Stratification of additional genomic features by TMB and Tcell–inflamed GEP

TMB和tcell炎癥性GEP對其他基因組特征的分層

• The patient groups defined by TMB and GEP status show notable differences in clinical response to pembrolizumab.

  以TMB和GEP狀態定義的患者組對pembrolizumab的臨床反應有顯著差異。

• Inparticular,the two groups with only one positive biomarker indicative of potential for pembrolizumab response(TMBhi GEPlo or TMBlo GEPhi) have markedly lower response rates than the TMBhi GEPhi group,suggesting that mechanisms of resistance to pembrolizumab may exist that are specific to each respective group.

  特別是，只有一個陽性生物標志物提示pembrolizumab潛在應答的兩組(TMBhi GEPlo或TMBlo GEPhi)的應答率明顯低于TMBhi GEPhi組，這表明對pembrolizumab的耐藥機制可能存在，且可能針對每一組。

  In order to identify potential mechanisms of resistance,we assessed molecular differences among tumors that belong to differentTMB-andTcell– inflamed GEP–defined groups through analyses in TCGA molecular database.

  為了確定潛在的耐藥機制，我們通過對TCGA分子數據庫的分析，評估了不同tmb和tcell -炎性gep組腫瘤之間的分子差異。

  First, we compared the correlation of genes in the transcriptome with GEP in TMBhi and in TMBlo tumors separately.

  首先，我們分別比較了TMBhi和TMBlo腫瘤中轉錄組基因與GEP的相關性。

  Both distributions of correlationsdivergedfromanormaldistribution because of a pattern of significant skewing toward positive correlations with the T cell– inflamed GEP, consistent with robust coregulation of gene expression markers of cell types present in a cytolytic TME.

  這兩種相關性的分布都偏離了正態分布，因為一種與T細胞發炎的GEP呈顯著正相關的模式，這與溶細胞TME中細胞類型的基因表達標志物的強大協同調控一致。

  However, there were no major differences in the correlations of individual genes with the T cell–inflamed GEP betweenTMBhi (TMB>100mutationsperexome) and TMBlo (TMB ≤ 100 mutations per exome tumors (r = 0.76;P < 1 × 10 ?20) (Fig. 5B), suggesting a lack of qualitative difference in T cell inflammation markers as a function of tumor neoantigenicity.

  然而，單個基因與T細胞炎癥性GEP的相關性在tmbhi ((TMB>100突變/外顯子組)和TMBlo (TMB≤100個突變每個外顯體腫瘤)之間沒有顯著差異(r = 0.76;P < 1×10?20)(圖5B)，表明T細胞炎癥標志物作為腫瘤新抗原性的功能缺乏定性差異。

  [圖片上傳失敗...(image-3537dd-1556593615660)]

  Notably,muchsmallerdeviations from a normal distribution were observed in the negative range of correlations with GEP in both TMBhi andTMBlo tumors,suggesting the absence of major pan-cancer transcriptional signatures strongly associated with T cell exclusion.

  值得注意的是，在TMBhi和tmblo腫瘤中，GEP與正態分布的負相關范圍內的偏差要小得多，這表明沒有與T細胞排斥密切相關的主要泛癌轉錄特征。

  To understand the origin of the skewness toward positive correlations with the T cell– inflamed GEP, genes positively correlated with the T cell–inflamed GEP(r>0.15)were classified into two sets by using cutoffs defined by deviations from a normal distribution of the correlation with the T cell–inflamed GEP at 83% and 98% quantiles, respectively (Fig. 5C).

  理解偏態向正相關性的起源與T細胞- GEP發炎,基因與T cell-inflamed GEP呈正相關(r > 0.15)分為兩組通過達標由偏離正態分布的相關性與T cell-inflamed GEP 分別83%和98%分位數,(圖5C)。

  
  
  fig5C

• Set 1 comprisedgenesthathadaSpearmancorrelationr> 0.6 with the T cell–inflamed GEP (the lower bound for the correlation of individual genes inthe signature with the signature as awhole), whereas set 2 genes had correlations with GEP that ranged between 0.15 and 0.6.

  Set 1與T細胞感染的GEP(簽名中單個基因與整個簽名相關性的下界)相關，而Set 2與GEP相關，范圍在0.15到0.6之間。

• Additionally,genes negatively correlated with the T cell– inflamed GEP and divergent from a normal distribution (r < ?0.15 at 14% quantile) were grouped in set 3

  此外，與T細胞炎癥性GEP呈負相關并偏離正態分布(r < - 0.15, 14%分位數)的基因分組在set 3中.

As expected, a strong enrichment of genes relatedtoTcell–inflamed cytolytic processeswas observed in set 1 (table S5).

正如所料，在set 1中觀察到與全細胞炎癥溶細胞過程相關的基因大量富集(表S5)。

By contrast, set 2 showed enrichment in genes specific to other cell types in the TME, including vascular endothelium and myeloid infiltrate, but did not show enrichmentofgenesforT cell–inflamed cytolytic processes or tumorcell–intrinsic pathways.

