Our preliminary results support a prominent role for dysregulation of RNA binding proteins (RBPs) in cancer progression. In fact, our analysis of the cancer genome atlas (TCGA) transcriptome data shows that RBPs, as a group, are significantly more dysregulated in cancer than transcription factors. We propose a multi-faceted set of computational and experimental studies to systematically identify the set of RBPs that causally contribute to cancer progression and to characterize their downstream effector mechanisms. In one strategy, we propose to identify dysregulated RBPs by first discovering their cis-regulatory recognition elements in the 3? and 5? UTR of genes that show dynamic mRNA expression?both between tumor vs. normal samples, and across each of the 25 cancer cohorts in TCGA. This will be accomplished using information-theoretic algorithms that discover de novo linear and structural RNA motif elements with high sensitivity and low false discovery rates. We have previously shown that such RNA motifs are the binding sites for RBPs that modulate mRNA stability, a subset of which regulate tumorigenesis and metastasis. The genes harboring these motifs constitute an orphan RBP regulon (or RBP module) with a suspected role in cancer progression. In order to identify clinically significant RBP modules, we propose to develop a computational framework that quantifies the degree to which the expression of each module stratifies patient survival across the TCGA primary tumor samples. Our preliminary results have led to the discovery of many such modules with remarkable stratification of patient survival across multiple cancer types. For the subset of the most clinically prognostic modules, we will identify their cognate RBPs using both biochemical and CRISPR-based parallel genetic screens. In a complementary strategy, we will computationally identify such clinically prognostic RBP modules from a compendium of ENCODE transcriptome data obtained following shRNA knockdowns of each of ~250 RBPs. In order to identify RBPs that causally contribute to cancer progression, we propose to develop a parallel CRISPR loss-of-function screen for all RBPs in mouse xenograft models of tumor formation and metastasis. We will then conduct a more focused CRISPR screen on the top ~20 RBPs that show both significant patient survival stratification and mouse in vivo tumor effects in our primary comprehensive screen. The top validated RBPs will then be individually characterized for their roles in a variety of in vitro and in vivo cancer cell phenotypes. Finally, we propose to develop a parallel mouse in vivo CRISPR epistasis platform to efficiently determine the specific downstream genes through which the RBP exerts its effects on tumor formation and metastasis. Our integrated computational/experimental strategy will expand our molecular understanding of a largely unexplored domain of cancer pathway dysregulation and potentially reveal new principles at work. Furthermore, our focus on causal pathways of cancer progression will impact the diagnostic, prognostic, and therapeutic precision with which we approach clinical oncology.

Public Health Relevance

The proposal describes an inter-disciplinary project whose goal is to systematically determine the role of RNA- binding proteins (a set of cellular regulatory proteins that recognize, bind, and regulate mRNAs) in the process of cancer progression. Our preliminary data suggests that these regulators play a disproportionately important role in cancer and our project is a multi-faceted computational/experimental strategy to characterize this landscape. If successful, the project will expand our understanding of cancer pathways in novel directions that may have significant impact on diagnosis and treatment of a variety of cancers.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Research Project (R01)
Project #
1R01CA257153-01
Application #
10142732
Study Section
Cancer Genetics Study Section (CG)
Program Officer
Fingerman, Ian M
Project Start
2021-02-12
Project End
2026-01-31
Budget Start
2021-02-12
Budget End
2022-01-31
Support Year
1
Fiscal Year
2021
Total Cost
Indirect Cost
Name
Columbia University (N.Y.)
Department
Biology
Type
Graduate Schools
DUNS #
049179401
City
New York
State
NY
Country
United States
Zip Code
10027