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Precision Medicine and Imaging

Integrated Genomic and ImmunophenotypicClassification of Pancreatic Cancer RevealsThree Distinct Subtypes with Prognostic/Predictive SignificanceMartin Wartenberg1, Silvia Cibin1, Inti Zlobec1, Erik Vassella1,Serenella Eppenberger-Castori2, Luigi Terracciano2, Micha David Eichmann1,Mathias Worni3, Beat Gloor3, Aurel Perren1, and Eva Karamitopoulou1

Abstract

Purpose: Current clinical classification of pancreatic ductaladenocarcinoma (PDAC) is unable to predict prognosis orresponse to chemo- or immunotherapy and does not take intoaccount the host reaction to PDAC cells. Our aim is to classifyPDAC according to host- and tumor-related factors into clin-ically/biologically relevant subtypes by integrating molecularand microenvironmental findings.

Experimental Design: A well-characterized PDAC cohort(n ¼ 110) underwent next-generation sequencing with a hotspot cancer panelwhile next-generation tissuemicroarrays wereimmunostained for CD3, CD4, CD8, CD20, PD-L1, p63,hyaluronan-mediated motility receptor (RHAMM), and DNAmismatch repair proteins. Previous data on FOXP3 were inte-grated. Immune cell counts and protein expression were cor-related with tumor-derived driver mutations, clinicopathologicfeatures (TNM 8th edition, 2017), survival, and epithelial–mesenchymal transition (EMT)–like tumor budding.

Results: Three PDAC subtypes were identified: the"immune escape" (54%), poor in T and B cells andenriched in FOXP3þ regulatory T cells (Treg), with high-

grade budding, frequent CDKN2A, SMAD4, and PIK3CAmutations, and poor outcome; the "immune rich" (35%),rich in T and B cells and poorer in FOXP3þ Tregs, withinfrequent budding, lower CDKN2A and PIK3CA mutationrate, and better outcome and a subpopulation with tertiarylymphoid tissue (TLT), mutations in DNA damage responsegenes (STK11 and ATM), and the best outcome; and the"immune exhausted" (11%), with immunogenic microenvi-ronment and two subpopulations—one with PD-L1 expres-sion and a high PIK3CA mutation rate and a microsatellite-unstable subpopulation with a high prevalence of JAK3muta-tions. The combination of low budding, low stromalFOXP3 counts, presence of TLTs, and absence of CDKN2Amutations confers significant survival advantage in patientswith PDAC.

Conclusions: Immune host responses correlate withtumor characteristics, leading to morphologically recogniz-able PDAC subtypes with prognostic/predictive significance.Clin Cancer Res; 24(18); 4444–54. �2018 AACR.

See related commentary by Khalil and O'Reilly, p. 4355

IntroductionPancreatic ductal adenocarcinoma (PDAC) is considered a

highly immunosuppressive and heterogeneous neoplasm (1, 2).Despite improved knowledge regarding the genetic backgroundand an increasing understanding of the tumor microenviron-ment, targeted therapies and immunotherapy, although effi-cient for many solid malignancies, including metastatic mela-noma and lung cancer, have not yielded any clinical benefit in

PDAC (2, 3). Moreover, there are currently no approved treat-ments that target driver mutations in PDAC, such as KRAS,TP53, CDKN2A, and SMAD4 (3). Ongoing studies aiming totarget the stroma and the immune microenvironment eitheralone or in combination are expected to lead to more endur-able treatment outcomes (4–6). Specifically, inhibition of thebromodomain and extraterminal (BET) family of epigeneticreaders was shown to block stroma-inducible transcriptionalregulation in vitro and tumor progression in vivo (4). Moreover,expression of antitumor immunity genes to uncover biomarkerspredictive of response to systemic therapies has been under-taken (5). Furthermore, mutations in the oncogenes PIK3CA,FGFR3, and RAS/RAF family members, as well as the tumorsuppressor TP53, were linked to changes in immune infiltration,supporting the relevance of tumor genetics to questions ofefficacy and resistance in checkpoint blockade therapies (6).A detailed characterization of the tumor microenvironmentcombining tumor- and host-associated factors may lead to theidentification of novel biomarkers and pathways for a moretargeted approach to the use of immunotherapy and/or othercombinatorial treatments to overcome the immunosuppressivemechanisms in the PDAC microenvironment.

1Institute of Pathology, University of Bern, Bern, Switzerland. 2Institute ofPathology, University of Basel, Basel, Switzerland. 3Department of VisceralSurgery, Insel University Hospital, University of Bern, Bern, Switzerland.

Note: Supplementary data for this article are available at Clinical CancerResearch Online (http://clincancerres.aacrjournals.org/).

M. Wartenberg and S. Cibin contributed equally to this article.

Corresponding Author: Eva Karamitopoulou, Institute of Pathology, Universityof Bern, Murtenstrasse 31, Bern 3008, Switzerland. Phone: 0041 31 632 8768;Fax: 0041 632 4995; E-mail: [email protected]

doi: 10.1158/1078-0432.CCR-17-3401

�2018 American Association for Cancer Research.

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The immune microenvironment is also of importance for thelocal aggressiveness of tumor cells and PDAC progression, asspecific immune expression signatures may render the tumormicroenvironment permissible for single cancer cell invasion(7). Aggressive PDACs are characterized by increased numbersof dissociative growing tumor cells at the invasive front withepithelial–mesenchymal transition (EMT)–like features, coined"tumor buds," that have been shown to represent an independentadverse prognostic factor inmany gastrointestinal cancers, includ-ing PDAC (8–14). Themicroenvironment surrounding the tumorbuds is therefore especially interesting, as it likely promotes theirmigration, angioinvasion, and metastatic potential. We recentlyreported that PDACs with EMT-like tumor budding display atumor-favoring immunemicroenvironment that supports furthertumor growth (15).

