四月week4文献阅读1：Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy

基于阻断的PD-1检查点的免疫治疗泛肿瘤基因组生物标志物

INTRODUCTION：

Immunotherapy targeting the programmed cell death protein–1 (PD-1) axis elicits durable antitumor responses in multiple cancer types.

针对程序性细胞死亡蛋白-1 (PD-1)轴的免疫治疗可在多种癌症类型中引起持久的抗肿瘤反应。

However, clinical responses vary, and biomarkers predictive of response may help to identify patients who will derive the greatest therapeutic benefit.

然而，临床反应不同，生物标志物预测反应可能有助于确定谁将获得最大的治疗效益。

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.

临床验证的预测抗PD-1单克隆抗体pembrolizumab反应的生物标志物包括PD-1配体1 (PD-L1)在特定癌症中的表达以及无论肿瘤类型如何的高的微卫星不稳定性(MSI-H)。

Tumor mutational burden (TMB) and T cell–inflamed gene expression profile (GEP) are emerging predictive biomarkers for pembrolizumab.

肿瘤突变负担(TMB)和T细胞炎症基因表达谱(GEP)是pembrolizumab新的预测生物标志物。

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.

PD-L1和GEP都是T细胞炎症性肿瘤微环境(TME)的炎症标志物，而TMB和MSI-H则是由体细胞肿瘤突变产生的肿瘤抗原性的间接指标。

However, the relationship between these two categories of biomarkers is notwell characterized.

然而，这两类生物标志物之间的关系还没有很好地描述。

名词解释：
PD-1配体1 (PD-L1)在特定癌症中的表达，T细胞炎症基因表达谱(GEP)。
肿瘤突变负担(TMB)，高的微卫星不稳定性(MSI-H)。
PD-L1和GEP都是T细胞炎症性肿瘤微环境(TME)的炎症标志物，而TMB和MSI-H则是由体细胞肿瘤突变产生的肿瘤抗原性的间接指标。

RATIONALE 理论依据

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.

本研究评估了TMB和T细胞炎症性GEP联合预测pembrolizumab在>300例晚期实体瘤和黑色素瘤患者中的临床反应的潜力，这些患者来自4个主要临床试验的22种肿瘤类型。

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.

为了评估TMB和GEP的个体和联合临床效用，根据预先定义的TMB和GEP的截断值，将患者分为四个生物标志物定义的临床反应组[GEP低和TMB低(GEPlo TMBlo)，GEP低和TMB高(GEPlo TMBhi)， GEP高TMB低和GEPhi TMBhi]。

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.

这些患者定义的生物标志物组进一步用于指导大分子数据库中肿瘤的转录组和外显体分析[癌症基因组图谱(TCGA)] (n = 6384个肿瘤)，以确定可能调节反应和耐药性的生物学靶标模式。

RESULTS:

TMB and GEP exhibited only modest correlation and were independently predictive of response across the KEYNOTE clinical datasets.

TMB和GEP仅表现出适度的相关性，并独立预测基调临床数据集的反应。

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).

我们发现，GEP高 TMB高患者的客观反应率最高(37 - 57%)，GEP高 TMB低患者的客观反应率最低(12 - 35%)，GEP低TMB高患者的客观反应率最低(11 - 42%)，GEP低 TMB低患者的客观反应率最低(0 - 9%)(见图)。

Additionally, longer progression-free survival times were seen in patients with higher levels of both TMB and GEP.

此外，TMB和GEP水平较高的患者无进展生存时间更长。

Findings were comparable when TMB and PD-L1 expression were jointly assessed.

联合评估TMB和PD-L1表达时，结果具有可比性。

Within TCGA database,GEP and TMB again had a low correlation, demonstrating the potential to jointly stratify transcriptomic and genomic features across cancer types.

在TCGA数据库中，GEP和TMB再次具有较低的相关性，显示了跨癌症类型联合分层转录组和基因组特征的潜力。

Specific gene expression patterns reflective of TME biology showed significant associations with TMB, GEP, or both.

反映TME生物学的特定基因表达模式与TMB、GEP或两者均有显著关联。

In particular, gene set enrichment analysis identified proliferative and stromal, myeloid, and vascular biology corresponding to specific TMB-defined subgroups within GEPhi tumors.

特别是，基因集富集分析确定了与GEP高肿瘤中特定TMB定义的亚群相对应的增殖和基质、髓细胞和血管生物学。

In TMBhi tumors, indication-dependent somatic DNA alterations in key cancer driver genes showed a strong negative association with GEP.

在TMB高肿瘤中，关键肿瘤驱动基因的指示依赖性体细胞DNA改变与GEP呈显著负相关。

CONCLUSION:

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.

这一分析表明，TMB和炎症生物标志物(T细胞炎症性GEP和PD-L1表达)可以联合将人类癌症分为对pembrolizumab单药治疗有不同临床反应的组，并识别与这些组相关的潜在的、可靶向的生物学模式。

TMB and inflammatory biomarkers independently predict response and may capture distinct features of neoantigenicity and T cell activation, respectively.

