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• W3128955216 abstract "Immunotherapy is a potential way to save the lives of patients with bladder cancer, but it only benefits approximately 20% of them. A total of 4,028 bladder cancer patients were collected for this study. Unsupervised non-negative matrix factorization and the nearest template prediction algorithms were employed for the classification. We identified the immune and non-immune classes from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA-BLCA) training cohort. The 150 most differentially expressed genes between these two classes were extracted, and the classification reappeared in 20 validation cohorts. For the activated and exhausted subgroups, a stromal activation signature was assessed by the NTP algorithm. Patients in the immune class showed highly enriched signatures of immunocytes, while the exhausted subgroup also exhibited activated transforming growth factor (TGF)-β1, and cancer-associated extracellular matrix signatures. Patients in the immune-activated subgroup showed a lower genetic alteration and better overall survival. Anti-PD-1/PD-L1 immunotherapy was more beneficial for the immune-activated subgroup, while immune checkpoint blockade therapy plus a TGF-β inhibitor or an EP300 inhibitor might achieve greater efficacy for patients in the immune-exhausted subgroup. Novel immune molecular classifier was identified for the innovative immunotherapy of patients with bladder cancer. Immunotherapy is a potential way to save the lives of patients with bladder cancer, but it only benefits approximately 20% of them. A total of 4,028 bladder cancer patients were collected for this study. Unsupervised non-negative matrix factorization and the nearest template prediction algorithms were employed for the classification. We identified the immune and non-immune classes from The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA-BLCA) training cohort. The 150 most differentially expressed genes between these two classes were extracted, and the classification reappeared in 20 validation cohorts. For the activated and exhausted subgroups, a stromal activation signature was assessed by the NTP algorithm. Patients in the immune class showed highly enriched signatures of immunocytes, while the exhausted subgroup also exhibited activated transforming growth factor (TGF)-β1, and cancer-associated extracellular matrix signatures. Patients in the immune-activated subgroup showed a lower genetic alteration and better overall survival. Anti-PD-1/PD-L1 immunotherapy was more beneficial for the immune-activated subgroup, while immune checkpoint blockade therapy plus a TGF-β inhibitor or an EP300 inhibitor might achieve greater efficacy for patients in the immune-exhausted subgroup. Novel immune molecular classifier was identified for the innovative immunotherapy of patients with bladder cancer. IntroductionBladder cancer is the 10th most frequent tumor globally and exhibits a high rate of recurrence.1Sanchez A. Wszolek M.F. Niemierko A. Clayman R.H. Drumm M. Rodríguez D. Feldman A.S. Dahl D.M. Heney N.M. Shipley W.U. et al.Incidence, Clinicopathological Risk Factors, Management and Outcomes of Nonmuscle Invasive Recurrence after Complete Response to Trimodality Therapy for Muscle Invasive Bladder Cancer.J. Urol. 2018; 199: 407-415Crossref PubMed Scopus (26) Google Scholar The major challenge of clinical care of bladder cancer is the short-term recurrence of non-muscle-invasive bladder cancer (NMIBC), as well as the shortened overall survival of muscle-invasive bladder cancer (MIBC) patients, especially those with distant metastases, the 5-year survival rate of whom less than 10%.2Hautmann R.E. Gschwend J.E. de Petriconi R.C. Kron M. Volkmer B.G. Cystectomy for transitional cell carcinoma of the bladder: results of a surgery only series in the neobladder era.J. Urol. 2006; 176 (discussion 491–492): 486-492Crossref PubMed Scopus (318) Google Scholar,3Chamie K. Litwin M.S. Bassett J.C. Daskivich T.J. Lai J. Hanley J.M. Konety B.R. Saigal C.S. Urologic Diseases in America ProjectRecurrence of high-risk bladder cancer: a population-based analysis.Cancer. 