相比之下，set 2顯示了TME中其他細胞類型特異性基因的富集，包括血管內皮和髓樣浸潤，但沒有顯示豐富的細胞炎癥溶細胞過程或腫瘤細胞固有通路。

Genes in set 1 and set 2 were further grouped as modules of gene coexpression by K-means clustering(K=10 forset2,andK=4 forset1).

通過K-means聚類(K=10 forset2, K=4 forset1)，將set1和set2中的基因進一步分組為基因共表達模塊。

Modules in set 1 did not show a strong association with TMB, consistent with the weak associations between TMB and the T cell–inflamed GEP described above.

set 1中的模塊沒有顯示出與TMB的強相關性，這與上面描述的TMB與T細胞感染的GEP之間的弱相關性一致。

However, severalmodules in set 2 (table S6) displayed distinct patterns of correlation or anticorrelation with TMB.

然而，set 2(表S6)中的幾個模塊顯示了與TMB不同的關聯或抗腐蝕模式。

Annotation of the genes in the modules that were most strongly correlated and anti correlated with TMB (modules 4 and 5, respectively), revealed enrichment in biology related to cell proliferation(module4) and vasculature (module 5). These data suggest that distinct patterns of underlying biology can be identified by using TMB and the T cell– inflamed GEP to categorize tumors(Fig.5D).

注釋的基因最強烈相關的模塊和反與TMB (分別為模塊4和5),揭示了富集在生物學相關細胞增殖(module4)和脈管系統(模塊5)。這些數據表明,不同的潛在生物學模式可以使用TMB和T細胞——發炎GEP對腫瘤進行分類(Fig.5D)。
(正態偏離追尋加基因富集分析，找到了弱相關的因素。)

figD

• The association of the average expression of these gene modules(modules4 and 5)with TMB and T cell –inflamed GEP is represented in Fig. 5D in the up per l eft and lower right panels,respectively, by using the cytolytic module 1 from set 1 in the upper right panel as a reference.

  聯系的平均表達這些基因模塊(modules4和5)TMB和T細胞發炎GEP表示在圖5 d / 左和右下板,分別利用細胞溶解的模塊1組1在右上角面板作為參考。

  The group of genes in set 3 that were anticorrelated with the T cell–inflamed GEP (r < ?0.15) was also investigated;

  還研究了set 3中與T細胞炎癥GEP負相關的基因組(r < - 0.15);

  however, the biological annotation of the resulting coexpression moduleswaslessinformativethanthatforgenes positively correlated with the T cell–inflamed GEP.

  然而，與與T細胞感染的GEP正相關的基因相比，由此產生的共表達調控的生物學注釋所提供的信息更少。

  However, some modules in this group were anticorrelated with TMB as well as with T cell –inflamed GEP.

  然而，這一組中的一些模塊與TMB以及T細胞炎癥的GEP有負關系。

  In particular, a module enriched in stromal and Wnt signaling elements was identified in tumors with both TMBlo and T cell –inflamed GEPlo (Fig. 5D, lower left panel).

  特別是，在TMBlo和T細胞發炎的GEPlo腫瘤中發現了一個富含基質和Wnt信號元件的模塊(圖5D，左下面板)。

An additional analysis was performed by interrogating the entire transcriptome for genes associated with TMB in T cell–inflamed tumors, independently of the GEP-based clustering approach described above.

通過詢問整個轉錄組，獨立于上述基于gep的聚類方法，對T細胞炎癥性腫瘤中與TMB相關的基因進行了額外的分析。

• Similar to the analysis of modules, this analysis showed that genes that positively correlated with TMB were enriched for proliferation whereas those that were anticorrelated with TMB were related to vascular and stromal biology (table S7).

  與模塊分析相似，本分析表明，與TMB正相關的基因在增殖方面得到了富集，而與TMB抗負相關的基因則與血管和基質生物學相關(表S7)。

  Consistent with these analyses,the distribution of previously identified signaturesof stromalbiology, proliferation, cytolytic activity, and Wnt signaling (13, 32–34) also showed similar patterns of association with TMB and theTcell–inflamed GEP(fig.S6).

  與這些分析一致，先前識別的基質生物學、增殖、細胞水解活性和Wnt信號的分布(13,32 - 34)也顯示了與TMB和tcell炎癥的GEP相似的關聯模式(圖s6)。

  However,in this analysis, we were not able to identify a gene expression signature of TMBhi that was as predictive as TMB itselff or response to pembrolizumab

  然而，在這項分析中，我們不能確定TMBhi的基因表達特征，這與TMB本身或對pembrolizumab的反應一樣具有預測性。

A complementary approach was used to identify genomic determinants of low cytolytic transcriptomic activity (absence of a T cell–inflamed GEP) in tumors with TMBhi as potential drivers of immune evasion in a mutagen-rich context.

