Abstract 1066: Pre-clinical combination of a PARP inhibitor, talazoparib, and carboplatin in triple-negative breast cancer
Bibliographic record
Abstract
Abstract Background: PARP inhibitors, such as talazoparib, demonstrated an improvement in progression-free survival as monotherapy in germline BRCA1/2-mutated (BRCA-MUT) HER2-negative locally advanced/metastatic breast cancer patients. BRCA-MUT tumors constitute 15-20% of triple-negative breast cancers (TNBCs). TNBCs are an aggressive subtype and lack overexpression of hormone receptors and HER2. Here, we evaluate the impact of the combination of talazoparib and a chemotherapeutic agent, carboplatin, to better understand which TNBCs will benefit from this combination, and the manner in which different sequencing strategies can impact primary tumor growth and distant metastasis development. Methods: We performed a 10-day chemosensitivity assay using 7 BRCA-MUT and BRCA wild-type TNBC cell lines. We tested 9 concentrations of talazoparib (Pfizer), and carboplatin (Selleckchem) each. Post-treatment, we used automated high-content imaging with Operetta (Perkin Elmer). We calculated IC50 values for talazoparib alone, CI (Combination Index) values, the DNA damage response (product of the percentage of cells positive for 53BP1 and mean 53BP1 foci per cell), and the apoptotic index (percentage of cells positive for cleaved-PARP). We also used an orthotopic xenograft model of MDAMB231 in NSG (NOD scid gamma) mice, with 8-14 mice in each treatment group. We treated mice with carboplatin (C) (35 mg/kg intraperitoneally) in combination with talazoparib (T) (0.03 mg/kg oral gavage) using 2 dosing strategies: a) concomitant administration of C + T; and b) T first, followed by C three days later, each compared to vehicle control. Results: We found a synergistic effect of the combination of talazoparib and carboplatin (CI < 1) in all 7 cell lines, with the greatest synergy (CI< 0.65) identified in three cell lines that were PARPi (PARP inhibitor)-resistant, namely HCC1143, MDAMB231, and Hs578T. For these three cell lines, we found that the mean 53BP1 product score increased by 7-16 fold in combination versus talazoparib alone at 0.2 µM, and the apoptotic index also increased by 4-26 fold at the same conditions. Concomitant administration of talazoparib and carboplatin in the MDAMB231 xenograft resulted in a 53.1% inhibition in primary tumor volume in comparison to control (P=0.0004), whereas sequential administration resulted in a 69.2% primary tumor volume inhibition (P<0.0001). The talazoparib first combination approach also resulted in a 53.9% decrease in lung micrometastasis (P=0.0003). Conclusion: The combination of talazoparib and carboplatin demonstrated a synergistic effect in 7 TNBC cell lines. We found greater DNA damage and cell death amongst PARPi-resistant cell lines and at lower concentrations of talazoparib when combined with carboplatin. In-vivo, sequential administration of talazoparib and carboplatin was the most effective approach to inhibit primary tumor growth. Citation Format: Alexia Cotte, Michèle Beniey, Takrima Haque, Nelly Béchir, Audrey Hubert, Korotoum W. Diallo, Danh Tran-Thanh, Saima N. Hassan. Pre-clinical combination of a PARP inhibitor, talazoparib, and carboplatin in triple-negative breast cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 1066.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".