2013; 119: 3219-3227Crossref PubMed Scopus (192) Google Scholar In the tumor mass, the normal cells, blood vessels, and cytokines that surround and support the vitality of tumor cells compose the tumor microenvironment (TME). Crosstalk exists between the tumor and the TME. Tumor cells can alter the TME, and the TME can promote the growth and spread of tumors.From the molecular side, bladder cancer is composed of a multitude of heterogenetic characteristics, including gene mutations, gene copy number alterations, and neoantigens, as well as the infiltration of immunocytes. Several teams have established molecular classifications among bladder cancer. Mo et al.4Mo Q. Nikolos F. Chen F. Tramel Z. Lee Y.C. Hayashi K. Xiao J. Shen J. Chan K.S. Prognostic Power of a Tumor Differentiation Gene Signature for Bladder Urothelial Carcinomas.J. Natl. Cancer Inst. 2018; 110: 448-459Crossref PubMed Scopus (63) Google Scholar generated an 18-gene tumor signature in MIBC patients that can reflect the urothelial differentiation and predict clinical outcomes; basal and differentiated groups are the two groups with the highest and lowest risk scores, respectively. Damrauer et al.5Damrauer J.S. Hoadley K.A. Chism D.D. Fan C. Tiganelli C.J. Wobker S.E. Yeh J.J. Milowsky M.I. Iyer G. Parker J.S. Kim W.Y. Intrinsic subtypes of high-grade bladder cancer reflect the hallmarks of breast cancer biology.Proc. Natl. Acad. Sci. USA. 2014; 111: 3110-3115Crossref PubMed Scopus (575) Google Scholar developed BASE47, a 47 gene-based classifier, for the separate of luminal-like or basal-like subtypes of MIBC tumors. Robertson et al.6Robertson A.G. Kim J. Al-Ahmadie H. Bellmunt J. Guo G. Cherniack A.D. Hinoue T. Laird P.W. Hoadley K.A. Akbani R. et al.TCGA Research NetworkComprehensive Molecular Characterization of Muscle-Invasive Bladder Cancer.Cell. 2017; 171: 540-556.e25Abstract Full Text Full Text PDF PubMed Scopus (1078) Google Scholar further generated a consensus hierarchical clustering of luminal-papillary, luminal-infiltrated, luminal, basal/squamous, and neuronal subtypes. However, most of the molecular classifiers only focused on the clinical outcomes, not the tumor immune microenvironment. Therefore, our goals were to provide comprehensive insight into the immune response of bladder cancer patients with diverse inner molecular features and generate the classifier to screen the patients suited for immunotherapy.The non-negative matrix factorization (NMF) algorithm is a multiplicative updates algorithm; it can decompose a non-negative matrix V into two non-negative matrices, W and H.7Devarajan K. Nonnegative matrix factorization: an analytical and interpretive tool in computational biology.PLoS Comput. Biol. 2008; 4: e1000029Crossref PubMed Scopus (265) Google Scholar Similar to principal component analysis (PCA) or independent component analysis (ICA), the NMF algorithm can also use a limited number of components to reflect the original observed data, which might contain huge volumes.8Gaujoux R. Seoighe C. A flexible R package for nonnegative matrix factorization.BMC Bioinformatics. 2010; 11: 367Crossref PubMed Scopus (654) Google Scholar NMF has been applied to reveal biomarkers, classify tumor subtypes, and predict the prognosis of tumors in recent works.9Zeng Z. Vo A.H. Mao C. Clare S.E. Khan S.A. Luo Y. Cancer classification and pathway discovery using non-negative matrix factorization.J. Biomed. Inform. 2019; 96: 103247Crossref PubMed Scopus (16) Google Scholar, 10Esposito F. Boccarelli A. Del Buono N. An NMF-Based Methodology for Selecting Biomarkers in the Landscape of Genes of Heterogeneous Cancer-Associated Fibroblast Populations.Bioinform. Biol. Insights. 2020; 14 (1177932220906827)Crossref PubMed Scopus (9) Google Scholar, 11Meng J. Zhou Y. Lu X. Bian Z. Chen Y. Zhou J. Zhang L. Hao Z. Zhang M. Liang C. Immune response drives outcomes in prostate cancer: implications for immunotherapy.Mol. Oncol. 2020; (Published online December 18, 2020)https://doi.org/10.1002/1878-0261.12887Crossref Scopus (29) Google Scholar, 12Zhou Y.J. Zhu G.Q. Lu X.F. Zheng K.I. Wang Q.W. Chen J.N. Zhang Q.W. Yan F.R. Li X.B. Identification and validation of tumour microenvironment-based immune molecular subgroups for gastric cancer: immunotherapeutic implications.Cancer Immunol. Immunother. 