Cancer Systems Biology Consortium

The CSBC U54 Research Center @ Vanderbilt


Center Title

The CSBC U54 Research Center at Vanderbilt

Overall Project Title

Phenotype Transitions in Small Cell Lung Cancer

Center Website

Under construction

Center Summary

Our Center addresses a fundamental issue in oncology: the pervasive and ubiquitous occurrence of phenotypic heterogeneity in any cancer, at all stages of progression. The overarching goal of the Center is to produce a quantitative understanding of plasticity and dynamics of cancer cell phenotypes in a tumor, as we believe this knowledge holds the promise of major advances in treatment. Both genetic and epigenetic factors contribute to tumor heterogeneity. Furthermore, multidirectional interactions of distinct tumor cell phenotypes amongst themselves and with host cells shape the evolutionary trajectory of a tumor, including its metastatic properties. Thus, heterogeneity is a complex, multiscale problem (from genes to molecules to cells to tissues), intrinsically unsuitable to reductionist approaches. Rather, we consider a systems-level approach to current challenges, which include: 1) identifying useful quantitative metrics of a tumor phenotypic space; 2) defining deterministic and stochastic components of heterogeneity at molecular and cellular levels; 3) deriving emergent tumor phenotype dynamics from single-cell behavior; and 4) designing effective treatment strategies based on this system-level knowledge.

To tackle these challenges, we frame tumors as complex adaptive systems and apply concepts from dynamical systems theory. Furthermore, we resolved to focus a multitude of theoretical and experimental approaches on one single cancer type, Small Cell Lung Cancer (SCLC), which at the moment is a disease with dismal outcomes and no improvement in treatment approaches for over half a century.

SCLC is an ideal model system to study the impact of phenotypic heterogeneity on progression and treatment resistance. Virtually all tumors exhibit degrees of plasticity, but SCLC is on the extreme side, facilitating data interpretation and possibly reducing the influence of confounding factors. For instance, SCLC tumors exhibits rapid relapse after initial high levels of response to chemoradiation treatment. Relapse is not associated with recurrent acquired resistance mutations, pointing to a major role for phenotypic state transitions. SCLC initiation and promotion involve obligatory biallelic inactivation of tumor suppressors p53 and Rb, while driver oncogenes appear to be absent. Nonetheless, diverse intra-tumor SCLC cellular phenotypes emerge in patients and mouse model tumors, supporting the idea that SCLC aggressiveness and treatment escape depend of phenotype diversity. Our working hypothesis is that heterogeneous phenotypes and their interaction dynamics form a robust SCLC tumor ecosystem adaptable to perturbations and treatment. In our Center, the overarching goal is to test this hypothesis with a combination of experimental and modeling approaches and if correct, at least in part, demonstrate its general value for other cancer types.

Principal Investigators

Vito Quaranta, MD

Vito Quaranta, MD, trained in cell biology and immunology, is the Director of the Quantitative Systems Biology Center, and Professor of Biochemistry and Pharmacology at Vanderbilt University School of Medicine. Having studied cancer and the tumor microenvironment for most of his career, for over a decade he has been implementing cutting-edge interdisciplinary efforts melding mathematics, engineering, computation and biology to solve the problem of cancer invasion and metastasis. He has co-developed multiscale mathematical models that predict tumor aggressiveness based on the physical properties of extracellular matrix and adhesive properties of cancer cells. Dr. Quaranta has established single-cell techniques to quantify the rate of proliferation of single cells in response to perturbations (e.g., Fractional Proliferation), which can be applied in high-throughput fashion to measure the dynamics of cancer cell response to drugs. He is also expert in single-cell methodologies to evaluate mechanism of action of cancer targeted therapy, based on the merging of automated time-lapse microscopy with image analysis and computational modeling. To explore the roots of differential cell behavior and tumor heterogeneity, Dr. Quaranta studies the dynamics of transcription factor and signaling networks that define and maintain cell identity, and ultimately contribute to forming the phenotypic landscape of the tumor microenvironment.

Participating Investigators

Leonard A. Harris, Ph.D

Leonard A. Harris, Ph.D., is a post-doctoral fellow in the Quaranta and Lopez laboratories, with extensive experience in mathematical modeling. He is interested in understanding and uncovering the molecular basis for cancer cell heterogeneity and its connection to drug treatment resistance using a systems biology approach combining computational modeling and in vitro and in vivo experimentation. Formally trained in chemical engineering, Leonard is a leader in the development of BioNetGen (BNGL) rule-based modeling, and an expert in stochastic simulations with the Gillespie method.

Jonathan M. Lehman, M.D., Ph.D.

Jonathan M. Lehman, M.D., Ph.D., is a physician scientist with a research focus on SCLC. One of his original contributions in this field is the emphasis on the underappreciated role of metabolism in SCLC and SSTR2 signaling with its influence on AMPKa phosphorylation in metabolic flux and survival. Jon’s role in the Center will encompass the characterization of SCLC phenotypes both in cell lines and PDXs that he initiates and curates. He works in close collaboration with Pierre Massioni, MD, Director of Cancer Early Detection and Prevention Initiative and an expert in lung cancer.

Carlos F. Lopez, Ph.D

Carlos F. Lopez, Ph.D., is an Assistant Professor of Biochemistry, Biomedical Informatics and Pharmacology at VUSM. He is formally trained in the fields of chemistry, physics, biophysics, biochemistry, and molecular cell biology with a unique emphasis on the development and application of computational modeling techniques. Carlos and his group build cancer models across multiple spatiotemporal time-scales, using techniques from statistical mechanics, molecular simulation, mesoscale modeling, reaction kinetics, bridging the gap between single cells and cell populations. He has a deep commitment to interface experimental and theoretical groups in order to test and expand hypotheses, with the ultimate goal of achieving a fundamental understanding of the rules that govern cancer as a complex system.

Christine M. Lovly, M.D., Ph.D.

Christine M. Lovly, M.D., Ph.D., is an Assistant Professor of Medicine at Vanderbilt University. Research in the Lovly laboratory is directed at understanding and developing improved therapeutic strategies for specific clinically relevant molecular subsets of lung cancer. Her laboratory has a particular focus on mechanisms of sensitivity and resistance to agents used in clinical practice. Christine has positioned herself at the forefront of translational SCLC research with emphasis on liquid biopsies.

Julien Sage, Ph.D.

Julien Sage, Ph.D., is a Professor of Pediatrics and Genetics at Stanford University and the co-Director of the Stanford Cancer Biology PhD program. His laboratory focuses on mouse models of cancer, especially for tumor types mutant for the retinoblastoma (RB) tumor suppressor gene such as small cell lung cancer (SCLC). His laboratory has developed novel mouse models for SCLC and has used these models to investigate key signaling pathways in SCLC, including the role of developmental signaling pathways such as Notch and Hedgehog, as well as paracrine and autocrine signals that promote SCLC growth.

Alissa M. Weaver, M.D., Ph.D.

Alissa M. Weaver, M.D., Ph.D., is the Cornelius Vanderbilt Professor of Cell and Developmental Biology and Pathology, Microbiology, and Immunology. She was appointed as AAAS fellow in 2016. The overall goal of her research is to understand cellular and molecular mechanisms of tumor progression.  Her laboratory has a particular focus on the role of membrane trafficking and cytoskeletal rearrangements in promoting tumor invasion and metastasis, especially the role of exosomes and other extracellular vesicles in driving those and other tumor-relevant processes. In this project, her laboratory will focus on the role of secretion in driving SCLC cell-cell interactions, phenotype dynamics, and aggressiveness.

Core Directors and Participating Faculty

Yu Shyr, Ph.D.

Yu Shyr, Ph.D., is the Chair of the Department of Biostatistics at Vanderbilt University School of Medicine. He is expert in biostatistical methodologies for clinical trial design, high-dimensional data preprocessing, and statistical or bioinformatics approaches to big data. He is on the advisory board of several Cancer Centers and SPOREs, and is Principal Investigator of a UO1 grant for the Barrett’s esophagus translational research network coordinating center (BETRNetCC), and PI for data coordination of the NCI SCLC Consortium. Dr. Shyr’s current research interests focus on developing statistical bioinformatics methods for analyzing next-generation sequencing data including sample size requirements for studies based on cell population and single-cell RNA sequencing.

Jonathan M. Irish, Ph.D.

Jonathan M. Irish, Ph.D., is Assistant Professor in the Department of Cell & Developmental Biology, and Pathology, Microbiology & Immunology at VUSM. He is Scientific Director of the Cancer & Immunology Core and the Mass Cytometry Center of Excellence. Research areas in Jonathan’s laboratory include neural stem cell and immune cell signaling interactions in brain tumors; machine learning algorithms to identify healthy and malignant cell types; and, immune mechanisms of disease and therapy response. The central goal of his research is a sytems-level understanding of changes at the single-cell level that alter signaling and lead to therapy-resistant populations in cancer and immunological diseases.

Ken Lau, Ph.D.

Ken Lau, Ph.D., is Assistant Professor of Cell and Developmental Biology at Vanderbilt University. His background is in computational modeling combined with experimentation to study complex biological phenomena. Ken is interested in developing a systems-level understanding of the molecular basis for cell decisions, by utilizing high-dimensional, high-resolution data generated by technologies such as mass cytometry on single cells from fixed tissue, multiplex tissue imaging, and single-cell RNA-seq. To interpret such data, he recently developed p-Creode, an unsupervised algorithm that produces multi-branching graphs and infers a statistically robust hierarchy of cell-state transitions that define developmental trajectories. Ken’s overarching goal is to unravel heterogeneity and plasticity of epithelial and cancer cell populations, and translate such knowledge into therapeutic approaches.

Qi Liu, Ph.D.

Qi Liu, Ph.D., is Assistant Professor of Biomedical Informatics at VUSM. She is expert in bioinformatics methodology development and next generation sequencing data analysis. Her research interests include integrative system biology approaches to the biological basis of complex diseases, and transcriptional and post-transcriptional regulation networks.


Project 1: Modeling the SCLC Phenotypic Space

Leaders: Vito Quaranta, Carlos F. Lopez
Co IS: Christine M. Lovly, Jonathan M. Lehman

Figure1: Potential Sources and Dynamics of Tumor Heterogeneity in the SCLC Phenotypic Landscape. Diversity of tumor cell phenotypic states can occur based on genetic or epigenetic changes that allow cells to adapt to various circumstances, including the microenvironment. Additional features, such as stochasticity of molecular states and cellular interactions (e.g. through secretion) will also contribute to transitions between phenotypic states and differential biological functions of tumors, such as altered ratios of drug resistant, sensitive, and quiescent cell populations

The overarching goal of this project is to develop a global blueprint of SCLC phenotypic space, clarifying bias imposed by genomic alterations, and epigenetic basis for phenotype transition or selection in response to drugs. We leverage our expertise in modeling the origin of heterogeneous phenotypes from transcription factor (TF) networks inferred from transcriptomics data. Simulations of TF network dynamics via logic-based models enable the identification of attractors, which roughly correspond to SCLC differentiation states defined by profiles of activated or silenced TFs. We define a gene ontology metric to identify biological similarities and differences between phenotypes across a variety of experimental systems including human cell lines, patient-derived xenografts and primary tumors, as well as tumors from SCLC genetically engineered mouse models (GEMMs). The resulting phenotype map informs studies aimed at connecting model systems to patients. A limitation of SCLC is the scarcity of patient specimens, since biopsies or surgery are rarely performed beyond initial diagnosis. We circumvent this barrier by using instead liquid biopsies of circulating, cell-free DNA as a clinical proxy for the primary tumor, allowing a connection between genomic alterations and phenotypic space states of SCLC tumors. To evaluate the relative role of state transitions vs. selection underneath SCLC plasticity and drug treatment evasion, we use DNA barcoding and information theory techniques to quantify rates of diversification of SCLC phenotypes in response perturbations. Specifically, we map trajectories of cells within the SCLC phenotype space as cells adapt and evade treatment. In summary, we propose to develop a comprehensive view of SCLC phenotypic heterogeneity, linking transcriptomic, genomic, and functional features of SCLC cells across diverse experimental model systems and patient primary tumor specimens. We will link these observations to clinically measurable variables, develop a unified map of phenotypic response dynamics in response to therapy, and analyze the role of plasticity in phenotype shifting and evolution of drug resistance., providing possible novel avenues to SCLC treatment strategies.

Figure 2: Mutational analysis of plasma cfDNA from 27 patients with SCLC. Summary of mutations identified by patient at any time point. Patients are separated by stage of disease at diagnosis (LS-limited stage; ES- extensive stage). Alterations are color coded per the figure legend below the image. The mutation frequencies for each gene are plotted on the right panel.

Project 2: Phenotype interactions and dynamics in SCLC tumors

Leaders: Alissa M. Weaver, Julien Sage, Carlos F. Lopez
Key Personnel: Leonard A. Harris

Figure 3: Schematic of the preliminary SCLC population dynamics (PopD) model. Black arrows represent cell fate (division, death, differentiation); red arrows represent positive feedback; red circles represent negative feedback; ∅ represents cell death.

In this project, we assess interactions of distinct SCLC cell phenotypes with each other, directed at forming an ecosystem that supports progression and treatment escape. Using a combination of mathematical modeling and experimentation, we aim to quantitatively appraise the relative role of each phenotype in driving the dynamics of tumor aggressiveness as a whole under a variety of perturbation. Cell population models will be simulated both deterministically, to predict the average behavior of a tumor, and stochastically, to evaluate the role of intrinsic and extrinsic noise. The overarching goal is to predict dependencies that are targetable, both at the molecular and cellular level, and then test these predictions in vitro and in experimental SCLC GEMMs.
Tumor-propagating cells (TPCs) in SCLC GEMM tumors are neuroendocrine and strongly tumorigenic. We also characterized cell populations derived from these TPCs with distinct phenotypes, including non-neuroendocrine subpopulations that can promote the growth and the spread of neuroendocrine TPCs. With a combination of experimental and mathematical approaches we will predict key interactions between SCLC phenotypic subpopulations to uncover fragility/intervention points that could be used for treatment. We will also determine whether specific molecular cargoes carried by purified extracellular vesicles and/or secreted as soluble factors affect SCLC phenotype maintenance, growth, and survival dynamics in vitro and in vivo. The identification of secreted factors involved in SCLC communication will allow disruption of feedback loops for cell-population level mathematical model validation and eventual therapeutic intervention.


Single Cell Biology and Data Analysis (SCB-DA) Core

Leaders: Jonathan Irish, Yu Shyr
Co-Is: Ken S. Lau, Qi Liu

This Center shared resource provides two critical services: 1) state-of-the-art single-cell analytic experimental procedures, including single-cell RNA-sequencing on several platforms, and mass cytometry analyses; 2) bioinformatics and statistical data analysis services, including dimensionality reduction for interpretation and visualization of high-dimensional data. These services support in a cost-efficient manner analysis and integration of data related to phenotypic heterogeneity in SCLC. The SCB-DA Core will drive development and/or application of cutting edge single-cell and bioinformatics technologies, such as the MEM score metric for separation of cell subpopulations with, e.g., mass cytometry, or the p-Creode algorithm to infer phenotypic trajectories.

Outreach Core

Leaders: Alissa M. Weaver
Co-Lead: Carlos F. Lopez

In addition to cutting edge cancer systems biology research, we are committed to reaching out to the community to disseminate our techniques, modeling approaches and accomplishments. Activities include outreach through: 1) our website; 2) hands-on workshops focused on single-cell technologies and modeling approaches; and 3) seminars and symposia. Our Center investigators are leaders in the area of tumor heterogeneity, network biology, single-cell biology, analysis and modeling of complex systems. They will continue to innovate and drive forward these fields as part of the Center. As new tools and approaches are developed, we will disseminate them through hands-on workshops and online modules.

Measuring, Modeling and Controlling Heterogeneity (M2CH)


Center Title

M2CH Center for Cancer Systems Biology (M2CH-CCSB)

Overall Project Title

Measuring, Modeling and Controlling Heterogeneity (M2CH)

Participating Sites

Oregon Health & Science University
University of California, Berkeley

Center Website

Center Summary

The overall goal of our M2CH Center for Cancer Systems Biology (M2CH-CCSB) is to improve management of triple negative breast cancer (TNBC) by developing systems level strategies to prevent the emergence of cancer subpopulations that are resistant to treatment. We postulate that heterogeneity arising from epigenomic instability intrinsic to cancer cells and diverse signals from extrinsic microenvironments in which cancer cells reside are root causes of resistance.

We learn how intrinsic and extrinsic factors influence differentiation state, proliferation and therapeutic response in TNBC through experimental manipulation and computational modeling of cancer cell lines, 3D engineered multicellular systems, xenografts and clinical specimens. We deploy single cell ‘omic and imaging technologies that allow quantitative assessment of molecular, cellular, and structural heterogeneity. We interpret these data using computational models that define control networks and structures in heterogeneous systems as well as transitions between states of therapeutic resistance and sensitivity.

This is accomplished in three related Projects and three Cores. Project 1 focuses on measuring and managing resistance-associated heterogeneity intrinsic to cancer cells. Project 2 focuses on identifying resistance-associated signals from the microenvironment and on mitigating effects from these signals on therapeutic response. Project 3 applies spatial systems biology approaches to TNBC specimens and multicell type models thereof to discover molecular control networks that influence how cell intrinsic plasticity and microenvironment signaling alter therapeutic responses in complex tissues. All Projects analyze core cell lines, patient derived cultures, and FDA approved, pathway-targeted drugs (afatinib, ruxolotinib, trametinib, BYL719, cabozantinib, and everolimus).

An Imaging Management and Analysis Core provides infrastructure and image analytics that enables efficient image data management, quantitative analysis of image features, and visualization of images and metadata generated using multiscale light and electron microscopy. An Outreach Core makes available educational materials, experimental and computational tools and data to the scientific community.


Principal Investigators

Joe W. Gray, Ph.D.

Joe W. Gray, Ph.D., a physicist and an engineer by training, is the Gordon Moore Endowed Chair in the Department of Biomedical Engineering and serves as Director for the Center for Spatial Systems Biomedicine and Associate Director for Biophysical Oncology at the Knight Cancer Institute at Oregon Health & Science University. He also serves as co-PI of an NIH LINCS project on effects of the microenvironment on cell phenotypes and a clinical trial aimed at understanding cancer evolution under treatment through Serial Measurements of Molecular and Architectural Responses to Therapy with which the M2CH-CCSB interacts. Dr. Gray’s research program applies experimental and mathematical tools to elucidate mechanisms by which genomic, transcriptional and proteomic abnormalities occur in selected cancers including how these abnormalities alter the multiscale architecture of cancers and their microenvironments cancer pathophysiology and how these changes contribute to cancer progression and response to therapy. He brings substantial experience in development and application of advanced measurement technologies. He has contributed to development of cytometric techniques for cell and genome analysis including high speed chromosome sorting, BrdUrd/DNA analysis of cell proliferation, Fluorescence In Situ Hybridization, Comparative Genomic Hybridization and End Sequence Profiling. More recently, Dr. Gray has elucidated mechanisms of cancer progression and developed systems biology approaches for identification of molecular markers that predict and determine response to therapeutic treatment. Currently, he is applying multiscale image analysis approaches to identify multiscale (Ängstroms to millimeters) structures that lead to therapeutic resistance.

Rosalie Sears, Ph.D.