More recently, an integrated genomic analysis of PDACs iden-tified four molecular subtypes—squamous, pancreatic progeni-tor, immunogenic, and aberrantly differentiated endocrine exo-crine—indicating differences in themolecular evolution of PDACsubtypes and identifying opportunities for therapeutic develop-ment (16–18).

We hypothesized that the local immune phenotype will be ofimportance for more than the immunogenic "gene program." Wetherefore analyzed the immune response patterns in the tumormicroenvironment of PDAC from awell-characterized cohort andcorrelated the immune pattern to tumor-related factors, such asthe mutational background and EMT-like tumor budding as wellas clinicopathologic features, including survival of the patients, toclassify PDAC into clinically/biologically relevant subgroups. Wealso attempted to accommodate these subgroups to the above-mentioned molecular subtypes of PDAC according to Baileyand colleagues (16). Our specific aims were to identify valuableclues for the different immunosuppressive mechanisms ofPDACs, outline a morphologic substrate for the recognition ofthe molecular subtypes in daily clinical practice, and provide abasis for a more successful and individualized therapeuticapproach.

Materials and MethodsPatients and tissues

Histomorphologic data from 110 PDACs, stage I to III, surgi-cally resected between 2002 and 2011, were reviewed from thecorresponding hematoxylin and eosin (H&E)–stained slideswhile clinical data were obtained from corresponding reports(database). Clinicopathologic information for all patients includ-ed age; gender; tumor diameter; number of positive lymph nodes;total number of lymph nodes harvested; pathologic tumor–node–metastasis (pTNM) stage; perineural, blood vessel, andlymphatic invasion; and resection margin status. PreoperativeCA19-9 values were available from 70 patients (SupplementaryTable S1). Tumors treated neoadjuvantly, stage IV tumors, as wellas carcinomas with histology other than ductal adenocarcinoma(acinar cell carcinomas, adenosquamous carcinomas, neuroen-docrine carcinomas, mixed subtypes, and mucinous neoplasmswith associated invasive carcinoma) were excluded from thestudy. Staging was performed using the AJCC Cancer StagingManual 8th edition (19). This study was conducted in accordancewith recognized ethical guidelines (Declaration of Helsinki),including written informed consent from the patients or theirliving relatives, and the use of the material was approved by thelocal ethics committee of the University of Bern (Bern, Switzer-land; KEK Nr 200/14).

Next-generation tissue microarray constructionTissue microarrays were constructed using the next-generation

tissue microarray (ngTMA) approach (20). For each patient, oneH&E-stained whole tissue slide containing representativeregions of tumor center and invasion front was scanned (Pan-noramic P250; 3DHISTECH). Using a tissue microarray anno-tation tool of 0.6 mm in diameter, slides were digitally anno-tated as follows: four regions of tumor center (green) and fourregions of invasion front (red) including regions of highesttumor budding if available. Next, corresponding formalin-fixed(10% buffered formalin) paraffin-embedded tissue blocks wereloaded into an automated tissue microarrayer (TMA GrandMaster; 3DHISTECH). The digital slides were aligned with thecorresponding donor block. Annotated regions were cored fromthe donor block and transferred to the recipient ngTMA. Threedifferent ngTMA blocks were produced, resulting in 960 spotsfor evaluation.

Assessment of tumor buddingTumor budding has been evaluated for the purposes of

another study according to the International Tumor BuddingConsensus Conference (ITBCC) method (21) and was definedaccording to ITBCC as single tumor cells or tumor cell clustersof up to four cells. Whole tissue sections of the PDACs, stainedwith H&E as in the routine diagnostics, were used. Two expe-rienced pathologists (E. Karamitopoulou and M. Wartenberg)independently searched all tumor slides throughout at lowmagnification. Densest budding area (hot spot) was selectedby visual estimation regardless of the topographic area withinthe tumor (intratumoral or at the invasive front). Tumor budsin this area were counted at �20 magnification (field area,0.785 mm2). Density of tumor buds was assigned into fourgroups: no budding (BD-0): 0 buds; low budding (BD-1): 1–4buds; intermediate budding (BD-2): 5–9 buds; and high bud-ding (BD-3):�10 buds. For simplicity reasons, categories 0 and1 (no and low budding) were grouped together, as only very

Translational RelevanceBy integrating immune cell background, molecular, and

histomorphologic data, we describe three distinct, clinically/biologically relevant pancreatic ductal adenocarcinoma(PDAC) subtypes: "immune escape," "immune rich," and"immune exhausted." These largely correspond to previouslydescribed molecular PDAC subtypes, thus providing a recog-nizable morphologic substrate integrating host immuneresponse patterns with tumor-associated factors, includingmolecular features and biologic behavior of the tumors. Thiswill enable the translation ofmolecular findings into clinicallyrelevant information and may provide a basis for a moresuccessful and individualized therapeutic approach. Indeed,mutational status appears to correlate with the tumor aggres-siveness and the immune host response in the PDAC micro-environment. A combination of tumor-related (CDKN2Amutations and epithelial–mesenchymal transition–like tumorbudding) and host-related (stromal FOXP3þ regulatory T cellcounts and tertiary lymphoid tissue) microenvironmentalfeatures significantly affects prognosis in PDAC.

Immunophenotypic and Molecular Classification of PDAC

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few patients had 0 buds. The interobserver agreement wasassessed using the Kappa statistic (k) for categorical countsand the intraclass correlation coefficient (ICC) for the numberof buds.

