Molecular Correlates of Metastasis by Systematic Pan-Cancer Analysis Across the Cancer Genome Atlas

Author Manuscript Published OnlineFirst on November 6, 2018; DOI: 10.1158/1541-7786.MCR-18-0601 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Molecular correlates of metastasis by systematic pan-cancer analysis across The Cancer Genome Atlas

Fengju Chen1*, Yiqun Zhang1*, Sooryanarayana Varambally2,3, Chad J. Creighton1,4,5,6

1. Dan L. Duncan Comprehensive Cancer Center Division of Biostatistics, Baylor College of Medicine, Houston, TX, USA. 2. Comprehensive Cancer Center, University of Alabama at Birmingham, Birmingham, AL 35233, USA 3. Molecular and Cellular Pathology, Department of Pathology, University of Alabama at Birmingham, Birmingham, AL 35233, USA 4. Department of Bioinformatics and Computational Biology, The University of Texas MD Anderson Cancer Center, Houston, TX, USA 5. Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX 77030, USA 6. Department of Medicine, Baylor College of Medicine, Houston, TX, USA

* co-first authors

Running title: Pan-cancer metastasis correlates Abbreviations: TCGA, The Cancer Genome Atlas; RNA-seq, RNA sequencing; RPPA, reverse- phase protein arrays

Correspondence to: Chad J. Creighton ([email protected]) One Baylor Plaza, MS305 Houston, TX 77030

Disclosure of potential conflicts of interest: The authors have no conflicts of interest.

Downloaded from mcr.aacrjournals.org on September 28, 2021. © 2018 American Association for Cancer Research. Author Manuscript Published OnlineFirst on November 6, 2018; DOI: 10.1158/1541-7786.MCR-18-0601 Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited.

Abstract

Tumor metastasis is a major contributor to cancer patient mortality, but the process remains poorly understood. Molecular comparisons between primary tumors and metastases can provide insights into the pathways and processes involved. Here, we systematically analyzed and cataloged molecular correlates of metastasis using The Cancer Genome Atlas (TCGA) datasets across 11 different cancer types, these data involving 4,473 primary tumor samples and 395 tumor metastasis samples (including 369 from melanoma). For each cancer type, widespread differences in gene transcription between primary and metastasis samples were observed. For several cancer types, metastasis-associated genes from TCGA comparisons were found to overlap extensively with external results from independent profiling datasets of metastatic tumors. While some differential expression patterns associated with metastasis were found to be shared across multiple cancer types, by and large each cancer type showed a metastasis signature that was distinctive from those of the other cancer types. Functional categories of genes enriched in multiple cancer type-specific metastasis over-expression signatures included cellular response to stress, DNA repair, oxidation-reduction process, protein deubiquitination, and receptor activity. The TCGA-derived prostate cancer metastasis signature in particular could define a subset of aggressive primary prostate cancer. Transglutaminase 2 protein and mRNA were both elevated in metastases from breast and melanoma cancers.

Alterations in microRNAs and in DNA methylation were also identified.

Implications: Our findings suggest that there are different molecular pathways to metastasis involved in different cancers. Our catalog of alterations provides a resource for future studies investigating the role of specific genes in metastasis.

Introduction

Metastases are formed by cancer cells that have left the primary tumor mass to form new colonies at sites throughout the human body(1). Tumor metastasis remains a major contributor to cancer patient deaths(2). Metastasis is a multi-step process, which includes localized invasion, intravasation into lymphatic or blood vessels, traversal of the bloodstream, extravasation from the bloodstream, formation of micrometastasis, and colonization(1, 2). The process of metastasis and the factors governing cancer spread and establishment at secondary locations remains poorly understood(3). Only a small fraction of cancer cells from the primary tumor may go on to successfully establish distant, macroscopic metastasis, and while the tumor microenvironment is understood to play an important role(3), the molecular state of the cancer cells in a macroscopic metastasis may widely differ from that of the cancer cells in the associated primary tumor.