TMB和炎症生物标志物可以独立预测反应，并可能分别捕捉到新抗原性和T细胞活化的不同特征。

This approach may provide a precision medicine framework for rationally constructing and evaluating anti–PD-1– and/or –PD-L1–based combination therapy regimens.

该方法可为合理构建和评价抗pd -1和/或- pd - l1联合治疗方案提供精确的医学框架。

Abstract

Programmed cell death protein–1 (PD-1) and programmed cell death ligand–1 (PD-L1) checkpoint blockade immunotherapyelicits durable antitumoreffects in multiple cancers, yet not all patients respond.

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)检查点阻断免疫治疗在多种癌症中具有持久的抗肿瘤作用，但并非所有患者都有反应。

We report the evaluation of >300 patient samples across 22 tumor types from four KEYNOTE clinical trials.

我们报告了来自四个主要临床试验的22种肿瘤类型的>300例患者样本的评估。

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.

肿瘤突变负担(TMB)和t细胞炎症基因表达谱(GEP)在识别PD-1抗体pembrolizumab应答者和无应答者方面显示出联合预测作用。

TMB and GEP were independently predictive of response and demonstrated low correlation,suggesting that they capture distinct features of neoantigenicity and T cell activation.

TMB和GEP对反应具有独立的预测作用，且相关性较低，说明它们捕获了新抗原性和T细胞活化的不同特征。

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.

对肿瘤基因组图谱数据库的分析表明，TMB与GEP相关性较低，联合分层分析揭示了生物标志物所定义的靶向性生物学模式。

These biomarkers may have utility in clinical trial design by guiding rational selection of anti–PD-1 monotherapy and combination immunotherapy regimens.

这些生物标志物可指导抗pd -1单药和联合免疫治疗方案的合理选择，在临床试验设计中具有实用价值。

(TMB和GEP对反应具有独立的预测,或联合预测，肿瘤突变负担(TMB)和t细胞炎症基因表达谱(GEP)在识别PD-1抗体pembrolizumab，预测抗PD-1单克隆抗体pembrolizumab反应，应答者和无应答者（客观反应率），相关性函数，各指标输入后的预测的反应结果（反应率的等级划分）)

• Emerging immune-relevant biomarkers for checkpoint blockade immunotherapy response can be placed broadly into two categories: those related to tumor neoepitope burden, such as microsatellite instability (MSI) or high tumor mutational burden (TMB), and those indicative of a T cell–inflamed tumor microenvironment (TME).

  检查点阻断免疫治疗反应的新兴免疫相关生物标志物可大致分为两类:一类是与肿瘤新表位负担相关的生物标志物，如微卫星不稳定性(MSI)或高肿瘤突变负担(TMB)，另一类是提示T细胞炎症性肿瘤微环境(TME)。

• The latter include programmed cell deathlig and–1(PD-L1)protein expression on tumor and immune cells, which in many cases is up-regulated in response to local T cell– derived interferon-g (IFN-g),and gene signatures of activated T cells (1–3).

  后者包括肿瘤细胞和免疫细胞上的程序性细胞死亡配体- 1(PD-L1)蛋白表达，在许多情况下，这种蛋白表达会随着局部T细胞来源的干扰素-g (IFN-g)和活化T细胞的基因信号而上调(1-3)。

• TMB is correlated with clinical response to cytotoxic T lymphocyte– associated antigen–4 blockade in advanced melanoma (4–6) and with anti–programmed cell deathprotein–1(PD-1)and/orPD-L1blockadein melanoma(7),non–smallcelllungcancer(NSCLC) (8, 9), colorectal and gastric cancers (10, 11), and urothelialcancer (12).

  TMB与晚期黑色素瘤(4-6)中细胞毒性T淋巴细胞相关抗原- 4阻断的临床反应相关，与抗程序性细胞死亡蛋白-1 (PD-1)和/orPD-L1blockadein黑色素瘤(7)、非小细胞肺癌(8,9)、结直肠癌和胃癌(10,11)、泌尿系癌(12)相关。

• Similarly, tumors with MSI thathavehighlevelsofbothsingle-nucleotideand frameshift mutations [high MSI (MSI-H)] are responsive to anti–PD-1 therapy in colorectal cancer and other malignancies (10, 11).

  同样，MSI高水平单核苷酸和移码突变[高MSI (MSI- h)]的肿瘤对结直肠癌和其他恶性肿瘤的抗pd -1治疗也有反应(10,11)。

  Expression of genes related to immunecytolytic activity have also been shown to be associated with clinical response to checkpoint blockade in certain tumors (13, 14).

  与免疫溶细胞活性相关的基因表达也被证明与某些肿瘤对检查点阻断的临床反应有关(13,14)。

  Recently, a T cell–inflamed gene expression profile (GEP) was shown to predict response to anti–PD-1–directed therapy (15).

  最近，T细胞炎症基因表达谱(GEP)被证明可以预测抗pd -1定向治疗的反应(15)。

• However,the inter play between these two distinct categories of biomarkers has not been well characterized across cancer types with respect to their ability either to independently or jointly predict response to immunotherapy or to reveal underlying genomic and/or transcriptomic features of tumor antigenicity and TME.

