Abstract 2149: Use of PDXs and patient-derived cell lines to uncover unconventional drug therapies and combinations for the treatment of drug-resistant cancers
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,002 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».