Molecular comparisons between primary tumors and metastases can potentially provide insights into the pathways and processes involved with cancer disease progression(4, 5). Numerous independent studies have carried out gene expression profiling of metastasis versus primary cancer for individual cancer types(4-18). In addition to individual studies by cancer type, “pan- cancer” molecular analyses would allow for examining similarities and differences among the molecular alterations that may be associated with metastasis across diverse cancer types. The recently published “MET500” dataset includes transcriptome profiling data for metastasis samples from ~500 patients, involving over 30 primary sites and biopsied from over 22 organs(19); however, the MET500 dataset does not include any data on primary cancers. The

Cancer Genome Atlas (TCGA), a large-scale initiative to comprehensively profile over 10,000 cancer cases at the molecular level, includes data on some metastasis samples as well as on primary samples. Other than the TCGA-sponsored melanoma marker study(20), the metastasis samples were not featured in the respective marker analyses by cancer type that were led by

TCGA network, as the project as a whole was focused on primary disease. The advantages of analyzing TCGA data for metastasis-associated molecular correlations include the multiple cancer types having been profiled on a common platform that involves multiple levels of molecular data in addition to mRNA expression.

In this present study, we systematically analyzed and cataloged molecular correlates of metastasis using TCGA datasets, across 11 different cancer types for which metastasis versus primary data were available. Molecular profiling data platforms analyzed included mRNA expression, protein expression, microRNA expression, and DNA methylation. Significantly altered genes, as identified in a given cancer type, were compared across the other cancer types, as well as across results from other profiling datasets from studies outside of TCGA.

Materials and methods

TCGA patient cohort

Results are based upon data generated by TCGA Research Network (https://gdc.cancer.gov/).

Molecular data were aggregated from public repositories. Tumors analyzed in this study spanned 11 different TCGA projects, each project representing a specific cancer type, listed as follows: BRCA, Breast invasive carcinoma; CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CRC, Colorectal adenocarcinoma (combining COAD and READ projects); ESCA, Esophageal carcinoma; HNSC, Head and Neck squamous cell carcinoma;

PAAD, Pancreatic adenocarcinoma; PCPG, Pheochromocytoma and Paraganglioma; PRAD,

Prostate adenocarcinoma; SARC, Sarcoma; SKCM, Skin Cutaneous Melanoma; THCA, Thyroid carcinoma. Cancer molecular profiling data were generated through informed consent as part of previously published studies and analyzed in accordance with each original study’s data use guidelines and restrictions. Metastasis versus primary samples were inferred using the TCGA sample code (“06” versus “01,” respectively), which is the two digit code following the TCGA

legacy sample name (e.g. metastasis sample “TCGA-V1-A9O5-06” and primary sample “TCGA-

ZG-A9L9-01”).

Datasets

RNA-seq data were obtained from The Broad Institute Firehose pipeline

(http://gdac.broadinstitute.org/). All RNA-seq samples were aligned using the by UNC RNA-seq

V2 pipeline(21). Expression of coding genes was quantified for 20531 features based on the gene models defined in the TCGA Gene Annotation File (GAF). Gene expression was quantified by counting the number of reads overlapping each gene model’s exons and converted to Reads per Kilobase Mapped (RPKM) values by dividing by the transcribed gene length, defined in the

GAF and by the total number of reads aligned to genes. Proteomic data generated by RPPA across 7663 patient tumors (“Level 4” data) were obtained from The Cancer Proteome Atlas

(http://tcpaportal.org/tcpa/)(22). The miRNA-seq dataset was obtained from TCGA PanCanAtlas project (https://gdc.cancer.gov/about-data/publications/pancanatlas)(23), which dataset involved batch correction according to Illumina GAIIx or HiSeq 2000 platforms. DNA methylation profiles for 450K Illumina array platform were obtained from The Broad Institute Firehose pipeline

(http://gdac.broadinstitute.org/).

