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Identifying and targeting collateral lethal vulnerabilities in cancers

识别并针对癌症的附带致命弱点

基本信息

批准号：
10563469
负责人：
RONALD ANTHONY DEPINHO
金额：
$ 96.7万
依托单位：
UNIVERSITY OF TX MD ANDERSON CAN CTR
依托单位国家：
美国
项目类别：
财政年份：
2023
资助国家：
美国
起止时间：
2023-04-01 至 2028-03-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10563469
关键词：
1p36 ARID1A gene Adult Alleles Apoptotic Biochemical Biological Biological Assay CDKN2A gene CRISPR/Cas technology Cancer cell line Cell Culture Techniques Cell Line Cell Proliferation Cell Survival Cell division Cell physiology Cells Chromosomes Cleavage Stimulation Factor Clustered Regularly Interspaced Short Palindromic Repeats Common Neoplasm Complement Complex Computer Analysis Computing Methodologies Coupled Cultured Cells Data Defect Dependence Diphosphates Endoplasmic Reticulum Engineering Enzymes Event Extinction Face Gene Deletion Gene Family Genes Genetic Genetic Transcription Genome Genomics Goals Housekeeping Human Individual Knock-out Lactate Dehydrogenase Ligase MADH4 gene Malignant Neoplasms Mammalian Cell Maps Measurement Measures Mediating Metabolism Methods Mitochondria Mitosis Mitotic spindle Modeling Monitor Mus NF1 gene NF1 tumor suppressor Normal Cell Nuclear Envelope Organoids Orthologous Gene PTEN gene Patients Phosphatidylserine Synthase Phosphatidylserines Poly A Polyadenylation Probability Proteins Proteomics Pyruvate RB1 gene Reiterated Genes Ribose-Phosphate Pyrophosphokinase Sample Size Signal Transduction Survival Analysis System Systemic Therapy TP53 gene Toxic effect Tumor Suppressor Genes Tumor Suppressor Proteins U1 small nuclear RNA Validation cancer cell cancer therapy cancer type candidate validation deep learning design drug discovery enzyme activity gain of function genome-wide genomic locus improved outcome in vivo live cell imaging mRNA Precursor malic enzyme member metabolomics model organism mutant new therapeutic target nucleotide metabolism overexpression paralogous gene pharmacologic public database receptor expression research study response screening small hairpin RNA targeted treatment tool transcriptomics tumor validation studies vector whole genome

项目摘要

Identifying and targeting collateral lethal vulnerabilities in cancers Abstract/Summary Genomic deletions targeting major tumor suppressor genes frequently include adjacent passenger genes, encoding cell essential housekeeping functions. These cancer cells survive due to co-expressing functionally redundant paralogs residing in non-deleted regions of the genome. As such, these “collateral deletions” in tumor suppressor loci can confer cancer cell-specific vulnerabilities through targeted extinction of the remaining paralog. Our “collateral lethality” concept was first demonstrated in GBM with deletion of the 1p36 tumor suppressor locus encompassing ENO1, resulting in profound sensitivity to ENO2 depletion or pharmacologic inhibition (Muller et al. 2012). Subsequently, we demonstrated that deletion of mitochondrial malic enzyme 2 (ME2) in the SMAD4 locus engendered lethality upon shRNA-mediated depletion of the remaining mitochondrial malic enzyme activity encoded by the ME3 paralog (Dey et al. 2017). To systematically and comprehensively identify collateral lethal targets in cancer, we first analyzed the Broad Institute’s Cancer Dependency Map (DepMap), a publicly accessible database hosting essentiality scores of 17386 genes from a pooled genome- scale CRISPR knockout study conducted in 1054 cancer cell lines of diverse lineages (Dempster et al. 2019; Ghandi et al. 2019). The computational efforts yielded multiple collateral lethal candidate pairs including REEP3/4, PTDSS1/2, INTS6/INTS6L, PRPS1/2, LDHA/B, and CSTF2/CSTF2T via a two-class comparison method that regressed cell line sensitivity vectors against whole-genome CCLE expression and copy number data to predict paralog-depletion based sensitivity. While computational analysis of DepMap data can identify some candidate collateral lethal pairs, small sample sizes for many collateral deletions have stymied robust conclusions. Building on these computational methods, here we will apply the CRISPR/Cas12a polygenic knockout platform to systematically identify collateral lethal pairs anchored in deletion events targeting common tumor suppressor gene loci such as TP53, CDKN2A/B, ARID1A, PTEN, SMAD4, RB1 and NF1 across cancer types. This conceptual and experimental framework, coupled with a cell/organoid/tumor validation platform, seeks to identify and stringently validate collateral lethal target sets that can then be channeled into a drug discovery pipeline with the goal of expanding precision cancer treatments.

识别和瞄准癌症的附带致命弱点