Metadata supporting Association of germline variation with the survival of women with BRCA1/2 pathogenic variants and breast cancer
Bibliographic record
Abstract
This metadata record describes the data generated and analysed in the study "Association of germline variation with the survival of women with BRCA1/2 pathogenic variants and breast cancer". The study investigates genetic survival associations in pathogenic variant carriers from Consortium of Investigators of Modifiers of BRCA1/2 (CIMBA), genotyped on the OncoArray. Data availability and sourcesA subset of the genotype data that support the findings of this study is publicly available via dbGaP https://identifiers.org/dbgap:phs001321.v1.p1 CIMBA 1000 Genomes-imputed genotype data is protected in accordance with the informed consent received from the study participants and therefore cannot be made publicly available. Requests for data can be made to the CIMBA Data Access Coordination Committee. DACC approval is required to access data from the BCFR-ON, EMBRACE, GC-HBOC, HEBCS, HEBON, IHCC, IPOBCS, MCGILL, and OUH studies Phenotype data is stored in a relational database and an output would be a text file. Imputed genotype data can be requested in the QCTOOL dosage format (https://www.well.ox.ac.uk/~gav/qctool_v2/documentation/genotype_file_formats.html), which has been used in these analyses The contact for data access requests is Lesley McGuffog (ljm26@medschl.cam.ac.uk), Data Manager, Department of Public Health and Primary Care, University of Cambridge Newly discovered survival SNPs were characterized in silico utilizing data from the 1000 genomes and Encode projects as integrated in databases LDlink, RegulomeDB and GeneCards. Candidate genes’ mRNA expression and patient survival was tested in the METABRIC data in European Genome-phenome Archive: EGAD00010000434 (1,302 breast cancer patients). BCAC survival summary results are available from the University of Cambridge BCAC site All summary results will be made available on the CIMBA website upon publication of the related article: http://cimba.ccge.medschl.cam.ac.uk The data that support each table and figure in the related article are summarised in the excel file in this data record. BackgroundThis study investigates the survival of women carrying germline pathogenic BRCA1 or BRCA2 variants. These are the two most important genes linked to breast cancer susceptibility. The great variation in survival rates between tumors with similar characteristics and stage suggests a heritable component, e.g. genetic differences in metastatic potential sensitivity to adjuvant therapy or host factors, like tumor microenvironment interaction, immune surveillance, and efficiency in drug metabolism. Both candidate gene and genome-wide approaches have been employed to find genetic determinants patient prognosis and treatment outcome prediction. Participants women of European ancestry diagnosed with invasive breast cancer before the age of 70 years, enrolled in studies participating in CIMBA. CIMBA studies included in analysis if sufficient follow-up data are available, at least 15 study subjects at risk during the time when five events occurred. Patients were followed from the diagnosis of the first primary breast cancer until death of any causeand censored after 15 years or when lost from follow-up Supplementary table 1 of the related article lists all CIMBA studies, characteristics, sample and cohort sizes. Overall sample sizes: 21 studies for carrier of BRCA1 variants (n = 3,008) 15 studies for carriers of BRCA2 variants (n = 2,009).
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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 | 0.009 | 0.072 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.010 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.282 | 0.086 |
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".