Oncotarget

Research Papers:

Comprehensive immune transcriptomic analysis in bladder cancer reveals subtype specific immune gene expression patterns of prognostic relevance

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Oncotarget. 2017; 8:70982-71001. https://doi.org/10.18632/oncotarget.20237

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Runhan Ren, Kathrin Tyryshkin, Charles H. Graham, Madhuri Koti, D. Robert Siemens _

Abstract

Runhan Ren1,2, Kathrin Tyryshkin3, Charles H. Graham1,2, Madhuri Koti2,4,5 and D. Robert Siemens1,2

1Department of Urology, Queen’s University, Kingston, ON, Canada

2Department of Biomedical and Molecular Sciences, Queen’s University, Kingston, ON, Canada

3Department of Pathology and Molecular Medicine, Queen’s University, Kingston, ON, Canada

4Cancer Biology and Genetics Division, Queen’s Cancer Research Institute, Queen’s University, Kingston, ON, Canada

5Department of Obstetrics and Gynecology, Queen’s University, Kingston, ON, Canada

Correspondence to:

D. Robert Siemens, email: siemensr@kgh.kari.net

Keywords: MIBC, immune biomarkers, immunotherapy, TCGA, interferon

Received: December 14, 2016     Accepted: May 21, 2017     Published: August 09, 2017

ABSTRACT

Recent efforts on genome wide profiling of muscle invasive bladder cancer (MIBC) have led to its classification into distinct genomic and transcriptomic molecular subtypes that exhibit variability in prognosis. Evolving evidence from recent immunotherapy trials has demonstrated the significance of pre-existing tumour immune profiles that could guide treatment decisions. To identify immune gene expression patterns associated with the molecular subtypes, we performed a comprehensive in silico immune transcriptomic profiling, utilizing transcriptomic data from 347 MIBC cases from The Cancer Genome Atlas (TCGA). To investigate subtype-associated immune gene expression patterns, we assembled 924 immune response genes and specifically those involved in T-cell cytotoxicity and the Type I/II interferon pathways. A set of 157 ranked genes was able to distinguish the four subtypes in an unsupervised analysis in an original training cohort (n=122) and an expanded, validation cohort (n=225). The most common overrepresented pathways distinguishing the four molecular subtypes, included JAK/STAT signaling, Toll-like receptor signaling, interleukin signaling, and T-cell activation. Some of the most enriched biological processes were responses to IFN-γ, antigen processing and presentation, cytokine mediated signaling, hemopoeisis, cell proliferation and cellular defense response in the TCGA cluster IV. Our novel findings provide further insights into the association between genomic subtypes and immune activation in MIBC and may open novel opportunities for their exploitation towards precise treatment with immunotherapy.


Comprehensive immune transcriptomic analysis in bladder cancer reveals subtype specific immune gene expression patterns of prognostic relevance | Ren | Oncotarget

INTRODUCTION

Urothelial bladder cancer (UBC) is the fifth most common cancer worldwide [1] and is one of the most management intensive cancers in North America [2]. Although the majority of incident cases of UBC are non-invasive at presentation, muscle invasive bladder cancer (MIBC) represents very aggressive disease with rapid progression to metastases [3] and poor overall survival despite intensive local and systemic therapy. Current standards for localized MIBC include radical cystectomy with or without perioperative cisplatin-based chemotherapy [3]. Unfortunately, many suffer early disease recurrence and, despite palliative chemotherapy, median survival rates are generally less than one year [4]. The optimal management of patients with higher risk UBC is ambiguous with a significant need for better prediction tools and enhanced therapeutics [5].

MIBCs are highly heterogeneous tumours. Recent investigations based on molecular profiling of specimens from large UBC cohorts have led to their classification into molecular subtypes that display distinct genomic and transcriptomic features, resembling those seen in breast cancer [3], [68]. Interestingly, these subtypes may exhibit distinct associations with treatment response and survival [8, 9]. Although different groups have classified UBC into two [8], three [3], four [6], or five [7] subtypes, there is a consensus that the top separation occurs as the basal and luminal subtypes [10]. Basal tumours, enriched with EGFR and hypoxia-inducible factor 1 expression, are often metastatic at presentation, possess squamous and sarcomatoid histological features, and have epithelial-to-mesenchymal transition cell biomarkers [11]. In comparison, luminal cancers have papillary features and commonly FGFR3, ERBB2, and ERBB3 activating mutations [11]. The Cancer Genome Atlas Network (TCGA) bladder analysis working group classified bladder tumours into four clusters named I, II, III, and IV [6]. Clusters I and II correspond to the luminal subtype, while III and IV represent the basal subtype [12]. Tumours in Cluster I are enriched in FGFR3 overexpression due to mutations and amplification and show better overall survival, whereas those in cluster II, designated “p53-like” tumours, express active p53 gene signatures and are resistant to neoadjuvant cisplatin-based combination chemotherapy [3]. Cluster IV shares features with the claudin-low subtype of breast cancer, express immune checkpoint molecules, and were actively immunosuppressed, despite having an enriched immune gene signature [13]. In particular, Kardos et al. [13] demonstrated that immune infiltration was not correlated with predicted neoantigen burden, but from unopposed NF-kB activity from downregulated PPARγ signaling.

