Tumor progression from a tractable to an intractable, drug-resistant form represents perhaps the most formidable challenge both in terms of basic elucidation of tumor biology mechanisms and in terms of its translational and clinical implications. Even though not all tumors will spontaneously progress to a metastatic drug-resistant stage; our ability to identify the patients at greater risk of progression is extremely limited. For tumors destined to progress, the challenge presents two distinct, yet highly complementary perspectives. Macroscopically, progression occurs because either pharmacologically actionable mechanisms do not yet exist for a specific malignancy or because drug resistance ensues, due to genetic and epigenetic mechanisms. For instance, while 70% of HER2+ breast adenocarcinomas initially respond to trastuzumab, 70% of these will eventually relapse to trastuzumab-resistant tumors. The same dismal outcome is reflected across most targeted therapeutics. Microscopically, however, emergence of drug resistance is rooted in the exceedingly heterogeneous nature of cancer, both across individuals (inter-tumor) and, more importantly, across individual tumor cells (intra-tumor). The goal of this proposal is the development of a novel methodological framework integrating both experimental and computational approaches to systematically elucidate the mechanisms by which tumor heterogeneity drives tumor progression and emergence of drug resistance. It will focus specifically on the study of intra-tumor heterogeneity at the single cell level to identify the rane of independent molecular events contributing to sub-clonal expansion and emergence of drug-resistant niches. The methodological advances resulting from these studies will be broadly disseminated and will be applicable to the unbiased analysis of any human malignancy for which appropriate data is available. These methodologies will be hypothesis generating, producing comprehensive repertoire of high-likelihood molecular mechanisms that will be experimentally validated. Within this broader context, the primary focus of the proposed research will be on extending network-based methodologies, which were successfully applied to multicellular samples, to study the impact of tumor heterogeneity on progression and drug resistance, at the single cell level. We will focus on three sources of heterogeneity: (a) genetically distinct tumor subclones, (b) epigenetically reprogrammed yet isogenic tumor sub-populations, and (c) normal cells (e.g. stromal or immune system related), whose presence modulates tumor cell behavior and response to therapeutic agents. Our hypothesis is that elucidating tumor-related mechanisms in single cells is critical to the development of better strategies for prevention, diagnosis, and treatment of the disease.

Public Health Relevance

Tumor progression from a tractable form to an intractable, drug-resistant form represents perhaps the most formidable challenge both in terms of basic elucidation of tumor biology mechanisms and in terms of its translational and clinical implications. The emergence of drug resistance is rooted in the exceedingly heterogeneous nature of cancer, both across individuals (inter-tumor) and, more importantly, across individual tumor cells (intra-tumor). The goal of this proposal is the development of a novel methodological framework integrating both experimental and computational approaches to study the impact of tumor heterogeneity on progression and drug resistance, at the single cell level.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Unknown (R35)
Project #
5R35CA197745-03
Application #
9319711
Study Section
Special Emphasis Panel (ZCA1)
Program Officer
Li, Jerry
Project Start
2015-08-14
Project End
2022-07-31
Budget Start
2017-08-01
Budget End
2018-07-31
Support Year
3
Fiscal Year
2017
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biochemistry
Type
Schools of Medicine
DUNS #
621889815
City
New York
State
NY
Country
United States
Zip Code
10032
Ding, Hongxu; Wang, Wanxin; Califano, Andrea (2018) iterClust: a statistical framework for iterative clustering analysis. Bioinformatics 34:2865-2866
Thorsson, V├ęsteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14
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
Tomljanovic, Zeljko; Patel, Mitesh; Shin, William et al. (2018) ZCCHC17 is a master regulator of synaptic gene expression in Alzheimer's disease. Bioinformatics 34:367-371
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
Boboila, Shuobo; Lopez, Gonzalo; Yu, Jiyang et al. (2018) Transcription factor activating protein 4 is synthetically lethal and a master regulator of MYCN-amplified neuroblastoma. Oncogene 37:5451-5465
Talos, Flaminia; Mitrofanova, Antonina; Bergren, Sarah K et al. (2017) A computational systems approach identifies synergistic specification genes that facilitate lineage conversion to prostate tissue. Nat Commun 8:14662
Chen, Xiaowei; Deng, Huan; Churchill, Michael J et al. (2017) Bone Marrow Myeloid Cells Regulate Myeloid-Biased Hematopoietic Stem Cells via a Histamine-Dependent Feedback Loop. Cell Stem Cell 21:747-760.e7

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