Abstract P5-13-05: Profiling of APOBEC3 dysregulation in breast cancer
Bibliographic record
Abstract
Abstract DNA of many breast tumors is barraged by C-to-T/G mutations within TCW (W:T,A). These mutations are attributed to the aberrant expression and activity of APOBEC3 enzymes. They have been shown to account for many driver mutations in genes such as PIK3CA, ERBB2, and PPP2R1A, however their precise source and also their roles in tumor development, evolution, and patient survival are debated. Currently, quantification of APOBEC3 expression changes in tumor cells is confounded by the ubiquitous expression of these enzymes in immune infiltrating cells. In this study, we used a novel quantitative biology approach to determine the expression profiles of APOBEC3 enzymes in breast tumor and tumor microenvironment cells from >1,000 patients. We combined diverse datasets including tumor/matched normal RNAseqs, tumor somatic mutations, cell line RNAseqs and mutations, estimates of tumor purities and immune cell compositions, and expression of purified cell populations to show that in breast cancer there is only a single APOBEC3 dysregulation process. This process is subtype-independent and is represented by APOBEC3B upregulation and and extreme APOBEC3C downregulation. Compared to all other tumor types, breast tumors are affected the most by this process. Citation Format: Hamid Hamidi, Azad Khosh, Hamzeh Rahimi, Diako Ebrahimi. Profiling of APOBEC3 dysregulation in breast cancer [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P5-13-05.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 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".