Abstract 1191: Translational Proof-of-Concept (TransPoC), a not-for-profit research organization enabling access to large-scale translational oncology platforms: The Patient-Derived Xenograft network
Notice bibliographique
Résumé
Abstract TransPoC is a not-for-profit research organization that will deliver open-access, large-scale translational oncology platforms to enable greater clinical proof-of-concept success for new cancer therapies. TransPoC will comprise three platforms: 1. CPN - Cancer Cell “PoC” Network for screening compounds against 1000+ genomically-characterized cell lines; 2. MPN- Mouse “PoC” Network - a multi-site platform for mouse preclinical trials using genomically-characterized Patient-Derived Xenograft (PDX) models; 3. BioIT - analysis and integration of genomic information and pharmacological profiling data. Here we present an overview of the Mouse “PoC” Network, define a path to implementation of multi-center pre-clinical trials in mice and describe a pilot study to demonstrate the feasibility of implementing such a network. PDX models are increasingly used in pre-clinical studies as they capture and retain the histological, molecular, and genetic heterogeneity of the original tumor compared to cell line derived xenografts and are therefore a closer representation of a patient's tumor in situ. To enable transformative preclinical studies, models need to be characterized in a manner similar to tumor samples in The Cancer Genome Atlas and the International Cancer Genome Consortium, and must be assembled in sufficient quantity to capture clinically relevant major cancer (sub)types. To achieve this, TransPoC is building a global network of mouse PDX “hospitals” with genomic and metabolomic profiles characterized in a consistent manner. In addition, each mouse hospital will utilize common SOPs to generate comparable pharmacology data sets across sites that will include testing standard of care agents. BioIT will enable deep interrogation of data sets and provide pipelines for pharmacogenomics correlates of response to both standard and novel agents. To date, the network has collated over 2,000 PDX models and will enable sponsors to execute multi-center pre-clinical trials in a manner similar to those used in multi-institutional cooperative clinical trials. To demonstrate the viability of MPN, a pilot study has been initiated at 6 sites located in Canada, Italy, China and USA to evaluate the activity of MEK and RAF inhibitors against a panel of BRAF/KRAS mutant melanoma and colorectal cancer PDX models. An update on the initial tolerability, PK/PD/efficacy studies and molecular characterization of PDX models in the network will be presented. TransPoC continues to recruit new sites and characterize their PDX models for incorporation into MPN for use by TransPoC sponsors. Through this effort TransPoC enables rapid assessment of standard and novel investigational therapies to determine their therapeutic potential for translation to clinical trials with a mission to improve the chance of observing clinical proof-of-concept. Citation Format: Peter G. Smith, David Sutton, Andrea Bertotti, Livio Trusolino, Susan Airhart, Ming S. Tsao, Bradly G. Wouters, S. Gail Eckhardt, Lai Wang, Tim Heffernan, David Verbel, Andrea Gerken, Peter Fekkes, Lihua Yu, Lihua Yu, Markus Warmuth. Translational Proof-of-Concept (TransPoC), a not-for-profit research organization enabling access to large-scale translational oncology platforms: The Patient-Derived Xenograft network. [abstract]. In: Proceedings of the 105th Annual Meeting of the American Association for Cancer Research; 2014 Apr 5-9; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2014;74(19 Suppl):Abstract nr 1191. doi:10.1158/1538-7445.AM2014-1191
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 distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,003 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,001 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,001 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
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 tête enseignante, 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 ».