Given the urgent need of alternative approaches in MIBC treatment, there has been a growing interest in immunotherapies, such as those targeting the immune checkpoints: CTLA-4, PD-L1, and PD-1 [14]. Atezolizumab, a PD-L1 inhibitor, has been recently approved by the FDA for bladder cancer that progressed during or following chemotherapy [15]. Evolving evidence based on the success of immune checkpoint blockade therapies in melanoma and non-small cell lung cancer has confirmed the significance of the pre-treatment tumour immune state as a strong prognostic and response predictive indicator [14, 16]. An important feature, key to the success of immunotherapy, is the spatial organization of cytotoxic CD8+ tumour infiltrating lymphocytes (TILs) in the epithelial and stromal compartments and their activation status [17]. Higher density of CD3+ and CD8+ TILs have been associated with increased disease-free and overall survival in melanoma, head and neck, breast, bladder, urothelial, ovarian, colorectal, prostatic, and lung cancer; however, their activation status determines their prognostic significance in most cancers [18, 19]. In particular the expression of interferons (IFNs), which play a central role in anti-tumour immune responses, are emerging as prognostic and predictive biomarkers of both chemotherapy and immunotherapy [20]. Higher infiltration with CD4+ and regulatory subsets of TILs and higher CD68 to CD3 ratios are associated with poor prognosis in bladder cancer [2123]. In particular PD-L1, IDO, FOXP3, TIM3, and LAG3 are expressed in T-cell-inflamed, and β-catenin, PPAR-γ, and FGFR3 in non-T-cell-inflamed urothelial tumours [17]. Although the pre-treatment expression of PD-1/PD-L1 initially showed some predictive value, it has recently failed to perform as a good biomarker in the recent clinical trials due to their transient nature of expression [2325].

As reported in other cancers sites, it is likely that the pre-existing tumour immune landscape in UBC could be an additive determinant of response to chemotherapy as well as immune-based therapies leading to more precise prognostication, patient stratification, and informed treatment decisions [26]. To our knowledge there are no previous studies in MIBC that have evaluated the association between immune transcriptomic alterations, specifically those mediated by IFNs and cytotoxic pathway genes, and their potential associations with their distinct molecular sub-populations. In the current study, we performed a comprehensive in silico immune transcriptomic profiling of MIBC using the publicly available TCGA global transcriptomic datasets in order to determine whether the known molecular subtypes of MIBC are associated with specific immune gene signatures. The findings from our study may not only provide insights into the association between genomic subtypes and immune activation, but may also open novel opportunities for improving the management of MIBC.

RESULTS

We aimed to determine whether the previously defined four TCGA MIBC clusters exhibit differences in their immune gene expression patterns that could be of potential significance in informing treatment decisions for immunotherapies and other combinatorial treatment approaches.

Clinicopathological features of TCGA MIBC cohort

The TCGA cohort as reported earlier, consisted of chemotherapy-naïve, muscle-invasive, high-grade urothelial tumors (T2-T4a, Nx, Mx) [6]. Inclusion criteria reviewed by five expert genitourinary pathologists involved: tumour nuclei ≥ 60% of total, ≤20% tumour necrosis in the specimen, and variant histology ≤50% [6].

Immune gene expression patterns across MIBC clusters

First, the 122 samples previously used to identify the four clusters by TCGA [6] were treated as a discovery group to determine immune gene expression profiles across clusters. A total of 377 genes derived from the NanoString panel discriminating among the clusters were identified using a feature selection technique (Supplementary Table 1). The performance of these genes to accurately distinguish the four TCGA clusters was evaluated by clustering of cohort 1 (Figure 1). This set of genes was then used to assign samples in the validation set to the four clusters. Similar to cohort 1, the genes were able to distinguish the four clusters in cohort 2 by supervised and unsupervised analysis (Figure 2A and 2B). Similar unsupervised analysis was done using the top 5% of genes derived from all immune panels (n=157) (Supplementary Table 2) on both cohorts (Figure 3A and 3B). A recent updated analysis of the current TCGA bladder tumour cohort shows that clusters I-IV remained stable [28], supporting our classification approach in cohorts 1 and 2.

Figure 1:

Figure 1: Distinct immune gene expression levels in cohort 1 (n=122) between the four TCGA bladder cancer subtypes based on the top 20% (377 NanosString panel genes) using the feature selection algorithm. Red indicates high expression, and blue indicates low expression.

Cohort 2 (n=225) assigned to clusters based on Euclidian distance to the cluster centroids generated from the cohort 1 (n=122).

Figure 2: Cohort 2 (n=225) assigned to clusters based on Euclidian distance to the cluster centroids generated from the cohort 1 (n=122). Supervised (A) and unsupervised (B) analysis based on the samples and 377 NanoString panel genes.

Figure 3:

Figure 3: Unsupervised analysis of both the cohort 1 (A) and cohort 2 (B) using the top 5% (n=157) genes. Unsupervised grouping shows gradient of under expression in cluster I to overexpression in cluster IV.

Differential pre-existing expression patterns of interferon associated genes

The four cluster patterns were also noted for the top 20% ranking IFN-γ associated genes upon hierarchical clustering analysis in both training and validation cohorts (Figure 4A and 4B). A gradient of under-expression of IFN-γ associated genes in cluster I to overexpression in cluster IV is observed in both. A similar pattern was also noted in the top 20% ranking IFN-α (Figure 5A and 5B) and cytotoxic genes (Figure 6A and 6B). Most importantly, key IFN response genes and downstream T-cell recruiting target chemokine genes, CXCL9, CXCL10, and CXCL11, and their common receptor CXCR3, showed increased expression in clusters III and IV. Similarly, others in the list included the key players in IFN response such as IFITM2, CCL5, IRF4 and others.

