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

TMB和tcell炎癥性GEP對(duì)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(tài)定義的患者組對(duì)pembrolizumab的臨床反應(yīng)有顯著差異肾扰。

• 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.

  特別是晴裹，只有一個(gè)陽(yáng)性生物標(biāo)志物提示pembrolizumab潛在應(yīng)答的兩組(TMBhi GEPlo或TMBlo GEPhi)的應(yīng)答率明顯低于TMBhi GEPhi組赐俗，這表明對(duì)pembrolizumab的耐藥機(jī)制可能存在拾积，且可能針對(duì)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(jī)制檐束，我們通過(guò)對(duì)TCGA分子數(shù)據(jù)庫(kù)的分析骗随，評(píng)估了不同tmb和tcell -炎性gep組腫瘤之間的分子差異轰豆。

  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(zhuǎn)錄組基因與GEP的相關(guān)性弓千。

  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(guān)性的分布都偏離了正態(tài)分布衡便，因?yàn)橐环N與T細(xì)胞發(fā)炎的GEP呈顯著正相關(guān)的模式，這與溶細(xì)胞TME中細(xì)胞類型的基因表達(dá)標(biāo)志物的強(qiáng)大協(xié)同調(diào)控一致洋访。

  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.

  然而镣陕，單個(gè)基因與T細(xì)胞炎癥性GEP的相關(guān)性在tmbhi ((TMB>100突變/外顯子組)和TMBlo (TMB≤100個(gè)突變每個(gè)外顯體腫瘤)之間沒(méi)有顯著差異(r = 0.76;P < 1×10?20)(圖5B)，表明T細(xì)胞炎癥標(biāo)志物作為腫瘤新抗原性的功能缺乏定性差異姻政。

  [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.

  值得注意的是呆抑，在TMBhi和tmblo腫瘤中，GEP與正態(tài)分布的負(fù)相關(guān)范圍內(nèi)的偏差要小得多汁展，這表明沒(méi)有與T細(xì)胞排斥密切相關(guān)的主要泛癌轉(zhuǎn)錄特征鹊碍。

  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(tài)向正相關(guān)性的起源與T細(xì)胞- GEP發(fā)炎,基因與T cell-inflamed GEP呈正相關(guān)(r > 0.15)分為兩組通過(guò)達(dá)標(biāo)由偏離正態(tài)分布的相關(guān)性與T cell-inflamed GEP 分別83%和98%分位數(shù),(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(xì)胞感染的GEP(簽名中單個(gè)基因與整個(gè)簽名相關(guān)性的下界)相關(guān)食绿，而Set 2與GEP相關(guān)侈咕，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

  此外器紧，與T細(xì)胞炎癥性GEP呈負(fù)相關(guān)并偏離正態(tài)分布(r < - 0.15, 14%分位數(shù))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).

正如所料耀销，在set 1中觀察到與全細(xì)胞炎癥溶細(xì)胞過(guò)程相關(guān)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.

相比之下品洛，set 2顯示了TME中其他細(xì)胞類型特異性基因的富集树姨，包括血管內(nèi)皮和髓樣浸潤(rùn)，但沒(méi)有顯示豐富的細(xì)胞炎癥溶細(xì)胞過(guò)程或腫瘤細(xì)胞固有通路桥状。

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(guò)K-means聚類(K=10 forset2, K=4 forset1)帽揪，將set1和set2中的基因進(jìn)一步分組為基因共表達(dá)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(méi)有顯示出與TMB的強(qiáng)相關(guān)性辅斟，這與上面描述的TMB與T細(xì)胞感染的GEP之間的弱相關(guān)性一致转晰。

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(gè)模塊顯示了與TMB不同的關(guān)聯(lián)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(qiáng)烈相關(guān)的模塊和反與TMB (分別為模塊4和5),揭示了富集在生物學(xué)相關(guān)細(xì)胞增殖(module4)和脈管系統(tǒng)(模塊5)查邢。這些數(shù)據(jù)表明,不同的潛在生物學(xué)模式可以使用TMB和T細(xì)胞——發(fā)炎GEP對(duì)腫瘤進(jìn)行分類(Fig.5D)蔗崎。
(正態(tài)偏離追尋加基因富集分析，找到了弱相關(guān)的因素扰藕。)

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

  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(xì)胞炎癥GEP負(fù)相關(guān)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(xì)胞感染的GEP正相關(guān)的基因相比邓深，由此產(chǎn)生的共表達(dá)調(diào)控的生物學(xué)注釋所提供的信息更少未桥。

  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(xì)胞炎癥的GEP有負(fù)關(guān)系芥备。

  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).

