The Predictive and Prognostic Value of T- and B-cell Transcriptomic Signatures for Clinical Response to Immune Checkpoint Blockade in Pleural Mesothelioma

The Predictive and Prognostic Value of T- and B-cell Transcriptomic Signatures for Clinical Response to Immune Checkpoint Blockade in Pleural Mesothelioma | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search The Predictive and Prognostic Value of T- and B-cell Transcriptomic Signatures for Clinical Response to Immune Checkpoint Blockade in Pleural Mesothelioma Jasper van Genugten , Daniel Faulkner , Jens C. Hahne , Maria Disselhorst , Lodewyk Wessels , Dean Fennell , Paul Baas doi: https://doi.org/10.1101/2025.05.12.25326806 Jasper van Genugten 1 Thoracic Oncology Dept, NKI-AVL - Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital , Amsterdam, Netherlands ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Daniel Faulkner 2 National Institute for Health Research Biomedical Research Centre & Cancer Research UK Experimental Cancer Medicine Centre, University of Leicester , Leicester, UK ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jens C. Hahne 2 National Institute for Health Research Biomedical Research Centre & Cancer Research UK Experimental Cancer Medicine Centre, University of Leicester , Leicester, UK ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Maria Disselhorst 3 Pulmonology Department, Noordwest Ziekenhuisgroep - Alkmaar , Alkmaar, Netherlands ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Lodewyk Wessels 4 Computational Cancer Biology Dept, Netherlands Cancer Institute , Amsterdam, Netherlands Find this author on Google Scholar Find this author on PubMed Search for this author on this site Dean Fennell 2 National Institute for Health Research Biomedical Research Centre & Cancer Research UK Experimental Cancer Medicine Centre, University of Leicester , Leicester, UK ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Paul Baas 1 Thoracic Oncology Dept, NKI-AVL - Netherlands Cancer Institute/Antoni van Leeuwenhoek Hospital , Amsterdam, Netherlands ; Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: p.baas{at}nki.nl Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background Malignant pleural mesothelioma (PM) is an aggressive cancer with limited treatment options. Although immune checkpoint blockade (ICB) with nivolumab plus ipilimumab improves overall survival and has become standard-of-care, responses are variable and there are no predictive biomarkers for treatment outcome among PM patients with epithelioid histology. Methods We developed an eight-gene T-cell and six-gene B-cell transcriptomic signature. To quantify T- and B-cell infiltration, a geometric-mean T- and B-cell expression score was calculated for each patient. Immune profiles were generated in three ICB-naïve PM cohorts (n = 448), and Kaplan-Meier analysis was used to evaluate prognostic value of the signatures. Predictive value was tested in five independent ICB-treated cohorts (n = 104). Results In ICB-naïve patients, high B-cell infiltration was associated with longer overall survival (hazard ratio (HR): 0.50, 95% confidence interval (CI): 0.31-0.82), whereas T-cell infiltration had no prognostic value. Among patients treated with 2 nd line treatment with nivolumab plus ipilimumab, high T-cell infiltration predicted better objective response and improved overall survival (HR: 0.06, 95% CI: 0.01-0.36). This effect was absent in patients treated with 2 nd line nivolumab alone or any other anti-PD1/PDL1 drug combined with non-immunotherapeutics. B-cell infiltration showed no predictive value in any ICB-treated group. Conclusions The eight-gene T-cell signature is a specific predictor of outcome after 2 nd line treatment with nivolumab plus ipilimumab, while B-cell infiltration is prognostic in ICB-naïve disease. Introduction Malignant pleural mesothelioma (PM) is an asbestos-related pleural cancer with a 5-year overall survival between 5-12% 1 - 3 . Platinum-based chemotherapy has historically been