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Abstract B004: An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities

2024· article· en· W4399505421 on OpenAlexaffabout
Peter Lin, Linda Z. Penn

Bibliographic record

VenueMolecular Cancer Therapeutics · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicAdvanced biosensing and bioanalysis techniques
Canadian institutionsUniversity Health NetworkUniversity of Toronto
Fundersnot available
KeywordsCRISPRSynthetic lethalityBiologyComputational biologyContext (archaeology)CancerGeneCancer researchBreast cancerCancer cellDrug discoveryGeneticsBioinformaticsDNA repair

Abstract

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Abstract c-MYC (MYC) is a central regulatory protein that is dysregulated in >50% of all human cancers and is linked to aggressive disease. Developing MYC inhibitors would revolutionize cancer treatment; however, developing small molecules that directly target MYC is challenging. An alternative approach is to identify and inhibit critical MYC partner proteins to inactivate MYC and trigger cancer cell death. Inhibiting these targets therapeutically can result in synthetic lethality (MYC-SL), which can be exploited in MYC-dysregulated cancers. To identify MYC-SL targets, we performed a genome-wide CRISPR knock-out screen using an isogenic pair of normal and MYC-driven breast cancer cells. In contrast to other screens, this model is dependent on MYC and recapitulates human disease at both pathological and molecular levels in vivo. We identified high-priority hits to validate from the screen using two independent approaches: 1) a traditional gene-set enrichment analysis to identify highly represented biological pathways; and 2) analyzing the Cancer Dependency Map (DEPMAP) to select hits that are likely to be robust beyond the context of our screening conditions. Using a traditional gene-set enrichment analysis approach, we identified topoisomerase 1 (TOP1) as an actionable vulnerability that can be targeted with clinically approved inhibitors. Genetic and pharmacological inhibition of TOP1 resulted in MYC-driven cell death compared to that in control cells. Finally, drug response to TOP1 inhibitors significantly correlated with MYC levels and activity across panels of breast cancer cell lines and patient-derived organoids, highlighting TOP1 as a promising target for MYC-driven cancers. As a secondary approach to interpreting our CRISPR screen hits, we analyzed DEPMAP to identify MYC-SLs that are differentially essential in MYC-dependent cancer cells. Specifically, data from RNA interference screens in hundreds of cancer cell lines were used to describe the response of these cells to MYC knockdown. These data were used to define MYC-dependent and MYC-independent cell lines within the context of this analysis. These two groups were then investigated for their in silico response to the knockdown of each of our MYC-SL hits. MYC-SLs, which were also differentially essential in MYC-dependent cancer cells from DEPMAP, were prioritized for further investigation. Critical MYC cofactors that have been validated by us and others (e.g., CDK9) were identified, providing confidence in this approach, and rationalizing ongoing investigations. Together, this work features two successful strategies to prioritize hits from hundreds of synthetic-lethal genome-wide CRISPR screens to identify novel MYC-driven vulnerabilities in cancer. Citation Format: Peter Lin, Linda Penn. An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Expanding and Translating Cancer Synthetic Vulnerabilities; 2024 Jun 10-13; Montreal, Quebec, Canada. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(6 Suppl):Abstract nr B004.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.331
Teacher spread0.305 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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