Multi-omics indicators of long-term survival benefits after immune checkpoint inhibitor therapy.
Cell Rep Methods
; 3(10): 100596, 2023 Oct 23.
Article
en En
| MEDLINE
| ID: mdl-37738982
ABSTRACT
Molecular indicators of long-term survival (LTS) in response to immune-checkpoint inhibitor (ICI) treatment have the potential to provide both mechanistic and therapeutic insights. In this study, we construct predictive models of LTS following ICI therapy based on data from 158 clinical trials involving 21,023 patients of 25 cancer types with available 1-year overall survival (OS) rates. We present evidence for the use of 1-year OS rate as a surrogate for LTS. Based on these and corresponding TCGA multi-omics data, total neoantigen, metabolism score, CD8+ T cell, and MHC_score were identified as predictive biomarkers. These were integrated into a Gaussian process regression model that estimates "long-term survival predictive score of immunotherapy" (iLSPS). We found that iLSPS outperformed the predictive capabilities of individual biomarkers and successfully predicted LTS of patient groups with melanoma and lung cancer. Our study explores the feasibility of modeling LTS based on multi-omics indicators and machine-learning methods.
Palabras clave
Texto completo:
1
Colección:
01-internacional
Base de datos:
MEDLINE
Contexto en salud:
6_ODS3_enfermedades_notrasmisibles
Problema de salud:
6_malignant_skin_melanoma
/
6_other_respiratory_diseases
/
6_trachea_bronchus_lung_cancer
Asunto principal:
Neoplasias Pulmonares
/
Melanoma
Tipo de estudio:
Prognostic_studies
Límite:
Humans
Idioma:
En
Revista:
Cell Rep Methods
Año:
2023
Tipo del documento:
Article