standard-of-care 4 , while the role of surgery remains controversial 5 - 6 . The CheckMate 743 phase III trial recently demonstrated that immune checkpoint blockade (ICB) treatment with nivolumab plus ipilimumab improved survival for some PM patients with a median overall survival (mOS) benefit of 18.1 months compared to 14.1 months with chemotherapy 7 . However, only ∼42% of patients respond, and immune-related toxicity combined with high treatment cost highlight the need for robust predictive biomarkers to stratify patients 7 - 8 . PD-L1 expression is the most studied predictive biomarker for ICB in PM 7 , 9 - 16 , and might have limited value in predicting response and survival after treatment with nivolumab plus ipilimumab 7 , 14 - 15 . In the CheckMate 743 trial, PD-L1 ≥ 1% predicted a benefit of ICB treatment compared to chemotherapy 7 . However, within the nivolumab plus ipilimumab arm PD-L1 expression failed to predict differences in treatment response or overall survival (17.3 vs. 18.0 months) 7 . Given the mixed treatment responses and limited predictive value of PD-L1 expression, novel biomarkers to stratify patients for nivolumab plus ipilimumab are urgently needed. Proposed alternative biomarkers include tumor mutational burden (TMB), DNA-repair defects, and chromosomal rearrangements 17 - 18 . However, while genetic alterations and TMB are predictive in some cancer types 19 - 20 , the low mutation rate and lack of microsatellite instability of PM limits their utility in this disease 21 . The tumor immune microenvironment may hold greater promise in PM 22 - 23 , as in other cancer types T- and B-cell infiltration were shown to predict ICB benefit 24 - 28 . Here, we establish transcriptomic signatures for T- and B-cells to characterize T- and B-cell infiltration in bulk RNA-sequencing data from three independent ICB-naïve 21 , 29 - 30 and five ICB-treated PM cohorts 15 - 16 , 31 - 33 . We retrospectively evaluate their prognostic value in ICB-untreated patients and their predictive value for response to 2 nd line ICB treatment in PM patients. Materials & Methods Patient cohorts All patients were diagnosed with histologically confirmed PM. Baseline characteristics, including age, sex, asbestos exposure, clinical stage (AJCC), histology, ECOG performance score, and prior chemotherapy, are summarized in Table 1 . As expected, most patients were male and had epithelioid histology. View this table: View inline View popup Download powerpoint Table 1: Baseline characteristics of the PM cohorts. The T- and B-cell signatures were analyzed in bulk RNA-sequencing data from three ICB-naïve cohorts: Bueno (n = 211) 21 , Zhang (n = 150) 29 , and Hmeljak (n = 87) 30 . We also studied RNA-sequencing data from five phase II clinical trial cohorts: NivoMes (2 nd line nivolumab; n = 26) 16 , INITIATE (2 nd line nivolumab plus ipilimumab; n = 21) 15 , MiST3 (2 nd line pembrolizumab plus bemcentinib; n = 16) 31 , MiST4 (2 nd line atezolizumab plus bevacizumab; n = 21) 32 , and MiST5 (2 nd line dostarlimab plus niraparib; n = 20) 33 . All analyses were performed in diagnostic or pre-treatment biopsies. A subset of patients (14.7% and 14.0%) from the Bueno 21 and Zhang 29 cohorts received chemotherapy prior to biopsy collection, and all patients in these two cohorts subsequently underwent surgery. All patients in the clinical trial cohorts received at least one line of platinum-containing chemotherapy before ICB-treatment. Ethics approval This study was approved by the Netherlands Cancer Institute IRB (IRBd24-234). RNA-sequencing data processing Read count RNA-sequencing data from Bueno 21 , Zhang 29 , NivoMes 16 , INITIATE 15 , and the MiST3/4/5 31 - 33 cohorts were normalized using the DESeq2 R package 34 . For the Hmeljak cohort 30 , RSEM-normalized data were retrieved from the Firebrowse portal. Since our analyses of T- and B-cell infiltration relied on ranking of samples based on T- and B-cell signature scores within each cohort, we do not expect these normalization