Abstract A035: Comparing the clinical and genomic landscapes of acral, mucosal, and cutaneous metastatic melanomas treated with immune checkpoint inhibitors
Notice bibliographique
Résumé
Abstract Background: Melanoma is a melanocytic malignancy that is classified into different subtypes, including cutaneous (CM), acral (AM), and mucosal melanoma (MM). Immune checkpoint inhibitors (ICI) targeting PD-1 and/or CTLA-4 have emerged as the standard of care for advanced metastatic melanoma. We investigated responses to ICIs, genomic profiles, and molecular differences among these different subtypes of melanoma. Methods: We performed a multi-center retrospective cohort study including patients with metastatic melanoma who received anti-PD1 +/- CTLA4 inhibitor ICI for metastatic disease. We employed the AACR GENIE (v13.1) cancer database, DepMap portal, and Enrichr web tool with MSigDB Hallmark 2020 database to analyze the incidence and distribution of significant alterations, differentially expressed genes, and pathway enrichment within distinct melanoma subtypes. Results: We identified 337 patients with advanced melanoma who received anti-PD1 +/- anti-CTLA4 for metastatic disease. CM was the most frequent melanoma subtype (81%), followed by MM (12%), and AM (8%). Patients with AM had the shortest OS and PFS CM (OS 3.4 years, PFS of 1.1 years), AM median (OS 1.4 years, PFS 3.8 months), and MM (OS 1.7 years, PFS 6 months; OS, P= 0.028; PFS P=0.029). Patients with CM or MM experienced longer OS with anti-PD1 +/- CTLA4 vs. anti-PD1 monotherapy, but no survival advantage was observed in patients with AM. In the GENIE dataset, we identified 2015/374/177 samples with CM/MM/AM, respectively. AM and MM patients were significantly more likely to be Female, Black or Asian than CM patients. BRAF V600 mutations were most frequent in CM (40%), followed by MM (7%) and AM (14%). However, MM and AM had an increase in alterations in cell cycle regulator genes. The incidence of CDK4 and CCND1 amplification, respectively was highest in AM (17%, 16%), followed by MM (6%, 6%) and CM (2%, 3%). We compared the pathways represented by differentially altered genes in AM vs. CM: G2M-Checkpoint (Q<0.05) and E2F targets (Q<0.05) pathways were enriched in AM. We also compared RNAseq data from n=4) AM and n=20 CM cell lines, and found the same pathways enriched amongst genes that were differentially expressed between AM and CM: G2M-Checkpoint (Q<0.01) and E2F targets (Q<0.01) Conclusions: Our findings highlight comparatively poor outcomes with ICIs in Acral Melanoma. AM has lower rates of actionable BRAF mutations; as such, very few patients with metastatic AM have access to effective 1st or 2nd line therapies. Thus, there is an urgent need to explore alternative therapeutic targets. Our data implicate cell cycle inhibitors as potentially important components of novel treatment strategies that could augment immunotherapy efficacy for patients with metastatic AM. Citation Format: Sadaf Solati, April A. N. Rose, Anna Spreafico, Adrian Sacher, Denis Yahiaoui, Wilson H. Miller. Comparing the clinical and genomic landscapes of acral, mucosal, and cutaneous metastatic melanomas treated with immune checkpoint inhibitors [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2023 Oct 1-4; Toronto, Ontario, Canada. Philadelphia (PA): AACR; Cancer Immunol Res 2023;11(12 Suppl):Abstract nr A035.
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 | 0,002 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,002 |
| 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 ».