计算机科学
基因组
人工智能
免疫疗法
癌症
深度学习
机器学习
计算生物学
医学
生物
遗传学
内科学
基因
作者
Kai Yan,Zhiheng Zhou,Sihao Liu,Guanghui Wang,Guiying Yan,Edwin Wang
标识
DOI:10.1109/jbhi.2025.3555596
摘要
The emergence of immune checkpoint inhibitors (ICIs) has significantly advanced cancer treatment. However, only 15-30% of the cancer patients respond to ICI treatment, which stimulates and enhances host immunity to eliminate tumor cells. ICI treatment is very expensive and has potential adverse reactions; therefore, it is crucial to develop a method which enables to accurately and rapidly assess a patient's suitability before ICI treatment. We complied germline whole-genome sequencing (WES) data of 37 melanoma patients who have been treated with ICIs and sequenced in our lab previously, and the WES data of other 700 ICI-treated cancer patients in public domain. Using these data, we proposed a novel double-channel attention neural network (DANN) model to predict cancer ICI-response and validate the predictions. DANN achieved a mean accuracy and AUC of 0.95 and 0.98, respectively, which outperformed traditional machine learning methods. Enrichment analysis of the DANN-identified genes indicated that cancer patients whose in-born genomic variants might mainly affect host immune system in a wide-ranging manner, and then affect ICI response. Finally, we found a set of 12 genes bearing genomic variants were significantly associated with cancer patient survivals after ICI treatment.
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