Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis

Yin He; Xiaosheng Wang

doi:10.1016/j.compbiomed.2023.107591

Identifying biomarkers associated with immunotherapy response in melanoma by multi-omics analysis

Comput Biol Med. 2023 Dec:167:107591. doi: 10.1016/j.compbiomed.2023.107591. Epub 2023 Oct 18.

Authors

Yin He¹, Xiaosheng Wang²

Affiliations

¹ Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China; Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China; Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China.
² Biomedical Informatics Research Lab, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China; Cancer Genomics Research Center, School of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, 211198, China; Big Data Research Institute, China Pharmaceutical University, Nanjing, 211198, China. Electronic address: xiaosheng.wang@cpu.edu.cn.

PMID: 37875043
DOI: 10.1016/j.compbiomed.2023.107591

Abstract

Despite immune checkpoint inhibitors (ICIs) have shown the greatest success in melanoma treatment, only a subset of melanoma patients responds well to ICIs. Thus, identifying predictive biomarkers for immunotherapy response is crucial. In this study, we took complementary advantages of immunotherapy data and The Cancer Genome Atlas (TCGA) multi-omics data to explore the predictive biomarkers for the response to immunotherapy in melanoma. We first predicted responsive and non-responsive melanomas in the TCGA skin cutaneous melanoma (SKCM) cohort based on both somatic mutation and transcriptome datasets which involved immunotherapy data for melanoma. This method identified 170 responsive and 56 non-responsive melanomas in TCGA-SKCM. Based on the TCGA-SKCM data, we performed a comprehensive comparison of multi-omics molecular features between responsive and non-responsive melanomas. We identified the molecular features significantly associated with immunotherapy response in melanoma at the genome, transcriptome, epigenome, and proteome levels, respectively. Our analysis confirmed certain immunotherapy response-associated biomarkers, such as tumor mutation burden (TMB), copy number alteration (CNA), intratumor heterogeneity (ITH), PD-L1 expression, and tumor immunity. Moreover, we identified some novel molecular features associated with immunotherapy response: (1) the activation of mast cells and dendritic cells correlating negatively with immunotherapy response; (2) the enrichment of many oncogenic pathways correlating positively with immunotherapy response, such as JAK-STAT, RAS, MAPK, HIF-1, PI3K-Akt, and VEGF pathways; and (3) a number of microRNAs and proteins whose expression correlates with immunotherapy response. In addition, the mTOR signaling pathway has a negative association with immunotherapy response. The novel biomarkers have potential predictive values in immunotherapy response and warrant further investigation.

Keywords: Biomarker; Immune checkpoint inhibitor; Immunotherapy response; Melanoma; Multi-omics.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Biomarkers, Tumor / genetics
Humans
Immunotherapy / methods
Melanoma* / genetics
Melanoma* / therapy
Melanoma, Cutaneous Malignant
Multiomics
Phosphatidylinositol 3-Kinases
Skin Neoplasms* / genetics
Skin Neoplasms* / pathology
Skin Neoplasms* / therapy

Substances

Phosphatidylinositol 3-Kinases
Biomarkers, Tumor