The Tumor Immune Microenvironment Drives a Prognostic Relevance That Correlates with Bladder Cancer Subtypes Carolin Pfannstiel1; Pamela L

Published OnlineFirst April 15, 2019; DOI: 10.1158/2326-6066.CIR-18-0758

Research Article Cancer Immunology Research The Tumor Immune Microenvironment Drives a Prognostic Relevance That Correlates with Bladder Cancer Subtypes Carolin Pfannstiel1; Pamela L. Strissel2; on behalf of the BRIDGE Consortium, Germany, Katherine B. Chiappinelli3; Danijel Sikic4, Sven Wach4, Ralph M. Wirtz5; on behalf of the BRIDGE Consortium, Germany, Adrian Wullweber1; Helge Taubert4, Johannes Breyer6, Wolfgang Otto6, Thomas Worst7, Maximilian Burger6, Bernd Wullich5, Christian Bolenz8; on behalf of the BRIDGE Consortium, Germany, Nicole Fuhrich1, Carol I. Geppert1; Veronika Weyerer1, Robert Stoehr1, Simone Bertz1, Bastian Keck4, Franziska Erlmeier9, Philipp Erben7, Arndt Hartmann1, Reiner Strick2, and Markus Eckstein1; on behalf of the BRIDGE Consortium, Germany

Abstract

Muscle-invasive bladder cancer (MIBC) represents phenotype with upregulated immune checkpoints associat- approximately two-thirds of invasive urothelial bladder ed with poor survival. Within the TIME are tertiary lym- cancers (UBC) and has high morbidity and mortality. Men phoid structures (TLS), which can mediate antitumor activ- are over 3-fold more frequently affected by UBC than ity via immune cells. High TLS amounts and close tumor women. Despite intensive efforts to improve patient treat- distance correlated significantly with an inflamed pheno- ment and outcome, two-thirds of patients with UBC will type and favorable survival. The uninflamed and evasion have a recurrence or disease progression within 5 years. We phenotypes showed lowest TLS numbers, farthest tumor demonstrated that the quantity and spatial distribution of distances, and shortest survival. High inflammation also stromal tumor-infiltrating lymphocytes (sTIL) within the correlated with increased neoantigen load and mutational tumor immune microenvironment (TIME) predict stages of burden. Patients treated with adjuvant chemotherapy tumor inflammation, subtypes, and patient survival and showed a favorable prognosis, which was dependent on correlate with expression of immune checkpoints in an high sTILs. Determination of sTILs and tumor subtypes may analysis of 542 patients with MIBC. High sTILs indicated stratify therapy success and patient survival, and considering an inflamed subtype with an 80% 5-year DSS, and a lack of sTILs can easily be quantified using simple morphologic immune infiltrates identified an uninflamed subtype with a parameters, like hematoxylin and eosin, sTILs can be imple- survival rate of less than 25%. A separate immune evading mented for predicting patient survival in a routine manner.

Introduction tumors and/or lymph node metastasis (2). MIBC is characterized Urothelial bladder cancer (UBC) is one of the 10th most by a poor 5-year survival of 40%–60% (2). common malignancies worldwide and the second most common Molecular characterization of MIBC demonstrates two major cancer of the genitourinary tract (1). Current therapy for muscle- phenotypes, luminal and basal tumors with signiﬁcant prognostic invasive bladder cancer (MIBC) consists of radical cystectomy, and predictive value (3, 4). Immunotherapeutic agents offer with bilateral lymphadenectomy in combination with platinum- new therapy options for patients with metastasized UBC (5). The based perioperative chemotherapy, in patients with extravesical FDA has approved immunotherapeutic agents targeting PD-1 or

1Institute of Pathology, University Hospital Erlangen, Friedrich-Alexander Note: Supplementary data for this article are available at Cancer Immunology Universitat€ Erlangen-Nurnberg,€ Erlangen, Germany. 2Department of Research Online (http://cancerimmunolres.aacrjournals.org/). Gynecology and Obstetrics, University Hospital Erlangen, Friedrich- Alexander Universitat€ Erlangen-Nurnberg,€ Erlangen, Germany. 3Depart- R. Strick, and M. Eckstein are the cosenior authors of this article. ment of Microbiology, Immunology, and Tropical Medicine, The George C. Pfannstiel and P.L. Strissel are co-ﬁrst authors of this article. Washington University School of Medicine and Health Sciences, Washington D.C. 4Department of Urology and Pediatric Urology, University Hospital Corresponding Authors: Markus Eckstein, University Hospital Erlangen, Erlangen, Friedrich-Alexander Universitat€ Erlangen-Nurnberg,€ Erlangen, Krankenhausstraße 8-10, Erlangen 91054, Germany. Phone: 49-09131-85- Germany. 5STRATIFYER Molecular Pathology, Cologne, Germany. 6Depart- 22525; Fax: 49-09131-85-25674; E-mail: [email protected]; and ment of Urology, Caritas Hospital St. Josef, University of Regensburg, Reiner Strick, [email protected] Regensburg, Germany. 7Department of Urology, University Hospital Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim, Germany. Cancer Immunol Res 2019;7:923–38 8Department of Urology, University Hospital Ulm, University of Ulm, Ulm, doi: 10.1158/2326-6066.CIR-18-0758 Germany. 9Institute of Pathology, University Hospital TU Munich, TU Munich, Munich, Germany. 2019 American Association for Cancer Research.

www.aacrjournals.org 923

Pfannstiel et al.

PD-L1, which have demonstrated prolonged stable responses in sTILs assessment metastatic UBC (6). Therapeutic response of many tumors, sTILs were analyzed on hematoxylin and eosin (H&E)-stained including UBC, correlates with amounts of tumor-infiltrating tissue sections of both cohorts by two observers (M. Eckstein and lymphocytes (TIL), tertiary lymphoid structures (TLS), the C. Pfannstiel). TCGA images were accessed via the Cancer Digital number of neoantigens, and tumor mutational burden (TMB; Slide Archive (cancer.digitalslidearchive.net). sTILs were scored in refs. 5, 7–9). the desmoplastic tumor stroma according to a validated meth- TILs in the tumor immune microenvironment (TIME) have odology of the International Working Group on TILs (Supple- been researched for over 40 years, with one initial article mentary Fig. S1A and S1B; refs. 13, 20). Large fibrotic and necrotic describing infiltrating T cells in UBC (10). TILs can consist of areas and sTILs within noninvasive papillary tumor regions were þ T, B, and natural killer (NK) cells. CD8 cytotoxic T-effector excluded (20). sTILs of normal healthy bladder tissues (n ¼ 65) cells (TEFF) play a critical role in restraining tumor develop- were also scored according to the above criteria (Supplementary þ ment, whereas CD4 Th cells can have pro- or antitumor Fig. S1A). The normal tissue samples were collected from healthy effects (11). TILs predict a better prognosis for patients with patients without the diagnosis of bladder cancer or inflammatory different tumor types, including colorectal, breast, and ovarian diseases of the bladder. cancers (12–15). Immune gene expression patterns are enriched in UBC and expression of immune checkpoint genes CCC-EMN: Tissue microarray is enhanced in tumors, for example, T-cell inflammation–relat- FFPE blocks with at least 50% tumor (minimal tumor size ed genes (16, 17). Only limited data of small patient cohorts 5 5 mm), well delimited invasion borders, and without have indicated positive prognostic relevance of TILs for patient necrotic areas or granulomatous inflammation were selected survival (18–20). Because the presence of TILs seems to predict from each patient out of the CCC-EMN cohort (n ¼ 148). H&E- response to immuno-oncologic treatments, characterization of stained slides were scanned (Panoramic P250, 3DHistech) the MIBC TIME is needed (5). and annotated with CaseViewer 2.0 (3DHistech). Four cores We present comprehensive data on the role of stromal tumor- (diameter 1 mm; two cores from the invasion front and infiltrating lymphocytes (sTIL) in MIBC. sTILs quantities pre- two cores from the tumor center) were isolated using the dicted different immune phenotypes, survival, and tumor sub- TMA-Grandmaster (3DHistech; refs. 21, 22). types in 542 patients with MIBC. Five inflammation-associated genes correlated with sTILs and delineated high, low, or absent CCC-EMN: IHC and analysis of PD-L1 and TLSs levels of tumor inflammation, which correlated with tumor sub- IHC was performed on 4-mm tissue sections on a Ventana types and patient survival. Using IHC, we revealed detailed BenchMark Ultra autostainer (Ventana) according to accredited knowledge about spatial tumor organization and identified three staining protocols (https://www.dakks.de/en) using the follow- different inflammatory phenotypes and a unique tumor evasion ing antibodies: PD-L1 (SP263 assay, Ventana), CD3 (F7.2.38, phenotype, all affecting patient outcomes. We further showed a monoclonal mouse; Thermo Fisher Scientific; dilution 1:50), CD8 significant correlation of sTILs with APOBEC mutational load, (C8/144B, mouse monoclonal; Thermo Fisher Scientific; dilution neoantigens, and TMB. 1:50), CD79a (JCB117, mouse monoclonal; Zytomed Systems; dilution 1:50), PD-1 (NAT105, mouse monoclonal; Ventana), CD68 (PG-M1, mouse monoclonal; Thermo Fisher Scientific; Materials and Methods dilution 1:60), and CD56 (MRQ-42, monoclonal mouse; Cell Study cohorts and histopathologic review Marque; dilution 1:50; example pictures depicted in Supplemen- We investigated two independent MIBC cohorts: (i) the Com- tary Fig. S1C). þ prehensive Cancer Center Erlangen Metropol Region Nuremberg PD-L1 immune cells (IC) and tumor cells were scored on cohort (CCC-EMN; n ¼ 135); and (ii) The Cancer Genome Atlas TMAs by two pathologists (A. Hartmann and M. Eckstein) accord- bladder cancer cohort (TCGA BLCA) cohort (n ¼ 407). Patients ing to the distributor's PD-L1 scoring algorithm (21, 22). Posi- within both cohorts were treated with radical cystectomy and tivity of tumor cells was scored as a percentage with specific, bilateral lymph node dissection. The CCC-EMN cohort initially membranous staining using the PD-L1 assay described above consisted of 148 MIBC cases, of which 13 were excluded due to (Ventana SP263). Number of ICs was scored as the percentage the following issues: 8 were metastasized and for 5 cases, no ratio of the area occupied by positive ICs per tumor area (22). Data representative tissue was available. Out of the TCGA BLCA cohort ranges based from the hierarchical cluster analysis for defining (n ¼ 413), 6 cases were excluded due to missing RNAseq data and absence, very low, low, moderate, or high expression of these DNA mutational analysis. No further exclusion criterions were variables were as follows: (i) sTILs: very low ¼ interquartile applied. range (IQR) 1%–5%, low ¼ IQR 5%–20%, moderate ¼ IQR þ For analysis in the CCC-EMN cohort, standard formalin-fixed 10%–27.5%, and high ¼ IQR 15%–75%; (ii) PD-1 ICs: IQR paraffin-embedded (FFPE) tumor tissues (paraffin blocks) were 0.81–3.16, low ¼ IQR 2.0–4.57, moderate ¼ IQR 3.85–5.1, þ collected from pathologic routine case files. The blocks were and high ¼ IQR 4.90–7.12; (iii) PD-L1 ICs: very low ¼ IQR stored at room temperature until further use. For each analysis, 0%–0.5%, low ¼ IQR 0.-3.0%, high ¼ IQR 7.0%–15.7%; (iv) þ fresh tissue cuts were taken from the blocks. PD-L1 TCs: absent ¼ IQR 0%, moderate ¼ IQR 0%–27.5%, The CCC-EMN cohort was reevaluated and classified accord- high ¼ IQR 53.8%–100%. ing to the most recent TNM classification (2017) and the WHO TLSs were scored on digitalized whole mount IHC sections 2016 classification of genitourinary tumors. Clinical, patho- stained with the abovementioned antibodies by two observers logic, and molecular data of the TCGA cohort were updated (M. Eckstein and C. Pfannstiel). Slides were scanned with a slide accordingly (3). Clinicopathologic data are depicted in Sup- scanner (Panoramic P250, 3DHistech) and viewed with the Case- plementary Table S1. Viewer 2.0 (3DHistech). Total amounts of TLSs were scored in the