differences (DESeq2 vs. RSEM) to impact the results of these analyses. Each cohort was normalized and analyzed independently, since no cross-cohort pooling was performed, inter-dataset correction was not required. Transcriptomic T- and B-cell signatures We developed transcriptomic signatures to quantify T- and B-cell infiltration ( Table 2 ). The T-cell signature was based on T-cell receptor (CD3D, CD3E, CD3G, CD8A, and CD8B) and effector genes (GZMA, GZMB, PRF1) 35 , while the B-cell signature was based on B-cell receptor (CD19, BANK1, FCRL1) and membrane genes (CR2, MS4A1, CXCR5) 36 . Single-cell RNA-sequencing data from the Human Protein Atlas 37 was used to validate cell-type specificity of these markers. View this table: View inline View popup Download powerpoint Table 2: Composition of the T- and B-cell signatures. Quantification of T- and B-cell infiltration For each sample a T- and B-cell score was calculated based on the log-transformed mean of signature gene expression, with a pseudo count of 1 added to each value 38 : Spearman correlations were used to assess the correlation between T-cell score and CD4, CD8, and PD-L1 immunohistochemistry (IHC) staining in the INITIATE cohort 15 , and to assess the correlation between T- and B-cell scores and immune checkpoint (PD1, PDL1, CTLA4) expression in the Bueno cohort 21 . Clustering of ICB-naïve PM patients We profiled the landscape of T- and B-cells in ICB-naïve patients through unsupervised hierarchical clustering of the samples based on the T- and B-cell signature gene panels. The ConsensusClusterPlus R package 39 was used for clustering (parameters: maxK =20, reps =2000, pItem =0.8, pFeature =1, clusterAlg =“hc”, distance =“pearson”), and optimal cluster number ( k ) was determined based on the delta area plots. Heatmaps were created for each cohort at optimal k using the log 10 -transformed gene expression values, and plotted in Microsoft Excel with colors indicating the range between lowest and highest expression values for each gene separately. Patients were classified into one of three recurrent immune phenotypes, group one: T-cell -- ;B-cell -- , group two: T-cell + ;B-cell ++ , group three: T-cell ++ ;B-cell + , with all other intermediate samples classified as “rest”. Survival analyses To test the associations between T- and B-cell infiltration and overall survival in PM patients, we used univariate Kaplan-Meier survival analysis with log-rank tests to compare survival differences. Hazard ratios (HRs) and 95% confidence intervals (CIs) were calculated for group comparisons and to create Forest plots. Based on available data and numbers, patients were ranked by T- and B-cell scores and classified into three subgroups for T- and B-cell scores: Top quartile (25%; high) versus (vs.) bottom three quartiles (75%; low). Top half (50%; high) vs. bottom half (50%; low). Top three quartiles (75%; high) vs. bottom quartile (25%; low). Statistical significance for group comparisons was adjusted for multiple-testing by Bonferroni correction (6 tests per cohort). A threshold for T-cell score of the top three quartiles (75% high) vs. bottom quartile (25% low) was determined based on analysis from the INITIATE 15 and NivoMes 16 cohorts. Multivariate Cox-analysis was performed to assess the interaction between T- and B-cell score and histology in the Bueno 21 , Zhang 29 cohorts. Statistical considerations Statistical significance was set at p < 0.05. All RNA-sequencing data processing and normalization, and T- and B-cell score calculations were performed in R-Studio. GraphPad Prism v.10.0.0 was used for statistics and plotting, and heatmaps for T- and B-cell-based subgroups of ICB-naïve patients were created in Microsoft Excel for Windows. Results Validation of T- and B-cell signatures We developed an eight-gene T-cell and six-gene B-cell transcriptomic signature to evaluate immune infiltration in PM. The cell-type specificity of these signatures was confirmed by single-cell RNA-sequencing data from the Human Protein