四月week4文獻閱讀1：Pan-tumor genomic biomarkers for PD-1 checkpoint blockade–based immunotherapy

基于阻斷的PD-1檢查點的免疫治療泛腫瘤基因組生物標志物

INTRODUCTION：

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

針對程序性細胞死亡蛋白-1 (PD-1)軸的免疫治療可在多種癌癥類型中引起持久的抗腫瘤反應。

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

然而，臨床反應不同，生物標志物預測反應可能有助于確定誰將獲得最大的治療效益。

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.

臨床驗證的預測抗PD-1單克隆抗體pembrolizumab反應的生物標志物包括PD-1配體1 (PD-L1)在特定癌癥中的表達以及無論腫瘤類型如何的高的微衛星不穩定性(MSI-H)。

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

腫瘤突變負擔(TMB)和T細胞炎癥基因表達譜(GEP)是pembrolizumab新的預測生物標志物。

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細胞炎癥性腫瘤微環境(TME)的炎癥標志物，而TMB和MSI-H則是由體細胞腫瘤突變產生的腫瘤抗原性的間接指標。

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

然而，這兩類生物標志物之間的關系還沒有很好地描述。

名詞解釋：
PD-1配體1 (PD-L1)在特定癌癥中的表達，T細胞炎癥基因表達譜(GEP)。
腫瘤突變負擔(TMB)，高的微衛星不穩定性(MSI-H)。
PD-L1和GEP都是T細胞炎癥性腫瘤微環境(TME)的炎癥標志物，而TMB和MSI-H則是由體細胞腫瘤突變產生的腫瘤抗原性的間接指標。

RATIONALE 理論依據

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.

本研究評估了TMB和T細胞炎癥性GEP聯合預測pembrolizumab在>300例晚期實體瘤和黑色素瘤患者中的臨床反應的潛力，這些患者來自4個主要臨床試驗的22種腫瘤類型。

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.

為了評估TMB和GEP的個體和聯合臨床效用，根據預先定義的TMB和GEP的截斷值，將患者分為四個生物標志物定義的臨床反應組[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.

這些患者定義的生物標志物組進一步用于指導大分子數據庫中腫瘤的轉錄組和外顯體分析[癌癥基因組圖譜(TCGA)] (n = 6384個腫瘤)，以確定可能調節反應和耐藥性的生物學靶標模式。

RESULTS:

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

TMB和GEP僅表現出適度的相關性，并獨立預測基調臨床數據集的反應。

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).

我們發現，GEP高 TMB高患者的客觀反應率最高(37 - 57%)，GEP高 TMB低患者的客觀反應率最低(12 - 35%)，GEP低TMB高患者的客觀反應率最低(11 - 42%)，GEP低 TMB低患者的客觀反應率最低(0 - 9%)(見圖)。

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

此外，TMB和GEP水平較高的患者無進展生存時間更長。

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

聯合評估TMB和PD-L1表達時，結果具有可比性。

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數據庫中，GEP和TMB再次具有較低的相關性，顯示了跨癌癥類型聯合分層轉錄組和基因組特征的潛力。

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

反映TME生物學的特定基因表達模式與TMB、GEP或兩者均有顯著關聯。

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

特別是，基因集富集分析確定了與GEP高腫瘤中特定TMB定義的亞群相對應的增殖和基質、髓細胞和血管生物學。

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

在TMB高腫瘤中，關鍵腫瘤驅動基因的指示依賴性體細胞DNA改變與GEP呈顯著負相關。

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.

這一分析表明，TMB和炎癥生物標志物(T細胞炎癥性GEP和PD-L1表達)可以聯合將人類癌癥分為對pembrolizumab單藥治療有不同臨床反應的組，并識別與這些組相關的潛在的、可靶向的生物學模式。

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

TMB和炎癥生物標志物可以獨立預測反應，并可能分別捕捉到新抗原性和T細胞活化的不同特征。

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

該方法可為合理構建和評價抗pd -1和/或- pd - l1聯合治療方案提供精確的醫學框架。

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.

程序性細胞死亡蛋白-1 (PD-1)和程序性細胞死亡配體-1 (PD-L1)檢查點阻斷免疫治療在多種癌癥中具有持久的抗腫瘤作用，但并非所有患者都有反應。

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

我們報告了來自四個主要臨床試驗的22種腫瘤類型的>300例患者樣本的評估。

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.