Differential analyses by molecular feature

For mRNA, miRNA, and RPPA data platforms, differential expression between comparison groups was assessed using Pearson’s correlation on log-transformed values (base 2). For cancer types with more than one metastasis profile, the Pearson’s correlation p-value is equivalent to a t-test; for cancer types with just one metastasis profile, significant genes in effect represented outliers with large differences at the edge or outside of the distribution as defined by the primary samples. Differential analyses between metastasis and primary by alternate methods for RNA-seq data were found to be largely concordant with results by the Pearson’s

method (Supplementary Figure S1). For DNA methylation platform, differential expression between comparison groups was assessed using Pearson’s correlation on logit-transformed values (natural log). For SKCM datasets, a linear regression model was also carried out for each gene, with dependent variable (continuous variable) of expression and with independent variables: metastasis/primary (categorical variable) + estimated tumor purity(24) (continuous variable). False Discovery Rates (FDRs) were estimated using the method of Storey and

Tibshirini(25). For selecting top features for a given data platform and cancer type, FDR<10% was used as a cutoff; for SKCM datasets, top features were also significant with p<0.05 for linear model incorporating tumor purity as a covariate. Visualization using heat maps was performed using both JavaTreeview (version 1.1.6r4)(26) and matrix2png (version 1.2.1)(27). R software (version 3.1.0) was used for generation of box plots.

Pathway and network analyses

Enrichment of GO annotation terms within sets of differentially expressed genes was evaluated using SigTerms software(28) and one-sided Fisher’s exact tests, with FDRs estimated using the method of Storey and Tibshirini(25). Protein interaction network analysis used the entire set of human protein–protein interactions cataloged in Entrez Gene (downloaded June 2017). Entrez gene interactions with yeast two-hybrid experiments providing the only support for the interaction were not included in the analysis. Graphical visualization of networks was generated using Cytoscape(29).

Analysis of external expression profiling datasets

We examined the following external gene expression profiling datasets of metastasis versus primary samples (listed by Gene Expression Omnibus or ArrayExpress accession number):

BRCA studies E-MTAB-4003(8), GSE100534(9), and GSE110590(5); CRC studies

GSE50760(10), GSE22834(11), and GSE41258(12); PAAD studies GSE42952(13) and

GSE19281(14); PRAD studies GSE21034(7), GSE3933(6), and GSE6099(4); SKCM studies

GSE65904(15), GSE17275(16), and GSE46517(17); and THCA study GSE60542(18).

Differential expression between comparison groups was assessed using t-test on log- transformed values (base 2). For the purposes of comparing results of external datasets with

TCGA metastasis signatures, where multiple expression array features referred to the same gene, the feature with the smallest p-value for differences between metastasis and primary tumors (either direction) was used to represent the gene. For patient survival associations involving the TCGA PRAD metastasis signature, we examined external gene expression profiling datasets of primary prostate cancer from Taylor et al. (GSE21034)(7), Sboner et al.

(GSE16560)(30), and Nakagawa et al. (GSE10645)(31), assigning a metastasis signature score to each external tumor profile using our previously described “t-score” metric(21); log2- transformed values within each dataset were for normalized to standard devations from the median across the primary sample profiles. In the same way, the t-score metric was also used in applying the TCGA metastasis gene signature for a given cancer type to the primary sample mRNA profiles in TCGA for that cancer type.

We also examined tissue specific mRNA signatures, in order to determine whether these might overlap with the cancer metastasis-specific mRNA signatures that were identified. Gene expression data (TPM values) from GTEx Analysis version 7 release were obtained from the

GTEx Portal (https://www.gtexportal.org). Genes with average TPM values greater than five units across the normal tissue samples were used in this analysis, which involved 12769 unique genes in total. Using log-transformed values, for each tissue in GTEx dataset that would be associated with one of the cancer types analyzed in the present study (Breast:BRCA,

Skin:SKCM, Cervix Uteri:CESC, Colon:CRC, Esophagus:ESCA, Muscle:SARC, Nerve:PCPG,

Pancreas:PAAD, Prostate:PRAD, Thyroid:THCA), the top 500 genes positively correlated with that tissue as compared to all other tissues were determined (t-test using log-transformed data).

For a given cancer type, both the genes over-expressed in metastasis and the genes under-

expressed in metastasis were each compared with the set of tissue-specific mRNA markers from GTEx corresponding to that cancer type, with the significant of overlap determined using one-sided Fisher’s exact tests. In the same way, we examined GTEx-derived markers of tissues representing common sites of metastasis (adrenal gland, brain, liver, lung) for significant overlap with TCGA-derived metastasis over-expressed genes.

Statistical analysis

All p-values were two-sided unless otherwise specified.

