Abstract C008: Patient-derived organoids and precision medicine: Insights from the PASS-01 clinical trial in PDAC
Notice bibliographique
Résumé
Abstract Introduction: Pancreatic ductal adenocarcinoma (PDAC) is challenging as most patients are diagnosed at an advanced stage with limited treatment options. Tailoring therapies for individual patients is paramount due to the aggressive nature of the disease. Our work explores patient- derived organoids (PDOs) as in vitro tumor models to dissect molecular signatures and conduct pharmacotyping, aiming to enhance precision medicine. Methods: In the PASS-01 stage IV PDAC clinical trial, biopsies were collected for molecular correlatives, including establishment of PDO models. Tissue samples were collected from patients at six institutes across the United States and Canada, highlighting our ability to integrate PDO models into a robust, multi- institutional clinical framework. Most biopsies were collected from liver metastases, followed by primary pancreas tumors, peritoneal, omental, lymph node, lung, and brain metastases. KRAS mutation status by ddPCR was used to validate neoplastic cells in the organoid cultures and identify pseudonormal outgrowth. Established PDO lines were subjected to high throughput drug screening for 120+ compounds, comprising both standard-of-care (as monotherapy and in combination therapies) and experimental agents. Subsequently, validated PDO lines were expanded, biobanked and harvested for RNA and DNA sequencing. Results: During the trial, 186 biopsies from 183 enrolled patients were shipped to CSHL for PDO establishment. The overall malignant PDO establishment rate was 50% across all biopsy sites and patients. Interestingly, PDOs could be generated more often from patients that rapidly progressed on therapy (p = 0.03). Furthermore, by comparing the characteristics of the primary biopsies to the established PDO, PDOs were more frequently generated from Moffitt subtype classical (64% establishment) compared to basal (35%), and from those with a KRASminor imbalance (67% establishment) compared with KRAS wild-type (36%), balanced (58%), and KRASmajor imbalance (52%). The average time from tissue receipt to first drug screen data was 65 days, with six PDOs screened in under 30 days. This turnaround time enabled PDO therapeutic data to be presented at monthly molecular tumor boards. Consequently, these data were considered alongside other clinical trial correlates to aid in selection of second-line therapies when patients progressed. Conclusions: The PASS-01 trial facilitated the real-time generation of PDO models and reporting of therapeutic results. We identified correlations between PDO establishment and patient characteristics, and improved the methods to detect pseudonormal outgrowth. PDO pharmacoptyping identified sensitivity that correlated with patient outcomes on GnP, while also highlighting challenges of using empiric drug testing for chemotherapy sensitivity. Moving forward we aim to continue incorporation of PDOs and tumor molecular profiling to aid in patient therapy selection. We anticipate this work to be crucial as targeted therapies, including KRAS inhibitors, become mainstream in PDAC care. Citation Format: Amber N. Habowski, Dennis Plenker, Hardik Patel, Caitlin Tsang, Luce St. Surin, Fatim Kouassi, Deepthi Budagavi, Grainne M O'Kane, Stephanie Ramotar, Kenneth H Yu, Faiyaz Notta, Andrew Aguirre, Brian Wolpin, Dan Laheru, Daniel A King, Elizabeth M Jaffee, Jennifer J Knox, David A Tuveson. Patient-derived organoids and precision medicine: Insights from the PASS-01 clinical trial in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C008.
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 | 0,006 | 0,003 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,001 |
| Communication savante | 0,001 | 0,000 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 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 ».