  然而，这两种截然不同的生物标志物之间的相互作用还没有被很好地跨类型描述，因为它们既能独立又能共同预测免疫治疗的反应，也能揭示肿瘤抗原性和TME的潜在基因组和/或转录组特征。

• We evaluated the relation ship between somatic TMB and clinical response to anti–PD-1 immuno therapy with pembrolizumab.

  我们评估了躯体TMB与pembrolizumab抗pd -1免疫治疗的临床反应之间的关系。

• Twenty-twocancer types were included in the discovery and validation cohorts and were analyzed for the independent and joint predictive values of TMB and T cell –inflamed GEP.

  在发现和验证组中纳入了20种肿瘤类型，并分析了TMB和T细胞炎症性GEP的独立和联合预测值。

• Additionally, by using large molecular databases [e.g.

  此外，通过使用大型分子数据库[例如。

• The Cancer Genome Atlas (TCGA) (16)],we explored transcriptomic and genetic features associated with the presence or absence of either of these two markers.

  在癌症基因组图谱(TCGA)(16)中，我们探索了存在或不存在这两种标记的转录组学和遗传学特征。

Study cohorts and tumor and mutation type

研究群体、肿瘤和突变类型

• The predictive values of TMB and the T cell– inflamed GEP were first assessed separately by rigorous stepwise testing in four cohorts of patients across the pembrolizumab clinical development program (one discovery, one pan-tumor validation, and two single-indication summary cohorts).

  分别评估TMB和T细胞炎症性GEP的预测值，首先通过严格的逐步测试四组患者在pembrolizumab临床发展项目(一个发现、一个泛肿瘤验证和两个单指征总结组)。

• TMB was evaluated by whole-exome sequencing (WES) of germline and tumor DNA, and the T cell–inflamed GEP was analyzed by targeted gene expression profiling of tumor RNA (with the NanoString platform) from formalinfixed, paraffin-embedded (FFPE) pretreatment samples.

采用生殖系和肿瘤DNA全外显子组测序(WES)评价TMB，采用石蜡包埋(FFPE)预处理样品肿瘤RNA靶向基因表达谱(NanoString platform)分析T细胞感染的GEP。

The initial discovery cohort for TMB comprised patients with PD-L1–positive head and neck squamous cell carcinoma (HNSCC) from a phase 1b clinical trial (KEYNOTE-012 B1 cohort;n = 34 patients), and the pan-tumor validation cohort consisted of patients with PD-L1–positive advanced solid tumors (n = 119 patients) from two multi cohort phase 1b trials across 20 cancer types[KEYNOTE-028(17cohorts;n=80patients) and KEYNOTE-012 (A, C, and D cohorts;n = 39 patients)].

初始发现TMB队列由PD-L1-positive患者头颈部鳞状细胞癌(HNSCC) 1 b期临床试验(主题- 012 B1组;n = 34名患者),和pan-tumor验证队列由PD-L1-positive的实体肿瘤患者(n = 119例)从两个多队列1 b阶段试验20个癌症类型(主题- 028(17组;n = 80名患者)和主题- 012 (a, C和D组,n = 39病人)]。

The HNSCC single-indication cohort (n=107patients)include dpatients in the phase 1b KEYNOTE-012 B1 cohort and additional patientswithPD-L1–unselected HNSCC(n=73patients) from the KEYNOTE-012 B2 cohort.

HNSCC单指征队列(n=107例患者)包括1b期KEYNOTE-012 B1组患者和来自KEYNOTE-012 B2组的pd - l1 -未选HNSCC患者(n=73例患者)。

The melanoma single-indication cohort included patients with advanced melanoma from the phase 1b(KEYNOTE-001;n=30patients)and the phase 3 (KEYNOTE-006 pembrolizumab arm;n = 59 patients)trials.

黑色素瘤单指征队列包括来自1b期(KEYNOTE-001;n=30例)和3期(KEYNOTE-006 pembrolizumab arm;n = 59例)试验的晚期黑色素瘤患者。

The clinical characteristics of each cohort are listed in table S1, and the characteristics of all patients included in this study are listed in table S2.

各队列的临床特征见表S1，本研究纳入的所有患者的临床特征见表S2。

The distribution of tumor mutational signatures across the study cohorts largely reflected recognized cancer subtype–dependent determinants of mutagenesis (17) (table S3 and fig. S1).

整个研究群体中肿瘤突变特征的分布在很大程度上反映了突变的公认的癌症亚型依赖性决定因素(17)(表S3和图S1)。

The dominant mutational signatures varied across tumor types in the pan-cancer cohort, with higher TMB associated with tissue-specific signatures, such as smoking in small cell lung cancer;apolipoprotein B mRNA editing enzyme, catalytic polypeptide–like (APOBEC) in genitourinary tumors;and mismatch repair (MMR) in gastrointestinal cancer.