一種互補的方法被用來確定在突變體豐富的情況下，TMBhi作為免疫逃避的潛在驅動因素的腫瘤中，低溶細胞轉錄組活性(沒有T細胞發炎的GEP)的基因組決定因素。

As described above, the transcriptomic correlation oftheT cell–inflamed GEPinTMBhi tumors(Fig.5B) showed a distribution that skewed toward positive correlation with GEP, suggesting the absence of a robust transcriptome signal in tumors with TMBhi and GEPlo.

如上所述，t細胞炎癥GEP和TMBhi腫瘤的轉錄組相關性(圖5b)呈與GEP呈正相關的分布，提示TMBhi和GEPlo腫瘤中缺乏一個強的轉錄組信號。

Therefore, DNA alterations in TCGA were explored to reveal potential negative associations of somatic mutations with GEP by using a previously reported approach(13)butfocusingspecificallyontumors withTMBhi.

因此，利用先前報道的方法(13)研究TCGA的DNA變化，以TMBhi為重點，揭示了體細胞突變與GEP之間潛在的負相關關系。

Among known cancer drivers serinethreonine kinase 11 (STK11) [also known as liver kinase B1 (LKB1)] mutation in lung adenocarcinoma,Kelch-likeECH-associated protein1(KEAP1) mutation in lung adenocarcinoma and lung squamous cell carcinoma, and adenomatous polyposis coli (APC) mutation in colorectal cancer showed highly significant negative associations with the T cell–inflamed GEP (Fig. 6). Notably,

已知的癌癥驅動serinethreonine激酶11 (STK11)(也稱為肝激酶B1 (LKB1)]在肺腺癌突變,Kelch-likeECH-associated protein1 (KEAP1)突變在肺腺癌和肺鱗狀細胞癌,腺瘤息肉桿菌(APC)的突變結直腸癌顯示高度顯著的負相關T cell-inflamed GEP(圖6)。

fig6

Notably,none of these associations passed the nominal significance level(P<0.01)in the pan-cancer analysis, suggesting a potential cancer type–specific role for these somatic alterations.

值得注意的是,，這些關聯在泛癌分析中均未超過名義顯著性水平(P<0.01)，提示這些軀體改變可能與癌癥類型特異性有關。

Other genes demonstrating negative associations with the T cell– inflamed GEP were either of low frequency or were not known cancer drivers (Fig. 6B).

其他與T細胞炎癥性GEP呈負相關的基因要么是低頻率的，要么是未知的癌癥驅動因素(圖6B)。

fig6B

(深入研究基因相關關系，與與GEP正/負相關的基因)

Discussion

• Several studies have shown that either TMBhi or cytolyticelementsoftheTMEareassociatedwith clinical response to checkpoint blockade immunotherapy in some tumor types (4–9, 11–13, 15).

  幾項研究表明，TMBhi或溶細胞因子與某些腫瘤類型(4 - 9,11 - 13,15)對檢查點阻斷免疫治療的臨床反應有關。

  However, the relationship between these two central aspects of tumor immunobiology and their combined associationwithclinical response to checkpoint blockade immunotherapy has not been well-studied across multiple cancer types.

  然而，腫瘤免疫生物學的這兩個核心方面之間的關系，以及它們與檢查點阻斷免疫治療的臨床反應的聯合關系，在多種癌癥類型中尚未得到很好的研究。

• Here, we show that TMB and a T cell–inflamed GEP are tissue-agnostic measures of distinct aspects oftumor immunobiology and independently predict response to anti–PD-1 therapy in multiple tumors.

  在這里，我們證明TMB和T細胞炎癥的GEP是腫瘤免疫生物學不同方面的組織不可知的測量方法，并獨立預測抗pd -1治療在多種腫瘤中的反應。

  In particular, limited clinical responses to pembrolizumab occurred in patients with low levels of both TMB and T cell– inflamed GEP, whereas the greatest response rates were seen in patients with high levels of bothbiomarkers.

  特別是，對pembrolizumab的臨床反應有限發生在TMB和T細胞炎癥性GEP水平較低的患者中，而對這兩種生物標志物水平較高的患者的反應率最高。

• Similarly,improvedresponses were seen in patients who had high levels of both PD-L1 IHC expression and TMB, reflective of the relationship of PD-L1and GEP to a Tcell– inflamed TME.

  同樣，PD-L1 IHC和TMB表達水平高的患者反應也有所改善，這反映了PD-L1和GEP與Tcell炎癥性TME的關系。

• These observations suggest that using inflammatory biomarkers such as the T cell –inflamed GEP or PD-L1 jointly with TMB may helpto identifypatientswho are responsive toanti–PD-1 therapies.

  這些觀察表明，使用炎癥生物標志物，如T細胞發炎的GEP或PD-L1與TMB聯合使用，可能有助于識別對抗pd -1治療有反應的患者。

AdditionalIHCassayshave been developed thatmeasureproteinmarkersof a cytolytic T cell environment, and evaluating their performance characteristics in conjunction with TMB in future studies may be useful (14, 35).

