Inference of sample-specific genetic interactions to increase accuracy of indication prioritization in oncology clinical trials and facilitate exploration of combined therapy opportunities.
Bibliographic record
Abstract
3123 Background: Precision oncology is growing rapidly in parallel with advances in high throughput sequencing. Development of new anti-cancer therapies is, however, still associated with low efficacy issues, leading to phase II and III clinical trial failures. Improved methodologies are required to identify clinical and molecular patient profiles associated with good drug response to inform decisions on indication prioritization. Methods: We used a sample-specific Genetic Interaction Graph Inference (ssGI2) algorithm, integrating bulk tumor transcriptomic data as well as data collected from 120 public databases and scientific literature in oncology, to infer genetic interactions (GI). More than 10,000 genes from 17,000 samples, covering 195 oncology ICD10 codes, were used to infer GIs for each individual sample. GIs involving a given drug target are selected from a compendium of 17,000 networks of 2M GIs each, and ranked based on their prevalence in the patient cohort and data-support. The mean Z-scored expression of genes from the top ranked GIs were subsequently used to predict drug response for each patient and to calculate the response rate for each indication. Detailed information on each drug target’s genetic interactors was used to characterize the drug’s mechanisms of action and explore opportunities for combined therapies. We investigated our method's ability to predict good responders using four FDA approved immune and targeted therapies (pembrolizumab, nivolumab, ipilimumab and sorafenib) across seven clinical studies. Importantly this methodology is suitable for drugs with no clinical studies available. Results: Our results show that the prediction of good responders can be achieved with Precision-Recall AUC on average 13% higher than predictions based on drug target expression level solely, in five out of seven studies. Also, for each drug target, between 30 to 140 genetic interactors with good performance (Precision=0.92; Recall=0.61) were identified, suggesting potential synergistic effects of drugs, some of which have already been confirmed by clinical studies on combined therapies. Conclusions: Our ssGI2-derived signatures are powerful predictors of good response to a drug even without available clinical data. Applying this methodology at a pre-clinical stage will significantly de-risk clinical trials, particularly for novel therapies, and could also support investigation of new combined therapies.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".