显性突变签名在pan-cancer队列中肿瘤类型多样,具有较高TMB与组织相关的特征,如吸烟在小细胞肺癌;载脂蛋白B信使rna编辑酶,催化polypeptide-like (APOBEC)在泌尿系肿瘤;和错配修复(MMR)在胃肠道癌症。

Dominant signatures in the single-indication cohorts were more homogenous,with an APOBEC signature in the HNSCC cohort (61% of tumors) and an ultraviolet (UV) light exposure signature in melanoma (in 78% of the tumors, >30% of mutations were UV light induced).

单指征组的优势特征更为均匀，在HNSCC组中有APOBEC特征(61%的肿瘤)，在黑色素瘤中有紫外(UV)照射特征(78%的肿瘤中，30%的突变是由紫外线诱导的)。

Association of TMB and Tcell–inflamed GEP with clinical response

TMB和t细胞炎症性GEP与临床反应的关系

• Clinical response associations were assessed on the basis of best overall response (BOR) and progression-free survival (PFS) by RECIST 1.1.

  根据RECIST 1.1的最佳总体反应(BOR)和无进展生存(PFS)评估临床反应相关性。

  （总体反应和无进展的什么具体指标被定为有反应和无反应？）

• BOR and PFS associations with TMB and the Tcell–inflamed GEP were assessed in all patients who had WES and transcriptomic data available

  在所有有WES和转录组数据的患者中评估BOR和PFS与TMB和tcell炎症性GEP的关系

  We first assessed the predictive value of each individual genomic biomarker separately across the different cohorts.

  我们首先评估了不同群体中每个单独的基因组生物标志物的预测价值。

• In the HNSCCB1 discovery cohort,higher TMB predicted a greater frequency of clinical response (BOR) (P = 0.0123).

  在HNSCCB1发现队列中，较高的TMB预示着更高的临床反应频率(BOR) (P = 0.0123)。

• This was validated by using the pan-tumor cohort,in which TMB was again associated with BOR (P < 0.001) (Fig.1A).

  这是通过使用泛肿瘤队列证实的，其中TMB再次与BOR相关(P < 0.001)(图1A)。

  Higher Tcell–inflamed GEP scores were also positively associated with BOR in the pan-tumorcohort(P<0.01)(Fig.1B),showing that a T cell –activated tumor environment also affects response in addition to TMB.

  在泛肿瘤队列中，T细胞炎症性GEP评分较高也与BOR呈正相关(P<0.01)(图1B)，这表明T细胞激活的肿瘤环境除了影响TMB外，还影响反应。

  
  
  fig1AB

• Similarly,both TMB and T cell–inflamed GEP scores were positively associated with BOR in the single-indication cohorts of HNSCC (P < 0.05 and P < 0.001, respectively) and melanoma (P < 0.05 for both) patients (Fig.1,AandB).

  同样，在HNSCC单指征组(P < 0.05, P < 0.001)和黑色素瘤(P < 0.05)患者中，TMB和T细胞炎症性GEP评分均与BOR呈正相关(图1、AandB)。

  In this study,we did not evaluate the effect of human papillomavirus (HPV)antigens on the association of TMB with response in the HNSCC cohort;however,we have previously described the association of TMB with clinical outcome in a larger, overlapping group of HNSCC patients (KEYNOTE-012 B1 and B2 cohorts)stratified by HPV status(18).

  在这项研究中,我们没有评估的影响人类乳头状瘤病毒(HPV)抗原的联合与TMB响应在HNSCC队列反应;然而,我们曾描述了在一个更大的,重叠群HNSCC病人(主题- 012 B1和B2组)分层的人乳头状瘤病毒状态(18)TMB 的与临床结果的结合。

  Although we found that TMB was more strongly associated with BOR in HPV-negative patients than in HPV positive patients,those exploratory findings await validation in larger, independent studies

  虽然我们发现TMB在HPV阴性患者中与BOR的相关性比在HPV阳性患者中更强，但这些探索性的发现有待更大规模的独立研究的验证

  The clinical utility of TMB in predicting BOR was generally high, and degrees of utility were similar across cancer types,with areas under the receiveroperatingcharacteristiccurves(AUROCs) of 0.740, 0.617, and 0.602 in the pan-tumor, HNSCC, and melanoma cohorts, respectively.

  TMB在预测BOR方面的临床实用价值普遍较高，不同癌症类型的实用程度相似，在泛肿瘤、HNSCC和黑色素瘤患者中，接受手术特征曲线(AUROCs)下的区域分别为0.740、0.617和0.602。

  Similar results were observed for the T cell– inflamed GEP across the cohorts (AUROCs = 0.782,0.768,and0.638,respectively)(Fig.1C).

  各组T细胞炎症性GEP的结果相似(AUROCs = 0.782,0.768, 0.638)(图1c)。

  The potential performance of a targeted sequencing– based TMB assay was simulated by using the genes in the Foundation Medicine targeted sequencing platform(19).The corresponding AUROC across the cohorts was comparable to that observed by usingWES(0.721),suggesting potential translatability to a targeted panel diagnostic.

