Abstract 5342: Identifying therapeutic targets in combination with PARP inhibitors using a 63-gene signature in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Triple-negative breast cancer (TNBC) is a difficult-to-treat breast cancer with limited therapeutic options. PARP inhibitors (PARPi) have emerged as a promising targeted therapeutic for TNBC patients with germline mutations in BRCA1/2, recently demonstrating inhibition of micrometastatic disease. However, studies have also suggested that PARPi may have efficacy in TNBC, regardless of BRCA mutation status. We have previously identified a 63-gene signature associated with the DNA damage response to PARPi, with an overall accuracy of 86% in a cohort of patient-derived xenografts and a predicted sensitivity to PARPi in 45% of untreated TNBC patients. Additionally, our 63-gene signature can be used to identify genes implicated in intrinsic resistance. Therefore, we hypothesize that genes from our 63-gene signature can be used to identify potential targets for combination therapies with PARPi. We selected six candidate genes from our 63-gene signature: BARD1, BUB1, FEN1, RRM2, EXO1, and USP1. For each of the selected genes, we performed siRNA knockdown experiments in MDAMB231, a TNBC cell line. Using flow cytometry, we found that talazoparib, a potent PARPi plus siBARD1, increased the proportion of cells in G2 phase, with 80% γ-H2AX-positive cells and 60% cleaved-caspase-3-positive cells. Interestingly, the DNA damage response was comparable to what was observed with the combination of talazoparib and carboplatin. Talazoparib + siBUB1 also induced DNA damage in 51% of cells (P<0.001), and apoptosis in 20% of cells (P=0.002). Similar effects for enhanced DNA damage and apoptosis with talazoparib was observed for siUSP1, siFEN1, and siEXO1. Furthermore, the combination of siBARD1 + talazoparib resulted in a 49% reduction in cell migration in comparison to siBARD1 alone (P=0.0008), and the combination of siBUB1 and talazoparib demonstrated a 44% reduction in cell migration in comparison to talazoparib alone (P=0.04). Moreover, we evaluated gene expression in different breast cancer subtypes, and correlated with prognosis in a cohort of 881 untreated breast cancer patients. We found elevated expression of RRM2, FEN1, and EXO1 amongst more aggressive breast cancer subtypes (basal and HER2), and that their overexpression were associated with a poorer 10-year distant metastasis-free survival (RRM2, P<0.00001; FEN1, P=0.00012; EXO1, P<0.00001). Taken together, genes from our 63-gene signature can be used to identify therapeutic targets that have demonstrated either enhanced DNA damage, cell death, or cell migration in combination with PARPi. With prognostic implications, these targets also demonstrate therapeutic potential in the clinic and warrant further investigation. Citation Format: Audrey Hubert, Alexia Cotte, Nelly Bechir, Takrima Haque, Saima N. Hassan. Identifying therapeutic targets in combination with PARP inhibitors using a 63-gene signature in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5342.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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 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".