An Immunoevasive Strategy Through Clinically-Relevant Pan-Cancer Genomic and Transcriptomic Alterations of JAK-STAT Signaling Components

Home , CISH, IRF8, PIAS3, STAT5

bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

An immunoevasive strategy through clinically-relevant pan-cancer genomic and transcriptomic alterations of JAK-STAT signaling components

Wai Hoong Chang1 and Alvina G. Lai1,

1Nufﬁeld Department of Medicine, University of Oxford, Old Road Campus, Oxford, OX3 7FZ, United Kingdom

Since its discovery almost three decades ago, the Janus ki- Although cytokines are responsible for inflammation in nase (JAK)-signal transducer and activator of transcription cancer, spontaneous eradication of tumors by endoge- (STAT) pathway has paved the road for understanding inflam- nous immune processes rarely occurs. Moreover, the matory and immunity processes related to a wide range of hu- dynamic interaction between tumor cells and host immu- man pathologies including cancer. Several studies have demon- nity shields tumors from immunological ablation, which strated the importance of JAK-STAT pathway components in overall limits the efficacy of immunotherapy in the clinic. regulating tumor initiation and metastatic progression, yet, the extent of how genetic alterations influence patient outcome is far from being understood. Focusing on 133 genes involved in Cytokines can be pro- or anti-inflammatory and are inter- JAK-STAT signaling, we found that copy number alterations dependent on each other’s function to maintain immune underpin transcriptional dysregulation that differs within and homeostasis(3). Discovered as a critical regulator of cytokine between cancer types. Integrated analyses on over 18,000 tu- signaling, the Janus kinase (JAK) - signal transducer and mors representing 21 cancer types revealed a core set of 28 JAK- activator of transcription (STAT) pathway allows cytokines STAT pathway genes that correlated with survival outcomes in to transduce extracellular signals into the nucleus to regulate brain, renal, lung and endometrial cancers. High JAK-STAT gene expression implicated in a myriad of developmental scores were associated with increased mortality rates in brain processes including cellular growth, differentiation and host and renal cancers, but not in lung and endometrial cancers defense(4). JAK proteins interact with cytokine receptors to where hyperactive JAK-STAT signaling is a positive prognostic factor. Patients with aberrant JAK-STAT signaling demon- phosphorylate signaling substrates including STATs. Unlike strated pan-cancer molecular features associated with misex- normal cells which phosphorylate STATs temporarily, pression of genes in other oncogenic pathways (Wnt, MAPK, several STAT proteins were found to be persistently phos- TGF-β, PPAR and VEGF). Brain and renal tumors with hyper- phorylated and activated in cancer(5). Studies have shown active JAK-STAT signaling had increased regulatory T cell gene that persistent activation of STAT3 and STAT5 promote (Treg) expression. A combined model uniting JAK-STAT and inflammation of the microenvironment, tumor proliferation, Tregs allowed further delineation of risk groups where patients invasion and suppress anti-tumor immunity(5). Particularly with high JAK-STAT and Treg scores consistently performed in cancers associated with chronic inflammation such as liver the worst. Providing a pan-cancer perspective of clinically- and colorectal cancers(6, 7), STAT3 activation by growth relevant JAK-STAT alterations, this study could serve as a factors or interleukins suppresses T cell activation and framework for future research investigating anti-tumor immu- promotes the recruitment of anti-immunity factors such as nity using combination therapy involving JAK-STAT and immune checkpoint inhibitors. myeloid-derived suppressor cells and regulatory T cells(8, 9).

JAK-STAT | pan-cancer | tumor immunity | glioma | renal cancer Given its fundamental roles in interpreting environmental Correspondence: [email protected] cues to drive a cascade of signaling events that control growth and immune processes, it is essential to dissect Introduction cell type-specific roles of the JAK-STAT pathway in a pan-cancer context. This is made possible by advances in In their quest to survive and prosper, tumor cells are high-throughput sequencing initiatives and as many genetic armored with a unique ability to manipulate the host’s alterations have become targetable, detail understanding on immune system and promote pro-inflammatory pathways. genetic variations would be essential to identify particular Inflammation can both initiate and stimulate cancer progres- weaknesses in individual tumors in order to boost therapeutic sion, and in turn, tumor cells can create an inflammatory success. We predict that genetic alterations in JAK-STAT microenvironment to sustain their growth further(1, 2). pathway genes do not occur equally between and within Cytokines are secretable molecules that influence immune cancer types. Moreover, detection of rare alterations would and inflammatory processes of nearby and distant cells. require a large sample size to unravel genes that are altered