此外，已經開發出一種方法來測量溶細胞T細胞環境的蛋白標記，并在未來的研究中結合TMB來評估它們的性能特征，這可能是有用的(14,35)

More broadly, our study demonstrates the orthogonal relationship between universal measures oftumor antigenicityandtumorinfiltrationthat can occur by activated T cells (14, 36–38).

更廣泛地說，我們的研究表明，普遍的腫瘤抗原檢測方法與激活的T細胞可能發生的腫瘤浸潤之間存在正交關系(14,36 - 38)。

Although these are upstream and downstream components, respectively, of a robust antitumor T cell response, there is sufficient intervening biology such that biomarkers for each process can provide complementary information.

雖然這些分別是抗腫瘤T細胞反應的上游和下游成分，但有足夠的生物干預，使得每個過程的生物標志物可以提供補充信息。

As an increasing number ofPD-1– and PD-L1– based combination regimens show clinical benefit, it will become challenging to determine the relative utility of each regimen for an individual patient.

隨著越來越多基于pd -1和PD-L1的聯合方案顯示出臨床效益，確定每種方案對單個患者的相對效用將變得具有挑戰性。

A refined setof biomarker toolsthatcan stratify underlying patterns of tumor immunobiology may enable rational and biology-driven personalization of these various treatment regimens mens, such as selection of patients with tumors typically less responsive to immunotherapy.

一套能夠對腫瘤免疫生物學潛在模式進行分層的生物標志物工具，可能會使這些不同治療方案的患者在生物學驅動下實現合理的個性化，比如選擇對免疫治療通常反應較慢的腫瘤患者。

Our datademonstratethatTMBandaTcell–inflamed GEP can be used to categorize tumors into discrete subgroups that exhibit distinct patterns of potentially targetable biology to enhance clinical response.

我們的數據策略是，帶狀細胞炎癥性GEP可用于將腫瘤劃分為不同的亞組，這些亞組具有不同的潛在靶向生物學模式，以增強臨床反應。

These patterns include tumor type– agnostic signatures of proliferative, vascular, myeloid, and stromal biology, as well as tumor type–specificdysregulationoftumorcell–intrinsic signaling pathways.

這些模式包括腫瘤類型不可知的特征增殖，血管，骨髓和基質生物學，以及腫瘤類型特異性的腫瘤細胞固有信號通路失調。

Although the utility of TMB, T cell –inflamed GEP, and PD-L1, as well as other emerging tumor-agnostic biomarkers, will need to be prospectively validated for use in predicting response to various immunotherapy regimens, including combination therapies, the findings reported here suggest a rationale for further exploring the utility of these biomarkers as guides for precision cancer immunotherapy.

盡管TMB的效用,T細胞發炎GEP和PD-L1,以及其他新興tumor-agnostic生物標記,需要前瞻性驗證用于預測應對各種免疫治療方案,包括聯合療法,研究結果報道在這里建議理由進一步探索這些生物標記物的效用作為精密癌癥免疫治療的指南。

（利用這種相關關系，預測免疫治療的有效性，提供臨床指導方案，希望在以后可以T細胞發炎GEP和PD-L1的分子信息獲得腫瘤的特征，腫瘤類型，特性性腫瘤固有的信號通路等信息，指導臨床治療）

Materials and methods Clinical tumor samples

• Associations of TMB and the T cell–inflamed GEP with BOR and PFS were evaluated by using tumor samples from subgroups of patients treated with pembrolizumab in clinical trials who had WES data available.

  通過使用臨床試驗中接受pembrolizumab治療的患者亞組的腫瘤樣本，對TMB和T細胞炎癥性GEP與BOR和PFS的關系進行了評估，這些患者擁有WES數據。

  These included a discovery cohortofpatientswithHNSCC(KEYNOTE-012 B1),a pan-tumor validation cohort(KEYNOTE012/028), and single-indication cohorts of patients with HNSCC(KEYNOTE-012B1+B2) and melanoma(KN001and006).

  其中包括一項與HNSCC(KEYNOTE-012B1)的患者的發現(cohortofpatientswithHNSCC)，一個泛腫瘤驗證隊列(KEYNOTE012/028)，以及與HNSCC(KEYNOTE-012B1+B2)和黑色素瘤(kn001和006)患者的單指征隊列。

• The discovery cohort included 34 of 297 total enrolled patients with PDL1–selected (≥1%, modified proportion score or interface pattern, QualTek IHC) (39) HNSCC (B1 cohort).