Results

TCGA cohort of primary and metastasis samples

Our study utilized 4,473 primary tumor samples and 395 tumor metastasis samples, involving

4,839 human cancer cases representing 11 different major types, for which TCGA generated data on one or more of the following molecular characterization platforms (Supplementary Data

S1): RNA sequencing (4446 primaries and 393 metastases), reverse-phase protein array

(RPPA, 3194 and 267), microRNA sequencing (4350 and 378), and DNA methylation arrays

(3913 and 391). Of the cancer types studied, TCGA SKCM (melanoma) data involved the most metastasis samples (n=369), followed by THCA (thyroid, n=8), and BRCA (breast, n=7); CESC

(cervical), HNSC (head and neck), and PCPG (pheochromocytoma and paraganglioma) cancer types each involved two metastasis samples; CRC (colorectal), ESCA (esophageal), PAAD

(pancreas), PRAD (prostate), and SARC (sarcoma) each involved one metastatic sample. Just

29 of the 395 metastasis samples had a primary pair from the same patient, and so unpaired analyses between primary and metastasis were made the focus of this study. In terms of somatic DNA copy by SNP array platform, only SKCM metastasis samples had available data, with no data generated on primaries. Somatic mutation calls by whole-exome sequencing were

considered too sparse for carrying out comparisons within each cancer type, with the exception of SKCM, which data have been studied previously(20).

Differential mRNA patterns associated with metastasis by TCGA cancer type

We first set out to define differentially expressed mRNAs (based on RNA sequencing platform) between primary and metastasis samples for each cancer type. For each cancer type, the top differentially expressed mRNAs (genes) in metastasis greatly exceeded chance expected

(Figure 1, Supplementary Data S2, and Supplementary Data S3). Using a False Discovery Rate

(FDR) cutoff of 10%, the numbers of top significant genes ranged from 43 for PCPG to 10,084 for SKCM, with the other cancer types having between 178 and 1205 top genes. For cancer types with only one metastasis sample, significant genes in effect represented outliers with large differences at the edge or outside of the distribution as defined by the primary samples

(Supplementary Figure S1). The limitations with metastasis signatures as defined by a single sample would include false negatives (e.g. in cases where the distributions between primary and metastasis would overlap) and questions as to the generalizability of the signature to other metastasis cases, where the latter may be partially addressable by comparisons with results from external datasets (see below). We examined differences involving estimated tumor purities(24), as gene expression patterns in cancer can reflect non-cancer as well as cancer cells(32). Of all the cancer types examined, only SKCM showed significantly lower tumor purity in metastasis versus primary (p=7.6E-7, t-test, Supplementary Figure S2). Using linear models incorporating purity as a covariate, on the order of 8,038 genes remained significantly differentially expressed in SKCM, out of the above 10,084 genes (Figure 1).

While some differential expression patterns associated with metastasis were found to be shared across multiple cancer types, by and large each cancer type showed a metastasis signature that was distinctive from those of the other cancer types. In comparing the respective expression

signatures of metastasis from each cancer type to each other, some amount of gene set overlap was observed (Figure 2A). In a number of cases, the overlap in signatures between any two cancer types was statistically significant, even if the overlap itself involved a fraction of genes

(e.g. on the order of 10%). A set of 821 genes were found significant (FDR<10%) with same direction of change for two or more cancer types (Figure 2B). Of these genes, 65 were significant for three or more cancer types, including genes with previously demonstrated functional roles in metastasis such as EPL3(33), MYCNOS(34), and FOXF2(35). Just eight genes (BEND4, CD5L, CELA1, CLEC4M, CYP17A1, DCAF8L2, FAM151A, SPIC) were over- expressed in metastasis (FDR<10%) for four or more cancer types. We furthermore examined whether any of the metastasis signature genes (considering over-expressed and under- expressed gene sets separately) would be enriched for normal tissue-specific mRNA markers associated with the given cancer type (as obtained using GTEx data). Of 10 different tissue specific marker gene sets, only a nominally significant association (p<0.001, one-sided Fisher’s exact test) was observed between SKCM metastasis under-expressed genes and gene markers associated with GTEx mRNA markers of normal skin tissues.

