Abstract ES10-2: Mining noncoding mutations for drivers of development in ER alpha-positive breast cancer
Bibliographic record
Abstract
Abstract Large-scale analysis of whole genome sequencing of breast tumors has identified thousands of genetic alterations, such as single-nucleotide variants (SNVs), outside of gene coding sequences. Delineating the functional impact of these noncoding SNVs is challenging, limiting their inclusion in precision genomics medicine pipelines. Pan-cancer attempts to detect evidence for positive selection within individual CREs identified promoters of a limited number of genes such as TERT. By considering the enrichment of noncoding mutations in cistromes as opposed to individual CREs, we identify the enrichment of noncoding SNVs in cistromes of breast cancer driver transcription factors, including FOXA1. This parallels enrichment of breast cancer genetic predispositions over these transcription factor cistromes. Furthermore, somatic SNVs accumulate in the regulatory plexus of some of these driver transcription factors providing evidence for a multi-targeted approach to disrupt their activity in breast cancer. In conclusion, our results support a functional impact of somatic SNVs converging on the cistrome of driver transcription factors in breast cancer. Altogether, we show how to interrogate noncoding SNVs to delineate the oncogenic determinants of breast cancer to be considered for therapeutic intervention. Citation Format: Lupien M. Mining noncoding mutations for drivers of development in ER alpha-positive breast cancer [abstract]. In: Proceedings of the 2018 San Antonio Breast Cancer Symposium; 2018 Dec 4-8; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2019;79(4 Suppl):Abstract nr ES10-2.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".