IHCngTMAs were sectioned at 3 mm, dewaxed, and rehydrated in

dH2O and stained immunohistochemically for CD3 (1:400,clone SP7, Abcam ab16669), CD4 (1:100, clone CD4/4B12,Dako M7310), CD8 (1:100, clone C8/144B, Dako M7103),CD20 (1:100, clone L26, Dako M0755), hyaluronan-mediatedmotility receptor (RHAMMl 1:100, clone 2D6, 1:50; LeicaBiosystems), p63 (1:40, clone 7JUL, Novocastra NCL-L-p63),PD-L1 (1:400, clone E1L3N, Cell Signaling Technology 13684),MLH1 (1:200, clone ES05, Novocastra NCL-L-MLH1), PMS2(1:100, clone A16-4, BD Pharmingen 556415), MSH2 (1:500,clone G219-1129, Cell Marque 286M-15), and MSH6 (1:50,clone PU29, Novocastra NCL-L-MSH6). Antigen retrieval wasperformed with Tris-HCl pH 9 for 30 minutes at 95�C. Anti-body testing and staining protocols have been established, andstaining was performed by an automated Leica BOND RXsystem (Leica BOND RX, Leica Biosystems) with the BondPolymer Refine Detection Kit (with DAB as chromogen) andBond Polymer Refine Red Detection Kit for the double staining(Leica Biosystems).

Normalization and scoringProtein expression was evaluated by estimating the percent-

age of positive cells per TMA punch. Because each patient hadmultiple tumor cores taken from different regions within thetumor, the percentage of positive tumor cells across all coreswas averaged.

Proportions of PD-L1–positive carcinoma cells were scoredaccording to a six-step system (22). Accordingly, carcinomacells were considered positive if the cell membrane was par-tially or completely stained. Cytoplasmic staining was disre-garded, and necrotic areas were excluded. The following cate-gories were used: score 0: 0% to 1%, score 1: �1%, score 2:�5%, score 3: �10%, score 4: �25%, and score 5: �50%.

Scoring of RHAMM IHC was adapted from R€uschoff andcolleagues (23). In more detail, score 0 was assigned when nopositivity was observed; score 1 was assigned when high-powermagnification (�20–�40) was needed to detect expression;score 2 was recorded when staining was observed under medi-um power (�10); and score 3 was assigned when positivestaining was observed at low magnification (�5). In addition,the total number of tumor buds and proportion of budsexpressing RHAMM (score �1) were recorded for each tumor.Cases with more than 20% RHAMM-positive buds wereclassified as "high RHAMM–expressing buds," and cases withless than 20% RHAMM-positive buds were classified as "lowRHAMM–expressing buds" based on ROC analysis (24).

For the immune cell counts, both intraepithelial and stromalcounts were assessed by visual estimation on a numerical scale.Because a single tissue microarray spot may contain variousdegrees of stroma versus tumor epithelial content, the percentageof stroma and tumor tissue per spot was recorded. Cell countswere normalized for tumor cell and stromal content of eachspot. Evaluation was performed blinded to clinical endpoints.To verify the robustness of the data by visual estimation, stainedTMA slides were digitally analyzed using the Visiopharm Inte-

grator System (VIS) for Windows 8.1, version 6.5.0.2303(Visiopharm A/S). A custom Analysis Protocol Package (APP)was developed with the VIS author module. The thus obtainedarea measurements and cell counts were exported. To estimatethe interrater reliability for the counts of stromal and intraepithe-lial lymphocytes, and tumor buds between visual estimationand digital image analysis, Pearson r and Krippendorff a valueswere calculated using R, including the "irr" package.

Analysis of FOXP3 and PTEN was performed previously(15, 25).

Next-generation sequencingLibrary preparation for targeted next-generation sequencing withthe IonTorrent Platform. Library preparation using the IonAmpliSeq Cancer Hotspot Panel v2 (Thermo Fisher Scientific) wasperformed following the manufacturer's instructions and asdescribed previously (26–28). The Ion AmpliSeq Cancer HotspotPanel v2 was designed to allow amplification-based capture andsequencing of coding regions of 50 cancer-related genes (https://tools.thermofisher.com/content/sfs/brochures/Ion-AmpliSeq-Cancer-Hotspot-Panel-Flyer.pdf). This panel includes 207 primerpairs and requires a minimum initial amount of 10 ng of DNAas input. After DNA extraction and quantification, multiplex PCRfor target enrichment was performed using genomic DNA mixedwith primer pools and the Ion AmpliSeq HiFi Master Mix (IonAmpliSeq Library Kit 2.0; Thermo Fisher Scientific) for 2 minutesat 99�C, followedby22 cycles of 99�C for 15 seconds and60�C for4 minutes and holding at 10�C. The obtained PCR productswere subsequentially treated with 2 mL of FuPa reagent (IonAmpliSeq Library Kit 2.0; Thermo Fisher Scientific) to partiallydigest the primer sequences and then phosphorylated at 50�C for20minutes, followed by 55�C for 20minutes, and finally at 60�Cfor 20minutes. The generated ampliconswere ligated to Ionbeadsadapters (Ion AmpliSeq Library Kit 2.0; Thermo Fisher Scientific)and sample-specific barcodes from the Ion Xpress Barcode Adap-ters Kit (ThermoFisher Scientific) for 30minutes at 22�Cand then72�C for 10 minutes. Afterward, the generated barcoded librarieswere purified using Agencourt AMPure XP reagents (BeckmanCoulter). The library concentration was determined using theIon Universal Library Quantitation Kit (Thermo Fisher Scientific)and the amplicons size controlled with the 2100 Bioanalyzer(Agilent Technologies). A total of 40 pmol/L for each librarywere loaded into the Ion Chef System using the Ion PGM IC200 Sequencing Kit (Thermo Fisher Scientific) for fully automatedemulsion PCR and chip loading (37 samplesweremultiplexed onone 530 chip). Finally, loaded chips were sequenced (500 flows)using the Ion S5 Sequencing Kit using the S5XL Sequencer(Thermo Fisher Scientific).

