Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy.
Pan-tumor genomic biomarkers for PD-1 checkpoint blockade-based immunotherapy.
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DOI：
10.1126/science.aar3593
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发表时间：
2018-10-12
期刊：
影响因子：
--
通讯作者：
Kaufman D
中科院分区：
文献类型：
--
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
针对程序性细胞死亡蛋白1（PD-1）轴的免疫疗法会在多种癌症中引起持久的抗肿瘤反应，但是，临床反应变化，并且可以预测反应的生物标志物可以识别出获得最大治疗效果的患者生物标志物可以预测对抗PD-1单克隆抗体pembrolizumab的反应，包括特定癌症中的PD-1配体1（PD-L1）表达微卫星不稳定性（MSI-H），无论肿瘤突变燃烧（TMB）和T细胞 - 内基因表达谱（GEP）是PD-L1和GEP的新兴生物标志物。 - 肿瘤微环境（TME），而TMB和MSI-H是由体细胞突变产生的肿瘤抗原性的间接度量。生物标志物的类别没有很好地表征。
这项研究评估了TMB和T细胞 - INFLEAME GEP的潜力，可以在> 300个患者样本中预测pembrolizumab的临床反应，并在四个Keynote临床试验中进行了22种肿瘤类型的患者样品。在TMB和GEP中，患者在四个生物标志物定义的临床反应组中分层[GEP低和TMB低（GEPLO TMBLO），GEP低TMB高（GEPLO TMBHI），GEPHI TMBLO和GEPHI TMBHI]基于TMB和GEP的预定级临界值，这些患者定义的生物标志物进一步用于指导大型分子数据库中的肿瘤的转录组和外显子。地图集（TCGA）]（n = 6384肿瘤），以识别可能调节反应和抗性的生物学模式。
TMB和GEP仅暴露了适度的相关性，并且在主题演讲中的反应独立预测，我们发现Gephi TMBHI患者的客观反应率很高（37至57％） ）和GEPLO TMBHI（11％至42％），在Geplo TMBLO（0到9％）中减少或不存在（请参阅图）。当TMB和PD-L1表达在TCGA数据库中共同评估时，TMB和GEP水平较高的患者都可以观察到。跨癌症的特定基因表达模式反映了TME生物学的特征，显示了与TMB，GEP或两者的显着关联在TMBHI肿瘤中，关键癌症驱动基因的指示依赖性体细胞DNA改变与GEP有很强的负相关性。
该分析表明，TMB和炎性生物标志物（T细胞 - 燃料INFLANED GEP和PD-L1表达）可以将人类癌症分为对Pembrolizumab单一疗法的临床反应不同的组，并识别与这些组相关的潜在的，可靶向的生物学的模式生物标志物可以独立预测反应，并可能分别捕获新抗原性和T细胞激活的不同特征。用于合理构建和评估抗PD-1 –和/或基于PD-L1的组合疗法治疗方案的精确医学框架。
程序性细胞死亡蛋白– 1（PD-1）和程序性细胞死亡配体– 1（PD-L1）检查点封锁免疫疗法在多种癌症中引起持久的抗肿瘤作用，但并非所有患者都会做出反应。我们报告评估> 300例患者样本22种来自四个主题临床试验的肿瘤类型。Tumor突变伯嫩（TMB）和TCELL-炎症基因表达谱（GEP）暴露的关节预测效用，以识别响应者和对PD-1抗体Pembrolizumab.TMB和GEP的反应是独立预测响应的，并且表现出低相关性，表明它们捕获了新抗原性的不同特征，对癌症基因组数据库数据库和GEP的分析具有低疗法。 ，通过联合分层的分析揭示了靶向抗性生物学的生物标志物定义的模式。这些生物标志物可能在临床上具有实用性试验设计通过指导抗PD-1单一疗法和联合免疫疗法方案的合理选择。
生物标志物对pembrolizumab单一疗法的反应鉴定了靶向抗性生物学T细胞存在TME，低TMB的基质和/或内皮因子，而Neoantigenicity却阻碍了其活性高。新抗原性和一个T细胞浸入TME，其含有活化的T细胞和其他具有细胞溶解作用的免疫细胞。
Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types. However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit. Clinically validated biomarkers predictive of response to the anti–PD-1 monoclonal antibody pembrolizumab include PD-1 ligand 1 (PD-L1) expression in specific cancers and high microsatellite instability (MSI-H) regardless of tumor type. Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab. Both PD-L1 and GEP are inflammatory biomarkers indicative of a T cell–inflamed tumor microenvironment (TME), whereas TMB and MSI-H are indirect measures of tumor antigenicity generated by somatic tumor mutations. However, the relationship between these two categories of biomarkers is not well characterized. This study assessed the potential for TMB and a T cell–inflamed GEP to jointly predict clinical response to pembrolizumab in >300 patient samples with advanced solid tumors and melanoma across 22 tumor types from four KEYNOTE clinical trials. To assess the individual and joint clinical utility of TMB and GEP, patients were stratified in four biomarker–defined clinical response groups [GEP low and TMB low (GEPlo TMBlo), GEP low and TMB high (GEPlo TMBhi), GEPhi TMBlo, and GEPhi TMBhi] based on predefined cutoffs for TMB and GEP. These patient–defined biomarker groups were further used to guide transcriptome and exome analyses of tumors in a large molecular database [The Cancer Genome Atlas (TCGA)] (n = 6384 tumors) to identify targetable patterns of biology that may modulate response and resistance. TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets. We found that objective response rates were strongest in patients with GEPhi TMBhi (37 to 57%), moderate in those with GEPhi TMBlo (12 to 35%) and GEPlo TMBhi (11 to 42%), and reduced or absent in those with GEPlo TMBlo (0 to 9%) (see the figure). Additionally, longer progression–free survival times were seen in patients with higher levels of both TMB and GEP. Findings were comparable when TMB and PD-L1 expression were jointly assessed. Within TCGA database, GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types. Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both. In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors. In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP. This analysis shows that TMB and inflammatory biomarkers (T cell–inflamed GEP and PD-L1 expression) can jointly stratify human cancers into groups with different clinical responses to pembrolizumab monotherapy and identify patterns of underlying, targetable biology related to these groups. TMB and inflammatory biomarkers independently predict response and may capture distinct features of neoantigenicity and T cell activation, respectively. This approach may provide a precision medicine framework for rationally constructing and evaluating anti–PD-1– and/or –PD-L1–based combination therapy regimens. Programmed cell death protein–1 (PD-1) and programmed cell death ligand–1 (PD-L1) checkpoint blockade immunotherapy elicits durable antitumor effects in multiple cancers, yet not all patients respond.We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials.Tumor mutational burden (TMB) and a Tcell–inflamed gene expression profile (GEP) exhibited joint predictive utility in identifying responders and nonresponders to the PD-1 antibody pembrolizumab.TMB and GEP were independently predictive of response and demonstrated low correlation, suggesting that they capture distinct features of neoantigenicity and Tcell activation. Analysis of The Cancer Genome Atlas database showed TMB and GEP to have a low correlation, and analysis by joint stratification revealed biomarker-defined patterns of targetable-resistance biology.These biomarkers may have utility in clinical trial design by guiding rational selection of anti–PD-1 monotherapy and combination immunotherapy regimens. Biomarker-defined responses to pembrolizumab monotherapy identify targetable resistance biology.(A) Tumors have low TMB and low neoantigenicity and lack a T cell–inflamed TME. (B) Tumors can evade the immune response despite high TMB and high neoantigenicity. (C) Although T cells are present, stromal and/or endothelial factors in the TME, low TMB, and low neoantigenicity impede their activity. (D) Tumors have high TMB, high neoantigenicity, and a T cell–inflamed TME, typified by activated T cells and other immune cells with cytolytic roles.
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DOI：
10.1093/annonc/mdu479
发表时间：
2015-01
期刊：
Annals of oncology : official journal of the European Society for Medical Oncology
影响因子：
--
作者：
Favero F;Joshi T;Marquard AM;Birkbak NJ;Krzystanek M;Li Q;Szallasi Z;Eklund AC
通讯作者：
Eklund AC
影响因子：
7.2
作者：
Lu, Chunwan;Redd, Priscilla S.;Liu, Kebin
通讯作者：
Liu, Kebin
影响因子：
10.1
作者：
Johnson DB;Frampton GM;Rioth MJ;Yusko E;Xu Y;Guo X;Ennis RC;Fabrizio D;Chalmers ZR;Greenbowe J;Ali SM;Balasubramanian S;Sun JX;He Y;Frederick DT;Puzanov I;Balko JM;Cates JM;Ross JS;Sanders C;Robins H;Shyr Y;Miller VA;Stephens PJ;Sullivan RJ;Sosman JA;Lovly CM
通讯作者：
Lovly CM
影响因子：
64.8
作者：
Matsushita, Hirokazu;Vesely, Matthew D.;Koboldt, Daniel C.;Rickert, Charles G.;Uppaluri, Ravindra;Magrini, Vincent J.;Arthur, Cora D.;White, J. Michael;Chen, Yee-Shiuan;Shea, Lauren K.;Hundal, Jasreet;Wendl, Michael C.;Demeter, Ryan;Wylie, Todd;Allison, James P.;Smyth, Mark J.;Old, Lloyd J.;Mardis, Elaine R.;Schreiber, Robert D.
通讯作者：
Schreiber, Robert D.
影响因子：
4
作者：
Alexandrov, Ludmil B.;Stratton, Michael R.
通讯作者：
Stratton, Michael R.