Chang et al. | bioRχiv | March 13, 2019 | 1–11 bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. within specific histological subtypes of cancer. With over of signature genes: IL7, IFNG, MPL, IL11, IL2RA, IL21R, 18,000 samples representing 21 cancer types, we took the OSMR, IL20RA, IFNGR1, CDKN1A, CISH, SOCS1, IL10, opportunity to systematically characterize genetic alterations IL10RA, STAT2, IL24, IL23A, PIAS3, IFNLR1, EPO, within 133 JAK-STAT pathway genes to uncover shared TSLP, BCL2, IL20RB, IL11RA, PTPN6, IL13, IL17D and commonalities and differences. To identify functionally IL15RA. Regulatory T cell (Treg) scores were determined relevant alterations, genetic polymorphisms were overlaid by taking the mean expression of 31 Treg genes identified with transcript expression profiles and correlated with clin- from the overlap of four Treg signatures to generate a more ical outcomes. Our study identifies a core set of candidate representative gene set that is cell type-independent(12–15). JAK-STAT drivers that correlated with tumor progression and that predict overall survival outcomes in brain, re- Multidimensional scaling, survival and differential nal, lung and endometrial cancers converging on similar expression analyses: We previously published detailed downstream oncogenic pathways. This work provides a methods on the above analyses(16–18) and thus the methods rich source of cancer type-dependent alterations that could will not be repeated here. Briefly, multidimensional scaling serve as novel therapeutic targets to support underexploited analyses based on Euclidean distance in Fig. 2F were treatment initiatives targeting JAK-STAT signaling in cancer. performed using the vegan package in R(19). Permuta- tional multivariate analysis of variance (PERMANOVA) was used to determine statistical significance between Methods tumor and non-tumor samples. Survival analyses were performed using Cox proportional hazards regression All plots were generated using R and the Kaplan-Meier method coupled with the log-rank packages (pheatmap and ggplot2). test. Predictive performance of the 28-gene signature was assessed using the receiver operating characteristic Cancer cohorts and JAK-STAT pathway genes: 133 analysis. To determine the prognostic significance of a JAK-STAT pathway genes were obtained from the Kyoto En- combined model uniting the JAK-STAT signature and IRF8 cyclopedia of Genes and Genomes (KEGG) database listed expression or Treg scores, patients were separated into four in Table S1. Genomic, transcriptomic and clinical datasets of survival categories based on median 28-gene scores and 21 cancer types (n=18,484) were retrieved from The Cancer IRF8 expression or Treg scores for Kaplan-Meier and Cox Genome Atlas (TCGA)(10). The following is a list of cancer regression analyses. To determine the transcriptional effects cohorts and corresponding TCGA abbreviations in parenthe- of aberrant JAK-STAT signaling, differential expression ses: bladder urothelial carcinoma (BLCA), breast invasive analyses were performed on patients within the 4th versus carcinoma (BRCA), cervical squamous cell carcinoma 1st survival quartiles (stratified using the 28-gene signature). and endocervical adenocarcinoma (CESC), cholangiocarcinoma (CHOL), colon adenocarcinoma (COAD), Functional enrichment and transcription factor anal- esophageal carcinoma (ESCA), glioblastoma multiforme yses: Differentially expressed genes (DEGs) identified (GBM), glioma (GBMLGG), head and neck squamous cell above were mapped to KEGG and Gene Ontology carcinoma (HNSC), kidney chromophobe (KICH), pan- (GO) databases using GeneCodis(20) to determine sig- kidney cohort (KIPAN), kidney renal clear cell carcinoma nificantly enriched pathways and biological processes. (KIRC), kidney renal papillary cell carcinoma (KIRP), liver DEGs were also mapped to ENCODE and ChEA hepatocellular carcinoma (LIHC), lung adenocarcinoma databases using Enrichr(21, 22) to identify transcrip- (LUAD), lung squamous cell carcinoma (LUSC), pancreatic tion factors that were significant regulators of the DEGs. adenocarcinoma (PAAD), sarcoma (SARC), stomach adenocarcinoma (STAD), stomach and esophageal carcinoma (STES) and uterine corpus endometrial carcinoma (UCEC). Results Copy number variation analyses: Level 4 GISTIC copy Copy number and transcriptome analyses reveal con- number variation datasets were downloaded from the served driver mutations in JAK-STAT pathway genes Broad Institute GDAC Firehose(11). Discrete amplification and deletion indicators were obtained from GISTIC We interrogated genomic and transcriptomic landscape of gene-level tables. Genes with GISTIC values of 2 were 133 JAK-STAT pathway genes in 18,484 patients across annotated as deep amplification events, while genes with 21 cancer types (Table S1). To investigate the effects of values of -2 were annotated as deep (homozygous) deletion JAK-STAT pathway genomic alterations on transcriptional events. Shallow amplification and deletion events were output, we first analyzed copy number variation (CNV) of all annotated for genes with values of +1 and -1 respectively. 133 genes. CNVs were classified into four categories: low- level amplifications, deep amplifications, low-level deletions Calculating JAK-STAT and regulatory T cell scores: A (heterozygous deletions) and deep deletions (homozygous JAK-STAT 28-gene signature was developed from putative deletions). Lung squamous cell carcinoma (LUSC) and gain- or loss-of-function candidates. For each patient, 28- papillary renal cell carcinoma (KIRP) had the highest and gene scores were calculated from the average log2 expression lowest fraction of samples with deleted JAK-STAT pathway

2 | bioRχiv Chang et al. | Prognoses of JAK-STAT driver genes bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Somatic losses Somatic gains 1.00 Deep amplification

0.75 Shallow amplification

0.50 No alteration

0.25 Shallow deletion

SCNA fraction SCNA Deep deletion 0.00

1.0 GBM Central GBMLGG nervous system HNSC Head & neck LUAD Thorax LUSC 0.8 BRCA Breast ESCA CHOL COAD Gastrointestinal 0.6 LIHC tract PAAD STAD STES

Fraction with SCNA BLCA 0.4 KICH Genitourinary KIPAN tract SCNA fraction by cancer type SCNA KIRC KIRP 0.2 CESC Gynecologic UCEC SARC Soft tissue

GBM Central nervous system 6 GBMLGG HNSC Head & neck LUAD Thorax 4 LUSC BRCA Breast ESCA 2 CHOL COAD Gastrointestinal LIHC tract 0 PAAD STAD

Log2 fold change STES -2 BLCA KICH Genitourinary tract -4 KIPAN KIRC Transcript differential expression Transcript KIRP -6 CESC Gynecologic UCEC SARC Soft tissue CISH IL5RA IL17D J MPL IFNLR1 IFNGR1 IL20RA BCL2 J CNTFR IL11RA IL10RA IL15 IL2 IL6ST IL13 TSLP IL12B IL21R SOCS1 PTPN6 IFNG IL23A S TA T2 IL11 CDKN1A IL15RA IL2RA EPO PIAS3 IL10 IL24 IL19 IL20 BCL2L1 IL7 IL20RB IL7R OSMR AK1 AK2