Sequencing data analysis.Raw data (FASTQ files) for each samplewere processed for the alignment of sequencing reads withthe human genome reference (hg19) using the Torrent Suitesoftware v5.2 (Thermo Fisher Scientific). The alignment pipe-line also included signaling processing, base calling, qualityscore assignment, adapter trimming, and control of mappingquality. Local realignment and quality base score recalibrationwere also carried out using the Genome Analysis Toolkit(GATK). Coverage metrics for each amplicon (minimal accept-able coverage threshold was set at 500�) were obtained byrunning the Coverage Analysis plugin software v5.2.2 (ThermoFisher Scientific). Base calling was performed using the Ion

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https://tools.thermofisher.com/content/sfs/brochures/Ion-AmpliSeq-Cancer-Hotspot-Panel-Flyer.pdf




Reporter v5.2 (Thermo Fisher Scientific). In addition, MuTect4and VarScan25 algorithms were used to call somatic single-nucleotide variants and VarScan25, Strelka6, and Scalpel7for insertions and deletions (indels). We chose these specificmutations caller strategy as they allow identification of somaticmutations down to 5% mutant allele fraction (i.e., 5% of thereads harboring a given mutation). Finally, all candidate muta-tions were manually reviewed using the Integrative GenomicsViewer8 (29).

Statistical analysisSpearman correlation coefficients (rs) were used to determine

the strength of the relationship between immune cell counts andclinicopathologic tumor characteristics. For consistency, scoreswere then dichotomized according to the mean into low andhigh groups. The c2 test was used to determine the association oflow and high cell counts with categorical variables and t test fortumor size and age. The presence of association between cate-gorical variables or between normally distributed variables, suchas age, was calculated with the Wilcoxon–Mann–Whitney test ort test, respectively. Classification trees were used to calculatethreshold values and predictive algorithms for survival. Univar-iate Cox regression analysis was performed to determine thepresence of association of continuous variables with survivalprior to dichotomization. Kaplan–Meier survival curves wereplotted and survival time differences analyzed using the log-ranktest. Multivariable Cox regression analysis was performed todetermine the independent prognostic effect of the feature afteradjustment for potential confounders. HRs and 95% confidenceintervals (CI) were used to determine effect size. Pearson cor-relation coefficient was used to evaluate the strength of the linearrelationship between CD68 counts by a human observer andby software. All P values were two sided and considered signif-icant when P < 0.05. Analyses were conducted on SPSS (V21),SAS (V9.3), and Statistical Package Software R [Version 3.4.1(2017-06-30), www.r-project.org].

The study design is shown in Supplementary Fig. S1. Thestudy was designed to comply with the REporting recommen-dations for tumor MARKer (REMARK) guidelines for tumormarker prognostic studies (30).

ResultsInterobserver agreement for tumor budding

After comparing the budding scores, the ICC for buddingdensity between the two pathologists was 0.85, and the weightedkappa value for categorical assessments was 0.62 (0.5–0.73),suggesting substantial agreement.

Intraepithelial and stromal immune cell countsWe evaluated both intraepithelial and stromal immune cell

counts (CD3, CD4, CD8, and CD20) per tumor. Normalizedintraepithelial counts were generally very low (0–14.7/0.274mm2) and showed only few correlations with clinicopathologicfeatures. On the contrary, stromal counts were more abundant(0–347, 5/0.274 mm2) and showed an inverse correlation tomany adverse clinicopathologic features (SupplementaryTable S2). Representative findings of the immune cell infiltrateswith respect to PDAC phenotypes are shown in Figs. 1–3 andSupplementary Fig. S2.

Presence of tertiary lymphoid tissue (TLT; 21/120 patients;17.5%) was correlated with low tumor budding (P ¼ 0.0011)

and increased overall survival (OS, P < 0.0001), as well as disease-free survival (DFS, P¼ 0.0067). The impact of TLT on survival wasfound to be independent after adjusting for TNM and buddingitself (Supplementary Table S3; Fig. 3).

An increased stromal CD8/FOXP3 [effector T cells (Teff)/regulatory T cells (Treg)] ratio correlated with a lower rate oflymph nodes (P ¼ 0.0391) and DFS (P ¼ 0.0449), as well asbetter OS in univariate (P ¼ 0.019), but not in multivariateanalysis. Overall, patients with low tumor budding (BD-1),presence of TLT, and low FOXP3 stromal counts had thebest prognosis (Table 1). Stromal CD8/FOXP3 ratio showedan inverse correlation with tumor budding (SupplementaryFig. S3). Correlation between digitally analyzed TMA slidesand visual estimation is shown in Supplementary Fig. S4.Substantial correlations were found between stromal countsby visual estimation and by software (Pearson r 0.87). Thedensity of CD3, CD8, and FOXP3 counts for each subtype isshown in Supplementary Fig. S5.

Tumor cell subtyping by IHCNuclear p63 protein expression, a marker of a squamous

phenotype (31), was focally observed in 18% of the PDACs(Fig. 1), frequently also in the tumor buds. Eight percent of thep63-positive cases belonged to the high budding category (BD-3,median: 26 buds).