  發現隊列包括297例入選患者中的34例(PDL1-selected≥1%，modified proportion score or interface pattern, QualTek IHC) (39) HNSCC (B1隊列)。

• The pan-tumor cohort comprised patients with PD-L1–positive (≥1%, modified proportion score or interface pattern, QualTek IHC) (39) advanced solid tumors pooled from two multicohort trials, including 39 of 297 total enrolled patients in KEYNOTE-012(cohortsA,C,and D:triple-negative breast cancer, urothelial cancer, and gastric cancer,respectively)and 80 of 450 total enrolled patients in KEYNOTE-028(17of20cohorts with anal, biliary, carcinoid, cervical, colorectal,endometrial,esophageal,estrogen receptor–positive human epidermal growth factor receptor-2–negative breast, pancreatic, salivary gland, prostate, small cell lung, thyroid, and vulvar cancers and neuroendocrine tumors, mesothelioma, and leiomyosarcoma).

  pan-tumor隊列由PD-L1-positive患者(分數比例≥1%,修改或接口模式,QualTek包含IHC)(39)高級實體腫瘤合并兩個multicohort試驗,包括39 297總登記病人的主題- 012 (cohortsA, C和D:三陰性乳腺癌,移行細胞癌,胃癌,分別)450年和80年總登記病人主題- 028 (17 of20cohorts肛門、膽道良性腫瘤,宮頸,結直腸、子宮內膜、食管、雌激素受體陽性的人表皮生長因子受體2陰性的乳腺、胰腺、唾液腺、前列腺、小細胞肺癌、甲狀腺、外陰癌、神經內分泌腫瘤、間皮瘤和平滑肌肉瘤)。

  Single-indication cohorts included 107 HNSCC patients from the KEYNOTE-012PD-L1– positive(≥1%, modified proportion scoreor interface pattern, QualTek IHC) (39) B1 ( n = 34) and PD-L1–unselectedB2(n=73)cohorts(40,41)and patients with advanced melanoma from the pembrolizumab arms of the KEYNOTE-001 (n = 30 of 668 total enrolled patients) and KEYNOTE006 (n=59 of 834 total enrolled patients)studies (26, 42).

  Single-indication組包括107 HNSCC病人的 KEYNOTE- 012 - pd - l1 -積極(≥1%,修改比例scoreor接口模式,QualTek包含IHC) (39) B1 (n = 34)和PD-L1-unselectedB2 (n = 73)組(40、41)和晚期黑色素瘤患者pembrolizumab武器的 KEYNOTE- 001 (n = 30 668總登記的病人)和KEYNOTE006 (n = 59 834總登記病人)的研究(26日42)。

  Tissue specimens were obtained with the approval ofthe institutional review boards, and patients provided informed consent [clinical trial registration: KEYNOTE-012 (NCT01848834);KEYNOTE-028 (NCT02054806);KEYNOTE-001 (NCT01295827);KEYNOTE-006 (NCT01866319)].

  組織標本獲得機構審查委員會批準，患者提供知情同意[臨床試驗注冊:KEYNOTE-012 (NCT01848834);KEYNOTE-028 (NCT02054806);KEYNOTE-001 (NCT01295827);KEYNOTE-006 (NCT01866319)].

Clinical end points

• BOR was assessed in the discovery HNSCC, pantumor, and HNSCC cohorts by central radiology reviewandinthemelanomacohortbyintegrated radiology and oncologist assessment.

  BOR在發現HNSCC、pantumor和HNSCC組中通過中央放射學評論和綜合放射學和腫瘤學評估進行評估。

• For BOR, a responder was defined as a patient with a partial response(PR)orcompleteresponse(CR),andPFS wasdefinedasthetimefromthestartoftreatment to documented evidence of progressive disease or death.

  對于BOR，應答者被定義為部分應答(PR)或完全應答(CR)的患者，pfs被定義為從開始治療到有記錄的漸進性疾病或死亡證據的時間。

• BOR and PFS were both assessed in the all patients-as-treated populations,defined as those who had received ≥1dose of study drug,in each cohort

  在每個隊列中，所有接受治療的患者(定義為接受≥1劑量研究藥物的人群)均進行BOR和PFS評估

Processing of tissue samples

• DNA sequencing (WES) and RNA analysis (gene expression profiling) were performed by using FFPE sections of pretreatment tumor samples fromtheabove-listedstudies.

  采用上述研究中預處理腫瘤標本的FFPE切片進行DNA測序(WES)和RNA分析(基因表達譜)。

• WESwasperformed onbothgermlineandtumorsamples,andgene expression profiling was performed on tumor samples.

  我們對細胞和腫瘤樣本進行了檢測，并對腫瘤樣本進行了基因表達譜分析。

• With a fresh scalpel, the tissue was either macrodissected from the marked tumor area (tissue containing <20% tumor) or scraped fromtheentiresectionandtransferredtoa1.5-ml tube containing 200 ml of 100% ethanol

  用新鮮的手術刀，從標記的腫瘤區域(腫瘤組織小于20%)大范圍切除組織，或從整個切片上刮取組織，轉移到一個1.5 ml的試管中，試管中含有200 ml的100%乙醇

Gene expression (RNA) profiling: NanoString methodology

• The previously described T cell–inflamed GEP was derived by using a stepwise derivation process of discovery, validation, and refinement of candidate genesets acrossawidevariety of solid tumors(15).

