Abstract 1058: Immune biomarkers for recurrence in stage II and III melanoma to guide patient stratification
Notice bibliographique
Résumé
Abstract Stage II-III melanoma patients lack needed biomarkers to avoid over-treatment and determine need for combination immunotherapy. A “hot” tumor immune micro-environment (TIME) is predictive of survival and immunotherapy benefit in multiple tumor types. We have previously identified the ratio of CD8+/CD68+ cells as well as a 53-gene melanoma immune panel (MIP) as indicative of a favorable “hot” TIME. CD8/CD68 ratio and MIP were evaluated in specimens from 213 previously untreated patients including 164 from Roswell Park Comprehensive Cancer Center (RPCCC) and 49 from Geisinger Health Systems (GHS, n=36). Tumors were stained with Sox10, CD8, CD68, MPO, PDL1 and HLA-DR using the MOTiF workflow (Akoya Biosciences, AB). Image analysis was completed using inForm software (AB). RNA was extracted and NanoString analysis performed using published methods. Receiving Operator Curves (ROC) were generated and KM curves using cutoffs selected using maximally selected log-rank statistics to quantify survival benefit. We tested whether the CD8/CD68 ratio correlated with disease specific survival (DSS) and distant metastatic recurrence (DMR). In the RPCCC cohort (n=164), 138 specimens were evaluated by a pathologist and stained of which 15 were excluded based on tissue quality, and inForm analysis was completed on 80. Of these 80, 16 had DMR of whom 8 had died. ROC showed CD8/CD68 ratio to correlate with DMR (p=0.0397). KM analysis showed high CD8/CD68 ratio correlated with DMR free survival (p=0.00079). Within the GHS samples, 36 of 49 patients had sufficient quality tissue, of whom 13 had died. DMR data was not available. A high CD8/CD68 ratio correlated with DSS by ROC curve (p=0.027) and KM analysis (p=0.0009). MIP was run on 166 patients of whom 147 passed quality control for analysis. MIP correlated with DSS in the RPCCC cohort by ROC analysis (p=0.025) and cutoff defined based on prior cohort showed correlation with DSS (p=0.01) and trend towards correlation with DMRFS (p=0.065). To include diverse samples in the training set, MIP was retrained with the addition of 81 patients from Columbia University (CUMC). KM analysis performed using the retrained signature showed a favorable signature correlated with DSS (p=0.032) and DMRFS (p=0.00077). Immune based biomarkers including CD8/CD68 ratio and immune gene signature have potential to assist clinicians in selecting early-stage melanoma patients for more intensive immunotherapy regimens. Full analysis of the data from the RPCCC and GHS cohorts will be presented at the conference. Citation Format: Gerardo A. Espinoza, Chenxin Zhang, Jee-Young Moon, Divya B. Kenchappa, Matteo Abbruzzese, Lydia Bioh, Yadriel M. Bracero, Sharmin Sultana, Ajay Singh, Thazin Aung, John J. Krolewski, Robyn D. Gartrell, Larisa J. Geskin, Lawrence W. Leung, Tammie Ferringer, Rui Chang, Basil Horst, Kent Nastiuk, Yvonne M. Saenger. Immune biomarkers for recurrence in stage II and III melanoma to guide patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1058.
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 | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 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 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 ».