Positive RHAMM staining (score �1) was observed in 49% ofthe tumors and high RHAMM–expressing buds were seen in 31%of the RHAMM-positive cases (15% of the cohort; Figs. 1 and 2).All cases with high RHAMM–expressing buds belonged to thehigh budding category (BD-3; see also PDAC subtypes below).

Overall, 27% of the cohort showed a membranous PD-L1positivity in the tumor cells, with most of them showing onlyfocal positivity: 40% of the positive cases exhibited positivityin <5% of the tumor cells, and 70% of the cases exhibitedpositivity in �10% of the tumor cells. Cases with scores 4 and5 (staining in �25% of the tumor cells) constituted 30% ofthe PD-L1–positive cases, and 7% of the whole cohort belong-ed to the high budding category (BD-3, mean number of buds:24.4) and had reduced OS (median: 10 months). Moreover,they showed similar immunophenotypical features, beingcharacterized by a high CD8/FOXP3 stromal ratio (median:19.32) and a generally immune cell–rich microenvironment(Table 1; Supplementary Fig. S2).

Very few cases (4% of the cohort) showed an IHC loss ofthe DNA mismatch repair proteins (MLH1, PMS2, MSH2, andMSH6) in different combinations [microsatellite instability-high(MSI-H); Supplementary Fig. S6]. The patients in this categorywere generally older (median: 69 years), and the tumors werecharacterized by a microenvironment rich in immune cells with avery high stromal CD8/FOXP3 ratio (55.2%).

Next-generation sequencingThe genes included in the Ion AmpliSeq Cancer Hotspot

Panel v2 are depicted in Supplementary Table S4A. KRASmutation (mostly in exon 2) was detectable in 92.8% of allcases (allelic frequency between 7% and 71%) and was the solemutation detected in 15% of the PDACs. The KRAS wild-typecases (7.2%) showed mutations in TP53, CDKN2A, ATM,and/or BRAF. TP53 was the second most common mutation(65.4%, allelic frequency between 6% and 67%), in 98% as co-mutation with KRAS and/or other genes. SMAD4 was mutated





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in 25% of the PDACs, and CDKN2A mutations were found in15.3% of the cohort (allelic frequency between 6% and 33%).Mutations in other genes were less frequent (SupplementaryTable S4B; Fig. 4A). Seven percent of the cases also showedmany subclonal populations with minor allelic frequency(<5%). Twenty-eight percent of the cases showed mutationsin genes additional to the four main drivers (KRAS, TP53,SMAD4, and CDKN2A). The number of mutations did notimpact survival (P ¼ 0.0819). All detected mutations aredepicted in Supplementary Fig. S7 (OncoPrint).

CA19-9 valuesPreoperative carbohydrate antigen 19-9 (CA 19-9) values were

available from 70 patients. They ranged between 10 and 6,835U/mL (median: 270 U/mL) and correlated with OS and DFS(P < 0.0001). Patients with CA19-9 values <100 U/mL (21.4% ofthe cohort) survived longer, whereas patients with CA19-9 values>800 U/mL (28.6% of the cohort) had the worst prognosis(Supplementary Fig. S8).

PDAC subtypesOn the basis of the immune cell composition of the micro-

environment, we were able to define three distinct groupsof PDAC with clinicopathologic and tumor cell–specificimplications:

i. The "immune-escape" (54%) subtype has a microenviron-ment poor in T (CD3, CD4, and CD8) and B cells (CD20)but enriched in FOXP3þ Tregs. These PDACs show morpho-logically high tumor budding, focal p63 expression (espe-cially in the tumor buds), and adverse clinicopathologicfeatures (Table 1; Fig. 1). This group is molecularly charac-terized by KRAS mutations in 94%, along with a high TP53,CDKN2A, SMAD4, and PIK3CA mutation rate (Fig. 4Aand B). Although the prevalence of MET and ERBB4 muta-tions was generally low, all detectable mutations in thesegenes belong to this category. All this correlates with apoor outcome (median OS: 10 months). Moreover,immune-escape cases often showed high preoperativeCA19-9 levels (median: 800 U/mL; Supplementary Fig.S8), while the majority of the cases with high RHAMM–

expressing buds belonged to this category (69% of the caseswith high RHAMM–expressing buds, representing 22% ofthe immune-escape cases; Fig. 1; Supplementary Fig. S9).

ii. The "immune-rich" (35%) subtype has a microenvironmentrich in T (CD8, CD3, and CD4) and B cells (CD20) andpoorer in FOXP3þ Tregs. These tumors exhibit infrequenttumor budding and favorable clinicopathologic features(Table 1; Fig. 2). Molecularly, they are characterized (apartfrom KRAS mutations in 94.5%) by a lower CDKN2A,SMAD4, and PIK3CA mutation rate as compared with the

Figure 1.

PDACs of the "immune-escape" subtype showing high-grade tumor budding (BD-3), strong RHAMM expression, especially in the tumor buds, focal expressionof p63, and reduced CD8 and CD3 counts, along with increased FOXP3 counts in the tumor microenvironment (�200, �400).

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previous subtype. The few GNAS and IDH2 mutationsidentified in our cohort are present in this category (Fig.4A and B). All these characteristics contribute to a betteroutcome (median OS: 19 months). The presence of TLTdefined a subpopulation of the immune-rich subtype (44%of the immune-rich cases, 17.5% of the whole cohort)with an additional high prevalence of STK11, ATM, andSMARCB1 mutations, even more favorable features (Sup-plementary Table S3; Table 1; Fig. 3), and the best outcome

(median OS: 23 months). Only 12% of the cases with highRHAMM–expressing buds belonged to this category (5% ofthe immune-rich cases; Fig. 2; Supplementary Fig. S9).Patients of the immune-rich subtype had the lowest preop-erative CA19-9 levels (median: 109 U/mL; SupplementaryFig. S8).

iii. The "immune-exhausted" (11% of all cases) subtype consistsof two subpopulations showing considerable similarities

Figure 2.