2020; 69: 1057-1069Crossref PubMed Scopus (23) Google ScholarWe enrolled 4,028 patients with bladder cancer from independent cohorts. The NMF and nearest template prediction (NTP) algorithms were applied to distinguish patients with different immunophenotypes in The Cancer Genome Atlas Bladder Urothelial Carcinoma (TCGA-BLCA) training cohort and reappeared in the validation cohorts. The novel definition of these immunophenotypes could provide illumination for the immunotherapy of patients with bladder cancer.ResultsIdentification of the immune module and derivation of the immune class of bladder cancer4,028 bladder cancer patients were involved, along with the matched overall survival data, clinicopathological information, and gene expression profiles (Figure 1). We performed virtual microdissection using the NMF algorithm in the TCGA-BLCA training cohort. To obtain the robust immune module, we preset the respective module numbers as five to 10. When the total module number was nine, the first module strongly enriched patients with high immune enrichment scores, which were defined as the immune module (Figure 2A). The top 150 weighted genes in the immune module were defined as exemplar genes that reflected the characteristics of the immune module (Table S1). According to the ontological analysis of biological processes, these genes are associated with the activation of immunocytes, T helper 1 (Th1)/Th2 cell differentiation, T cell receptor signaling, and B cell receptor signaling (all p < 0.05; Table S2). Subsequently, we redefined the 408 bladder patients into immune-enriched or non-immune-enriched groups via the consensus clustering analysis of the 150 exemplar genes (Figure 2B). Furthermore, multidimensional scaling (MDS) random forest (RF) was further applied to define a more precise classification for the immune and non-immune classes (Figure 2C). In Figure 2D, the distributions of the 408 bladder cancer patients among the NMF modules, immune module weight, exemplar gene clustering, final immune classes, and immune enrichment score are shown.Figure 2Recognition of the immune classes by the non-negative matrix factorization (NMF) algorithmShow full caption(A) Nine modules were generated from the NMF algorithm, and the module gathered patients with high immune enrichment score were recognized as the immune module. (B) Heatmap showing the top 150 exemplar genes expression among immune-enriched and non-immune-enriched clusters, divided by consensus clustering. (C) The multidimensional scaling random forest further modified the clusters to immune and non-immune classes. (D) The distributions of patients in different NMF modules, immune module weight, exemplar gene clustering, final immune classes, and immune enrichment score.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Several immune-associated signatures (Table S3) were collected to help confirm the classification of the immune or non-immune classes, and the enrichment score of each signature for each patient was determined by single-sample gene set enrichment analysis (ssGSEA). We observed the increased enrichment of immunocytes in the immune class as compare with the non-immune class, including T cells (as reflected by the signatures of 13 T cell signature, T cells, CD8+ T cells, and T. NK. Metagene), B cells (as reflected by the signatures of B cell clusters and B.P. metagene), macrophages, tertiary lymphoid structure (TLS), cytolytic activity score (CYT), and interferon (IFN) signatures (all p < 0.05; Figure 3A). We also analyzed the activated KEGG signaling pathways by GSEA, revealing that immune cell pathways (including T cell-, B cell-, natural killer cell-, and leukocyte-associated pathways), immune response pathways (including chemokine signaling pathways, antigen processing presentation, cell adhesion molecules, and complement coagulation cascades), and proinflammatory pathways (including FC-Epsilon-RI-, NOD-like receptor-, and FC gamma R-mediated phagocytosis pathways) were all activated in the immune class (Figure S1). From the results of Figures 2, 3A (top panel), and S1 and Tables S1, S2, and S3, we microdissected the immune and non-immune classes in the TCGA-BLCA cohort, and activated immune-associated signatures