从体内免疫编辑预测新抗原免疫原性
Predicting Neoantigen Immunogenicity from In Vivo Immune Editing
bioRxiv · 2026 年 9 月 18 日 · Timothy J Sears, Ko-Han Lee, Maria Munoz Perez 等 10 人
新抗原（肿瘤突变产生、可被免疫系统识别的小片段）的免疫原性预测是个体化癌症疫苗、肿瘤浸润淋巴细胞（TIL）治疗和TCR-T细胞工程的基础，但现有计算预测器多依赖体外呈递指标或实验反应数据，通常只在单一治疗场景中验证。研究者提出NEMo，一个基于CD8和CD4双区室的机器学习分类器，训练信号来自免疫检查点阻断（ICB）治疗中连续测序肿瘤的新抗原体内编辑（被识别的克隆随时间被选择性清除）。使用模拟机制的NeoPrecis特征后，NEMo在四个独立未见临床场景中找回了实验确认的免疫原性新抗原，包括免疫原性筛查、个体化癌症疫苗、TIL治疗和放疗±ICB的ctDNA队列，且对无进展生存期的分层强于ELISPOT确认的反应性。编辑信号还揭示了免疫逃逸格局：致癌驱动突变和受限于丢失或沉默HLA等位基因的新抗原被系统性地“豁免”编辑。尚未经同行评审。
为什么推荐给您：利用体内免疫编辑作为训练信号的新预测框架，跨四类临床场景验证，可能改变新抗原筛选方式。
不需要生物学背景，多打比方
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摘要Abstract
Neoantigen immunogenicity prediction is fundamental to personalized cancer vaccines, tumor-infiltrating lymphocyte (TIL) therapy, and TCR-T cell engineering. Existing computational predictors rely primarily on in-vitro correlates of peptide presentation or models trained against assay-based reactivity, and they are typically validated within a single therapeutic setting. We reasoned that the most direct evidence of neoantigen immunogenicity is longitudinal in-vivo elimination: under immune checkpoint blockade (ICB), subclones bearing recognized neoantigens are selectively depleted over time. Here, we present the Neoantigen Elimination Model (NEMo), a two-compartment (CD8 and CD4) machine learning classifier trained on the in-vivo editing (IVE) of neoantigens across serially sequenced, ICB-treated tumors. By using mechanistically inspired NeoPrecis features designed to capture determinants of immunogenicity beyond MHC binding affinity, NEMo recovered assay-confirmed immunogenic neoantigens across four independent, unseen clinical settings -- pre-existing immunogenicity screening, personalized cancer vaccines, TIL therapy, and a radiotherapy +/- ICB ctDNA cohort -- and stratified progression-free survival more strongly than ELISPOT-confirmed reactivity. The editing signal further revealed an immune-evasion architecture in which oncogenic drivers and neoantigens restricted to lost or silenced HLA alleles are systematically spared from editing.