PDACs of the "immune-rich" subtype showing low-grade tumor budding (BD0-1), absence of RHAMM and p63 staining, and increased CD8 and CD3counts, along with decreased FOXP3 counts in the tumor microenvironment (x200).

Figure 3.

PDACs of the "immune-rich subtype with TLT," with the presence of TLT (HE, CD3, and CD8, x200). Kaplan–Meier curve for OS (P < 0.0001).






concerning their microenvironmental features: one with highPD-L1 expression (65% of the immune-exhausted cases, 7%of the whole cohort), frequent tumor budding, and a highPIK3CA mutation rate, along with an immunogenic micro-environment with a high CD8/FOXP3 ratio and unfavorableoutcome (median OS: 10 months; Table 1; SupplementaryFig. S2), and a small, microsatellite-unstable subpopulation(35% of the immune-exhausted cases, 4% of the wholecohort) with loss of DNAmismatch repair proteins in variouscombinations, an antitumoral immune microenvironmentwith the highest CD8/FOXP3 ratio, and a high prevalence ofJAK3 in addition to PIK3CA mutations (SupplementaryFig. S5; Fig. 4A). Cases with high RHAMM–expressing budswere also highly represented in the PD-L1 category (19% ofthe cases with high RHAMM–expressing buds, representing43% of the PD-L1 cases; Supplementary Fig. S9).

KRAS mutations were present in 94% to 100% of the casesin all subtypes.

Prognostic impactThe prognostic impact of the three main PDAC subtypes is

depicted in Fig. 5. The immune-rich subtype has better prog-nosis, whereas the immune-escape and immune-exhaustedsubtypes have worse prognosis (P < 0.001 for OS and P ¼0.004 for DFS). Moreover, the various combinations of tumorbudding, stromal FOXP3 counts, and CDKN2Amutations seemto significantly affect survival in PDAC. Stromal FOXP3 countscan substratify low-grade budders into two further categories,with low budding/low stromal FOXP3 having the best prog-nosis, whereas the presence of CDKN2 mutations seems to addindependent prognostic information and leads to substratifica-tion of the high-grade budders (Supplementary Fig. S10). Thus,PDACs with high tumor budding, presence of CDKN2A

Table 1. PDAC subtypes (with subpopulations) and clinicopathologic features

Features Immune escape Immune rich Immune rich with TLTs Immune exhausted PD-L1þ Immune exhausted MSI-H

Age (median) 63.4 66 65 51 69Sex (m/f) 1.1 1.2 1 1.5 4Size (median) 3.4 3.9 3.2 3.7 2.8Grade 3 (%) 44.2 30 23 40 40T3 (%) 15.7 18 14 40 0N0 (%) 18.6 10 38 30 0N1 (%) 51.2 65 44 20 70N2 (%) 30.2 25 18 50 30M1 (%) 10.5 7 0 20 0L1 (%) 87 74 67 75 100V1 (%) 26 26 0 20 20R1 (%) 33 26 9.5 60 50DFS (months, median) 5 7 9 5 6OS (months, median) 10 14 23 10 10Tumor buds (median) 22.4 5 5 26 11.7Teff/Treg IE (median) 1.8 4.5 5.5 0.9 1.4Teff/Treg S (median) 0.4 6.5 28.8 19.3 55.2CD8/buds (median) 2 16.7 17.8 4.8 10.6

Abbreviations: IE, intraepithelial; m/f, male/female; S, stromal.

Figure 4.Molecular alterations and PDAC subtypes. A, The heatmap depictspercentage of the cases of each subtype that harbor a particular mutation.B, The heatmap shows the percentage of the mutations of each particulargene that is present in the most frequent phenotypes.

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mutations, and high stromal FOXP3 counts have the worstprognosis in our cohort (Supplementary Fig. S10; Supplemen-tary Table S5A and S5B).

DiscussionHere, we show that the immune response patterns in the

microenvironment of PDAC, in correlation with the mutationalbackground and the clinicopathologic features, define three dis-tinct subtypes with clinical and prognostic relevance. Theimmune-exhausted subtype might be helpful in stratifying pati-ents for different immunomodulatory therapies.

One of our most interesting findings are the differencesbetween the immune-escape and the immune-rich subtype,which seem to have reverse microenvironmental response pat-terns and almost the opposite mutational background. Theimmune-escape category is characterized by a high CDKN2A,SMAD4, and PIK3CA mutation rate, high-grade tumor budding,and a microenvironment rich in FOXP3þ Tregs and poor in T andB cells, whereas the immune-rich subtype (with or without TLT)is characterized by a lower CDKN2A, SMAD4, and PIK3CAmuta-tion rate and a high prevalence of STK11, ATM, and SMARCB1mutations, along with infrequent tumor budding and a micro-environment rich in T and B cells and poorer in FOXP3þ Tregs.This leads to different morphologic and clinicopathologic fea-tures and impacts clinical outcome. Furthermore, it suggests thattumor budding cells, which are significantly more frequent inthe immune-escape subtype, are embedded in and probablyinteract with the tumor-permissive microenvironment, whiletheir close interaction with the FOXP3-Tregs opens possibilitiesfor therapeutic intervention (31). Although this interplaywith theimmune cells guarantees the survival of the prognostically unfa-vorable tumor buds, there is evidence that other componentsof the tumor microenvironment, like surrounding stromal cells,also support EMT-like tumor budding by expressing high levels ofE-cadherin suppressors and/or by contributing to miRNA dysre-gulation (32, 33). In addition, increasing evidence has linkedEMTto cancer stem cell features, thus supporting the suggestion thatEMT-like tumor buds may represent a population of migratingcancer stem cells (34). For example, theWNT pathway, one of thecommon pathways undergoing genetic alterations in PDAC (35)and strongly associated with the promotion of a stem cell–like

phenotype (36), is involved in the development of EMT-liketumor budding (9, 32). Furthermore, it has been shown thatEMT-like cells (like tumor budding cells) share similar molecularcharacteristics with cancer stem cells, for example, they are bothdrug resistant and have higher metastatic potential (11, 37). Thismay further contribute to the worse prognosis of the immune-escape PDAC subtype.

