Genomic alterations in breast cancer: level of evidence for actionability according to ESMO Scale for Clinical Actionability of molecular Targets (ESCAT)
Bibliographic record
Abstract
Better knowledge of the tumor genomic landscapes has helped to develop more effective targeted drugs. However, there is no tool to interpret targetability of genomic alterations assessed by next-generation sequencing in the context of clinical practice. Our aim is to rank the level of evidence of individual recurrent genomic alterations observed in breast cancer based on the ESMO Scale for Clinical Actionability of molecular Targets (ESCAT) in order to help the clinicians to prioritize treatment. Analyses of databases suggested that there are around 40 recurrent driver alterations in breast cancer. ERBB2 amplification, germline BRCA1/2 mutations, PIK3CA mutations were classified tier of evidence IA based on large randomized trials showing antitumor activity of targeted therapies in patients presenting the alterations. NTRK fusions and microsatellite instability (MSI) were ranked IC. ESR1 mutations and PTEN loss were ranked tier IIA, and ERBB2 mutations and AKT1 mutations tier IIB. Somatic BRCA 1/2 mutations, MDM2 amplifications and ERBB 3 mutations were ranked tier III. Seventeen genes were ranked tier IV based on preclinical evidence. Finally, FGFR1 and CCND1 were ranked tier X alterations because previous studies have shown lack of actionability.
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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 | 0.029 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".