Abstract IA02: Utilizing patient-derived xenografts for prognostication and biomarker discovery
Notice bibliographique
Résumé
Abstract Overall outcomes for human papilloma virus (HPV)-negative head and neck squamous cell carcinoma (HNSCC) remain poor with 5-year overall survival rates of 50-60%. Treatment often includes surgery, and clinicopathologic features are used to identify patients in need of adjuvant therapies such as radiation therapy (RT) or radiation plus concurrent chemotherapy (CRT). It is clear from the rate of locoregional or distant failures that more accurate methods of risk stratification would greatly improve outcomes for HPV-negative HNSCC patients. This requires biomarkers to identify patients who will benefit from adjuvant RT or CRT, but currently there are no validated molecular biomarkers that have been clinically implemented for the personalized treatment of HNSCC. In addition to biomarkers for better risk stratification, there is also a need for novel therapeutic strategies leading to improved outcomes. Recently, patient-derived xenografts (PDXs) have been shown to faithfully recapitulate human tumor biology and predict drug responses, supporting their relevance as preclinical models for new drug development. Upon subcutaneous implantation of HPV-negative HNSCC specimens into NOD/SCID/IL2Rγ-/- mice, 161 of 243 samples (66%) successfully formed PDXs. Using univariable and multivariable analyses, the ability to form a PDX correlated significantly with adverse clinical outcomes, and specifically, patients with palpable PDX-formation within 8 weeks experienced particularly poor outcomes (hazard ratio for overall survival = 3.0). A cohort of engrafting and nonengrafting samples were sequenced using a targeted sequencing panel designed for both mutational and copy number alteration detection. The overall frequency of somatic genomic alterations detected was similar to The Cancer Genome Atlas cohort and interestingly, successful engraftment correlated to amplification of the CCND1 gene. Ten HPV-negative PDX models were treated with the CDK4/6 inhibitor, abemaciclib; 5 of 6 models with CCND1 amplifications and/or CDKN2A mutations responded to abemaciclib treatment, whereas only 1 of 4 models lacking these alterations responded. We also carried out a PDX clinical trial on 20 models using the PI3Kα inhibitor, BYL719. Interestingly, while previous studies using in vitro cell line studies and limited numbers of xenograft models derived from various tumor types have suggested PIK3CA hotspot mutations predict for response to PI3K inhibitors, we found that BYL719 was almost globally tumoristatic, regardless of PIK3CA mutational status. Our results demonstrate the potential of using PDX models to individualize treatment for patients at high risk of relapse following definitive treatment, to identify novel therapies and predictive biomarkers, and to interrogate drug resistance mechanisms. Citation Format: Christina Karamboulas, Jeffrey P. Bruce, Kara M. Ruicci, Wei Xu, Anthony C. Nichols, Laurie Ailles. Utilizing patient-derived xenografts for prognostication and biomarker discovery [abstract]. In: Proceedings of the AACR-AHNS Head and Neck Cancer Conference: Optimizing Survival and Quality of Life through Basic, Clinical, and Translational Research; 2019 Apr 29-30; Austin, TX. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(12_Suppl_2):Abstract nr IA02.
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 | 0,001 | 0,008 |
| 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 | 0,001 |
| Communication savante | 0,000 | 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) | 0,000 | 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 ».