Topoisomerase 1 Inhibition in MYC-Driven Cancer Promotes Aberrant R-Loop Accumulation to Induce Synthetic Lethality
Bibliographic record
Abstract
MYC is a central regulator of gene transcription and is frequently dysregulated in human cancers. As targeting MYC directly is challenging, an alternative strategy is to identify specific proteins or processes required for MYC to function as a potent cancer driver that can be targeted to result in synthetic lethality. To identify potential targets in MYC-driven cancers, we performed a genome-wide CRISPR knockout screen using an isogenic pair of breast cancer cell lines in which MYC dysregulation is the switch from benign to transformed tumor growth. Proteins that regulate R-loops were identified as a potential class of synthetic lethal targets. Dysregulated MYC elevated global transcription and coincident R-loop accumulation. Topoisomerase 1 (TOP1), a regulator of R-loops by DNA topology, was validated to be a vulnerability in cells with high MYC activity. Genetic knockdown of TOP1 in MYC-transformed cells resulted in reduced colony formation compared with control cells, demonstrating synthetic lethality. Overexpression of RNaseH1, a riboendonuclease that specifically degrades R-loops, rescued the reduction in clonogenicity induced by TOP1 deficiency, demonstrating that this vulnerability is driven by aberrant R-loop accumulation. Genetic and pharmacologic TOP1 inhibition selectively reduced the fitness of MYC-transformed tumors in vivo. Finally, drug response to TOP1 inhibitors (i.e., topotecan) significantly correlated with MYC levels and activity across panels of breast cancer cell lines and patient-derived organoids. Together, these results highlight TOP1 as a promising target for MYC-driven cancers. SIGNIFICANCE: CRISPR screening reveals topoisomerase 1 as an immediately actionable vulnerability in cancers harboring MYC as a driver oncoprotein that can be targeted with clinically approved inhibitors.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".