Research Category: Tumor-Immune

Overview

Center Title

Stanford University Center for Cancer Systems Biology

Overall Project Title

Modeling the role of lymph node metastases in tumor-mediated immune suppression

Center Website

http://ccsb.stanford.edu

Center Summary

Our Research Center aims to identify the mechanisms in which tumor cells instruct the immune system to tolerant them by focusing on the understudied role of lymph node invasion in tumor-mediated immunosuppression.

Overall Distant metastasis is the primary cause of cancer-related death. To colonize distant tissues, cancer cells must migrate while evading elimination by the immune system. Evidence suggests that key steps in the induction process of immune tolerance occur early in the metastatic cascade, located at regional lymph nodes. However, the nature of the interactions between tumors and immune cells remains poorly understood, particularly for those occurring within the lymph nodes. Even though lymph nodes are in fact commonly assessed in cancer patients to determine disease stage and treatment plan, they are understudied in the context of metastatic progression.

We hypothesize that lymph node metastasis constitutes an essential, first step in the metastatic cascade of cancer progression. Based on our preliminary findings, we speculate that such metastases act locally upon the adaptive immune system within the nodes to begin to induce systemic tolerance of the tumor. We will explore, compare and test this hypothesis in two malignancies: (i) melanoma and (ii) head and neck squamous cell carcinoma. We have assembled a multidisciplinary team whose coordinated efforts will involve the application of genomic and single-cell in-situ imaging technologies on preclinical and human samples to explore the evidence and mechanisms of the induction of immunosuppression in the lymph nodes. We propose three inter-connected Research Projects that focus our scientific theme on different platforms: murine models (Project 1), high-dimensional in-situ imaging (Project 2), and integrative computational analysis (Project 3). All three projects will utilize a shared resource core dedicated to the acquisition of patient samples and associated clinical annotation and data management (Biospecimen and Data Management Core. These efforts will yield highly multiplexed, multi-scale datasets which will be analyzed by novel bio-computational methods to reconstruct intracellular and intercellular molecular interaction networks in order to identify, then functionally validate, critical mediators of tumor immunosuppression.

Our ultimate objective is to advance our understanding of the systemic consequences of lymph node metastases and identify new therapeutic approaches to cancer immunotherapy. Our findings promise to provide critical insights into blocking metastatic progression and thereby preventing cancer-related deaths.

Investigators

Principal Investigators

Participating Investigators

Projects

Project 1: Murine modeling of tumor-mediated immunosuppression

We hypothesize that lymph node (LN) metastasis constitutes an essential step in the metastatic cascade of melanomas and head and neck tumors in that such metastases act locally upon the adaptive immune system within the nodes to induce tolerance to the tumor and that leukocytes recirculating from these nodes carry the tolerance to distant sites. Our objectives are to establish whether LN metastases induce perturbations in anti-tumor immunity and to identify the mechanisms of these perturbations. We will a) characterize differences in local and systemic immune responses to metastatic tumors; b) identify differential regulators of tolerance induction by metastatic cells through the use of genomic profiling; and c) identify the molecular mediators of metastatic tolerance induction in mice and humans. Through the use of serial in vivo passaging, we have developed a panel of syngeneic melanoma cell lines that exhibit enhanced LN metastatic potential. We will compare the activation states of these immune cells, cytokine profiles, T cell polarization, and cytolytic activity toward tumor cells using single cell proteomic methods (Project 2). Using cytokine profiling and RNA sequencing on the lines, we will apply computational systems biology approaches (Project 3) to identify the molecules relevant for induction of tolerance. If our hypothesis is proven correct that LN metastasis is an obligate step in the generation of systemic disease due to tolerance induction, targeting the molecules responsible for LN metastasis induced tolerance could prevent and treat metastatic disease.

