Abstract 2149: Use of PDXs and patient-derived cell lines to uncover unconventional drug therapies and combinations for the treatment of drug-resistant cancers
Bibliographic record
Abstract
Abstract The use of patient derived models for pre-clinical drug testing provides a unique opportunity for testing non-conventional therapies and novel drug combinations to overcome drug resistance in cancer. We have established 22 models from breast cancer, 20 PDXs were engrafted in house and two were generated from live tissue obtained from PDXs established in a partner lab. The overall engraftment rate in house was of 24%. Engraftment varied according to BC subtype: 38% (3/8) for ER-HER2+ and 33% (17/51) for TNBC. No PDXs could be generated from ER+PR+Her2- tumors (0/13) or ER+HER2+ breast cancers (0/11). Post-chemotherapy drug resistant tumors engrafted with better success (18/55) compared to non-treated tumors (2/28). In 3 patients, we established PDX models with tissues collected at different anatomical sites, enabling the study of tumor heterogeneity. For one patient, 2 PDX models were established from serial samples after different lines of chemotherapy. Molecular profiling at the copy number and mutation level has confirmed the molecular stability of our models over engraftment passages. Drug studies were performed to validate clinical drug response and models were also treated with unconventional drugs and drug combinations to overcome drug resistance. Drugs that resulted in significant tumor regression in drug resistant TNBC models included irinotecan as well as olaparib in combination with an angiogenesis inhibitor. We successfully established one PDX model from a recurrent Her2+ gastric tumor resistant to trastuzumab. In this model, treatment with T-DM1 alone as well as with pertuzumab/trastuzumab resulted in complete tumor regression, while everolimus significantly inhibited tumor growth. This PDX study provided an alternative clinical management for the patient who subsequently received T-DM1 and showed clinical response, validating the results obtained in the PDX model. To develop a more agile model, we also established conditionally reprogrammed cell lines directly from patient tumors or from PDX models. Drug resistant conditionally reprogrammed cells from Her2+ breast cancers harboring a FGFR1 amplification were found to be sensitive to FGFR inhibitors, chloroquine, and a CDK4/6 inhibitor. Correlation of drug responses between conditionally reprogrammed cells and matched PDX models will be presented. These live tumor models enable timely and clinically pertinent drug testing, which has the potential to expand the known therapeutic arsenal as well as provide truly personalized therapy to cancer patients. Citation Format: Marguerite Buchanan, Catherine Chabot, Josiane Lafleur, Urszula Krzemien, Cathy Lan, Olga Savichtcheva, Jean-François Boileau, Cristiano Ferrario, Paul Savage, Morag Park, Gerald Batist, Adriana Aguilar-Mahecha, Mark Basik. Use of PDXs and patient-derived cell lines to uncover unconventional drug therapies and combinations for the treatment of drug-resistant cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2149.
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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 | 0.001 | 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 | 0.001 | 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".