Abstract 4108: The use of conditionally reprogrammed cells for high throughput screening for novel drug combinations to treat drug resistant triple negative breast cancers
Bibliographic record
Abstract
Abstract Triple negative breast cancer (TNBC) is the most aggressive form of breast cancer, with prognosis tightly dependent on its responsiveness to cytotoxic chemotherapy. Indeed, overcoming chemoresistance is a major unmet need in this disease. Patient derived xenografts (PDXs) are now widely used in research since they faithfully represent the patient's tumor. However, testing novel drug combinations is a challenge in these models since it is time consuming and quite expensive to test each novel combination. The development of conditionally reprogrammed cells (CRCs) offers the opportunity to perform high throughput screens on clinically relevant models in a more timely and less expensive manner. To this end, we developed a 7 CRCs from PDXs of drug-resistant TNBC tumors. We characterized these CRCs at the molecular level and validated drug resistance to confirm they matched treatment response in both clinical and PDX tumors from which they were derived. These CRCs underwent high-throughput screens of both compound and shRNA libraries of genes targeted by FDA-approved drugs to identify novel drug combinations as well as genetic vulnerabilities that could re-sensitize these cells to carboplatin or paclitaxel chemotherapy. We found that drugs such as mitomycin C and oxfendazole were putative chemo-sensitizers while genes such as ATR and CDK2 were identified as novel genetic vulnerabilities. Treatment of these drug-resistant cells with combinations of carboplatin with mitomycin C or with an ATR inhibitor resulted in synergistic growth inhibition in proliferation and colony formation assays. We are further validating these combinations in organoid models derived from PDXs. These results have the potential to lead to novel therapeutic strategies against chemoresistant TNBCs. Citation Format: Adriana Aguilar-Mahecha, Catherine Chabot, Nancy Santos-Martinez, Cedric Darini, Midhet Hajira, Tim Kong, Genevieve Morin, Sidong Huang, Morag Park, Mark Basik. The use of conditionally reprogrammed cells for high throughput screening for novel drug combinations to treat drug resistant triple negative breast cancers [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 4108.
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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".