Figure 4:

Figure 4: Supervised heat map of top 20% of IFN-γ associated pathway genes in both cohort 1 (A) and cohort 2 (B).

Figure 5:

Figure 5: Supervised heat map of top 20% of IFN-α associated pathway genes in both discovery (A) and validation (B) groups.

Figure 6:

Figure 6: Supervised heat map of top 20% of cytotoxic associated pathway genes in both cohort 1 (A) and cohort 2 (B).

Antigen processing pathways are overrepresented in T-cell inflamed MIBC clusters

We determined the Gene Ontology functional annotations of the differentially expressed genes that distinguished the four clusters, using both the 377 and 157 genes as input gene lists. Using the overrepresentation statistic in PANTHER, we calculated the probability of highly populated protein classes and gene ontology classes among the two gene lists (Table 2a and 2b). The most enriched GO biological processes in the 377-gene list were response to IFN-γ, antigen processing and presentation, cytokine mediated signaling, hemopoeisis, cell proliferation and cellular defense response (Supplementary Table 3). The top five overrepresented pathways included JAK/STAT signaling pathway, Toll-like receptor signaling pathway, interleukin signalling pathway, and T-cell activation (Figure 7). Interestingly, similar analysis using the top ranking 157 genes as input list, revealed only the B-cell activation, T-cell activation, and inflammation mediated by chemokine and cytokine signaling pathways as the only three overrepresented pathways. Response to IFN-γ, hemopoiesis, macrophage activation and cell proliferation were the most overrepresented GO biological processes in the top ranked 157 genes (Supplementary Table 4).

Table 1: Custom designed immune gene panel of 924 genes, consisting of IFN-α and IFN-γ pathway genes from GSEA and immune response genes defined by the NanoString nCounter PanCancer immune Pathways Panel