and signaling pathways were observed in the immune class.Figure 3The diverse immune characteristics and heterogeneity of genetic phenotypes of non-immune class, immune-activated subgroup, and immune-exhausted subgroupShow full caption(A) Division and characterization of three immunophenotypes. CYT, cytolytic activity score; TITR, tumor-infiltrating Tregs; MDSC, myeloid-derived suppressor cell; TLS, tertiary lymphoid structure; C-ECM, cancer-associated extracellular matrix. (B) Difference of tumor-infiltrating lymphocyte abundance. (C) Difference in the PD-L1 mRNA expression level. (D) Difference in gene copy number alterations, including amplification and deletion, among arm levels and focal levels. (E) Difference the tumor mutation burden. (F) Difference in tumor neoantigens. (G) Specific mutant genes in the immune-activated subgroup. (H) Specific mutant genes in the immune-exhausted subgroup. WT, wild-type; IM-Act, immune-activated subgroup; IM-Exh, immune-exhausted subgroup.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Tumor immune microenvironment immunophenotypes are distinguished by the activation of stromal cellsFibroblasts, mesenchymal stromal cells (MSCs), and the extracellular matrix (ECM) are the key components of the tumor stroma and support and connect tumor cells.13Valkenburg K.C. de Groot A.E. Pienta K.J. Targeting the tumour stroma to improve cancer therapy.Nat. Rev. Clin. Oncol. 2018; 15: 366-381Crossref PubMed Scopus (473) Google Scholar Especially during the late stages of tumors, genetic and epigenetic alterations of tumor cells are driven by activated stromal components.14Hanahan D. Coussens L.M. Accessories to the crime: functions of cells recruited to the tumor microenvironment.Cancer Cell. 2012; 21: 309-322Abstract Full Text Full Text PDF PubMed Scopus (2789) Google Scholar MSCs act as inherent regulators of tumors and can secrete inhibiting soluble factors and alter cell surface markers to suppress the immune microenvironment. MSCs can regulate the expression of PD-L1 and impact the proliferation and induction of T regulatory cells (Tregs).15Sivanathan K.N. Gronthos S. Rojas-Canales D. Thierry B. Coates P.T. Interferon-gamma modification of mesenchymal stem cells: implications of autologous and allogeneic mesenchymal stem cell therapy in allotransplantation.Stem Cell Rev. Rep. 2014; 10: 351-375Crossref PubMed Scopus (108) Google Scholar,16van Megen K.M. van ’t Wout E.T. Lages Motta J. Dekker B. Nikolic T. Roep B.O. Activated Mesenchymal Stromal Cells Process and Present Antigens Regulating Adaptive Immunity.Front. Immunol. 2019; 10: 694Crossref PubMed Scopus (37) Google Scholar MSCs suppress immune processes by decreasing the expression of proinflammatory factors, including IFN-γ, tumor necrosis factor (TNF)-α, and interleukin (IL)-1β, or by promoting the expression of type 2 factors IL-10 and IL13.17Soboslay P.T. Lüder C.G. Riesch S. Geiger S.M. Banla M. Batchassi E. Stadler A. Schulz-Key H. Regulatory effects of Th1-type (IFN-gamma, IL-12) and Th2-type cytokines (IL-10, IL-13) on parasite-specific cellular responsiveness in Onchocerca volvulus-infected humans and exposed endemic controls.Immunology. 1999; 97: 219-225Crossref PubMed Scopus (36) Google Scholar, 18Aggarwal S. Pittenger M.F. Human mesenchymal stem cells modulate allogeneic immune cell responses.Blood. 2005; 105: 1815-1822Crossref PubMed Scopus (3599) Google Scholar, 19Selleri S. Dieng M.M. Nicoletti S. Louis I. Beausejour C. Le Deist F. Haddad E. Cord-blood-derived mesenchymal stromal cells downmodulate CD4+ T-cell activation by inducing IL-10-producing Th1 cells.Stem Cells Dev. 2013; 22: 1063-1075Crossref PubMed Scopus (29) Google Scholar For this reason, the previously defined stromal activated signature was used for the further separation of patients with high immunocyte infiltration to immune-activated and immune-exhausted immunophenotypes, which could reflect the immune response status. A total of 11.0% (45/408) of bladder cancer patients in the TCGA-BLCA cohort were recognized as the immune-activated subgroup, while the remaining 110 patients (27.0%, 110/408) belonged to the immune-exhausted subgroup, with the activated stromal phenotype (Table 2; Figure 3A). Cancer-associated ECM (C-EC) regulated by the fibroblasts can recruit immunosuppressive cells, transforming growth factor (TGF)-β is an accepted immunosuppressor in the immune microenvironment, and Tregs and MDSCs can reflect the immune-exhausted status in the TME.20Batlle E. Massagué J. Transforming Growth Factor-β Signaling in Immunity and Cancer.Immunity. 