Fig. 1. Pan-cancer genomic and transcriptomic alterations of JAK-STAT pathway genes. Stacked bar charts depict the fraction of altered samples for each of the 40 putative driver genes. Heatmap in the center illustrates the fraction of somatic gains and losses per gene within each cancer type. Heatmap below illustrates differential expression profiles (tumor vs. non-tumor) for each gene. Hierarchical clustering is performed on columns (genes) using Euclidean distance metric. Cancer abbreviations are listed in the Methods section. genes respectively (Table S2). In terms of gene amplification, with CNV profiles and we identified 40 driver genes rep- this was also the highest in lung squamous cell carcinoma resenting potential loss- or gain-of-function (Fig. 1). In at (LUSC) and the lowest in pancreatic adenocarcinoma least 7 cancer types, 18 genes were recurrently deleted and (PAAD) (Table S2). To identify pan-cancer CNV events, downregulated (log2 fold-change < -0.5, P < 0.01), while a we focused on genes that were deleted or amplified in at non-overlapping set of 22 genes were recurrently amplified least one-third of cancer types ( 7 cancers). We identified and upregulated (log2 fold-change > 0.5, P < 0.01) (Fig. 1). 71 and 49 genes that were recurrently deleted and amplified respectively in at least 20% of samples within each cancer JAK-STAT driver genes predict over- type and at least 7 cancer types (Table S2). Esophageal all survival in diverse cancer types carcinoma (ESCA) had 70 genes that were recurrently deleted while only four recurrently deleted genes were found Focusing on the 40 driver genes identified above, we next in papillary renal cell carcinoma (KIRP). When considering analyzed whether expression levels of individual genes were recurrent gene amplifications, lung adenocarcinoma (LUAD) associated with overall survival outcomes. Cox proportional had the highest number of gains (48 genes), while the lowest hazards regression analyses demonstrated that all 40 genes number of gene gains was observed in glioma (GBMLGG) harbored prognostic information in at least one cancer type. (3 genes) (Table S2). CNV events associated with tran- IL11, PTPN6 and CISH were significantly associated with scriptional changes could represent candidate driver genes. survival outcomes in patients from 9 cancer cohorts (Fig. Loss-of-function genes can be identified from genes that 2A). In contrast, IL19, CNTFR and JAK2 were some of were recurrently deleted and downregulated at the transcript the least prognostic genes (Fig. 2A). When deciphering level. Similarly, genes that were concomitantly gained and the contribution of individual genes across cancer types, upregulated could represent a gain-of-function. Differential we observed that the glioma (GBMLGG) cohort had the expression profiles (tumor vs. non-tumor) were intersected highest number of prognostic genes (31/40 genes) (Fig.

Chang et al. | Prognoses of JAK-STAT driver genes bioRχiv | 3 bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Fig. 2. Prognostic significance of JAK-STAT driver genes. (A) Heatmap illustrates hazard ratio values obtained from Cox proportional hazards regression on 40 candidate drivers across all cancer types. (B) Heatmap illustrates Spearman’s correlation coefficient values comparing hazard ratios of the 40 driver genes. Highly correlated genes are highlighted in red and are demarcated by a red box. (C) Box plots represent the distribution of 28-gene scores derived from highly correlated JAK-STAT driver genes. Cancers are ranked from high to low median scores. (D) Heatmap depicts the Z-scores for each of the 28 genes by cancer types. (E) Kaplan-Meier analyses confirmed prognosis of the 28-gene signature in five cancer cohorts. Patients are separated into 4th and 1st survival quartiles based on their 28-gene scores. P values are obtained from log-rank tests. (F) Ordination plots of multidimensional scaling analyses using signature genes reveal significant differences between tumor and non-tumor samples. P values are obtained from PERMANOVA tests. (G) Expression distribution of 28-gene scores in tumors stratified by stage (s1, s2, s3 and s4). P values are determined using ANOVA.

4 | bioRχiv Chang et al. | Prognoses of JAK-STAT driver genes bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A B Glioma Astrocytoma Pan-kidney Renal clear cell 1.00 1.00 +++++++ 1.00 +++++++++++++++++++++++++++++++++++++++ 1.00 +++++++++++++++++++ ++ ++++ + +++++++++++++++++++++++++++++++++++++++++++++++ + + +++ ++ +++ ++++++ + ++ ++++++++++ + + + +++++ +++ ++++ + +++ ++++++++++++++++++++++++++++ +++ ++++++++++++ ++++++++++++++++++ + ++++ + + ++ ++++++++ ++++ + +++ ++ ++++++++ + ++ + ++++ 0.75 ++ + + + + ++ +++++++++ + + + + +++ + ++ +++ +++ +++++++ + + 0.75 + ++ + + ++ ++ +++ ++++++ + + + +++ + ++ +++++ +++ 0.75 +++ + ++ +++++++ 0.75 ++ + + ++ + + 0.50 + +++ + ++++ + ++ +++++ + ++ + ++ + + 0.50 ++ + +++ +++ +++++ +++++ al probability + + + + ++ + + + Classifier AUC + ++ + ++ + +++ 0.25 ++ + vi v All glioma 0.853 + al probability ++++ + + Astrocytoma 0.878 0.50 + 0.50 +++ + vi v Oligoastrocytoma 0.951 Su r 0.25 + 0.00 p = 0.015 + + + 0.00 0.25 0.50 0.75 1.00 Su r + + + 0.00 0.25 0.25 0 1 2 3 4 5 p < 0.0001 + + p < 0.0001 Pan-kidney Years 1.00 Number at risk 0.00 0.00 Q1 49 37 25 20 8 5 0.75 Q4 49 29 16 8 5 2 0 1 2 3 4 5 0 1 2 3 4 5 Years Years Number at risk Number at risk 0.50 s1_Q1 229 200 145 109 89 55 s1_Q1 131 117 99 86 69 45 s1_Q2 229 201 159 121 86 66 s1_Q2 130 115 92 73 52 37 Oligoastrocytoma s2_Q1 52 48 42 39 32 27 s2_Q1 29 26 21 19 14 13 1.00 + +++++ s2_Q2 51 46 35 30 25 19 s2_Q2 28 26 22 19 16 11 0.25 Classifier AUC +++++++++ s3_Q1 94 76 56 44 32 24 s3_Q1 60 45 38 30 21 17 ++ + +++++ s3_Q2 95 76 64 48 36 20 s3_Q2 61 52 44 32 25 12 Signature 0.800 ++ + s4_Q1 52 31 24 17 15 14 s4_Q1 42 28 22 17 15 12 Signature + TNM 0.838 ++ s4_Q2 53 29 20 15 11 5 s4_Q2 41 25 17 13 9 5 0.00 0.75 ++ + 0.00 0.25 0.50 0.75 1.00 + +

