A Comparative Genomic Approach for Identifying Synthetic Lethal Interactions in Human Cancer
Bibliographic record
Abstract
Synthetic lethal interactions enable a novel approach for discovering specific genetic vulnerabilities in cancer cells that can be exploited for the development of therapeutics. Despite successes in model organisms such as yeast, discovering synthetic lethal interactions on a large scale in human cells remains a significant challenge. We describe a comparative genomic strategy for identifying cancer-relevant synthetic lethal interactions whereby candidate interactions are prioritized on the basis of genetic interaction data available in yeast, followed by targeted testing of candidate interactions in human cell lines. As a proof of principle, we describe two novel synthetic lethal interactions in human cells discovered by this approach, one between the tumor suppressor gene SMARCB1 and PSMA4, and another between alveolar soft-part sarcoma-associated ASPSCR1 and PSMC2. These results suggest therapeutic targets for cancers harboring mutations in SMARCB1 or ASPSCR1 and highlight the potential of a targeted, cross-species strategy for identifying synthetic lethal interactions relevant to human cancer.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".