Rosalie Sears, Ph.D. is a Professor in the Department of Molecular and Medical Genetics and a senior member of the Knight Cancer Institute and the Center for Spatial Systems Biomedicine at Oregon Health & Science University. She is also Co-Director of the Brenden-Colson Center for Pancreatic Care at OHSU, where she has built the infrastructure for deep omic analysis of longitudinally collected patient samples as well as propagation of primary patient tumor cells. This infrastructure is contributing to a multi-organ site clinical trial at OHSU aimed at understanding cancer evolution under treatment through the M2CH-CCSB. The Sears Lab studies cellular signaling pathways that control tumor cell behavior, with an emphasis on the management of tumor phenotypic heterogeneity and cell plasticity contributing to therapeutic resistance. Her research focuses on the convergence of signaling pathways on the c-Myc oncoprotein and how this impacts Myc’s expression, activity, and its regulation of cell fate. Myc is constitutively overexpressed in the majority of human tumors and studies have demonstrated that this affects both tumor cell state (proliferation, differentiation, metabolism) as well as cross-talk with the tumor microenvironment affecting immune surveillance and vasculature. Dr. Sears’ work reveals new ways to target post-translational activation of Myc that suppress tumor cell plasticity and phenotypic heterogeneity.

Claire Tomlin, Ph.D.

Claire Tomlin, Ph.D. is the Charles A. Desoer Professor of Engineering in the Department of Electrical Engineering and Computer Sciences at the University of California, Berkeley. Dr. Tomlin works in the area of dynamics, control and learning for hybrid systems, with applications to biology and engineering. She and her group have designed mechanistic and data-driven ODE models of the HER2+ signaling network in breast cancer, which led to design and test a pre-treatment scheme for steering the signaling network, and she has worked on understanding and developing models of phenotypic heterogeneity in breast cancer. In addition, she has developed high fidelity and deep mechanistic models of embryonic development, through a coupled process of biological experiments and mathematical modeling and analysis tasks in Drosophila, as well as models to understand the spatio-temporal architecture and molecular details of Planar Cell Polarity signaling. Her group develops tools for control system analysis and design using reachability and machine learning.

Emek Demir, Ph.D.

Emek Demir, Ph.D. is an Associate Professor of Molecular & Medical Genetics at Oregon Health & Science University. Dr. Demir’s overall research integrates multi modal profiles and pathway information to provide mechanistic explanations with a specific focus on cancer. His work on pathway informatics led to the development of the largest human pathway database, Pathway Commons and numerous tools and algorithms.

Center Administrator

Heidi Feiler, Ph.D.

Heidi Feiler, Ph.D. is a Research Associate Professor and the Deputy Director, Center for Spatial System Biomedicine at OHSU. She serves as Center Administrator for the M2CH-CCSB and is the primary contact for the logistical and organization aspects of the center to ensure that the goals are achieved. She works with the Center leadership to establish program governance, and communications strategies within the Center, with the External Advisory Committee, the CSB Consortium, the CSBC/PS-ON Coordinating Center (Sage Bionetworks), NCI, and other NCI large data initiatives. She oversees the center’s resources and budgets, compliance and reporting activities, the intra-center pilot project proposal process, and provides logistics support to the Outreach Core, to coordinate the Center SAGE-Synapse website and DREAM Challenge events, workshops and symposia. She also coordinates personnel exchanges and ensures that resources available through the M2CH-CCSB are available to the researchers in the CSBC and the broader research community.

Participating Investigators

Ellen Langer, Ph.D.

Ellen Langer, Ph.D. is a Postdoctoral Researcher in Rosalie Sears’ laboratory in the Department of Molecular and Medical Genetics at Oregon Health & Science University. Her current work focuses on mechanisms of cellular plasticity in cancer cells as well as in stromal cells within the tumor microenvironment.

Andrew Adey, Ph.D.

Andrew Adey, Ph.D. is an Assistant Professor in the Department of Molecular and Medical Genetics at Oregon Health & Science University. His past work has involved the development of numerous genome sequencing technologies for the acquisition of sequence contiguity information and for the interrogation of a variety of epigenomic properties. His current work involves the development of single cell genome and epigenome technology platforms to profile thousands of single cells in parallel.

James Korkola, Ph.D.

James Korkola, Ph.D. is an Assistant Professor in the Department of Biomedical Engineering and member of the Knight Cancer Institute and OHSU Center for Spatial Systems Biomedicine. Dr. Korkola’s laboratory focuses on how the microenvironment alters the phenotype of cancer cells, including their proliferative potential, differentiation state, and response to targeted therapeutics. He has developed and implemented MicroEnvironment MicroArray technology at OHSU to study simple combinatorial microenvironment factors for their effects on cancer cell phenotypes. His laboratory has applied MEMA technology to the study of HER2+ breast cancer, where it was found that different factors within the microenvironment can confer resistance to lapatinib, but in tumor subtype specific manner. Currently, Dr. Korkola is applying MEMA technology to other subtypes of breast cancer to identify microenvironment modifiers of drug response.

Laura Heiser, Ph.D.

Laura Heiser, Ph.D. is an Assistant Professor in the Department of Biomedical Engineering and member of the Knight Cancer Institute and OHSU Center for Spatial Systems Biomedicine. Her laboratory is focused on understanding mechanisms of therapeutic response and resistance, using novel imaging and omic analysis techniques to identify phenotypic changes associated with molecular aberrations and therapeutic response, and studying the influence of the microenvironment on cancer cells.

Xiaolin Nan, PhD

Xiaolin Nan, PhD is an Assistant Professor in the Department of Biomedical Engineering, and a member of the Knight Cancer Institute and the OHSU Center for Spatial Systems Biomedicine. Dr. Nan’s biophysics laboratory develops quantitative single-molecule and superresolution microscopy techniques for high resolution imaging of cells and tissue sections in up to tens of colors. The Nan Lab is also applying these powerful imaging tools to investigate the spatiotemporal regulation of the HER family receptors and the Ras GTPases at the nanometer and single-molecule scales.

Young Hwan Chang, Ph.D.

Young Hwan Chang, Ph.D. is an Assistant Professor of Biomedical Engineering and Computational Biology and a member of the OHSU Center for Spatial Systems Biomedicine. His current work focuses on developing image analysis tools for multiplexed imaging data to characterize subtypes and understand spatial distribution of cancer cells and their interactions with components of the microenvironment.

Damir Sudar, M.S.

Damir Sudar, M.S. is a Staff Scientist at Quantitative Imaging Systems, LLC (Qi), a member of OHSU Center for Spatial Systems Biomedicine, and a Visiting Scientist at Lawrence Berkeley National Laboratory. He develops automated microscopy techniques, image data processing and analysis software, and image data management systems. He is interested in integrating imaging modalities over multiple spatial, temporal, and functional/structural scales. He co-manages the Imaging Core, developing and optimizing image data management and image analysis software capabilities, overseeing microscopic imaging and data analysis, and work to enable image data sharing.

Michel Nederlof, Ph.D.

Michel Nederlof, Ph.D. is President and Chief Technology Officer at Quantitative Imaging Systems (Qi) and a member of the OHSU Center for Spatial Systems Biomedicine. He has 30 years of experience in digital imaging microscopy for life sciences, ranging from basic technological developments, to advanced image analysis, High Content Screening and clinical applications. He founded six biotechnology companies that have created pioneering products in the areas of microscopy research, pharmaceutical drug discovery, and clinical diagnostics. As CTO of Qi, he oversees all technical development on software and analytics integration and is responsible for the imaging workflow of many projects and involved in most aspects from microscopy image acquisition, to analysis, and visualization. Qi co-manages the Imaging Core for the M2CH-CCSB and provides image analytics.


Schematic illustration of the interplay between cancer cell intrinsic networks and extrinsic signals that influence aspects of cancer biology and response to therapy.

Overview of the Measuring, Modeling and Controlling Heterogeneity Center for Cancer Systems Biology.

Project 1 focuses on measuring and managing resistance-associated heterogeneity intrinsic to cancer cells. Project 2 focuses on identifying resistance-associated signals from the microenvironment and on mitigating effects from these signals on therapeutic response. Project 3 applies spatial systems biology approaches to TNBC specimens and multicell type models thereof to discover molecular control networks that influence how cell intrinsic plasticity and microenvironment signaling alter therapeutic responses in complex tissues.

Project 1: Therapeutic Management of Lineage- and Differentiation-state Plasticity

PIs: Sears, Demir; co-Is: Tomlin, Demir, Adey, Langer

Heterogeneity in differentiation state in a TNBC tumor and cell line demonstrated by immunofluorescence staining for differentiation-associated proteins.

TNBC is an aggressive disease characterized by high intratumor heterogeneity and poor patient outcome. In preliminary experiments, we identified subpopulations of tumor cells in primary TNBC as well as in basal-like TNBC cell lines that are characterized by differential expression of luminal, basal, and mesenchymal differentiation-state markers. We have observed that distinct classes of targeted therapeutics have the capacity to eliminate or enrich specific differentiation-state subpopulations within these lines, steering heterogeneous cancer cell populations toward increased homogeneity. Importantly, we identified synergistic combinatorial treatments that targeted either pathway dependencies predicted by master regulator analysis of residual cells or epigenetic regulators found to contribute to a cell’s transition to a resistant state. The overall goal of this project, therefore, is to understand cell intrinsic regulation of therapeutic response in phenotypically heterogeneous TNBC in order to develop targeting strategies to kill all co-existing subpopulations. We focus on phenotypic heterogeneity, as this can represent the combination of genetic and epigenetic factors, and we take advantage of clinically relevant therapeutics that drive heterogeneous populations toward homogeneity. We hypothesize that a systems biology approach of measuring and computationally modeling the functional pathways underlying phenotypic state changes in response to state-aggregating therapeutics will reveal common escape routes and regulators of cell plasticity, which allow us to predict effective combinatorial therapeutic strategies that eliminate all cancer subpopulations. We address this hypothesis by (1) examining and computationally modeling phenotype state changes in multiple genetically diverse, heterogeneous TNBC cell lines in response to targeted therapeutics that induce homogeneity using high-content imaging and single cell expression analysis, (2) determining whether clonal expansion or differentiation state plasticity drives the dynamic phenotype changes following targeted therapy and modeling the molecular network changes that underlie these transitions, and (3) determining epigenetic regulation underlying state transitions and developing combinatorial strategies that overcome therapeutic resistance in heterogeneous TNBC cells in vitro and in vivo. Together, these aims support our goal to measure and model cell intrinsic responses to clinically relevant targeted therapeutics and to predict synergistic drug combinations that more effectively control heterogeneous TNBC. Integration of this work with Projects 2 and 3 allows us to incorporate extrinsic regulators of these intrinsic mechanisms and to iteratively refine control strategies for this devastating disease.

Project 2: Managing Microenvironment-mediated Heterogeneity and Resistance

PIs: Heiser, Korkola; co-Is: Demir

Schematic illustration of the process used to use microenvironment microarrays to interrogate the effects of microenvironmental proteins on therapeutic responses.

The inability to effectively treat TNBC is thought to be in part due to its heterogeneity, as cells are highly plastic and able to respond rapidly to therapeutic insults to steer into drug resistant states. One aspect that is likely to strongly influence TNBC plasticity, heterogeneity, and response to therapy is the microenvironment (ME) in which cells reside. Interactions with extracellular matrix proteins or soluble factors like growth factors and cytokines can profoundly change phenotypic properties of TNBC cells, and mounting evidence suggests that such ME factors also influence response to therapy. We hypothesize that the ME impacts therapeutic response of TNBC, and that consideration of signals from the ME in treatment decisions are likely to lead to improved therapeutic control and patient outcomes. We couple experimental assessment of TNBC response to targeted therapeutics in the presence of defined combinatorial ME perturbations (MEPs) with concomitant expression profiling and computational approaches to define underlying pathway signatures to identify vulnerabilities in residual cancer cells that could be exploited for therapeutic benefit. This is accomplished using microenvironment microarrays to identify MEPs that confer resistance to six targeted therapeutics in TNBC cell lines and primary patient-derived xenograft (PDX) samples. We perform expression profiling by RNA-Seq at fixed time points on TNBC cells grown in the presence of resistance conferring MEPs plus therapeutic and use computational approaches to identify underlying reduced dimensionality network signatures (PREdictors of CEllular Phenotypes to guide Therapeutic Strategies, PRECEPTS) that are altered as a result of interactions of cells with MEP and drug. These altered PRECEPTS signatures represent candidates for therapeutic intervention, and are tested using drug combinations in an attempt to overcome ME-mediated resistance. We perform dynamic imaging and expression profiling of the response of TNBC cells to resistance conferring MEPs plus drug and identify PRECEPTS signatures that are dynamically altered. Such PRECEPTS signatures represent potential transition vulnerabilities that could be targeted for therapeutic intervention, which we test experimentally using drug combination treatments of TNBC cells. These approaches are closely coordinated with those of Projects 1 and 3 in the use of common cell lines, drugs, and reagents and to maximize the information that we derive from the experiments. This approach enables the discovery of new drug combinations that could be deployed clinically to improve outcome in TNBC patients with primary and disseminated disease.

Project 3: Understanding the Impact of Microscale and Nanoscale Heterogeneity and Resistance

PIs: Gray, Tomlin; co-Is: Nan, Demir, Chang

Integration of omic and spatial features to identify molecular networks that control molecular heterogeneity.

We use a spatial systems approach to identify molecular networks that control development of resistance-associated heterogeneity in TNBCs and to use this information to devise multidrug treatments that are effective in heterogeneous TNBCs. Our focus is on heterogeneity that arises from epigenomic plasticity intrinsic to cancer cells and from extrinsic signals from the diverse microenvironments into which TNBC cells disperse. Individual cells within a TNBC exhibit variable phenotypes and respond variably to treatment so that establishing durable control of TNBCs is notoriously difficult. We explore the mechanisms by which individual cells in TNBC tissues respond to perturbations induced by microenvironment interactions and/or drugs. Our approach is based on the concept that the phenotype and response to therapy of every cell in a heterogeneous TNBC tissue is influenced by its intrinsic epigenomic status and by the microenvironmental signals it receives. In short, every cancer cell-microenvironment-drug interaction in a heterogeneous experimental tissue or clinical specimen is an independent experiment of nature. We analyze ensembles of such interactions in TNBC tissues before and after treatment to determine the impact of local environmental signals on cancer cell phenotype and therapeutic response.

We accomplish this using cyclic multiplex immunofluorescence to stain cancer cells for quantitative analysis of proliferative status, differentiation state, and expression levels of proteins that report on control network activity. We quantify cancer cell-microenvironment interactions at the microscale using multicolor fluorescence microscopy and at the nanoscale using multispectral super resolution fluorescence microscopy and 3D scanning electron microscopy. We use custom image analysis techniques developed in the Imaging Core to quantify cell and microenvironment components and machine/deep learning strategies to identify microenvironment-cancer cell interactions that influence phenotype. This work guides development of dynamic models of spatially dependent control network-microenvironment interactions that can be used to devise therapeutic strategies to control TNBCs. The approach is statistically powerful since every tissue section contains details about tens of thousands of cell-microenvironment interactions. We accomplish this by; (1) developing cyclic multiplex immunofluorescence, multiscale image analysis, and machine learning procedures needed to identify molecular control networks in individual cells in TNBC tissues that respond to signals from microenvironmental cells and proteins (MEPs) and that influence phenotype and/or therapeutic response, (2) elucidating the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in bioprinted tissues. (3) assessing the effects of microenvironmental cells and high impact proteins on TNBC control network activity, phenotype, and therapeutic response in TNBC xenografts and clinical TNBC specimens.


Imaging Management and Analysis Core

(Gray, Nederlof, Sudar)
The Imaging Management and Analysis Core (Imaging Core) provides a common infrastructure for image acquisition, efficient image data management, and quantitative analysis for all projects. It deploys and integrates images from multiple platforms including multiwell high content imaging, microenvironment microarrays, cyclic immunofluorescence workflows and correlative light/electron microscopy. This includes: (1) developing customized workflows for the three high-content microscope systems to acquire and store images and metadata and all derived and associated measurement data, using an open source OMERO image database and our Annot experiment tracking database, (b) providing automated scene segmentation and feature extraction solutions, and (c) developing novel visualization methods to interactively analyze quantitative imaging data, metadata, and externally linked data. These tools and methods are integrated into a highly efficient workflow for acquiring, managing, analyzing, and visualizing the types of high-content imaging data that are generated in Project 1 for assessing phenotypic state changes from drug therapy, and in Project 2 for analyzing the effects of the microenvironment, alone and in combination with drugs, on differentiation state. These data and visualization tools are made available to the community through the Outreach Core in close collaboration with Sage Synapse. The Imaging Core supports Project 3 and image analysis and visualization requirements in all projects by further developing our Dynamic Visualization Engine framework for visualization methods that integrate image data at the level of individual cells, images, and assays; experimental metadata and external annotations; image analysis features such as segmentation results; and interactive quantitative graphs that allow drilling down to all levels of underlying data.

Outreach Core

(Heiser, Feiler, Sudar)
The Outreach Core disseminates results, methods and tools from the Center to the broader research community. It accomplishes this by; (a) hosting symposia, workshops, and think tanks that present, train, and extend the state of the art in techniques useful for understanding heterogeneity in cancer, (b) providing access to the bioimaging and genomic data using a custom portal for access, visualization, and interactive analysis based on OMERO (Open Microscopy Environment image database) and Sage Synapse; and (c) conducting DREAM crowd-sourcing activities to seek analytical solutions to spatial systems biology questions, and to identify new methods and approaches for analyzing these data.

Administrative Core

(Gray, Tomin, Demir, Sears, Feiler)
The Administrative Core provides leadership and supports and coordinates: (a) communications and interactions within the Center and across the CSBC, (b) proposal preparation, management, reporting, and compliance activities, (c) oversight of budgetary and intellectual property issues, (d) oversight of the M2CH-CCSB computational infrastructure and the Resource Sharing Plan, (e) project integration and evaluation (including pilot projects), and (f) external review of the Center, including assembly of an External Advisory Committee (EAC).

Complexity, Cooperation and Community in Cancer ([email protected])


Center Title

Complexity, Cooperation and Community in Cancer ([email protected])

Center Website

Center Summary

The overall goal for the UCI Center for Cancer Systems Biology ([email protected]), also known as the Center for Complexity, Cooperation and Community in Cancer, is to understand the principles that underlie why cancers are organized as they are. Our approaches stem from the idea that cancer cells proliferate and evolve in complex environments that have been highly selected for the robust control of growth and differentiation, and thus the behaviors of cancer cells can only be fully understood in the context of the design principles underlying such control. [email protected] is a joint effort by the systems biology and cancer biology communities at the University of California Irvine (UCI), as represented by two campus-wide research organizations, the Center for Complex Biological Systems and the Cancer Research Institute of the UCI Chao Family Comprehensive Cancer Center.

[email protected] is carrying out three coordinated, team-oriented research projects on the role of context, cooperation and community in the initiation and progression of cancer. All three projects seek to understand how the in vivo behaviors of transformed cells are constrained by rules inherited from the communities of diverse, interacting cell types and lineage hierarchies within which those cells arise. Project 1 leverages new observations from xenograft models of colon cancer to investigate non-genetic heterogeneity in solid tumors, both its origins and its relevance to tumor growth and response to therapy. Project 2 investigates the cellular origins of melanoma, seeking to clarify the relationship between melanoma and the benign lesions (melanocytic nevi) that are driven by a common oncogenic event. This work focuses on interactions among melanocyte precursors, within the skin environment, and under conditions that promote progression from benign to malignant. Project 3 focuses on improving models of chronic myeloid leukemia (CML) and its treatment, taking into account interactions between hierarchical lineages, intercellular feedback, and dynamics.

All three projects combine mathematical modeling, genomics, and experimental manipulation of animal models. Mathematical modeling is central to all three projects, not just as a means to analyze large data sets, but as a way of identifying, with the most generality, the architectures of cell interaction and feedback that can explain generic features of cancer cell behavior. All three projects develop models with design principles built on similar concepts: cell state transitions, proliferation and quiescence, positive and negative feedback, but in different contexts that derive from the different cancer types (spatial vs. non-spatial; focus on self-organizing pattern vs. focus on control; emphasis on model and parameter identification vs. emphasis on replicating qualitative behaviors). A common theme is the idea that bi- and multistability that arises as a result of feedback can potentially explain bifurcating system behaviors, such as nevi and melanoma in the same mouse, spatial patterns of heterogeneity, or resistance to cancer therapy, without having to attribute such events to new mutational “hits.”