腫瘤突變負擔(TMB)和t細胞炎癥基因表達譜(GEP)在識別PD-1抗體pembrolizumab應答者和無應答者方面顯示出聯合預測作用。

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對反應具有獨立的預測作用，且相關性較低，說明它們捕獲了新抗原性和T細胞活化的不同特征。

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.

對腫瘤基因組圖譜數據庫的分析表明，TMB與GEP相關性較低，聯合分層分析揭示了生物標志物所定義的靶向性生物學模式。

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

這些生物標志物可指導抗pd -1單藥和聯合免疫治療方案的合理選擇，在臨床試驗設計中具有實用價值。

(TMB和GEP對反應具有獨立的預測,或聯合預測，腫瘤突變負擔(TMB)和t細胞炎癥基因表達譜(GEP)在識別PD-1抗體pembrolizumab，預測抗PD-1單克隆抗體pembrolizumab反應，應答者和無應答者（客觀反應率），相關性函數，各指標輸入后的預測的反應結果（反應率的等級劃分）)

• 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).

  檢查點阻斷免疫治療反應的新興免疫相關生物標志物可大致分為兩類:一類是與腫瘤新表位負擔相關的生物標志物，如微衛星不穩定性(MSI)或高腫瘤突變負擔(TMB)，另一類是提示T細胞炎癥性腫瘤微環境(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).

  后者包括腫瘤細胞和免疫細胞上的程序性細胞死亡配體- 1(PD-L1)蛋白表達，在許多情況下，這種蛋白表達會隨著局部T細胞來源的干擾素-g (IFN-g)和活化T細胞的基因信號而上調(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與晚期黑色素瘤(4-6)中細胞毒性T淋巴細胞相關抗原- 4阻斷的臨床反應相關，與抗程序性細胞死亡蛋白-1 (PD-1)和/orPD-L1blockadein黑色素瘤(7)、非小細胞肺癌(8,9)、結直腸癌和胃癌(10,11)、泌尿系癌(12)相關。

• 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).

  同樣，MSI高水平單核苷酸和移碼突變[高MSI (MSI- h)]的腫瘤對結直腸癌和其他惡性腫瘤的抗pd -1治療也有反應(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).

  與免疫溶細胞活性相關的基因表達也被證明與某些腫瘤對檢查點阻斷的臨床反應有關(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細胞炎癥基因表達譜(GEP)被證明可以預測抗pd -1定向治療的反應(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，這兩種截然不同的生物標志物之間的相互作用還沒有被很好地跨類型描述，因為它們既能獨立又能共同預測免疫治療的反應，也能揭示腫瘤抗原性和TME的潛在基因組和/或轉錄組特征。

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

  我們評估了軀體TMB與pembrolizumab抗pd -1免疫治療的臨床反應之間的關系。

• 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.

  在發現和驗證組中納入了20種腫瘤類型，并分析了TMB和T細胞炎癥性GEP的獨立和聯合預測值。

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

  此外，通過使用大型分子數據庫[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.

  在癌癥基因組圖譜(TCGA)(16)中，我們探索了存在或不存在這兩種標記的轉錄組學和遺傳學特征。

Study cohorts and tumor and mutation type

研究群體、腫瘤和突變類型

• 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).

  分別評估TMB和T細胞炎癥性GEP的預測值，首先通過嚴格的逐步測試四組患者在pembrolizumab臨床發展項目(一個發現、一個泛腫瘤驗證和兩個單指征總結組)。

• 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.

采用生殖系和腫瘤DNA全外顯子組測序(WES)評價TMB，采用石蠟包埋(FFPE)預處理樣品腫瘤RNA靶向基因表達譜(NanoString platform)分析T細胞感染的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)].

初始發現TMB隊列由PD-L1-positive患者頭頸部鱗狀細胞癌(HNSCC) 1 b期臨床試驗(主題- 012 B1組;n = 34名患者),和pan-tumor驗證隊列由PD-L1-positive的實體腫瘤患者(n = 119例)從兩個多隊列1 b階段試驗20個癌癥類型(主題- 028(17組;n = 80名患者)和主題- 012 (a, C和D組,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單指征隊列(n=107例患者)包括1b期KEYNOTE-012 B1組患者和來自KEYNOTE-012 B2組的pd - l1 -未選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.