  特別是冬耿，在TMBlo和T細(xì)胞發(fā)炎的GEPlo腫瘤中發(fā)現(xiàn)了一個(gè)富含基質(zhì)和Wnt信號(hào)元件的模塊(圖5D，左下面板)萌壳。

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(guò)詢問(wèn)整個(gè)轉(zhuǎn)錄組亦镶，獨(dú)立于上述基于gep的聚類方法，對(duì)T細(xì)胞炎癥性腫瘤中與TMB相關(guān)的基因進(jìn)行了額外的分析袱瓮。

• 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).

  與模塊分析相似缤骨，本分析表明，與TMB正相關(guān)的基因在增殖方面得到了富集懂讯，而與TMB抗負(fù)相關(guān)的基因則與血管和基質(zhì)生物學(xué)相關(guān)(表S7)荷憋。

  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(shí)別的基質(zhì)生物學(xué)褐望、增殖勒庄、細(xì)胞水解活性和Wnt信號(hào)的分布(13,32 - 34)也顯示了與TMB和tcell炎癥的GEP相似的關(guān)聯(lián)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

  然而瘫里，在這項(xiàng)分析中实蔽，我們不能確定TMBhi的基因表達(dá)特征，這與TMB本身或?qū)embrolizumab的反應(yīng)一樣具有預(yù)測(cè)性谨读。

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(bǔ)的方法被用來(lái)確定在突變體豐富的情況下局装，TMBhi作為免疫逃避的潛在驅(qū)動(dòng)因素的腫瘤中，低溶細(xì)胞轉(zhuǎn)錄組活性(沒(méi)有T細(xì)胞發(fā)炎的GEP)的基因組決定因素劳殖。

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.

如上所述铐尚，t細(xì)胞炎癥GEP和TMBhi腫瘤的轉(zhuǎn)錄組相關(guān)性(圖5b)呈與GEP呈正相關(guān)的分布，提示TMBhi和GEPlo腫瘤中缺乏一個(gè)強(qiáng)的轉(zhuǎn)錄組信號(hào)哆姻。

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.

因此宣增，利用先前報(bào)道的方法(13)研究TCGA的DNA變化，以TMBhi為重點(diǎn)矛缨，揭示了體細(xì)胞突變與GEP之間潛在的負(fù)相關(guān)關(guān)系爹脾。

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(qū)動(dòng)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?cè)诜蜗侔┖头西[狀細(xì)胞癌,腺瘤息肉桿菌(APC)的突變結(jié)直腸癌顯示高度顯著的負(fù)相關(guān)T cell-inflamed GEP(圖6)帖旨。

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(guān)聯(lián)在泛癌分析中均未超過(guò)名義顯著性水平(P<0.01)灵妨，提示這些軀體改變可能與癌癥類型特異性有關(guān)解阅。

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(xì)胞炎癥性GEP呈負(fù)相關(guān)的基因要么是低頻率的，要么是未知的癌癥驅(qū)動(dòng)因素(圖6B)泌霍。

fig6B

(深入研究基因相關(guān)關(guān)系货抄，與與GEP正/負(fù)相關(guān)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(xiàng)研究表明，TMBhi或溶細(xì)胞因子與某些腫瘤類型(4 - 9,11 - 13,15)對(duì)檢查點(diǎn)阻斷免疫治療的臨床反應(yīng)有關(guān)烹吵。

  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.

  然而碉熄，腫瘤免疫生物學(xué)的這兩個(gè)核心方面之間的關(guān)系，以及它們與檢查點(diǎn)阻斷免疫治療的臨床反應(yīng)的聯(lián)合關(guān)系肋拔，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.