0.50 + al probability Renal clear cell vi v Lung 1.00 Endometrium

Su r 0.25 1.00 +++++ 1.00 ++++ +++ +++++ p = 0.037 ++++++++ ++++++ +++ + + ++++++++++++ + ++++ ++++++ ++ +++++++++++++++++++++++ ++ +++++++ ++++++ + +++ + +++++++++++++ ++++++ ++++++++++++ 0.75 ++ ++++++ +++ +++ ++ ++ ++++ +++++++++ + +++++ + +++ +++ ++++++++++++ + 0.00 + + ++ +++++++ ++ + ++ +++ + ++++++++++++++++++ +++ ++ ++ 0 1 2 3 4 5 + ++++++ 0.50 0.75 + + ++++ 0.75 + Years ++ + + ++ + ++++ + ++ + + + + ++ + + + + + + ++ +++ + ++ 0.25 Classifier AUC Number at risk + + + +++ ++++ + + +++ Signature 0.789 + ++++ + Q1 33 29 18 17 7 4 al probability + Signature + TNM 0.836 0.50 ++++ + 0.50 0.00 Q4 33 26 14 8 4 4 vi v + +++ + + 0.00 0.25 0.50 0.75 1.00 + Su r + + + + + 0.25 0.25 Lung Endometrium p < 0.0001 p < 0.0001 1.00 1.00 0.00 0.00 0.75 0.75 0 1 2 3 4 5 0 1 2 3 4 5 Years Years 0.50 0.50 Number at risk Number at risk s1_Q1 115 99 54 38 30 22 s1_Q1 122 114 84 58 44 30 s1_Q2 114 101 54 33 16 13 s1_Q2 122 103 81 62 49 39 s2_Q1 52 39 22 12 3 2 s2_Q1 14 12 10 9 7 4 0.25 Classifier AUC 0.25 Classifier AUC s2_Q2 49 37 24 11 8 5 s2_Q2 14 10 9 7 6 5 Signature 0.703 s3_Q1 32 23 10 3 1 1 s3_Q1 39 32 20 12 8 3 Signature 0.713 s3_Q2 30 22 12 8 6 3 s3_Q2 39 35 27 15 14 12 Signature + TNM 0.724 Signature + TNM 0.760 s4_Q1 10 9 4 1 1 1 s4_Q1 9 7 4 2 2 1 0.00 0.00 s4_Q2 10 9 5 3 0 0 s4_Q2 9 8 6 5 3 2 0.00 0.25 0.50 0.75 1.00 0.00 0.25 0.50 0.75 1.00

Fig. 3. The 28-gene signature is independent of tumor, node and metastasis (TNM) stage. (A) Receiver operating characteristic analysis is used to assess the predictive performance of the signature and TNM stage. For glioma patients, area under the curves (AUCs) are compared between histological subtypes. (B) Kaplan-Meier analyses of patients stratified by tumor stage or, in the case of glioma, by histological subtype and the 28-gene signature. For histological subtypes of glioma, log-rank tests are used to compare patients within the 1st and 4th survival quartiles. For the other cancers, patients are first stratified according to TNM stage followed by median-stratification into low- and high-score groups using the 28-gene signature. P values are obtained from log-rank tests.

2A). On the other hand, none of the 40 driver candidates driver genes demonstrated that they exhibited a wide range were prognostic in esophageal carcinoma (ESCA) and of expression depending on the cellular context where they cholangiocarcinoma (CHOL), which suggests the minimal could serve as potential candidates for therapy. For example, contribution of JAK-STAT signaling in driving tumor IL7, IL15RA, IL21R, IL10, OSMR, IFNGR1, IL10RA, progression in these cancer types (Fig. 2A). To identify a IL2RA, IFNG, IL24, SOCS1, IL20RA, IL11 and IL23A core set of prognostic genes denoting pan-cancer signifi- were highly expressed in gastrointestinal cancers (PAAD, cance, we performed Spearman’s correlation analyses on STAD and STES) (Fig. 2D). When the 28-gene scores hazard ratio (HR) values obtained from Cox regression and were employed for patient stratification, we observed that identified 28 highly-correlated genes: IL7, IFNG, MPL, the JAK-STAT signature conferred prognostic information IL11, IL2RA, IL21R, OSMR, IL20RA, IFNGR1, CDKN1A, in five diverse cancer cohorts (Fig. 2E). Intriguingly, the CISH, SOCS1, IL10, IL10RA, STAT2, IL24, IL23A, PIAS3, significance of the signature in predicting overall survival IFNLR1, EPO, TSLP, BCL2, IL20RB, IL11RA, PTPN6, was cancer type-dependent. Kaplan-Meier analyses and IL13, IL17D and IL15RA (Fig. 2B). These genes were col- log-rank tests revealed that patients with high scores (4th lectively regarded as a pan-cancer JAK-STAT signature. To quartile) had higher death risks in glioma (P<0.0001), determine the extent of JAK-STAT pathway variation across pan-kidney (consisting of chromophobe renal cell, clear cancer types, we calculated an activity score based on the cell renal cell and papillary renal cell cancers; P<0.0001) mean expression of the 28 genes. When cancers were sorted and clear cell renal cell (P<0.0001) cohorts (Fig. 2E). In according to their pathway activity scores, chromophobe contrast, high expression of signature genes was linked to renal cell cancer (KICH) had the lowest median score while improved survival rates in lung (P=0.025) and endometrial the highest median score was observed in head and neck (P=0.032) cancers (Fig. 2E). These results were indepen- cancer (HNSC) (Fig. 2C). Hierarchical clustering of the 28 dently corroborated using Cox regression analyses: glioma

