All files

Identification of tumor neoantigens is indispensable for the development of cancer immunotherapies. However, we are still lacking knowledge about the potential neoantigens derived from sequences outside protein-coding regions. Here, we comprehensively characterized the immunopeptidome landscape by integrating multi-omics data in acute myeloid leukemia (AML). Both canonical and non-canonical MHC-associated peptides (MAPs) in AML were identified. We found that the quality and characteristics of ncMAPs are comparable or superior to cMAPs, indicating ncMAPs are indispensable sources for tumor neoantigens. We further proposed a computational framework to prioritize the neoantigens by integrating additional transcriptome and immunopeptidome in normal tissues. Notably, 6 of prioritized 13 neoantigens were derived from ncMAPs. The expressions of corresponding source genes were highly related to infiltrations of immune cells. Finally, a risk model was developed, which exhibited good performance for clinical prognosis in AML. Our findings expand potential cancer immunotherapy targets and provide in-depth insights into AML treatment, laying a new foundation for precision therapies in AML.

肿瘤新抗原（tumor neoantigens）的识别对于癌症免疫治疗的研发不可或缺。然而，目前学界对于源自蛋白质编码区以外序列的潜在新抗原仍缺乏充分认知。本研究通过整合急性髓系白血病（acute myeloid leukemia，AML）的多组学（multi-omics）数据，全面解析了免疫肽组（immunopeptidome）的图谱。本研究在AML中鉴定出了经典与非经典MHC相关肽（MHC-associated peptides，MAPs），其中经典MHC相关肽（canonical MHC-associated peptides，cMAPs）与非经典MHC相关肽（non-canonical MHC-associated peptides，ncMAPs）均被成功鉴定。我们发现，ncMAPs的质量与特征可与cMAPs媲美，甚至更优，这表明ncMAPs是肿瘤新抗原不可或缺的来源。本研究进一步提出了一种计算框架，通过整合正常组织的额外转录组与免疫肽组数据，对新抗原进行优先级排序。值得注意的是，在经该框架筛选出的13种新抗原中，有6种源自ncMAPs。这些新抗原对应源基因的表达水平与免疫细胞浸润程度密切相关。最后，本研究构建了一款临床风险预测模型，该模型在AML患者的预后评估中表现优异。本研究的发现拓展了潜在癌症免疫治疗靶点的范围，为AML治疗提供了深入见解，为AML的精准治疗奠定了全新基础。