  利用基础医学靶向测序平台(19)中的基因，模拟了基于靶向测序的TMB检测的潜在性能与各组中相应的AUROC值与WES观察值相当(0.721)，表明其潜在的可译性可用于靶板诊断。

  Taken together,these data imply that both TMB and the Tcell–inflamed GEP have comparable performance characteristics and potential diagnostic utility

  综上所述，这些数据表明TMB和tcell炎症性GEP具有相似的性能特征和潜在的诊断价值

We next evaluated the joint utility of the two genomic biomarkers in predicting response.

接下来，我们评估了这两种基因组生物标志物在预测反应中的联合效用。

The correlation between TMB and GEP was low in the pan-tumor and melanoma cohorts(Spearman correlation coefficient r = 0.221, P < 0.05, andr = 0.252, P < 0.05, respectively), and there was no correlation in the HNSCC cohort (r =−0.020, P = 0.841)(Fig.2A).

在泛肿瘤组和黑色素瘤组中，TMB与GEP的相关性较低(Spearman相关系数r = 0.221, P < 0.05, r = 0.252, P < 0.05)，在HNSCC组中无相关性(r = - 0.020, P = 0.841)(图2a)。

fig2AB

This lack of correlation,combined with the observed individual predictive values, suggested that TMB and theTcell–inflamed GEP are independent predictive measures of response to pembrolizumab.

这种相关性的缺乏，结合观察到的个体预测值，表明TMB和tcell炎症的GEP是对pembrolizumab反应的独立预测措施。

When tested in a multivariate model adjusted for each measure, both TMB and T cell–inflamed GEP retained significant predictive value in the pan-tumor(P=0.0028and0.0051, respectively) and HNSCC (P = 0.0013 and 0.0004) cohorts, whereas only GEP remained significant in the melanoma cohort (P = 0.1644 and 0.026).

在对每一项指标进行调整的多元模型中进行测试时，TMB和T细胞炎症性GEP在泛肿瘤(P=0.0028和0.0051)和HNSCC (P= 0.0013和0.0004)组中均保留了显著的预测价值，而在黑色素瘤组中只有GEP保持显著的预测价值(P= 0.1644和0.026)。

Although a portion of the patients in this study were PD-L1 selected, these relationships were observed even in those cohorts of patients that were not PD-L1 selected.

虽然本研究中有一部分患者选择PD-L1，但即使在未选择PD-L1的患者中也观察到了这些关系。

• We evaluated the association of the genomic biomarkers with PD-L1 immunohistochemistry (IHC) scores (fig. S2).

  我们评估了基因组生物标志物与PD-L1免疫组化(IHC)评分的相关性(图S2)。

  TMB was significantly but moderately correlated with PD-L1 in the pan tumor cohort[combinedpositivescore(CPS),r= 0.330;P = 0.0038] and showed no association withPD-L1 in the HNSCC cohort(CPS, r=0.020;P = 0.8084) or in the melanoma cohort [melanoma (MEL) score, r = 0.049;P = 0.6473].

  在泛肿瘤队列中，TMB与PD-L1显著但中度相关[联合阳性(CPS)，r= 0.330;P = 0.0038]，在HNSCC队列中，TMB与PD-L1无相关性(CPS, r=0.020;或在黑色素瘤队列中[黑色素瘤(MEL)评分，r = 0.049;P = 0.6473)。

  In contrast, GEP was more significantly correlated with PD-L1 in the pan-tumor, HNSCC, and melanoma cohorts (r = 0.49, 0.51, and 0.53, respectively;all P values < 0.001), consistent with the known regulation of PD-L1 gene expression by T cell–derived IFN-g (1–3).

  相比之下，GEP与泛肿瘤组、HNSCC组和黑色素瘤组PD-L1的相关性更显著(r分别为0.49、0.51和0.53;所有P值均< 0.001)，与T细胞来源的IFN-g调控PD-L1基因表达的已知规律一致(1-3)。

  This correlation suggests that a PD-L1 IHC–based assay is relevant in assessing a T cell–inflamed TME.

  这种相关性表明，基于PD-L1 IHC的检测与评估T细胞感染的TME有关。

  As seen with high TMB(TMBhi) and high GEP scores (GEPhi), responses in patients who had both TMBhi and greater PD-L1 expression (PD-L1+;CPS≥1) were greater than those in patients who had low levels of both TMB and PD-L1 expression.

  从高TMB(TMBhi)和高GEP评分(GEPhi)可以看出，TMB高和PD-L1表达(PD-L1+)同时存在的患者的反应;CPS≥1)明显高于TMB和PD-L1表达水平较低的患者。

• We next studied the potential joint utility of TMB and GEP for patient stratification and treatment outcome prediction.

  接下来，我们研究了TMB和GEP在患者分层和治疗结果预测方面的潜在联合应用。

  Clinical response was evaluated on the basis of cut points associated with the Youden Index (derived from the AUROCs for TMB in each cohort) and a discovery cutoff of −0.318 for the T cell–inflamed GEP score (selected via analysis of pan-cancer data) (15).