All three projects are served by a core facility for investigating tumor cells at the single cell level, providing access to the latest in single-cell genomic, transcriptomic and other technologies, which are needed to provide the kind of data that can adequately constrain models.

An Administrative Core provides the organizational framework and logistic support for the center. The Administrative Core also solicits, reviews and administers pilot project grants initiated both by faculty and by trainees (graduate students and postdoctoral fellows). An Outreach Core promotes cancer systems biology to the research community, targeting researchers and trainees at all career stages through a wide variety of educational and professional development activities.


Principal Investigators

John Lowengrub, Ph.D.

John Lowengrub, Ph.D., Chancellor’s Professor of Mathematics and Biomedical Engineering, is a systems biologist and mathematician whose research efforts focus on developing mathematically rigorous and biologically-justified models of solid tumors. His research is highly interdisciplinary and has involved collaborations with many of the faculty participating in this center. In general, Dr. Lowengrub’s research aims to use mathematical modeling to understand how misregulated feedback signaling, metabolic reprogramming and interactions between a vascularized tumor and its microenvironment can drive tumor progression and dictate the optimal course of treatment. Recently, Dr. Lowengrub’s research group has provided an explanation of how cancer stem cells drive the development of invasive fingers (joint work with MPI Lander) and crosstalk with the stroma and vascular network. In addition, Dr. Lowengrub’s research group has developed models that explain how Wnt-driven spatiotemporal patterns of different metabolic states can emerge in vascularized tumor xenografts with genetically identical cells (joint work with MPI Waterman). Dr. Lowengrub currently serves as a co-leader of the Systems, Pathways and Targets (SPT) program of the Chao Family Comprehensive Cancer Center, a program that brings together cell biologists, immunologists, geneticists, developmental biologists, systems biologists, computational scientists, and clinicians. He is deeply committed to education in mathematical biology, cancer biology and systems biology, and spearheaded the development of an interdisciplinary M.S./Ph.D. graduate program at UCI that spans 10 departments and 5 schools; he now serves as Director of that program.

Arthur Lander, M.D., Ph.D.

Arthur Lander, M.D., Ph.D., Bren Professor of Developmental and Cell Biology and Professor of Biomedical Engineering and Logic & Philosophy of Science, originally trained in biochemistry, medicine, neurobiology and developmental biology, but his research over the last 15 years has focused primarily on the systems biology of morphogenesis and growth. His interests in the feedback control of growth, as well as his early clinical training, led him to an interest in cancer biology that ultimately resulted in several collaborative papers with the Lowengrub group, and his service as an MPI on a multi-investigator RC2 grant from the NCI. He has 18 years of experience as director of T32 training grants (three different ones), 8 years of service as the Chair of Developmental and Cell Biology, 10 years of experience as founding director of the UCI transgenic mouse core facility, and 16 years of experience as the founding director of the Center for Complex Biological Systems, UCI’s NIGMS-designated National Center for Systems Biology, which supports the research and educational activities of over 100 Systems Biology-affiliated faculty. Dr. Lander has served as chair of an NIH Developmental Biology study section and, from 2013 to 2017, as a member and Chair of the MABS (modeling and analysis of biological systems) NIH study section.

Marian Waterman, Ph.D.

Marian Waterman, Ph.D., Professor of Microbiology and Molecular Genetics, is the Director of the UCI Cancer Research Institute and Deputy Director of the Chao Family Comprehensive Cancer Center. She focuses her research on mechanisms of Wnt signaling that direct cellular phenotypes in cancer and stem cells. Dr. Waterman discovered one of the first members of the LEF/TCF transcription factor family and since then has focused on how these factors function in normal and diseased tissue. Dr. Waterman collaborates with physician scientists, mass spectroscopists, biomedical engineers, biophysicists at the Laboratory for Fluorescence Dynamics (Gratton, Digman), and mathematicians (including MPI Lowengrub). The overarching goal of her research is to understand how gene expression, metabolism, development, carcinogenesis and Wnt signaling are connected. As Director of the Cancer Research Institute, she also coordinates member leadership for a T32 training grant in Cancer Biology and Therapeutics; an American Cancer Society seed grant mechanism for junior faculty; the Cancer Biology track for the Molecular and Cellular Biology cross-campus graduate program; and a popular and competitive summer internship program for high school students. As Deputy Director of the Chao Family Comprehensive Cancer Center, Dr. Waterman coordinates the basic science programs and Shared Resources and works with the Director Richard Van Etten to advance strategic initiatives of the center and aims of the NCI cancer center support grant. At the national level, Dr. Waterman has served as chair of an NIH cancer biology study section (CAMP; 2010-2016), as chair of an American Cancer Society review panel (DMC; 2003-2007), and a member of ACS council for extramural grants (2010-2014).

Participating Investigators

Steven D. Allison, Ph.D.

Steven D. Allison, Ph.D., is an Associate Professor in the Departments of Ecology and Evolutionary Biology and Earth System Science. He is broadly trained as a biologist but with a focus on the role of micro-organisms in ecosystem scale processes, such as carbon cycling. His research spans scales from genomes to the globe, and he applies both experimental and theoretical approaches to analyze microbial functioning. Most of his experimental work has focused on the regulation, environmental responses, and ecosystem consequences of extracellular enzymes in environmental systems. More recently, his research has increasingly focused on theory and models. There is a clear need for new models that scale up microbial processes, and he brings a unique experimental perspective to the models he has developed. One set of individual-based models he created has potential applications to human health. These models are spatially-explicit and represent interactions among different cell types. The variation across cell types is flexible and informed by empirical data. One of the main results from this set of models is that emergent properties at the ecosystem scale are dependent on microbial interactions involving metabolite production. In some cases, evolutionary processes that optimize individual cell performance serve to undermine system-level functioning. This type of prediction is relevant in cancer treatments where targeting particular cellular interactions might provide a means of arresting tumor development and other system-scale symptoms.

Michelle Digman, Ph.D.

Michelle Digman, Ph.D., is an Assistant Professor in the Department of Biomedical Engineering. Her research focuses on developing fluctuation imaging techniques to study protein dynamics in real time and space inside live cells and tissues. In addition, her lab is developing a metabolic index for organs, tissue sections, tumors, embryos and single cells using the FLIM/Phasor method. This fast computational approach allows for the detection of metabolic states at the single cell level will help create a reference for metabolically active or non-active cells under specific conditions. She is also working on characterizing the competitive recruitment and binding activity of DNA repair proteins upon DNA single strand and double strand laser induced DNA damage using high spatiotemporal resolution scanning correlation spectroscopies. She has extensive expertise in measuring protein dynamics in both nuclear and cytosolic compartments in living cells as her background is multidisciplinary in the areas in biochemistry and biophysics. She has coauthored over 80 peer reviewed manuscripts including PNAS, Biophysical Journal, EMBO, and Nature Communication. She set up her lab as Assistant Professor in July 2013 and is focused in the quantitative understanding of cell function including cell invasion, proliferation, differentiation and apoptosis. She has several peer reviewed papers on using the pair correlation method to calculate the diffusive rout of proteins in the nucleus to understand nuclear epigenetic regulation in cancer cells. She is developing imaging methods with high spatial-temporal resolution in correlation analysis to elucidate signaling protein network interactions in the 2D and 3D microenvironment. Using a bioengineering approach, she can mimic the tissue microenvironment and quantitatively measure changes in matrix reorganization to dissect key molecular mechanisms that govern tumor cell invasion. She also developed the phasor/FLIM method for mapping and identifying intrinsic autofluorescence markers in tissues and for fluorescent FRET biosensors (Rho, Rac and CDC42) to measure signaling and activation. She has extensive expertise in measuring protein dynamics in live cells. Among these techniques, which she co-developed, the raster image correlation spectroscopy (RICS) method, the Number and Molecular Brightness (N&B) technique, the Phasor/FLIM and the pair correlation spectroscopy (pCF) method, are now being used by researchers in Biology.

Robert A. Edwards, M.D., Ph.D.

Robert A. Edwards, M.D., Ph.D., is an Associate Professor in the Department of Pathology & Laboratory Medicine. Dr. Edwards is the Director of the Experimental Tissue Resource (ETR) within the Chao Family Comprehensive Cancer Center. Research in his laboratory is focused on understanding how chronic inflammatory signals in the intestine promote colorectal cancer (CRC). As a Pathology resident and post-doctoral fellow, he studied a new mouse tumor model that links inflammation and colon cancer, via knockout of the G-protein subunit Gi2. Upon establishing his laboratory at UC Irvine, he developed collaborations with Dr. Steve Lipkin and MPI Waterman to study how this mouse model connects to human colon cancer. For the past 10 years, his work has highlighted network links between inflammation, hypoxia, Wnt and Notch signals in the tumor microenvironment, both in animal models and in clinical isolates of human CRC.

Anand K. Ganesan, M.D., Ph.D.

Anand K. Ganesan, M.D., Ph.D., is an Associate Professor of Dermatology and Biological Chemistry. His research focuses on understanding how the melanocyte interacts with the environment and other cells within the skin during the process of normal tanning and transformation. He has recently published studies identifying novel pathways that drive the early steps in melanoma progression, studies identifying novel pathways that regulate melanogenesis in vivo, and studies characterizing how melanoma tumors modulates the immune response during progression. His published work has used in vitro cell based approaches to understand the cell biology of melanocytes, in vivo approaches to understand how melanocytes interact with other skin and immune cells in mouse models, and studies with human tissue to ensure that the findings in these models are relevant to human disease. His clinical practice focuses on caring for patients with atypical moles and determining whether these lesions have malignant potential or not. He has recently redirected his research efforts to answer this important question and developed models to study nevus development, regression, and progression non-invasively. The eventual goal of this line of investigation is to develop better algorithms to identify and remove nevi evolving to melanoma at an early stage before they become a problem.

Christopher C.W. Hughes, Ph.D.

Christopher C.W. Hughes, Ph.D., is the Francisco J. Ayala Chair of the Department of Molecular Biology & Biochemistry and a Professor in the Department of Biomedical Engineering, where he serves as Director of the Edwards Lifesciences Center for Advanced Cardiovascular Technology. Dr. Hughes is also co-Leader of the Onco-Imaging and Biotechnology (OIB) Program, part of the Chao Family Comprehensive Cancer Center at UCI, and in 2014 was elected a Fellow of the American Association for the Advancement of Science (AAAS). His research focuses on the development and growth of blood vessels. The work in his lab spans multiple scales – from understanding the basic molecular mechanisms of angiogenesis (the growth of new blood vessels), to engineering of artificial tissues. Recently his lab has been pioneering “Body-on-Chip” technology, which allows for micro-organs – heart, pancreas, tumor, etc. – to be grown in the lab, each with its own blood vessel network. In addition to his research, Dr. Hughes works extensively with the non-profit organization, cureHHT, which provides patient support and research advocacy on behalf of those suffering from the rare vascular disorder Hereditary Hemorrhagic Telangiectasia. Professor Hughes serves as Chair of the foundation’s Global Research and Medical Advisory Board. He has worked with Marian Waterman on several projects related to tumor angiogenesis and wnt signaling, and has co-mentored a student with John Lowengrub

Kai Kessenbrock, Ph.D.

Kai Kessenbrock, Ph.D., is an Assistant Professor in the Department of Biological Chemistry. His research experiences include: a) 5 years of working on inflammatory diseases using mouse models and human samples during his PhD thesis; and b) 6 years of postdoctoral training studying the role of microenvironmental factors in the regulation of breast epithelial stem cell function and breast cancer in single cell resolution. His lab at UC Irvine is focusing on single cell analyses of the epigenetic landscapes and the transcriptional signatures in individual cancer cells in order to ultimately understand how one may therapeutically tackle the clinical problem of tumor heterogeneity. Dr. Kessenbrock is highly experienced in tissue dissociation, cell preparation, cell screening and analysis using the C1 Fluidigm and 10X Chromium platforms. The goals of his research are to use enhanced understanding of tumor initiation to improve methods for early detection of cancer in order to treat cancer patients before it turns into a life-threatening condition.

Natalia Komarova, Ph.D.

Natalia Komarova, Ph.D., is a Professor in the Departments of Mathematics and Ecology and Evolutionary Biology. She has extensive experience with mathematical models that describe the in vivo dynamics of human diseases, most notably cancer. In particular, she is very familiar with stochastic modeling approaches to describe cancer and with the types of approaches required to predict age-incidence patterns from mathematical models of in vivo biological processes. She has a strong background in applied mathematics, the area in which I performed my doctoral studies at the University of Arizona. She has been working extensively in various fields of mathematical biology since her postdoctoral position at the Institute for Advanced Study in Princeton, including biomedical fields, as well as general evolutionary dynamics and problems in social and behavioral sciences. She has been PI and investigator on a number of NIH and NSF grants, has collaborated with a variety of experimental groups, and has successfully led and coordinated scientific work in the context of larger projects.

Tatiana B. Krasieva, Ph.D.

Tatiana B. Krasieva, Ph.D., is a Project Scientist at the Beckman Laser Institute and the Department of Surgery. She has been involved with the development of optical microscopy methods and its applications through Laser Microbeam and Medical Program (LAMMP) since 1991. She has extensive expertise in developing in-vitro, ex-vivo, and in-vivo microscopy applications, experimental design and data interpretation in the field of optical microscopy, including conventional techniques, multiphoton excited fluorescence, second harmonic generation, fluorescence life-time imaging, optical coherence microscopy and spectroscopy. She has developed an optical method based on fluorescence spectroscopy and fluorescence lifetime imaging for identification and separation of two pigments – eumelanin and pheomelanin, two major constituents of human skin. She also has expertise in development of novel murine model imaging in vivo microscopy applications (skin, brain) and in laser ablation of tissue in the laboratory setting and experience in imaging regeneration after laser injury.

Devon A. Lawson, Ph.D.

Devon A. Lawson, Ph.D., is an Assistant Professor, Department of Physiology and Biophysics. The goal of her research program is to understand the cellular and molecular basis of breast cancer metastasis, with an emphasis on using new single-cell genomics technologies to investigate the role of genetic and phenotypic diversity in different phases of the metastatic process. Her research takes an interdisciplinary approach, combining recent advances in human in mouse modeling, sequencing technology, bioinformatics and computational biology, systems biology, and mathematical modeling to investigate the metastasis problem at an integrated level. Since metastasis remains the cause of most cancer-related mortality, enhanced understanding of the metastatic process is needed to effectively treat and prevent metastatic progression. The ultimate goal of her research is to utilize new insights to develop biomarkers for early detection of metastatic cells, and identify new therapeutic strategies to prevent and treat metastatic disease.

Harry Mangalam, Ph.D.

Harry Mangalam, Ph.D., is a research computing specialist in the Office of Information Technology. He is deeply familiar with a wide range of research computing domains and works with researchers to help them accelerate their work in large scale visualization, bioinformatics, evolutionary biology, high-throughput sequencing, large scale data processing, compute cluster planning & implementation, system administration, data center planning, and grant preparation. He is experienced at integrating multiple pieces of software into a pipeline or complex script, usually addressed with Perl, Python, and R/Bioconductor. He is an expert in C and is familiar with the GNU build toolchain. Dr. Mangalam has performed bioinformatics contract work for the Epidemic Outbreak Surveillance taskforce (now part of the Homeland Security Department), GeneCodes, the CDC, Allergan, Accelerys, and startups. He has also worked in the Science group on their Genomics Knowledge Platform (GKP), which provided both syntactic and semantic integration of biological information from a number of sources through their “Biological Object Model”. In addition, Dr. Mangalam has significant experience in storing and analyzing results from large scale gene expression through projects involving the GeneX gene expression database project, an Open Source Gene Expression database. He has created the sequence analysis application tacg and it’s CGI Web interface tacgi to make a small, fast (~30X faster than GCG or EMBOSS), free, and capable molecular biology tool available for Linux/Unix. Dr. Mangalam has taught introductory linux classes, written online documents on various subjects in scientific computing, and written useful Open Source applications, including the clusterfork, a cluster administration tool, parsyncfp, a parallel, load-balancing wrapper for rsync, and scut, a regular-expression-aware data slicer.

Edwin S. Monuki, M.D., Ph.D.

Edwin S. Monuki, M.D., Ph.D., is the Warren L. Bostick Professor and Chair of the Department of Pathology & Laboratory Medicine. His laboratory investigates basic forebrain development to inform human disorders and stem cell culture strategies with clinical potential. The two forebrain structures of particular interest to his group are the cerebral cortex, the seat of higher cognitive functions, and the choroid plexus, the source of cerebrospinal fluid (CSF) that bathes and nourishes the brain and spinal cord. Over the past several years, the experimental work in his lab has benefitted tremendously from collaborations with systems biologists. Dr. Monuki is working closely with systems biologists to develop an educational program in systems pathology in the School of Medicine at UC Irvine.

Ali Mortazavi, Ph.D.

Ali Mortazavi, Ph.D., is an Assistant Professor of Developmental and Cell Biology. His research interests are in the application of genomic methods to answer fundamental questions in the transcriptional regulation of development using a combination of functional sequencing assays and computational methods. He is particularly interested in understanding how homologous gene regulatory networks are encoded in the human and mouse genomes. He also has a separate interest in comparative genomics and development of nematode parasites with a particular focus on the genus Steinernema. His lab combines experimental work and computational analysis primarily in hematopoietic, skeletal muscle, and embryonic stem cells in human, mouse, and other mammals to understand which regulatory elements are conserved, which elements are not conserved but functional, and which elements regulates what genes. His laboratory has focused over the last five years on doing comparative analyses of regulatory elements and their long-range interactions in human and mouse using a combination of ChIP-seq, ChIA-PET, DNase-seq, ATAC-seq and RNA-seq using ever lower amounts of starting material down to single cells when practical. His long term goals are (a) to develop models that take into account the complex interplay of promoters and enhancers in controlling gene expression and (b) to understand the common and species specific parts of the human and mouse Gene Regulatory Networks for homologous cell types and tissues in order to translate seamlessly results between human and mouse models of disease.

Qing Nie, Ph.D.

Qing Nie, Ph.D., is a Professor of Mathematics, Biomedical Engineering, and Developmental and Cell Biology. He is a fellow of the American Association for the Advancement of Science (AAAS) and American Physical Society (APS). Dr. Nie is also the Director of the Mathematical and Computational Biology Graduate Gateway Program and an Associate Director of the Mathematical, Computational and Systems Biology interdisciplinary graduate program. Originally trained in scientific computing and mechanics during my Ph.D. study and as a postdoctoral fellow working on fluids and materials, during the past 15 years Dr. Nie has devoted his research effort to systems biology of morphogenesis, regulatory networks, cell signaling, and stem cells with emphasis on addressing challenging and complex biological questions in close collaboration with experimentalists. One of his major research aims is to develop predictive models and powerful computational tools that target specific and important questions including developmental patterning, stem cells, spatial dynamics, and stochastic dynamics in cell signaling. Dr. Nie is a leader in interdisciplinary training at the interface between mathematics and biology and is a MPI of a NIH T32 pre-doctoral training grant on Mathematical, Computational, and Systems Biology.

Nilamani Jena, Ph.D.

Nilamani Jena, Ph.D., is a Senior Project Scientist in the Department of Hematology and Oncology. His graduate and postdoctoral studies focused on the molecular pathogenesis of human hematologic malignancies. As a graduate student with Dr. Carlo Croce, he identified a novel pathway of regulation of the BCL-2 antiapoptotic protein via phosphorylation. As a postdoctoral fellow with Dr. George Daley, he demonstrated a specific role for Cyclin D2 in transformation and proliferation of BCR-ABL1-transformed B lymphoid cells. In his subsequent work as a postdoctoral fellow and now as a Senior Project Scientist in the research group of Dr. Rick Van Etten at UC Irvine, he has gained expertise on study of lymphoid development, hematopoietic stem cell isolation, retrovirus mediated gene transfer, bone marrow transplantation, analysis of development of leukemia in mice, and treatment of mice with leukemia with experimental therapy. He also has extensive experience in recombinant DNA techniques, in vitro cell culture, production of recombinant retrovirus, analysis of cells using FACS, analysis of proteins by immunoblotting.