  在這里呀酸，我們證明TMB和T細(xì)胞炎癥的GEP是腫瘤免疫生物學(xué)不同方面的組織不可知的測(cè)量方法凉蜂，并獨(dú)立預(yù)測(cè)抗pd -1治療在多種腫瘤中的反應(yīng)。

  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.

  特別是性誉，對(duì)pembrolizumab的臨床反應(yīng)有限發(fā)生在TMB和T細(xì)胞炎癥性GEP水平較低的患者中窿吩，而對(duì)這兩種生物標(biāo)志物水平較高的患者的反應(yīng)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.

  同樣错览，PD-L1 IHC和TMB表達(dá)水平高的患者反應(yīng)也有所改善纫雁，這反映了PD-L1和GEP與Tcell炎癥性TME的關(guān)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.

  這些觀察表明倾哺，使用炎癥生物標(biāo)志物轧邪，如T細(xì)胞發(fā)炎的GEP或PD-L1與TMB聯(lián)合使用，可能有助于識(shí)別對(duì)抗pd -1治療有反應(yīng)的患者羞海。

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).

此外忌愚，已經(jīng)開(kāi)發(fā)出一種方法來(lái)測(cè)量溶細(xì)胞T細(xì)胞環(huán)境的蛋白標(biāo)記，并在未來(lái)的研究中結(jié)合TMB來(lái)評(píng)估它們的性能特征却邓，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(shuō)硕糊，我們的研究表明，普遍的腫瘤抗原檢測(cè)方法與激活的T細(xì)胞可能發(fā)生的腫瘤浸潤(rùn)之間存在正交關(guān)系(14,36 - 38)腊徙。

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(xì)胞反應(yīng)的上游和下游成分简十，但有足夠的生物干預(yù)，使得每個(gè)過(guò)程的生物標(biāo)志物可以提供補(bǔ)充信息撬腾。

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(lái)越多基于pd -1和PD-L1的聯(lián)合方案顯示出臨床效益螟蝙，確定每種方案對(duì)單個(gè)患者的相對(duì)效用將變得具有挑戰(zhàn)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?qū)δ[瘤免疫生物學(xué)潛在模式進(jìn)行分層的生物標(biāo)志物工具时鸵，可能會(huì)使這些不同治療方案的患者在生物學(xué)驅(qū)動(dòng)下實(shí)現(xiàn)合理的個(gè)性化胶逢，比如選擇對(duì)免疫治療通常反應(yīng)較慢的腫瘤患者厅瞎。

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(shù)據(jù)策略是，帶狀細(xì)胞炎癥性GEP可用于將腫瘤劃分為不同的亞組初坠，這些亞組具有不同的潛在靶向生物學(xué)模式和簸，以增強(qiáng)臨床反應(yīng)。

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碟刺，血管锁保，骨髓和基質(zhì)生物學(xué)，以及腫瘤類型特異性的腫瘤細(xì)胞固有信號(hào)通路失調(diào)半沽。

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(xì)胞發(fā)炎GEP和PD-L1,以及其他新興tumor-agnostic生物標(biāo)記,需要前瞻性驗(yàn)證用于預(yù)測(cè)應(yīng)對(duì)各種免疫治療方案,包括聯(lián)合療法,研究結(jié)果報(bào)道在這里建議理由進(jìn)一步探索這些生物標(biāo)記物的效用作為精密癌癥免疫治療的指南爽柒。

（利用這種相關(guān)關(guān)系，預(yù)測(cè)免疫治療的有效性者填，提供臨床指導(dǎo)方案浩村，希望在以后可以T細(xì)胞發(fā)炎GEP和PD-L1的分子信息獲得腫瘤的特征，腫瘤類型占哟，特性性腫瘤固有的信號(hào)通路等信息心墅，指導(dǎo)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(guò)使用臨床試驗(yàn)中接受pembrolizumab治療的患者亞組的腫瘤樣本，對(duì)TMB和T細(xì)胞炎癥性GEP與BOR和PFS的關(guān)系進(jìn)行了評(píng)估榨乎，這些患者擁有WES數(shù)據(jù)怎燥。

