Additional file 2 of Association of germline genetic variants with breast cancer-specific survival in patient subgroups defined by clinic-pathological variables related to tumor biology and type of systemic treatment
Bibliographic record
Abstract
Additional file 2: Supplementary Methods, Supplementary Table S5, Supplementary Table S6, Supplementary Table S7, Supplementary Figure S1, Supplementary Figure S2, Supplementary Figure S3, Supplementary Figure S4, Supplementary Figure S5, Supplementary Figure S6, Supplementary Figure S7, Supplementary Figure S8, Supplementary Figure S9, Supplementary Figure S10, Supplementary Figure S11, Supplementary Figure S12. The Supplementary methods section includes details about: multiple imputation of missing data; power calculations. Supplementary Table S5. Shows an overview of number of breast cancer patients, breast cancer deaths and follow-up information by subgroup and endpoint. Supplementary Table S6. Shows a list of imputed variables with corresponding percentage of missing values, imputation method and processing. Supplementary Table S7. Shows an overview of the GWAS significant associations (P < 5 E-08) and noteworthy (BFDP< 0.15) associations from the unadjusted 15-year and 5-year breast cancer-specific survival analyses by subgroup. Supplementary Figure S1. Shows the Q-Q plots of the meta-analysis results of all variants for 15-year breast cancer-specific survival. Supplementary Figure S2. Shows the Q-Q plots of the meta-analysis results of all variants for 5-year breast cancer-specific survival. Supplementary Figure S3. Shows the Q-Q plots of the meta-analysis results for 15-year breast cancer-specific survival and the corresponding genome inflation factors by minor allele frequency. Supplementary Figure S4. Shows the Q-Q plots of the meta-analysis results for 5-year breast cancer-specific survival and the corresponding genome inflation factors by minor allele frequency. Supplementary Figure S5. Shows the regional plots of genome-wide significant (P < 5E-08) independent associated variants from the 15-year and 5-year genome-wide breast cancer-specific survival analyses. Supplementary Figure S6. Shows the regional plots of noteworthy (BFDP< 0.15), non-genome-wide significant (P > 5E-08) variants from the 15-year and 5-year genome-wide breast cancer-specific survival analyses. Supplementary Figure S7 shows the unadjusted association of variant rs5934618 with 15-year breast cancer-specific survival by tumor grade and in all breast cancer patients. Supplementary Figure S8. Shows the functional annotation and position of variants rs5934618, rs4830644, rs3810742, rs4830642, and rs72611496 relative to TBL1X. Supplementary Figure S9. Shows Kaplan-Meier distant metastasis-free survival plots (based on KMPlotter data) for high versus low expression level of gene TBL1X by tumor grade. Supplementary Figure S10. Shows the association of genetic variants with TBL1X expression, based on GTEx v8 data on samples of normal breast tissue from 396 individuals (male and female). Supplementary Figure S11. Shows a Kaplan-Meier distant metastasis-free survival plot for high versus low expression level of gene GRIP2, restricted to patients with an ER- tumor who received chemotherapy (based on KMPlotter data). Supplementary Figure S12. Shows a Kaplan-Meier distant metastasis-free survival plot for high versus low expression level of gene ARAP2, restricted to patients with an ER- tumor who received chemotherapy (based on KMPlotter data).
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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 | 0.002 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.878 | 0.130 |
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".