Immune gene panel - NanoString nCounter PanCancer immune panel

A2M

C1R

CCND3

CD44

CLEC5A

ABCB1

C1S

CCR1

CD46

CLEC6A

ABL1

C2

CCR2

CD47

CLEC7A

ADA

C3

CCR3

CD48

CLU

ADORA2A

C3AR1

CCR4

CD5

CMA1

AICDA

C4BPA

CCR5

CD53

CMKLR1

AIRE

C5

CCR6

CD55

COL3A1

AKT3

C6

CCR7

CD58

COLEC12

ALCAM

C7

CCR9

CD59

CR1

AMBP

C8A

CCRL2

CD6

CR2

AMICA1

C8B

CD14

CD63

CREB1

ANP32B

C8G

CD160

CD68

CREB5

ANXA1

C9

CD163

CD7

CREBBP

APOE

CAMP

CD164

CD70

CRP

APP

CARD11

CD180

CD74

CSF1

ARG1

CARD9

CD19

CD79A

CSF1R

ARG2

CASP1

CD1A

CD79B

CSF2

ATF1

CASP10

CD1B

CD80

CSF2RB

ATF2

CASP3

CD1C

CD81

CSF3

ATG10

CASP8

CD1D

CD83

CSF3R

ATG12

CCL1

CD1E

CD84

CT45A1

ATG16L1

CCL11

CD2

CD86

CTAG1B

ATG5

CCL13

CD200

CD8A

CTAGE1

ATG7

CCL14

CD207

CD8B

CTCFL

ATM

CCL15

CD209

CD9

CTLA4

AXL

CCL16

CD22

CD96

CTSG

BAGE

CCL17

CD24

CD97

CTSH

BATF

CCL18

CD244

CD99

CTSL1

BAX

CCL19

CD247

CDH1

CTSS

BCL10

CCL2

CD27

CDH5

CTSW

BCL2

CCL20

CD274

CDK1

CX3CL1

BCL2L1

CCL21

CD276

CDKN1A

CX3CR1

BCL6

CCL22

CD28

CEACAM1

CXCL1

BID

CCL23

CD33

CEACAM6

CXCL10

BIRC5

CCL24

CD34

CEACAM8

CXCL11

BLK

CCL25

CD36

CEBPB

CXCL12

BLNK

CCL26

CD37

CFB

CXCL13

BMI1

CCL27

CD38

CFD

CXCL14

BST1

CCL28

CD3D

CFI

CXCL16

BST2

CCL3

CD3E

CFP

CXCL2

BTK

CCL3L1

CD3EAP

CHIT1

CXCL3

BTLA

CCL4

CD3G

CHUK

CXCL5

C1QA

CCL5

CD4

CKLF

CXCL6

C1QB

CCL7

CD40

CLEC4A

CXCL9

C1QBP

CCL8

CD40LG

CLEC4C

CXCR1

CXCR2

FOS

IFI27

IL19

IRAK4

CXCR3

FOXJ1

IFI35

IL1A

IRF1

CXCR4

FOXP3

IFIH1

IL1B

IRF2

CXCR5

FPR2

IFIT1

IL1R1

IRF3

CXCR6

FUT5

IFIT2

IL1R2

IRF4

CYBB

FUT7

IFITM1

IL1RAP

IRF5

CYFIP2

FYN

IFITM2

IL1RAPL2

IRF7

CYLD

GAGE1

IFNA1

IL1RL1

IRF8

DDX43

GATA3

IFNA17

IL1RL2

IRGM

DDX58

GNLY

IFNA2

IL1RN

ISG15

DEFB1

GPI

IFNA7

IL2

ISG20

DMBT1

GPR44

IFNA8

IL21

ITCH

DOCK9

GTF3C1

IFNAR1

IL21R

ITGA1

DPP4

GZMA

IFNAR2

IL22

ITGA2

DUSP4

GZMB

IFNB1

IL22RA1

ITGA2B

DUSP6

GZMH

IFNG

IL22RA2

ITGA4

EBI3

GZMK

IFNGR1

IL23A

ITGA5

ECSIT

GZMM

IGF1R

IL23R

ITGA6

EGR1

HAMP

IGF2R

IL24

ITGAE

EGR2

HAVCR2

IGLL1

IL25

ITGAL

ELANE

HCK

IKBKB

IL26

ITGAM

ELK1

HLA-A

IKBKE

IL27

ITGAX

ENG

HLA-B

IKBKG

IL28A

ITGB1

ENTPD1

HLA-C

IL10

IL29

ITGB2

EOMES

HLA-DMA

IL10RA

IL2RA

ITGB3

EP300

HLA-DMB

IL11

IL2RB

ITGB4

EPCAM

HLA-DOB

IL11RA

IL2RG

ITK

ETS1

HLA-DPA1

IL12A

IL3

JAK1

EWSR1

HLA-DPB1

IL12B

IL32

JAK2

F12

HLA-DQA1

IL12RB1

IL34

JAK3

F13A1

HLA-DQB1

IL12RB2

IL3RA

JAM3

F2RL1

HLA-DRA

IL13

IL4

KIR2DL1

FADD

HLA-E

IL13RA1

IL4R

KIR2DL3

FAS

HLA-G

IL13RA2

IL5

KIR3DL1

FCER1A

HMGB1

IL15

IL5RA

KIR3DL2

FCER1G

HRAS

IL15RA

IL6

KIR3DL3

FCER2

HSD11B1

IL16

IL6R

KIT

FCGR1A

ICAM1

IL17A

IL6ST

KLRB1

FCGR2A

ICAM2

IL17B

IL7

KLRC1

FCGR2B

ICAM3

IL17F

IL7R

KLRC2

FCGR3A

ICAM4

IL17RA

IL9

KLRD1

FEZ1

ICOS

IL17RB

ILF3

KLRF1

FLT3

ICOSLG

IL18

INPP5D

KLRG1

FLT3LG

IDO1

IL18R1

IRAK1

KLRK1

FN1

IFI16

IL18RAP

IRAK2

LAG3

LAIR2

MAPK3

NT5E

RELB

STAT2

LAMP1

MAPK8

NUP107

REPS1

STAT3

LAMP2

MAPKAPK2

OAS3

RIPK2

STAT4

LAMP3

MARCO

OSM

ROPN1

STAT5B

LBP

MASP1

PASD1

RORA

STAT6

LCK

MASP2

PAX5

RORC

SYCP1

LCN2

MAVS

PBK

RPS6

SYK

LCP1

MBL2

PDCD1

RRAD

SYT17

LGALS3

MCAM

PDCD1LG2

RUNX1

TAB1

LIF

MEF2C

PDGFC

RUNX3

TAL1

LILRA1

MEFV

PDGFRB

S100A12

TANK

LILRA4

MERTK

PECAM1

S100A7

TAP1

LILRA5

MFGE8

PIK3CD

S100A8

TAP2

LILRB1

MICA

PIK3CG

S100B

TAPBP

LILRB2

MICB

PIN1

SAA1

TARP

LILRB3

MIF

PLA2G1B

SBNO2

TBK1

LRP1

MME

PLA2G6

SELE

TBX21

LRRN3

MNX1

PLAU

SELL

TCF7

LTA

MPPED1

PLAUR

SELPLG

TFE3

LTB

MR1