  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(xiàng)與HNSCC(KEYNOTE-012B1)的患者的發(fā)現(xiàn)(cohortofpatientswithHNSCC)，一個(gè)泛腫瘤驗(yàn)證隊(duì)列(KEYNOTE012/028)蜜暑，以及與HNSCC(KEYNOTE-012B1+B2)和黑色素瘤(kn001和006)患者的單指征隊(duì)列铐姚。

• 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(fā)現(xiàn)隊(duì)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(duì)列)肛捍。

• 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(duì)列由PD-L1-positive患者(分?jǐn)?shù)比例≥1%,修改或接口模式,QualTek包含IHC)(39)高級(jí)實(shí)體腫瘤合并兩個(gè)multicohort試驗(yàn),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(xì)胞癌,胃癌,分別)450年和80年總登記病人主題- 028 (17 of20cohorts肛門隐绵、膽道良性腫瘤,宮頸,結(jié)直腸、子宮內(nèi)膜篇梭、食管氢橙、雌激素受體陽(yáng)性的人表皮生長(zhǎng)因子受體2陰性的乳腺、胰腺恬偷、唾液腺悍手、前列腺、小細(xì)胞肺癌袍患、甲狀腺坦康、外陰癌、神經(jīng)內(nèi)分泌腫瘤诡延、間皮瘤和平滑肌肉瘤)滞欠。

  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)肆良。

  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(biāo)本獲得機(jī)構(gòu)審查委員會(huì)批準(zhǔn)筛璧，患者提供知情同意[臨床試驗(yàn)注冊(cè):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(fā)現(xiàn)HNSCC逸绎、pantumor和HNSCC組中通過(guò)中央放射學(xué)評(píng)論和綜合放射學(xué)和腫瘤學(xué)評(píng)估進(jìn)行評(píng)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(duì)于BOR夭谤，應(yīng)答者被定義為部分應(yīng)答(PR)或完全應(yīng)答(CR)的患者棺牧，pfs被定義為從開(kāi)始治療到有記錄的漸進(jìn)性疾病或死亡證據(jù)的時(shí)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(gè)隊(duì)列中朗儒，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(jìn)行BOR和PFS評(píng)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(yù)處理腫瘤標(biāo)本的FFPE切片進(jìn)行DNA測(cè)序(WES)和RNA分析(基因表達(dá)譜)颊乘。

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

  我們對(duì)細(xì)胞和腫瘤樣本進(jìn)行了檢測(cè)，并對(duì)腫瘤樣本進(jìn)行了基因表達(dá)譜分析醉锄。

• 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(shù)刀乏悄，從標(biāo)記的腫瘤區(qū)域(腫瘤組織小于20%)大范圍切除組織，或從整個(gè)切片上刮取組織恳不，轉(zhuǎn)移到一個(gè)1.5 ml的試管中檩小，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(xì)胞炎癥性GEP是通過(guò)發(fā)現(xiàn)、驗(yàn)證和純化多種實(shí)體腫瘤候選基因集的逐步衍生過(guò)程而得到的(15)妆够。

• 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识啦、CD27、CD274 (PD-L1)神妹、CD276 (B7-H3)、CD8A家妆、CMKLR1鸵荠、CXCL9、CXCR6伤极、HLA-DQA1蛹找、HLA-DRB1、HLA-E哨坪、IDO1庸疾、LAG3、NKG7当编、PDCD1LG2 (PDL2)届慈、PSMB10、STAT1忿偷、tigit等18個(gè)與抗原表達(dá)金顿、趨化因子表達(dá)、細(xì)胞水解活性鲤桥、適應(yīng)性免疫耐受相關(guān)的炎癥基因組成揍拆。

• 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(cè)試樣品中測(cè)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).