  先前描述的T細胞炎癥性GEP是通過發現、驗證和純化多種實體腫瘤候選基因集的逐步衍生過程而得到的(15)。

• The GEP was composed of 18 inflammatory genes related to antigen presentation, chemokine expression, cytolytic activity, and adaptive immune resistance, including CCL5, CD27, CD274 (PD-L1), CD276 (B7-H3), CD8A, CMKLR1, CXCL9, CXCR6, HLA-DQA1, HLA-DRB1, HLA-E, IDO1, LAG3, NKG7, PDCD1LG2 (PDL2), PSMB10,STAT1,andTIGIT.

  GEP由CCL5、CD27、CD274 (PD-L1)、CD276 (B7-H3)、CD8A、CMKLR1、CXCL9、CXCR6、HLA-DQA1、HLA-DRB1、HLA-E、IDO1、LAG3、NKG7、PDCD1LG2 (PDL2)、PSMB10、STAT1、tigit等18個與抗原表達、趨化因子表達、細胞水解活性、適應性免疫耐受相關的炎癥基因組成。

• For GEP analysis,total RNA was isolated from 5-mm-thick FFPE sections of tumor tissue fixed on positively charged slides (Ambion Recover All total nucleic acid isolationkit for FFPE;catalog no.AM1975) at ALMAC, United Kingdom.

  GEP分析從固定于帶正電荷載玻片上的腫瘤組織5 mm厚的FFPE切片中提取總RNA (Ambion回收所有用于FFPE的總核酸分離試劑盒;

• Total RNA concentrations were measured using the NanoDrop ND1000 (Thermo Fisher Scientific) in 1.5 ml of test sample.

  使用NanoDrop ND1000 (Thermo Fisher Scientific)在1.5 ml的測試樣品中測定總RNA濃度。

• Gene expression analysis was conducted on the NanoString nCounter gene expression platform (NanoString Technologies, Seattle, WA) as described previously (15).

  如前所述，在NanoString nCounter基因表達平臺(NanoString Technologies， Seattle，WA)上進行基因表達分析(15)。

• Per sample, 50 ng of total RNA was mixed in a final volume of 5 to 7 ml with a 3 ′-biotinylated capture probe and 5′-reporter probe tagged with a fluorescent barcode, from the desired custom gene expression codeset (HUIMR680_V2_C2406+PLS_SPIKE80_C2765 for Batch 1 and HUIMR800_C3176 for Batch 2), containing probes designed to function as positive and negative hybridization controls.

  每個樣本,50 ng的總RNA混合在最后一卷5到7毫升3′生物素化的捕獲探針和5′記者探針熒光條碼標記,從基因表達所需的自定義代碼集(HUIMR680_V2_C2406 + PLS_SPIKE80_C2765批次1和HUIMR800_C3176批2),包含探測器設計定位為積極的和消極的雜化控制。

  Probes and target transcripts were hybridized overnight at 65°C for 14 to 18 hours as permanufacturers’recommendations.

  探針和目標轉錄本在65°C條件下雜交14 - 18小時，這是制造商的建議。

Hybridized samples were run on the NanoString nCounter preparationstationbyusingahigh-sensitivityprotocol where excess capture and reporter probes wereremovedandtranscript-specificternarycomplexes were immobilized on a streptavidin-coated cartridge.

雜交樣品在納米字符串非計數器制備站進行，使用高靈敏度的協議，其中過量的捕獲和報告探針是由轉錄特異性的復合物固定在一個鏈霉親和素涂層墨盒。

The cartridge samples were scanned at maximum resolution by using the nCounter digital analyzer.

使用nCounter數字分析儀以最大分辨率掃描墨盒樣品。

GEP scores were calculated as a weighted sum of normalized expression values for the 18 genes.

GEP評分計算為18個基因歸一化表達值的加權和。

Quality control of the gene expression data followed an approach similar to that of the NanoString clinical-grade assay, with theuseofjointcriteriathatassessedtherelationships between housekeeping genes and the negative control probes plus a weighted score evaluating the GEP gene counts versus background subtracted counts.

基因表達數據的質量控制采用了一種類似于納米字符串臨床級檢測的方法，使用聯合標準分析了內家基因和陰性對照探針之間的關系，并使用加權評分來評估GEP基因計數與背景減除計數之間的關系。

For housekeeping normalization, raw counts for the individual genes were log10 transformed and then normalized by subtracting the arithmetric mean of the log10 counts for a set of 11 housekeeping genes.
對于管家化，對單個基因的原始計數進行log10轉換，然后通過減去一組11個管家化基因的log10計數的算術平均值進行歸一化。

WES pipeline

Somatic single-nucleotide variant (SNV) calling

• Whole-exome sequence reads were aligned to reference human genome GRCh37 by using bwa mem(43)followedbypreprocessingstepsincluding duplicate marking, indel realignment, and baserecalibrationwithPicard(v1.114)andGATK (Genome Analysis Toolkit, v2) (44) to generate analysis-ready BAM files.

