The quantitative, model driven approaches that constitute the underpinning of systems biology are emerging as an increasingly critical methodological repertoire for a truly ?precise? implementation of precision cancer medicine. This is especially relevant in view of the increasing limitations of current approaches based on the oncogene addiction paradigm. Even though pharmacological inhibition of oncogenes harboring activating alterations has emerged as a valuable rationale for targeted therapy, >75% of all adult malignancies lack any actionable alteration or present with undruggable ones and inhibitors of canonical oncogenes have shown lackluster response in the clinic. Most critically, following initial and at times remarkable response, targeted therapy almost invariably leads to relapse to drug-resistant disease. Systematic treatment of hundreds of cell lines with hundreds of compounds has shown that, with few notable exceptions, mutations are far from representing optimal predictors of targeted agent sensitivity. This is not surprising, as drug sensitivity clearly represents a complex polygenic phenotype, requiring equally complex and tumor-specific models. This center proposal encompasses studies across multiple levels of granularity, representing the full complexity of the tumor phenotype: from tumor/microenvironment interactions to single cell plasticity, supporting tumors reprograming to distinct isogenic states associated with progression or drug resistance. Specifically, three complementary directions will be pursued: First, elucidation of the regulatory module architecture (tumor checkpoint) and specific proteins within these modules (master regulators) that comprise the dysregulated mechanisms presiding over tumor homeostasis (I.e., a cell's ability to maintain its tumor state independent of mutational landscape and endogenous/exogenous signal heterogeneity). This will be accomplished by developing model-based approaches to analyze omics data representing distinct compartments of the tumor, ranging from tumor bulk, to stroma/tumor compartments, to single cells, following physiologic, genetic, and pharmacologic perturbations. Second, study the mechanisms by which tumor state can be altered to induce progression or drug resistance by adopting and extending approaches for the study of physiologic differentiation and reprograming. This analysis will integrate subclonal genomic characterization, using innovative computational models, with single cell data from primary tumors and patient derived xenografts to elucidate the mechanisms presiding over tumor plasticity. Finally, using the mechanistic regulatory frameworks emerging from these studies to elucidate actionable tumor dependencies leading to irreversible collapse of tumor homeostasis in vitro and in vivo. This will be accomplished by assembling and experimentally validating both probabilistic and kinetic models of tumor checkpoint regulation, assembled from time-series data following systematic small molecule perturbations. Center-developed software and methods will be disseminated to the Research Community, using proven strategies.

Public Health Relevance

Columbia Center for Cancer Systems Therapeutics (CaST) will leverage proven methodologies and novel and integrative systems biology approaches to foster a highly innovative framework for the rapid, patient-centric prioritization and evaluation of cancer therapeutic strategies targeting target tumor homeostasis, including single agent and combination therapy.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA209997-05
Application #
9976471
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Hughes, Shannon K
Project Start
2016-08-08
Project End
2021-07-31
Budget Start
2020-08-01
Budget End
2021-07-31
Support Year
5
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Internal Medicine/Medicine
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14
Arnes, Luis; Liu, Zhaoqi; Wang, Jiguang et al. (2018) Comprehensive characterisation of compartment-specific long non-coding RNAs associated with pancreatic ductal adenocarcinoma. Gut :
Risom, Tyler; Langer, Ellen M; Chapman, Margaret P et al. (2018) Differentiation-state plasticity is a targetable resistance mechanism in basal-like breast cancer. Nat Commun 9:3815
Ding, Hongxu; Douglass Jr, Eugene F; Sonabend, Adam M et al. (2018) Quantitative assessment of protein activity in orphan tissues and single cells using the metaVIPER algorithm. Nat Commun 9:1471
Rajbhandari, Presha; Lopez, Gonzalo; Capdevila, Claudia et al. (2018) Cross-Cohort Analysis Identifies a TEAD4-MYCN Positive Feedback Loop as the Core Regulatory Element of High-Risk Neuroblastoma. Cancer Discov 8:582-599
Cesana, Marcella; Guo, Michael H; Cacchiarelli, Davide et al. (2018) A CLK3-HMGA2 Alternative Splicing Axis Impacts Human Hematopoietic Stem Cell Molecular Identity throughout Development. Cell Stem Cell 22:575-588.e7
Yuan, Jinzhou; Levitin, Hanna Mendes; Frattini, Veronique et al. (2018) Single-cell transcriptome analysis of lineage diversity in high-grade glioma. Genome Med 10:57
Tzoneva, Gannie; Dieck, Chelsea L; Oshima, Koichi et al. (2018) Clonal evolution mechanisms in NT5C2 mutant-relapsed acute lymphoblastic leukaemia. Nature 553:511-514
Chiu, Hua-Sheng; Martínez, María Rodríguez; Komissarova, Elena V et al. (2018) The number of titrated microRNA species dictates ceRNA regulation. Nucleic Acids Res 46:4354-4369
Ding, Hongxu; Wang, Wanxin; Califano, Andrea (2018) iterClust: a statistical framework for iterative clustering analysis. Bioinformatics 34:2865-2866

Showing the most recent 10 out of 27 publications