Abstract 4529: Characterization of PARP inhibitor combination therapies in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Introduction: Triple-negative breast cancer (TNBC) is the most aggressive type of breast cancer, with limited targeted treatment options. TNBC patients can, however, benefit from targeted drugs known as Poly (ADP-Ribose) Polymerase inhibitors (PARPi). PARPi disrupt DNA repair pathways by targeting tumors with germline BRCA1/2 mutations (BRCA-MUT) via synthetic lethality and PARP-DNA trapping mechanisms. Only 15-20% of the TNBC patient population are BRCA-MUT and can thus benefit from PARPi. However, preclinical studies and clinical trials have suggested that PARPi can also be effective in BRCA1/2 wild-type (BRCA-WT) cancer cells that have genomic phenotypes similar to BRCA-MUT cells, a phenomenon known as BRCAness. Previously, we used whole transcriptome analysis in a panel of TNBC cell lines to identify a 63-gene signature for BRCAness. The 63-gene signature was shown to predict response to PARPi with an accuracy of 86% in patient-derived xenografts, and was present in 45% of TNBC patients. We hypothesize that targeting genes in this 63-gene signature can enhance the sensitivity of PARPi in BRCA-WT and BRCA-MUT TNBC cells. Our aim is to identify and target genes from this signature that are involved in DNA synthesis and repair pathways, to identify effective combination strategies with PARPi. Methods: Using a PARPi-resistant TNBC cell line, MDAMB231, we carried out an siRNA screen of six genes from the 63-gene signature (BARD1, BUB1, RRM2, FEN1, EXO1, and USP1), chosen based on DNA repair functions and small-molecule inhibitor availability. The gene knockdowns were combined with the application of a potent PARPi, talazoparib, to determine the impact on DNA damage and cell death in TNBC cell lines. We then focused on targeting FEN1 function with the inhibitor LNT1. Using the Chou & Talalay combination index, we combined LNT1 with talazoparib to determine drug synergy in TNBC cell lines. Results: The individual siRNA knockdowns of BARD1, BUB1, FEN1, EXO1, and USP1, in combination with talazoparib, led to increased γ-H2AX and cleaved-caspase 3 levels, indicating augmentation of DNA damage and apoptosis. In particular, the FEN1 inhibitor, LNT1 demonstrated efficacy as a single-agent and synergy in combination with talazoparib in both BRCA-WT and BRCA-MUT TNBC cell lines. Conclusions: The siRNA screens, in combination with talazoparib, show that there are indeed targets within the 63-gene signature that can be used with PARPi that enhance DNA damage and cell death. The drug synergy shown between LNT1 and talazoparib in both BRCA-WT and BRCA-MUT TNBC cells suggests that LNT1 with PARPi could be an effective targeted combination approach, with great potential for TNBC patients. Citation Format: Elicia Fyle, Djihane Abdesselam, Mallory Frederick, Saima N. Hassan. Characterization of PARP inhibitor combination therapies in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4529.
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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 | 0.001 | 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".