2019; 50: 924-940Abstract Full Text Full Text PDF PubMed Scopus (781) Google Scholar, 21Furukawa A. Wisel S.A. Tang Q. Impact of Immune-Modulatory Drugs on Regulatory T Cell.Transplantation. 2016; 100: 2288-2300Crossref PubMed Scopus (73) Google Scholar, 22Groth C. Hu X. Weber R. Fleming V. Altevogt P. Utikal J. Umansky V. Immunosuppression mediated by myeloid-derived suppressor cells (MDSCs) during tumour progression.Br. J. Cancer. 2019; 120: 16-25Crossref PubMed Scopus (282) Google Scholar, 23Berraondo P. Sanmamed M.F. Ochoa M.C. Etxeberria I. Aznar M.A. Pérez-Gracia J.L. Rodríguez-Ruiz M.E. Ponz-Sarvise M. Castañón E. Melero I. Cytokines in clinical cancer immunotherapy.Br. J. Cancer. 2019; 120: 6-15Crossref PubMed Scopus (373) Google Scholar These signatures were evaluated by ssGSEA, and we revealed that the tumor-infiltrating Tregs (TITRs), WNT/TGF-β, TGF-β1-activated, and C-ECM signatures were higher in the immune-exhausted subgroup than in the immune-activated subgroup (all p < 0.05; Figure 3A; Figure S2). TIM-3 and LAG3 are reported to be associated with immune exhaustion status,24Dong Y. Li X. Zhang L. Zhu Q. Chen C. Bao J. Chen Y. CD4+ T cell exhaustion revealed by high PD-1 and LAG-3 expression and the loss of helper T cell function in chronic hepatitis B.BMC Immunol. 2019; 20: 27Crossref PubMed Scopus (54) Google Scholar,25Liu J.F. Wu L. Yang L.L. Deng W.W. Mao L. Wu H. Zhang W.F. Sun Z.J. Blockade of TIM3 relieves immunosuppression through reducing regulatory T cells in head and neck cancer.J. Exp. Clin. Cancer Res. 2018; 37: 44Crossref PubMed Scopus (59) Google Scholar and we also found similar results in the immune-exhausted subgroups; increased TIM-3 (p = 0.008) and LAG3 (p = 0.218) were observed in the immune-exhausted subgroup (Figure S2). Based on the results from Figure 3A (bottom panel) and Figure S2, we separated the immune class into the immune-activated and immune-exhausted subgroups. The stromal enrichment score, TITR, myeloid-derived suppressor cell (MDSC), and WNT/TGF-β signatures increased in the immune-exhausted subgroup, as validated by the immune-exhausted markers TIM-3 and LAG3.Table 2Summary of the clinicopathological parameters of the TCGA-BLCA, GSE32894, and E-MTAB-1803 cohortsTCGA-BLCA (n = 408)GSE32894 (n = 308)E-MTAB-1803 (n = 70)Age≤7023014342>7017816528SexMale30122859Female1078011StageaSix samples lacked T stage data in the TCGA database, and two samples lacked data in GEO: GSE32894.Ta–116–T11197–T21918524T3157728T443118GradebThree samples lacked grade data in the TCGA database, and three samples lacked date in GEO: GSE32894.G1/low2148–G2–1034G3/high38415466SmokingcTwo samples lacked alive status data in the TCGA database, and 84 samples lacked data in GEO: GSE32894.No109––Yes286––StatusAlive22919928Dead1772542a Six samples lacked T stage data in the TCGA database, and two samples lacked data in GEO: GSE32894.b Three samples lacked grade data in the TCGA database, and three samples lacked date in GEO: GSE32894.c Two samples lacked alive status data in the TCGA database, and 84 samples lacked data in GEO: GSE32894. Open table in a new tab Heterogeneity of genetic phenotypes among the immune classesTo confirm the infiltration of immunocytes among the immune and non-immune classes, as distinguished by the mRNA expression profiles of the exemplar genes, we compared the tumor-infiltrating lymphocyte (TIL) abundance of 408 bladder cancer patients, which was anteriorly estimated by hematoxylin and eosin (H&E) staining,26Saltz J. Gupta R. Hou L. Kurc T. Singh P. Nguyen V. Samaras D. Shroyer K.R. Zhao T. Batiste R. et al.Cancer Genome Atlas Research NetworkSpatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images.Cell Rep. 2018; 23: 181-193.e7Abstract Full Text Full Text PDF PubMed Scopus (400) Google Scholar and observed that the TIL abundance was higher in the immune class than in the non-immune class (p < 0.001; Figure 