PMCH

SEMG1

TFEB

LTBR

MRC1

PNMA1

SERPINB2

TFRC

LTF

MS4A1

POU2AF1

SERPING1

TGFB1

LTK

MS4A2

POU2F2

SH2B2

TGFB2

LY86

MSR1

PPARG

SH2D1A

THBD

LY9

MST1R

PPBP

SH2D1B

THBS1

LY96

MUC1

PRAME

SIGIRR

THY1

LYN

MX1

PRF1

SIGLEC1

TICAM1

MAF

MYD88

PRG2

SLAMF1

TICAM2

MAGEA1

NCAM1

PRKCD

SLAMF6

TIGIT

MAGEA12

NCF4

PRKCE

SLAMF7

TIRAP

MAGEA3

NCR1

PRM1

SLC11A1

TLR1

MAGEA4

NEFL

PSEN1

SMAD2

TLR10

MAGEB2

NFATC1

PSEN2

SMAD3

TLR2

MAGEC1

NFATC2

PSMB10

SMPD3

TLR3

MAGEC2

NFATC3

PSMB7

SOCS1

TLR4

MAP2K1

NFATC4

PSMB8

SPA17

TLR5

MAP2K2

NFKB1

PSMB9

SPACA3

TLR6

MAP2K4

NFKB2

PSMD7

SPINK5

TLR7

MAP3K1

NFKBIA

PTGS2

SPN

TLR8

MAP3K5

NLRC5

PTPRC

SPO11

TLR9

MAP3K7

NLRP3

PVR

SPP1

TMEFF2

MAP4K2

NOD1

PYCARD

SSX1

TNF

MAPK1

NOD2

RAG1

SSX4

TNFAIP3

MAPK11

NOTCH1

REL

ST6GAL1

TNFRSF10B

MAPK14

NRP1

RELA

STAT1

TNFRSF10C

TNFRSF11A

TNFRSF4

TNFSF18

TREM1

VEGFA

TNFRSF11B

TNFRSF8

TNFSF4

TREM2

VEGFC

TNFRSF12A

TNFRSF9

TNFSF8

TTK

XCL2

TNFRSF13B

TNFSF10

TOLLIP

TXK

XCR1

TNFRSF13C

TNFSF11

TP53

TXNIP

YTHDF2

TNFRSF14

TNFSF12

TPSAB1

TYK2

ZAP70

TNFRSF17

TNFSF13

TPTE

UBC

ZNF205

TNFRSF18

TNFSF13B

TRAF2

ULBP2

TNFRSF1A

TNFSF14

TRAF3

USP9Y

TNFRSF1B

TNFSF15

TRAF6

VCAM1

Immune gene panel – IFN-γ associated genes

ADAR

CSF2RB

IFI44

LGALS3BP

PNP

APOL6

CXCL10

IFI44L

LY6E

PNPT1

ARID5B

CXCL11

IFIH1

LYSMD2

PRIC285

ARL4A

CXCL9

IFIT1

MAR-01

PSMA2

AUTS2

DDX58

IFIT2

METTL7B

PSMA3

B2M

DDX60

IFIT3

MT2A

PSMB10

BANK1

DHX58

IFITM2

MTHFD2

PSMB2

BATF2

EIF2AK2

IFITM3

MVP

PSMB8

BPGM

EIF4E3

IFNAR2

MX1

PSMB9

BST2

EPSTI1

IL10RA

MX2

PSME1

BTG1

FAS

IL15

MYD88

PSME2

C1R

FCGR1A

IL15RA

NAMPT

PTGS2

C1S

FGL2

IL18BP

NCOA3

PTPN1

CASP1

FPR1

IL2RB

NFKB1

PTPN2

CASP3

FTSJD2

IL4R

NFKBIA

PTPN6

CASP4

GBP4

IL6

NLRC5

RAPGEF6

CASP7

GBP6

IL7

NMI

RBCK1

CASP8

GCH1

IRF1

NOD1

RIPK1

CCL2

GPR18

IRF2

NUP93

RIPK2

CCL5

GZMA

IRF4

OAS2

RNF213

CCL7

HERC6

IRF5

OAS3

RNF31

CD274

HIF1A

IRF7

OASL

RSAD2

CD38

HLA-A

IRF8

OGFR

RTP4

CD40

HLA-B

IRF9

P2RY14

SAMD9L

CD69

HLA-DMA

ISG15

PARP12

SAMHD1

CD74

HLA-DQA1

ISG20

PARP14

SECTM1

CD86

HLA-DRB1

ISOC1

PDE4B

SELP

CDKN1A

HLA-G

ITGB7

PELI1

SERPING1

CFB

ICAM1

JAK2

PFKP

SLAMF7

CFH

IDO1

KLRK1

PIM1

SLC25A28

CIITA

IFI27

LAP3

PLA2G4A

SOCS1

CMKLR1

IFI30

LATS2

PLSCR1

SOCS3

CMPK2

IFI35

LCP2

PML

SOD2

SP110

STAT2

TNFAIP3

TRIM25

VAMP8

SPPL2A

STAT3

TNFAIP6

TRIM26

VCAM1

SRI

STAT4

TNFSF10

TXNIP

WARS

SSPN

TAP1

TOR1B

UBE2L6

XAF1

ST3GAL5

TAPBP

TRAFD1

UPP1

XCL1

ST8SIA4

TDRD7

TRIM14

USP18

ZBP1

STAT1

TNFAIP2

TRIM21

VAMP5

ZNFX1

Immune gene panel – IFN-α associated genes

ADAR

FAM125A

IL4R

OGFR

SLC25A28

B2M

FAM46A

IL7

PARP12

SP110

BATF2

FTSJD2

IRF1

PARP14

STAT2

BST2

GBP2

IRF2

PARP9

TAP1

C1S

GBP4

IRF7

PLSCR1

TDRD7

CASP1

GMPR

IRF9

PNPT1

TMEM140

CASP8

HERC6

ISG15

PRIC285

TRAFD1

CCRL2

HLA-C

ISG20

PROCR

TRIM14

CD47

IFI27

LAMP3

PSMA3

TRIM21

CD74

IFI30

LAP3

PSMB8

TRIM25

CMPK2

IFI35

LGALS3BP

PSMB9

TRIM26

CNP

IFI44

LPAR6

PSME1

TRIM5

CSF1

IFI44L

LY6E

PSME2

TXNIP

CXCL10

IFIH1

MOV10

RIPK2

UBA7

CXCL11

IFIT2

MX1

RNF31

UBE2L6

DDX60

IFIT3

NCOA7

RSAD2

USP18

DHX58

IFITM1

NMI

RTP4

WARS

EIF2AK2

IFITM2

NUB1

SAMD9

ELF1

IFITM3

OAS1

SAMD9L

EPSTI1

IL15

OASL

SELL

Immune gene panel – cytotoxic associated genes

ABCB1

CCL8

CTLA4

FFAR3

HERC6

ADAM19

CCR5

CXCL10

FUT2

HESX1

ADAM3A

CD163L1

CXCL11

GAS6

HS3ST3B1

ADORA3

CD28

CXCL9

GBP6

IDO1

ANKRD22

CD300E

DNA2

GIMAP4

IFNB1

APOL3

CD38

DPCD

GIMAP5

IFNG

ARNT2

CD5

ENPP2

