Abstract B24: Genomic prediction of response to PARP inhibition in breast cancer
Bibliographic record
Abstract
Abstract Efficacy of PARP inhibition has been demonstrated in several cancer types including prostate, ovarian, and breast. Most recently, anti-PARP therapy was shown to be effective in combination with carboplatin in triple-negative breast cancer patients in the neoadjuvant setting. However, it is not yet well known which subset of triple-negative breast cancers will benefit from single-agent anti-PARP therapy. We determined the therapeutic efficacy of three PARP inhibitors: veliparib, olaparib, and BMN 673, in a panel of eight triple-negative breast cancer cell lines. We used a 10-day in-vitro assay, after which we fixed the cells and determined 53BP1 expression using immunofluorescence and high-content imaging. We used cell counts to derive IC50 values and enumerated 53BP1 foci per cell to determine EC50 values. We used pre-treatment whole-transcriptome data to identify genes associated with 53BP1 response using gene set enrichment and pathway enrichment analysis. We determined the prevalence of these genes in a dataset of triple-negative breast cancer patients, and performed survival analysis. We found PARP inhibition to be effective in both BRCA-mutant and BRCA wild-type breast cancer cell lines. BMN 673 was the most potent PARP inhibitor, with the lowest concentrations required for DNA damage (measured by 53BP1 expression) and cell kill (measured by cell count), followed by olaparib, and then veliparib. We found a strong correlation between the IC50 values for cell count and the EC50 values for 53BP1 response. We identified a gene set associated with 53BP1 response, which was involved with three major pathways: DNA repair, cell cycle, and programmed cell death. These genes were found to be downregulated in triple-negative breast cancer patients. Patients with aberrations in these genes demonstrated poorer overall survival (P = 0.03). In conclusion, we identified a gene set involved with DNA repair, cell-cycle, and programmed cell death, which was associated with poor outcomes in triple-negative breast cancer patients that could potentially benefit from anti-PARP therapy. Citation Format: Saima Hassan, Amanda Esch, Laura M. Heiser, Joe W. Gray. Genomic prediction of response to PARP inhibition in breast cancer [abstract]. In: Proceedings of the AACR Special Conference on DNA Repair: Tumor Development and Therapeutic Response; 2016 Nov 2-5; Montreal, QC, Canada. Philadelphia (PA): AACR; Mol Cancer Res 2017;15(4_Suppl):Abstract nr B24.
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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 | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".