The expression pattern as well as the molecular and clinicalcharacteristics of the immune-escape phenotype largely over-lapwith the squamous subtypedescribedbyBailey and colleagues(16) or the quasi-mesenchymal subtype described by Collissonand colleagues (17), whereas the pattern that characterizes theimmune-rich phenotype is more compatible with the pancreaticprogenitor subtype described by Bailey and colleagues (16). Wecould thus assume that the presence of high-grade budding andincreased FOXP3 counts can be regarded as the morphologicsignature of the squamous subtype,whereas thepicture associatedwith low-grade budding and low stromal FOXP3 can be regardedas the morphologic substrate of the pancreatic progenitor type.Our results are also compatible with Siemers and colleagues (6),who observed a strong link betweenCDKN2Amutation (as in ourimmune-escape subtype) and reduced immune cell infiltratesacross 40 tumor cohorts. Most importantly, the molecular differ-ences create opportunities for various treatment modalitiestoward a more individualized therapy approach. For example,METpathway–targeted anticancer therapiesmaybemore effectivein PDACs of the immune-escape subtype, to additionally targetEMT-like tumor budding, as mutations in the receptor tyrosinekinases MET and ERBB4 were more frequent in the immune-escape cases (38, 39). In a recent study, dual MET and ERBBinhibition was shown to overcome intratumor plasticity in osi-mertinib-resistant advanced non–small cell lung cancer (40).Given the limited therapy options for PDAC, administration ofsuch combinatorial treatment can be exploited in PDACs of theimmune-escape subtype.

SMAD4 acts as the central mediator of TGFb signaling, andits inactivation is frequently found in PDACs (3, 41). The roleof the TGFb pathway as a tumor promoter or suppressor is stilla matter of debate. It is generally accepted that TGFb functionsas a tumor suppressor in the early phase of tumorigenesisbut can be converted to a tumor promoter during cancerprogression (42), thereby contributing to a permissive

Figure 5.

Kaplan–Meier curves showing theimpact of the three main PDACsubtypes on OS and DFS.






microenvironment for tumor growth and metastasis (43). Ourown results show that inactivating SMAD4 mutations are moreprevalent in the unfavorable immune-escape subtype, compat-ible with a tumor suppressor activity.

The CD20þ and CD3þ stromal infiltrates can give rise to twodifferent, sometimes interrelated patterns, including the looseinfiltrates and the formation of TLT (44). Presence of TLT(a subtype of the immune-rich phenotype) conveyed a strongantitumor impact, resulting in favorable features, with very lowtumor budding and a strong and independent survival advantagein our cohort. This is in agreement with recently publisheddata associating rich B-lymphocyte infiltrates and especiallyTLT with better prognosis in PDAC (44). Moreover, PDACs ofthe "immune rich with TLT" subpopulation frequently exhibitmutations in DNA damage repair genes (ATM and STK11),potentially leading to an increased number of tumor antigensand/or to defective DNA repair that may have direct therapeuticimplications (45). Mutations in STK11 have been found topromote activation of the PI3K/AKT/mTOR pathway, pointingto new drug targets (46).

A further interesting finding is the identification of animmune-exhausted subtype with two subpopulations, show-ing similarities to the immunogenic subtype by Bailey andcolleagues (16). The one characterized by high PD-L1 expres-sion exhibits unusual characteristics combining unfavorableclinicopathologic features like frequent tumor budding, alongwith an immune-rich microenvironment and increased Teff/Treg ratio. Molecularly, these tumors are characterized by ahigher frequency of PIK3CA mutations as well as a higherrate of PTEN loss (80%) compared with the other subtypes.Consistent with this, PIK3CA mutations have been associatedwith higher estimates of CD8þ T cells and natural killer (NK)cells, along with decreased Treg/CD8 ratios, across multipletumor types (47). Moreover, in an orthotopic mouse pancreaticcancer model, overexpression of PD-L1 correlated with lackof PTEN expression, which aberrantly activated the PI3K/Akt/mTOR signaling pathway (48). Our findings further indicatethat even though these PDACs show molecular and immuno-phenotypic similarities to the immune-rich subtype, the anti-tumor effect of the immune response is probably cancelledby the PD-1/PD-L1 blockade, rendering the microenviron-ment tumor permissive and allowing for the formation oftumor buds, provoking a biologic behavior more similar tothe immune-escape phenotype.

The second subpopulation of the immune-exhausted sub-type consists of a small number of MSI-H PDACs character-ized by an immune-rich microenvironment while showingprevalence of PIK3CA and JAK3 mutations. Consistent withthis, a recent study showed that the gene encoding mutLho-molog-1 (MLH1), a key component of the DNA mismatchrepair system, is silenced by promoter methylation in cellsharboring JAK mutations (49). Interestingly, both subpopula-tions of the immune-exhausted subtype (PD-L1 and MSI-H)show remarkable microenvironmental similarities. Althoughthese patients may represent good candidates for the admin-istration of checkpoint inhibitors, the unusual characteristicsand low frequency of these tumors may explain the limitedsuccess of immunotherapy in PDAC (50).