Project 2: Spatial architecture of tumor-mediated immunosuppression

Beyond the internal genomic and epigenetic events that occur to drive a cell towards outright carcinogenesis and then metastasis, there co-exist the ordered events a cancer imposes on immune cells it encounters on its progression towards advanced disease. Induction of tolerance, avoidance of apoptosis, and even recruitment of the immune system to aid a tumor’s growth are all poorly understood processes. We propose to undertake deep phenotyping of the 2D and 3D architecture of tumour-lymph node micro-environment—wherein it is expected some of the initial phases of the tumor’s avoidance and recruitment mechanisms are implemented. How is the architecture of the immune environment disrupted in the face of tumor metastasis? Are their micro-communities (as defined by particular cell-cell interactions) whose presence or absence defines an outcome in progression of the tumor? To this end we have developed a technology (ABSeq) that enables us to sensitively and quantitatively image tumors with 60 markers per 3 hours (scalable to 480 in a time-dependent manner) with markers selected from a range of intracellular or surface epitopes (recognized by antibodies) or RNAs. The hypothesis is that an orchestrated corruption of immune surveillance is initiated by cancers as they progress, and that the micro-scale architecture of the lymph node (by way of which cells are talking to whom and what broader effects occur across the lymph node and beyond) is disrupted in a defined manner. A major aim of the research is to, with ABSeq, define the 2D and 3D architecture and communities of immune and cancer cells in draining lymph nodes from 2 cancers (melanoma and head and neck cancer) in murine models and with human samples. Databases of 2D and 3D microenvironments will be publicly created and mined for associations that define the architectural changes that occur as tumors progress and initiate tolerance.

Project 3: Integrative computational modeling of tumor-mediated immunosuppression

We will develop and apply computational tools to integrate the complex datasets generated by our Center in order to identify candidate mediators of tumor-immune interactions that induce immunosuppression for functional validation. To enrich our ability for interpretation, we will explore signatures of the immune system in a pan-cancer analysis using the TCGA datasets annotated with time to distant metastasis, in the context of node-negative and nodepositive patients. We hypothesize that pan-cancer genes whose expression is strongly associated with time to distant metastasis are more likely to be associated with tumor-intrinsic or microenvironmental processes driving metastasis progression, thus we will prioritize these genes in our integrative computational analysis of our melanoma and head and neck squamous cell carcinoma datasets. Using the RNAseq data generated by our study, we will develop and apply novel network-based computational methods for reconstructing the interactions between malignant and immune subpopulations. Moreover, we will develop and apply new approaches to integrate the spatial information from high dimensional single cell in situ images from Project 2 with the gene expression datasets to further refine our inferences of candidate mediators of immunosuppression. The datasets and computational resources developed by our Project, and Center at large, will not only enable use to deeply explore the role of lymph nodes in tumor-mediated immunosuppression, but will also provide the community with powerful resources for understanding systemic influences on the forces governing metastatic dissemination.

Cores

Core 1: Biospecimen and Data Management Core

Our Biospecimen and Data Management Core will be utilized by all the Research Projects in our Center. All three Research Projects aim to identify mechanisms by which tumor cells, metastatic to regional lymph nodes, interface with the host immune system to perturb systemic immunity to induce global immune tolerance. While we will methodically study this process in murine models (Project 1), analysis of human tumor and lymph node specimens is paramount for two primary reasons. The first reason is to ensure the translation of the murine discovery approach to human disease. The second reason is to facilitate the discovery of critical mediators of tumor-induced immunosuppression directly from the human samples, then follow with functional validation in our murine models. Hence it is critical that we establish the necessary infrastructure to procure viable human tumor specimens and autologous immune tumorinfiltrating immune cells. Importantly, matched sets of fresh primary tumor cells, metastatic tumor cells, tumor-infiltrating immune cells (from the primary tumor and the lymph nodes) and circulating immune cells will be collected to address the hypotheses and goals of the Research Projects. Furthermore, in this shared core, a database of clinical annotation (aka clinical metadata) for these matched samples will be created and maintained. This clinical metadata is critical for interpretation of our molecular and imaging data given tumor and patient heterogeneity and will likely provide important insight into how the data generated by the proposed projects may be used to guide treatment and predict outcome. Our proposed Biospecimen and Data Management Core that will focus on collecting tissue and collating all the molecular data on our two index cancer models: melanoma and head and neck squamous cell carcinoma (HNSCC).