Atlas 37 , where these signatures showed at least 13-fold and 110-fold enrichment in T-cells and B-cells, respectively, compared to other cell types ( Fig. 1a ). The T-cell score correlated with immunohistochemistry (IHC)-based CD4 + T-cell infiltration (Spearman r: 0.6301; p-value: 0.0029) and CD8 + T-cell infiltration (Spearman r: 0.7293; p-value: 0.0003) in the INITIATE 15 cohort (n = 21), but not with PD-L1 expression (Spearman r: 0.2180; p-value: 0.3558) ( Fig. 1b-d ). Download figure Open in new tab Figure 1: T- and B-cell transcriptional signatures. a ) The T- and B-cell signature genes are specifically expressed in T-cells and B-cells, respectively, in the single-cell RNA-sequencing dataset from The Human Protein Atlas. ( b-d ) Correlation between sample T-cell score and IHC-based ( b ) CD4 + T-cells, ( c ) CD8 + T-cells, and ( d ) PD-L1 expression in samples from the INITIATE trial (n = 21). Red shading indicates the 95% confidence interval around a linear regression line. Immune profiling in ICB-naïve PM cohorts Through unsupervised hierarchical clustering we identified three recurrent immune phenotypes in ICB-naïve PM patients, group one: T-cell -- ;B-cell -- , group two: T-cell + ;B-cell ++ , and group three: T-cell ++ ;B-cell + , with an additional “rest” group for all other intermediate phenotypes ( Fig. 2a-c ). The frequency distribution of these groups was similar across cohorts, with 29.3-40.3% classified as T-cell -- ;B-cell -- , 9.0-21.3% as T-cell + ;B-cell ++ , and 24.0-31.8% as T-cell ++ ;B-cell + ( Fig. 2d ). Download figure Open in new tab Figure 2: Landscape of T- and B-cell signature gene expression in three ICB-naïve PM cohorts: ( a ) Bueno et al. (2016), ( b ) Zhang et al. (2021), ( c ) Hmeljak et al. (2018). ( d ) Frequency of the recurrent immune subtypes in the three ICB-naïve PM cohorts. Kaplan-Meier survival analysis was performed to evaluate the prognostic value of these immune subgroups in ICB-naïve PM patients, and we found that the B-cell-high subgroup (T-cell + ;B-cell ++ ) was associated with improved overall survival in the Bueno 21 , Zhang 29 cohorts, but not in the Hmeljak 30 cohort ( Fig. 3a-c ). Approximately 10-20% of PM patients have significantly increased B-cell score ( Fig. 2d ), and a threshold-based analysis of patients with the 10% highest B-cell scores confirmed its prognostic value in the Bueno 21 (HR: 0.61, 95% CI: 0.39-0.94, p-value: 0.0269) and Zhang 29 (HR: 0.50, 95% CI: 0.31-0.82, p-value: 0.0058) cohorts, but not the Hmeljak 30 cohort (HR: 0.83, 95% CI: 0.37-1.846, p-value: 0.6452) ( Fig. 3d-f ). Download figure Open in new tab Figure 3: Survival of ICB-naive PM patients based on T- and B-cell immune infiltration. ( a-c ) Overall survival in the four recurrent T- and B-cell based immune subtypes in the three ICB-naïve PM cohorts. ( d-f ) Overall survival of the top 10% B-cell high versus bottom 90% B-cell low tumors in the three ICB-naïve PM cohorts. To test for interaction between T- and B-cell score and histological subtype, these factors were included as covariates in a Cox regression analysis ( Suppl. Fig. 1 ), demonstrating that the T-cell + ;B-cell ++ subgroup remained significantly associated with improved overall survival in both the Bueno 21 (HR: 0.45, 95% CI: 0.21-0.84, p: 0.019) and Zhang 29 (HR: 0.53, 95% CI: 0.32-0.85, p: 0.010) cohorts. Predictive value of T- and B-cells for ICB response Next, we assessed the predictive value of T- and B-cell score in ICB-treated PM patients. An optimal cut-off threshold for T- and B-cell score was determined by testing quartile-based cut-off levels (corrected for multiple-testing) in the INITIATE 15 (2 nd line nivolumab plus ipilimumab) and NivoMes 16 (2 nd line nivolumab-only) cohorts. An optimal cut-off threshold for T-cell infiltration was determined at the top three quartiles (75%; high) vs. bottom quartile (25%; low), while B-cell infiltration was not significant at any cut-off level ( Suppl. Fig. 2 ). Expression of the T-cell score was significantly higher