  临床反应的评估基于与Youden指数相关的切点(来自每个队列中TMB的AUROCs)和发现T细胞炎症的GEP评分的- 0.318截止点(通过分析泛癌数据选择)(15)。

  Rates of response to pembrolizumab were greater in patients with TMBhi (greater than or equal to Youden Index cut points)than in those with low TMB (TMBlo) (less than Youden Index cut points) and were similarly greater for those with higher T cell–inflamed GEP scores (greater than or equal to the cutoff of −0.318) than for those with lower scores (less than the −0.318 cutoff) (Fig. 2B).

  TMBhi患者(大于或等于Youden指数减少点)pembrolizumab反应率要大于那些(TMBlo)低的(少于Youden指数减少点),同样大的得分更高的T cell-inflamed GEP(大于或等于截止−0.318)大于那些成绩差的(小于−0.318截止)(图2 b)。

  The highest objective response rate was observed for patients within each cohort who had both TMBhi and GEPhi.

  在每个队列中，同时患有TMBhi和GEPhi的患者的客观反应率最高。

  Additionally, among patients with both TMBlo andlowTcell–inflamed GEP scores(GEPlo),no responses were observed in the pan-tumor and HNSCC cohorts and only one response was observed in the melanoma cohort, suggesting greater sensitivity for the combination of biomarkers.

  此外，在TMB低和低细胞炎症性GEP评分(GEPlo)患者中，泛肿瘤组和HNSCC组均未观察到反应，而黑色素瘤组仅观察到一种反应，这表明对生物标志物组合的敏感性更高。

  Patients who had high scores for only one of the biomarkers (TMBlo GEPhi and TMBhi GEPlo) had moderate responses (Fig. 2B).

  只有一种生物标志物(TMBlo GEPhi和TMBhi GEPlo)得分较高的患者反应中等(图2B)。

  These data suggest the potential for greater positive and negative predictive value when these biomarkers are used together in the setting of PD-1– directed monotherapy

  这些数据表明，当这些生物标志物同时用于PD-1定向单药治疗时，可能具有更大的阳性和阴性预测值
  

  fig2B

Patient stratification by TMB and GEP was also differentially associated with PFS.

患者TMB和GEP分层与PFS也有差异。

In all three cohorts, hazard ratios associated with PFS were<1.0(implyingPFSbenefit)among patients with high versus low TMB and high versus low Tcell–inflamed GEP scores.

在所有三个队列中，与PFS相关的危险比在TMB高与低、tcell炎症的GEP评分高与低的患者中均<1.0(暗pfsbenefit)。

The most pronounced PFS-associated hazardratios were observed for TMBhi GEPhi tumors in the pan-tumor (Fig. 3A), HNSCC (Fig. 3B), and melanoma cohorts (Fig. 3C).

在泛肿瘤(图3A)、HNSCC(图3B)和黑色素瘤(图3C)中，最显著的与pfs相关的hazardratios被观察到用于TMBhi GEPhi肿瘤。

fig3AB

fig3C

The greatest differential was observed in eachcohortforpatientswithTMBhi GEPhi versus patients with TMBlo GEPlo.

最大的差异出现在患有tmbhi GEPhi的患者与患有TMBlo GEPlo的患者之间。

Patients who had greater levels of either TMB or GEP (TMBhi or GEPhi) versus low levels of these biomarkers (TMBlo or GEPlo) also had longer PFS

TMB或GEP (TMBhi或GEPhi)水平较高的患者与这些生物标志物(TMBlo或GEPlo)水平较低的患者相比，PFS也较长。

We also explored the feasibility and potential clinical value of identifying a pan-cancer threshold for TMB across our cohorts that maximizes its joint predictive utility with GEP by using a method similar to that of Panda et al.(20).

我们还通过与Panda等人(20)类似的方法，探索了在我们的研究群体中确定TMB的泛癌阈值的可行性和潜在的临床价值，该阈值可以最大化其与GEP的联合预测效用。

A TMB cutoff of ≥123 mutations per exome maximized the effect size of the difference in GEP distributions between tumors having TMB less than and greater than the cutoff.

每个外显子突变数≥123的TMB截断使肿瘤中TMB小于或大于截断值的GEP分布差异的效应大小最大化。

The response rates to pembrolizumab in theTMB-GEP–defined groups of each clinical cohort were comparable tothoseobservedbyusingthecohort-specificcut points for TMB reported above (fig. S3).

在每个临床队列的TMB- gep定义的组中，pembrolizumab的应答率与使用上述TMB的特定切点观察到的应答率相当(图S3)。

The hazard ratios observed for PFS were also generally similar with the use of the TMB cutoff of ≥123mutations per exome(fig.S4).

使用每个外显子组≥123个突变的TMB截止值，观察到PFS的危险比也大致相似(图s4)。

Apan-tumor threshold may be further optimized with the availability of additional data beyond those in ourstudy.

在我们的研究之外，随着更多数据的可用性，pan-tumor阈值可能会进一步优化。

For example,apan-tumorTMB threshold of ≥175 mutations per exome was recently reported for response to pembrolizumab (21).