黑色素瘤單指征隊列包括來自1b期(KEYNOTE-001;n=30例)和3期(KEYNOTE-006 pembrolizumab arm;n = 59例)試驗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.

各隊列的臨床特征見表S1，本研究納入的所有患者的臨床特征見表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).

整個研究群體中腫瘤突變特征的分布在很大程度上反映了突變的公認的癌癥亞型依賴性決定因素(17)(表S3和圖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.

顯性突變簽名在pan-cancer隊列中腫瘤類型多樣,具有較高TMB與組織相關的特征,如吸煙在小細胞肺癌;載脂蛋白B信使rna編輯酶,催化polypeptide-like (APOBEC)在泌尿系腫瘤;和錯配修復(MMR)在胃腸道癌癥。

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).

單指征組的優勢特征更為均勻，在HNSCC組中有APOBEC特征(61%的腫瘤)，在黑色素瘤中有紫外(UV)照射特征(78%的腫瘤中，30%的突變是由紫外線誘導的)。

Association of TMB and Tcell–inflamed GEP with clinical response

TMB和t細胞炎癥性GEP與臨床反應的關系

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

  根據RECIST 1.1的最佳總體反應(BOR)和無進展生存(PFS)評估臨床反應相關性。

  （總體反應和無進展的什么具體指標被定為有反應和無反應？）

• 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和轉錄組數據的患者中評估BOR和PFS與TMB和tcell炎癥性GEP的關系

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

  我們首先評估了不同群體中每個單獨的基因組生物標志物的預測價值。

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

  在HNSCCB1發現隊列中，較高的TMB預示著更高的臨床反應頻率(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).

  這是通過使用泛腫瘤隊列證實的，其中TMB再次與BOR相關(P < 0.001)(圖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.

  在泛腫瘤隊列中，T細胞炎癥性GEP評分較高也與BOR呈正相關(P<0.01)(圖1B)，這表明T細胞激活的腫瘤環境除了影響TMB外，還影響反應。

  
  
  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).

  同樣，在HNSCC單指征組(P < 0.05, P < 0.001)和黑色素瘤(P < 0.05)患者中，TMB和T細胞炎癥性GEP評分均與BOR呈正相關(圖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).

  在這項研究中,我們沒有評估的影響人類乳頭狀瘤病毒(HPV)抗原的聯合與TMB響應在HNSCC隊列反應;然而,我們曾描述了在一個更大的,重疊群HNSCC病人(主題- 012 B1和B2組)分層的人乳頭狀瘤病毒狀態(18)TMB 的與臨床結果的結合。

  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

  雖然我們發現TMB在HPV陰性患者中與BOR的相關性比在HPV陽性患者中更強，但這些探索性的發現有待更大規模的獨立研究的驗證

  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在預測BOR方面的臨床實用價值普遍較高，不同癌癥類型的實用程度相似，在泛腫瘤、HNSCC和黑色素瘤患者中，接受手術特征曲線(AUROCs)下的區域分別為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).

  各組T細胞炎癥性GEP的結果相似(AUROCs = 0.782,0.768, 0.638)(圖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.

  利用基礎醫學靶向測序平臺(19)中的基因，模擬了基于靶向測序的TMB檢測的潛在性能與各組中相應的AUROC值與WES觀察值相當(0.721)，表明其潛在的可譯性可用于靶板診斷。

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

  綜上所述，這些數據表明TMB和tcell炎癥性GEP具有相似的性能特征和潛在的診斷價值

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

接下來，我們評估了這兩種基因組生物標志物在預測反應中的聯合效用。

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).

在泛腫瘤組和黑色素瘤組中，TMB與GEP的相關性較低(Spearman相關系數r = 0.221, P < 0.05, r = 0.252, P < 0.05)，在HNSCC組中無相關性(r = - 0.020, P = 0.841)(圖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.

這種相關性的缺乏，結合觀察到的個體預測值，表明TMB和tcell炎癥的GEP是對pembrolizumab反應的獨立預測措施。

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).

在對每一項指標進行調整的多元模型中進行測試時，TMB和T細胞炎癥性GEP在泛腫瘤(P=0.0028和0.0051)和HNSCC (P= 0.0013和0.0004)組中均保留了顯著的預測價值，而在黑色素瘤組中只有GEP保持顯著的預測價值(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.