GIMAP7

IL10

ATF5

CD8A

F13A1

GIMAP8

IL12RB2

BATF2

CDC7

F2R

GNLY

IL15

BCL2

CDK6

FAM20A

GPR171

IL15RA

BCL2L14

CH25H

FAM26F

GZMB

IL21

C5orf39

CMKLR1

FAM40B

GZMK

IL2RA

CASC5

CMPK2

FBXO39

HAMP

IL4I1

CCL7

CRABP1

FCGR2B

HAPLN3

IRF4

ISG15

MYBL1

PRKCQ

SDS

TMEM229B

ITGA9

NKG7

PTGER2

SH2D1A

TNFSF18

KIAA1199

OR2A5

RGL1

SLA2

TRAT1

KLHDC1

OR5D14

RHBDF2

SLAMF1

TSHZ3

KLRD1

OR6K6

RNASE2

SMOX

TXK

LILRB5

P2RX5

RRM2

SOCS1

TYMS

MERTK

PLEKHO1

RTP4

SSTR2

USP18

MKI67

PRF1

RUNX3

STAB1

MS4A6E

PRKCA

SAMD3

STAT4

Table 2: Gene Ontology classes enriched in the overall 377 genes (a) and 157 top ranked genes (b) GO categories defined by PANTHER pathway tool [27, 33]

Table 2a

Analysis Type:

PANTHER Overrepresentation Test (release 20160715)

Annotation Version and Release Date:

PANTHER version 11.1 Released 2016-10-24

Analyzed List:

377genes (Homo sapiens)

Reference List:

Homo sapiens (all genes in database)

Bonferroni correction:

TRUE

Bonferroni count:

158

PANTHER Pathways

Homo sapiens - REFLIST (20972)

377genes (385)

377genes (expected)

377genes (over/under)

377 genes (fold Enrichment)

377genes (P-value)

JAK/STAT signaling pathway (P00038)

17

6

0.31

+

19.23

1.50E-04

Toll receptor signaling pathway (P00054)

60

21

1.1

+

19.07

5.00E-18

Interleukin signaling pathway (P00036)

98

23

1.8

+

12.78

4.53E-16

T cell activation (P00053)

96

22

1.76

+

12.48

4.03E-15

B cell activation (P00010)

72

15

1.32

+

11.35

1.83E-09

p38 MAPK pathway (P05918)

42

7

0.77

+

9.08

2.49E-03

Apoptosis signaling pathway (P00006)

122

20

2.24

+

8.93

5.24E-11

Inflammation mediated by chemokine and cytokine signaling pathway (P00031)

261

36

4.79

+

7.51

3.53E-18

Blood coagulation (P00011)

47

6

0.86

+

6.95

4.23E-02

VEGF signaling pathway (P00056)

72

8

1.32

+

6.05

1.08E-02

Ras Pathway (P04393)

76

8

1.4

+

5.73

1.56E-02

CCKR signaling map (P06959)

173

17

3.18

+

5.35

6.02E-06

p53 pathway (P00059)

88

8

1.62

+

4.95

4.19E-02

Angiogenesis (P00005)

176

16

3.23

+

4.95

4.23E-05

Integrin signalling pathway (P00034)

192

17

3.52

+

4.82

2.59E-05

EGF receptor signaling pathway (P00018)

139

11

2.55

+

4.31

1.07E-02

Gonadotropin-releasing hormone receptor pathway (P06664)

235

15

4.31

+

3.48

6.36E-03

Unclassified (UNCLASSIFIED)

18333

232

336.55

-

0.69

0.00E+00

Table 2b

Analysis Type:

PANTHER Overrepresentation Test (release 20160715)

Annotation Version and Release Date:

PANTHER version 11.1 Released 2016-10-24

Analyzed List:

157 genes (Homo sapiens)

Reference List:

Homo sapiens (all genes in database)

Bonferroni correction:

TRUE

Bonferroni count:

158

PANTHER Pathways

Homo sapiens - REFLIST (20972)

157 genes Input (158)

157 genes Input (expected)

157 genes Input (over/under)

157 genes Input (fold Enrichment)

157 genes Input (P-value)

JAK/STAT signaling pathway (P00038)

17

3

0.13

+

23.42

4.94E-02

Interleukin signaling pathway (P00036)

98

9

0.74

+

12.19

1.20E-05

B cell activation (P00010)

72

6

0.54

+

11.06

3.25E-03

Toll receptor signaling pathway (P00054)

60

5

0.45

+

11.06

1.62E-02

T cell activation (P00053)

96

7

0.72

+

9.68

1.55E-03

Inflammation mediated by chemokine and cytokine signaling pathway (P00031)

261

12

1.97

+

6.1

1.34E-04

Integrin signalling pathway (P00034)

192

8

1.45

+

5.53

1.86E-02

Unclassified (UNCLASSIFIED)

18333

96

138.12

-

0.7

0.00E+00

Bar graph depicting distribution of fold enrichment levels of biological pathways defined by PANTHER based analysis in the 377 genes that show differential expression patterns in the four TCGA MIBC clusters.

