The Columbia University Center for Cancer Systems Therapeutics (CaST) was created in 2016 as one of the inaugural centers in the National Cancer Institute’s Cancer Systems Biology Consortium (CSBC). The CSBC aims to address challenges of complexity in basic and translational cancer research through the use of experimental biology combined with in silico modeling, multi-dimensional data analysis, and systems engineering.

As a member of the CSBC, CaST’s overarching aim is to facilitate the development of effective therapeutics for the treatment of cancer. A significant fraction of patients with aggressive cancers present with no pharmacologically actionable mutations and limited sensitivity to immune checkpoint blockade, thus deriving only modest clinical benefit from targeted and immune therapy (1). Tumor heterogeneity adds further complexity by fostering reprogramming, adaptation, selection, and ultimately expansion of drug-resistant cells, as well as emergence of an immunosuppressive tumor microenvironment (TME), which are ultimately responsible for patient relapse and poor outcome (2). We propose that addressing these challenges—i.e., identifying more universal, mechanistic targets for pharmacological intervention and assessing their potential value in highly heterogeneous, poor outcome tumors—will be increasingly dependent on the availability of detailed, comprehensive, and cell type-specific molecular interaction networks, which underlie both the cell-autonomous behavior of cancer cells and their interaction with TME subpopulations.

Recent studies by CaST investigators have shown that individual cancer cells can persist in only a handful of transcriptionally distinct “stable states” within a tumor mass, as determined by mutations and paracrine/endocrine signals (3, 4), thus potentially supporting the development of more universal treatments. TME subpopulations also comprise discrete sets of molecularly distinct states, some of which are highly predictive of tumor outcome, for instance due to their ability to implement and maintain an immunosuppressive milieu or to facilitate metastatic progression (5, 6). We postulate that single-cell-level assessment of stable cell states, state transitions, and state-state interactions will help produce a generalizable framework to elucidate pharmacologically actionable, state-specific dependencies, for both transformed and healthy TME cells, at a much finer level of granularity compared to previous bulk-tissue, pan-cancer studies (3). Thus, by integrating proven methodologies with novel systems biology approaches, CaST Center investigators will develop and disseminate an innovative network-based framework for the systematic elucidation of inter- and intra-molecular mechanisms contributing to the stability of tumor-related cell states, their ligand/receptor-mediated interactions with other subpopulations in the TME, and their pharmacologically actionable molecular dependencies. Our goals include:

  • Enabling the structure-informed dissection of cancer-specific intracellular and paracrine networks, by integrating structure-based, proteomics, and functional approaches. These networks will comprise both intra-cellular interactions and cell-cell paracrine interactions inducing a pro-malignant TME.
  • Elucidating and validating the mechanisms presiding over the implementation and maintenance of co-existing malignant and TME-related cell states at the single cell level. This will be accomplished by leveraging regulatory and signaling networks reverse engineered de novo from multiple data sources, as well as cutting-edge single cell technologies. The focus of this effort will be on tumor progression to brain metastasis—an event associated with worst prognosis—in the context of melanoma (SKCM) and non-small cell lung cancer (NSCLC), the two malignancies with the largest contribution to this phenotype.
  • Developing and utilizing novel methodologies for the tissue-specific, proteome-wide characterization of drug mechanism of action and polypharmacology. The goal is to enable prediction of drugs that selectively deplete individual subpopulations, representing either malignant or pro-malignant TME-related cell states, with optimal potential for monotherapy or combination therapy.

To achieve these goals CaST has assembled a multidisciplinary scientific team comprising faculty from several departments and institutes at Columbia University. CaST investigators are experts in systems biology, functional and structural data analysis, high-throughput technologies for single-cell profiling, high-throughput genetic and pharmacological screening, experimental methods for studying tumor evolution in vivo, and software development.

Additionally, through its Outreach Core, CaST will vigorously pursue the dissemination of its research products, by strengthening connections with the CSBC and young scientists; forging new partnerships with cancer researchers and clinicians; cultivating the next generation of diversity-promoting scientists; and fostering systems approaches in science, education, and clinical translation.

Ultimately, CaST aims to use the remarkable opportunities that systems biology now offers to identify more effective, widely applicable strategies for precision cancer medicine.


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2. Tirosh I, Izar B, Prakadan SM, Wadsworth MH, 2nd, Treacy D, Trombetta JJ, Rotem A, Rodman C, Lian C, Murphy G, Fallahi-Sichani M, Dutton-Regester K, Lin JR, Cohen O, Shah P, Lu D, Genshaft AS, Hughes TK, Ziegler CG, Kazer SW, Gaillard A, Kolb KE, Villani AC, Johannessen CM, Andreev AY, Van Allen EM, Bertagnolli M, Sorger PK, Sullivan RJ, Flaherty KT, Frederick DT, Jane-Valbuena J, Yoon CH, Rozenblatt-Rosen O, Shalek AK, Regev A, Garraway LA. Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq. Science. 2016;352(6282):189-96. doi: 10.1126/science.aad0501. PubMed PMID: 27124452; PMCID: PMC4944528.

3. Paull EO, Aytes A, Jones SJ, Subramaniam PS, Giorgi FM, Douglass EF, Tagore S, Chu B, Vasciaveo A, Zheng S, Verhaak R, Abate-Shen C, Alvarez MJ, Califano A. A modular master regulator landscape controls cancer transcriptional identity. Cell. 2021;184(2):334-51 e20. Epub 20210111. doi: 10.1016/j.cell.2020.11.045. PubMed PMID: 33434495; PMCID: PMC8103356.

4. Laise P, Bosker G, Sun X, Shen Y, Douglass EF, Karan C, Realubit RB, Pampou S, Califano A, Alvarez MJ. The Host Cell ViroCheckpoint: Identification and Pharmacologic Targeting of Novel Mechanistic Determinants of Coronavirus-Mediated Hijacked Cell States. bioRxiv. 2020. Epub 20200517. doi: 10.1101/2020.05.12.091256. PubMed PMID: 32511361; PMCID: PMC7263489.

5. Obradovic A, Chowdhury N, Haake SM, Ager C, Wang V, Vlahos L, Guo XV, Aggen DH, Rathmell WK, Jonasch E, Johnson JE, Roth M, Beckermann KE, Rini BI, McKiernan J, Califano A, Drake CG. Single-cell protein activity analysis identifies recurrence-associated renal tumor macrophages. Cell. 2021;184(11):2988-3005 e16. Epub 20210520. doi: 10.1016/j.cell.2021.04.038. PubMed PMID: 34019793; PMCID: PMC8479759.

6. Elyada E, Bolisetty M, Laise P, Flynn WF, Courtois ET, Burkhart RA, Teinor JA, Belleau P, Biffi G, Lucito MS, Sivajothi S, Armstrong TD, Engle DD, Yu KH, Hao Y, Wolfgang CL, Park Y, Preall J, Jaffee EM, Califano A, Robson P, Tuveson DA. Cross-Species Single-Cell Analysis of Pancreatic Ductal Adenocarcinoma Reveals Antigen-Presenting Cancer-Associated Fibroblasts. Cancer Discov. 2019;9(8):1102-23. Epub 20190613. doi: 10.1158/2159-8290.CD-19-0094. PubMed PMID: 31197017; PMCID: PMC6727976.