Chang et al. | Prognoses of JAK-STAT driver genes bioRχiv | 5 bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

(HR=6.832, P<0.0001), pan-kidney (HR=3.335, P<0.0001), transcriptional defects caused by aberrant activation of clear cell renal cell (HR=4.292, P<0.0001), lung (HR=0.624, JAK-STAT. Differential expression analyses performed P=0.028) and endometrium (HR=0.504, P=0.027) (Table between the 4th and 1st quartile patients revealed that a S3). Since high expression of signature genes was associated striking number of over 200 differentially expressed genes with poor survival outcomes in brain and renal cancers, as (DEGs) were shared between all five prognostic cohorts expected, we observed a significant increase in expression (Fig. 4A; Table S4). Significant overlaps were observed in scores according to tumor stage (Fig. 2G). An opposite DEGs where 555 genes were found in at least four cohorts, trend was observed in lung and endometrial cancers where 1,009 genes in at least three cohorts and 2,034 genes in more aggressive tumors had lower expression scores (Fig. at least two cohorts (Fig. 4A; Table S4). The highest 2F). Lastly, multidimensional scaling analyses of signature number of DEGs was observed in glioma (2,847 genes), genes in the five cohorts revealed significant differences followed by pan-kidney (2,810 genes), lung (1,221 genes), between tumor and non-tumor samples, implying that clear cell renal cell (1,161 genes) and endometrial (782 dysregulated JAK-STAT signaling may serve as a diagnostic genes) cancers (Fig. 4A; Table S4). To determine their marker for early detection in pre-cancerous lesions (Fig. 2F). functional roles, the DEGs were mapped to Gene Ontology (GO) and KEGG databases. All five cohorts exhibited The JAK-STAT 28-gene signature remarkably similar patterns of enriched biological processes is an independent prognostic factor (Fig. 4B). Pan-cancer enrichments of ontologies related to inflammation and immune function were observed, e.g., Multivariate Cox regression analyses confirmed that the sig- cytokine and chemokine signaling, T cell and B cell receptor nature was independent of tumor, node and metastasis (TNM) signaling, natural killer cell-mediated processes, Toll-like stage: glioma (HR=2.377, P=0.018), pan-kidney (HR=2.468, receptor signaling and NOD-like receptor signaling (Fig. P<0.0001), clear cell renal cell (HR=2.552, P=0.00047), 4B). Additionally, genes associated with other oncogenic lung (HR=0.636, P=0.031) and endometrium (HR=0.434, pathways (Wnt, MAPK, TGF-β, PPAR and VEGF)(23–25) P=0.033) (Table S3). Given that the signature was an in- were frequently altered at both transcriptional and genomic dependent predictor of overall survival, we reasoned that its levels (Fig. 4B and 4C). CNV analyses performed on predictive performance could be increased when used in con- DEGs that were found to be in common in at least three junction with TNM staging. Employing the receiver oper- cohorts demonstrated that transcriptional dysregulation of ating characteristic (ROC) analysis, we demonstrated that the aforementioned oncogenic pathways was attributed to a combined model uniting the signature and TNM staging activating or inactivating CNVs (Fig. 4C). For instance, could outperform the signature (higher area under the curve except for THBS1 and THBS2, a vast majority of TGF-β [AUC] values) when it was considered alone: pan-kidney DEGs exhibited somatic gains (Fig. 4C). To further explore (0.838 vs. 0.800), clear cell renal cell (0.836 vs. 0.789), which upstream transcriptional regulators were involved, we lung (0.724 vs. 0.703) and endometrium (0.760 vs. 0.713) mapped the DEGs to ENCODE and ChEA databases (Fig. (Fig. 3A). Independently, Kaplan-Meier analyses and log- 4B). Interestingly, we observed significant enrichment of rank tests confirmed that the signature allowed further delin- transcription factors (TFs) involved in modulating immune eation of risk groups within similarly-staged tumors: pan- function: IRF8(26), RUNX1(27), RELA(28) and EZH2(29) kidney (P<0.0001), clear cell renal cell (P<0.0001), lung (Fig. 4B). Taken together, our analyses revealed that (P<0.0001) and endometrium (P<0.0001) (Fig. 3B). pan-cancer JAK-STAT drivers underpin numerous aspects of tumor oncogenesis and immunity, which play important High JAK-STAT scores were associated with decreased roles in tumor progression and ultimately patient prognosis. survival rates in glioma patients. We confirmed that this was also true for histological subtypes of glioma: astrocytoma (P=0.015) and oligoastrocytoma (P=0.037) (Fig. 3B). IRF8 and JAK-STAT pathway synergistically in- Independently confirmed using Cox regression, patients fluence survival outcomes in glioma and renal cancer within the 4th survival quartile had lower survival rates: astrocytoma (HR=2.377, P=0.018) and oligoastrocytoma IRF8 was among one of the most enriched TFs implicated (HR=2.730, P=0.038) (Table S3). In terms of the signa- in the regulation of transcriptional outputs of patients with ture’s predictive performance, ROC analyses revealed that dysregulated JAK-STAT signaling (Fig. 4B). As a member it performed the best in oligoastrocytoma (AUC=0.951), of the interferon regulatory factor family, IRF8 is needed for followed by astrocytoma (AUC=0.878) and glioma patients the development of immune cells and is often regarded as a when considered as a full cohort (AUC=0.853) (Fig. 3A). tumor suppressor gene since a loss of function is associated with increased metastatic potential(30, 31). Thus, we predict Consequences of dysregulated JAK-STAT signal- that tumors with low expression of IRF8 would be more ing and significant crosstalk with tumor immunity aggressive. To evaluate the combined relationship between JAK-STAT signaling and IRF8 expression, patients were Since dysregulated JAK-STAT signaling was associated categorized into four groups based on median IRF8 and JAK- with survival outcomes (Fig. 2 and Fig. 3), we reasoned STAT scores: 1) high-high, 2) low-low, 3) high IRF8 and low that patients from diverse cancer types might harbor similar 28-gene score and 4) low IRF8 and high 28-gene score. The

