The MHC-I-dependent neoantigen presentation pathway predicts response rate to PD-1/PD-L1 blockade

Immune checkpoint inhibitors produce durable antitumor effects in various cancers, but not all patients respond. High tumor mutational burden (TMB) is a known predictor of clinical benefit. In this study, we focused on the MHC-I-dependent neoantigen presentation pathway to enhance predictive capabilities beyond TMB. Using pan-cancer immunogenomic analyses of somatic mutation data from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC), we analyzed 33 cancer types. Objective response rates (ORRs) to PD-1/PD-L1 inhibitors were evaluated in relation to immune characteristics, including TMB, neoantigen load, MHC-I gene expression, and CD8+ T cell fraction. Spearman's rank correlation was used to assess these relationships. TMB showed the strongest correlation with ORR (r = 0.783, P = 2.17 × 10⁻⁵). However, integrating TMB, HLA-A expression, and CD8+ T cell fraction significantly improved predictive accuracy (r = 0.865, P = 1.80 × 10⁻⁶). Validation in external cohorts confirmed these findings, revealing notable differences in MHC-I pathway activity between responders and non-responders to immunotherapy. Our results demonstrate that the MHC-I antigen presentation pathway is strongly associated with response to PD-1/PD-L1 inhibitors. Importantly, combining antigen expression, processing, presentation, and recognition features provides superior predictive power compared to TMB alone. This integrated approach could improve treatment outcome predictions and advance personalized immunotherapy strategies.

Authors

Institutions

Publication Details

Journal
Biomolecules and Biomedicine
Published
2024-11-26
DOI
https://doi.org/10.17305/bb.2024.11069
Citations
2
Primary Topic
Cancer Immunotherapy and Biomarkers
Type
article
Field-Weighted Citation Impact
0.57
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The MHC-I-dependent neoantigen presentation pathway predicts response rate to PD-1/PD-L1 blockade

Binghua Li, Decai Yu, Yuchen Zhang, Yanchao Xu et al.
2 citations
Biomolecules and Biomedicine
Cancer Immunotherapy and Biomarkers
0.57
article

The MHC-I-dependent neoantigen presentation pathway predicts response rate to PD-1/PD-L1 blockade

Binghua Li, Decai Yu, Yuchen Zhang, Yanchao Xu, Yang Chen, Jiajun Shi, Xiang Jiang
article en
2 citations

Abstract

Immune checkpoint inhibitors produce durable antitumor effects in various cancers, but not all patients respond. High tumor mutational burden (TMB) is a known predictor of clinical benefit. In this study, we focused on the MHC-I-dependent neoantigen presentation pathway to enhance predictive capabilities beyond TMB. Using pan-cancer immunogenomic analyses of somatic mutation data from The Cancer Genome Atlas (TCGA) and The International Cancer Genome Consortium (ICGC), we analyzed 33 cancer types. Objective response rates (ORRs) to PD-1/PD-L1 inhibitors were evaluated in relation to immune characteristics, including TMB, neoantigen load, MHC-I gene expression, and CD8+ T cell fraction. Spearman's rank correlation was used to assess these relationships. TMB showed the strongest correlation with ORR (r = 0.783, P = 2.17 × 10⁻⁵). However, integrating TMB, HLA-A expression, and CD8+ T cell fraction significantly improved predictive accuracy (r = 0.865, P = 1.80 × 10⁻⁶). Validation in external cohorts confirmed these findings, revealing notable differences in MHC-I pathway activity between responders and non-responders to immunotherapy. Our results demonstrate that the MHC-I antigen presentation pathway is strongly associated with response to PD-1/PD-L1 inhibitors. Importantly, combining antigen expression, processing, presentation, and recognition features provides superior predictive power compared to TMB alone. This integrated approach could improve treatment outcome predictions and advance personalized immunotherapy strategies.

Biomolecules and BiomedicineVol. 25(6)
Nanjing Drum Tower Hospital (CN)
Openalex Percentile: Top 26%
Cancer Immunotherapy and Biomarkers
0.57
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.