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

Overview

Center Title

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

Center Website

BildLab.org

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.

Investigators

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

Projects

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.

Cores

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.