924 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

tumor surrounding tissue and within the tumor tissue. TLSs were RNA/cDNA concentration was assessed via a Qubit 4.0 Fluorom- defined by the following criteria: (i) germinal centers with eter (Thermo Fisher Scientific). Expression of CD3Z (CD247), þ þ þ þ CD79a B cells; (ii) CD68 macrophages; and (iii) CD3 CD8 CD8A, PD-L1 (CD274), PD-1 (PDCD1), and CXCL9 were assessed TEFF cells. The TLS minimal distance (in mm) was also measured in triplicates by qRT-PCR on a Versant kPCR system (Siemens) with the CaseViewer 2.0 (3DHistech). TLS distance of TLSs with according to the following protocol using approved qRT-PCR-Kits direct contact to the tumor mass at the invasive margin or with from STRATIFYER Molecular Pathology (STRATIFYER; refs. 24, localization in the tumor was scored as 0 mm. 25): 5 minutes at 50C, 20 seconds at 95C followed by 40 cycles of 15 seconds at 95C and 60 seconds at 60C. Forty amplification CCC-EMN: Automated image analysis by the Definiens cycles were applied, and the cycle quantification threshold (Cq) Developer Software values of target genes and the reference gene for each sample (S) For this analysis, TMA sections stained for CD3, CD8, PD-1, were estimated as the median of triplicate measurements. Primers CD68, and CD56 were scanned with a slide scanner (Panoramic were purchased from Eurogentec. Primer sequences are depicted P250, 3DHistech). The files were imported into the Definiens in Supplementary Table S2. Developer Software (Definiens AG). Tumor, normal, and stroma Data were normalized, using three reference genes (RPL37A, tissue and empty spaces on all TMA cores were segregated by a B2M, CALM2) according to MIQE guidelines (26). Final values machine-learning approach (Composer module of Definiens were generated using DCt from the total number of cycles to ensure Developer Software). Correct area detection was visually checked that normalized gene expression obtained is proportional to the for all cores with the Definiens Tissue Studio (Definiens AG) corresponding mRNA expression level. "region of interest correction" and revised to include digitally undetected tumor cells and exclude staining artifacts (diamino- Gene expression normalization of adjuvant chemotherapy þ þ þ benzidine remnants) and empty spaces. CD3 , CD8 , PD-1 , cohort þ þ CD68 , and CD56 immune cells (IC) were then quantified TocomparegeneexpressiondataofPD-1 (PDCD1), CD3Z automatically by the software algorithm. Cell counts were (CD247), CD8A, PD-L1 (CD274), and CXCL9 in the cohort of 2 obtained as counts per mm and log2-transformed for further 102 patients treated with adjuvant platinum-containing che- analysis. motherapy, gene expression was normalized by a common approach to calculate Z-Scores [x ¼ sample value (40DCT value CCC-EMN: Molecular subtyping via NanoString in CCC-EMN-cohort; log2-transformed value in the TCGA- RNA was isolated from five 10-mm FFPE sections with at least cohort); m ¼ median gene expression in entire cohort; s ¼ SD 50% tumor of using the Maxwell Promega RNA purification Kit of the cohort]. (Promega). Sections were deparaffinized with xylol and rehy- ðÞx m Z ¼ drated with ethanol. Fractionated tumor tissue was digested with Score s 30 mL Proteinase K (Promega), and RNA was extracted by an automated magnetic bead-based approach using the Promega Reference cohort for patients out of the CCC-EMN-Cohort Maxwell (Promega). Purified RNA was measured via a Qubit 4.0 was the entire CCC-EMN-Cohort (n ¼ 135). Reference cohort Fluorometer (Thermo Fisher Scientific) according to NanoString for patients out of the TCGA-Cohort was the entire TCGA- assay recommendations. Four-hundred nanograms of RNA of Cohort (n ¼ 407).Thiscohortconsistsof69patientsofthe each sample was hybridized with 8 mL of the NanoString detection TCGA BLCA cohort and 33 patients of the CCC-EMN cohort. probe–containing mastermix for 16 hours at 67C on a Master- Clinicopathologic data of this cohort are displayed in Supple- cycler X50p (Eppendorf AG). After hybridization, samples were mentary Table S1. loaded in standard 12-stripe NanoString tubes and purified by the nCounter Prep-station (NanoString). Purified samples were load- TCGA BLCA: Gene expression and mutational data analysis ed in the analysis cartridge by the Prep-station, and the cartridge Assignments for mRNA subtypes, APOBEC mutational containing purified RNA hybrids was analyzed via the MaxFlex load, mutational signatures (MSig), mutational burden [total nCounter system (NanoString). According to NanoString recom- single-nucleotide variant (SNV) count], and amount of mendations, no technical replicates were analyzed. A sample of an predicted neoantigens were adapted without changes from MIBC patient was used as an external tissue control. For internal Robertson and colleagues (3). Tertile assignments of mutation- controls, every codeset library of NanoString contains premade al burden and neoantigen load were kindly provided by Jaegil positive and negative control. A custom NanoString panel of 21 Kim (Broad Institute, Boston, MA) and Gordon Robertson genes for luminal and basal differentiation were selected accord- (BC Cancer Agency, Vancouver, Canada). RSEM RNA-seq data ing to MD Anderson Cancer Center (MDACC, Houston, TX) were accessed via cBioportal (http://www.cbioportal.org/) and subtyping approach (ref. 23; Supplementary Table S2). Gene log2-transformed (27). counts were normalized using two reference genes (SDHA, HPRT1) and log2-transformed for further analysis by the nSolver TCGA BLCA: IC population using the CIBERSORT algorithm 4.0 software (NanoString). Further analysis was carried out with The relative fraction of 22 IC populations based on the JMP SAS 13.2 (SAS Institute). whole-exome TCGA RNA-seq dataset was calculated using the CIBERSORT algorithm (https://cibersort.stanford.edu/) with CCC-EMN: qRT-PCR 1,000 permutations (28). RNA was extracted on the basis of a magnetic bead approach (RNA-Xtract-Kit, STARTIFYER) using a single 10-mm FFPE section TCGA BLCA: Antigen-presenting cell signature with tumor content of at least 50% on a STRATIFYER XTRAKT Cell surface expression of genes in plasmacytoid dendritic platform (STRATIFYER, Molecular Pathology GmbH; ref. 21). cells (pDC), conventional dendritic cells (cDC), and M1 and

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 925

Pfannstiel et al.

