Research Paper Volume 14, Issue 10 pp 4586—4605
Construction and validation of an immunoediting-based optimized neoantigen load (ioTNL) model to predict the response and prognosis of immune checkpoint therapy in various cancers
- 1 Department of Respiratory and Critical Care Medicine, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shanxi 710061, China
- 2 YuceNeo Technology Co., Ltd., Shenzhen 518000, China
- 3 YuceBio Technology Co., Ltd., Shenzhen 518020, China
Received: December 28, 2021 Accepted: April 12, 2022 Published: May 25, 2022
https://doi.org/10.18632/aging.204101How to Cite
Copyright: © 2022 Su et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Background: Only a minority of patients clinically benefit from immune checkpoint therapy. Tumor clones with neoantigens have immunogenicity; therefore, they are eliminated by T-cell-mediated immune editing. Identifying neoantigen clones with the ability to induce immune elimination may better predict the clinical outcome of immunotherapy.
Methods: We developed ioTNL model, which indicates the immunoediting-based optimized tumor neoantigen load, by identifying tumor clones that could induce immune elimination. Data of more than two hundred patients from our patient pool and previously reported studies who underwent anti-PD-(L)1 therapy were collected to validate the prediction performance of ioTNL model. Clonal architectures, immune editing scores and ioTNL scores were identified. The association between the response as well as prognosis and the ioTNL were evaluated. Panel sequencing of genes from 2,469 patients within 20 cancer types was performed to profile the landscape of immunoediting.
Results: As expected, the ioTNL score could predict the response in patients who underwent immune checkpoint inhibitor (ICI) immunotherapy for various cancers, including non-small cell lung cancer (NSCLC; p = 0.0066), skin cutaneous melanoma (SKCM; p = 0.026) and nasopharyngeal carcinoma (NPC; p = 0.0025). Patients with a high ioTNL score demonstrated longer survival than those with a low score. We verified the ioTNL on our cohort through panel sequencing and found that the ioTNL was associated with the response (p = 0.025) and prognosis (p = 0.00082) in anti-PD-(L)1 monotherapy. In addition, we found that the immune editing score correlated with the tumor mutation burden (TMB) and the objective response rate of immunotherapy.
Conclusions: Identifying neoantigen clones with the ability to induce immune elimination would better predict the efficacy of immunotherapy. We have proved that the reliable method of ioTNL can be applied to whole-exome sequencing (WES) and panel data and would have a broad application in precision diagnosis in immunotherapy.
Abbreviations
ioTNL: immunoediting based optimized neoantigen load; NSCLC: non-small cell lung cancer; SKCM: skin cutaneous melanoma; NPC: nasopharyngeal carcinoma; ICC: intrahepatic cholangiocarcinoma; anti-PD-(L)1: anti-programmed death-(ligand) 1; MHC: major histocompatibility complex; TCR: T-cell receptor; HLA: human leukocyte antigen; InDels: insertions and deletions; ORR: objective response rate; NOR: non-objective response; DCB: durable clinical benefit; NDB: non-durable clinical benefit; CR: complete response; PR: partial response; SD: stable disease; PD: disease progression; PFS: progression-free survival; OS: overall survival; TMB: tumor mutation burden; TNB: tumor neoantigen burden; ROC: receiver operating characteristic curve; AUC: area under curve; CCF: cancer cell fraction.