Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma.
Predicting prognosis and immunotherapeutic response of clear cell renal cell carcinoma.
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肾透明细胞癌的预后预测及免疫反应。
DOI：
10.3389/fphar.2022.984080
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发表时间：
2022
影响因子：
5.6
通讯作者：
中科院分区：
文献类型：
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作者：
免疫检查点抑制剂已经成为许多不同肿瘤的一种新的治疗策略，包括肾透明细胞癌(CcRCC)。然而，这些药物仅对部分慢性肾细胞癌患者有效，并可产生广泛的免疫相关不良反应。以往的研究发现，肾细胞癌不同于其他肿瘤，肿瘤突变负荷、人类白细胞抗原类型、免疫浸润程度等常见生物标志物不能预测肾细胞癌对免疫治疗的反应。因此，有必要进一步研究和构建相应的临床预测模型来预测免疫检查点抑制剂的疗效。我们整合了123例晚期肾细胞癌患者的PBRM1突变数据、转录组数据、内源性逆转录病毒数据和基因拷贝数数据，这些患者参与了PD-1抑制剂的前瞻性临床试验(包括Check Mate 009、Check Mate 010和Check Mate 025试验)。我们使用人工智能来优化突变数据的解释，并建立了临床预测模型，预测生存率(总生存期AUC：0.931；无进展生存期AUC：0.795)和对免疫治疗的反应(ORRAUC：0.763)。通过Bootstrap对模型进行了内部验证。对于诺模图模型，也生成了拟合良好的校准曲线。我们的模型在预测慢性肾细胞癌的生存和对免疫治疗的反应方面表现出良好的性能。
Immune checkpoint inhibitors have emerged as a novel therapeutic strategy for many different tumors, including clear cell renal cell carcinoma (ccRCC). However, these drugs are only effective in some ccRCC patients, and can produce a wide range of immune-related adverse reactions. Previous studies have found that ccRCC is different from other tumors, and common biomarkers such as tumor mutational burden, HLA type, and degree of immunological infiltration cannot predict the response of ccRCC to immunotherapy. Therefore, it is necessary to further research and construct corresponding clinical prediction models to predict the efficacy of Immune checkpoint inhibitors. We integrated PBRM1 mutation data, transcriptome data, endogenous retrovirus data, and gene copy number data from 123 patients with advanced ccRCC who participated in prospective clinical trials of PD-1 inhibitors (including CheckMate 009, CheckMate 010, and CheckMate 025 trials). We used AI to optimize mutation data interpretation and established clinical prediction models for survival (for overall survival AUC: 0.931; for progression-free survival AUC: 0.795) and response (ORR AUC: 0.763) to immunotherapy of ccRCC. The models were internally validated by bootstrap. Well-fitted calibration curves were also generated for the nomogram models. Our models showed good performance in predicting survival and response to immunotherapy of ccRCC.
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影响因子：
5.8
作者：
Miao D;Shi J;Xiong Z;Xiao W;Meng X;Lv Q;Xie K;Yang H;Zhang X
通讯作者：
Zhang X
影响因子：
50.3
作者：
Au L;Hatipoglu E;Robert de Massy M;Litchfield K;Beattie G;Rowan A;Schnidrig D;Thompson R;Byrne F;Horswell S;Fotiadis N;Hazell S;Nicol D;Shepherd STC;Fendler A;Mason R;Del Rosario L;Edmonds K;Lingard K;Sarker S;Mangwende M;Carlyle E;Attig J;Joshi K;Uddin I;Becker PD;Sunderland MW;Akarca A;Puccio I;Yang WW;Lund T;Dhillon K;Vasquez MD;Ghorani E;Xu H;Spencer C;López JI;Green A;Mahadeva U;Borg E;Mitchison M;Moore DA;Proctor I;Falzon M;Pickering L;Furness AJS;Reading JL;Salgado R;Marafioti T;Jamal-Hanjani M;PEACE Consortium;Kassiotis G;Chain B;Larkin J;Swanton C;Quezada SA;Turajlic S;TRACERx Renal Consortium
通讯作者：
TRACERx Renal Consortium
DOI：
10.1126/science.aan5951
发表时间：
2018-02-16
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Miao D;Margolis CA;Gao W;Voss MH;Li W;Martini DJ;Norton C;Bossé D;Wankowicz SM;Cullen D;Horak C;Wind-Rotolo M;Tracy A;Giannakis M;Hodi FS;Drake CG;Ball MW;Allaf ME;Snyder A;Hellmann MD;Ho T;Motzer RJ;Signoretti S;Kaelin WG Jr;Choueiri TK;Van Allen EM
通讯作者：
Van Allen EM
影响因子：
9.8
作者：
Jordan, Daniel M.;Kiezun, Adam;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.
DOI：
10.1158/1078-0432.ccr-15-2839
发表时间：
2016-11-15
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Choueiri TK;Fishman MN;Escudier B;McDermott DF;Drake CG;Kluger H;Stadler WM;Perez-Gracia JL;McNeel DG;Curti B;Harrison MR;Plimack ER;Appleman L;Fong L;Albiges L;Cohen L;Young TC;Chasalow SD;Ross-Macdonald P;Srivastava S;Jure-Kunkel M;Kurland JF;Simon JS;Sznol M
通讯作者：
Sznol M