Melanie L. Oakes, Ph.D.

Melanie L. Oakes, Ph.D., is a Project Scientist in the Department of Biological Chemistry and the Facilities Manager of the UCI Genomics High Throughput Facility (GHTF). As manager of the UCI Genomics High Throughput Facility, she brings the latest technologies to the UCI campus research community. She supervises a staff of five research associates in applications including current Affymetrix microarrays, next generation sequencing using the Illumina HiSeq 2500 and 4000 and Pacific Biosciences Sequel sequencers, Nanostring RNA analysis and BioNano Irys genome mapping system. Additionally, her team at the GHTF supports single cell gene expression with the Fluidigm C1 single cell auto prep system and the 10x Genomics Chromium platform. Her team tests and develops custom approaches to assist users in optimal applications of emergent technologies. Her research focused on using yeast as a model system to explore regulation of cellular growth with a specific focus on the regulation of transcription of ribosomal RNA. During the course of the work, she identified and characterized polymerase I transcription factors and took advantage of yeast genetics to create mutants and subsequently analyze ribosomal RNA synthesis, nucleolar structure and cell growth.

Bruce J. Tromberg, Ph.D.

Bruce J. Tromberg, Ph.D., is a Professor of Biomedical Engineering and Surgery. Dr. Tromberg is also the Director of the Beckman Laser Institute and Medical Clinic (BLI) and is the Director of the Laser Microbeam and Medical Program (LAMMP), an NIH P41 National Biomedical Technology Center. His lab has been involved in the development of biophotonic technologies for cancer detection, diagnosis, and treatment for more than 25 years. A major emphasis has been on the clinical translation of in vivo imaging technologies based on nonlinear optical microscopy (NLOM) and diffuse optical spectroscopic imaging (DOSI). He has extensive experience in building and supporting optical imaging technologies, including leading an ECOG-ACRIN national clinical trial of DOSI in breast cancer imaging and a UCI trial of NLOM in melanoma.

Richard Van Etten, M.D., Ph.D.

Richard Van Etten, M.D., Ph.D., is a Professor of Medicine and Biological Chemistry and the Director of the Chao Family Comprehensive Cancer Center at UCI. Dr. Van Etten has been involved in basic and translational cancer research for over 25 years as a faculty member at academic medical centers, initially at the Dana-Farber/Harvard Cancer Center and subsequently at Tufts University. He currently serves as a regular member of NCI Cancer Centers Subcommittee A. He is also a member of the Leukemia Committee of the Eastern Cooperative Oncology Group/ACRIN. His lab studies the molecular pathogenesis of human leukemia with a heavy emphasis on modeling these diseases in laboratory mice using retroviral/lentiviral gene transfer and bone marrow transplantation, and using conditional transgenic mouse technology. He has extensive experience in murine hematopoietic stem cell transplantation and in the analysis of healthy and diseased BM chimeric mice. Dr. Van Etten’s group has modeled adoptive immunotherapy of chronic myeloid leukemia (CML) and B-cell acute lymphoblastic leukemia (B-ALL), discovered novel signaling pathways in CML and B-ALL, and tested new approaches to targeted therapy of myeloid and lymphoid leukemia. Dr. Van Etten also a clinician specializing in hematologic malignancies, currently caring for many CML patients in various phases of the disease, and serving as institutional co-investigator on multiple clinical trials in blood cancer.

Dominik Wodarz, D. Phil.

Dominik Wodarz, D. Phil., is a Professor in the Department of Ecology and Evolutionary Biology. He has a broad background in mathematical models that describe biological processes, especially in the context of diseases and biomedical questions. He has extensive experience with modeling the in vivo dynamics of carcinogenesis, as well as with modeling the in vivo dynamics of viral infections and immune responses. He works with a variety of modeling approaches, including ordinary differential equations, stochastic models, as well as a variety of spatial modeling approaches including agent based models. He began this work as a D. Phil. student at the University of Oxford, expanded on it as a postdoctoral researcher at the Institute for Advanced Study in Princeton. Dr. Wodarz has been working in this area of research as a faculty member both at the Fred Hutchinson Cancer Research Center, and at the University of California Irvine. He has worked successfully with a number of experimental laboratories, and have previously led dual PI projects that involved combinations of mathematics and experiments.

Jie (Jenny) Wu, Ph.D.

Jie (Jenny) Wu, Ph.D., is a Project Scientist in the Department of Biological Chemistry and the UCI Genomics High Throughput Facility. She has a broad background in computational biology, with specific training and expertise in key research areas such as sequence analysis, network analysis, statistical analysis and software development. As a postdoctoral researcher at Boston University, she carried out whole genome sequence data analysis to automatically annotate newly sequenced genomes and developed software for visualization and exploration of functional association networks. At Delta Search Labs, she focused on integrating heterogeneous data types such as microarray data, genomics data, proteomics and metabolomics data from toxicological treatments, using pathway and network analysis with GeneGO metacore and IPA. As a Sr. Scientist at CODA and Verdezyne, she designed pipelines for whole genome sequencing data analysis to optimize protein expression using MATLAB, R and Perl. At UC Irvine, she has performed next generation sequencing data analysis including exome-sequencing, WGS and RNA-seq data, pathway and network analysis with WGCNA and IPA. Working with huge data sets on a daily basis, she has experience with Unix environment scripting, high performance computing and parallel programing. She is also familiar with popular NGS tools such as Bowtie, Samtools, IGV etc.

Xiaohui Xie, Ph.D.

Xiaohui Xie, Ph.D., is a Professor of Computer Science. His main research and teaching interests are in computational biology, bioinformatics and machine learning. He has extensive experience in sequence analysis, genomics, statistics including 1) large-scale genome analysis; 2) development of deep learning methods; 3) development of machine learning algorithms for probabilistic models; 4) data structures and algorithms for sequence alignment; 5) genome-wide regulatory element discovery; and 6) development of analysis tools for CLIP-seq, ChIP-seq, RNA-seq, epigenomics, and genetic variation detection. His group has developed popular tools for cancer genome analysis, including TEMT, a software package for analyzing RNA-seq in heterogeneous cancer tissues, and PyLOH and MixClone, two packages for analyzing cancer genome heterogeneity. Xie has also pioneered in the application of deep learning to genomic analysis. Dr. Xie also been actively involved in teaching machine learning, computational and systems biology to both undergraduate and graduate students.

Core Director

Suzanne B. Sandmeyer, Ph.D.

Suzanne B. Sandmeyer, Ph.D., is the Grace Beekhuis Bell Chair of Biological Chemistry and a Professor of Microbiology & Molecular Genetics and Chemical Engineering & Materials Science. She is the Director of the UCI Genomics and High-Throughput Facility (GHTF), the Associate Director of the UCI Institute for Genomics and Bioinformatics and the Vice Dean for Research in the School of Medicine. As Director of the UCI Genomics High-Throughput Facility, Dr. Sandmeyer strives to make emerging technologies available to investigators at UCI, and where possible beyond, and to facilitate development of such technologies. The GHTF currently provides Affymetrix microarray, Illumina HiSeq 4000 and PacBio Sequel sequencing, Nanostring RNA analysis, BioNano Long Range mapping, and 10X Genomics Chromium single-cell DNA sequencing to the UCI campus and outside clients. The GHTF also provides workshops for users in wet bench techniques and bioinformatics. Dr. Sandmeyer discovered the Ty3 retrotransposon in budding yeast which became a molecular model for Ty3/gypsy elements, one of the largest families of retrotransposons. Using this system and the power of yeast genetics and biochemistry she described the essential features of the Ty3 genome and encoded proteins, Ty3 host factors, virus-like particle assembly in association with RNA processing bodies, and integration targeting by transcription factors. She has expanded her research interests to development of the oleaginous yeast model, for metabolism, Yarrowia lipolytica. She recently published an annotated version of the genome for this organism and a study using transposon mutagenesis to identify essential genes of Yarrowia and test the requirements for growth under different conditions.

Project Manager

Sohail Jahid, Ph.D.

Sohail Jahid, Ph.D., is an Academic Coordinator at the Center for Complex Biological Systems. Her research experiences have included studying the role of microRNAs in the development of colon cancer. She identified a recurrent amplicon on mouse chromosome 8 that encodes microRNAs (miRs) 23a and 27a. miRs-23a and 27a levels are upregulated in mouse intestinal adenocarcinomas, primary tumors from stage I/II CRC patients, as well as in human CRC cell lines and cancer stem cells. She also studied the role of DNA mismatch repair proteins in genomic recombination. Mammalian mismatch repair (MMR) complexes include MLH/PMS proteins, which heterodimerize to form three distinct complexes: MLH1/PMS1, MLH1/PMS2, and MLH1/MLH3. MMR suppresses tumor formation via three mechanisms: repair of base substitution, repair of frameshift, and repair of small insertion-deletion mutation. She became interested in next generation cancer imaging, and joined Dr. Enrico Gratton’s laboratory to develop novel techniques to image cancer cell metastasis in vivo. While in his laboratory, she gained expertise in advanced imaging techniques and how they can be utilized to image the extravasation of cancer cells from the bloodstream. She has also studied RhoJ (a Cdc42 homologue), as a novel regulator of melanoma chemoresistance, and has investigated whether Pak inhibitors are useful agents to treat metastatic melanomas through both transgenic mouse and translational human biomarker studies.


Project 1: Patterned Heterogeneity in Colon Cancer

PIs: Christopher C.W. Hughes, Marian Waterman
Key Personnel: Steven D. Allison, Michelle Digman, Robert A. Edwards, Kai Kessenbrock, Arthur Lander, John Lowengrub, Qing Nie

Solid tumors are complex masses of cancer cells with a multitude of genetic, epigenetic, morphologic and metabolic phenotypes. This heterogeneous condition is a formidable barrier to treating cancer as it underlies the ability of tumors to adapt to nutrient starvation, immune challenges and to develop resistance to cancer treatments – the most common cause of mortality. Non-genetic heterogeneity in gene expression, signaling and metabolism are considered to be some of the most dynamic forms of heterogeneity and the most responsive to the tumor microenvironment. But we currently understand little about such heterogeneity, both mechanistically (what drives it) and functionally (how it helps the tumor). Non-genetic heterogeneity is very challenging to study, partly because of a limited toolbox and partly for lack of tractable model systems. Consequently, there are fundamental unknowns about how such heterogeneity arises and what its role in tumor growth and drug resistance really is.
In preliminary and published work, we observed heterogeneity in a xenograft model of colon cancer where the heterogeneity is patterned in a manner suggestive of a spatially self-organizing process (such as Turing-patterning). What is heterogeneous in these tumors is both Wnt signaling (thought to be the essential driver of proliferation in these cells), and metabolism (the balance between glycolysis and oxidative phosphorylation). In particular, the pattern consists of cell clusters, or spots, in which biomarkers of Wnt signaling are higher than in surrounding regions. These spots also mark regions of glycolytic metabolism. These patterns are likely connected through Wnt regulation of the expression of genes that control metabolism, as identified in our earlier work, and possibly through Wnt-Turing patterning of cancer cell subpopulations. Interestingly, glycolytic heterogeneity has recently been proposed to serve as the basis for resistance to anti-angiogenic therapy, one of the most important clinical problems in colorectal cancer. The short time scale of the xenografting (14-21 days), the reproducibility of the heterogeneity across genetically identical cell lines, and sites of injection, all suggest that this heterogeneity is not genetic.

In this project, Hughes, Waterman and their team seek to understand the causes of the observed heterogeneity in colon cancer, the reason why spatial patterns of heterogeneity develop spontaneously, the consequences of such heterogeneity for the growth of tumor cells, and whether this, or possibly other, forms of heterogeneity indeed drive resistance to therapy (and if so, why). They are addressing these questions by 1) explaining the relationships between heterogeneity, patterning and growth of colon tumors; 2) defining the general principles linking heterogeneity, patterning and growth in colon tumors; and 3) defining the link between heterogeneity and drug resistance.

Figure 1. Xenograft colon tumors reveal a spotted pattern of metabolic (A) and Wnt signaling (B) heterogeneity; concordant heterogeneity in metabolism and Wnt signaling is present in primary patient tumors (C). (D) shows a new vascularized microtumor (VMT) platform that supports human vessels (red) and colon tumors (green), NADH-fluorescence lifetime imaging (FLIM) of colon tumors in the VMT shows metabolic heterogeneity (D: lower right). (E) Single Cell Sequence analysis using Seurat/tSNE clustering reveals that SW480 xenograft tumors are strikingly heterogeneous with a cancer stem cell-like cluster (0: top left) that is missing in tumors expressing dominant negative LEF1 (dnLEF: bottom left). In (E/Middle Panel) Ligand and receptor pairs among several cell clusters of the human and mouse (right panels in E) cells are shown. (F) Candidate mathematical model for simulating SW480 tumors.

The foundation of the project is multi-scale modeling of stochastic and self-organizing processes that potentially explains overt differences in tumor growth, patterning of heterogeneity and metabolism, and the most likely mechanisms for drug resistance. The experimental tools pair xenograft studies with a novel platform for generating fully vascularized micro-tumors in vitro; the use of fluorescence lifetime imaging to read out the metabolic states of unlabeled, living cells, and the use of single cell transcriptomics to identify cell states, the gene expression signatures that define them and signaling and adhesion molecules that mediate communication among the cells. Modeling predictions of strategies that re-establish drug sensitivity will be tested via genetic engineering (CRISPR/Cas9) or small molecule drug therapies. The overarching goal of the work is to discover deep insights into the origins and consequences of tumor heterogeneity in an especially manipulable, and clinically relevant tumor system. The integration of this work with Projects 2 and 3, which focus on different cancer types (melanoma and chronic myeloid leukemia, respectively), enables us to identify general principles that underlie how the in vivo behaviors of transformed cells are constrained by rules inherited from the communities of diverse, interacting cell types and lineage hierarchies within which those cells arise.

Project 2: Understanding the Cellular Origins of Melanoma

PIs: Anand Ganesan, Arthur Lander
Key Personnel: Devon A. Lawson, John Lowengrub, Bruce Tromberg, Tatiana Krasieva

Melanoma, a tumor resistant to therapy in late stages, is curable by excision when caught early. Early melanomas can be difficult to distinguish from benign, pigmented “moles”, i.e. melanocytic nevi; this leads to unnecessary excision of many normal nevi while early melanomas are often missed. Nevi and melanomas share more than morphological features: Clinical and experimental data show that ~90% of nevi are initiated when melanocytes acquire an activating mutation in the BRAF oncogene, the same oncogenic mutation observed in >60% of melanomas. Yet nevi spontaneously stop growing. This is usually attributed to “oncogene-induced senescence,” but the fact that nevi readily re-grow after incomplete excision, or in response to UV-irradiation, and can sometimes evolve to melanoma, suggest nevi are not “senescent” but reversibly growth-arrested. Nevi also spontaneously regress, a process that appears to involve the immune system.

In preliminary work, we investigated nevus dynamics in a mouse model of inducible Braf activation, which mimics human nevus formation and also produces melanomas either at low frequency or when additional oncogenic mutations are added (e.g., in Pten). We found that as we activate Braf in more melanocytes, such that nevi become more numerous and closely-spaced, the smaller individual nevi become—as if nevi, when close together enough, inhibit each other’s growth. Such behavior is predicted by mathematical models of growth control based on feedback through diffusible signaling molecules. Such models achieve robust control when feedback regulates decisions between self-renewal and progression to alternate cell states or fates. Interestingly, when we look closely at the nevi in this model, we see that there are, in fact, two distinct cell types: highly pigmented nevus body cells and a scattered, lightly pigmented melanocyte population that forms a “veil” around the pigmented cells that had not been observed before. These veil cells are usually not seen unless the melanocyte lineage is fluorescently-labeled with GFP. Single cell RNA-sequencing suggests that these cells likely communicate through ligands and receptors they differentially express.

In this project, Ganesan, Lander and their team seek to understand the role of the nevus body and veil cell types in mouse models that produce both nevi and melanoma, and to identify both the nature of how growth is controlled in nevi and the means by which melanoma cells escape from it. The goal of the work is to build a solid molecular and cellular framework on which to base clinical decisions about melanoma prevention, detection and treatment. They are addressing these issues by 1) Explaining feedback growth dynamics in melanocytic nevi; 2) Elucidating how melanomas escape growth control mechanisms that arrest nevi; and 3) Revealing how the immune system targets nevi, and how this affects melanoma development.

Figure 2. (A) BRAF-mutant (Tyrosinase::CreERT2; BraffloxV600E/+) mice are crossed with ROSAmT/mG mice (TCBR) to GFP-label BRAF-mutant melanocytes. (B) Fluorescence emission (confocal and MPM) 3-D imaging of skin of live mice (as in Fig. 1; total depth = 90, 115 and 190μm for 1x, 2x, and 3x representative stacks, respectively). Green = GFP; red = td Tomato. (C,D). Continuum modeling, in which cells at an arrested (quiescent) lineage stage feedback on the self-renewal probability of dividing cells. Model snapshots (D) show cell density (see heatmap) as a function of time (T= cell cycles) and location. Top and bottom rows are for low and high seeding densities, respectively, that represent different levels of nevi induction. (E) Single Cell RNA-seq of melanocytic nevi. Control mice (Tyrosinase::CreERT2; ROSAmTmG; TCR) and TCBR mice, as labeled. Left: tSNE clustering of gene expression for individual cells. Right: A portion of the tSNE map, showing contributions of TCR and TCBR mice as well as expression of selected marker genes.


This project integrates multiscale mathematical modeling with experiments in mice using a nevus-forming inducible activated Braf model, and a version of the same model that combines Braf activation with inducible loss of one allele of Pten, leading to the reliable production of both nevi and melanoma tumors. We are developing hypotheses that can explain the spatiotemporal dynamics and spatial statistics of nevus and melanoma development in these models, including potential bifurcations that account for the development of both nevi and melanoma in the same mouse. We are investigating the reasons why some cells escape from growth control, while others do not. We anticipate that this is unlikely to be due to a requirement for inactivation of the other Pten allele, and instead believe that escape may more likely be due to the spatial dynamics of collective feedback. The results are expected to shed light on signaling pathways that could be manipulated to prevent or treat melanoma. Live cell imaging, focused laser ablation, immunohistochemistry, and time-course single cell RNA-sequencing are used to identify potential positive and negative feedback regulators that drive the mathematical models, and experiments are used to test model-based predictions concerning the roles that such molecules play. Finally, an investigation of spontaneous regression, which occurs with both mouse and human nevi, provides clues into how the immune system efficiently recognizes melanocyte overgrowth. Since immunotherapy has recently emerged as a promising therapy for melanoma, this study is expected to reveal whether immunotherapy leverages an existing immune program for eliminating nevi, and if so, how that program is carried out, and how melanomas typically escape from it. Such information should aid in developing new prevention and therapeutic strategies for this devastating disease. The integration of this work with Projects 1 and 3 occurs through the use of scRNA-seq as a tool for hypothesis generation and development and through the application of mathematical models that have similar underlying structures (cell state transitions, proliferation and quiescence, positive and negative feedback), but differ in their context (e.g., spatial in Projects 1 and 2 vs. non-spatial in Project 3).

