Association Analysis of Somatic Copy Number Alteration Burden With Breast Cancer Survival
Bibliographic record
Abstract
The increasing prevalence of diagnosed breast cancer cases emphasizes the urgent demand for developing new prognostic breast cancer biomarkers. Copy number alteration (CNA) burden measured as the percentage of the genome affected by CNAs has emerged as a potential candidate to this aim. Using somatic CNA data obtained from METABRIC (Molecular Taxonomy of Breast Cancer International Consortium), we implemented Kaplan-Meier estimators and Cox proportional hazards models to examine the association of CNA burden with patient's overall survival (OS) and disease specific survival (DSS). We also evaluated the association by considering patients' age and tumor subtypes using stratified Cox models. We delineated the distribution of CNA burden in sample genomes and highlighted chromosomes 1, 8, and 16 as the carriers of the highest CNA burden. We identified a strong association between CNA burden and age as well as CNA burden and breast cancer PAM50 subtypes. We found that controlling the effects of both age (bound by 45-year) and PAM50 subtypes on patient survival using stratified Cox models, would still result in significant association between CNA burden and patients overall survival in both Discovery and Validation data. The same trend was observed in disease specific survival when only PAM50 subtypes were controlled in the stratified Cox models. Our analysis showed that there is a significant association between CNA burden and breast cancer survival. This result is also validated by using TCGA (The Cancer Genome Atlas) data. CNA burden of breast cancer patients has a considerable potential to be used as a novel prognostic biomarker.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".