An analysis of mutational signatures in breast cancer among young women
Bibliographic record
Abstract
BACKGROUND. Approximately 7% of breast cancer cases are diagnosed among women 40 years of age or younger, and over 40% of all cancer diagnoses in this population are breast cancer. In 1969-2012, there was an increasing incidence of breast cancer among young (≤40 years) Canadian women, and in 1995-2006 there was also an increase in incidence of 3% and 1% among European women 20-29 years and 30-39 years respectively. Younger age is related to later stage at diagnosis, and worse outcomes even when controlling for disease stage and other prognostic features. The most aggressive tumour subtypes, human epidermal growth factor receptor 2 positive and triple-negative breast cancer have a higher prevalence among young women. Clinical and molecular differences between tumours diagnosed at different ages motivates study of differences in patterns of mutations at a genomic level. Mutational signatures are patterns in the type and three-nucleotide neighbourhood of somatic single-nucleotide variants across the exome, which may provide insight into the exposures and processes leading to carcinogenesis. METHODS. Clinical and variant call data for 1098 breast cancer patients was taken from The Cancer Genome Atlas. Cases were separated into young (≤40 years) and old (>40 years) age groups, and the mutational load was found for each. The SomaticSignatures R package was used to determine the mutational spectrum of one young patient. This package will be used to construct mutational spectra for each age group, then decompose them into mutational signatures. RESULTS. There were 91 and 951 patients in the young and old categories respectively. The young group had a median mutational load of 166.5 mutations, slightly less than the median of 198 in the older group. The mutational spectrum showed a high frequency of TCA>TTA and TCT>TTT variants, which is similar to the previously identified Signature 2, and similarly a high frequency of TCA>TGA and TCT>TGT transitions which resembles Signature 13. These two signatures are commonly found in the same tumour, and are thought to be caused by cytidine deaminase activity of AID/APOBEC proteins. CONCLUSIONS. Patterns in a mutational spectrum suggest that Signatures 2 and 13 may be present in at least one young breast cancer tumour, but this approach should be continued to confirm this hypothesis. Further research should be conducted with a larger sample of young breast cancer patients with known exposures to risk factors. *Indicates supervisor
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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 | 0.000 |
| 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".