Project 3: Modeling malignant myelopoiesis to increase efficacy of targeted leukemia therapy

PIs: Richard Van Etten

Key Personnel: Kai Kessenbrock, Natalia Komarova, John S. Lowengrub, Qing Nie, Dominik Wodarz, Xiaohui Xie, Nilamani Jena
Chronic myeloid leukemia (CML), one of the most prevalent of human leukemias, is a natural model of dysregulated granulopoiesis driven by a single genetic abnormality in a hematopoietic stem cell, the BCR-ABL1 gene fusion. Although therapy with tyrosine kinase inhibitors (TKIs) such as imatinib mesylate has dramatically lowered the death rate in CML, lifelong treatment is needed and the associated economic costs are significant. Two major unaddressed questions are to understand the mechanism of primary resistance to TKI therapy (affecting ~10-15% of newly diagnosed CML patients), and to identify strategies to increase the frequency of complete molecular remission (CMR) in patients treated with TKIs and subsequently the rate of treatment-free remission (TFR), which may represent a surrogate for permanent cure of the disease. The scientific premise of this project is that new and clinically relevant insights into the biology of CML and its response to therapy can be gained by a more physiologically accurate mathematical model of the disease.

While mathematical models of CML have been developed by several groups, these models tend to be highly simplified and largely omit feedback interactions among the different hematopoietic components. These models are designed to fit clinical data sets of the response of patient populations to TKI therapy but unfortunately, they have not proven useful for understanding primary TKI resistance or for predicting TFR. In preliminary work, we found that nonlinear models that incorporate feedback are more robust, have a better fit to alternative patterns of patient response than simple linear models used previously, and allow predictions about the effects of interventions affecting parameters (such as cell cycle status) subject to feedback mechanisms on the response to TKI therapy. Preliminary analyses from prototype feedback models have already raised two provocative hypotheses about CML. The first is that the initial response to TKI therapy may depend on the relative size of the leukemic stem cell clone, which will be analyzed by TKI treatment of mice engrafted with different levels of BCR-ABL1+ stem cells. The second is that interventions that increase leukemic stem cell cycling may sensitize this population to killing by TKIs.

In this project, Van Etten and his team seek to develop improved mathematical models of chronic phase CML and the response to TKI therapy, to validate these models using data from a binary transgenic mouse model of CML and from human CML patients, and to utilize the models to test several hypotheses about the response of CML to therapy and to predict strategies for improving the treatment-free remission rate in CML. We address these issues by 1) Developing data-driven, dynamic models of CML hematopoiesis incorporating feedback control; 2) Measuring granulocytopoiesis parameters in a CML mouse model and in human CML patients; 3) Testing model-driven hypotheses about the response of CML to therapy and informing strategies for improving the treatment-free remission rate in CML.

Figure 3. (A) Illustrative nonlinear feedback models of hematopoiesis. The red box indicates the model used in (B). (B) Increased leukemic stem cell (LSC) cycling predicts faster LSC decline on TKI therapy. The results correspond to different LSC cycling parameters applied (expressed as ratio of LSC to multipotential progenitor (MPP) cycling rate). Note the increasing negative slope of the second linear phase with increased cycling. (C,D) Selective decrement of HSC compartment increases MPP proliferation. Mice were irradiated (50 cGy), injected 24h post-radiation with BrdU and analyzed 12h later. (C) 50 cGy irradiation significantly (P=0.018) decreased the size (%LSK) of the HSC compartment (right panel) at 36h without a significant effect on the MPP compartment (left panel). (D) Reduction in HSC pool by low-dose radiation dramatically increases proliferation of the MPP compartment, supporting the existence of a negative feedback loop in the model above. (E) t-SNE analysis of scRNA-seq data from the MPP population isolated from mice with BCR-ABL1-induced CML shows eight separate subpopulations. (F) Expression of sorted lineage markers mapped on the t-SNE plot from (E). (G) Expression of GFP (marking leukemic MPP) and MPP/HSC markers mapped onto the t-SNE plot from (E).

Machine-based automated model selection methods are being utilized to arrive at a mathematical model that maintains appropriate stability and homeostasis, responds physiologically to stress and depletion of different cell compartments, and conforms to the limited existing qualitative data on CML hematopoiesis derived from mouse models and patient studies. To validate and inform potential models, binary/conditional BCR-ABL1 transgenic donor mice are being used to generate mixed BM chimeras via transplantation of high doses of unfractionated marrow cells without use of conditioning radiation. Recipients bearing a clone of BCR-ABL1+ cells have leukemia induced by withdrawal of doxycycline, leukemic mice are treated with BrdU or subjected to short-term stable isotope labeling with D2-glucose, marrow and spleen stem and progenitor compartments isolated by flow cytometry, and cell cycle and kinetic parameters estimated to inform the mathematical models. Single cell transcriptome profiling is used to investigate the heterogeneity of the MPP compartment and to discover potential regulatory mechanisms mediated by cytokine/receptor interactions. Feedback relationships are tested via direct in vivo manipulation using depleting monoclonal antibodies and through a novel mouse that targets metronidazole cytotoxicity to specific cell compartments. Parallel studies of human CML progenitor flux will be carried out through a clinical protocol of short-term D2-glucose labeling in patients presenting with suspected CML prior to diagnostic BM biopsy. The response to TKI therapy is also being analyzed. The integration of this work with

Projects 1 and 2 occurs through the use of scRNA-seq as a tool for hypothesis generation and development and through the application of nonlinear mathematical models that have similar underlying structures (cell state transitions, proliferation and quiescence, positive and negative feedback), although the models in Project 3 are non-spatial.


Single Cell Analysis Core

Core Director: Suzanne Sandmeyer
Key personnel: Kai Kessenbrock, Devon A. Lawson, Melanie Oakes, Ali Mortazavi, Jie (Jenny) Wu

This core provides technical, bioinformatic, and training support for the Center. The goal of the Single Cell Analysis Core technical support is to make cost-effective high-throughput analysis of single cells optimal for and accessible to each of the three Projects. Technical support consists of providing staff and instrument infrastructure, as well as advising on design of experimental strategies, facilitating sharing of protocols for process development; providing single-cell services including microscopy, protein localization, cell sorting, library production, and sequencing; and working with investigators to innovate in these technologies

Administrative Core

Core Director: John S. Lowengrub

Key personnel: Arthur Lander, Ed Monuki, Marian Waterman
The Administrative Core (AC) provides the administrative, communication and oversight needs for the three projects and the single cell analysis and outreach cores. The AC defines, approves and reviews membership in the Center and provides logistical support for Center members, including assisting investigators with meeting regulatory hurdles associated with research. In addition, the AC solicits, reviews and administers two types of pilot project grants: one directed at graduate students and postdocs and the other directed at faculty. The AC guides the integration of the Center into the Cancer Systems Biology Consortium by facilitating the sharing of Center resources and member expertise across the Consortium, by participating in the Annual Consortium meeting and by matching Center members with counterparts in the Consortium to foster new collaborations.

Outreach Core

Core Director: Arthur Lander
Key personnel: Ed Monuki; Sohail Jahid

The Outreach Core of the UCI cancer systems biology center promotes cancer systems biology to the research community, targeting researchers and trainees at all career stages, and disseminates advances and capabilities of cancer systems biology to the cancer research and broader communities. These goals are accomplished through a variety of activities including symposia, seminars, “short courses”, interest groups, “bootcamps”, a visiting scientist program, and an annual retreat. Included among the activities of the core are programs aimed at mentoring junior faculty, programs to provide undergraduate and pre-college students with exposure to cancer systems biology research, and activities to increase public awareness of the advances and capabilities of cancer systems biology. The core monitors its effectiveness through periodic evaluation, and coordinates and integrates with activities of the larger NCI Cancer Systems Biology consortium

The Cancer Cell Map Initiative


Center Title

The Cancer Cell Map Initiative

Center Website

Center Summary

There now exists a vast amount of sequence data from tumors associated with many different cancer types, and efforts are ongoing to extract mechanistic insight from this information. Given all of this progress, what is now needed is an integrated computational and experimental strategy that will help place these alterations into context of the higher order biological mechanisms in cancer cells. This is the goal of the Cancer Cell Map Initiative, which will create a resource that can be used for cancer genome interpretation. This will allow us to identify key complexes and pathways to be studied in greater mechanistic detail to get a deeper understanding about the biology underlying different cancer states. Genomic data derived from tumor sequencing studies identifies key genes implicated in different cancer cells. Integrated physical and genetic networks based on these factors will help put the mutations into biological context, enabling the discovery of new disease genes as interacting partners become apparent. Ultimately, all of this knowledge will translate into improved ability to stratify and treat patients based on the particular networks that are altered.


The Co-Directors of the CCMI are Drs. Nevan Krogan (UCSF) and Trey Ideker (UC San Diego). The research interests and expertise of the CCMI faculty members run the gamut from structural biologists to cancer specialists.

Dr. Nevan Krogan

Dr. Nevan Krogan is a Professor in the Department of Cellular and Molecular Pharmacology at UCSF whose lab focuses on generating, analyzing and visualizing large-scale, quantitative genetic and physical interaction maps to further understand cell physiology.

Dr. Trey Ideker

Dr. Trey Ideker is a Professor in the Departments of Medicine, Bioengineering and Computer Science at UC San Diego whose lab focuses on mapping the molecular networks underlying cancer and using these networks to guide the development of novel therapies and diagnostics.

Dr. David Agard

Dr. David Agard is a renowned structural biologist at UCSF whose research interests include the mechanism of Hsp90 chaperone function and its role in human disease, microtubule nucleation and centrosome structure, and the structure and cell biology of phage encoded tubulins.

Dr. Prashant Mali

Dr. Prashant Mali is a leader in the exploding field of genome editing. As a postdoctoral fellow in Dr. George Church’s lab at Harvard, he published one of the first papers using CRISPR/Cas9 to edit the human genome. His lab at UC San Diego is developing a range of novel applications for genome engineering including his work on screening for genetic interactions.

Dr. Jill Mesirov

Dr. Jill Mesirov, previously the Associate Director and Chief Informatics Officer at the Broad Institute, was recently appointed Associate Vice Chancellor for Computational Health Sciences at UC San Diego. Her team has developed many popular analysis and visualization software packages, such as Gene Set Enrichment Analysis, GenePattern and the Integrative Genomics Viewer.

Dr. Alan Ashworth

Dr. Alan Ashworth is the President of the Helen Diller Family Comprehensive Cancer Center at UCSF and has spearheaded the understanding of synthetic lethality in cancer, showing that these insights can form the basis of new therapeutic approaches.

Dr. Jennifer Grandis

Dr. Jennifer Grandis is the Associate Vice Chancellor of Clinical and Translational Research at UCSF and is a leading researcher in head and neck cancer. Her lab has made many seminal discoveries about the molecular mechanisms underlying the resistance to EGFR inhibitors in head and neck cancer.

Dr. Silvio Gutkind

Dr. Silvio Gutkind, previously the Chief of the Oral and Pharyngeal Cancer Branch at the National Institute of Dental and Craniofacial Research, is now the Associate Director for Basic Science at the UC San Diego Moores Cancer Center. Dr. Gutkind is a leading expert in signaling pathways and 3D cell culture models of head and neck cancer.

Dr. Laura Esserman

Dr. Laura Esserman is the Director of the Breast Care Center at the UCSF Helen Diller Family Comprehensive Cancer Center. She is also the PI for the I-SPY 2 Trial, a large clinical trial that is screening multiple drugs from multiple companies with the hope of dramatically increasing the rate of identifying safe and effective new treatments for breast cancer.

Dr. Laura van ’t Veer

Dr. Laura van ’t Veer, an expert on personalized medicine, is Leader of the Breast Oncology Program and Associate Director Applied Genomics at the UCSF Helen Diller Family Comprehensive Cancer Center. Her research aims to advance patient management by using knowledge of the genetic makeup of both the tumor and the patient to optimally assign systemic therapy.


Project 1: Systematic Identification of Driver Networks in Cancer

Project Leaders: Krogan and van ‘t Veer
Co-Investigators: Agard, Ashworth, Ideker, Grandis and Gutkind

A vast number of mutations contribute to cancer, but the observed non-random combinations of those leading to transformation highlight the importance of hallmark pathways and networks in cancer progression. While many pathways have been implicated in cancer, attributes such as tumor heterogeneity, tissue of origin, and degree of progression lead to each case exhibiting a unique subset of altered pathways. Taken together, this diversity among cancer types and their origins has complicated the development of targeted cancer treatments. We propose to systematically identify the protein networks driving cancer, across a range of tumor types starting with head and neck squamous cell carcinoma and breast cancer. Coupled with functional validation and high-resolution structural analysis of the key protein interactions and complexes, we anticipate major insights into the underlying tumor biology as well as the potential to unravel genetic vulnerabilities of therapeutic relevance.

Project 2: Mapping the Pharmacogenetic Landscape for Precision Medicine

Project Leaders: Ashworth and Mali
Co-Investigators: Esserman, Grandis, Gutkind, Ideker, Krogan, Mesirov and van ‘t Veer

It is well known that cancer is tremendously heterogeneous with few tumors having the same set of mutated, amplified, or deleted genes. Clearly these molecular differences alter a tumor’s responsiveness to chemotherapy, but current knowledge of how the tumor genotype influences drug sensitivity is poor. We will seek to vastly increase our understanding of pharmacogenetic interactions in cancer (gene-gene and gene-drug interactions). Recognizing that oncogenic transformation requires alteration of the function of many genes, we will use state-of-the-art high-throughput epistasis mapping and data analysis pipelines to systematically interrogate the function and pairwise interactions of a panel cancer driver genes and therapeutic targets in both head and neck squamous cell carcinoma and breast cancer, expecting to identify many new synthetic lethal relationships. Anticipating the discovery of multiple therapeutically relevant synthetic lethal interactions, we have already formulated a plan for rapid clinical testing of the most promising hits as new treatment arms on the I-SPY 2 trial in breast cancer. Through this work, we expect to develop fundamental new insights into the genetic logic and functional synergies underlying cancer pathways as well as to greatly expand the ability of clinicians to practice precision oncology.

Project 3: Using Networks to Seed Hierarchical Whole-Cell Models of Cancer

Project Leaders: Ideker and Mesirov
Co-Investigators: Esserman, Grandis, Gutkind and van ‘t Veer

Knowledge of cell biology is often modeled in the form of molecular networks and interaction maps, consisting of sets of genes and gene-gene (or protein-protein) pairwise interactions. In reality, however, biological systems are not simply one large protein network, but consist of a deep and dynamic hierarchy of functional subsystems ranging across many orders of magnitude in scale. Here, we move beyond basic interaction maps to instead use molecular interaction data to develop hierarchical structure/function models of the cancer cell. This hierarchical structure will be developed using the protein-protein interaction data generated here and backstopped by public networks; it will provide an objective definition of a cancer cell by systematically identifying the hierarchical relations among its associated systems of genes and proteins. We will next use this descriptive hierarchy to seed a predictive whole-cell model of cancer. This hierarchical model will be validated and revised by applying it to predict therapeutic responses in PDXs of head and neck and breast tumors as well as inform an ongoing I-SPY 2 breast cancer clinical trial.


Core 1: Functional Genomics

Core Leaders: Krogan and Mali
Co-Investigators: Ashworth

The Functional Genomics Core provides cutting-edge innovative technologies for the functional characterization of the genome in a reliable, reproducible and cost-efficient manner. We provide combinatorial genetic knockout by CRISPR, CRISPRi and CRISPRa, mass spectrometry characterization, and expertise in data processing for these experimental platforms. This core is partnered with two well established facilities: the Thermo Fisher Scientific Proteomics Facility for Disease Target Discovery located at the J. David Gladstone Institutes and the UCSD Institute for Genomic Medicine Genomics Center sequencing facility. We are also creating a new CRISPR screening core that leverages our foundational expertise in genome engineering.

Core 2: Bioinformatics Infrastructure and Services

Core Leaders: Ideker and Mesirov

The Bioinformatics Infrastructure and Services Core provides support to all three CCMI projects at all stages of research and publication. The Core is made up of three major components: Cytoscape and the Cytoscape Cyberinfrastructure (CI); the Network Data Exchange (NDEx); and the CCMI Data and Analysis Portal. Cytoscape provides a range of tools for the analysis and visualization of biological networks. NDEx provides the database infrastructure to support the sharing, review and dissemination of network data and models. It also enables a consolidated access point to public biological network resources for use by CCMI investigators. Finally, the CCMI Data and Analysis Portal provides a common access point for software tools and pipelines and for their associated data. This component will be supported by GenePattern Notebooks, which will facilitate the development of workflow pipelines and the sharing and reproducibility of analyses.

Quantitative and functional characterization of therapeutic resistance in cancer


Center Title

Quantitative and functional characterization of therapeutic resistance in cancer

Center Website

In progress

Center Summary

Fig. 1. Overview of Center and integration of Projects and Cores.

Despite tremendous advances in understanding of cancer pathogenesis, the treatment of individual patients with either conventional chemotherapy or targeted agents remains highly empiric. Current efforts to predict drug efficacy are generally focused on genetic and transcriptional markers of pathway activation or drug binding, such as resistance mutations that sterically hinder small molecule binding or activate parallel or orthogonal signaling pathways. These markers exist in a very small fraction of all cancers, such that most patients are treated with little or no understanding of whether they will respond to an individual therapy. This results in many patients receiving ineffective and/or unnecessarily toxic therapies. There is a desperate need to change this paradigm. The ideal for characterizing therapeutic sensitivity would allow for: real-time decision making, identification of rare subpopulations with therapeutic resistance, analysis of very small samples (e.g. MRD), and maintenance of viable individual cells for downstream assays to characterize phenotypic, genotypic, transcriptional and other determinants of sensitivity.

As shown in Fig. 1, the overall goal of our center is to address this need using new strategies for predicting therapeutic response in which paired phenotypic and genomic properties are measured at the single-cell level. Phenotypic properties will include both physical parameters (e.g. mass, mass accumulation rate) and molecular markers (e.g. protein secretion, surface immunophenotype) that are rapidly affected by effective therapeutics and precede longer-term phenotypes (e.g. loss of viability). Because these properties are measured for each single cell, clonal architectures based on therapeutic response will be established across each tumor sample by incorporating molecular and physical parameter data from large numbers of cells. In settings of deep treatment response, pre-treatment and MRD samples will be compared to define the effects of therapy on clonal architecture. The cells that exhibit particular functional properties (e.g. phenotypic non-responders) will be isolated and analyzed for genomic determinants of these properties. These data will then be incorporated into mathematical models to design and optimize therapeutic approaches that overcome the heterogeneity within individual tumors responsible for treatment failure. By pursuing this approach, our center will establish a framework that enables an iterative cycle between novel single-cell measurements from clinically-relevant specimens and computational approaches that result in testable predictions.


Principal Investigators

Dr. Scott Manalis

Dr. Scott Manalis has an undergraduate and doctorate degree in physics and applied physics, respectively, and his faculty appointment is in the departments of biological and mechanical engineering and an intramural member of MIT’s cancer center (Koch Institute for Integrative Cancer Research). He was the PI of MIT’s PSOC for Single Cell Dynamics in Cancer from 2012-2016 and a leader of a project and core within the center since it started in 2009. His lab developed suspended microchannel resonators for measuring the mass and mass accumulation rate of single cells with unprecedented precision – a capability that is used extensively in this center for measuring ex vivo drug sensitivity of tumor cells.

Dr. Douglas Lauffenburger

Dr. Douglas Lauffenburger is a biological engineer, formally educated in chemical engineering but with research program focused on quantitative, multi-variate studies of cell biology since beginning his academic faculty career in 1979. He is an affiliate member of the Koch Institute for Integrative Cancer Research, and has served as PI of the NCI-funded MIT Integrative Cancer Biology Program for the period 2007-2014. His expertise is in integration of computational analysis and modeling with quantitative cell biology and biochemistry experiments, toward development and testing of mathematical models for how phenotypic cell functions depend on cellular and extracellular molecular properties. He has extensive experience in combined experimental/computational studies of relating molecular properties and phenotypic behaviors on a single-cell basis.

