Challenges and opportunities for oncology drug repurposing informed by synthetic lethality
Bibliographic record
Abstract
Although two-thirds of cancers arise from loss-of-function mutations in tumor suppressor genes, there are few approved targeted therapies linked to these alterations. Synthetic lethality offers a promising strategy to treat such cancers by targeting vulnerabilities unique to cancer cells with these mutations. To identify clinically relevant synthetic lethal interactions, we analyzed genome-wide CRISPR/Cas9 knock-out (KO) viability screens from the Cancer Dependency Map and evaluated their clinical relevance in patient tumors through mutual exclusivity, a pattern indicative of synthetic lethality. Indeed, we found significant enrichment of mutual exclusivity for interactions involving cancer driver genes compared to non-driver mutations. To identify therapeutic opportunities, we integrated drug sensitivity data to identify inhibitors that mimic the effects of CRISPR-mediated KO. This approach revealed potential drug repurposing opportunities, including BRD2 inhibitors for bladder cancers with ARID1A mutations and SIN3A-mutated cell lines showing sensitivity to nicotinamide phosphoribosyltransferase (NAMPT) inhibitors. However, we discovered that pharmacological inhibitors often fail to phenocopy KO of matched drug targets, with only a small fraction of drugs inducing similar effects. This discrepancy reveals fundamental differences between pharmacological and genetic perturbations, emphasizing the need for approaches that directly assess the interplay of loss-of-function mutations and drug activity in cancer models.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".