in responders to 2 nd line nivolumab plus ipilimumab ( Fig. 4a ), and high T-cell score predicted significantly improved mOS in these patients (HR: 0.06, 95% CI: 0.01-0.36, p-value: 0.0021) ( Fig. 4b ). This effect was not observed in the nivolumab-only group (HR: 0.84, 95% CI: 0.33-2.13, p-value: 0.6552) ( Fig. 4c,d ). In contrast, B-cell score had no predictive value for mOS across cut-off levels ( Suppl. Fig. 2 ). Download figure Open in new tab Figure 4: Association between T-cell score, objective response, and overall survival in PM patients. ( a , c ) Expression of T-cell score stratified by treatment response in the INITIATE and NivoMes cohorts. ( b , d ) Overall survival in T-cell low (bottom 25%) versus T-cell high (top 75%) PM tumors in the INITIATE, and NivoMes cohorts. Expanding the mOS analysis to patients treated with an anti-PD1/PDL1 drug combined with non-immunotherapeutic drugs 31 - 33 ( Fig. 5 ), we found that patients with high T-cell score (top three quartiles; 75%) showed a trend towards improved survival after 2 nd line dostarlimab plus niraparib 33 (HR: 0.26, 95% CI: 0.06-1.06). T-cell score had no predictive value for 2 nd line pembrolizumab plus bemcentinib 31 (HR: 2.56, 95% CI: 0.66-9.86) or 2 nd line atezolizumab plus bevacizumab 32 (HR: 0.46, 95% CI: 0.06-3.45). Importantly, the T-cell score had no overall prognostic value in ICB-naïve patients ( Fig. 5 ), indicating the specificity of this marker for predicting response to 2 nd line nivolumab plus ipilimumab. Download figure Open in new tab Figure 5: Hazard ratio for survival in the T-cell low (bottom 25%) versus T-cell high (top 75%) subgroups in the three ICB-naïve and five ICB-treated PM cohorts. In summary, our eight-gene T-cell signature is a specific predictive biomarker for the combination of nivolumab (anti-PD1/PDL1) plus ipilimumab (anti-CTLA4) ( Fig. 4a,b ), but not for anti-PD1 monotherapy ( Fig. 4c,d ) or other non-immunotherapeutic drug combinations ( Fig. 5 ). Discussion This study explored the predictive and prognostic value of an eight-gene T-cell signature and a six-gene B-cell signature for ICB response in PM. Our results indicate that T-cell infiltration is a specific predictive biomarker for overall survival after combination treatment with 2 nd line nivolumab plus ipilimumab, but not for anti-PD1 monotherapy or combinations of anti-PD1/PDL1 with non-immunotherapeutic drugs. The T-cell signature did not show overall prognostic value in ICB-naïve patients, indicating its potential clinical utility as a specific marker for overall survival after treatment with 1 st or 2 nd line treatment with nivolumab plus ipilimumab in PM. Clinical responses to 1 st and 2 nd line nivolumab plus ipilimumab in PM are variable 7 , 14 - 15 , highlighting the need for robust biomarkers. While PD-L1 expression has been widely investigated as a predictive biomarker for ICB in PM 7 , 9 - 16 , only two trials demonstrated significant predictive value among patients treated with nivolumab plus ipilimumab 14 - 15 . The INITIATE phase II trial of 2 nd line treatment with nivolumab plus ipilimumab in 36 patients found that PD-L1 expression (>1%) significantly predicted mOS (HR: 0.16, 95% CI: 0.04 - 0.73) 15 , while the non-comparative IFCT-1501 MAPS2 trial of 2 nd line treatment with nivolumab plus ipilimumab in 108 patients found that PD-L1 expression was associated with improved objective response rate, but not 12-week disease control rate or overall survival 14 . The larger phase III CheckMate 743 trial 7 , which compared 1 st line treatment with nivolumab plus ipilimumab to treatment with chemotherapy in 605 PM patients, found that while PD-L1 expression >1% predicted better mOS after treatment with nivolumab plus ipilimumab compared to chemotherapy (HR: 0.69, 95% CI: 0.55-0.87), it did not predict differences in mOS within the patient group who received nivolumab plus ipilimumab (17.3 months vs. 18.0 months for the <1% vs. ≥1% PD-L1 groups, respectively) 7 . By establishing an RNA-based