雖然本研究中有一部分患者選擇PD-L1，但即使在未選擇PD-L1的患者中也觀察到了這些關系。

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

  我們評估了基因組生物標志物與PD-L1免疫組化(IHC)評分的相關性(圖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].

  在泛腫瘤隊列中，TMB與PD-L1顯著但中度相關[聯合陽性(CPS)，r= 0.330;P = 0.0038]，在HNSCC隊列中，TMB與PD-L1無相關性(CPS, r=0.020;或在黑色素瘤隊列中[黑色素瘤(MEL)評分，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與泛腫瘤組、HNSCC組和黑色素瘤組PD-L1的相關性更顯著(r分別為0.49、0.51和0.53;所有P值均< 0.001)，與T細胞來源的IFN-g調控PD-L1基因表達的已知規律一致(1-3)。

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

  這種相關性表明，基于PD-L1 IHC的檢測與評估T細胞感染的TME有關。

  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.

  從高TMB(TMBhi)和高GEP評分(GEPhi)可以看出，TMB高和PD-L1表達(PD-L1+)同時存在的患者的反應;CPS≥1)明顯高于TMB和PD-L1表達水平較低的患者。

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

  接下來，我們研究了TMB和GEP在患者分層和治療結果預測方面的潛在聯合應用。

  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).

  臨床反應的評估基于與Youden指數相關的切點(來自每個隊列中TMB的AUROCs)和發現T細胞炎癥的GEP評分的- 0.318截止點(通過分析泛癌數據選擇)(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指數減少點)pembrolizumab反應率要大于那些(TMBlo)低的(少于Youden指數減少點),同樣大的得分更高的T cell-inflamed GEP(大于或等于截止?0.318)大于那些成績差的(小于?0.318截止)(圖2 b)。

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

  在每個隊列中，同時患有TMBhi和GEPhi的患者的客觀反應率最高。

  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低和低細胞炎癥性GEP評分(GEPlo)患者中，泛腫瘤組和HNSCC組均未觀察到反應，而黑色素瘤組僅觀察到一種反應，這表明對生物標志物組合的敏感性更高。

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

  只有一種生物標志物(TMBlo GEPhi和TMBhi GEPlo)得分較高的患者反應中等(圖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

  這些數據表明，當這些生物標志物同時用于PD-1定向單藥治療時，可能具有更大的陽性和陰性預測值
  

  fig2B

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

患者TMB和GEP分層與PFS也有差異。

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.

在所有三個隊列中，與PFS相關的危險比在TMB高與低、tcell炎癥的GEP評分高與低的患者中均<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).

在泛腫瘤(圖3A)、HNSCC(圖3B)和黑色素瘤(圖3C)中，最顯著的與pfs相關的hazardratios被觀察到用于TMBhi GEPhi腫瘤。

fig3AB

fig3C

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

最大的差異出現在患有tmbhi GEPhi的患者與患有TMBlo GEPlo的患者之間。

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)水平較高的患者與這些生物標志物(TMBlo或GEPlo)水平較低的患者相比，PFS也較長。

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).

我們還通過與Panda等人(20)類似的方法，探索了在我們的研究群體中確定TMB的泛癌閾值的可行性和潛在的臨床價值，該閾值可以最大化其與GEP的聯合預測效用。

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.

每個外顯子突變數≥123的TMB截斷使腫瘤中TMB小于或大于截斷值的GEP分布差異的效應大小最大化。

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).

在每個臨床隊列的TMB- gep定義的組中，pembrolizumab的應答率與使用上述TMB的特定切點觀察到的應答率相當(圖S3)。

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

使用每個外顯子組≥123個突變的TMB截止值，觀察到PFS的危險比也大致相似(圖s4)。

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

在我們的研究之外，隨著更多數據的可用性，pan-tumor閾值可能會進一步優化。

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

例如，最近報道了每個外顯子組≥175個突變的apan-tumorTMB閾值對pembrolizumab的應答(21)。

** (TMB,TME,預測反應應答，用PFC,BOR評估，分腫瘤類型)**

Association of other DNA-based measures with response

其他基于dna的措施與反應的關聯

• 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的突變狀態與反應無關的預測價值也在這些隊列中進行了評估，包括預測的新抗原特征、吸煙狀況、中風導致的突變、紫外線照射、DNA轉位、同源重組缺陷和MSI。

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

  除了MSI，沒有任何一種特別的基因改變測量方法比TMB評估提供了額外的有意義的預測價值的改善。

• 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).