M2 macrophages, as well as genes related to antigen processing Ethical aspects and presentation, were extracted from the open access pathway This study was approved by the ethical review board of the analysis database (https://pathcards.genecards.org/) provided Friedrich-Alexander-University Erlangen-Nurnberg€ (Erlangen, by the Weizmann Institute (Rehovot, Israel; Supplementary Germany; approval number: no. 3755 and 329_16B) in accor- Table S3). dance with the Declaration of Helsinki. All patients gave a written informed consent. TCGA BLCA: IC inflammation gene signature fl fi Genes for the IC in ammation signature de ning immune cell Results clusters (ICC) were obtained by coexpression analyses performed with cBioPortal (http://www.cbioportal.org/; ref. 27). The cutoff High sTILs associate with increased immune cells and for gene expression of CD3Z and CD8A was set at 0.5 SDs from the checkpoint gene expression Tumors within the CCC-EMN cohort with high infiltration of mean cohort expression (Z-score; see section below for calcula- þ þ n ¼ sTILs were characterized by dense infiltrates of CD3 , CD8 , tions). Genes of interest ( 59) with coexpression correlation þ þ þ þ (Spearman) of higher than 0.70 were selected (Supplementary CD68 , CD56 , PD-1 , PD-L1 ICs and high numbers of TLSs Table S4). The gene list (n ¼ 59) is also represented in Supple- (Fig. 1A–C; Supplementary Fig. S1A–S1C). This was in contrast to mentary Table S4. normal healthy bladder tissue, where significantly lower amounts of sTILs were noted (Supplementary Fig. S1A). We found no Cutoffs and cut-off calculations association between sTILs and different tumor stages. To test whether sTIL phenotypes render information about the spatial The p-quantiles p ¼ 1 and p ¼ 2 were calculated with JMP SAS 3 3 organization of the TIME at the invasive front and tumor center, 13.2 (SAS Institute; tertile split; T1 < p ¼ 1 ;p¼ 1 < T2 < p ¼ 2 ; 3 3 3 we performed a hierarchical cluster analysis (Fig. 1A). Four ¼ 2 T3 > p 3 ) to split the respective cohorts into three homogenous immune tumor phenotypes emerged: (i) cluster A (uninflamed; parts. Tertile ranges in respective subgroups are depicted in 36% of cases)—very low infiltration of sTILs (median sTILs: 3%), þ þ þ Supplementary Table S5. PD-1 and PD-L1 ICs, and absent PD-L1 TCs (both median: 0%); (ii) cluster B (inflamed-low; 29% of cases)—low sTILs þ þ Statistical analysis (median sTILs: 10%), including low PD-1 and PD-L1 ICs þ Descriptive statistics were employed to characterize the distri- (median: 0.5%) and absence of PD-L1 TCs (median: 0%); (iii) butions of continuous variables (mean, SD, SEM, quartiles, cluster C (inflamed-high; 22% of cases)—high sTILs (median þ þ median, range) and nominal variables (frequency, percentage). sTILs: 35%), high PD-1 and high PD-L1 IC expression (median: þ Disease-specific (DSS) and overall survival (OS) curves were 10%), moderate PD-L1 TCs (median: 0%; upper quartile estimated by the Kaplan–Meier method. Median potential fol- 27.5%); and (iv) cluster D (evasion; 13% of cases)—moderate þ þ low-up was calculated by Reverse Kaplan–Meier analysis. For each sTILs (median sTILs: 15%), moderate PD-1 and low PD-L1 ICs þ subgroup, the number of patients at risk at various milestone time (median: 1.5%) and high PD-L1 TCs (median: 80%). A disease- points are shown in a table below the survival plot. Statistical specific 5-year survival of patients with inflamed-high tumors comparisons of subgroups included nonparametric Wilcoxon (cluster C) was 80.0% (95% CI: 59.6%–91.6%), whereas less than rank-sum test and Kruskal–Wallis test for continuous variables, 24.1% (95% CI: 13.8%–38.6%) of patients with uninflamed Pearson x2 test for nominal variables, and log-rank test for time- tumors (cluster A) survived a 5-year period (Fig. 1D). Patients to-event outcomes. Although a number of related statistical tests with the evasive tumor phenotype (cluster D) with very high were conducted (such as TCGA/all, TCGA/basal, TCGA/luminal), PD-L1 TC expression (4.4-fold compared with cluster C) showed significance adjustment for multiple testing was not performed. an unfavorable 5-year DSS of 29.3% (95% CI: 11.9%– Multivariable survival analyses were conducted using Cox pro- 55.9%; Fig. 1D). Amount of sTILs correlated strongly with differ- portional hazards regression modeling to assess the magnitude of ent immune cell populations at the invasion front and the tumor impact (i.e., the HR) of immune phenotype or sTILs, while center (Fig. 1E). adjusting for well-established prognostic clinicopathologic vari- Gene expression of T-cell genes [CD3Z (CD247), CD8A], a ables [e.g., pT-Stage, pN-Stage, lymphovascular invasion (L), age, T-cell chemoattractant induced by IFNg (CXCL9), and two gender, presence of distant metastasis, and tumor grading immune checkpoints [PD-1 (PDCD1) and PD-L1 (CD274)] asso- (WHO2016 and WHO1973)]. Variables for final multivariable ciated with high infiltration of sTILs (Fig. 2A and B). Clustering models were selected by step-wise back and forward selection these five genes revealed three hierarchical clusters corresponding that is depicted in detail in Supplementary Table S6 for each with declining amounts of sTILs (median sTILs 40% vs. 3% in the applied model. Indicator variables were employed to model cluster "no" expression; Fig. 2A and B). Tumors with an evasion multilevel nominal variables. All P values were two-sided, and a phenotype assembled throughout all three immune gene clusters P value <0.05 was considered statistically significant. Cluster (Fig. 2A). analysis was performed by unsupervised hierarchical clustering We then quantified TLSs in the peri- and intratumoral stroma, based on average link algorithm (WPGMA, weighted pair- which mediate T cells to the tumor and promote interactions with group method using arithmetic averages) using Euclidean dis- antigen-presenting cells to generate an antitumoral immune tance as the metric scale. All statistical analyses were performed response (refs. 29, 30; Supplementary Fig. S2A and S2B). Tumors by GraphPad Prism 7.2 (GraphPad Software Inc.) and JMP with an inflamed-high phenotype had the highest number of TLSs SAS 13.2 (SAS). R 3.3.3 (https://www.r-project.org/) was (median: 11 TLSs), whereas the uninflamed and evasion pheno- employed to obtain tertile splits of total single nucleotide types had the lowest [uninflamed median: 0 TLS; evasion median: variants and predicted neoantigens in the TCGA cohort as 3 TLSs (Fig. 1C)]. The inflamed phenotype correlated with close described previously (3). distances (mm) of TLSs to the tumor-invasive front (median: 37

926 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

mm vs. median: 423 mm in uninflamed tumors; Fig. 1C). Close- The TIME of MIBC correlates with the prognostic impact of sTILs range localization of TLSs to the tumor (129.5 mm; median We predicted that the presence of significantly higher sTILs split), as well as high overall number of TLSs (4 TLS; median- would correlate with increased antitumor immune responses. split) associated with a significantly higher DSS and emerged as an Distinct IC populations within the TCGA cohort were calculat- independent predictor for better DSS in multivariable Cox regres- ed (28). Nonparametric Spearman rank correlation revealed sion analysis (Supplementary Fig. S2C and S2D; Supplementary correlations of sTILs with the relative amount of all IC popula- þ Table S6). tions (r ¼ 0.63): CD8 T cells (r ¼ 0.49), NK cells (r ¼ 0.52), þ CD4 T cells (r ¼ 0.58), as well as M1 macrophages (r ¼ 0.63) High infiltration with sTILs identifies patients with favorable and M2 macrophages (r ¼ 0.43; Fig. 3D; Supplementary Table prognosis S3). Unsupervised hierarchical cluster analysis of 59 inflamma- Because sTILs can fight tumors, we predicted that sTILs tory immune cell–related genes in the TCGA cohort was per- positively influence bladder cancer patient survival. To test formed to determine correlation of sTILs and IC inflammatory this, we applied tertile splitting of the cohort into three genes (Supplementary Table S4). Three distinct hierarchical inflam- equal parts (Supplementary Table S5). Tertile splits revealed mation immune cell clusters (ICC) were detected: high inflamma- that patients with high infiltration of sTILs and high T-cell– tion (ICC-high), low inflammation (ICC-low), and uninflamed associated gene expression have a significantly better DSS (Fig. 3A). Inflamed tumors demonstrated high sTIL infiltration and (Fig. 2C and D). High sTIL infiltration also was an independent correlated with the basal squamous phenotype (70.3% of ICC-high predictor for better DSS in a multivariable Cox regression tumors) and a portion of luminal tumors (ICC-high for 1.0% model (multivariable P < 0.0001 for sTILs indicator; Fig. 2C; luminal, 5.9% luminal papillary, and 22.8% luminal- Supplementary Table S6). infiltrated; Fig. 3A). A total of 65.9% of luminal papillary tumors were assigned as uninflamed, with lower or no sTILs (Fig. 3A). sTIL infiltration is dependent on bladder cancer subtypes We then separately analyzed basal (n ¼ 142) and luminal MIBCs can be classified into molecular subtypes (3, 4, 23): tumors (n ¼ 243; Supplementary Table S1) of the TCGA cohort luminal, basal, and neuronal/neuroendocrine-like. Depending for the same 59 immune genes and sTILs to investigate their upon gene expression signatures, several lineage subclasses importance in the specific subgroups. Three inflammation clusters were also noted by different groups: p53-/EMT-like luminal significantly correlated with sTILs in basal tumors (Fig. 4A; Sup- tumors (MDACC subtyping; ref. 23) and less aggressive luminal plementary Fig. S4A and S4B). Basal MIBC has been reported as papillary tumors, as well as the epithelial mesenchymal tran- the most aggressive subtype, with poor OS (3, 23). However, we sition (EMT)-enriched luminal and luminal-infiltrated tumors found that patients with basal tumors and high infiltration of [TCGA (3)]. To investigate whether sTILs were associated with sTILs have a favorable prognosis (multivariable P < 0.0001 for tumor subtypes, we performed gene expression subtyping of sTILs indicator; Fig. 4B), which is consistent with the inflamed the CCC-EMN cohort using MDACC subtyping (Supplemen- basal-type breast cancer (13). Three inflammation clusters corre- tary Fig. S3A; ref. 23). Forty-five tumors were assigned as lating with sTILs were also present in luminal tumors (Fig. 5A; luminal EMT-like/p53-like, 28 as luminal, 52 as basal, and Supplementary Fig. S5A–S5C). Luminal-infiltrated tumors within 10 as double negative, whereas 8 showed partial neuroendo- the ICC-high cluster demonstrated high sTIL infiltration, whereas crine differentiation (Supplementary Fig. S3A; ref. 23). Basal luminal papillary tumors segregated into the ICC-low and unin- tumors exhibited the highest sTIL infiltration (Supplementary flamed gene expression clusters (60% in ICC-low, 85% in Fig. S3A). To validate this, we performed unsupervised hierar- uninflamed; Fig. 5A). Although luminal and luminal-infiltrated chical clustering with the same 21 target genes in 407 tumors tumors are considered aggressive with poor prognosis (3), we [TCGA BLCA cohort (Supplementary Fig. S3B)]. A total of demonstrated that high infiltration of sTILs were an independent 114 tumors were classified as luminal EMT/p53-like, 147 as predictor for better OS in these respective subgroups (multivar- luminal, 126 as basal, and 20 were double negative (Supple- iable P ¼ 0.027 for sTILs indicator; Fig. 5B; Supplementary mentary Fig. S3B). Congruent with our findings from the CCC- Table S6). EMN cohort, basal tumors demonstrated the highest sTIL To understand the basis for increased sTILs in the TIME, we infiltration (Supplementary Fig. S3A and S3B). A correlation tested whether increased somatic tumor mutations could lead of sTILs with tumor subtypes of the TCGA cohort also con- to presentation of novel peptides (neoantigens) recognized by firmed that basal squamous tumors exhibited the highest sTILs the immune system and antitumor activity (8, 31). Tumors compared with luminal-infiltrated tumors (second highest with high nonsynonymous singlenucleotidevariants/high count), and luminal and luminal papillary tumors (TCGA TMB (SNV), a high APOBEC-driven mutational burden, and subtyping; Fig. 3A). high neoantigen load are indicators for prolonged survival (32). Although these early molecular events have been described for Prognostic impact of sTILs in the TCGA cohort MIBC (3), a direct correlation of these factors with downstream To investigate the prognostic impact of sTILs, we applied tertile- immunologic cells, like sTILs, has not been determined. We splitting. The tertile split was an independent predictor for better found significantly elevated SNVs, APOBEC-mediated muta- OS in multivariable Cox regression analysis (multivariable P ¼ tions, and neoantigens in inflamed tumors within the TCGA 0.0002 for sTILs indicator; Fig. 3B; Supplementary Table S6). cohort (Fig. 6A–D; Supplementary Fig. S6). Increased expres- Congruent with a high sTIL infiltration, gene expression of sion of genes mediating antigen presentation, like HLA, also the specific immune cell genes CD8A, CD3Z, CXCL9, and FOXP3 associated with high sTILs and the basal subtype (Supplemen- and immune cell checkpoint–regulatory genes PDCD1 (PD-1), tary Fig. S7A; Supplementary Table S7). The infiltration of sTILs CD274 (PD-L1), LAG3, CTLA4, and IDO1 was significantly higher was significantly higher in subpopulations with high TMB, high in groups with high sTILs (Fig. 3C). neoantigen load, high APOBEC-mediated mutational burden,

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 927

Pfannstiel et al.

