Rigorous software pipeline for clinical somatic mutation analyses of solid tumors
Bibliographic record
Abstract
Abstract Mutational analyses of tumor DNA guide the use of targeted therapies and checkpoint inhibitors in management of solid tumors. Reducing false positive mutation calls without compromising sensitivity as gene panels increase in size, and whole exome and genome sequencing enters clinical use, remains a major challenge. Aiming for robust somatic mutation analyses in the clinical setting, we have developed VARify, an integrated, accurate and computationally efficient software for cancer genome analyses encompassing all steps from pre-processing of sequencing reads to mutation identification. Benchmarking to two state-of-the-art open-source somatic mutation analysis pipelines demonstrated accurate detection of clinically actionable point mutations, all while strongly reducing the number of false positive mutations reported, at comparable or faster speed. Further, the VARify output classified microsatellite unstable colorectal cancers by tumor mutation burden better than the other pipelines. In comparisons where the same tumors were subjected to different panel enrichment and sequencing technologies, VARify had the most consistent intersection of consensus mutations. False positive calls were produced when the same data was used as tumor and reference by the other pipelines, while VARify did not produce such calls. The calling uniformity across sequencing technologies of VARify and its tumor-only analysis derivative pipeline ALTOmate was also demonstrated. Taken together, these two novel pipelines can improve clinical mutation analysis to the benefit of cancer patients.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 0.013 |
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".