  在泛腫瘤組、HNSCC組和黑色素瘤組中，預測的新抗原載量與TMB高度相關(r分別為0.87、0.83和0.90)，與預期一致(圖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).

  在泛腫瘤隊列中，大多數誘變過程的測量與BOR顯著相關(例如，預測新抗原載量和吸煙;兩個P值均= 0.001)，與PFS顯著相關的趨勢相似(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的方法推斷MSI(22)，識別出2例MSI- h腫瘤(胃和膽道氣管癌)患者，均為應答者;采用標準MSI聚合酶鏈反應(PCR)方法檢測患者的MSI狀態。

  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).

  在黑色素瘤隊列中，紫外線誘導突變的百分比與tmb相關(r=0.77;P < 1×10?10)(圖S1)，與反應顯著相關(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.

  這些數據表明，由多種誘變過程引起的非同義突變能夠增強腫瘤的抗原性，而對PD1檢查點阻斷的反應則具有可比性。

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

體細胞突變克隆性(Somatic mutation clonality)和拷貝數變異(copy number variation, CNV)分別與PD-1檢查點阻斷反應相關(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.

克隆性與非克隆性腫瘤(克隆性分別為1與<1)的分析顯示，在泛腫瘤隊列中克隆性腫瘤的治療反應率(18%比10%)要高，但在hnscc(21%比23%)或黑色素瘤(44%比41%)隊列中沒有差異。

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.

在匯集的數據集中，克隆性與TMB之間存在較低且不顯著的總體相關性(r = 0.05;P>0.05)，這表明在基于TMB的生物標志物的應用中，克隆性評估具有潛在價值。

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，與反應呈負相關，但只有在高黑素瘤組才有統計學意義(AUROCs = 0.48、0.35和0.42;在泛腫瘤組、HSNCC組和黑色素瘤組中，P=不顯著，分別為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

泛腫瘤組(r= - 0.03)、HNSCC組(r= 0.16)和黑色素瘤組(r= - 0.12)中，TMB和CNV負荷之間的相關性較低(P>0.05)，這表明CNV在基于生物標志物的預測應答者和無應答者中具有潛在的互補作用

（其它與抗原反應相關研究）

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

TMB和t cell炎癥的GEP關系可以應用于基因組數據庫的多種腫瘤類型

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).

探索我們的研究結果的普遍性和實用的分層模式在腫瘤類型,在TMB,T細胞發炎GEP和相關基因功能進一步探索TCGA (n = 9963患者轉錄組數據,其中6384也有WES數據)(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(每個外顯子組的WES評分≤100個突變)和T細胞炎癥性GEP評分(數據頂部三分位以下)進行分層，使用與定義泛腫瘤隊列中臨床反應組的患病率相同的截斷值(圖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)。

與我們的臨床數據一致，TMB與T細胞炎癥性GEP呈低而顯著的相關性(r=0.30;P <1×10?4)，TMB與PDL1基因表達(r= 0.16;P <1×10?4)，TMB與PD-L2基因表達(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和細胞毒T細胞IFN-g誘導的PD-L1和PD-L2表達與T細胞炎癥性GEP高度相關(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腫瘤是TMBhi合并細胞炎性和非炎性腫瘤的一個亞型。

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

即使在這些表現出非常高的突變負擔的腫瘤中，GEP和TMB之間的適度相關性也得到了保留。

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子群的頻率,在我們的數據集被確認為臨床上最敏感反應的人口,不同癌癥類型不同(圖4 b),在腫瘤患者通常反應pembrolizumab更加多,如黑色素瘤和非小細胞肺癌(25、26)和前列腺癌等腫瘤患者中，在膠質母細胞瘤代表名額不足,但通常抗免疫療法(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細胞炎癥和細胞溶解的過程的研究領域(13 29-31),T cell-inflamed GEP代表特征是派生的一個逐步的過程發現,驗證和改進相關的候選基因集跨多個實體腫瘤病人應對pembrolizumab NanoString平臺使用富含免疫基因(15),因此代表了一種普遍的代表。

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數據集中，我們觀察到GEP和其他幾個先前發表的反映T細胞炎癥性TME與細胞溶解過程相關的轉錄特征之間有很強的相關性(r > 0.9)(圖5A)。

（公共數據中驗證相光關系）

fig5A