Immune phenotypes Immune phenotypes D C B A A: Uninflamed B: Inflamed low Intrinsic subtypes D: Inflamed high 100% E: Evasion phenotype sTILs (%) Intrinsic subtypes 0% Double negave Luminal +3 log2 (fold change) -3 Luminal EMT/p53-like Basal

*** *** B *** *** D − *** 10 * P < 0.0001; HR = 0.15 [95%-CI 0.05 0.36]* *** 50 *** 100 *** *** *** 8 40 *** 30 6 Center+IF) ; Center+IF) 50 2 (%; Center+IF) 20 *** 4 IC TC (%; IC (log + + 2 10 + PD-L1 PD-L1 0 0 PD-1 0 Probability to survive to survive (%) Probability n h h n gh o ig o ed i i h sion . low m h . f .hig asi a f. as amed v l Inf. lown Ev fl Inf. low Eva In Inf E nf I in Inf Log-rank: P < 0.0001 i n ninflamed Un U U Paents at risk (n) *** C 60 4,000 ** *** Uninﬂamed 48 18 15 11 4 3 2 0

* ** 3,000 Inf. low 39 22 12 9 7 3 2 0 40 m m) ** 2,000 Inf. high 30 24 20 18 15 6 6 0

20 Evasion 18 8 7 5 3 3 3 0 1,000 to tumor ( Amount TLS (n) *Mulvariable P-value for indicator „Immune Phenotype“; shown mulvariable TLS shortest distance 0 0 HR: inﬂamed high vs. uninﬂamed

w d h d o igh ion e e . l h s sion f f. lam In n Eva Inf. low va I inf Inf. higE E Un Uninflam Invasion front Tumor center ) ) 15 15 2 15 15 15 15 2 ) ) ) ) 2 2 2 2 log 10 10 10 10 10 10

5 5 (CD56, 5 5 5 5 CD8 T cells (log CD8 T cells (log CD3 T cells (log 0 0 0 CD3 T cells (log 0 0 0 NK cells 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 NL cells (CD56, log 0 20 40 60 80 100 sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) ρ = 0.73 [95%-CI 0.64−0.80] ρ = 0.65 [95%-CI 0.54−0.75] ρ = 0.78 [95%-CI 0.71−0.84] ρ = 0.68 [95%-CI 0.57−0.76] ρ = 0.63 [95%-CI 0.51−0.72] ρ = 0.71 [95%-CI 0.61−0.78] ) ) 2 2 ) ) 2 15 15 100 15 2 15 100 (log 80 (log 80 IC 10 10 10 IC 10 60 60 40 5 5 5 5 40 20 20 PD-L1 IC (%) PD-L1 IC (%) 0 0 0 0 0 0 PD-1 - Posive 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 0 20 40 60 80 100 PD-1 - Posive 0 20 40 60 80 100 0 20 40 60 80 100 Macrophages (CD68, log Macrophages (CD68, log sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) ρ = 0.54 [95%-CI 0.40−0.65] ρ = 0.63 [95%-CI 0.51−0.72] ρ = 0.59 [95%-CI 0.46−0.69] ρ = 0.50 [95%-CI 0.36−0.62] ρ = 0.75 [95%-CI 0.67−0.82] ρ = 0.60 [95%-CI 0.47−0.70]

100 100

50 TC (%) 50 PD-L1 PD-L1 TC (%) 0 0 0 20 40 60 80 100 0 20 40 60 80100 sTILs (%) sTILs (%) ρ = 0.43 [95%-CI 0.28−0.56] ρ = 0.36 [95%-CI 0.19−0.50]

928 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

A B *** CCC-EMN-Cohort: Immune gene cluster (n = 135) 100 *** 80 *** 60 40

sTILs (%) 20 Immune gene cluster High Low No 0 Intrinsic subtype w Immune phenotype No o gh 100% L Hi sTILs (%) 0% Immune gene cluster

Intrinsic subtypes Immune phenotypes +3 40-ΔCt fold change -3 Double negave A: Uninflamed Luminal B: Inflamed low Luminal EMT/p53-like D: Inflamed high Basal E: Evasion Phenotype sTILs Terle Split *** *** C D 45 *** 45 *** 40 ** ) − ) ) t P < 0.0001; HR = 0.20 [95%-CI 0.09 0.41]* *** t t 40 40 ** ** C 35 ** 35 35 30 (40-Δ C (40-Δ (40-Δ C 30 30 25 25 25 PD-1 CD8A 20 PD-L1 20 20

1 1 1 2 3 T T2 T3 T T2 T3 T T T *** *** 40 *** 50 *** ) )

** t ** t 40 35 C 30 30 (40-Δ 20 Log-rank: P < 0.0001 25 10 CD3Z (40-Δ C 20 CXCL9 0 Disease-speciﬁc survival (months) Paents at risk (n) 2 1 3 T1 T T3 T T2 T T1 (<5%) 41 18 13 10 5 4 3 0

T2 (≥5-15%) 53 24 17 13 6 3 3 0

T3 (>15%) 41 28 22 17 12 7 6 0 *Mulvariable P-value for indicator „sTILs terles“; shown mulvariable HR: T3 vs. T1

Figure 2. sTILs and immune gene expression in the CCC-EMN Cohort (n ¼ 135). A, Immune cell gene expression using unsupervised hierarchical cluster analyses based on the subtypes and phenotypes discussed in Fig. 1A. B, Immune gene clusters' inﬁltration of sTILs. C, Kaplan–Meier regression of DSS based on sTILs tertile split (tertile 1–3: T1–T3). Log-rank P < 0.0001; multivariable P < 0.0001 for indicator "sTILs tertiles." Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "sTILs tertiles" derived by multivariable Cox regression is depicted above the survival curves followed by the multivariable HR for Tertile group 3 versus Tertile group 1. The table below the survival plot shows patients at risk in different tertile split groups (ﬁrst column total amount of patients in each split group) with following columns showing the number of patients at risk in 20-month increments. D, Immune gene expression (PDCD1, CD274, CXCL9, CD3A, CD3Z) based on tertile groups (, P < 0.05; , P < 0.01; , P < 0.001 tested by Mann–Whitney test).

and distinct mutational signatures described by Robertson and num-based chemotherapeutic agents enhance antitumor immu- colleagues (MSig; ref. 3; Fig. 6B). nomodulatory mechanisms, such as recruitment and stimulating proliferation of TEFF cells (33). To investigate whether patients sTILs correlate with better survival after adjuvant platinum- treated with cis- or carbo-platinum adjuvant chemotherapy containing chemotherapy showed a dependency on sTIL levels and proﬁted from therapy, High sTIL inﬁltration is an indicator for a favorable outcome we combined 69 patients from the TCGA and 33 patients after peri-operative chemotherapy in breast cancer (13). Plati- from the CCC-EMN cohorts (Supplementary Table S1 and S5;

Figure 1. Hierarchical clustering of tumor immune cell phenotypes of the CCC-EMN cohort. A, sTILs and macrophages according to immune cell phenotypes (left) and spatial tumor location (IF, invasion front; center, tumor center; TC, tumor cells; IC, immune cells). Unsupervised hierarchical cluster analyses (average-linkage 2 algorithm) representing the absolute cell counts per mm (log2-transformed) revealed four tumor immune phenotypes (indicated on the left and below the heatmap). Four clusters were revealed: Cluster A (uninflamed), cluster B (inflamed-low), cluster C (inflamed-high), and cluster D (evasion. sTILs in percent (shown below heatmap, scale in percent, left). B, Percentage of PD-L1þ TCs (left) and ICs (middle), as well as count of PD-1þ ICs (right) according to immune phenotypes described in A. C, Number and distances (mm) of tertiary lymphoid structure (TLS)-containing lymphocytes and their association with tumor phenotypes shown in A (, P < 0.05; , P < 0.01; , P < 0.001; tested by Mann–Whitney test). D, Kaplan–Meier regression of disease-specific survival (DSS) based on immune cell phenotypes. Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "immune phenotypes" derived by multivariable Cox regression is depicted above the survival curves, followed by the multivariable HR for inflamed-high versus uninflamed. Table shows total patients/phenotype with the following columns showing the number of patients at risk in 20-month increments. E, Nonparametric Spearman rank correlations (Spearman r) of sTILs scores with counts of CD3þ and CD8þ T cells, CD68þ macrophages, PD-1þ immune cells, CD56þ NK cells, and PD-L1 scoring of ICs and TCs.

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 929

Pfannstiel et al.