Core 2: Outreach Core

Stanford University has a diverse range of faculty in biostatistics, computer science, applied mathematics, cancer biology, immunology, biomedical engineering and many other disciplines. A number of transformative technologies such as DNA microarrays, flow cytometry, single cell transcriptomics, and applications of next generation sequencing had their genesis in labs here. We are therefore in a unique environment for promoting cancer systems biology to a wide audience of researchers, the relevance of whose expertise to the field may not be obvious a priori. We envisage that our efforts to promote our research and those of others to the Stanford community will seed new collaborations across disciplines. We will conduct outreach through regular seminar series and meetings, an annual symposium, funding for pilot projects, as well as participation in the CSBC Postdoctoral Exchange Program that will place researchers at reciprocal institutions to have short-term dense exposure to relevant research objectives and expertise at other institutions. And finally, important collaborative possibilities with the other CSBC Research Centers will be facilitated by their updates provided at annual meetings, as well as in our newsletter. The great value of this new NCI Consortium will be harnessed by ongoing building of an expansive investigator network across universities and research centers.

Overview

Center Title

The CSBC Research Center for Cancer Systems Immunology at MSKCC

Participating Institutions

Memorial Sloan-Kettering Cancer Center (MSKCC)
Dana Farber Cancer Center

Center Website

https://www.mskcc.org/research-areas/programs-centers/cancer-systems-immunology

Center Summary

Exciting clinical breakthroughs with checkpoint blockade antibodies and adoptive T cell transfers demonstrate the power of harnessing the immune system to eliminate cancer. However, fundamental challenges remain. Only a subset of patients and cancer types—especially hematological malignancies and melanoma—show significant clinical responses. What properties of tumors determine clinical responses? How can we produce clinical responses in cancers currently unbeatable?

The CSBC Research Center for Cancer Systems Immunology at MSKCC addresses these challenges towards predictable and effective cancer immunotherapy. We assembled a multidisciplinary team of computational biologists, immunologists and cancer scientists; together we will deepen our fundamental understanding of cancer-immune system interactions at the molecular, cellular, and systems levels.
We investigate cancer-immune system interactions at three stages of disease progression: cancer initiation and early tumorigenesis (Project I); established and progressing tumors (Project II); and latent disease and metastasis (Project III). All projects will use cutting-edge single-cell droplet sequencing technologies and computational analyses (Shared Resource Core).

Investigators

Principal Investigators

Computational Investigators

Experimental Investigators

Clinical collaborator

Projects

Project 1: Tumor-specific T Cell State Dynamics and Heterogeneity in Early Tumorigenesis

Project 1 aims to define the molecular and epigenetic characteristics and mechanisms that cause tumor-specific T cell to differente to a dysfunctional state during early tumorigenesis. In Aim 1 we will define the chromatin states and transcription factor networks that mediate the transition from plastic to fixed T cell dysfunction states. In Aim 2, we will elucidate the mutational landscape, stromal and immune population dynamics over the course of tumor development and assess how tumor-specific, dysfunctional T cell populations and cell states co-evolve with these changes; exploit single-cell technologies to dissect diversity of T cell states, and develop a novel single cell technology to capture TCR sequences together with gene expression profiles to connect tumor antigen specificity with cell states; ultimately, develop computational and mathematical models of T cell differentiation states to predict responsiveness to therapeutic interventions (e.g. checkpoint blockade). In Aim 3, we will define cell states and heterogeneity of tumor-specific T cells from human solid tumors, and predict and validate changes in their cell states in response to therapeutic interventions (e.g. checkpoint blockade) at a population and single-cell level. Identifying cell states and epigenetic programs of tumor-specific T cells that mediate plasticity or imprinting of cellular hyporesponsiveness will not only provide new insights into the genomic control circuitry of T cell differentiation and dysfunction but may point to novel strategies for cellular reprogramming of cancer-specific T cells for cancer therapy.

