Microsatellite genotyping reveals a signature in breast cancer exomes
Bibliographic record
Abstract
Genomic instability at microsatellite loci is a hallmark of many cancers, including breast cancer. However, much of the genomic variation and many of the hereditary components responsible for breast cancer remain undetected. We hypothesized that variation at microsatellites could provide additional genomic markers for breast cancer risk assessment. A total of 1,345 germline and tumor DNA samples from individuals diagnosed with breast cancer, exome sequenced as part of The Cancer Genome Atlas, were analyzed for microsatellite variation. The comparison group for our analysis, representing healthy individuals, consisted of 249 females which were exome sequenced as part of the 1,000 Genomes Project. We applied our microsatellite-based genotyping pipeline to identify 55 microsatellite loci that can distinguish between the germline of individuals diagnosed with breast cancer and healthy individuals with a sensitivity of 88.4 % and a specificity of 77.1 %. Further, we identified additional microsatellite loci that are potentially useful for distinguishing between breast cancer subtypes, revealing a possible fifth subtype. These findings are of clinical interest as possible risk diagnostics and reveal genes that may be of potential therapeutic value, including genes previously not associated with breast cancer.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".