  通過使用bwa mem(43)和預處理步驟(包括重復標記、indel重新排列和使用picard (v1.114)和gatk(基因組分析工具包v2)(44)對整個外顯子組序列進行比對，以參考人類基因組GRCh37。

  MuTect-called SNVs present in the Single Nucleotide Polymorphism Database (dbSNP, v141) (46) but not in the CatalogueofSomaticMutationsinCancer (COSMIC, v68)(47)werefilteredout.TheSNVswithmutant reads of <4 in tumor samples were also eliminated.

  MuTect-called SNVs出現在單核苷酸多態性數據庫(dbSNP, v141)(46)中，但沒有出現在體細胞突變目錄sincancer (COSMIC, v68)(47)中，在腫瘤樣本中，突變讀數<4的sns也被剔除。

  TMB for a subject was defined as the sum of somatic nonsynonymous SNVs thatpassed all the filters described

  受試者的TMB被定義為通過所有描述的過濾器的軀體非同義snv之和

HLA class I typing

• HLA-I major loci,A,B and C, weretyped atfourdigit resolution by using OptiType (v1.0) (48).

  HLA-I主要位點A、B和C使用OptiType (v1.0)(48)以四位數分辨率輸入。

• For output typed alleles not found in the NetMHC(v3.4)(49)inputlist,thecorresponding supertype was identified for each allele (50, 51) and the supertype-representativeallele was used for NetMHC.

  對于NetMHC(v3.4)(49)inputlist中沒有發現的輸出型等位基因，為每個等位基因(50,51)確定相應的超型，并將supertype- representative等位基因用于NetMHC。

SNV annotation and neoantigen detection

SNV注釋和新抗原檢測

• Somatic mutations were annotated with VEP (Variant Effect Predictor) (52), and nonsynony mousmutations in protein codin gregions were counted for TMB.

  用VEP(變異效應預測因子)標記體細胞突變(52)，計算TMB中蛋白codin gregions中的非同步突變。

• All possible 9-mer peptide sequences with mutated amino acid inside for each nonsynonymous mutation locus were extracted and binding affinities for patient HLA-AandHLAB alleles were computed by using NetMHC (v3.4).

  提取每個非同義突變位點所有可能含有突變氨基酸的9-mer肽段序列，使用NetMHC計算患者HLA-AandHLAB等位基因的結合親和力(v3.4)。

  The 9-mer peptide with the highest binding affinity with the HLAalleles from a nonsynonymous mutation locus was selected as the representative antigen for the mutation.

  選擇非同義突變位點與hla等位基因結合親和力最高的9-mer肽段作為該突變的代表性抗原。

  Representative antigens with HLA-A or -B binding affinity of <50 nM were considered neoantigens.

  具有代表性的HLA-A或-B結合親和力<50 nM的抗原被認為是新抗原。

Microsatellite instability (MSI) calling

• MSI phenotype was detected by applying mSINGS on WES data from tumor samples (22).

  將mSINGS應用于腫瘤樣本的WES數據，檢測MSI表型(22)。

• The stability of each mononucleotide microsatellite locus was evaluated, and the proportion of unstable microsatellite loci was determined as the MSI score.

  對每個單核苷酸微衛星位點的穩定性進行評價，確定不穩定微衛星位點的比例作為MSI評分。

  Samples with an MSI score of more than 20% were classified as MSI-high (MSI-H) positive.

  MSI評分超過20%的樣本為MSI-high (MSI- h)陽性。

• MSIwasconfirmedbyPCRbyusingthe Promega MSI analysis system, version 1.2.

  通過使用Promega MSI分析系統1.2版，msiwas得到了確認。

Mutation signature analysis

突變特征分析

• Mutational signature analysis was performed by using the deconstructSigs package (v1.6.0) in R thatselectsthecombinationofknownmutational signaturesthatcanaccountfortheobservedmutational profile in each sample (53).

  使用R中的解構tsigs包(v1.6.0)進行突變特征分析，該包選擇能夠解釋每個樣本中觀察到的突變剖面的已知突變特征的組合(53)。

• Exome regions were defined by Agilent Sureselect V5 target region.

  由安捷倫Sureselect V5靶區定義外顯子區。

• Only somatic mutations in exome regions were considered, and trinucleotide counts were normalized by the number of times each trinucleotide context was observed in the exome region.

  只考慮外顯體區域的體細胞突變，并根據外顯體區域中觀察到的每個三核苷酸上下文的次數對三核苷酸計數進行標準化。

• Mutational signatures as defined by Alexandrov et al.(54) and named as signatures.

  由Alexandrov等人(54)定義并命名為簽名的突變簽名。

• nature 2013 were the target signature set to be screened.

  《自然》雜志(nature)將于2013年對目標簽名進行篩選。

• The relationships of these various mutational signatures, including specific nucleotide changes, DNA repair, smoking, neoantigen, TP53, and APOBEC, with BOR and PFS were evaluated in patient samples in the pan-tumor cohort.