Project 2: The tumor ecosystem in cancer progression and immunotherapeutic response

The goal of this project is to identify the ecological interactions between cancer and immune cells that govern cancer dynamics and response to therapy. A tumor can be considered an ecosystem or organ, where multiple accessory cell types are interconnected and communicate with each other and with tumor cells, which serve as their clients. We seek to identify key cellular and molecular regulatory elements in the tumor microenvironment and potential means of their manipulation for therapeutic benefit through systems analysis and modeling of functional interactions in the tumor ecosystem on different scales including cellular, protein, metabolite, and gene expression dynamics at a population and single cell levels. In Aim 1, we will explore the multiple accessory cell types and their interactions with tumor cells using experimental models of skin and lung cancer in mice and in human cancer patients. The accessory cells include myeloid cells, dendritic cells, innate lymphoid cells (ILC), neutrophils, eosinophils, endothelial cells, fibroblasts, and regulatory T (Treg) cells. We will also investigate features of mediators of anti-tumor immunity including NK cells, CD4 and CD8 T cells and their specialized subsets. In Aim 2, we will use perturbation of the tumor ecology impacting its progression in mice and human patients by established and novel immunotherapeutic modalities including PD1 and CTLA4 blockade and Treg cell depletion. The impact of these perturbations will be assessed through comprehensive analysis of cellular dynamics and states in relation to biological and clinical outcomes to generate predictive models. In Aim 3, we will then validate key interaction components in the tumor ecosystem by modeling cell-cell interactions in vitro using tissue mimetic systems and in silico using agent-based models.

Project 3: Latent Metastasis: Immune Regulation of Disseminated Cancer Stem Cells

The goal of Project 3 is to discover mechanisms that critically regulate immune evasion by disseminated tumor cells (DTCs) and their evolution as latent metastatic entities. We will use an integrated approach that combines single-cell interrogation methods with unique biological models of latent metastasis from breast cancer and lung adenocarcinoma, and novel computational strategies. Distant metastasis underlies the overwhelming majority of cancer-related deaths and its inception is exceedingly variable. Residual DTCs may outgrow immediately or, more frequently, linger in a viable state of replicative quiescence or mass dormancy for months to years after infiltrating distant organs. This latency state of DTCs is accompanied with significant resistance to anti-neoplastic therapy, which typically targets actively dividing tumor cells. Moreover, latent DTCs somehow evade immune surveillance. The biology underlying these adaptive abilities remains poorly understood and factors governing DTC population dynamics, whether stochastic or deterministic, remain unknown. We aim to address this significant knowledge gap by combining massively parallel, single-cell RNA expression profiling using a bead-based molecular barcoding technology with unsupervised learning methods to identify stable/transitory cell states within latent, residual disease and their molecular control mechanisms. As a complementary approach, individual cell responses to molecular perturbations will be dynamically tracked by live cell imaging. In Aim 1 we propose to determine whether the latent state pre-exists in the primary tumor or is induced by the stress of immunosurveillance in a host tissue. In Aim 2 we will model the evolutionary dynamics of metastatic cells as they exit latency under growth permissive and immunoediting conditions. In Aim 3 we will identify key regulators of metastatic immune evasion by probing transcriptional heterogeneity in quiescent subpopulations differentially sensitive to NK-cell mediated elimination. The amalgamation of these approaches, combined with our deep understanding of the biology of cancer metastasis, will promote the discovery of therapeutic strategies to eradicate or control metastasis from its earliest stages of inception.

Cores

Shared Resource Core

Single-cell sequencing technology and computational analysis
The Research Center’s Shared Resource Core will provide a broadly applicable set of experimental technologies and computational tools for high-throughput single-cell RNA-seq (scRNA-seq) of heterogeneous populations. Single-cell resolution is crucial for elucidating the functional cell subpopulations in both the tumor and its immune microenvironment and can be harnessed to elucidate regulatory relationships within and between these cell subpopulations. To achieve our goals requires a technology that can collect many thousands of cells per tumor and computational methods to analyze and interpret the complex data collected. We will use an improved version of in-drop scRNA-seq, which provides an unprecedented increase in throughput for automated capture and library preparation for single cells in a cost effective manner. The technology will be used to interrogate cell populations for Projects I, II, and III. The Shared Resource will provide the computation, visualization and analysis methods needed to interpret the collected single cell data and integrate it with other data types.