Antigen presentation and tumor immunogenicity in cancer immunotherapy response prediction.

Shixiang Wang,Zaoke He,Xue-Song Liu,Huimin Li,Xuan Wang

doi:10.7554/elife.49020

Abstract

Immunotherapy, represented by immune checkpoint inhibitors (ICI), is transforming the treatment of cancer. However, only a small percentage of patients show response to ICI, and there is an unmet need for biomarkers that will identify patients who are more likely to respond to immunotherapy. The fundamental basis for ICI response is the immunogenicity of a tumor, which is primarily determined by tumor antigenicity and antigen presentation efficiency. Here, we propose a method to measure tumor immunogenicity score (TIGS), which combines tumor mutational burden (TMB) and an expression signature of the antigen processing and presenting machinery (APM). In both correlation with pan-cancer ICI objective response rates (ORR) and ICI clinical response prediction for individual patients, TIGS consistently showed improved performance compared to TMB and other known prediction biomarkers for ICI response. This study suggests that TIGS is an effective tumor-inherent biomarker for ICI-response prediction.

Highlights

Immunotherapy, represented by immune checkpoint inhibitors (ICI), including anti-PD-1 antibodies, anti-PD-L1 antibodies, anti CTLA-4 antibodies or their combinations, is transforming the treatment of cancer
The results suggest that inducing tumor cells to produce more of the machinery that presents protein fragments to the immune system could increase their responsiveness to immunotherapy
Several steps are involved in this process, including: 1) peptide generation and trimming in the proteasome; 2) peptide transport; 3) assembly of the MHC class loading complex in the endoplasmic reticulum (ER); and 4) antigen presentation on cell surface (Leone et al, 2013)

Summary

Introduction

Immunotherapy, represented by immune checkpoint inhibitors (ICI), including anti-PD-1 antibodies, anti-PD-L1 antibodies, anti CTLA-4 antibodies or their combinations, is transforming the treatment of cancer. Multiple factors are reported to affect ICI effectiveness, including: PD-L1 expression (Herbst et al, 2014; Shukuya and Carbone, 2016), TMB (Rizvi et al, 2015; Snyder et al, 2014), DNA mismatch repair deficiency (Le et al, 2015), the degree of cytotoxic T cell infiltration (Tang et al, 2016), mutational signature (Miao et al, 2018; Wang et al, 2018), antigen presentation defects (Chowell et al, 2018; Zaretsky et al, 2016), interferon signaling (Ayers et al, 2017), tumor aneuploidy (Davoli et al, 2017) and T-cell signatures (Jiang et al, 2018). These biomarkers have various rates of accuracy and utility, and the identification of a robust ICI-response biomarker is still a critical challenge in the field (Nishino et al, 2017)

Methods

Results

Conclusion