瑞士洛桑大学Michal Bassani-Sternberg等研究人员合作发现，机器学习方法和统一数据集可改进免疫原新抗原预测。相关论文于2023年10月9日在线发表在《免疫》杂志上。

研究人员重新处理了120名癌症患者的全外显子组测序和RNA测序（RNA-seq）数据，这些数据来自两个外部大规模新抗原免疫原性筛选试验和一个内部的11名患者数据集，结果发现了46017个体细胞单核苷酸变异突变和1781445个新肽，其中212个突变和178个新肽具有免疫原性。除了用于确定新抗原优先级的常用特征外，新肽在蛋白质I类人类白细胞抗原（HLA）呈递热点中的位置、结合杂合性以及突变基因在致癌中的作用等因素也可预测免疫原性。

分类器准确预测了不同数据集的新抗原免疫原性，并将其排名提高了30%。除了对新抗原排序的机器学习方法有了深入的了解，研究人员还提供了统一数据集，这些数据集对开发基于新抗原的免疫疗法的配套算法并为其设定基准非常有价值。

研究人员表示，准确选择能与I类人类白细胞抗原（HLA）结合并被自体T细胞识别的新抗原是许多癌症免疫疗法流水线的关键步骤。

附：英文原文

Title: Machine learning methods and harmonized datasets improve immunogenic neoantigen prediction

Author: Markus Müller, Florian Huber, Marion Arnaud, Anne I. Kraemer, Emma Ricart Altimiras, Justine Michaux, Marie Taillandier-Coindard, Johanna Chiffelle, Baptiste Murgues, Talita Gehret, Aymeric Auger, Brian J. Stevenson, George Coukos, Alexandre Harari, Michal Bassani-Sternberg

Issue&Volume: 2023-10-09

Abstract: The accurate selection of neoantigens that bind to class I human leukocyte antigen (HLA) and are recognized by autologous T cells is a crucial step in many cancer immunotherapy pipelines. We reprocessed whole-exome sequencing and RNA sequencing (RNA-seq) data from 120 cancer patients from two external large-scale neoantigen immunogenicity screening assays combined with an in-house dataset of 11 patients and identified 46,017 somatic single-nucleotide variant mutations and 1,781,445 neo-peptides, of which 212 mutations and 178 neo-peptides were immunogenic. Beyond features commonly used for neoantigen prioritization, factors such as the location of neo-peptides within protein HLA presentation hotspots, binding promiscuity, and the role of the mutated gene in oncogenicity were predictive for immunogenicity. The classifiers accurately predicted neoantigen immunogenicity across datasets and improved their ranking by up to 30%. Besides insights into machine learning methods for neoantigen ranking, we have provided homogenized datasets valuable for developing and benchmarking companion algorithms for neoantigen-based immunotherapies.

DOI: 10.1016/j.immuni.2023.09.002

Source: https://www.cell.com/immunity/fulltext/S1074-7613(23)00406-5