Importantly, we find an increased number of cases withhigh RHAMM–expressing tumor buds in our immune-escapeand immune-exhausted phenotypes as compared with

the immune-rich subtype. High RHAMM expression has beenassociated with adverse prognosis in many carcinomas (51–53)and hematologic malignancies (54); experimental data suggestthat targeting RHAMM could inhibit metastatic disseminationand cancer progression (55). In addition, it has been suggestedthat high RHAMM expression in malignant cells may evokean antigen-specific antitumoral immune response, makingRHAMM a promising target for cancer immunotherapy (56).Our results are consistent with these findings and also withCasalegno-Gardu~no and colleagues (57) who have shown thatpersistent RHAMM expression and decreasing CD8þ T-cellresponses (like in our immune-escape subtype) might indicatethe immune escape of leukemia cells.

Interestingly, we also find a good correlation between CA19-9 values and PDAC subtypes as well as with OS and DFS in ourcohort. CA19-9, a modified Lewis(a) blood group antigen thatis a component of glycoproteins and mucins, is the onlyestablished pancreatic cancer biomarker (58). Indeed, cases ofthe immune-escape phenotype showed significantly higher CA-19-9 values, followed by the immune-exhausted cases, whereasthe immune-rich phenotype exhibited the lowest values. Ourfindings are in accordance with previous studies, where anassociation between high CA-19-9 values and worse prognosishas been demonstrated (59).

Antitumor immunity is currently believed to be conditionedby the number of neoantigens that represent mutated peptidesshed from tumor cells and considered as nonself (2, 60). Tumorswith high mutational burdens can thus elicit an antitumorimmune response (2). Although the level of neoepitopes islower in PDAC compared with highly immunogenic tumors, asubset of PDACs harbor a significant neoantigen number (2).Consistent with this, we show that PDACs with an immune-richmicroenvironment generally exhibit a higher mutational burden,even in low-range mutations and/or a high prevalence ofPIK3CA mutations, which favor higher immune cell infiltrates.Moreover, the balance of the immune cell infiltrates seems tocorrelate with the molecular and morphologic characteristics,influencing the clinical outcome. In a previous study (15), wehave shown that FOXP3 counts have an independent prognosticeffect in PDAC. Here, we additionally show that the prognosticimpact of FOXP3 is stronger in cases with low-grade tumorbudding, resulting in a subset of PDACs (low budding/lowFOXP3) with especially good prognosis. On the contrary, tumorswith high FOXP3 infiltrates show high-grade tumor buddingand an adverse prognosis, although in these cases, the presenceofCDKN2Amutations has an additive adverse prognostic impact.

This study may be hampered by the small number of casesand the use of ngTMAs for immune cell and protein evalua-tion. It has, however, several strong points: The study cohort iswell characterized with full clinicopathologic information,multiple tumor punches from center and front were includedto account for heterogeneity, and the new AJCC TNM stagingsystem (8th ed.) has been implemented. Moreover, the fre-quency and type of the identified mutations of the examinedgenes are comparable with recently published The CancerGenome Atlas (TCGA) data for pancreatic cancer (18). Inaddition, the study was designed to conform with the REMARKguidelines (30).

In conclusion, our findings indicate the presence of threedifferent PDAC subtypes with distinct microenvironmental,morphologic, clinicopathologic, and molecular characteristics.

Wartenberg et al.





The recognition of these subtypes in daily practice has prog-nostic and predictive implications and would help clinicaldecision-making toward a more individualized treatmentapproach for patients with PDAC.

Disclosure of Potential Conflicts of InterestNo potential conflicts of interest were disclosed.

DisclaimerThe funders had no involvement in the study design; in the collection,

analysis, and interpretation of the data; in the writing of the report; and in thedecision to submit the article for publication.

Authors' ContributionsConception and design: B. Gloor, A. Perren, E. KaramitopoulouDevelopment of methodology: M. Wartenberg, E. KaramitopoulouAcquisition of data (provided animals, acquired and managed patients,provided facilities, etc.): M. Wartenberg, S. Cibin, I. Zlobec, E. Vassella,L. Terracciano, M.D. Eichmann, M. Worni, B. Gloor, E. Karamitopoulou

Analysis and interpretation of data (e.g., statistical analysis, biostatistics,computational analysis):M.Wartenberg, I. Zlobec, E. Vassella, S. Eppenberger-Castori, L. Terracciano, M.D. Eichmann, M. Worni, B. GloorWriting, review, and/or revision of the manuscript: I. Zlobec, E. Vassella,M. Worni, B. Gloor, A. Perren, E. KaramitopoulouAdministrative, technical, or material support (i.e., reporting or organizingdata, constructing databases): M. WartenbergStudy supervision: E. Karamitopoulou

AcknowledgmentsThis project was supported by the Werner and Hedy Berger-Janser Founda-

tion and the Foundation for Clinical-Experimental Tumor-Research.

The costs of publication of this article were defrayed in part by thepayment of page charges. This article must therefore be hereby markedadvertisement in accordance with 18 U.S.C. Section 1734 solely to indicatethis fact.

Received November 14, 2017; revised February 26, 2018; accepted April 9,2018; published first April 16, 2018.

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Wartenberg et al.




2018;24:4444-4454. Published OnlineFirst April 16, 2018.Clin Cancer Res Martin Wartenberg, Silvia Cibin, Inti Zlobec, et al. Prognostic/Predictive SignificancePancreatic Cancer Reveals Three Distinct Subtypes with Integrated Genomic and Immunophenotypic Classification of

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