3B), consistent with the definition of these two groups. Furthermore, we also observed a higher PD-L1 expression in the immune class than in the non-immune class (p < 0.001; Figure 3C). Gene copy number alteration (CNA), tumor mutant burden (TMB), and neoantigens were reported to exhibit crosstalk with tumor immune activation. Patients in the non-immune class showed increased levels of deletion at both the arm and focal levels (pArm-del < 0.001, pFocal-del = 0.007) but not CNA amplification (pArm-Amp = 0.733, and pFocal-Amp = 0.065) (Figure 3D), which reflected the positive association of immune infiltration and gene CNA deletion. With the online tool TIMER, we twice confirmed that the association between immune infiltration and gene CNA deletion; deep deletion and arm-level deletion of PD-1, PD-L1 and CTLA-4; and the three major immune checkpoints were linked with decreased immunocyte infiltration, especially for CD4+ T cells, neutrophils, and dendritic cells (Figure S3).The TMB in the immune class was higher than that in the non-immune class (p = 0.01; Figure 3E), while the neoantigen level exhibited no difference (p = 0.109; Figure 3F). We further compared the specific gene mutations in the immune subgroups (Figure S4A). Mutations of TP53 (53.5% versus 43.1%, p = 0.051), TTN (52.9% versus 39.5%, p = 0.011), PIK3CA (28.0% versus 17.0%, p = 0.007), and RB1 (26.0% versus 13.0%, p < 0.001) appeared more frequently in the immune class than in the non-immune class (Figure S4B). ERBB2 (p = 0.035), KMT2A (p = 0.013), PKHD1 (p = 0.007), and MDN1 (p = 0.015) were the specific mutations noted in the immune-activated subgroup (Figure 3G), and patients with EP300 (p = 0.020), HMCN1 (p = 0.014), AKAP9 (p = 0.003), and MACF1 (p = 0.016) mutations were more highly enriched in the immune-exhausted subgroups (Figure 3H). The mutation of EP300 lead to the increased expression of EP300 (Figure S4C). Based on the results from Figures 3B–3H, S3, and S4, we can conclude that the immune class exhibits lower copy number deletion, higher TIL abundance, higher TMB, and higher PD-L1 level. The specific mutant genes in the immunophenotypes are diverse.Reappearance of the three immunophenotypes in external cohortsExternal cohorts with mRNA expression profiles were collected to recapitulate the three immunophenotypes defined by the NMF algorithm with respect to the microdissected and activated stroma signature (Figure 1; Tables 1 and 2). The top 150 increased differentially expressed genes (DEGs) between the immune and non-immune classes (Table S4) were chosen as the seed genes to regenerate the immune subclasses in the external cohorts using the GenePattern module NMFConsensus, and then immune-activated and immune-exhausted subgroups were further separated via the NTP method.Table 1Summary of the detailed information of the enrolled bladder cancer cohortsDatasetData arrayPatientsReferenceTCGA-BLCARNA sequencing408https://xenabrowser.net/datapages/?cohort=GDC%20TCGA%20Prostate%20Cancer%20(PRAD)&removeHub=https%3A%2F%2Fxena.treehouse.gi.ucsc.edu%3A443E-MTAB-4321RNA sequencing476https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-4321/IMvigor210Illumina HiSeq 2500348http://research-pub.gene.com/IMvigor210CoreBiologies/GSE32894Illumina HumanHT-12 V3.0 expression beadchip308https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32894GSE83586Affymetrix Human Gene 1.0 ST Array307https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE83586GSE87304Affymetrix Human Exon 1.0 ST Array305https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE87304GSE128702Affymetrix Human Exon 1.0 ST Array256https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128702GSE13507Illumina human-6 v2.0 expression beadchip164https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE13507GSE129871Illumina HiSeq 2000 (Homo sapiens)158https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE129871GSE120736Illumina HumanHT-12 V4.0 expression beadchip145https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE120736GSE39016Affymetrix Human Exon 1.0 ST Array141https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE39016GSE128701Affymetrix Human Exon 1.0 ST Array136https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128701GSE124035Affymetrix