Abstract 2311: Identification of therapeutic targets in HNSCC
Notice bibliographique
Résumé
Abstract HNSCC is 6th most common malignancy in the world. Despite advances in diagnosis and treatment, the survival rates remain low due in large part to metastatic disease. The underlying biology associated with metastatic disease and poor outcome in HNSCC remains unclear. Importantly, metastatic cells acquire new properties that permit them to invade surrounding tissues and seed metastasis at distant sites. While these acquired properties contribute to aggressiveness of metastatic cancer, they can be exploited to target metastatic cells selectively, sparing toxicity in normal tissues. We used functional genomic technologies to identify new therapeutic targets for advanced disease in HNSCC. These targets were identified by conducting whole genome shRNA screens in matched sets of cell lines derived from primary HNSCC tumors and their respective metastatic sites/recurrences. Since hypoxia is an important attribute of aggressive and therapy resistant subpopulations of HNSCC tumor cells, we also aimed to identify genes that became essential when cells are exposed to hypoxia. To complement the data from the functional screens and to further characterize our cell line collection, we utilized several high through put approaches including mutational analysis, proteomics and gene expression profiling. Moreover, we performed chemical screens using libraries of over 4000 FDA approved drugs with the aim to combine our functional genomic data with the results from the drug screens to discover drug/gene “hit” combinations that would provide a basis for development of novel anti-cancer therapies.While HNSCC derived cell lines represent a valuable tool to study this disease, their biology might not always accurately represent the biology of the tumors they were derived from. Therefore, we looked at the expression of the components of several key pathways that we discovered using the in vitro characterization in a patient material from over HNSCC surgical samples constructed into a TMA. Five of the hits belonged to the Notch signalling pathway and 4 others came from the proteomic analysis as they showed higher levels of surface expression in metastatic cells and/or under hypoxic conditions. Interestingly, higher proportion of patients with late stages of HNSCC belonged to the high Jag2 and Hey1 populations as compared to patients with low stages of HNSCC that mostly expressed low or intermediate levels of these proteins. This data is in line with our observations that Jag2 and Hey1 expression is elevated in cells derived from metastatic sites and is low in cells derived from primary tumors. Proportion of cells with high CD66 and TRAIL proteins was also higher among the late stage tumors suggesting that these biomarkers are of interest for further investigation. We discovered several molecular pathways that are important in metastatic/hypoxic HNSCC. We are investigating our ability to target these pathways using FDA approved drugs in order to achieve durable cures. Citation Format: Maria Kondratyev, Aleksandra Pesic, Anna Dvorkin-Sheva, Troy Ketela, Natalie Stickle, Laurie Aiiles, Reidar Grenman, Marianne Koritzinsky, Brad Wouters. Identification of therapeutic targets in HNSCC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2311.
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 | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,001 | 0,001 |
| 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 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 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 ».