Abstract 2410: Toward mutation analysis of regulatory elements: Epigenetic profiling of primary breast tumors
Bibliographic record
Abstract
Abstract Non-coding mutations found in regulatory elements can function as driver mutations in breast cancer by changing the binding affinity of transcription factors for DNA, thereby resulting in direct change of expression of genes that promote cancer development. Identifying such additional driver mutations can reveal the molecular mechanisms favorable to breast cancer development and progression, as well as reveal new biomarkers to better tailor personalized/precision cancer medicine. In this study we have collected 20 primary luminal breast tumors and optimized experimental workflow to dissociate solid tumors and map open chromatin using ATAC-seq. In our initial experiments using ATAC-seq profiling of bulk tumor tissues, we were able to call an average of 15x103 peaks. Subsequently, flow cytometry analysis showed the presence of 15-25% of immune cells in our primary tumors. Therefore, we have optimized a workflow to eliminate immune cells and focus mainly on epithelial tumor cells. Primary breast tumors were digested using collagenase and further dissociated with dispase. Cells were sorted into two populations (mammary epithelial and immune cells) using anti-CD45, anti-CD49f and anti-EpCAM antibodies. Sorted mammary epithelial cells were then used for ATAC- and RNA-seq library preparation as well as for generation of patient derived organoids. Our new workflow resulted in an increased number of called peaks (40x103 vs 15x103), as well as a significant increase in the percentage of unique peaks compared to bulk sequencing (45% vs 15%). By refining our workflow to enrich for tumour content, we will continue our ongoing effort to profile these open chromatin regions and contextualize the mutations within in a large cohort of luminal breast cancers using targeted sequencing. These data will be compared with large-scale whole genome data generated by our group and made publicly available by others. Citation Format: Samah El Ghamrasni, Paul Guilhamon, Rene Quevedo, Cindy Yang, Mathieu Lupien, Trevor Pugh. Toward mutation analysis of regulatory elements: Epigenetic profiling of primary breast tumors [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 2410. doi:10.1158/1538-7445.AM2017-2410
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".