Research Category: Microenvironment

Overview

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

https://www.ohsu.edu/m2ch

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.

Investigators

Principal Investigators

Center Administrator

Participating Investigators

Projects

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

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

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

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.

Cores

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).

Overview

Center Title

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

Center Website

http://casb.uci.edu

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.

Investigators

Principal Investigators

Participating Investigators

Core Director

Project Manager

Projects

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.

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.

 

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.

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.

Cores

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

Overview

Center Title

Quantitative and functional characterization of therapeutic resistance in cancer

Center Website

In progress

Center Summary

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.

Investigators

Principal Investigators

innovation

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.

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.

 

[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.

 

 

Projects

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.

Cores

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.

Overview

Center Title

Columbia University Center for Cancer Systems Therapeutics

Center Website

http://systemsbiology.columbia.edu/cast

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.

Investigators

Principal Investigators

Participating Investigators

Core Director

Project Manager

Projects

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.

Cores

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.