  如前所述茶凳，在NanoString nCounter基因表達(dá)平臺(tái)(NanoString Technologies嫂拴， Seattle播揪，WA)上進(jìn)行基因表達(dá)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(gè)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(biāo)記,從基因表達(dá)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(cè)器設(shè)計(jì)定位為積極的和消極的雜化控制筒狠。

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

  探針和目標(biāo)轉(zhuǎn)錄本在65°C條件下雜交14 - 18小時(shí)猪狈，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(jì)數(shù)器制備站進(jìn)行窟蓝，使用高靈敏度的協(xié)議罪裹，其中過(guò)量的捕獲和報(bào)告探針是由轉(zhuǎn)錄特異性的復(fù)合物固定在一個(gè)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(shù)字分析儀以最大分辨率掃描墨盒樣品运挫。

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

GEP評(píng)分計(jì)算為18個(gè)基因歸一化表達(dá)值的加權(quán)和状共。

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(dá)數(shù)據(jù)的質(zhì)量控制采用了一種類似于納米字符串臨床級(jí)檢測(cè)的方法，使用聯(lián)合標(biāo)準(zhǔn)分析了內(nèi)家基因和陰性對(duì)照探針之間的關(guān)系谁帕，并使用加權(quán)評(píng)分來(lái)評(píng)估GEP基因計(jì)數(shù)與背景減除計(jì)數(shù)之間的關(guān)系峡继。

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(duì)于管家化，對(duì)單個(gè)基因的原始計(jì)數(shù)進(jìn)行l(wèi)og10轉(zhuǎn)換匈挖，然后通過(guò)減去一組11個(gè)管家化基因的log10計(jì)數(shù)的算術(shù)平均值進(jìn)行歸一化碾牌。

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(guò)使用bwa mem(43)和預(yù)處理步驟(包括重復(fù)標(biāo)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(duì)整個(gè)外顯子組序列進(jìn)行比對(duì)儡循，以參考人類基因組GRCh37舶吗。

  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(xiàn)在單核苷酸多態(tài)性數(shù)據(jù)庫(kù)(dbSNP, v141)(46)中，但沒(méi)有出現(xiàn)在體細(xì)胞突變目錄sincancer (COSMIC, v68)(47)中，在腫瘤樣本中，突變讀數(shù)<4的sns也被剔除讶迁。

  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(guò)所有描述的過(guò)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(diǎn)A、B和C使用OptiType (v1.0)(48)以四位數(shù)分辨率輸入腹侣。

• 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(duì)于NetMHC(v3.4)(49)inputlist中沒(méi)有發(fā)現(xiàn)的輸出型等位基因，為每個(gè)等位基因(50,51)確定相應(yīng)的超型齿穗，并將supertype- representative等位基因用于NetMHC傲隶。

SNV annotation and neoantigen detection

SNV注釋和新抗原檢測(cè)

• 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(yīng)預(yù)測(cè)因子)標(biāo)記體細(xì)胞突變(52)，計(jì)算TMB中蛋白codin gregions中的非同步突變窃页。

• 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(gè)非同義突變位點(diǎn)所有可能含有突變氨基酸的9-mer肽段序列跺株，使用NetMHC計(jì)算患者HLA-AandHLAB等位基因的結(jié)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(diǎn)與hla等位基因結(jié)合親和力最高的9-mer肽段作為該突變的代表性抗原腮出。

  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(jié)合親和力<50 nM的抗原被認(rèn)為是新抗原帖鸦。

Microsatellite instability (MSI) calling

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

  將mSINGS應(yīng)用于腫瘤樣本的WES數(shù)據(jù)，檢測(cè)MSI表型(22)胚嘲。

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

  對(duì)每個(gè)單核苷酸微衛(wèi)星位點(diǎn)的穩(wěn)定性進(jìn)行評(píng)價(jià)作儿，確定不穩(wěn)定微衛(wèi)星位點(diǎn)的比例作為MSI評(píng)分。

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

  MSI評(píng)分超過(guò)20%的樣本為MSI-high (MSI- h)陽(yáng)性馋劈。

• MSIwasconfirmedbyPCRbyusingthe Promega MSI analysis system, version 1.2.

