The advent of single cell approaches has dramatically improved our ability to study human disease and to appreciate its hidden complexity. This has changed the study of cancer from a largely cell-autonomous perspective, determined by individual driver mutations, to one that focuses on the dysregulation of intracellular and cell-cell interaction networks regulating the interplay between malignant cells and cells in the Tumor Microenvironment (TME). In addition to the T cells that mediated immune response, many additional TME subpopulations have emerged as playing a key role in tumor maintenance and progression, from fibroblasts (1, 2) and macrophages (3) to cellular niches contributing the paracrine signals necessary to support metastasis (4, 5). These advances are transforming cancer research from studying the average of a mixture of co-existing cell states to the characterization of each individual cell state comprising a tumor mass.

Such intra-tumor heterogeneity creates two complementary yet intimately related issues. First, virtually isogenic, yet transcriptionally distinct malignant cell states may exist within the same tumor (6, 7). These states may not only elicit distinct drug sensitivity but can also defeat monotherapy by plastically reprogramming into each other, thus first escaping targeted or immune therapy (8) and then regenerating the full heterogeneity of the tumor (6). Thus, the development of novel methodologies to inventory molecularly distinct subpopulations of transformed cells that coexist in the TME and to elucidate their genetic and pharmacological dependencies represents a critical step in the eradication of aggressive and untreatable malignancies. Second, and equally important, cancer cells leverage aberrant paracrine signals to selectively recruit (or exclude) healthy cells and/or reprogram them to create a pro-malignant TME. For instance, this is accomplished by tumor-mediated recruitment of T regulatory cells (Tregs) and other immunosuppressive subpopulations, such as macrophages (3) and Cancer Associated Fibroblasts (CAFs) (1). We thus propose that the development of methods to inventory and target pro-malignant, non-transformed TME populations is a critical step towards improving treatment of human malignancies.

Figure 1: CaST Center at a glance. CaST is organized as a set of concentric frameworks centered around the emerging concept of Cell States and Cell State Dependencies (Layer 1, brown) and their role in defining inter-dependent malignant and TME-related subpopulations (Layer 2, purple). Elucidating the internal dependencies and interactions is predicated on the reverse engineering of Transcriptional, Signaling, 3D-Structure mediated PPI, and Paracrine networks (Layer 3, green). Reverse Engineering and Integrative Interrogation of these networks requires both analytical innovations (Layer 4, yellow, with novel algorithms shown in bold) and complementary technological innovations to (a) generate the necessary data for network reconstruction and (b) validate predictions arising from their interrogation (Layer 5, red). The Center is organized around three coherent and interdependent projects aimed at developing fully integrated computational and experimental methodologies and proof of concept (PoC) applications to address key basic-science and translational challenges in cancer. The Center is supported by three Cores that ensure proper management, access to cutting edge single cell technologies and methodology dissemination.

Rather than focusing on a specific tumor type, the proposed CaST Center is thus dedicated to developing novel network-based methodologies that integrate both computational and experimental approaches, to address these two critical challenges at the single cell level. Specifically, we will build upon technologies for the reverse engineering and interrogation of multi-layer cellular networks—comprising regulatory, signaling, protein-complex, and paracrine interactions—to produce a highly generalizable tumor- and mutation-agnostic framework for the study and treatment of human malignancies at the individual tumor cell state level (Figure 1). Thus, CaST will focus on the development of novel, interactome-based methodological frameworks that are generalizable to virtually any tumor context, as already shown in previous pancancer publications (9-12). This is consistent with a study of human malignancies that is increasingly focused on mechanism conservation and more universal dependencies, rather than on tumor organ sites. We will thus select a handful of tumor contexts as Proof-of-Concept (PoC) for different methodologies, based on (a) availability of critical reagents and datasets to successfully achieve PoC and (b) tumor biology expertise and track record of CaST investigators.  Specifically:

  • In Project 1, which focuses on the reverse engineering of multi-layer interactomes, we will study colon adenocarcinoma (COAD) due to availability of a unique, large-scale dataset of time-dependent phosphoproteomic profiles generated following drug perturbations and pancreatic ductal adenocarcinoma (PDAC) because it represents the archetype of stroma-rich tumors and is thus ideally suited to elucidate paracrine interactions between malignant and TME cells. LEARN MORE ABOUT PROJECT 1 
  • In Project 2, we will exclusively focus on brain metastases from the two malignancies that most contribute to this phenotype, melanoma (SKCM) and non-small-cell lung cancer (NSCLC). LEARN MORE ABOUT PROJECT 2 
  • Finally, in Project 3, we will leverage an extensive prostate carcinoma (PCa) GEMM resource to investigate the concept of cancer model fidelity and will extend our PDAC studies to predict and validate therapies aimed at depleting individual malignant and immunosuppressive cell states for combination therapy. LEARN MORE ABOUT PROJECT 3

All CaST-developed methodologies will be fully generalizable and adaptable to study other tumors with minimal parameter tuning. We believe that such an approach is most responsive to the CSBC mission and will provide tools that other consortium centers, and the broader cancer research community at large, will be able to leverage for their studies.   


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11. Broyde J, Simpson DR, Murray D, Paull EO, Chu BW, Tagore S, Jones SJ, Griffin AT, Giorgi FM, Lachmann A, Jackson P, Sweet-Cordero EA, Honig B, Califano A. Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses. Nat Biotechnol. 2021;39(2):215-24. Epub 2020/09/16. doi: 10.1038/s41587-020-0652-7. PubMed PMID: 32929263; PMCID: PMC7878435.

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13. Lo YH, Kolahi KS, Du Y, Chang CY, Krokhotin A, Nair A, Sobba WD, Karlsson K, Jones SJ, Longacre TA, Mah AT, Tercan B, Sockell A, Xu H, Seoane JA, Chen J, Shmulevich I, Weissman JS, Curtis C, Califano A, Fu H, Crabtree GR, Kuo CJ. A CRISPR/Cas9-engineered ARID1A-deficient human gastric cancer organoid model reveals essential and non-essential modes of oncogenic transformation. Cancer discovery. 2021. Epub 2021/01/17. doi: 10.1158/2159-8290.CD-20-1109. PubMed PMID: 33451982.

14. Affo S, Nair A, Brundu F, Ravichandra A, Bhattacharjee S, Matsuda M, Chin L, Filliol A, Wen W, Song X, Decker A, Worley J, Caviglia JM, Yu L, Yin D, Saito Y, Savage T, Wells RG, Mack M, Zender L, Arpaia N, Remotti HE, Rabadan R, Sims P, Leblond AL, Weber A, Riener MO, Stockwell BR, Gaublomme J, Llovet JM, Kalluri R, Michalopoulos GK, Seki E, Sia D, Chen X, Califano A, Schwabe RF. Promotion of cholangiocarcinoma growth by diverse cancer-associated fibroblast subpopulations. Cancer Cell. 2021;39(6):866-82 e11. Epub 2021/05/01. doi: 10.1016/j.ccell.2021.03.012. PubMed PMID: 33930309; PMCID: PMC8241235.