A +3 log2 (fold change) -3

Intrinsic subtypes (TCGA) Angen presenng cell Luminal signature (AGPS) Luminal inﬁltrated No Luminal papillary Low Neuronal High Basal squamous

Immune cell cluster (ICC) Uninﬂamed Low High

Distribuon of TCGA intrinsic subtypes across Immune cell cluster ICC high ICC low Uninﬂamed

5.9% 5.6% 7% 7.3% 9.1% 11% 22.8% 19.7% 6.7% 37.3% 70.3%

30.3% 65.9%

*** ** ** * 100 * 100 *** Immune cell cluster (ICC) ICC High ICC Low Uninﬂamed 80 80 AGPS 60 60 Intrinsic subtypes (TCGA) 100% 40 40 sTILs (%) sTILs (%) sTILs (%) 20 20 0% 0 0

al s d h n u ow ig ated o mi ame Cl Ch u am l IC C papillaryL u inf I l infiltr n na l lsq U mi ina Lu Basa Lum BC*** ) TCGA: sTILs Terle split ) *** *** ) 2 2 15 15 10 *** 2 *** *** P = 0.0002; HR = 0.43 [95%-CI 0.28−0.65 ]* 8 *** 10 *** 10 *** 6

(RSEM log 4 5 5 2 PD-1 (RSEM log PD-L1 (RSEM log FOXP3 0 0 0 2 3 2 T1 T T3 T1 T2 T T1 T T3 )

) *** *** 2

15 ) 15 15 2

2 *** *** *** *** *** 10 *** 10 10 *** Log-rank: P < 0.0001 5 5 5 CD3Z (RSEM log LAG3(RSEM log Paents at risk (n) 0 0 CTLA4 (RSEM log 0 T1 (<7%) 1 1 2 2 3 126 47 24 13 7 3 0 00 0 T T2 T3 T T T3 T1 T T

T2: ≥7%, <25% 139 55 27 16 11 5 2 22 0 *** *** *** ) ) 2 20 20 ) 15 T3 (≥25%) 2 142 69 31 21 7 6 6 43 0 *** *** 2 *** 15 *** 15 *** *** *Mulvariable P-value for indicator „sTILs terles“; shown mulvariable HR: T3 vs. T1 10 10 10 5 5 5 IDO1 (RSEM log CD8A(RSEM log 0 CXCL9 (RSEM log 0 0 3 3 3 T1 T2 T T1 T2 T T1 T2 T

D 0.20 0.4 0.15 1.5 0.3 0.4 0.15 0.3 0.3 0.10 1.0 0.2 0.10 0.2 0.2 T cells (rao) 0.05 0.5 T cells (rao) 0.1 + 0.05 0.1 + 0.1 M φ -M1 (rao) M φ -M1 (rao) NK Cells (rao) CD8 CD4 0.00 0.0 0.00 0.0 0.0 0.0 Total immune cells (rao) 0 20406080100 020406080100 0 20406080100 0 20406080100 0 20406080100 0 20406080100 sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) sTILs (%) ρ = 0.63 [95%-CI 0.57−0.69] ρ = 0.43 [95%-CI 0.35−0.51] ρ = 0.52 [95%-CI 0.44−0.59] ρ = 0.63 [95%-CI 0.56−0.68] ρ = 0.49 [95%-CI 0.41−0.56] ρ = 0.58 [95%-CI 0.51−0.64]

Figure 3. TCGA-TIME (n ¼ 407). A, Unsupervised hierarchical clustering of 59 genes related to immune cell inflammation revealed three immune cell clusters (ICC): Inflamed-high, inflamed-low, and uninflamed. Distribution of tumor subtypes and correlation with the antigen-presenting cell signature (AGPS) are indicated below ICC clusters, as well as in pie distributions (right) and graphs with sTILs (%) (right). B, Kaplan–Meier regression of survival based on sTILs tertile split (T1–T3). Log-rank P < 0.0001; multivariable P ¼ 0.0002 for indicator "sTILs tertiles." Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "sTILs tertiles" derived by multivariable Cox regression is depicted above the survival curves followed by the multivariable HR for "Tertile group 3" versus "Tertile group 1". Table shows tertile split groups (first column total number of patients) with following columns

showing the number of patients at risk in 20-month increments. C, Cytotoxic TEFF and Th cells (CXCL9, CD3A, CD3Z) and immune checkpoint [CTLA4, IDO1, PDCD-1 (PD-1), CD274 (PD-L1), LAG3] gene expression based on tertile split (, P < 0.05; , P < 0.01; , P < 0.001; tested by Mann–Whitney test). D, Spearman rank correlations (Spearman r) of sTILs and different immune cell populations calculated by CIBERSORT algorithm.

930 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

A +3 Log (fold change) -3 2

Angen presenng cell Immune cell cluster (ICC) signature (AGPS) Uninﬂamed No Low Low High High

*** ** 100 ** 80

60 (%)

40 sTILs

ed igh m low h la C C f IC in IC n U

Immune cell cluster (ICC) ICC High ICC Low Uninﬂamed AGPS 100% sTILs (%) 0% B TCGA Basal: sTILs Terle split

P < 0.0001 HR = 0.27 [95%-CI 0.15−0.48]*

Log-rank: P < 0.0001

Paents at risk (n)

T1 (<15%) 40 11 6 4 2 0 0 00 0

T2 (≥15%;<35%) 48 23 14 9 7 4 4 44 4

T3 (≥35%) 53 29 14 9 3 3 3 10 0

*Mulvariable P-value for indicator „sTILs terles“; shown mulvariable HR: T3 vs. T1

Figure 4. TCGA-TIME in basal tumors (n ¼ 142). A, Unsupervised hierarchical clustering of 59 genes related to immune cell inﬂammation clusters (ICC) and association with the AGPS (below) and sTILs (%; below; scale in % on left) in basal differentiated tumors. Right graph shows sTILs (%) based on ICC clusters (, P < 0.05; , P < 0.01; , P < 0.001) tested by Mann–Whitney test. B, Kaplan–Meier regression showing survival based on sTILs tertile split (T1–T3). Log-rank P < 0.0001; multivariable P ¼ 0.0002 for indicator "sTILs tertiles". Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "sTILs tertiles" derived by multivariable Cox regression is depicted above the survival curves followed by the multivariable HR for Tertile group 3 versus Tertile group 1. Table shows Tertile split groups (ﬁrst column total number of patients) with following columns showing the number of patients at risk in 20-month increments.

Supplementary Fig. S7B). Unsupervised hierarchical clustering Tertile splitting revealed that high sTILs associated with of PDCD1, CD274, CD3Z, CD8A,andCXCL9 deﬁned three increased patient overall survival after chemotherapy immune gene expression clusters. The high cluster was repre- [multivariable P ¼ 0.0001 for sTILs indicator; multivariable sented by 61.9% basal tumors and 38.1% luminal tumors with HR ¼ 0.22 (Tertile group 3 vs. Tertile group 1); Fig. 7C; the highest sTILs (median sTILs count: 27% vs. 7% for low sTILs Supplementary Table S6] and increased immune cell inﬂam- and 5% for no sTILs; Fig. 7A and B; Supplementary Fig. S7A). mation–related gene expression (Fig. 7D). A total of 65.1% of

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 931

Pfannstiel et al.

Log (fold change) A +3 2 -3

Angen presenng cell Immune cell cluster (ICC) signature (AGPS) Uninﬂamed No Low Low High High Intrinsic subtypes (TCGA) Luminal inﬁltrated Luminal papillary Luminal

*** *** 100 *** 80 60 40 sTILs (%) 20 0

d w h ig Clo h C C I IC Uninflame

Immune cell cluster (ICC) ICC High ICC Low Uninﬂamed Intrinsic subtypes (TCGA) AGPS 100% sTILs (%) 0%

Distribuon of TCGA intrinsic subtypes across Immune cell cluster

ICC high ICC low Uninﬂamed 3% 4% 11% 21% 19% 24%

3% 60% 85% Intrinsic subtypes (TCGA) Luminal inﬁltrated Luminal papillary Luminal B TCGA Luminal: sTILs Terle split P = 0.027; HR = 0.48 [95%-CI 0.28−0.82]*

Log-rank: P = 0.0006

Paents at risk (n) T1 (<5%) 60 26 14 9 6 2 0 0

T2 (≥5; <15%) 80 33 18 8 4 2 0 0

T3 (≥15%) 103 46 18 13 5 4 2 0

*Mulvariable P-value for indicator „sTILs terles“; shown mulvariable HR: T3 vs. T1

Figure 5. TCGA-TIME in luminal tumors (n ¼ 243). A, Unsupervised hierarchical clustering of 59 genes related to immune cell inﬂammation clusters (ICC) and association with luminal subtypes, the AGPS (below) and sTILs % (below; scale % on left). Pie distributions below shows TCGA luminal subtypes in relation to ICC clusters and subtypes. Right graph shows sTILs (%) based on ICC clusters (, P < 0.05; , P < 0.01; , P < 0.001 tested by Mann–Whitney test). B, Kaplan–Meier regression showing survival based on sTILs tertile split (T1–T3). Log rank P < 0.0001; multivariable P ¼ 0.027 for indicator "sTILs tertiles." Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "sTILs tertiles" derived by multivariable Cox regression is depicted above the survival curves followed by the multivariable HR for Tertile group 3 versus Tertile group 1. Table shows tertile split groups (ﬁrst column total number of patients) with following columns showing the number of patients at risk in 20-month increments.

932 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

APOBEC Mutaonal load

A ** * Immune cell cluster (ICC) No Low High 10,000 * Uninﬂamed 100% ) *

10 Low 1,000 High 35.4 100 80% 44.4 55.4 10 60% 1 0.1 Total amount (log 40% 46.3 46.5 Vs) ad 36.6 SN l en lo 20% ta ig o nt a o 18.3 MB (t e 7.9 9.2 T N 0% ICC High ICC Low Uninﬂamed *** B *** *** *** 100 *** *** *** 100 ** 100 *** 100 *** 80 80 80 80 60 60 60 60

Figure 6. (%) Correlation of SNVs, neoantigens, 40 40 40 40 sTILs (%) sTILs (%) sTILs(%) sTILs APOBEC mutational load, and 20 20 20 20 mutational signatures in TCGA. A, Total 0 0 0 0 SNVs (SNVs/TMB; log10) and neoantigen Terle 2 1 2 3 w h 3 4 groups (T) T1 T T3 T T T No o load (log10), as well as the APOBEC L Hig ig T1 MSig 1MSig 2MSig MS mutation load based on different ICC T2 clusters within the entire TCGA cohort. T3 TMB Terles Neoangen terles APOBEC TCGA mutaonal B, Percentage of sTILs in the entire Mutaonal load signatures TCGA cohort based on tertile groups and TMB, neoantigen load, APOBEC- C APOBEC Mutaonal load mediated mutational burden, and ** * No Low High

distinct mutational signatures (MSig). ) 10,000 Immune cell cluster (ICC) 100% 10 Uninﬂamed ICC gene clusters' total SNVs/TMB, 1,000 Low 35.8 neoantigen load, and APOBEC 100 High 80% 43.1 54.1 mutational burden in basal (C)and 10 60% luminal subtypes (D)(P value 1 abbreviations , P 0.05; , P 0.01; < < Total amount (log 0.1 45.3 P – 40% , < 0.001 tested by Mann Whitney 49.2 ad o 35.1 test). nl ge 20% tal SNVs) ti (To 18.9 eoan 7.7 10.8 TMB N 0% Uninﬂamed ICC Low ICC High D APOBEC Mutaonal load High Low No * * ) 10,000 Immune cell cluster (ICC) 100%

10 Uninﬂamed 1,000 Low 35.7 High 80% 47.4 100 67.7 10 60%

1 Total amount (log 40% 51.4 ad 47.4 Vs) lo lSN en 20% ta g 29.0 (to 12.9 eoanti 5.3 3.3 TMB N 0% Uninﬂamed ICC Low ICC High

the tumors within Tertile group 3 were basal. These results Discussion indicated that patients with basal and luminal tumors, with Following tumor antigen presentation in lymph nodes, TILs high sTILs, benefitfromadjuvantplatinum-containing chemo- including T, B, and NK cells are primed to mount a powerful therapy. This is in line with reports demonstrating a favorable immunologic inflammatory response to tumor cells. Negative outcome of patients with breast cancer treated with neo- T-cell checkpoint inhibitors engage with ligands, like PD-1 with adjuvant chemotherapy for basal-like tumors with high PD-L1, to ensure a balanced immune response to avoid an sTILs (13, 34), and for neoadjuvant chemotherapy of patients autoimmune reaction (35). Unfortunately, within the TIME the with basal MIBC, which show increased chemoresponsive- resulting inflammatory immune response is not sufficient to ness (4). In line with these observations, a nonsignificant trend eradicate all tumor cells and is subject to regulation, including forbettersurvival(P ¼ 0.055) was found for patients with immunosuppression and inflammatory cytokines which fuel basal-differentiated MIBC undergoing adjuvant chemotherapy tumor progression (36). (Supplementary Fig. S7C).