Dr. William Hahn

Dr. William Hahn is a medical oncologist and professor of medicine at Harvard Medical School who has extensive experience in the genomic characterization and functional genomic analysis of cancers in vitro and in vivo. He is currently the Chief of the Division of Molecular and Cellular Oncology and Chair of the Executive Committee for Research at DFCI. Dr. Hahn is also an Institute Member of the Cancer Program at the Broad Institute. His lab has developed new experimental models of human cancer of defined genetic composition, created methods to perform systematic interrogation of gene function in mammalian cells and tissues and helped optimize new approaches to integrate genome scale data. Using these approaches, they have identified and credentialed new oncogenes and tumor suppressor genes and have performed preclinical studies that will form the foundation necessary for translational studies in patients.

Dr. Alex K. Shalek

Dr. Alex K. Shalek received training in physics, mathematics and chemistry, and his faculty appointment is in the Institute for Medical Engineering and Science and the department of Chemistry. He is also an Associate Member of the Broad and Ragon Institutes, where he has additional labs and access to an array of cutting-edge equipment, platforms and approaches. His expertise relevant to this proposal is on developing and utilizing nanoscale manipulation and measurement technologies to understand how small components (molecules, cells) drive systems of vast complexity (cellular responses, population behaviors). As a postdoctoral fellow, he developed a strategy that uses single-cell RNA-Seq to identify distinct cell states and circuits from the natural variation that exists between seemingly identical cells. His lab is determining how cell-to-cell variability arises from intra- and inter-cellular regulatory circuits in healthy and diseased states, as well as to explore the causes and consequences of cellular heterogeneity. These approaches are used extensively in this center.

Dr. David Weinstock

Dr. David Weinstock is an oncologist at DFCI, Associate Professor at Harvard Medical School and Associate Member of the Broad Institute. His laboratory has made a series of high-impact discoveries in leukemias, including the identification of CRLF2 as a targetable oncoprotein in poor-risk B-cell acute lymphoblastic leukemia (B-ALL), the therapeutic targeting of CRLF2-dependent leukemias with inhibitors of JAK2, HSP90, MDM2 and BRD4, the role of the nucleosome remodeling protein HMGN1 in cases with Down Syndrome, and the identification of alterations in G protein subunits as a mechanism of targeted therapy resistance. The Weinstock lab leads the effort to establish leukemia and lymphoma PDX models at the DFCI, Brigham and Women’s Hospital, Boston Children’s Hospital and Massachusetts General Hospital. He has already established a repository of >300 models serially passaged through immunodeficient mice and characterized by exome and transcriptome sequencing, and made these available open-source ( as well as through the Jackson Laboratories. He currently leads a multi-institutional Specialized Center of Research that is supported by the Leukemia and Lymphoma Society and focuses on lymphoid cancer biology and targeting.

Dr. Christopher Love

Dr. Christopher Love has an undergraduate and doctoral degree in physical chemistry, and his faculty appointment is in the department of chemical engineering. He is an intramural member of the Koch Institute for Integrative Cancer Research and an associate member at both the Broad Institute and Ragon Institute. He has been part of a working group on systems biology and engineering solutions for sparse samples in mucosal immunology (DAIDS/HVTN MIG). He has served as the primary PhD thesis advisor of graduate students from the departments of chemical engineering and biological engineering. His lab developed the nanowell technology that is used in our center. It has been optimized in all aspects of the technology, including imaging of cells in nanowells, time-resolved, multiplexed analysis of cytokines, and software tools for data extraction. New advances in plate formats allow for 24-array processing in parallel, and up to 16-channel imaging cytometry on a microscope. These capabilities will be employed in both projects.

Ms. Kristen Stevenson

Ms. Kristen Stevenson is a biostatistician and has worked for the past 10 years at DFCI in research of hematologic malignancies primarily including myelodysplastic syndrome, chronic lymphocytic leukemia, and acute lymphoblastic leukemia. She also has undergraduate degrees in chemical engineering, computer science, and industrial mathematics and statistics. Her expertise is in statistical modeling and analysis, power and samples size calculation, and experimental and clinical trial design.


There are three novel single-cell analysis approaches that extensively utilized in our center:

[1] Suspended microchannel resonator. Our approach for measuring cell growth, or mass accumulation rate, is based on a sensing technology known as the suspended microchannel resonator (SMR). When an object that is denser than water passes through the SMR, the net increase in mass (i.e. the buoyant mass of the object) lowers the resonant frequency. By continually measuring buoyant mass when the cell travels back and forth through the sensor, growth of individual bacteria, yeast, and mammalian cells has been quantified with a precision that has not been achievable by conventional microscopy. In our center, we will use a novel SMR array-based technique for high-throughput mass accumulation rate measurements that increases throughput by 100-fold without sacrificing precision. This is accomplished by an array of SMRs microfluidically connected in series, with “delay” channels in between each cantilever (Fig. 2). These delay channels give the cell time to continue changing mass as it flows between cantilevers. After a cell exits a cantilever, other cells are free to enter it and be weighed. We are not limited to flowing only one cell through the array at a time, but can have many cells flowing through the array in a queue, enabling precision growth measurements with a throughput approaching 100 cells per hour per device. In a collaboration between MIT and DFCI researchers, the serial SMR device has been validated for ex vivo drug susceptibility testing in several cell lines, patient-derived cell lines and primary cells from mouse models.

Fig. 2: A) Micrograph of serial SMR device. Cantilever lengths range from 380 to 470 μm to provide unique resonant frequencies, which are readout with integrated piezoresistors. The MEMS foundry CEA-LETI recently fabricated several 8” wafers, each of which includes ~200 hundred devices. B) Buoyant mass data from mouse lymphoblast cells extracted from frequency shifts of cantilevers, color-coded by cantilever. 7 and 9 μm polystyrene particles are added as calibration and negative control, respectively. Each cell requires ~20 min to pass through the serial array, which is sufficient for measuring its mass accumulation rate with high precision. Over the course of incubation with a selected drug (2-24 hrs), the serial SMR can detect changes in the growth of single cells to predict therapeutic response without the need for extended culture. In some cases, measuring single cell growth can result in drug sensitivity test that is sufficient more rapid than viability. This is important since the properties of primary cells change quickly when moved to an ex vivo environment.

Through a seed grant from the MIT/DFCI Bridge program (co-Principal Investigators: Manalis, Weinstock), two serial SMR platforms are now being operated in CLIA-approved space at the DFCI/Brigham and Women’s Cancer Center by a dedicated technician in the Weinstock lab. This was an essential step toward translating the SMR into a clinical assay for predicting therapeutic response in heterogeneous tumor populations directly from primary specimens.

[2] Single-cell genomic profiling. scRNA-Seq, an approach co-developed by the Shalek Lab in both basic and clinical settings, is utilized throughout Projects 1 and 2. In previous work, we have established and validated robust experimental and computational pipelines for scRNA-Seq. This work has demonstrated that scRNA-Seq enables unbiased identification of the cell types, states, and circuits that drive complex biological systems, such as tumors, at the single-cell level. More specifically, in the context of human cancer biopsies, we have optimized strategies for the capture and lysis of individual cancer, stromal, and immune cells, and developed computational approaches to identify the cell states of each and their molecular underpinnings. Here, scRNA-Seq will enable not only the precise identification of malignant and non-malignant cells from the bone marrow microenvironment, but also the nomination of putative extracellular factors that influence malignant cell drug responses through the identification of co-expressed gene programs downstream of secreted molecules or receptor-ligand pairs. Recent work in the Shalek Lab, performed in collaboration with the Regev (Broad, MIT) and Garraway (DFCI) Labs, identified intratumor and intertumor heterogeneity in melanoma biopsies and discovered patient-specific immune and stromal cell populations that correlate with prognosis and potentially explain some instances of resistance (Fig. 3). Here we propose to specifically examine the phenotype of cells with unaffected MAR in the presence of drug using scRNA-Seq in order to define resistant cell states and intuit treatment strategies that target them.

Fig. 3: scRNA-Seq in human cancer. scRNA-Seq of primary human melanoma samples demonstrates (A) inter- and intratumor heterogeneity among malignant (left) and non-malignant (right) cells. Further, in the context of malignant cells, (B) substantial variability is observed in the fractional abundance of a drug-resistant cell state (AXL-high) across patient samples (labeled MelXX). Adapted from Tirosh et al, Science, 2016.


[3] Nanowells. We will utilize a nascent technology for single-cell analysis that relies on dense arrays of subnanoliter wells to profile the phenotypic and molecular properties of tumor cells and for precise control of a cell’s microenvironment (Project 2). The majority of examples, and the development of this technology to date, has emphasized the characterization of the diversity and functions of antigen-specific B- and T-cells of the immune system. The Love Lab has developed a strategy for bioanalytics that uses these arrays in defined processes comprising one or more modular unit operations—each one reporting on different characteristics of the cells analyzed, such as phenotypic lineages or functions (Fig. 4). Examples include on-chip cytometry (51), quantitative measures of secreted proteins by a printing method called microengraving, single-cell cytolysis assays and gene expression. To date, this nanowell-based platform has been used to characterize mouse hybridomas, mouse B and T cells, yeast cells, and human PBMCs.

Innovative aspects of the nanowell technology relevant to this proposal include:

  • Modular operations that link complex phenotypic traits like viability to genotypic features (clonal lineages determined by single-cell sequencing)
  • Resolution and recovery of extremely rare cells (~1 in 10,000 or fewer), including circulating tumor cells, for clonal expansion or single-cell sequencing
  • Deep immunophenotyping by multi-spectral imaging cytometry (MuSIC) for up to 16 channels of data (comparable to state-of-the-art flow cytometers)
  • Quantitative measures of secretion from one or a few cells with sensitivities 10-100x greater than conventional clinical assays like ELISpot
  • Discrete co-cultures of multiple cells to measure intercellular communication among tumor cells, or mixed populations.




Fig. 5. Functional characterization of therapeutic resistance in cancer.Cells are treated in vivo before target populations are extracted, purified and measured immediately upon removal from the mouse or patient. Alternatively, the target population is treated ex vivo prior to the measurement. Upon measurement of mass accumulation rate (MAR), each cell is sorted into a well and lysed within ~1 min, thereby allowing scRNA-seq of a given cell to be linked to its MAR. Cells that maintain MAR similar to vehicle-treated cells after sufficient drug exposure are considered to be non-responders. The overall goal of this proposal is to determine mechanisms of resistance and make testable predictions to overcome that resistance through transcriptome analysis of phenotypically-defined single cells.

As illustrated in Fig. 5, scRNA-Seq will be acquired from cells that are determined by the SMR growth assay to be non-responders to a particular treatment (i.e. phenotypically drug resistant). These data will provide unprecedented insight into: i) pathways that distinguish response after target inhibition compared to untreated cells, ii) whether these same pathways distinguish response upon relapse and thus could be predicted at the time of minimal residual disease, and iii) pathways that associate with differential response to targeted therapeutics between different compartments (e.g. bone marrow and peripheral blood leukemia cells at the untreated and relaps ed time points). Based on these findings, we expect to nominate combination strategies that target the phenotypically-resistant cells as well as come up with drugs that might be appropriate targets currently not appreciated as being relevant for a particular cancer. Project 1 will focus on cell-intrinsic properties of tumor cells while Project 2 will focus on cell-extrinsic effects through the use of nanowells to control paracrine and juxtacrine interactions that mimic the microenvironment.


Project 1. Systematic discovery of cell-intrinsic mechanisms of cancer drug resistance

PIs: Manalis (lead) and Lauffenburger (co-lead), Shalek, Weinstock and Hahn
We aim to utilize high precision single-cell growth measurements together with single-cell RNA-Seq (scRNA-Seq) to profile the intrinsic factors that inform the responses of individual cancer cells to therapeutic interventions. We will ask to what extent paired phenotypic and transcriptomic measurements can identify pathways that mediate cell autonomous resistance and highlight therapeutic approaches to overcome that resistance. Cancer cells will be isolated from primary tumors or from patient-derived cell lines/xenografts of both leukemias (as a liquid tumor model) and colon/pancreatic cancers (as a solid tumor model). In contrast to Project 2, cells will be measured in isolation without mimicking aspects of the microenvironment. Over a period of many hours, we will examine distinct phenotypic attributes of the cells (mass and mass accumulation rate) and link these attributes to the transcriptome at the single-cell level. We will then determine cell intrinsic mechanisms for resistance by analyzing transcriptomic features of responding and non-responding tumor cells.

Project 2. Systematic discovery of cell-extrinsic mechanisms of cancer drug resistance

PIs: Shalek (lead), Hahn and Love (co-leads), Weinstock, Lauffenburger and Manalis
We aim to utilize high precision single-cell growth measurements and single-cell RNA-Seq (scRNA-Seq) together with nanowells to systematically examine how the extracellular factors present in leukemias and colon and pancreatic cancers influence drug responses. First, we will identify the signals and non-malignant cells present in each tumor type by performing scRNA-Seq on primary biopsies. We will then generate an atlas of implicated cell types/states and putative signaling molecules that may influence cancer cell drug responses in vivo. Second, we will use high precision single-cell growth measurement and nanowells to systematically uncover how these soluble factors and tumor cells inform on cancer cell drug responses; we will similarly examine the impact of previously implicated environmental factors as well as other cancer cells (of the same and different intrinsic states; from Project 1). By analyzing and modeling our results, we will uncover previously unknown microenvironmental synergies (e.g., cytokines, receptor-ligand pairing) that may modulate cancer cell drug responses in vivo. Collectively, these aims will afford an unprecedented view of the tumor microenvironment and shed light on current therapeutic bottlenecks, while suggesting potential new and more effect therapeutic inroads for treating cancer.


Core 1: Biospecimens and Patient-derived xenografts

PIs: Hahn (lead) and Weinstock (co-lead)
The overarching goal of Core 1 is to provide the infrastructure and professional expertise needed to bank primary leukemia, colon, and pancreatic cancer specimens, establish patient-derived xenograft (PDX), organoid, and cell models, and make these patient-derived resources available to all Program investigators, thereby facilitating the translational and laboratory-based research performed by CSBC investigators in Projects 1 and 2. Both projects will require access to patient samples and cells derived from PDX and organoid models. The core supports 3 functions: (1) cryopreservation and distribution of primary cells from patients with leukemia, colon, and pancreatic cancer, (2) establishment, characterization, and distribution of xenograft, organoid, and cell culture models of these diseases, and (3) isolation of cell populations from primary tumor samples and tissue models for single cell analysis. The functions of the core include documentation and tracking of informed consent, isolation and cryopreservation of viable tissue, plasma or serum, and genomic DNA from patient samples and distribution of samples to CSBC investigators. A large number of primary leukemia, colon and pancreatic tumor samples are currently available in our existing repositories for use by Project Investigators. Additional samples will be acquired from patients routinely evaluated in our gastrointestinal oncologic and hematologic malignancies clinics. In addition to distributing primary cells to program investigators, Core 1 will also maintain and distribute a bank of well characterized PDXs selected for passage in immunocompromised mice. Methods for engrafting leukemia cells and primary epithelial tumor tissues in immunodeficient mice are now well established, and we and others have successfully used PDX models for drug testing. Organoid culture approaches will be used to complement the PDX effort, and will provide an additional way of propagating and segregating patient-derived epithelial tumor cells and stromal cells. Core 1 will maintain a bank of PDX models and organoids and distribute to program investigators as needed. Overall, Core 1 has extensive experience in the processing, cryopreservation, flow cytometric analysis, purification, propagation, banking, and genomic characterization of primary specimens and tumor models, as well as their use in preclinical drug evaluation.

Core 2: Computational Analysis Core

PIs: Lauffenburger (lead), Shalek and Stevenson
The Computational Analysis Core (Core 2) will support Projects 1 & 2 by bringing necessary bioinformatics and computational methods to bear on the questions addressed by each project. More specifically, we will apply established computational pipelines to perform quality control on, and extract maximal information (cell types, states, circuits, and drivers) from, our rich data sets. We will help our CSBC team identify the cell types, states, and circuits active in the Leukemia and Colon and Pancreatic tumor models of Core 1 from the single-cell RNA-Seq data collected in Projects 1 & 2, as well as the molecular drivers of interesting behaviors. We will deploy numerical algorithms to uncover the predictive power of phenotypic measurements, alone or in combination, made via our Nanowell (NW) and Suspended Microchannel Resonator (SMR) platforms to explain cancer cell drug responses. When necessary, we will identify and/or develop new algorithms, enhancing our CSBC’s quantitative and multivariate capabilities. Finally, we will work closely with our CSBC team members to help with experimental design, ensuring that an appropriate number of cells/samples are processed to test hypotheses of interest and that appropriate statistical testing is performed. By providing comprehensive analysis (QC, power, gene set enrichment) and modeling capabilities, we will dramatically enhance our ability to draw crucial insights from our data. This will lead to new hypotheses that can be tested by designing new experiments.

Education and Outreach Core

PI: Lauffenburger
The mentorship of current and future trainees who can tackle cancer-related problems with computational systems biology approaches is an integral part of fulfilling our commitment to catalyze and generate new bodies of knowledge and fields of cancer study. To achieve this goal, we will: 1) Establish graduate student fellowships for students jointly mentored in computational systems biology, precision measurement or oncology. 2) Provide undergraduate research opportunities for MIT students to work in laboratories at DFCI. 3) Provide outreach to the biotech/pharmaceutical industry. 4) Establish an NCI CSBC Junior Investigators program. 5) Facilitate monthly meetings and annual retreats that will be open to the MIT/DFCI community. 6) Offer mini-courses and training in experimental and computational methods. 8) Provide outreach to the community. and 7) Establish and maintain a website for disseminating research activities of our center as well as relevant techniques and applications.

Administrative Core

The overall mission of the Administrative Unit is to provide oversight and coordination on administrative and fiscal aspects of the MIT/DFCI CSBC.

Center for Cancer Systems Pharmacology (CCSP)


Center Title

Center for Cancer Systems Pharmacology (CCSP)

Center Website

Center Summary

The Center for Cancer Systems Pharmacology (CCSP) based at Harvard Medical School focuses on constructing and validating network-level models of responsiveness and resistance (innate and acquired) to immune checkpoint inhibitor (ICI) and small molecule (targeted) therapies in human cancer. The over-arching goal is to improve the treatment of disease and advance molecular understanding of oncogenic transformation and opposing immune surveillance on the initiation and progression of human cancer. Our Center also studies the adverse effects of targeted therapies, with an initial focus on ICI toxicity in the skin.
Our approach involves the use of systems pharmacology tools and concepts to address the most significant questions encountered in the use of ICIs and targeted therapies individually and in combination in melanoma, in which both classes of drug can be highly effective individually, and also in triple negative breast cancer (TNBC) and glioblastoma multiforme (GBM) in which clinical responses are sporadic. In the long-term, expected outcomes include (i) translating clinical problems in melanoma, TNBC and GBM from the bedside to bench and then back to the bedside via new drug-disease pairings, drug combinations and response biomarkers (ii) developing, validating and applying to clinical trials innovative pharmacological concepts that consider the impact of cell-to-cell variability, micro environment, and dose and drug sequencing on outcomes and (iii) reducing the burden of therapy through improved understanding of mechanism-based drug toxicities and ways of mitigating them.

Conceptual focus of the CCSP Center. We study mechanisms of therapeutic and adverse response and of drug resistance in melanoma, in which both ICIs and targeted therapy are effective, and triple negative breast cancer (TNBC) and brain cancers, in which they are sporadic and a significant unmet need exists. (+) signs denote responsiveness to therapy.


Principal Investigators

Peter Sorger (PI)

Peter Sorger (PI), Otto Krayer Professor of Systems Biology; Founding Director of the Laboratory of Systems Pharmacology and Head of the Harvard Program in Therapeutic Science in which the Center for Cancer Systems Pharmacology (CCSP) is based. Dr. Sorger’s research focuses on the systems biology of mammalian signal transduction and the drugs that target oncogenic signaling proteins. His group uses single-cell and multiplexed imaging and mass spectrometry methods to constrain and validate physico-chemical models of oncogenesis and drug action.