Figure 7: Bar graph depicting distribution of fold enrichment levels of biological pathways defined by PANTHER based analysis in the 377 genes that show differential expression patterns in the four TCGA MIBC clusters. The enriched categories were obtained upon analysis using the statistical overrepresentation test defined by PANTHER tool [27].

DISCUSSION

Evolving research from correlative studies as well as clinical trials, including those for UBC, have emphasized the value of the pre-existing tumour immune state as a predictor of response to treatment and survival [29, 30]. Furthermore, UBC is associated with a comparatively high mutational burden [31], which could potentially contribute to increased immunogenicity making them more susceptible to novel immunotherapy-based approaches. In order to gain a better understanding of the pre-existing tumour immune landscape in UBC, we conducted a comprehensive in silico immune transcriptomic profiling of the MIBC tumours from the TCGA database.

Four distinct molecular subtypes in MIBC were defined recently based on the TCGA MIBC genome wide profiling datasets [6]. Indeed, variability in subtype nomenclature has been reported [10], which could be attributed to heterogeneity in tissue samples in addition to several other factors such as inclusion of NMIBC cases in classification schemes. However, since the TCGA bladder cohort is enriched for MIBC and gene expression based clusters have been well defined, we specifically used this cohort to address our question on immune gene expression patterns associated with genomic alterations. Based on the distinct immune signature between clusters in cohort 1 (n=122), we were able to assign cohort 2 (n=225) into the associated TCGA clusters using the top 20% of ranked immune genes from the training cohort. Further analysis on the IFN-γ, IFN-α, and cytotoxic genes were then compared in both cohorts. All of these analyses revealed an increased expression of immune-associated genes in Cluster IV and underactive immune environment in Cluster I. Given that specific genetic alterations associate with these molecular subtypes, it seems that anti-tumour immune responses could be partly driven by oncogenic drivers.

Cluster I has been previously reported to show higher expression of FGFR3 via mutations, amplifications, and other mechanisms [3]. Interestingly this cluster showed a distinct underactive tumour immune state with reduced expression of IFN genes and genes associated with T-helper type-1 response. It is indeed intriguing that tumours with FGFR3 mutations or overexpression as per previous classification [6], show an increased overall survival, which contradicts the underactive immune state observed here. In contrast, cases in cluster IV showed the most dominant immune response amongst all four clusters. Tumours in this cluster show decreased expression of PPAR-γ and GATA3, and significantly increased expression of IFN and antigen presentation pathway genes, in addition to MHC class II genes and those involved in T-cell cytolytic activity. Previous reports have shown that based on broader classifications, cases in cluster IV belong to the basal subtype, which shows poor overall survival [8], [9]. One potential contributor to these associations is the increased expression of immune checkpoint molecules such as CD274 (PD-L1), IDO1 and the immunosuppressive IL6 in clusters III and IV that potentially lead to increased resistance to cytotoxic killing and poor response to treatment and ultimately poor survival. As previously shown, this cluster also shows higher levels of EMT related genes, indicating more aggressive tumour phenotype [33]. Our recent report demonstrating that higher PD-L1 expression in cancer cells leads to increased drug resistance upon activation by IFN-γ or PD-1 [32] supports this notion. It could thus be speculated that the IFN-γ secreted by activated T-cells, reflected by the increased expression of GZMA in these clusters, could be inducing PD-L1 on the cancer cells with further interaction between these leading to T-cell dysfunction. However, the mechanistic basis of these significant associations needs to be explored further. In other cancers such as melanoma, colorectal, and ovarian, higher expression of IFN pathway genes and of those representing an active immune response is associated with a favourable treatment outcome and overall survival. Furthermore, it is also possible that factors other than anti-tumour immune responses contribute to increased survival rates in tumours with FGFR3 mutations in MIBC.

Increased expression of MHC class II genes CD74, HLA-DMB, and HLA-DQA1 indicate higher tumour antigen processing by the antigen presenting cells in clusters III and IV. This was also confirmed by gene ontology-based analysis, which reflected a dominance of response to IFN-γ, antigen processing and presentation, cytokine mediated signaling, and cell proliferation, NK cell and macrophage activation, and B cell mediated immunity. These enrichments not only confirm the increased active anti-tumour immune response in clusters IV and some of cluster III but also indicate the immunogenic nature of these clusters that could be potentially be driven by higher mutational burden and recognition of immune cells.