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 933

Pfannstiel et al.

A CXCL9 CD8A PD-1 CD3Z PD-L1 Immune gene cluster High Low No Intrinsic subtype 100%

sTILs (%) 0% Intrinsic subtypes +3 Z-Score (fold change) -3 Double negave Luminal Basal

B Intrinsic subtypes among sTILs C Adjuvant chemotherapy: sTILs Terle terle groups split 100% P = 0.0001; HR = 0.22 [95%-CI 0.10−0.47]* 10.5 27 80% 65.1 60%

89.5 40% 73

20% 34.9

0% Log-rank: P < 0.0001 T1 T2 T3 Paents at risk (n) Luminal Basal T1 (<5%) 22 11 4 3 2 0 0 00 0

T2 (≥5%; <15%) 37 16 9 6 2 2 2 00 0

T3 (≥15%) 43 33 20 11 8 5 4 44 0 *Mulvariable P-value for indicator „sTILs terles“; shown mulvariable HR: T3 vs. T1

D *** *** *** *** *** *** 4 3 *** 2 4 *** 3 *** *** 2 2 2 1 2 1 0 1 (Z-Score) (Z-Score) (Z-Score) (Z-Score)

0 0 0 (Z-Score) 0 -1 -1 -1 -2 -2 -2 PD-1 CD3Z CD8A

-2 PD-L1 -2 CXCL9 -4 -3 -3 -4 -3 3 1 T1 T2 T T1 T2 T3 T T2 T3 T1 T2 T3 T1 T2 T3

Figure 7. Prognostic impact of sTILs in patients treated with adjuvant chemotherapy (n ¼ 102). A, Unsupervised hierarchical clustering of five immune cell inflammation genes with a combined cohort of 69 patients with TCGA MIBC and 33 patients with MIBC from the CCC-EMN (n ¼ 102), treated with adjuvant chemotherapy (platinum-based). Immune cell gene clusters are shown below (high, low, no), stromal TILs (%; scale %, left) and the tumor subtypes. B, Percentage of luminal and basal subtypes according to sTILs tertile split (T1–T3). C, Kaplan–Meier regression of survival of patients with MIBC (n ¼ 102) treated with adjuvant chemotherapy (platinum-based) based on sTILs tertile split (T1–T3). Log-rank P < 0.0001; multivariable P ¼ 0.0001 for indicator "sTILs tertiles". Univariable log-rank P value is depicted in the lower left corner of the survival plot. P value for the entire indicator "sTILs tertiles" derived by multivariable Cox regression is depicted above the survival curves, followed by the multivariable HR for Tertile group 3 versus Tertile group 1. Table shows tertile split groups (first column total number of patients) with following columns showing the number of patients at risk in 20-month increments. D, Gene expression based on sTILs tertile split (T1–T3; , P < 0.05; , P < 0.01; , P < 0.001 tested by Mann–Whitney test).

The main impact of this study was the identification of a tumor inflammation was significantly enriched in basal tumors, role of sTILs in the TIME, whose specific quantities and whereas low inflammation mainly associated with luminal spatial organization phenotypes predicted antitumor inflam- subtypes. High sTILs and inflammation predicted improved mation that associated with patient survival, as well as with patient survival. High TMB, APOBEC mutation, and neoantigen molecular tumor subtypes. Using sTILs and a panel of five key load in MIBC correspond with a favorable 5-year OS of T-cell–specificandinflammation genes, we could delineate 75% (3). We showed that a high APOBEC signature mutational high, low, or absent tumor inflammation, which was like a load, high TMB, and neoantigen load could be responsible for 59 immune cell gene cluster within the TCGA cohort. High an increased expression of recognizable antigens with

934 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

consecutive upregulation of antigen-presenting–related genes, factor for poor responsiveness to checkpoint inhibition and lower such as MHC-I and MHC-II class HLA genes, leading to high survival of first-line–treated patients with low or absent PD-L1 sTILs infiltration and antitumor inflammation, and could expression (43). These patients should not solely be candidates explain improved patient outcome. for anti–PD-L1 or anti–PD-1 immune checkpoint inhibitor ther- IHC and gene expression analysis of the CCC-EMN-cohort apy, but need alternative strategies to possibly initiate an immune revealed immune cell phenotypes, which were spatially orga- response. Preclinical work from several groups has shown the nized and correlated with numbers of sTILs and amounts, potential of therapies that modulate the cancer epigenome distance, and localization of TLSs. We identified two inflamed to reverse immune evasion in the TIME. DNA methyltransferase phenotypes with favorable outcomes and an uninflamed phe- inhibitors (DNMTi) remove suppressive DNA methylation notype, as well as a tumor evasion phenotype with high PD-L1 marks and activate endogenous retro-elements that can be sensed TC expression, with the latter two both associated with adverse by the innate immune system, resulting in an antiviral outcomes. response (44–46). This antiviral IFN response recruits and acti- An important factor within the TIME are TLSs, which in addi- vates host immune cells to the TIME (47) and sensitizes mela- tion to lymph nodes, can also exhibit antitumor activity via noma (44), lung (47), and ovarian (47) mouse cancer models to antigen presentation, leading to an efflux of active immune immune checkpoint blockade therapy. Most favorable tumor cells (29, 30). Here, high amounts and close tumor distance of responses were seen when DNMTis were combined with another TLSs correlated significantly with a favorable DSS. Both the epigenetic therapy, like histone deacetylase inhibitors (47). Sep- uninflamed and evasion phenotypes showed the lowest amount arately, DNMTis can also reverse TEFF cell exhaustion, reinvigor- and the farthest TLS distances, resulting in the shortest DSS. These ating them to fight tumors and sensitizing them to immune findings support a negative regulation of immune cells occurring checkpoint blockade (48). Clinical trials are currently under way in the TIME. The inflamed-high phenotype, as well as tumors in several solid tumors testing the efficacy of this combination of enriched with TLSs, also exhibited high CXCL9. This was validated epigenetic and immune therapy (NCT02816021, NCT03019003, in the TCGA cohort, including other chemokines of the CXC and NCT01928576, NCT02959437). CC cytokine subfamilies, which play a positive role in antitumor Tumors with inflamed-high and evasion phenotypes were activity in the TIME. mainly represented by basal tumors, which harbor important The importance of investigating sTILs and the spatial arrange- implications for their treatment. For example, inflamed-high ment of the TIME stemmed from patients with bladder cancer tumors with moderate to high PD-1 and PD-L1 expression, treated with platinum-based chemotherapy or checkpoint inhi- exclusively on immune cells, seem to represent an immune bitors. High amounts of sTILs in patients with metastatic UBC phenotype with a normal restriction on active TEFF cells. A fraction treated with a platinum-based chemotherapy indicate prolonged of these tumors also showed increased PD-L1 expression on OS (37). Patients with metastatic UBC with high neoantigens and tumor cells. Because both phenotypes demonstrated a favorable þ TMB, as well as enrichment of CD8 TEFF cells, show higher prognosis with the highest DSS, this supports antitumor activity objective response rates toward atezolizumab (5). In liver metas- by sTILs. The immune evasion phenotype showed a worse prog- tasis, sTILs and PD-L1 expression are significantly reduced com- nosis, which could be due to having the highest constitutive pared with matched primary tumors (38), and patients respond PD-L1 tumor expression, which can lead to sTIL exhaustion (49). worse to checkpoint inhibition (6). We conclude all the tumor phenotypes above would be suitable Studies have demonstrated the relevance of UBC tumor for PD-L1 or PD-1–targeting therapies to further unleash an subtypes for response to checkpoint inhibition (6). In both antitumor immune response in inflamed-high tumors and reca- cohorts (CCC-EMN/TCGA), uninflamed immune phenotypes pacitate the antitumor immune responses in tumors with evasion were prominently enriched for luminal subtypes, whereas phenotype. inflamed-high subtypes were mainly basal tumors. Luminal All of the above findings, except the specific immune phe- tumors enriched with an EMT-like gene expression signature notypes by IHC, were confirmed in the TCGA cohort. Classical associated with low to moderate inflammation and immune luminal tumors are characterized by low proliferation, and we checkpoint expression. These tumors are aggressive tumors (3), now showed that they also have low amounts of sTILs, low to characterized by low proliferation with chemoresistance (3, absent expression of immune checkpoint markers, and a low 23). EMT-enriched luminal tumors have been shown to evade TMB. This indicated that neither chemotherapy nor immune immunosurveillance by upregulation of the TGFb axis (5). therapy are suitable for those tumors but support a potential Other immune evasion mechanisms could lead to sTILs exclu- benefit from other therapies, like epigenetic modulators, to sion: (i) tumor recruitment of suppressive myeloid cells, such ignite an immune response that could be enhanced by an as myeloid-derived suppressor cells (MDSC), tumor-associated immune checkpoint blockade (3, 6). The observed tumor macrophages (TAM), immature DCs, and immunosuppressive subtype dependency of immune infiltrates in MIBC is similar neutrophils, leading to poor tumor migration and invasion of to breast cancer (13, 50). Among the different immune phe- þ CD8 T cells (39, 40); (ii) establishment of a glucose-poor notypes revealed in this study, we propose that assessment of tumor microenvironment by tumor cells, limiting metabolism sTILsisaprognosticfactorthatallowsriskstratification after of TILs (41); (iii) intratumoral delivery of cytokines, chemo- radical cystectomy. We demonstrated an independent survival kines, and upregulation of other immune checkpoints by tumor advantage of high sTILs and immune cell phenotypes by cells to restrict T-cell function (42); and (iv) deregulation of analyzing all MIBCs, as well as specific subgroups, separately chemokine expression by tumor endothelial cells, preventing stratified by tumor differentiation or therapy. The prognostic T-cell homing (42). value of sTIL assessment is independent of basal and EMT-like The incapacitated immune response observed in uninflamed phenotypes. Thus, this assessment adds information for risk tumors in our study cohorts, mainly luminal tumors, may be a stratification of patients with aggressive tumor phenotypes

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 935

Pfannstiel et al.

