Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response.
Integration of tumor inflammation, cell proliferation, and traditional biomarkers improves prediction of immunotherapy resistance and response.
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肿瘤炎症、细胞增殖和传统生物标志物的整合改善了对免疫疗法抗性和反应的预测。
DOI：
10.1186/s40364-021-00308-6
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发表时间：
2021-07-07
影响因子：
11.1
通讯作者：
Conroy JM
中科院分区：
文献类型：
--
作者：
Pabla S;Seager RJ;Van Roey E;Gao S;Hoefer C;Nesline MK;DePietro P;Burgher B;Andreas J;Giamo V;Wang Y;Lenzo FL;Schoenborn M;Zhang S;Klein R;Glenn ST;Conroy JM
与癌症免疫疗法快速发展的形势同时出现的是对免疫肿瘤微环境（TME）的理解也在不断变化，这对这些疗法的成功至关重要。它们依赖于强大的宿主免疫反应，因此在诊断时需要对免疫TME进行临床级别的检测。在这项研究中，我们描述了一种稳定的肿瘤免疫原性特征，它描述了多种肿瘤类型中的免疫TME，并能够预测免疫检查点抑制剂（ICIs）的临床获益。
通过对涵盖35种组织学类型的1323例临床实体瘤病例进行无监督分析，从靶向RNA测序（RNA - seq）和基因表达分析中得出了一种肿瘤免疫原性特征标记（TIGS）。在非小细胞肺癌、黑色素瘤和肾细胞癌肿瘤组织块的回顾性队列中，单独以及结合肿瘤突变负荷（TMB）、程序性死亡受体配体1（PD - L1）免疫组化和细胞增殖生物标志物，评估了TIGS与ICI反应和生存的相关性。
RNA - seq图谱的无监督聚类揭示了一个161个基因的特征标记，其中T细胞和B细胞激活、干扰素γ、趋化因子、细胞因子和白细胞介素通路过度表达。这些基因的平均表达产生了三个不同的TIGS评分类别：强（n = 384/1323；29.02%）、中（n = 354/1323；26.76%）和弱（n = 585/1323；44.22%）。强TIGS肿瘤的ICI反应率提高到37%（30/81）；在非小细胞肺癌中反应率优势最高（客观缓解率 = 36.6%；16/44；p = 0.051）。同样，强TIGS肿瘤的总生存期呈上升趋势（中位生存期 = 25个月；p = 0.19）。将TIGS评分类别与通过细胞增殖量化的肿瘤影响相结合，结果显示高增殖且强TIGS的肿瘤与低增殖且弱TIGS的肿瘤相比，ICI客观缓解率显著更高[14.28%；p = 0.0006]。重要的是，我们注意到强TIGS且高[中位生存期 = 未达到；p = 0.025]或中[中位生存期 = 16.2个月；p = 0.025]增殖的肿瘤与弱TIGS、高增殖肿瘤[中位生存期 = 7.03个月]相比，生存期显著更好。重要的是，TIGS能够区分出那些被TMB和PD - L1判定为无反应的潜在ICI应答者亚群。
TIGS是一种对免疫TME全面且信息丰富的检测方法，它有效地描述了多种肿瘤中宿主对ICIs的免疫反应。结果表明，当与PD - L1、TMB和细胞增殖相结合时，TIGS为TME上的免疫和肿瘤影响提供了更全面的背景信息，以便应用于临床实践。
在线版本包含补充材料，可在10.1186/s40364 - 021 - 00308 - 6获取。
Contemporary to the rapidly evolving landscape of cancer immunotherapy is the equally changing understanding of immune tumor microenvironments (TMEs) which is crucial to the success of these therapies. Their reliance on a robust host immune response necessitates clinical grade measurements of immune TMEs at diagnosis. In this study, we describe a stable tumor immunogenic profile describing immune TMEs in multiple tumor types with ability to predict clinical benefit from immune checkpoint inhibitors (ICIs). A tumor immunogenic signature (TIGS) was derived from targeted RNA-sequencing (RNA-seq) and gene expression analysis of 1323 clinical solid tumor cases spanning 35 histologies using unsupervised analysis. TIGS correlation with ICI response and survival was assessed in a retrospective cohort of NSCLC, melanoma and RCC tumor blocks, alone and combined with TMB, PD-L1 IHC and cell proliferation biomarkers. Unsupervised clustering of RNA-seq profiles uncovered a 161 gene signature where T cell and B cell activation, IFNg, chemokine, cytokine and interleukin pathways are over-represented. Mean expression of these genes produced three distinct TIGS score categories: strong (n = 384/1323; 29.02%), moderate (n = 354/1323; 26.76%), and weak (n = 585/1323; 44.22%). Strong TIGS tumors presented an improved ICI response rate of 37% (30/81); with highest response rate advantage occurring in NSCLC (ORR = 36.6%; 16/44; p = 0.051). Similarly, overall survival for strong TIGS tumors trended upward (median = 25 months; p = 0.19). Integrating the TIGS score categories with neoplastic influence quantified via cell proliferation showed highly proliferative and strong TIGS tumors correlate with significantly higher ICI ORR than poorly proliferative and weak TIGS tumors [14.28%; p = 0.0006]. Importantly, we noted that strong TIGS and highly [median = not achieved; p = 0.025] or moderately [median = 16.2 months; p = 0.025] proliferative tumors had significantly better survival compared to weak TIGS, highly proliferative tumors [median = 7.03 months]. Importantly, TIGS discriminates subpopulations of potential ICI responders that were considered negative for response by TMB and PD-L1. TIGS is a comprehensive and informative measurement of immune TME that effectively characterizes host immune response to ICIs in multiple tumors. The results indicate that when combined with PD-L1, TMB and cell proliferation, TIGS provides greater context of both immune and neoplastic influences on the TME for implementation into clinical practice. The online version contains supplementary material available at 10.1186/s40364-021-00308-6.
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影响因子：
10.9
作者：
Morrison C;Pabla S;Conroy JM;Nesline MK;Glenn ST;Dressman D;Papanicolau-Sengos A;Burgher B;Andreas J;Giamo V;Qin M;Wang Y;Lenzo FL;Omilian A;Bshara W;Zibelman M;Ghatalia P;Dragnev K;Shirai K;Madden KG;Tafe LJ;Shah N;Kasuganti D;de la Cruz-Merino L;Araujo I;Saenger Y;Bogardus M;Villalona-Calero M;Diaz Z;Day R;Eisenberg M;Anderson SM;Puzanov I;Galluzzi L;Gardner M;Ernstoff MS
通讯作者：
Ernstoff MS
影响因子：
13.8
作者：
Yu, Yunfang;Zeng, Dongqiang;Yao, Herui
通讯作者：
Yao, Herui
DOI：
10.1126/science.aar3593
发表时间：
2018-10-12
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Cristescu R;Mogg R;Ayers M;Albright A;Murphy E;Yearley J;Sher X;Liu XQ;Lu H;Nebozhyn M;Zhang C;Lunceford JK;Joe A;Cheng J;Webber AL;Ibrahim N;Plimack ER;Ott PA;Seiwert TY;Ribas A;McClanahan TK;Tomassini JE;Loboda A;Kaufman D
通讯作者：
Kaufman D
影响因子：
7.2
作者：
Zhang, Tian;Pabla, Sarabjot;Morrison, Carl
通讯作者：
Morrison, Carl
影响因子：
15.9
作者：
Ayers, Mark;Lunceford, Jared;McClanahan, Terrill K.
通讯作者：
McClanahan, Terrill K.