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Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome.

ABSTRACT: CRISPR screens have accelerated the discovery of important cancer vulnerabilities. However, single-gene knockout phenotypes can be masked by redundancy among related genes. Paralogs constitute two-thirds of the human protein-coding genome, so existing methods are likely inadequate for assaying a large portion of gene function. Here, we develop paired guide RNAs for paralog genetic interaction mapping (pgPEN), a pooled CRISPR-Cas9 single- and double-knockout approach targeting more than 2,000 human paralogs. We apply pgPEN to two cell types and discover that 12% of human paralogs exhibit synthetic lethality in at least one context. We recover known synthetic lethal paralogs MEK1/MEK2, important drug targets CDK4/CDK6, and other synthetic lethal pairs including CCNL1/CCNL2. Additionally, we identify ten tumor suppressor paralog pairs whose compound loss promotes cell proliferation. These findings nominate drug targets and suggest that paralog genetic interactions could shape the landscape of positive and negative selection in cancer.

SUBMITTER: Parrish PCR

PROVIDER: S-EPMC8534300 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature

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Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome.

Parrish Phoebe C R PCR Thomas James D JD Gabel Austin M AM Kamlapurkar Shriya S Bradley Robert K RK Berger Alice H AH

Cell reports 20210801 9

CRISPR screens have accelerated the discovery of important cancer vulnerabilities. However, single-gene knockout phenotypes can be masked by redundancy among related genes. Paralogs constitute two-thirds of the human protein-coding genome, so existing methods are likely inadequate for assaying a large portion of gene function. Here, we develop paired guide RNAs for paralog genetic interaction mapping (pgPEN), a pooled CRISPR-Cas9 single- and double-knockout approach targeting more than 2,000 hum ...[more]

PMID: 34469736

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Project description:Emerging immunotherapies such as immune checkpoint blockade (ICB) and chimeric antigen receptor T-cell (CAR-T) therapy have revolutionized cancer treatment and have improved the survival of patients with multiple cancer types. Despite this success many patients are unresponsive to these treatments or relapse following treatment. CRISPR activation and knockout (KO) screens have been used to identify novel single gene targets that can enhance effector T cell function and promote immune cell targeting and eradication of tumors. However, cancer cells often employ multiple genes to promote an immunosuppressive pathway and thus modulating individual genes often has a limited effect. Paralogs are genes that originate from common ancestors and retain similar functions. They often have complex effects on a particular phenotype depending on factors like gene family similarity, each individual gene's expression and the physiological or pathological context. Some paralogs exhibit synthetic lethal interactions in cancer cell survival; however, a thorough investigation of paralog pairs that could enhance the efficacy of cancer immunotherapy is lacking. Here we introduce a sensitive computational approach that uses sgRNA sets enrichment analysis to identify cancer-intrinsic paralog pairs which have the potential to synergistically enhance T cell-mediated tumor destruction. We have further developed an ensemble learning model that uses an XGBoost classifier and incorporates features such as gene characteristics, sequence and structural similarities, protein-protein interaction (PPI) networks, and gene coevolution data to predict paralog pairs that are likely to enhance immunotherapy efficacy. We experimentally validated the functional significance of these predicted paralog pairs using double knockout (DKO) of identified paralog gene pairs as compared to single gene knockouts (SKOs). These data and analyses collectively provide a sensitive approach to identify previously undetected paralog pairs that can enhance cancer immunotherapy even when individual genes within the pair has a limited effect.

Dataset Information

Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome.

Publications

Discovery of synthetic lethal and tumor suppressor paralog pairs in the human genome.

Similar Datasets

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