Human Exon 1.0 ST Array133https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124305GSE86411Illumina HumanHT-12 WG-DASL V4.0 R2 expression beadchip132https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE86411GSE48276Illumina HumanHT-12 WG-DASL V4.0 R2 expression beadchip116https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE48276GSE128192Illumina HumanHT-12 WG-DASL V4.0 R2 expression beadchip112https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE128192GSE31684Affymetrix Human Genome U133 Plus 2.0 Array93https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31684GSE134292Illumina HiSeq 4000 (Homo sapiens)80https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE134292GSE93527Affymetrix Human Transcriptome Array 2.079https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE93527E-MTAB-1803Affymetrix GeneChip Human Genome U133 Plus 2.070https://www.ebi.ac.uk/arrayexpress/experiments/E-MTAB-1803/GSE69795Illumina HumanHT-12 WG-DASL V4.0 R2 expression beadchip61https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE69795 Open table in a new tab In the GSE32894 cohort, 60.7% (187/308) of patients belonged to the non-immune class, with the lower enrichment of immune-associated signatures; as for the remaining 121 patients, compared with the signatures of stromal enrichment, 42 patients divided into the immune-activated subgroup and 79 belonged to the immune-exhausted subgroup. High enrichment scores of TITR, MDSC, WNT/TGFβ, TGFβ-1-activated, and C-ECM signatures were observed in the immune-exhausted subgroup (all p < 0.01; Table 3; Figure S5).Table 3The distribution of three newly defined immunophenotypes in all enrolled cohortsDatasetNo. of patientsImmunophenotype distribution, n (%)Immune activatedImmune exhaustedNon-immuneTCGA-BLCA40845 (11.03)110 (26.96)253 (62.01)E-MTAB-432147674 (15.55)111 (23.32)291 (61.13)IMvigor21034885 (24.43)142 (40.8)121 (34.77)GSE3289430842 (13.64)79 (25.65)187 (60.71)GSE8358630763 (20.52)93 (30.29)151 (49.19)GSE8730430559 (19.34)85 (27.87)161 (52.79)GSE12870225672 (28.13)88 (34.38)96 (37.50)GSE1350716423 (14.02)36 (21.95)105 (64.02)GSE12987115826 (16.46)27 (17.09)105 (66.46)GSE12073614521 (14.48)35 (24.14)89 (61.38)GSE3901614116 (11.35)31 (21.99)94 (66.67)GSE12870113642 (30.88)34 (25.00)60 (44.12)GSE12403513332 (24.06)54 (40.6)47 (35.34)GSE8641113222 (16.67)36 (27.27)74 (56.06)GSE4827611624 (20.69)29 (25.00)63 (54.31)GSE12819211226 (23.21)36 (32.14)50 (44.64)GSE316849314 (15.05)34 (36.56)45 (48.39)GSE1342928013 (16.25)16 (20.00)51 (63.75)GSE935277913 (16.46)15 (18.99)51 (64.56)E-MTAB-18037013 (18.57)19 (27.14)38 (54.29)GSE69795619 (14.75)19 (31.15)33 (54.10) Open table in a new tab In the other cohorts, we also replicated the three immunophenotypes, and the results are displayed in Table 3 and Figures S5 and S6. In these cohorts, the distribution of immune-activated subgroups ranged from 11.3% to 30.9%, while the proportion of immune-exhausted subgroups ranged from 17.1% to 40.8%. We also observed increased scores for the immune enrichment signature and immune signaling signature in the 18 validation cohorts, as well as the other immunocyte signatures. As expected, the immune-exhausted subgroup showed increased enrichment scores for Tregs, TITR, MDSC, WNT/TGFβ, and C-ECM signatures. With the combined results from Tables 1, 2, 3, and S4 and Figures S5 and S6, our results suggest that the NMF and NTP algorithms could stably and precisely divide bladder patients into immune-activated, immune-exhausted, and non-immune phenotypes. The specific immune characteristics can reappear in all the enrolled bladder cancer cohorts.Favorable response to anti-PD-L1 therapy was observed in the immune-activated subgroupTo evaluate the response to immunotherapy of the bladder cancer patients with newly defined immunophenotypes, we collected the gene expression profile and clinical outcomes of 348 patients from the IMvigor210 cohort, a large phase II" @default.
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• W3128955216 title "Tumor immune microenvironment-based classifications of bladder cancer for enhancing the response rate of immunotherapy" @default.
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