例如，最近报道了每个外显子组≥175个突变的apan-tumorTMB阈值对pembrolizumab的应答(21)。

** (TMB,TME,预测反应应答，用PFC,BOR评估，分肿瘤类型)**

Association of other DNA-based measures with response

其他基于dna的措施与反应的关联

• The predictive value of other DNA-based measuresofmutationstatusinrelationtoresponse was also evaluated in these cohorts, including predicted neoantigen signature, smoking status, APOBEC-driven mutations, UV light exposure, DNA transversions, homologous recombination deficiency, and MSI.

  其他基于DNA的突变状态与反应无关的预测价值也在这些队列中进行了评估，包括预测的新抗原特征、吸烟状况、中风导致的突变、紫外线照射、DNA转位、同源重组缺陷和MSI。

  Aside from MSI, none of thesespecificmeasuresof geneticalterationprovided additional meaningful improvement in predictive value over TMB assessment alone.

  除了MSI，没有任何一种特别的基因改变测量方法比TMB评估提供了额外的有意义的预测价值的改善。

• The predicted neoantigen load was highly correlated with TMB in the pan-tumor, HNSCC, and melanoma cohorts (r = 0.87, 0.83, and 0.90, respectively), as expected (fig. S5).

  在泛肿瘤组、HNSCC组和黑色素瘤组中，预测的新抗原载量与TMB高度相关(r分别为0.87、0.83和0.90)，与预期一致(图S5)。

  In the pan-tumor cohort, most measures of mutagenic processes were significantly associated with BOR (e.g., predicted neoantigen load and smoking;both P values = 0.001), with similar relevant trends toward significant association with PFS (table S4).

  在泛肿瘤队列中，大多数诱变过程的测量与BOR显著相关(例如，预测新抗原载量和吸烟;两个P值均= 0.001)，与PFS显著相关的趋势相似(表S4)。

  By using a WES-based method to infer MSI (22), two patients with MSI-H tumors (gastric and biliary tractcarcinomas) wereidentified,and both were responders;the MSI status of these patients was confirmed with standard MSI polymerase chain reaction (PCR) methods.

  采用基于wesbased的方法推断MSI(22)，识别出2例MSI- h肿瘤(胃和胆道气管癌)患者，均为应答者;采用标准MSI聚合酶链反应(PCR)方法检测患者的MSI状态。

  In the melanoma cohort, the percentage of UV light– inducedmutationscorrelatedwithTMB(r=0.77;P < 1 × 10 −10) (fig. S1) and was significantly associated with response (P = 0.02).

  在黑色素瘤队列中，紫外线诱导突变的百分比与tmb相关(r=0.77;P < 1×10−10)(图S1)，与反应显著相关(P = 0.02)。

  These data suggest that nonsynonymous mutations arising from a wide variety of mutagenic processes are capable of enhancing the antigenicity of tumors, withcomparableeffects ontheresponse to PD1 checkpoint blockade.

  这些数据表明，由多种诱变过程引起的非同义突变能够增强肿瘤的抗原性，而对PD1检查点阻断的反应则具有可比性。

Somatic mutation clonality and copy number variation (CNV) have previously been reported topositivelyandnegativelyassociate,respectively, with response to PD-1 checkpoint blockade (23, 24).

体细胞突变克隆性(Somatic mutation clonality)和拷贝数变异(copy number variation, CNV)分别与PD-1检查点阻断反应相关(23,24)。

Inananalysisofclonalversusnonclonal tumors (clonality of 1 versus <1, respectively), the treatmentresponserateswerenumericallyhigher in clonal tumors in the pan-tumor cohort (18% versus10%)butnotdifferentintheHNSCC(21% versus 23%) or melanoma (44% versus 41%) cohort.

克隆性与非克隆性肿瘤(克隆性分别为1与<1)的分析显示，在泛肿瘤队列中克隆性肿瘤的治疗反应率(18%比10%)要高，但在hnscc(21%比23%)或黑色素瘤(44%比41%)队列中没有差异。

A low and nonsignificant overall correlation was observed between clonality and TMB (r = 0.05;P>0.05)inthepooleddataset,suggestinga potentialutilityofincluding clonality assessment in the application of a TMB-based biomarker.

在汇集的数据集中，克隆性与TMB之间存在较低且不显著的总体相关性(r = 0.05;P>0.05)，这表明在基于TMB的生物标志物的应用中，克隆性评估具有潜在价值。

Higher levels of CNV trended toward negative associations with response but approached statisticalsignificanceonlyintheHNSCCandmelanoma cohorts (AUROCs = 0.48, 0.35, and 0.42;P=notsignificant,0.1,and0.1forthepan-tumor, HSNCC, and melanoma cohorts, respectively).

