Abstract 1794: The combination of Talazoparib with anti-her2 drugs shows efficacy in drug resistant Her2+ and low Her2 PDX models
Bibliographic record
Abstract
Abstract Background: Her2+ breast cancer accounts for 15% of breast cancer cases and is primarily treated with a combination of Her2-targeted agents and chemotherapy. While use of targeted agents has substantially improved survival of these patients, clinical drug resistance is prevalent. DNA repair defects are a feature of cancer that has been exploited by targeted agents such as inhibitors of poly-ADP ribose polymerase (PARP). PARP inhibitors have been shown to be effective in patients with germline mutations in other DNA repair factors such as BRCA1/2. There is limited data for the use of PARP inhibitors in HER2+ breast cancers, and in tumors without BRCA1/2 mutations. Using a collection of HER2+ and HER2-low expressing breast cancer patient-derived xenografts (PDXs) that have differential responses to the Her2-targeted agents Trastuzumab, Pertuzumab, and T-DM1, we tested the combination of these agents with Talazoparib (Talzenna™), a clinically approved Parp inhibitor. Methods: Orthotopically engrafted PDX mouse models resistant to either Trastuzumab or T-DM1 were randomized into treatment arms including Trastuzumab or T-DM1 in combination with Talazoparib, and the single agents alone. Mice are monitored for body weight and change in tumour volume every 2-3 days, then sacrificed at endpoint for survival analysis. We have also generated PDX-derived cells (PDCs) from one model with which to validate in vivo results and perform mechanistic studies. All models are being characterized for copy number changes, somatic mutations, and protein expression by immunohistochemistry. Results: The combination of Talazoparib and either Trastuzumab or T-DM1 was effective in 3 PDX models, including a Trastuzumab-resistant HER2+ PDX, a T-DM1-resistant HER2+ PDX and a T-DM1-resistant HER2-low expressing PDX. The combinations resulted in significant delays in tumour growth (p<0.05), including a transient tumour regression with the combination T-DM1+Talazoparib in 1 PDX. Median overall survival time was improved with the combination of Trastuzumab +Talazoparib (p<0.05), while the combinations of T-DM1+Talazoparib increased survival time although not significantly. Notably all models have maintained Her2 expression through treatment. Conclusion: Our results suggest that combining HER-targeted agents with Talazoparib may benefit patients with advanced HER2-therapy resistant HER2+ and low HER2+ breast cancers. Exploratory biomarker analysis and mechanistic studies using PDCs are currently underway in our laboratory. Citation Format: Kathryn Bozek, Marguerite Buchanan, Cathy Lan, Josiane Lafleur, Cedric Darini, Urszula Krzemien, Adriana Aguilar-Mahecha, Mark Basik. The combination of Talazoparib with anti-her2 drugs shows efficacy in drug resistant Her2+ and low Her2 PDX models [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 1794.
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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 | 0.000 | 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".