Leveraging OncoMD to identify synthetic lethal interactions between recurrently mutated genes
Bibliographic record
Abstract
Most cancer therapies perform within a narrow therapeutic window causing numerous side effects and reducing the quality of life of cancer patients. Therefore, there is a need to develop new anti-cancer therapies of superior efficacy and minimal normal tissue toxicity. For this, identifying highly effective targets, which are essential for the survival of cancer cells, is required. A powerful genetic strategy in anticancer drug discovery is the exploitation of synthetic lethal interaction between genes. Synthetic lethal interaction is defined as interaction between two co-essential genes, in which inhibiting the function of either genes individually does not impact survival, but loss of function of both genes results in cell death. Cancer cells carry large number of mutations in many genes. These mutations can be grouped into gain-of-function, loss-of-function and function of unknown significance. Both gain and loss-of-function mutations occur in driver genes which are essential for cancer cells to grow and survive. MedGenome has built a database of somatic mutations – “Oncology Mutation Database” (OncoMD) by capturing data from published papers and public databases. We analyzed 2.2 million unique mutations in OncoMD and identified cancers in which certain mutation pairs occur more frequently than expected by chance. Analysis of these mutation pairs revealed well characterized genetic interactions between oncogenes and tumor suppressor genes and novel interactions that require further characterization. In conclusion, our analysis identifies cancer-specific susceptibilities that can be exploited for discovering novel drug targets.
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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 | 0.001 | 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".