CNV水平越高，与反应呈负相关，但只有在高黑素瘤组才有统计学意义(AUROCs = 0.48、0.35和0.42;在泛肿瘤组、HSNCC组和黑色素瘤组中，P=不显著，分别为0.1和0.1)。

Correlations between TMB and CNV load were low in the pan-tumor (r = −0.03), HNSCC (r = 0.16),andmelanoma(r=−0.12)cohorts(P>0.05 for all), suggesting a potential complementary role of CNV in biomarker-based prediction of responders versus nonresponders

泛肿瘤组(r= - 0.03)、HNSCC组(r= 0.16)和黑色素瘤组(r= - 0.12)中，TMB和CNV负荷之间的相关性较低(P>0.05)，这表明CNV在基于生物标志物的预测应答者和无应答者中具有潜在的互补作用

（其它与抗原反应相关研究）

TMB and Tcell–inflamed GEP relationships can be applied to a wide range of tumor types across genomic databases

TMB和t cell炎症的GEP关系可以应用于基因组数据库的多种肿瘤类型

To explore the generalizability of our findings and the utility of our stratification schema across tumor types,the relationship among TMB, T cell –inflamed GEP, and related genomic features was further explored in TCGA (n = 9963 patients with transcriptomic data,6384 of which also had WES data) (16).

探索我们的研究结果的普遍性和实用的分层模式在肿瘤类型,在TMB,T细胞发炎GEP和相关基因功能进一步探索TCGA (n = 9963患者转录组数据,其中6384也有WES数据)(16)。

Patients were stratified by TMB (WES score ≤ 100 mutations per exome) and T cell–inflamed GEP score (below the top tertile of data) by using cutoffs equivalent in terms of prevalence to those that were used to define the clinical response groups in the pan tumor cohort (Fig. 4A).

患者按TMB(每个外显子组的WES评分≤100个突变)和T细胞炎症性GEP评分(数据顶部三分位以下)进行分层，使用与定义泛肿瘤队列中临床反应组的患病率相同的截断值(图4A)。

fig4A

Consistent with our clinical data, TMB and the T cell–inflamed GEP were found to have low but significant correlations(r=0.30;P <1×10 −4),as did TMB and PDL1 gene expression (r = 0.16;P < 1 × 10 −4) and TMB and PD-L2 gene expression (r = 0.22;P < 1×10 - 4)。

与我们的临床数据一致，TMB与T细胞炎症性GEP呈低而显著的相关性(r=0.30;P <1×10−4)，TMB与PDL1基因表达(r= 0.16;P <1×10−4)，TMB与PD-L2基因表达(r= 0.22;P < 1×10 −4).

By contrast,both PD-L1 expression and PD-L2 expression, which are induced by IFN-g from activated Th1 and cytotoxic T cells (1–3), were highly correlated with the T cell–inflamed GEP (r = 0.61 and 0.72;P < 1 × 10 −10).

而活化Th1和细胞毒T细胞IFN-g诱导的PD-L1和PD-L2表达与T细胞炎症性GEP高度相关(r = 0.61和0.72;P < 1×10−10)。

MSI-H tumors made up a subset of tumors with TMBhi inbothTcell–inflamedandnoninflamedtumors.

MSI-H肿瘤是TMBhi合并细胞炎性和非炎性肿瘤的一个亚型。

Even in these tumors, which exhibit very high mutational burdens, the modest correlation between GEP and TMB was preserved.

即使在这些表现出非常高的突变负担的肿瘤中，GEP和TMB之间的适度相关性也得到了保留。

The frequency of the TMBhi GEPhi subgroup, which was identified as the most clinically responsive population in our datasets, varied across cancer types (Fig. 4B), with enrichment among patients with tumors that are generally more responsive to pembrolizumab, such as melanoma and NSCLC (25, 26), and underrepresentation among patients with tumors such as prostate cancer and glioblastoma that are typically more resistant to immunotherapy (27, 28).

TMBhi GEPhi子群的频率,在我们的数据集被确认为临床上最敏感反应的人口,不同癌症类型不同(图4 b),在肿瘤患者通常反应pembrolizumab更加多,如黑色素瘤和非小细胞肺癌(25、26)和前列腺癌等肿瘤患者中，在胶质母细胞瘤代表名额不足,但通常抗免疫疗法(27，28)。

fig4B

Rooted in the well-studied field of T cell inflammation and cytolytic process (13, 29–31), the T cell–inflamed GEP signature was derived by a stepwise process of discovery, validation, and refinement of candidate gene sets associated with patient response to pembrolizumab across multiple solid tumors with the use of a NanoString platform enriched in immune genes (15) and thus represents a universal signature.

根植于T细胞炎症和细胞溶解的过程的研究领域(13 29-31),T cell-inflamed GEP代表特征是派生的一个逐步的过程发现,验证和改进相关的候选基因集跨多个实体肿瘤病人应对pembrolizumab NanoString平台使用富含免疫基因(15),因此代表了一种普遍的代表。

Notably, in TCGA dataset, we observed a strong correlation (r > 0.9) between the GEP and several other previously published transcriptional signatures reflective of a T cell–inflamed TME associated with cytolytic processes (Fig. 5A).

值得注意的是，在TCGA数据集中，我们观察到GEP和其他几个先前发表的反映T细胞炎症性TME与细胞溶解过程相关的转录特征之间有很强的相关性(r > 0.9)(图5A)。

（公共数据中验证相光关系）

fig5A