Construction and validation of an immunoediting-based optimized neoantigen load (ioTNL) model to predict the response and prognosis of immune checkpoint therapy in various cancers.

Q4 Social Sciences Boletin Mexicano de Derecho Comparado Pub Date : 2022-05-25 DOI:10.18632/aging.204101
Xiaofan Su, Haoxuan Jin, Jiaqian Wang, Huiping Lu, Tiantian Gu, Zhibo Gao, Manxiang Li
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Abstract

Background: Only a minority of patients clinically benefit from immune checkpoint therapy. Tumor clones with neoantigens have immunogenicity; therefore, they are eliminated by T-cell-mediated immune editing. Identifying neoantigen clones with the ability to induce immune elimination may better predict the clinical outcome of immunotherapy.

Methods: We developed ioTNL model, which indicates the immunoediting-based optimized tumor neoantigen load, by identifying tumor clones that could induce immune elimination. Data of more than two hundred patients from our patient pool and previously reported studies who underwent anti-PD-(L)1 therapy were collected to validate the prediction performance of ioTNL model. Clonal architectures, immune editing scores and ioTNL scores were identified. The association between the response as well as prognosis and the ioTNL were evaluated. Panel sequencing of genes from 2,469 patients within 20 cancer types was performed to profile the landscape of immunoediting.

Results: As expected, the ioTNL score could predict the response in patients who underwent immune checkpoint inhibitor (ICI) immunotherapy for various cancers, including non-small cell lung cancer (NSCLC; p = 0.0066), skin cutaneous melanoma (SKCM; p = 0.026) and nasopharyngeal carcinoma (NPC; p = 0.0025). Patients with a high ioTNL score demonstrated longer survival than those with a low score. We verified the ioTNL on our cohort through panel sequencing and found that the ioTNL was associated with the response (p = 0.025) and prognosis (p = 0.00082) in anti-PD-(L)1 monotherapy. In addition, we found that the immune editing score correlated with the tumor mutation burden (TMB) and the objective response rate of immunotherapy.

Conclusions: Identifying neoantigen clones with the ability to induce immune elimination would better predict the efficacy of immunotherapy. We have proved that the reliable method of ioTNL can be applied to whole-exome sequencing (WES) and panel data and would have a broad application in precision diagnosis in immunotherapy.

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构建和验证基于免疫编辑的优化新抗原负荷(ioTNL)模型,以预测免疫检查点疗法对各种癌症的反应和预后。
背景:临床上,只有少数患者能从免疫检查点疗法中获益。具有新抗原的肿瘤克隆具有免疫原性;因此,它们会被T细胞介导的免疫编辑所消除。识别具有诱导免疫清除能力的新抗原克隆可以更好地预测免疫疗法的临床结果:方法:我们建立了ioTNL模型,该模型通过识别能诱导免疫清除的肿瘤克隆,显示基于免疫编辑的优化肿瘤新抗原负荷。为了验证ioTNL模型的预测性能,我们从我们的患者库和之前报道的研究中收集了两百多名接受抗PD-(L)1治疗的患者数据。我们确定了克隆结构、免疫编辑评分和ioTNL评分。评估了反应和预后与ioTNL之间的关联。对20种癌症类型中2469名患者的基因进行了基因组测序,以剖析免疫编辑的情况:不出所料,ioTNL评分可以预测接受免疫检查点抑制剂(ICI)免疫疗法的各种癌症患者的反应,包括非小细胞肺癌(NSCLC;p = 0.0066)、皮肤黑色素瘤(SKCM;p = 0.026)和鼻咽癌(NPC;p = 0.0025)。ioTNL评分高的患者生存期比评分低的患者长。我们通过面板测序验证了队列中的ioTNL,发现ioTNL与抗PD-(L)1单药治疗的反应(p = 0.025)和预后(p = 0.00082)相关。此外,我们还发现免疫编辑评分与肿瘤突变负荷(TMB)和免疫治疗的客观反应率相关:结论:识别具有诱导免疫清除能力的新抗原克隆可以更好地预测免疫疗法的疗效。我们证明了ioTNL的可靠方法可应用于全外显子组测序(WES)和面板数据,并将在免疫疗法的精准诊断中得到广泛应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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