6 | bioRχiv Chang et al. | Prognoses of JAK-STAT driver genes bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

A B GO C Gain Loss ANGPTL4 CD36 signal transduction CYP4A11 Renal clear cell P value FABP5 FABP6 cell adhesion FABP7 PPAR MMP1 0.015 PLTP 152 cell−cell signaling SCD 0.010 CACNA1E CACNA1F 70 transport CACNA1I 222 CACNA2D1 Pan-kidney 11 0.005 CACNA2D4 1047 159 18 carbohydrate metabolic process CACNG4 CD14 18 62 DUSP1 205 DUSP2 45 68 Lung cell differentiation EGF 33 98 FAS 106 FASLG 26 embryo development FGF9 235 Gene number FLNC 427 71 FOS 100 HSPA6 115 cell proliferation MAPK IL1A 78 4 IL1B 2 35 200 IL1R2 92 21 response to stress MAP3K8 0 MAP4K1 MAPT Glioma 1 NTRK1 1497 17 lipid metabolic process NTRK2 PDGFRA PRKCB 53 PTPN5 protein complex assembly PTPN7 RAC2 RASGRF1 y RASGRP2 ium Lung RASGRP4 Endometrium Glioma RPS6KA6 an−kidne P Endometr DKK1 Renal clear cell DKK2 FOSL1 FZD10 FZD2 LEF1 MMP7 NKD1 Wnt NKD2 KEGG ENCODE & ChEA PLCB1 PLCB2 Cytokine cytokine receptor interaction IRF8_CHEA SFRP2 Chemokine signaling pathway SFRP4 Cell adhesion molecules (CAMs) P value P value WNT10A WNT10B RUNX1_CHEA 0.04 T cell receptor signaling pathway WNT7B Natural killer cell mediated cytotoxicity 0.015 Jak STAT signaling pathway TP53_CHEA 0.03 COMP Primary immunodeficiency DCN 0.010 FST ECM receptor interaction ESR1_CHEA 0.02 IFNG Neuroactive ligand receptor interaction INHBA Calcium signaling pathway 0.005 LTBP1 RELA_ENCODE 0.01 PITX2 Toll like receptor signaling pathway TGF-β TGFB1 Complement and coagulation cascades TGFB2 Fc gamma R mediated phagocytosis TP63_CHEA THBS1 Combined Score Leukocyte transendothelial migration THBS2 MAPK signaling pathway AR_CHEA 10 Gene number NFATC2 B cell receptor signaling pathway PIK3CG NOD like receptor signaling pathway 30 20 PIK3R5 EZH2_CHEA PLA2G2A Focal adhesion PLA2G2D Wnt signaling pathway 60 30 PLA2G5 REST_CHEA VEGF PTGS2 PPAR signaling pathway 40 SH2D2A TGF beta signaling pathway 90 VEGFA Endomet r Glioma Lung P Renal clear cell Endomet r Glioma Lung P Renal clear cell an VEGF signaling pathway MYOD1_ENCODE an 50

Phosphatidylinositol signaling system kidn e kidn e ium Arachidonic acid metabolism VDR_CHEA ium y y Tryptophan metabolism

y y ium ium Lung Lung Glioma kidne Glioma an an−kidne P P 0 0.2 0.4 0.6 0.8 1.0 Endometr Endometr Renal clear cell Renal clear cell SCNA fraction