Sameer Chopra

Sameer Chopra, Instructor of Medicine, Harvard Medical School (HMS); Attending Physician, Dana-Farber Cancer Institute (DFCI); Research Associate, Harvard Program in Therapeutic Science. Dr. Chopra studies mechanisms of immune evasion in women’s cancers (breast, ovarian, endometrial) and their relationship to specific molecular and phenotypic aberrations commonly found in these tumors such as replication stress and genome instability. He aims to improve strategies for initiating and sustaining effective anti-tumor immune responses in patients using rational combinations of small molecule drugs, immunotherapy, and chemotherapy.

Conor Evans

Conor Evans, Assistant Professor at the Wellman Center for Photomedicine of Harvard Medical School at the Massachusetts General Hospital. Dr. Evans develops advanced imaging technologies for the direct visualization and quantification of pharmacokinetics and pharmacodynamics in situ. By leveraging tools such as coherent Raman imaging, image analysis, and machine learning, his group aims to improve our understanding of how cancer drugs reach and engage their targets.

Keith Flaherty

Keith Flaherty, Director of the Termeer Center for Targeted Therapies, Director of Clinical Research at the Massachusetts General Hospital; Professor of Medicine at Harvard Medical School. Dr. Flaherty focuses on understanding mechanisms of action and resistance to signal transduction targeted therapy and immunotherapy in melanoma, as well as the mechanistic interaction between the two modalities. Deep molecular analysis of serial tumor biopsies and rapid autopsy samples are used to motivate functional pre-clinical studies and to inform next generation clinical trials.

Jennifer Guerriero

Jennifer Guerriero, Director of the Breast Immunology Laboratory in the Women’s Cancer Program at Dana-Farber Cancer Institute; Instructor in Medicine at Harvard Medical School. Dr. Guerriero studies the role of tumor associated macrophages (TAMs) in promoting tumorigenesis and as a therapeutic target. Her research aims to understand the molecular and functional regulation of tumor macrophage phenotype and subsets, identify how tumor macrophages inhibit T cell function and limit the effectiveness of immunotherapy, and identify novel strategies to target macrophages therapeutically.

Marcia Haigis

Marcia Haigis, Associate Professor in the Department of Cell Biology at Harvard Medical School. Dr. Haigis studies the metabolic programs of mammalian cells and the pathways that regulate these programs. She focuses on metabolic reprogramming of tumor cells and immune cells within the tumor microenvironment to discover novel anti-tumor therapies.

Steve Hodi

Steve Hodi, Director of the Melanoma Center and the Center for Immuno-Oncology, and Institute Physician at the Dana Farber Cancer Institute; Sharon Crowley Martin Chair in Melanoma; Professor of Medicine at Harvard Medical School. Dr. Hodi studies mechanisms of immuno-therapy via clinical trials in multiple disease areas. He focuses on understanding determinants of response and the specific disease features that allow a subset of patients to experience extremely durable responses to immune checkpoint inhibition.

Benjamin Izar

Benjamin Izar, Medical Oncologist in the Department of Medical Oncology (Melanoma Disease Center) and Center for Immunology and Virology at the Dana-Farber Cancer Institute; Center for Cancer Precision Medicine at the Dana-Farber Cancer Institute; investigator at the Broad Institute of MIT and Harvard. Dr. Izar has pioneered the implementation of single-cell RNA-sequencing and single-cell protein imaging in clinical specimens and the development of patient-derived models to understand drug resistance to targeted therapies and immune checkpoint inhibitors. As a medical oncologist, he focuses on the treatment and understanding of drug resistance in melanoma.

Darrell Irvine

Darrell Irvine, Professor in the Departments of Biological Engineering and Materials Science & Engineering, Koch Institute for Integrative Cancer Research at the Massachusetts Institute of Technology, Ragon Institute of MGH, MIT, and Harvard; Howard Hughes Medical Institute Investigator. Dr. Irvine’s laboratory is focused on the development of combination immunotherapies leveraging innate and adaptive immune pathways as a means to eradicate established tumors. His group is particularly interested in combination therapies that trigger an “in situ” vaccination response.

Douglas Lauffenburger

Douglas Lauffenburger, Ford Professor of Biological Engineering, Chemical Engineering, and Biology Head, Department of Biological Engineering at the Massachusetts Institute of Technology. Dr. Lauffenburger studies cell dysregulation in complex pathophysiologies, emphasizing applications in cancer, inflammatory diseases, and vaccines. A central objective is gaining understanding of how multiple pathways and processes — both intracellular and intercellular – combine to govern cell functions and how they become dysregulated in disease.

Nicole Leboeuf

Nicole Leboeuf, Director, Center for Cutaneous Oncology at Dana-Farber/Brigham and Women’s Cancer Center, Director of Dermatologic Immunology; Clinic Director of Skin Toxicities from Anticancer Therapies Clinic; Assistant Professor at Harvard Medical School. Dr. Leboeuf provides urgent and ongoing expert dermatologic care for cancer patients with cutaneous adverse events from oncologic treatments. Her research focuses on profiling the immune infiltrate in the skin and blood of patients with cutaneous eruptions from immune checkpoint blockade.

Patrick Ott

Patrick Ott, Attending Physician in the Department of Medicine at Brigham and Women’s Hospital; Clinical Director of the Center for Immuno-Oncology and Melanoma Center at the Dana Farber Cancer Institute; and Assistant Professor at Harvard Medical School. Dr. Ott is interested in the development of new immunotherapeutic strategies for cancer patients. He is the principal investigator and site investigator of a large portfolio of clinical trials assessing novel agents and innovative combinatorial strategies for patients with melanoma and other cancers.

Sandro Santagata

Sandro Santagata, Associate Pathologist in Neuropathology at the Brigham and Women’s Hospital and the Dana Farber Cancer Institute; Assistant Professor in Pathology at Harvard Medical School. Dr. Santagata is interested in applying novel imaging methods, including tissue-based cyclic immunofluorescence (t-CyCIF) to brain tumor resection specimens from clinical trials in order to measure and model responses in tumors and their microenvironment before and after therapy.

Arlene Sharpe

Arlene Sharpe, George Fabyan Professor of Comparative Pathology, Head of the Division of Immunology; Co-Director of the Evergrande Center for Immunologic Diseases; Chair of the Department of Immunobiology at Harvard Medical School. Dr. Sharpe studies T-cell costimulation and its immunoregulatory functions in T cell tolerance and anti-tumor immunity. Her laboratory has been at the forefront of this field for over two decades, discovering T cell costimulatory pathways, and elucidated their functions, including the functions of B7-1 and B7-2, CTLA-4, ICOS, PD-1 and PD-1 ligands.


Project 1: Multi-scale modeling of adaptive drug resistance in BRAF-mutant melanoma

We are constructing families of computational models, at different levels of molecular detail, that capture and ultimately explain the diversity of phenomena associated with resistance to RAF/MEK inhibitors. This is accomplished by collecting time-series single-cell and population-level data from cells with diverse genotypes followed by time-resolved modeling using differential equations, logic-based models and supervised machine learning. These studies are performed initially in patient-derived cell lines and mouse models, but intended to provide hypotheses that can be tested in clinical trials.

Project 2: Measuring and modeling the tumor and immune microenvironment before and during therapy and at the time of drug resistance

We study changes in the tumor ecosystem induced by ICIs or targeted therapy and predictive of therapeutic response. The precise proportions and spatiotemporal arrangements of tumor, stromal and immune cells will be determined in tissue biopsies, and single-cell features will be extracted and associated with disease progression and therapeutic response using machine learning, deep learning and high-dimensional data analysis. Adverse responses in the skin and gut will also be investigated and compared to therapeutic responses at a mechanistic and clinical level.

Project 3: Mechanisms of immunotherapy action

We study ICIs alone or in combination in tumor-bearing mice to evaluate whether highly efficacious responses arise from co-targeting cells of single lineages (e.g. CD8+ effector T cells) or concurrent targeting of multiple cell lineages (e.g. CD8+ T cells, regulatory T cells), and to identify the tumor settings in which either strategy might prove more effective. Metabolic, signaling, and transcriptional changes associated with cellular responses to ICIs are assessed and modeled. Agent-based models are then used to study non-cell autonomous mechanisms that mediate therapeutic and adverse drug effects. We hope to thereby discover combinations of ICIs and specific patient populations in which therapeutic responses are high and toxicity is minimal.


Systems Pharmacology Core

The Systems Pharmacology Core provides all CCSP members, as well as individuals selected for funded internal research projects, access to a central, high quality resource for molecular profiling of cells and tissues and for data analysis. The core will join together four approaches based in the Laboratory of Systems Pharmacology (LSP) (i) deep, high throughput, and single cell RNA-Seq (ii) targeted and shotgun mass-spectrometry based proteomics (iii) high dimensional single cell imaging and (iv) data analysis and data science based on supervised and unsupervised machine learning as well as two technologies based in the laboratories of CCSP investigators (i) metabolomics profiling (via Haigis lab) and (ii) immune profiling of blood using flow cytometry and multiplex cytokine assays (via the DFCI Immune Profiling Lab). These activities do not take place in isolation, and all of our platform technologies work closely with HMS core facilities in a hub and spoke model..

Center for Cancer Systems Therapeutics (CaST)


Center Title

Columbia University Center for Cancer Systems Therapeutics

Center Website

Center Summary

The Columbia University Center for Cancer Systems Therapeutics (CaST) is developing a new conceptual framework capable of accounting for the extreme biological heterogeneity seen in cancer. Instead of focusing on the highly diverse, patient-specific spectrum of mutations that can initiate cancer, CaST is concentrating on the regulatory machinery found within cancer cells that is responsible for tumor homeostasis and tumor canalization. Just as regulatory networks have been shown to enable cells to differentiate during development and maintain stable phenotypes, CaST is testing the hypothesis that similar regulatory principles can be used to understand how cancer cells survive and propagate as tumors grow and respond to treatment. Understanding the regulatory logic behind tumor homeostasis and canalization over the time course of disease is critical for addressing several key challenges facing precision medicine; namely, how malignant tumors evade treatment, induce disease progression, and develop drug resistance. We are studying this machinery across multiple levels of granularity — including interactions between tumors and their microenvironment as well as single-cell heterogeneity and plasticity — representing the full, systems-wide complexity of the tumor phenotype.

Our approach is based on the proposition, validated repeatedly in previous research at Columbia, that tumor homeostasis is controlled by a small number of proteins and other gene products called master regulators (MRs), which work in concert within tightly autoregulated modules called tumor checkpoints to maintain cancer-related phenotypes. Similar to a traffic checkpoint, the aberrant signals that contribute to the implementation and maintenance of tumor cell state must converge on these modules, where they are integrated and translated into downstream transcriptional programs that generate the tumor signature. As our past research has shown, such tumor checkpoints are likely much more limited in number than the possible number of cancer-initiating mutations, and therefore constitute a unique kind of oncogene-independent “Achilles heel” of cancer, offering a distinct category of potential therapeutic targets. We aim to develop methods for systematically identifying master regulators of tumor homeostasis and tumor state transitions, and connect them to drugs capable of modulating them.


Principal Investigators

Andrea Califano, Ph.D.

Andrea Califano, Ph.D. is the Clyde and Helen Wu Professor of Chemical Systems Biology at Columbia University Medical Center. He is the Founding Chair of the Columbia University Department of Systems Biology, Director of the JP Sulzberger Columbia Genome Center, and Associate Director for Bioinformatics of the Herbert Irving Comprehensive Cancer Center. He is also the founder of Darwin Health. The Califano Lab uses a combination of computational and experimental methodologies to reconstruct the regulatory logic of human cells in a genome-wide fashion. He has shown that analysis of this logic can identify master regulator proteins responsible for human disease, including cancer and neurodegenerative syndromes, as well as for normal tissue development. In addition, his lab has developed methods for discovering compounds and compound combinations that can inactivate these proteins, thus providing valuable therapeutic strategies. These findings have been translated into several clinical studies, including an innovative set of N-of-1 clinical trials in which disease master regulators are identified and pharmacologically targeted on an individual patient basis, using a systems biology approach to precision medicine.

Barry Honig, Ph.D.

Barry Honig, Ph.D. is professor of Biochemistry and Molecular Biophysics and is director of the Center for Computational Biology and Bioinformatics (C2B2). He is a member of the National Academy of Sciences and the American Academy of Arts and Sciences, and is a Howard Hughes Medical Institute (HHMI) Investigator. Dr. Honig has developed methods that combine information about protein sequence with biophysical analysis to reveal how biological specificity is encoded on protein structures. His laboratory also uses methods from biophysics and bioinformatics to study the structure and function of proteins, nucleic acids, and membranes. His work includes fundamental theoretical research, the development of software tools, and applications to problems of biological importance.

Participating Investigators

Cory Abate-Shen, Ph.D.

Cory Abate-Shen, Ph.D. is the Michael and Stella Chernow Professor of Urologic Sciences, director of research in the Columbia University Department of Urology, and an associate director of the Herbert Irving Comprehensive Cancer Center and leader of its Prostate Program. In her research she investigates the molecular mechanisms of homeobox genes in development and cancer. Her laboratory has provided groundbreaking insights on the molecular bases of how homeoproteins achieve target gene recognition in vivo. She has also developed mouse models of prostate cancer that have been widely used to investigate the molecular bases of prostate tumorigenesis and as preclinical models for intervention and therapy.

Dimitris Anastassiou, Ph.D.

Dimitris Anastassiou, Ph.D. is Charles Batchelor Professor and director of the Genomic Information Systems Laboratory at the
Department of Electrical Engineering, and a member of the Department of Systems Biology, Center for Computational Biology and Bioinformatics, and the Center for Cancer Systems Therapeutics (CaST). His current research is focused on the discovery and elucidation of “pan-cancer” biomolecular mechanisms shared by multiple cancer types, as well as potential diagnostic, prognostic, and therapeutic applications associated with these mechanisms.

Filemon Dela Cruz, M.D.

Filemon Dela Cruz, M.D. is an assistant professor of pediatrics at Memorial Sloan Kettering Cancer Center. His current work focuses on the development and application of mouse models for the study of the development of solid tumors, with a particular focus on sarcomas.

Charles Karan, Ph.D.

Charles Karan, Ph.D. is the scientific director for the High-Throughput Screening Facility at the JP Sulzberger Columbia Genome Center. In this role, he assists researchers at Columbia and from other institutions to develop and implement high-throughput screening protocols tailored to the goals of individual research projects. He also manages all operations of the HTS facility.
Andrew Kung, M.D. is a physician-scientist and Chair of the Department of Pediatrics at Memorial Sloan Kettering. He oversees the clinical, research, and educational missions of the department. As a physician, he specializes in caring for patients using cancer genomics, precision medicine, and stem cell transplantation. In his research, he focuses on identifying the causes of pediatric cancers and developing new treatments to benefit children and teens with cancer.

Diana Murray, Ph.D.

Diana Murray, Ph.D. is a Research Scientist and the Program Director of Research and Outreach in the Department of Systems Biology. Her research focuses on integrating structure-based protein-protein interaction networks with gene regulatory networks to provide molecular-level descriptions for mechanisms underlying normal and aberrant cellular signaling. Building on a computational framework for examining phosphoinositide signaling, she will participate in CaST’s research by incorporating proteomic and genomic information on protein-lipid interactions into structure-informed network models. In addition, Dr. Murray is leading the CaST Outreach Corer.

Kenneth Olive, Ph.D.

Kenneth Olive, Ph.D. is Assistant Professor of Medicine and Pathology at the Columbia University College of Physicians & Surgeons. His laboratory performs preclinical therapeutics trials using advanced genetically engineered mouse models, with a particular emphasis on pancreatic cancer. The lab uses advanced small animal imaging technologies to track tumor response to treatment, as well as pharmacokinetic and pharmacodynamics analyses, functional imaging, microscopy, and biochemistry and molecular biology techniques to assess drug mechanisms and understand relevant signaling pathways.

Itsik Pe’er, Ph.D.

Itsik Pe’er, Ph.D. is an associate professor in the Department of Computer Science. His laboratory develops and applies computational methods for the analysis of high-throughput data in germline human genetics. Specifically, he has a strong interest in isolated populations such as Pacific Islanders and Ashkenazi Jews. Using high-throughput sequencing methods, Pe’er has focused on characterizing genetic variation that is unique to isolated populations, including the effects of such variation on phenotype.

Raul Rabadan, Ph.D.

Raul Rabadan, Ph.D. is an Associate Professor with joint appointments in the Departments of Systems Biology and Biomedical Informatics. He is also a member of the Scientific Advisory Board of the JP Sulzberger Columbia Genome Center, and codirector of the Center for Topology of Cancer Evolution and Heterogeneity, a center in the NCI’s Physical Sciences–Oncology Network. At Columbia University, Dr. Rabadan leads an interdisciplinary lab with researchers from the fields of mathematics, physics, computer science, engineering, and medicine who share the common goal of solving pressing biomedical problems through quantitative computational models. His work is focused on developing tools to analyze genomic data, and extracting relevant information to understand the molecular biology, population genetics, evolution, and epidemiology of cancer.

Nicholas Tatonetti, Ph.D.

Nicholas Tatonetti, Ph.D. is Herbert Irving Assistant Professor of Biomedical Informatics at Columbia University with interdisciplinary appointments in the Department of Systems Biology and the Department of Medicine. Dr. Tatonetti researches the use of observational clinical data and high-throughput molecular data to identify and explain the pharmacological effects of drugs and drug combinations. He also develops large-scale statistical and data mining techniques to address issues of bias and confounding in large observational data sets.

Dennis Vitkup, Ph.D.

Dennis Vitkup, Ph.D. is an associate professor in the Departments of Systems Biology and Biomedical Informatics. His laboratory develops and applies novel probabilistic techniques to analyze cellular networks, connecting network structure to function to phenotypes, including experimentally verifiable predictions. Research in the Vitkup Lab focuses on three main topics: 1) the global probabilistic reconstruction and analysis of metabolic networks based on completely sequenced genomes; 2) the development of methods to identify new human disease genes and genetic disease modules using probabilistic functional networks; and 3) the development of methods to combine mechanistic and probabilistic approaches for the dynamic simulation of biological pathways.

Harris Wang, Ph.D.

Harris Wang, Ph.D. is an Assistant Professor in the Department of Systems Biology and Department of Pathology and Cell Biology at Columbia University Medical Center. Using approaches from genome engineering, DNA synthesis, and next-generation sequencing, he is currently studying how genomes in microbial populations form, maintain themselves, and change over time, both within and across microbial communities. His goal is to use synthetic biology approaches to engineer ecologies of microbial populations, such as those found in the gut and elsewhere in the human body, in ways that could improve human health.

Core Director

Peter Sims, Ph.D.

Peter Sims, Ph.D. is an assistant professor in the Departments of Systems Biology, and Biochemistry and Molecular Biophysics at Columbia University, and associate director of the JP Sulzberger Columbia Genome Center. Trained in physical chemistry, he is interested in developing new tools for single-cell analysis, applying cutting-edge microscopy, next-generation sequencing, and microfabrication to enable unbiased, system-wide measurements of biological samples. He and his colleagues focus on single-cell transcriptomics and sequencing technology along with novel approaches to proteomics, where current tools lag far behind those available for nucleic acid analysis. He is leading the CaST Molecular Profiling Core.

Project Manager

Aris Floratos, Ph.D.

Aris Floratos, Ph.D. is an assistant professor in the Departments of Systems Biology and Biomedical Informatics and executive research director at the Center for Computational Biology and Bioinformatics. He has led the development of GeWorkbench, a free, open source bioinformatics application that gathers the Department’s software and databases into one integrated software platform.


