The immune checkpoint inhibitors have demonstrated promising clinical efficacy across various tumor types, yet the percentage of patients who benefit from them remains low. The bindings between tumor antigens and HLA-I/TCR molecules determine the antigen presentation and T-cell activation, thereby playing an important role in the immunotherapy response. In this paper, we propose UnifyImmun, a unified cross-attention transformer model designed to simultaneously predict the bindings of peptides to both receptors, providing more comprehensive evaluation of antigen immunogenicity. We devise a two-phase strategy using virtual adversarial training that enables these two tasks to reinforce each other mutually, by compelling the encoders to extract more expressive features. Our method demonstrates superior performance in predicting both pHLA and pTCR binding on multiple independent and external test sets. Notably, on a large-scale COVID-19 pTCR binding test set without any seen peptide in training set, our method outperforms the current state-of-the-art methods by more than 10\%. The predicted binding scores significantly correlate with the immunotherapy response and clinical outcomes on two clinical cohorts. Furthermore, the cross-attention scores and integrated gradients reveal the amino-acid sites critical for peptide binding to receptors. In essence, our approach marks a significant step toward comprehensive evaluation of antigen immunogenicity.

翻译：免疫检查点抑制剂已在多种肿瘤类型中展现出良好的临床疗效，但能从中获益的患者比例仍然较低。肿瘤抗原与HLA-I/TCR分子之间的结合决定了抗原呈递和T细胞活化，从而在免疫治疗应答中发挥重要作用。本文提出UnifyImmun，一种统一的交叉注意力Transformer模型，旨在同时预测肽段与这两种受体的结合，从而提供更全面的抗原免疫原性评估。我们设计了一种采用虚拟对抗训练的两阶段策略，通过迫使编码器提取更具表达力的特征，使这两个任务能够相互促进。我们的方法在多个独立外部测试集上预测pHLA和pTCR结合均表现出优越性能。值得注意的是，在一个训练集中未出现任何已知肽段的大规模COVID-19 pTCR结合测试集上，我们的方法比当前最先进方法的性能高出10%以上。预测的结合分数与两个临床队列的免疫治疗应答及临床结局显著相关。此外，交叉注意力分数和积分梯度揭示了肽段与受体结合的关键氨基酸位点。本质上，我们的方法标志着向全面评估抗原免疫原性迈出了重要一步。