Fig. 4. Aberrant JAK-STAT signaling drives malignant progression through crosstalk with other oncogenic pathways. (A) Venn diagram depicts the number of overlapping differentially expressed genes (DEGs) in five cohorts. Differential expression analyses are performed between patients stratified into the 4th and 1st survival quartiles using the JAK-STAT signature in five cancer cohorts. (B) Functional enrichment analyses are performed by mapping DEGs to the Gene Ontology and KEGG databases. Mapping of DEGs to ENCODE and ChEA databases identify enriched transcription factor binding associated with DEGs. (C) Somatic copy number alteration frequencies for DEGs identified from enriched pathways in B. Heatmaps depict the fraction of somatic gains and losses for DEGs found within five oncogenic pathways. Only genes that are altered in more than 10% of samples within each tumor type are shown. combined model encompassing JAK-STAT and IRF8 offered trieved genes implicated in regulatory T cell (Treg) function an additional resolution in patient stratification: glioma (full- from four studies and isolated 31 genes that were common in cohort, P<0.0001), astrocytoma (P=0.0007), pan-kidney all four Treg signatures(12–15). Treg scores were calculated (P<0.0001) and clear cell renal cell (P<0.0001) (Fig. 5A). for each patient based on the mean expression levels of Indeed, patients with low IRF8 and high 28-gene scores the 31 genes. Remarkably, we observed strong positive performed the worst in cancers where hyperactive JAK- correlations between Treg and JAK-STAT scores, suggesting STAT signaling was linked to adverse survival outcomes: that tumor tolerance was enhanced in patients with hyper- glioma (full-cohort: HR=5.826, P<0.0001), astrocytoma active JAK-STAT signaling: glioma (rho=0.85, P<0.0001), (HR=3.424, P=0.0032), pan-kidney (HR=5.131, P<0.0001) pan-kidney (rho=0.88, P<0.0001) and clear cell renal cell and clear cell renal cell (HR=5.389, P<0.0001) (Fig. 5B). (rho=0.83, P<0.0001) (Fig. 6A). As in the previous section, Our results support a model in which IRF8 influences patients were stratified into four categories based on median the behavior of tumors with aberrant JAK-STAT signaling. JAK-STAT and Treg scores for survival analyses. Log-rank tests and Cox regression analyses confirmed that elevated Treg activity further exacerbated disease phenotypes in Hyperactive JAK-STAT signal- patients where JAK-STAT scores were already high: glioma ing attenuates tumor immunity (full cohort, HR=6.183, P<0.0001), astrocytoma (HR=3.035, P=0.00042), pan-kidney (HR=3.133, P<0.0001) and clear Given the wide-ranging effects of JAK-STAT signaling on cell renal cell (HR=2.982, P<0.0001) (Fig. 6B and 6C). immune-related functions, we hypothesized that JAK-STAT Taken together, elevated JAK-STAT signaling may increase activity would correlate with immune cell infiltration. We re-

Chang et al. | Prognoses of JAK-STAT driver genes bioRχiv | 7 bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

the ability of tumors to escape immunosurveillance, resulting in more aggressive tumors and increased mortality rates.

Discussion Tumor-promoting and tumor-suppressing roles of JAK-STAT signaling is very much cell type-dependent (Fig. 1, Fig. 2, A Low signature & high IRF8 High signature & low IRF8 Fig. 3). JAK-STAT activation appears to drive oncogenic High signature & high IRF8 Low signature & low IRF8 progression in liver cancer(32) and infection with hepati- All Glioma Astrocytoma tis viruses could also induce pathway activation(33, 34). 1.00 +++++++++++++++++++++++++ 1.00 +++++++++ +++++ + +++++++++ ++++++++ + + + + ++ ++++++ +++ ++++++++++++++ ++ +++ Moreover, liver tumors with downregulated SOCS proteins ++++ ++++++++ ++ + + ++++ ++++++++ +++ ++ ++++++++ ++++ +++++++++++ +++ + +++ 0.75 + ++++++ 0.75 +++++++++ (inhibitors of JAK-STAT signaling) are associated with poor ++ +++++++ +++ + + ++ +++ ++ + + ++ + ++++ + + +++ ++ ++ + + +++++ prognosis(32). JAK and STAT3 promote cell proliferation, ++ ++ 0.50 + +++ 0.50 ++ + ++ + + + ++ ++++++++ ++

al probability +++ ++ invasion and migration in colorectal cancer through the reg- + ++ vi v +++ +++ +++ + + + 0.25 ++ 0.25 ulation of cell adhesion molecules and growth factors(35). Su r p < 0.0001 p = 0.0007 In contrast, phosphorylated STAT5 promotes cellular 0.00 0.00 differentiation and inhibits invasive properties in breast 0 1 2 3 4 5 0 1 2 3 4 5 Years Years Number at risk Number at risk cancer cells(36, 37). A decrease in STAT5 is also linked to 114 96 66 53 29 21 38 32 24 22 10 7 220 136 69 37 23 17 59 39 24 15 10 8 poorly differentiated morphology and advance histological 113 64 22 9 4 3 38 28 11 2 1 0 217 170 106 65 37 26 59 42 27 18 10 7 grades in breast tumors(38, 39). Similarly, in rectal cancers, patients with tumors positive for phosphorylated STAT3 had Pan-kidney Renal clear cell 1.00 +++++++++++++++ 1.00 ++++ improved survival outcomes(40). In contrast, high levels of ++ +++++++++++++++++++++++++++ ++++++++++++++ ++++++ +++++++ +++++ +++++ +++ +++++ +++++++ +++++++++++++++++ +++++++ +++++ +++ +++++ ++++ +++ ++ +++++ +++++++++++++++ +++ +++++ ++++++++++ phosphorylated STAT3 is associated with reduced survival +++++++++++++ ++++++ ++++++ +++++++++++ +++++ +++++++++ +++++++ + ++ ++ 0.75 ++++++++++ 0.75 ++ +++++++ +++++++ ++++++++++ +++ ++ ++ + ++ ++++++++++ +++ ++++++ rates in glioblastoma(41) and renal cancer(42), which inde- + + ++ + + +++ ++++++++++ + ++++++++ + + +++ +++ pendently corroborates our ﬁndings on the tumor-promoting 0.50 + + + 0.50 ++ al probability +++ effects of JAK-STAT signaling in these cancer types (Fig. 2 vi v + + + 0.25 0.25 Su r p < 0.0001 p < 0.0001 and 3). Given its ambiguous role, understanding the function of JAK-STAT signaling in a pan-cancer context would 0.00 0.00 0 1 2 3 4 5 0 1 2 3 4 5 be necessary to increase the success of therapy in tumors Years Years Number at risk Number at risk with abnormal pathway activity. In an integrated approach 121 99 74 62 49 34 86 75 66 56 41 30 323 267 208 162 118 81 177 155 124 99 75 48 121 93 73 55 40 28 86 57 45 31 20 11 employing genomic, transcriptomic and clinical datasets, 322 277 210 161 134 100 175 148 120 103 85 63 we elucidated pan-cancer patterns of JAK-STAT signaling converging on a core set of candidate driver genes known as B Hazard Ratio (95% CI) P -value All Glioma the JAK-STAT 28-gene signature (Fig. 2). We demonstrated High signature & low IRF8 vs. low signature & high IRF8 5.826 (3.635 - 9.337) < 0.0001 High signature & high IRF8 vs. low signature & high IRF8 4.384 (2.842 - 6.764) < 0.0001 prognosis of the signature in ﬁve cancer cohorts (n=2,976), Low signature & low IRF8 vs. low signature & high IRF8 1.096 (0.674 - 1.782) 0.72