(luminal EMT-like, basal tumors). These associations are in line Disclosure of Potential Conflicts of Interest with findings in breast cancer (13). High amount of sTILs R. Wirtz is an employee and has ownership interest (including stock, patents, predicted a benefit of adjuvant platinum-containing chemo- etc.) in STRATIFYER Molecular Pathology GmbH. M. Burger has received fi therapy. The most benefited subpopulation had enriched basal speakers bureau honoraria from Medac, Takeda, P zer, Bayer Healthcare, Cepheid, MSD, and BMS and is a consultant/advisory board member for Medac, differentiation, which has also been shown to be a favorable Astellas, and Janssen. S. Bertz has received speakers bureau honoraria from predictive factor for responsiveness to neoadjuvant chemother- Roche. A. Hartmann reports receiving a commercial research grant from apy (4). Platinum-containing chemotherapy has been shown to BioNTech, Cepheid, Janssen, and Roche, has received speakers bureau hono- increase DCs, eliminate MDSCs (51), enhance NK-cell–medi- raria from BMS, MSD, Roche, AbbVie, Boehringer Ingelheim, AstraZeneca, þ Novartis, Janssen, Cepheid, and NanoString, and has provided expert testimony ated antitumor toxicity (52), and increase CD8 TEFF-cell– mediated antitumor responses (53). Although cystectomy for Prosigna. M. Eckstein has received speakers bureau honoraria from Roche, Astellas, and AstraZeneca and is a consultant/advisory board member for removes the majority of tumor cells, this positive effect could AstraZeneca and Janssen Cilag. No potential conflicts of interest were disclosed be explained by enhanced antitumor immune responses, with by the other authors. increased local and circulating tumor cell clearance (54). Fur- ther support for the above shows that chemotherapeutic agents þ þ Authors' Contributions can induce proliferation of memory CD4 and CD8 T cells Conception and design: P.L. Strissel, R. Wirtz, M. Burger, B. Wullich, C. Bolenz, that are critical for sustained antitumor immunity (55). Beside P. Erben, A. Hartmann, R. Strick, M. Eckstein the enhancement effects of platinum-containing chemothera- Development of methodology: R. Wirtz, A. Hartmann, M. Eckstein py, our findings support the idea that memory T and B cells play Acquisition of data (provided animals, acquired and managed patients, a critical role in sustained antitumor activity after complete provided facilities, etc.): C. Pfannstiel, D. Sikic, R. Wirtz, A. Wullweber, tumor resection. Memory immune cells could perform tumor H. Taubert, W. Otto, M. Burger, C. Bolenz, N. Fuhrich, C.I. Geppert, cell clearance of circulating tumor cells or metastatic tumor R. Stoehr, S. Bertz, B. Keck, A. Hartmann, M. Eckstein Analysis and interpretation of data (e.g., statistical analysis, biostatistics, cells at distal sites, which would result in long-term protective computational analysis): C. Pfannstiel, P.L. Strissel, K.B. Chiappinelli, R. Wirtz, immunity to prevent relapse and improve patient survival. þ H. Taubert, M. Burger, N. Fuhrich, V. Weyerer, S. Bertz, F. Erlmeier, R. Strick, Studies regarding resident memory CD8 T cells in patients M. Eckstein with breast cancer demonstrate a correlation with improved Writing, review, and/or revision of the manuscript: C. Pfannstiel, P.L. Strissel, survival (56, 57). K.B. Chiappinelli, D. Sikic, S. Wach, A. Wullweber, H. Taubert, J. Breyer, W. Otto, Our current investigation supports a rationale that sTILs, TLSs, T. Worst, M. Burger, B. Wullich, C. Bolenz, C.I. Geppert, V. Weyerer, R. Stoehr, S. Bertz, B. Keck, F. Erlmeier, P. Erben, A. Hartmann, R. Strick, M. Eckstein and immune cell phenotypes, along with tumor subtypes of fi Administrative, technical, or material support (i.e., reporting or organizing MIBC, are factors for risk strati cation. On the basis of our data, constructing databases): C. Pfannstiel, S. Wach, A. Wullweber, W. Otto, findings, patients with the uninflamed and low-inflamed tumor B. Wullich, C. Bolenz, C.I. Geppert, R. Stoehr, M. Eckstein phenotypes with unfavorable prognosis (64 % of MIBC) would Study supervision: P.L. Strissel, W. Otto, C. Bolenz, A. Hartmann, R. Strick, need alternative therapies to (re)ignite the immune system. M. Eckstein Patients with inflamed-high tumors or with an evasion phenotype could benefit from immune checkpoint blockade. Our findings Acknowledgments could potentially improve MIBC patient treatment design to We thank Verena Popp, Elke Veltrup, Natascha Leicht, and Christa personalized therapy for a better outcome. Winkelmann for excellent technical assistance. Furthermore, we are grateful and thank Jaegil Kim, Gordon Robertson and Seth Lerner for providing Limitations are the use of OS data to calculate survival of the essential information about the TCGA provisional dataset and R-Scripts. This TCGA cohort and the merged adjuvant chemotherapy (only OS work was performed in (partial) fulfillment of the requirements for obtain- available). In TCGA survival analyses, age was the most adverse ing the degree "Dr. Med" (C. Pfannstiel). This study was funded from a risk factor for poor OS, which is most likely the cause for frequent, general scientific pool of each participating institution. nontumor-specific causes of death in these patients. However, all multivariable analyses in this study were adjusted for age and The costs of publication of this article were defrayed in part by the other available demographic characteristics in Cox proportional payment of page charges. This article must therefore be hereby marked hazard analyses. In the CCC-EMN cohort, limited numbers of advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate survival events in respective analytic subgroups may have con- this fact. tributed to precision and power issues. Thus, upcoming studies with larger case numbers are needed to further validate the Received October 22, 2018; revised January 9, 2019; accepted April 8, 2019; prognostic impact of immune phenotypes. published first April 15, 2019.

References 1. Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder cancer dicting response and survival after neoadjuvant chemotherapy. Eur Urol incidence and mortality: a global overview and recent trends. Eur Urol 2017;72:544–54. 2017;71:96–108. 5. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, et al. 2. Alfred Witjes J, Lebret T, Comperat EM, Cowan NC, De Santis M, Bruins TGFbeta attenuates tumour response to PD-L1 blockade by contributing to HM, et al. Updated 2016 EAU guidelines on muscle-invasive and meta- exclusion of T cells. Nature 2018;554:544–8. static bladder cancer. Eur Urol 2017;71:462–75. 6. Bellmunt J, Powles T, Vogelzang NJ. A review on the evolution of PD-1/ 3. Robertson AG, Kim J, Al-Ahmadie H, Bellmunt J, Guo G, Cherniack AD, PD-L1 immunotherapy for bladder cancer: The future is now. Cancer Treat et al. Comprehensive molecular characterization of muscle-invasive Rev 2017;54:58–67. bladder cancer. Cell 2018;174:1033. 7. Topalian SL, Drake CG, Pardoll DM. Immune checkpoint blockade: 4. Seiler R, Ashab HA, Erho N, van Rhijn BW, Winters B, Douglas J, et al. a common denominator approach to cancer therapy. Cancer Cell 2015; Impact of molecular subtypes in muscle-invasive bladder cancer on pre- 27:450–61.

