Additional file 1 of Patterns of genomic instability in > 2000 patients with ovarian cancer across six clinical trials evaluating olaparib
Bibliographic record
Abstract
Additional file 1: Table S1. Summary of ovarian cancer trials included in this analysis. Table S2. Assessment of mutation status and GIS in the ovarian cancer trials used in this analysis. Table S3. Full gene panel included in the Myriad tumor tissue testa (Myriad Genetic Laboratories, Inc.). Figure S1. GIS distribution by patient race in (A) all patients and (B) patients with a tBRCAm, by tumor histology in (C) all patients and (D) patients with a tBRCAm, and by primary tumor location in (E) all patients and (F) patients with a tBRCAm. Figure S2. GIS distribution in tumors with and without tBRCAm by individual study in PAOLA-1, OPINION, LIGHT, Study 19, SOLO1, and SOLO2. Figure S3. GIS distribution in tumors from patients (A) in response after first-line platinum-based chemotherapy and (B) with platinum-sensitive relapsed disease, and (C) GIS distribution by germline and somatic tumor BRCAm status and in non-tBRCAm tumors. Figure S4. (A) Gene-specific zygosity in tumors with BRCA1m or BRCA2m, (B) gene-specific zygosity and the rate of biallelic loss in tumors with a BRCAm, and (C) gene-specific zygosity and the rate of biallelic loss in tumors with germline BRCA1m or BRCA2m and somatic BRCA1m or BRCA2m. Figure S5. Gene-specific zygosity in patients with BRCA1m or BRCA2m by individual study. Figure S6. GIS distribution in (A) non-BRCA HRRm tumors from patients in response after first-line platinum-based chemotherapy and with platinum-sensitive relapsed disease and (B) in tumors with non-BRCA HRRm by individual study in PAOLA-1, OPINION, LIGHT, and Study 19. Figure S7. GIS distribution in patients with non-BRCA HRRm by individual study (PAOLA-1, OPINION, LIGHT, and Study 19). Figure S8. Genomic alterations detected in PAOLA-1, OPINION, LIGHT, and Study 19.
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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 | 0.003 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.744 | 0.044 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".