Abstract P072: Combining ATR inhibitors with carboplatin in chemoresistant TNBC conditionally reprogrammed cells and patient-derived xenografts
Bibliographic record
Abstract
Abstract Background: Breast cancer is the second leading cause of cancer related death worldwide in women and triple negative breast cancer subtype is associated with a poor prognosis. TNBC patients are treated mostly with chemotherapy. In addition to the standard of care, the DNA crosslinking agent Carboplatin is often used in the treatment of both early and metastatic TNBC. A fraction of patients presents a good response, but the majority are resistant or develop resistance to chemotherapy. Overcoming chemotherapy resistance in TNBC is a major clinical unmet need and the identification of novel treatments against chemoresistant TNBC is necessary. Method: We used conditional reprogramming to establish 5 patient-derived TNBC cell lines (CRCs) from patient-derived xenografts (PDXs) generated from chemotherapy resistant tumors. All cell lines were resistant to carboplatin, and we performed shRNA high throughput screens to identify genetic vulnerabilities that could resensitize these cells to Carboplatin. We identified ataxia telangiectasia and Rad3-related protein (ATR) as a target in one of the cell lines screened. Validation of this target was performed by targeted shRNA ATR knockdown and pharmacologically with commercially available ATR inhibitors both in vitro and in vivo. Results: We confirmed that ATR inhibition resensitizes TNBC CRC to Carboplatin with shRNA ATR. We used Ceralasertib and Elimusertib, 2 ATR inhibitors, to pharmacologically validate this hit. We tested the effect of the combination of each ATR inhibitors with carboplatin (IC25) using alamar blue assays and calculated the combination index with the Chou Talalay method in all 5 cell lines. We found the combination with Elimusertib to have a more potent synergy (Average CI=0.43) than the combination with Ceralasertib (Average CI=0.68). To further explore the mechanism of ATR inhibition in this combination we measured the key targets and observed a loss of ATR protein by western blot when the cells were exposed to the combination with Elimusertib-Carboplatin but not with Ceralasertib-Carboplatin. Pharmacological inhibition of ATR main effector Chk1 with Rabusertib or by knockdown did not recapitulate the synergy observed with Elimusertib-Carboplatin, suggesting the synergy observed is independent of Chk1 activity. Surprisingly, ATR inhibition or knockdown still modulated major cell cycle players. Finally, we performed in vivo validation using the PDX from which the cell line had been derived. Our results demonstrated that addition of low doses of Elimusertib to Carboplatin significantly delayed tumor growth compared to carboplatin treatment alone and prolonged survival in animal models. Conclusion: We highlighted the efficacy of ATR inhibition to resensitize drug resistant TNBC to Carboplatin as supported by our in vitro and in vivo results. Our data also sheds light on a potentially novel mechanism of action of the Elimusertib drug involving ATR loss of expression. Our findings imply that all ATR inhibitors are not equal, and they might be associated with different mechanisms of action. Citation Format: Juliet Guay. Combining ATR inhibitors with carboplatin in chemoresistant TNBC conditionally reprogrammed cells and patient-derived xenografts [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2021 Oct 7-10. Philadelphia (PA): AACR; Mol Cancer Ther 2021;20(12 Suppl):Abstract nr P072.
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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) | 0.002 | 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".