T-cell signature, we were able to retrospectively compare the prognostic and predictive value of T-cell infiltration in three large ICB-naïve 21 , 29 - 30 (n = 448 patients) and five ICB-treated 15 - 16 , 31 - 33 (n = 104 patients) PM cohorts. Our results indicate that this signature has potential for distinguishing between PM patients that do, or do not achieve clinical benefits after 2 nd line treatment with nivolumab plus ipilimumab. The signature did not predict clinical benefits for other anti-PD1/PDL1 treatment combinations, and did not have an overall prognostic value in ICB-naïve patients. These findings align with previous IHC-based analyses of CD4 + and CD8 + T-cell infiltration, which demonstrated that T-cell infiltration has predictive value for objective response rate after 2nd-line treatment with nivolumab plus ipilimumab 40 . Infiltration of B-cells has been linked to improved outcomes after ICB treatment in other cancers 26 - 28 , but we did not find any significant association between B-cell signature expression and ICB response in PM. This inconsistency might in part be explained by the small proportion of PM patients with elevated B-cell infiltration (9.0-21.3%) combined with the small sample size of the clinical trial cohorts used in this study (16-26 patients). Larger clinical trial cohorts will be required to definitively clarify the predictive value of B-cell infiltration for ICB response in PM. Interestingly, we found that there is a subgroup of 9.0-21.3% of B-cell-high tumors among ICB-naïve patients, which is associated with a significantly improved mOS compared to B-cell-low tumors. This finding aligns with previous reports 41 - 43 and suggests a role for B-cells in PM tumor control, potentially through an ongoing humoral anti-tumor response that is capable of suppressing remaining tumor cells after surgery. Our study has several limitations. Due to the rare nature of PM and the small sample size of available phase II clinical trial cohorts (16-26 patients), our ability to evaluate the predictive value of these markers in the clinically relevant non-epithelioid subgroup is limited. The non-epithelioid subgroup was shown to derive the greatest benefit from 1 st line nivolumab plus ipilimumab 7 , and a larger randomized study in both epithelioid and non-epithelioid tumors will be required to assess the predictive value of the T-cell signature in this subgroup. In addition, PM tumors are physically large (median tumor volume between 100 and 620 cm 3 ) 44 and show significant genetic and micro-environmental intra-tumor heterogeneity 29 , 45 . Single-biopsy-based biomarkers may therefore not accurately capture whole-tumor heterogeneity. Finally, bulk RNA-sequencing data lacks the single-cell resolution required to identify more specific immune populations - such as exhausted T-cells - that might have an even stronger predictive value. Future studies based on spatial transcriptomics in ICB-treated PM patients will refine the predictive potential of these immune subpopulations. In conclusion, we established high expression of an eight-gene T-cell signature as a potential biomarker for predicting response and mOS after 2 nd line nivolumab plus ipilimumab in PM. Larger randomized prospective studies in PM patients treated with 1 st line nivolumab plus ipilimumab are required to advance the clinical application of this biomarker. Data Availability Most of the datasets analyzed in this study (Zhang et al., and the INITIATE, NivoMes, MiST3, MiST4, and MiST5 clinical trial datasets) are not publicly available due to patient privacy and data sharing restrictions. The authors do not have permission to redistribute these datasets. Processed data from the study by Hmeljak et al. (2018) were retrieved from Firebrowse ( http://firebrowse.org/ ). Additional processed data and analysis scripts are available from the authors upon reasonable request. References 1. ↵ Selikoff , Irving J. , Jacob Churg , and E. 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