Project 1: Elucidating the regulatory logic that is responsible for maintaining cancer cell state, independent of specific initiating events and endogenous/exogenous perturbations


In project 1, CaST is attempting to move beyond current approaches for elucidating tumor checkpoints and master regulators, which are largely static and not designed to predict how tumors respond to pharmacological and genomic perturbations over time. Thus, we are developing novel methodologies to mechanistically uncover the regulatory machinery that maintains cancer cell state and governs cell state transition. We are also working to define the role of regulatory networks in implementing distinct tumor phenotypes, from metastatic progression, to immunoevasion, to drug resistance. This effort will leverage and integrate time-course data from gene expression and computationally inferred protein activity profiles, generated by small molecules and RNAi/CRISPR perturbations.

Project 2: Elucidating time-dependent mechanisms of genetic and epigenetic reprogramming of individual cancer cells underlying cancer-state transitions to drug resistance and progression

We are investigating cell state plasticity and tumor/microenvironment heterogeneity, which represent formidable obstacles to successful treatment of human malignancies. This interest is driven by a growing awareness that tumors can harbor distinct niches that include genetically distinct subclones (which have identical or distinct tumor checkpoints), as well as isogenic niches (which present with orthogonal checkpoints). Our goal is to compile a comprehensive inventory of tumor dependencies among such heterogeneous niches that can be targeted pharmacologically, We are studying these mechanisms by developing new methods to identify distinct tumor compartments and heterogeneity at the single-cell level.

Project 3: Developing novel methodologies for the systematic prioritization of compounds and compound combinations capable of abrogating tumorigenesis in vivo

We are developing and validating computational methods for the prioritization of master regulator-targeting drugs and drug combinations to either implement or prevent specific tumor state transitions. This includes inducing irreversible commitment to cell death, preventing progression to a malignant tumor stage, and rescuing drug sensitivity. In addition, we are attempting to address a broad range of questions related to the heterogeneity of tumor response, including mechanisms that allow tumor cells to compensate for MR-targeted therapy. We propose that addressing tumor plasticity and potential escape routes implemented by tumor state reprograming is going to be critically relevant for the chronic management of cancer in patients.


Molecular Profiling Core

A technical requirement for achieving CaST’s scientific goals is access to cost-effective, cutting-edge molecular profiling, high-throughput screening, and single-cell analysis tools utilizing robotics and microfluidics. The Molecular Profiling Core (MPC) provides three key capabilities: 1) PLATESeq: a novel platform for integrating high-throughput screening with genome-wide expression profiling; 2) microfluidics: a highly multiplexed, microfluidic implementation of PLATESeq that produces expression profiles across hundreds of single cells in parallel; and 3) single-cell whole genome sequencing: a pipeline that combines microscopy-based single cell isolation with whole genome amplification for single-cell whole genome sequencing.

Outreach Core

In addition to pursuing research, CaST conducts outreach to disseminate the software and methods developed by the Center, foster discussion on related issues within the larger scientific community, support community-based research efforts, and mentor young scientists. These activities include 1) supporting the DREAM challenges; 2) a cross-training program to help investigators gain experience with complementary methods and perspectives; 3) a “CaST Scholars” program that enables undergraduate students to participate in our research; and 4) organizing scientific meetings on related topics in cooperation with the New York Academy of Sciences Systems Biology Discussion Group.

Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution)


Center Title

Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution)

Center Website

Center Summary

Our Cancer Systems Biology Center of HoPE (Heterogeneity of Phenotypic Evolution) focuses on finding effective treatments for resistant tumors by studying the evolution of phenotypes emergent in late-stage breast and ovarian cancer. Our team will develop a suite of systems-based strategies to understand how genomic diversity, clonal evolution, and phenotypic change interact in the progression toward chemoresistant cancer. To evaluate their potential for therapy, we will then test these dynamic models in clinical trials. We hypothesize that acquired resistance emerges from selection acting on phenotypes during tumor evolution, and that simultaneously measuring and modeling subclone genotypes and phenotypes will identify new, and testable, therapeutic targets.

During treatment, the subclones from every patient’s tumor follow unique evolutionary and resistance trajectories. DNA sequencing has revealed significant subclone genotypic diversity within a single tumor, while RNA sequencing has established phenotypic diversity both across and within subclones. This diversity provides the variation required by evolution under the selective pressure created by the tumor microenvironment and treatment. Using computational tools to organize this complex variation, we will develop a new class of dynamical systems models of subclone evolution and acquisition of oncogenic phenotypes during treatment to identify key chemo-resistant cell states using our unique patient cohorts. These mechanistic models will identify points of vulnerability. Our clinical trials will be aimed at blocking transition of tumors to a resistant state by targeting critical resistant phenotypes.

Our Center is comprised of an Administrative, Education/Outreach, Translational, and Computational Cores, in addition to two complementary projects. The synergies are derived from: 1) the merged parameterization of the evolutionary models drawn from deep longitudinal patient progression studies (Project 1) and broad multisite metastatic tumor analyses (Project 2), both resulting in a model to identify resistant states for clinical targeting; and 2) an integrated computational and experimental framework and resources for dissecting tumor heterogeneity and evolution that will contribute to an improved capacity for personalized cancer therapy. Our multidisciplinary team of systems biologists, bioinformaticians, tumor biologists, pharmacologists, mathematical biologists, and clinicians will tackle these scientific challenges. We will create programs to educate the next generation of scientists in systems biology and inform the community about the latest scientific advances and their impact on treatment strategies. We will provide state of the art tools for the analysis of patient samples and tumor genomic complexity. These studies move beyond prior research by integrating cell population dynamics and cellular phenotypes with cellular genotypes, and will deliver approaches and a knowledge base to block or reverse the transition to a resistant state for advanced stage breast and ovarian cancer patients.


Our center is comprised of a multi-disciplinary team with complementary skillsets. Our team shares common goals and interests that have in many cases spanned a decade.

Principal Investigators

Dr. Andrea Bild

Dr. Andrea Bild trained as a pharmacologist with a specialization in genomics and cancer cell biology. Her research program has established the development and clinical translation of a systems biology framework for personalized medicine genomics. Specifically, her research has enabled 1) investigations of signaling pathways and networks in a physiologically relevant setting: patient tumors; 2) algorithms to personalize matching of effective drugs to patients; and 3) systems-guided clinical trials with novel therapeutic strategies. Dr. Bild has previously worked with every investigator that is part of this team, and has many shared publications and clinical trials with investigators on the grant. Dr. Bild has also developed and founded the Genome Science Program at the University of Utah in order to train students and scientists in genomics and systems biology as well as create a rich collaborative structure for faculty across campus with expertise in this field.

Dr. Fred Adler

Dr. Fred Adler trained as an applied mathematician with specialization in dynamical systems and mathematical ecology, and is Director of the Center for Quantitative Biology. His research program, through his joint appointment in the Departments of Mathematics and Biology, ranges from evolutionary ecology to mathematical oncology. These areas are unified through a commitment to finding key mechanisms in complex systems through mathematical modeling and close interaction with experimentalists and data. He has trained 17 graduate students across this broad range of topics, and his most influential research includes modeling the dynamics of cystic fibrosis to optimize the timing of lung transplantation, the dynamics of populations in spatially subdivided landscapes, and modeling of biodiversity in interacting communities. His expertise in collaboration across disciplinary boundaries, linking dynamical systems with data, and mentoring of trainees will effectively integrate the modeling research and computational core into all aspects of this project.

Professor David Bowtell

Professor David Bowtell is Head of the Cancer Genomics and Genetics Program at the Peter MacCallum Cancer Centre (Melbourne, AU), where he was Director of Research (2000-09) and holds a joint appointment as a Group Leader and Senior Principal Research Fellow at the Garvan Institute for Medical Research (Sydney, AU). He is a Visiting Professor at Dana Farber Harvard Cancer Center (Boston, MA). Prof. Bowtell has an extensive background in human cancer genomics. He is Principal Investigator (PI) for the Australian Ovarian Cancer Study (AOCS), one of the largest population-based cohort studies of ovarian cancer in the world, involving over 3000 women, and CASCADE, a rapid autopsy study. Prof. Bowtell’s research has focused on the classification of ovarian cancer and mechanisms of primary and acquired drug resistance.

Dr. Jeffrey Chang

Dr. Jeffrey Chang is a multidisciplinary cancer genomics researcher specializing in breast cancer, metastasis, and the epithelial-to-mesenchymal transition. He uses a range of approaches, including genomics, systems biology, and cell biology. He has developed novel methods for the analysis of gene expression signatures and next generation sequencing data using computer science and Bayesian statistical methods. In addition to his relevant research experience, Dr. Chang has had over 5 years’ experience as the co-founder and previous co-director of the computational and bioinformatics core at the University of Utah, and was the co-founder and former director of the Biopython project. Dr. Chang will co-direct studies on dissecting clonal structure and functional co-operativity in breast cancer metastasis. He will also direct the Computational Core.
Dr. Adam Cohen is a board certified medical oncologist with a master’s degree in mathematics. His research specializes in clinical trials, genomics, and biomarkers. He has developed and been the PI for three investigator-initiated clinical trials and has been the local PI for many multi-site trials. Dr. Cohen has successfully applied genomic biomarkers in his completed clinical trials, and is the PI for a tissue acquisition protocol that has been used in our successful collection of pleural effusion samples.

Dr. Gabor Marth

Dr. Gabor Marth, Professor of Human Genetics and Co-Director of the USTAR Center for Genetic Discovery, is a computational biologist with a long history of algorithm development for genomic data analysis. He developing sequence analysis tools in the C. elegans and the Human Genome Project, and participated in the SNP Consortium and the International HapMap Project. More recently, he played a leading role in the 1000 Genomes Project (1000GP), developing genomic data standards (SAM/BAM, VCF) that are now de facto standards in genomics. Dr. Marth has established critical variant detection algorithms for detecting somatic cancer mutations and for analyzing tumor tissue heterogeneity at the cellular level.
Dr. Philip Moos was trained as an engineer and cell and molecular biologist. He has used genomics in studies including delineation of patient phenotype segregation and pharmacological effects on cellular systems. He is also a senior administrator for several department and college-wide initiatives in the College of Pharmacy, including a role as Director of Graduate Studies and on the professional program admissions committee. This combination of genomic project expertise and administrative leadership will provide a strong base for his governance of the Translational and Outreach cores for this proposal.

Dr. Sunil Sharma

Dr. Sunil Sharma is Chief of Medical Oncology and an international expert in Drug Development at Huntsman Cancer Institute and University of Utah. Dr. Sharma has successfully directed over 100 clinical trials. He is the Director of Center for Investigational Therapeutics. Dr. Sharma is also an expert on epigenetics and Drug discovery in the epigenetic space. He is an author on more than 100 publications and his laboratory developed SP-2577, the Lysine Specific Demethylase (LSD-1) inhibitor. In addition, his laboratory and clinical trials program (Phase 1 Program) has developed novel therapeutics and biomarkers in the areas of epigenetics and signal transduction pathways.
Dr. Theresa Werner is a board certified medical oncologist who specializes in gynecologic and breast malignancies and clinical trials. Her clinical research program centers around targeted therapy for cancer patients with an emphasis on genomic biomarker driven treatment selection. Dr. Werner also serves as Medical Director of the Clinical Trials Office at Huntsman Cancer Institute and serves as PI on over 40 clinical trials at present. Dr. Werner has been integral in developing an infrastructure at our institution to enable successful complex clinical trials with coordination with radiology, pathology, surgery, pharmacy, and basic scientists.


Phenotype evolution during development of resistance in patients treated with chemotherapy. Shown are violin plots showing enrichment of EMT phenotype in single tumor cells both pre- and post-treatment.

Overview of our center’s research. Project 1, focused on evolution of tumors over time and approaches for reinstatement of chemosensitivity, will develop dynamic models that measure tumor cell chemo-response states overusing serial collections of patient tumors collected during chemotherapy treatment. Project 2 will identify common driver phenotypes in space, using multi-site metastatic cancer, and find therapeutic regimens targeting cooperative phenotypes in heterogeneous tumors. For both projects, clinical trials will test models for effective reversal of drug resistance evolution.

Project 1. Dynamic genomic and microenvironmental models of chemoresistance

PIs: Bild, Adler, Sharma; co-Is: Werner, Bowtell


Breast and ovarian cancers are heterogeneous diseases, as a typical tumor contains multiple “subclones”, which are defined as evolutionarily related subpopulations of cells with a different complement of somatically acquired DNA mutations and phenotypes. When chemotherapeutic agents are administered to the patient, some of these subclones may gain a selective advantage and develop resistance to the treatment, resulting in cancer relapse and progression. For this reason, it is imperative to identify these subclones and their evolution across treatment; and to understand how the genomic aberrations within these subclones drive resistance to chemotherapy. We will integrate experimental biology and computational models across temporal samples of patient tumors as they develop a resistant state in order to better understand and combat refractory and terminal cancer. To enable the study of tumor heterogeneity evolution in patients, we will utilize a highly unique collection of metastatic tumor cells from breast and ovarian cancer patients before, during, and after treatments, often across multiple courses of chemotherapy, as well as tumors from a clinical trial taken before and after therapy. We use deep sequencing to find genomic aberrations at each of these time points, and develop systems models to identify the subclones and follow phenotypic changes and their functional impacts of subclone evolution in response to chemotherapy. We hypothesize that 1) Dynamical systems models based on the evolution of subclone structure and acquisition of oncogenic phenotypes during treatment can identify key factors in the development of a chemo-resistant state; and 2) We can delay development of a chemo-resistant cancer state by inhibiting development of phenotypes that emerge over time commonly during treatment. We will model resistant cancer cell populations and both extrinsic and immune microenvironmental factors to identify critical features of acquired resistance and apply these models to a clinical trial aimed at blocking transition to a resistant cancer state. While these components can exhibit co-dependencies, by their nature they can also have vulnerabilities based on these interactive features, and if one can inhibit dependent relationships within a population it may be possible to shift the equilibrium of a tumor from a chemoresistant state to a sensitive state. The algorithms and procedures we are developing in this proposal will for a rational basis for real-time patient monitoring and making treatment choices for refractory patients. The outcomes of this research will deliver approaches to block or reverse the transition to a resistant state for advanced stage breast and ovarian cancer patients.

Project 2. Targeting cooperative phenotypes common in spatial heterogeneity

PIs: Chang, Cohen; co-I: Bowtell
Recent studies in primary tumors have found a remarkable degree of intratumor heterogeneity, where a single tumor is comprised of a range of subclones exhibiting a diversity of phenotypes, including molecular profiles, proliferation capacity, and response to therapies. Although heterogeneity is now widely reported, few studies have investigated the heterogeneity of metastatic tumors at the end stage, despite the fact that metastatic cancer is estimated to be responsible for over 90% of cancer deaths. For breast and ovarian cancer, tumors that progress to metastasis are refractory to treatment. Therefore, there is a great need to determine the mechanisms by which subclonal diversity can affect the metastatic phenotype and underlie the difficulties in treatment. Studying metastatic tumors is difficult due to the challenges in collecting patient tissues. While primary tissues are typically obtained through biopsy, this is rarely performed for metastatic sites. To address this difficulty, we have developed both a rapid autopsy strategy where we collect fresh samples of metastatic tumors within hours of patient death, as well as collections of metastatic tumor biopsies in the clinical trial setting prior to and after drug treatment. These collections enable us to profile multiple metastatic sites and investigate the association between metastatic sites and subclonal evolution in an isogenic background. We propose to leverage this unique data set to investigate the relationship between evolution of tumor subclones during metastatic progression and the phenotypic profiles of these tumors. We hypothesize that, despite the diversity in their genetic mutation profiles, metastatic tumors exhibit clonal dynamics that ultimately leads to convergence on more common cooperative phenotypic networks, and that targeting the key dependencies within this network will lead to increased collapse of the metastatic tumor population. To investigate this, we will profile the tumors by whole genome sequencing, whole exome sequencing, and single cell RNA sequencing. This data, coupled with our newly developed algorithms for dissecting subclonal populations using tree reconstruction algorithms, for eliciting phenotypes from gene expression profiles using Bayesian statistics, and for simulating phenotypic evolution using mathematical models from ecology; will enable us to understand (Aim 1) the subclonal heterogeneity that underlies metastatic initiation and progression; (Aim 2) how cooperative functions evolve to a chemo-refractory signaling network, and therapeutic strategies to target it; and (Aim 3) how these dynamics are manifested human tumors in a clinical trial. Our investigations represent the first characterization of the clonal dynamics of a large multisite metastatic cohort, and will provide a new framework for understanding and treating end-stage tumors based on the evolution of cooperative phenotypes. We will develop these models on patient samples and test them in a unique clinical trial, ensuring the physiological, if not clinical, relevance of our findings.


Translational Shared Resource Core (Moos)


The Translational Shared Resource Core will provide services for both research projects. This core is central to the proposal’s mission as it directly facilitates: 1) collection, processing, pharmacological testing and use of samples from our patient cohorts; and 2) generation and collection of DNA- and RNA-sequencing data, including single-cell sequencing. Specifically, we will collect, process, and maintain patient-derived cells for temporal and spatial samples from the patients we profile. In preparation for sequencing, we will enrich for tumor cells, and separate white blood cells and other normal cell types. For single-cell experiments, we will use microfluidics to isolate individual cells, prior to extracting DNA or RNA that will be amplified for sequencing. The core will also be responsible for comparing sequencing results for bulk tumors with results from single-cell sequencing, which will enable us to continue optimizing our single-cell sequencing process and to perform comparisons that help us better understand temporal and spatial tumor evolution, which is central to the success of the scientific projects.

Computational Core (Chang)

The main purpose of the Computational Core is to support the scientific goals of the center by ensuring correct and reproducible analysis of next generation sequencing data. This will be accomplished through five tasks. 1) Data management: we will store and maintain provenance of the raw and pre-processed data in archives that track relevant metadata, such as creation data and checks on secured servers. 2) Data Preprocessing: we will develop standardized approaches to pre-processing the data, and create reference versions of the pre-processed data ready for further analysis. 3) Algorithm Development: We will coordinate with investigators to develop algorithms for the modeling of tumor heterogeneity and its evolution. The Core will maintain the source code in a git repository and will identify stable working versions that are then tagged and archived. 4) Develop Pipelines: to facilitate the processing of the data, and to ensure its reproducibility, we will develop standardized pipelines for the preprocessing and analysis of the data. 5) Standardize Environments: The processing of next generation sequencing data requires a multitude of software programs, each of which can affect the final result. To mitigate variation due to differences in software, versions, or libraries, we will create standardized analysis environments equipped with validated software and libraries and distribute them as Docker containers.

Education and Outreach Core (Moos and Werner)

This core are to build a singular research community within the consortium, educate students and the community on the latest advances in systems biology, and reach out to cancer advocates and patients regarding the impact of systems biology and cancer heterogeneity on treatment strategies to build excitement for future developments. We will sponsor student exchange programs, seminars, and courses on systems biology, as well as participate in organizations that promote health and cancer awareness. We will invite leaders in systems biology—whose expertise and research interests complement and expand our own—to visit our center sites; meet with our faculty, postdocs, and students; and participate in workshops that encourage spirited dialog and broad participation. We will assess the effectiveness of our outreach efforts through surveys that will provide the feedback necessary to ensure we are meeting our goals.

Administrative Core

The key to our ongoing collaboration has been communication between groups on our weekly to bi-weekly calls and discussions, focused on coordinated project planning and discussion of experiments and results. The Administrative Core will provide oversight of the Center and will manage its day-to-day operations. Specifically, this Core will: 1) schedule and organize meetings among investigators twice a month; 2) provide monthly and yearly summaries of work accomplished, results, and to-do items; 3) manage the budget; 4) plan internal and external advisory panel meetings and implementation of feedback; 5) manage developmental research project selection and support; and 6) perform administrative tasks related to center management.