  通過(guò)使用Promega MSI分析系統(tǒng)1.2版攻锰，msiwas得到了確認(rèn)晾嘶。

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(gòu)tsigs包(v1.6.0)進(jìn)行突變特征分析，該包選擇能夠解釋每個(gè)樣本中觀察到的突變剖面的已知突變特征的組合(53)娶吞。

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

  由安捷倫Sureselect V5靶區(qū)定義外顯子區(qū)垒迂。

• 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(qū)域的體細(xì)胞突變，并根據(jù)外顯體區(qū)域中觀察到的每個(gè)三核苷酸上下文的次數(shù)對(duì)三核苷酸計(jì)數(shù)進(jìn)行標(biāo)準(zhǔn)化妒蛇。

• 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机断。

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

  《自然》雜志(nature)將于2013年對(duì)目標(biāo)簽名進(jìn)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绣夺，包括特異性核苷酸變化吏奸、DNA修復(fù)、吸煙陶耍、新抗原奋蔚、TP53和APOBEC，與BOR和PFS的關(guān)系在泛腫瘤隊(duì)列患者樣本中進(jìn)行了評(píng)估烈钞。

Allele-specific copy number and purity estimation

等位基因特異性拷貝數(shù)和純度估計(jì)

• 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(shù)比和SNP被輸入到Sequenza(56)泊碑，為每個(gè)樣本的細(xì)胞數(shù)量和分段等位基因特異性拷貝數(shù)提供最大的后向估計(jì)。

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為樣本毯欣，結(jié)合Sequenza輸出的等位基因特異性拷貝數(shù)和細(xì)胞密度估計(jì)馒过，輸入克隆體來(lái)估計(jì)所有體細(xì)胞snv的細(xì)胞流行率。

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

  通過(guò)PyClone的聚類過(guò)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(duì)腫瘤和HNSCC患者進(jìn)行預(yù)處理酗钞，采用免疫組化染色法檢測(cè)PD-L1的表達(dá)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(dá)水平以CPS表示沉桌，定義為腫瘤細(xì)胞(腫瘤細(xì)胞、淋巴細(xì)胞算吩、巨噬細(xì)胞)被腫瘤細(xì)胞總數(shù)×100分。

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

  CPS以前被報(bào)道為一個(gè)百分比佃扼，現(xiàn)在被報(bào)道為一個(gè)等價(jià)的無(wú)單位度量偎巢。

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(yàn)不同于用于確定PDL1陽(yáng)性(≥1%，修改的比例分?jǐn)?shù)或接口模式兼耀，QualTek IHC)的入選資格压昼，如上所述的泛腫瘤和HNSCC臨床隊(duì)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(duì)于黑色素瘤隊(duì)列瘤运，使用MEL評(píng)分通過(guò)IHC評(píng)估PD-L1水平窍霞，陽(yáng)性定義為至少1%的腫瘤和腫瘤免疫細(xì)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月拯坟，通過(guò)TCGA門戶網(wǎng)站(16)獲得6384例腫瘤的996363例腫瘤的基因表達(dá)數(shù)據(jù)和體細(xì)胞改變數(shù)據(jù)

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(duì)臨床樣本的回顧性統(tǒng)計(jì)分析是預(yù)先指定的但金，以盲法進(jìn)行，基因組終點(diǎn)的產(chǎn)生不涉及臨床結(jié)果郁季。

• 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(yàn)與BOR的相關(guān)性冷溃，用Cox比例危險(xiǎn)模型檢驗(yàn)與PFS的相關(guān)性钱磅。

• 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(jù)東部腫瘤合作組(ECOG)基線評(píng)分表現(xiàn)進(jìn)行調(diào)整。

• One-sided nominal P values were reported.

  單邊檢測(cè)似枕。

• 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(xù)變量之間的相關(guān)性采用Spearman相關(guān)進(jìn)行評(píng)估盖淡，連續(xù)變量與二元變量(如BOR)之間的相關(guān)性進(jìn)一步采用AUROC和秩和P值進(jìn)行評(píng)估。

• Statistical analyses and visualizations wereperformedwithMatlabR2010orwithR3.4.1.

  采用matlabr2010或withr3.4.1進(jìn)行統(tǒng)計(jì)分析和可視化凿歼。

• 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(shù)褪迟。

• 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(gè)探索性的泛腫瘤TMB閾值是通過(guò)使用每個(gè)隊(duì)列的TMB和GEP數(shù)據(jù)推導(dǎo)出來(lái)的，類似于前面描述的方法(20)