  這些不同的突變特征，包括特異性核苷酸變化、DNA修復、吸煙、新抗原、TP53和APOBEC，與BOR和PFS的關系在泛腫瘤隊列患者樣本中進行了評估。

Allele-specific copy number and purity estimation

等位基因特異性拷貝數和純度估計

• VarScan2 (55) output copy number ratio and SNP were input to Sequenza (56) to provide a maximum a posteriori estimation for cellularity and segmented allele-specific copy number for each sample.

  VarScan2(55)輸出拷貝數比和SNP被輸入到Sequenza(56)，為每個樣本的細胞數量和分段等位基因特異性拷貝數提供最大的后向估計。

Clonality

• Foreachsample,MuTect-calledsomaticSNVswith variant allele frequency information, combined with Sequenza output allele-specific copy number andcellularityestimation,wereinputtoPyClone to estimate cellular prevalence for all somatic SNVs.

  本研究以含有變異等位基因頻率信息的多克隆體snv為樣本，結合Sequenza輸出的等位基因特異性拷貝數和細胞密度估計，輸入克隆體來估計所有體細胞snv的細胞流行率。

• Mutational clonality was also inferred through the clustering process of PyClone

  通過PyClone的聚類過程推斷出突變克隆的克隆性

PD-L1 expression

• PD-L1 expression levels were evaluated in pretreatment samples by IHC staining by using the PD-L1 IHC 22C3 pharmDx kit (Agilent Technologies)inthepan-tumor and HNSCC cohorts(39);

  使用PD-L1 IHC 22C3藥物dx試劑盒(Agilent Technologies)對腫瘤和HNSCC患者進行預處理，采用免疫組化染色法檢測PD-L1的表達水平(39);

• expression levels were reported as the CPS, definedasthenumberofPD-L1–positivecells(tumor cells,lymphocytes,macrophages)dividedbythe total number of tumor cells × 100.

  表達水平以CPS表示，定義為腫瘤細胞(腫瘤細胞、淋巴細胞、巨噬細胞)被腫瘤細胞總數×100分。

• CPS was previously reported as a percentage and is now reportedas an equivalentunitlessmeasure.

  CPS以前被報道為一個百分比，現在被報道為一個等價的無單位度量。

This assay differs from the one used to determine PDL1 positivity (≥1%, modified proportion score or interface pattern, QualTek IHC) for enrollment eligibility as described above for the pan-tumor and HNSCC clinical cohorts (58).
本試驗不同于用于確定PDL1陽性(≥1%，修改的比例分數或接口模式，QualTek IHC)的入選資格，如上所述的泛腫瘤和HNSCC臨床隊列(58)。

• For the melanoma cohort, PD-L1 levels were assessed by IHC byusing the MEL score,andpositivitywasdefined as ascore of ≥2membranous PD-L1 staining in at least 1% of tumor and tumor immune cells (59)

  對于黑色素瘤隊列，使用MEL評分通過IHC評估PD-L1水平，陽性定義為至少1%的腫瘤和腫瘤免疫細胞中≥2膜性PD-L1染色(59)。

TCGA molecular data

Geneexpressiondatafor9963tumorsandsomatic alterations data for 6384 tumors were obtained through TCGA portal (16) as of September 2015

截至2015年9月，通過TCGA門戶網站(16)獲得6384例腫瘤的996363例腫瘤的基因表達數據和體細胞改變數據

Statistical methods

• The retrospective, statistical analysis of clinical samples in this study was prespecified and performed in a blinded fashion, with genomic end points generated without access to clinical outcomes.

  本研究中對臨床樣本的回顧性統計分析是預先指定的，以盲法進行，基因組終點的產生不涉及臨床結果。

• Associations with BOR were tested by using logistic regression, and associations with PFS were examined by using Cox proportional hazards models.

  用logistic回歸檢驗與BOR的相關性，用Cox比例危險模型檢驗與PFS的相關性。

• All models (logistic regression and Cox models) were adjusted for baseline Eastern Cooperative Oncology Group (ECOG) score performance.

  所有模型(logistic回歸和Cox模型)均根據東部腫瘤合作組(ECOG)基線評分表現進行調整。

• One-sided nominal P values were reported.

  單邊檢測。

• Associations between continuous variables were assessed by using Spearman correlation, and associations between continuous variables and binary variables (e.g., BOR) were further assessed by using AUROC and rank sum P values.

  連續變量之間的相關性采用Spearman相關進行評估，連續變量與二元變量(如BOR)之間的相關性進一步采用AUROC和秩和P值進行評估。

• Statistical analyses and visualizations wereperformedwithMatlabR2010orwithR3.4.1.

  采用matlabr2010或withr3.4.1進行統計分析和可視化。

• TMB cutoffs for the pan-tumor and singleindication clinical cohorts were the Youden Index values derived in AUROC analysis.

  全腫瘤和單指征患者的TMB切斷率是由AUROC分析得出的約登指數。

• An additional,exploratory,pan-tumor TMB threshold was derived by using TMB and GEP data across each cohort, similar to a previously described method (20

  另外一個探索性的泛腫瘤TMB閾值是通過使用每個隊列的TMB和GEP數據推導出來的，類似于前面描述的方法(20)