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检查点抑制剂治疗的黑色素瘤的多组学分析:确定反应和耐药的预测指标以及生物学不一致性的标志物。

Multiomic profiling of checkpoint inhibitor-treated melanoma: Identifying predictors of response and resistance, and markers of biological discordance.

作者信息

Newell Felicity, Pires da Silva Ines, Johansson Peter A, Menzies Alexander M, Wilmott James S, Addala Venkateswar, Carlino Matteo S, Rizos Helen, Nones Katia, Edwards Jarem J, Lakis Vanessa, Kazakoff Stephen H, Mukhopadhyay Pamela, Ferguson Peter M, Leonard Conrad, Koufariotis Lambros T, Wood Scott, Blank Christian U, Thompson John F, Spillane Andrew J, Saw Robyn P M, Shannon Kerwin F, Pearson John V, Mann Graham J, Hayward Nicholas K, Scolyer Richard A, Waddell Nicola, Long Georgina V

机构信息

QIMR Berghofer Medical Research Institute, Brisbane, QLD 4006, Australia.

Melanoma Institute Australia, The University of Sydney, Sydney, NSW 2065, Australia; Charles Perkins Centre, The University of Sydney, Sydney, NSW 2006, Australia; Faculty of Medicine and Health, The University of Sydney, Sydney, NSW 2006, Australia; Cancer Centre, Blacktown Hospital, Sydney, NSW 2148, Australia.

出版信息

Cancer Cell. 2022 Jan 10;40(1):88-102.e7. doi: 10.1016/j.ccell.2021.11.012. Epub 2021 Dec 23.

Abstract

We concurrently examine the whole genome, transcriptome, methylome, and immune cell infiltrates in baseline tumors from 77 patients with advanced cutaneous melanoma treated with anti-PD-1 with or without anti-CTLA-4. We show that high tumor mutation burden (TMB), neoantigen load, expression of IFNγ-related genes, programmed death ligand expression, low PSMB8 methylation (therefore high expression), and T cells in the tumor microenvironment are associated with response to immunotherapy. No specific mutation correlates with therapy response. A multivariable model combining the TMB and IFNγ-related gene expression robustly predicts response (89% sensitivity, 53% specificity, area under the curve [AUC], 0.84); tumors with high TMB and a high IFNγ signature show the best response to immunotherapy. This model validates in an independent cohort (80% sensitivity, 59% specificity, AUC, 0.79). Except for a JAK3 loss-of-function mutation, for patients who did not respond as predicted there is no obvious biological mechanism that clearly explained their outlier status, consistent with intratumor and intertumor heterogeneity in response to immunotherapy.

摘要

我们同时检测了77例接受抗PD-1联合或不联合抗CTLA-4治疗的晚期皮肤黑色素瘤患者基线肿瘤的全基因组、转录组、甲基化组和免疫细胞浸润情况。我们发现,高肿瘤突变负荷(TMB)、新抗原负荷、IFNγ相关基因表达、程序性死亡配体表达、低PSMB8甲基化(因此高表达)以及肿瘤微环境中的T细胞与免疫治疗反应相关。没有特定突变与治疗反应相关。结合TMB和IFNγ相关基因表达的多变量模型能够可靠地预测反应(敏感性89%,特异性53%,曲线下面积[AUC]为0.84);TMB高且IFNγ特征高的肿瘤对免疫治疗反应最佳。该模型在一个独立队列中得到验证(敏感性80%,特异性59%,AUC为0.79)。除了一个JAK3功能丧失突变外,对于未按预测反应的患者,没有明显的生物学机制能够清楚地解释他们的异常状态,这与肿瘤内和肿瘤间对免疫治疗反应的异质性一致。

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