936 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

TIME Drives Prognostic Relevance in MIBC

8. Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. 26. Bustin SA, Benes V, Garson JA, Hellemans J, Huggett J, Kubista M, et al. The Science 2015;348:69–74. MIQE guidelines: minimum information for publication of quantitative 9. Dieu-Nosjean MC, Antoine M, Danel C, Heudes D, Wislez M, Poulot V, real-time PCR experiments. Clin Chem 2009;55:611–22. et al. Long-term survival for patients with non-small-cell lung cancer with 27. Cerami E, Gao J, Dogrusoz U, Gross BE, Sumer SO, Aksoy BA, et al. The cBio intratumoral lymphoid structures. J Clin Oncol 2008;26:4410–7. cancer genomics portal: an open platform for exploring multidimensional 10. Catalona WJ, Mann R, Nime F, Potvin C, Harty JI, Gomolka D, et al. cancer genomics data. Cancer Discov 2012;2:401–4. Identification of complement-receptor lymphocytes (B cells) in lymph 28. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, et al. Robust nodes and tumor infiltrates. J Urol 1975;114:915–21. enumeration of cell subsets from tissue expression profiles. Nat Methods 11. Zitvogel L, Apetoh L, Ghiringhelli F, Andre F, Tesniere A, Kroemer G. The 2015;12:453–7. anticancer immune response: indispensable for therapeutic success? J Clin 29. Joshi NS, Akama-Garren EH, Lu Y, Lee DY, Chang GP, Li A, et al. Regulatory Invest 2008;118:1991–2001. T cells in tumor-associated tertiary lymphoid structures suppress anti- 12. Pages F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, et al. Interna- tumor T cell responses. Immunity 2015;43:579–90. tional validation of the consensus Immunoscore for the classification of 30. Buisseret L, Desmedt C, Garaud S, Fornili M, Wang X, Van den Eyden G, colon cancer: a prognostic and accuracy study. Lancet 2018;391:2128–39. et al. Reliability of tumor-infiltrating lymphocyte and tertiary lymphoid 13. Denkert C, von Minckwitz G, Darb-Esfahani S, Lederer B, Heppner BI, structure assessment in human breast cancer. Mod Pathol 2017;30: Weber KE, et al. Tumour-infiltrating lymphocytes and prognosis in differ- 1204–12. ent subtypes of breast cancer: a pooled analysis of 3771 patients treated 31. Giannakis M, Mu XJ, Shukla SA, Qian ZR, Cohen O, Nishihara R, et al. with neoadjuvant therapy. Lancet Oncol 2018;19:40–50. Genomic correlates of immune-cell infiltrates in colorectal carcinoma. 14. Goode EL, Block MS, Kalli KR, Vierkant RA, Chen W, Fogarty ZC, et al. Cell Rep 2016;15:857–65. Dose-response association of CD8þ tumor-infiltrating lymphocytes and 32. Middlebrooks CD, Banday AR, Matsuda K, Udquim KI, Onabajo OO, survival time in high-grade serous ovarian cancer. JAMA Oncol 2017;3: Paquin A, et al. Association of germline variants in the APOBEC3 region e173290. with cancer risk and enrichment with APOBEC-signature mutations in 15. Zhang L, Conejo-Garcia JR, Katsaros D, Gimotty PA, Massobrio M, Regnani tumors. Nat Genet 2016;48:1330–8. G, et al. Intratumoral T cells, recurrence, and survival in epithelial ovarian 33. de Biasi AR, Villena-Vargas J, Adusumilli PS. Cisplatin-induced antitumor cancer. N Engl J Med 2003;348:203–13. immunomodulation: a review of preclinical and clinical evidence. 16. Sweis RF, Spranger S, Bao R, Paner GP, Stadler WM, Steinberg G, et al. Clin Cancer Res 2014;20:5384–91. Molecular drivers of the non-T-cell-inflamed tumor microenvironment in 34. Gu-Trantien C, Loi S, Garaud S, Equeter C, Libin M, de Wind A, et al. CD4 urothelial bladder cancer. Cancer Immunol Res 2016;4:563–8. (þ) follicular helper T cell infiltration predicts breast cancer survival. J Clin 17. Ren R, Tyryshkin K, Graham CH, Koti M, Siemens DR. Comprehensive Invest 2013;123:2873–92. immune transcriptomic analysis in bladder cancer reveals subtype specific 35. Thommen DS, Schumacher TN. T Cell Dysfunction in Cancer. Cancer Cell immune gene expression patterns of prognostic relevance. Oncotarget 2018;33:547–62. 2017;8:70982–1001. 36. Maman S, Witz IP. A history of exploring cancer in context. Nat Rev Cancer 18. Sharma P, Shen Y, Wen S, Yamada S, Jungbluth AA, Gnjatic S, et al. 2018;18:359–76. CD8 tumor-infiltrating lymphocytes are predictive of survival in mus- 37. Huang HS, Su HY, Li PH, Chiang PH, Huang CH, Chen CH, et al. cle-invasive urothelial carcinoma. Proc Natl Acad Sci U S A 2007;104: Prognostic impact of tumor infiltrating lymphocytes on patients with 3967–72. metastatic urothelial carcinoma receiving platinum based chemotherapy. 19. Sjodahl G, Lovgren K, Lauss M, Chebil G, Patschan O, Gudjonsson S, et al. Sci Rep 2018;8:7485. Infiltration of CD3(þ) and CD68(þ) cells in bladder cancer is subtype 38. Eckstein M, Sikic D, Strissel PL, Erlmeier F, Germany BC. Evolution of specific and affects the outcome of patients with muscle-invasive tumors. PD-1 and PD-L1 gene and protein expression in primary tumors and Urol Oncol 2014;32:791–7. corresponding liver metastases of metastatic bladder cancer. Eur Urol 20. Hendry S, Salgado R, Gevaert T, Russell PA, John T, Thapa B, et al. Assessing 2018;74:527–9. tumor-infiltrating lymphocytes in solid tumors: a practical review for 39. Gabrilovich DI, Ostrand-Rosenberg S, Bronte V. Coordinated regulation of pathologists and proposal for a standardized method from the Interna- myeloid cells by tumours. Nat Rev Immunol 2012;12:253–68. tional Immuno-Oncology Biomarkers Working Group: Part 2: TILs in 40. Peranzoni E, Lemoine J, Vimeux L, Feuillet V, Barrin S, Kantari-Mimoun C, melanoma, gastrointestinal tract carcinomas, non-small cell lung carcino- et al. Macrophages impede CD8 T cells from reaching tumor cells and limit ma and mesothelioma, endometrial and ovarian carcinomas, squamous the efficacy of anti-PD-1 treatment. Proc Natl Acad Sci U S A 2018;115: cell carcinoma of the head and neck, genitourinary carcinomas, and E4041–50. primary brain tumors. Adv Anat Pathol 2017;24:311–35. 41. Ho PC, Bihuniak JD, Macintyre AN, Staron M, Liu X, Amezquita R, et al. 21. Eckstein M, Wirtz RM, Pfannstil C, Wach S, Stoehr R, Breyer J, et al. Phosphoenolpyruvate is a metabolic checkpoint of anti-tumor T cell A multicenter round robin test of PD-L1 expression assessment in urothe- responses. Cell 2015;162:1217–28. lial bladder cancer by immunohistochemistry and RT-qPCR with emphasis 42. Joyce JA, Fearon DT. T cell exclusion, immune privilege, and the tumor on prognosis prediction after radical cystectomy. Oncotarget 2018;9: microenvironment. Science 2015;348:74–80. 15001–14. 43. Gourd E. EMA restricts use of anti-PD-1 drugs for bladder cancer. 22. Eckstein M, Erben P, Kriegmair MC, Worst TS, Weiss CA, Wirtz RM, et al. Lancet Oncol 2018;19:e341. Performance of the Food and Drug Administration/EMA-approved pro- 44. Chiappinelli KB, Strissel PL, Desrichard A, Li H, Henke C, Akman B, grammed cell death ligand-1 assays in urothelial carcinoma with emphasis et al. Inhibiting DNA methylation causes an interferon response in on therapy stratification for first-line use of atezolizumab and pembroli- cancer via dsRNA including endogenous retroviruses. Cell 2015;162: zumab. Eur J Cancer 2019;106:234–43. 974–86. 23. Choi W, Porten S, Kim S, Willis D, Plimack ER, Hoffman-Censits J, et al. 45. Roulois D, Loo Yau H, Singhania R, Wang Y, Danesh A, Shen SY, et al. DNA- Identification of distinct basal and luminal subtypes of muscle-invasive demethylating agents target colorectal cancer cells by inducing viral mim- bladder cancer with different sensitivities to frontline chemotherapy. icry by endogenous transcripts. Cell 2015;162:961–73. Cancer Cell 2014;25:152–65. 46. Licht JD. DNA methylation inhibitors in cancer therapy: the immunity 24. Breyer J, Otto W, Wirtz RM, Wullich B, Keck B, Erben P, et al. ERBB2 dimension. Cell 2015;162:938–9. expression as potential risk-stratification for early cystectomy in patients 47. Topper MJ, Vaz M, Chiappinelli KB, DeStefano Shields CE, with pT1 bladder cancer and concomitant carcinoma in situ. Urol Int 2017; Niknafs N, Yen RC, et al. Epigenetic therapy ties MYC depletion 98:282–9. to reversing immune evasion and treating lung cancer. Cell 2017; 25. Breyer J, Wirtz RM, Otto W, Erben P, Worst TS, Stoehr R, et al. High PDL1 171:1284–300. mRNA expression predicts better survival of stage pT1 non-muscle-invasive 48. Ghoneim HE, Fan Y, Moustaki A, Abdelsamed HA, Dash P, Dogra P, et al. bladder cancer (NMIBC) patients. Cancer Immunol Immunother 2018;67: De novo epigenetic programs inhibit PD-1 blockade-mediated T cell 403–12. rejuvenation. Cell 2017;170:142–57.

www.aacrjournals.org Cancer Immunol Res; 7(6) June 2019 937

Pfannstiel et al.

49. Ribas A, Hu-Lieskovan S. What does PD-L1 positive or negative mean? J Exp 54. Bracci L, Schiavoni G, Sistigu A, Belardelli F. Immune-based mechanisms of Med 2016;213:2835–40. cytotoxic chemotherapy: implications for the design of novel and rationale- 50. Wang K, Xu J, Zhang T, Xue D. Tumor-inﬁltrating lymphocytes in breast based combined treatments against cancer. Cell Death Differ 2014;21: cancer predict the response to chemotherapy and survival outcome: a meta- 15–25. analysis. Oncotarget 2016;7:44288–98. 55. Schiavoni G, Mattei F, Di Pucchio T, Santini SM, Bracci L, Belardelli F, et al. 51. Chen J, Huang X, Huang G, Chen Y, Chen L, Song H. Preconditioning Cyclophosphamide induces type I interferon and augments the number of chemotherapy with cisplatin enhances the antitumor activity of cytokine- CD44(hi) T lymphocytes in mice: implications for strategies of chemoim- induced killer cells in a murine melanoma model. Cancer Biother Radio- munotherapy of cancer. Blood 2000;95:2024–30. pharm 2012;27:210–20. 56. Natarajan P, Liu D, Patel SR, Santhanakrishnan M, Beitler D, Liu J, 52. Gasser S, Orsulic S, Brown EJ, Raulet DH. The DNA damage pathway et al. CD4 depletion or CD40L blockade results in antigen-speciﬁc regulates innate immune system ligands of the NKG2D receptor. Nature tolerance in a red blood cell alloimmunization model. Front Immunol 2005;436:1186–90. 2017;8:907. 53. Vincent J, Mignot G, Chalmin F, Ladoire S, Bruchard M, Chevriaux A, et al. 57. Enamorado M, Iborra S, Priego E, Cueto FJ, Quintana JA, Martinez-Cano S, 5-Fluorouracil selectively kills tumor-associated myeloid-derived suppres- et al. Enhanced anti-tumour immunity requires the interplay between sor cells resulting in enhanced T cell-dependent antitumor immunity. resident and circulating memory CD8(þ) T cells. Nat Commun 2017;8: Cancer Res 2010;70:3052–61. 16073.

938 Cancer Immunol Res; 7(6) June 2019 Cancer Immunology Research

The Tumor Immune Microenvironment Drives a Prognostic Relevance That Correlates with Bladder Cancer Subtypes

Carolin Pfannstiel, Pamela L. Strissel, Katherine B. Chiappinelli, et al.

Cancer Immunol Res 2019;7:923-938. Published OnlineFirst April 15, 2019.

Updated version Access the most recent version of this article at: doi:10.1158/2326-6066.CIR-18-0758

Supplementary Access the most recent supplemental material at: Material http://cancerimmunolres.aacrjournals.org/content/suppl/2019/04/13/2326-6066.CIR-18-0758.DC1

Cited articles This article cites 57 articles, 12 of which you can access for free at: http://cancerimmunolres.aacrjournals.org/content/7/6/923.full#ref-list-1

Citing articles This article has been cited by 2 HighWire-hosted articles. Access the articles at: http://cancerimmunolres.aacrjournals.org/content/7/6/923.full#related-urls

E-mail alerts Sign up to receive free email-alerts related to this article or journal.

Reprints and To order reprints of this article or to subscribe to the journal, contact the AACR Publications Department Subscriptions at [email protected].

Permissions To request permission to re-use all or part of this article, use this link http://cancerimmunolres.aacrjournals.org/content/7/6/923. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC) Rightslink site.