Abstract 4039: An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities
Bibliographic record
Abstract
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, efforts to target MYC directly using small molecular inhibitors have historically failed. A promising 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 non-transformed and MYC-driven breast cancer cells. In contrast to other screens, this model is both dependent on MYC and recapitulates human disease at pathological and molecular levels in vivo. Finally, these hits were cross-referenced with our MYC protein-interactome data to reveal putative MYC partner proteins that are critical for MYC activity. From our screen results, we performed gene set enrichment analysis to identify biological activities that may represent core functional dependencies in MYC dysregulated cancer cells. Using this approach, we identified and validated topoisomerase 1 (TOP1) as an actionable vulnerability that can be targeted with clinically approved inhibitors. Genetic and pharmacological inhibition of TOP1 in multiple orthogonal assays resulted in MYC-driven cell death. Finally, drug response to TOP1 inhibitors 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. The recent accessibility of large-scale datasets detailing functional dependencies across hundreds of cancer cell lines offers an unprecedented opportunity to prioritize targets with greater translational relevance, which is a major limitation of the synthetic-lethal approach for target discovery. As a secondary analysis of our hits, we utilized DEPMAP data to classify cancer cell lines as relatively MYC-dependent or MYC-independent, enabling the in silico evaluation of each MYC-SL hit’s differential essentiality. MYC-SLs that exhibited selective essentiality in MYC-dependent cell lines were prioritized for further investigation. Results from this strategy revealed critical MYC cofactors that have been validated by us and others (e.g., CDK9), providing confidence in this approach. Excitingly, previously underexplored targets were identified with promising validation to-date. 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, Corey Lourenco, Jennifer Cruickshank, Luis Palomero, Jenna E. van Leeuwen, Amy H. Tong, Katherine Chan, Samah El Ghamrasni, Miquel Pujana, David W. Cescon, Jason Moffat, Linda Z. Penn. An isogenic CRISPR screen identifies novel MYC-driven vulnerabilities [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 4039.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
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".