Overall, our findings based on comprehensive immune transcriptomic analysis have significant implications in informing treatment decisions based on immune gene expression patterns. Specifically, since immune checkpoint blockade therapy has shown some promise in bladder cancer [15], near future biomarker driven clinical trials will benefit from these findings that emphasize appropriate patient stratification for treatment. Although recent reports have described the presence of T-cell inflamed and non-inflamed MIBC tumours [17, 33], no previous reports have identified associations between immune response and IFN-associated genes with the four molecular MIBC subtypes. Our study is limited by the fact that the TCGA dataset is enriched for MIBC and thus further validations in other cohorts need to be performed; however, these associations are timely and complement the ongoing and future clinical trials based on immune-based therapies. Clinical translation of our findings will most appropriately be addressed by validation of the most significant differentially expressed genes at both transcriptional and proteomic levels in retrospective and prospective pre-treatment bladder tumour specimens. Future investigations by integration of genomic alterations determined by exome and transcriptome sequencing data are key to identifying the genomic determinants of variability in immune response. Finally our study provides an improved understanding of the bladder cancer molecular subtype associated immune gene expression patterns and will significantly impact the design of novel immune based therapies.

MATERIALS AND METHODS

Patient tumour samples

The publicly available global transcriptomic sequencing (RNA-Seq) data from 412 MIBC cases, with the corresponding clinical information was downloaded from TCGA data portal (https://gdc-portal.nci.nih.gov/), now part of the National Cancer Institute’s Genetic Data Commons. The cohort was further divided into two cohorts for downstream analysis. For our training cohort (cohort 1) we used data from the previously defined 129 cases from TCGA that were divided into four clusters based on their integrated analysis of mRNA, miRNA, and protein data [6]. Since our objective was to define immune gene expression patterns in treatment naïve tumours, we excluded patients with any previous therapy. Thus a cohort of 122 MIBC cases, divided into four molecular clusters, was used for in silico immune profiling. The remaining 283 cases constituted the validation cohort (cohort 2) of which 225 had no prior therapy.

Design of immune pathway gene panel for in silico immune profiling

To investigate the presence of subtype associated immune signatures, we assembled a defined set of 924 immune related genes. This curated list (Table 1) primarily consisted of genes involved in IFN-α (97 genes), IFN-γ (200 genes), and cytotoxic (115 genes) pathways as defined by Gene Set Enrichment Analysis (GSEA) in combination with other immune genes. The nCounter PanCancer Immune profiling panel (722 genes), (http://www.nanostring.com/products/pancancer_immune/) was used to derive the immune response related genes.

Bioinformatics analysis of RNA-Seq data

We used the upper quartile-normalized RNA-seq data by expectation maximization (RSEM) available for all selected cases at the TCGA data portal. No additional normalization was performed and the expression data were log2 transformed. All downstream data analysis was performed in MATLAB (Mathworks, Inc., Natick, Massachusetts, USA). Focusing on the first set of 122 samples, where clustering information is known, we performed separate analyses of the genes in each of the four immune panels (NanoString, IFN-α, IFN-γ, and T cell cytotoxicity associated genes). Using a feature selection algorithm, genes were ranked based on their ability to discriminate samples belonging to one cluster from the remaining. The feature selection algorithm uses an ensemble of five different machine-learning techniques (unpublished). The analysis resulted in 16 ranking tables, four tables for each immune panel, where each table ranked genes on their ability to discriminate samples in one cluster from the rest.

In the hierarchical clustering analysis, the top 20% of genes in each ranking group were assembled to represent each immune panel, resulting in 377 genes (Nanostring), 44 genes (IFN-α), 91 genes (IFN-γ), and 62 genes (T cell cytotoxicity). A final feature selection ranking was performed where the combined unique set of 924 genes was used. The top 5% of genes in each of the four ranking groups were then merged, resulting in 157 unique genes.

Gene ontology analysis using PANTHER

We used the Protein Analysis Through Evolutionary Relationships (PANTHER), version 11.0, classification system (http://www.pantherdb.org/) [27] to determine dominant and enriched pathways in the top ranking 377 genes (NanoString panel) that were ranked based on their ability to discriminate samples across the four clusters. We then applied the statistical binomial overrepresentation test, as previously described in PANTHER [27], to derive the most dominant enriched pathways and gene ontology (GO) biological processes in our lists compared to the reference human genome. We performed these tests using both the 377 top ranking NanoString genes and 157 top ranked genes from all immune panels combined. The p-values were corrected for multiple testing using Bonferroni correction.

Validation of immune gene signature

The remaining 298 cases, not included in cohort 1, were treated as a validation group. From this cohort, patients with previous BCG therapy were excluded, leaving 225 cases. In order to assign samples in this set to each of the four clusters, only the top ranked 377 genes from the NanoString panel were used. First, for each cluster, two cluster centroids were computed using the expression data from cohort 1 (n=122; total of 8 cluster centroids). The cluster centroids were computed by taking the mean expression of samples in a given cluster (main cluster) and the mean expression of samples that do not belong to that cluster (alternative cluster). Then, for each sample in cohort 2, the Euclidean distance was computed to each of the 8 cluster centroids. A sample was assigned to a cluster with the smallest distance to the main cluster, but only if the distance to the main cluster was smaller than the distance to the alternative cluster. Alternatively, the sample was assigned to the ‘unclassified’ cluster. Using the newly assigned clustering information and ranked list of genes, unsupervised hierarchical clustering was performed.

ACKNOWLEDGMENTS

This study was supported by a South Eastern Ontario Academic Medical Organization Innovation grant awarded to D. Robert Siemens.

CONFLICTS OF INTEREST

All authors have no potential conflicts of interest.

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