Abstract 1112: Feasibility of real-time personalized patient-derived Tumorgraft® models for guiding systemic treatment in recurrent and/or metastatic head and neck cancer patients
Notice bibliographique
Résumé
Abstract Background: Recurrent/metastatic head and neck squamous cell carcinoma (RMHNSCC) is associated with poor quality of life and a poor prognosis with a median overall survival of 7 months. With limited systemic therapy options, there is an urgent need to identify predictive biomarkers for drug response. Patient derived xenografts (PDXs) have been demonstrated to preserve the histological features, heterogeneity, epigenetic and genetic profiles of the original tumor and appear to correlate well with objective tumor response in patients. This study aimed to test the feasibility of personalized PDXs for guiding systemic treatment in RMHNSCC. Methods: Eligible patients were consented and a fresh biopsy/surgical sample was obtained and implanted into mice (5-15 mice/patient) to establish TumorGraft® models. Engrafted tumors were excised and propagated into second generation models for drug testing with up to 4 drugs selected by the treating medical oncologist. Tumor dimensions were measured twice weekly and were reported as one of: progressive disease, stable disease (SD), partial response (PR), or complete response (CR) based on the percentage of tumor regression. Patients alive and suitable for chemotherapy were prescribed the regimen(s) observed to have the greatest response rate in their TumorGraft® models. Patients' responses to therapy were then observed. Results: Nine of 10 eligible patients had samples successfully engrafted with an average time to engraftment of 89.2 days (± SD 41.7 days). Drug testing was not performed on 5 patients as the patient either died or was not suitable for treatment. The remaining 4 patient TumorGraft® models underwent drug testing with the average time from engraftment completion to drug testing completion being 83.8 ± 59.9 days. Two of these patients then received xenograft-guided therapy. In one patient, paclitaxel demonstrated a partial response in the Tumorgraft®, however the patient's tumor did not respond and their clinical status rapidly deteriorated leading to death. In the second patient, cetuximab and paclitaxel demonstrated the best response in the TumorGraft® model. This patient had a sequential partial response to each drug including a 17 month response to cetuximab before progressing and transitioning to nivolumab. The patient remains alive with stable disease 3.5 years after diagnosis of recurrent disease. Conclusions: The main limitation of Tumorgraft® testing for this population is the time delay to obtain Tumorgraft® results. Despite this, Tumorgraft® testing is feasible for a subset of patients and appears to correlate with clinical benefit. Citation Format: Morgan D. Black, Allison Berger, Nicole Pinto, John Yoo, Kevin Fung, Danielle MacNeil, David A. Palma, Joseph S. Mymryk, Sara Kuruvilla, John W. Barrett, Suzanne Richter, Angela Davies, Eric W. Winquist, Anthony C. Nichols. Feasibility of real-time personalized patient-derived Tumorgraft® models for guiding systemic treatment in recurrent and/or metastatic head and neck cancer patients [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 1112.
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 | 0,000 | 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 | 0,000 | 0,000 |
| Science ouverte | 0,000 | 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 ».