Astrocytoma where its performance was independent of TNM stage High signature & low IRF8 vs. low signature & high IRF8 3.424 (1.512 - 7.753) 0.0032 High signature & high IRF8 vs. low signature & high IRF8 1.834 (0.885 - 3.801) 0.11 (Fig. 2 and 3). Patients with aberrant JAK-STAT signaling Low signature & low IRF8 vs. low signature & high IRF8 0.744 (0.308 - 1.799) 0.52 exhibited interactions with other major oncogenic pathways, Pan-kidney High signature & low IRF8 vs. low signature & high IRF8 5.131 (2.674 - 9.847) < 0.0001 including MAPK, Wnt, TGF-β, PPAR and VEGF (Fig. 4). High signature & high IRF8 vs. low signature & high IRF8 4.092 (2.204 - 7.598) < 0.0001 Low signature & low IRF8 vs. low signature & high IRF8 1.631 (0.849 - 3.129) 0.14 This suggests that co-regulation of intracellular signaling Renal clear cell cascades could have direct functional effects and combi- High signature & low IRF8 vs. low signature & high IRF8 5.389 (3.011 - 9.644) < 0.0001 High signature & high IRF8 vs. low signature & high IRF8 2.612 (1.494 - 4.566) < 0.0001 Low signature & low IRF8 vs. low signature & high IRF8 1.339 (0.782 - 2.430) 0.33 natorial therapies simultaneously targeting these pathways may improve treatment efﬁcacy and overcome resistance.

Fig. 5. Prognostic relevance of the crosstalk between JAK-STAT signaling and IRF8. We demonstrated that hyperactivation of JAK-STAT signal- (A) Patients are grouped into four categories based on median 28-gene and IRF8 expression values. Kaplan-Meier analyses are performed on the four patient groups ing promotes the loss of anti-tumor immunity in glioma and to determine the ability of the combined JAK-STAT-IRF8 model in determining over- renal cancer patients (Fig. 6). In tumor cells, constitutive all survival in glioma and renal cancers. P values are obtained from log-rank tests. activation of STAT3 inhibits anti-tumor immune response (B) Table inset shows univariate Cox proportional hazards analyses of the relationship between JAK-STAT signaling and IRF8. Significant P values are highlighted in by blocking the secretion of proinflammatory cytokines bold. CI = confidence interval. and suppressing dendritic cell function(43). Furthermore, hyperactivation of STAT3 is linked to abnormal differentiation of dendritic cells in colon cancer cells(44). STAT3 promotes interleukin-10-dependent Treg function(45) while STAT5 promotes Treg differentiation(46). We demonstrated that in glioma and renal cancer, JAK-STAT scores were strongly correlated with Treg expression scores, suggesting that persistent activation of JAK-STAT could promote tumor

8 | bioRχiv Chang et al. | Prognoses of JAK-STAT driver genes bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

Fig. 6. Tumors with hyperactive JAK-STAT signaling are hypoimmunogenic. (A) Scatter plots depict significant positive correlations between 28-gene and regulatory T cell (Treg) scores in glioma and renal cancer patients. Patients are grouped into four categories based on median 28-gene and Treg scores. Density plots at the x- and y-axes show the distribution of 28-gene and Treg scores. (B) Kaplan-Meier analyses are performed on the four patient groups to determine the ability of the combined JAK-STAT-Treg model in determining overall survival in glioma histological subtypes and renal cancer. P values are obtained from log-rank tests. (C) Table inset shows univariate Cox proportional hazards analyses of the relationship between JAK-STAT signaling and anti-tumor immunity. Significant P values are highlighted in bold. CI = confidence interval.

Chang et al. | Prognoses of JAK-STAT driver genes bioRχiv | 9 bioRxiv preprint doi: https://doi.org/10.1101/576645; this version posted March 14, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

immune evasion in these cancers (Fig. 6). Moreover, in tu- 9. Eric Tartour, H Pere, B Maillere, M Terme, N Merillon, J Taieb, F Sandoval, F Quintin- mors with high levels of JAK-STAT signaling and low IRF8 Colonna, K Lacerda, A Karadimou, and Others. Angiogenesis and immunity: a bidirectional link potentially relevant for the monitoring of antiangiogenic therapy and the development expression (a TF involved in regulating innate and adaptive of novel therapeutic combination with immunotherapy. Cancer and Metastasis Reviews, 30 immune responses), we observed a dramatic decrease in (1):83–95, 2011. 10. John N Weinstein, Eric A Collisson, Gordon B Mills, Kenna R Mills Shaw, Brad A Ozen- overall survival rates (Fig. 5). IRF8 is essential for dendritic berger, Kyle Ellrott, Ilya Shmulevich, Chris Sander, Joshua M Stuart, Cancer Genome At- cell development and proatherogenic immune responses(47). las Research Network, and Others. The cancer genome atlas pan-cancer analysis project. 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