Functional categories of genes represented by the cancer type-specific metastasis expression signatures were examined, using the Gene Ontology (GO) annotation terms (Supplementary

Data S4). Specific GO term categories were found enriched within the corresponding metastasis signatures of multiple cancer types (Figure 2C). Significantly enriched GO terms (FDR<10% using one-sided Fisher’s exact tests) found with the metastasis over-expressed genes for at least three cancer types included “cellular response to stress,” “DNA repair,” “oxidation- reduction process,” “protein deubiquitination,” and “receptor activity,” and significant GO terms within the under-expressed genes for at least three cancer types included “extracellular region,”

“proteolysis,” and “regulation of locomotion.” We took the genes related to receptor activity and genes high in metastasis (FDR<10%) for at least one cancer type, and we integrated these with

public databases of protein–protein interactions to generate a protein interaction network

(Figure 3), which allowed us to visualize the potential relationships involving these genes. While most of the genes in this network involved SKCM, a number of other genes involved a trend

(p<0.05, Pearson’s on log-transformed data) of higher expression in metastasis in two or more cancer types, and ten genes in the network were high (p<0.05) in three or more cancer types:

CR1, CR2, GP1BA, GRID2, GRM7, LHCGR, LRP2, MED14, P2RX2, and PTPRH. Similar types of interaction networks were also generated involving genes related to oxidation-reduction process or protein deubiquitination (Supplementary Figure S3). Genes involved in the immune checkpoint pathway were also examined in TCGA metastasis profiles (Supplementary Figure

S4), with these being elevated across SKCM metastasis samples as expected(20), as well as elevated in a portion of metastasis samples from other cancer types.

Metastasis-associated mRNA patterns as observed in datasets external to TCGA

To help assess their generalizability, we compared the gene expression signatures of metastasis, as defined for each cancer type using TCGA data, with metastasis expression signatures obtained from external datasets made available by previously published studies. We examined 15 external gene expression profiling datasets of metastasis versus primary samples, involving six cancer types (BRCA, CRC, PAAD, PRAD, SKCM, and THCA). For each of the cancer types surveyed, a significant number of genes where found to overlap with the results of at least one external dataset of the given cancer type, for either the metastasis over-expressed or under-expressed genes (Figure 4A). Perhaps in part because the CRC and THCA metastasis signatures each involved fewer genes, the CRC over-expressed genes showed some overlap but not significant overlap with CRC over-expressed genes from external datasets, and THCA under-expressed genes by TCGA did not show significant overlap with external dataset results.

For each cancer type, on the order of 35-70% of genes comprising the corresponding TCGA

metastasis signature showed a similar significant trend (p<0.05) in at least one external dataset of that cancer type (Figure 4B).

Notably, the external datasets often involved different sites of metastasis for a given cancer type; for example, the external PRAD datasets involved samples taken from various sites including lymph node, bone, lung, testes, and brain(4, 6, 7), implying that the TCGA PRAD signature, while derived from a single metastasis sample, would not be specific to a single site.

Similarly, breast metastasis in the GSE110590 dataset(5) involved a number of different sites, with the TCGA BRCA metastasis signature being manifested in samples from most of these sites (Figure 4C). Furthermore, we examined GTEx-derived markers of tissues representing common sites of metastasis (adrenal gland, brain, liver, lung) for significant overlap with TCGA- derived metastasis over-expressed genes; after multiple testing correction(25), only the GTEx liver signature was found to significantly overlap with metastasis genes associated with BRCA

(p<1E-7, one-sided Fisher’s exact test, with 20 of the 342 BRCA metastasis over-expressed genes also included in the top 500 genes highly expressed in normal liver), but not with genes from the other cancer types.

Previous studies have suggested that a subset of primary tumors resemble metastatic tumors with respect to gene expression patterns(36). For each cancer type in our TCGA cohort, we investigated the corresponding metastasis expression signatures in primary tumors. TCGA expression profiles of primary tumors were each scored for manifestation of the metastasis signature. Out of nine cancer types for which pathological stage or grade information were provided, five (CSEC, HNSC, PRAD, SKCM, and THCA) showed some statistical trend for positive correlation between the signature score and stage or grade across primary cancers

(one-sided p<=0.05, Pearson’s, Figure 5A). This association was notably strongest for PRAD

(prostate) cancer type (p<1E-30), to the extent that clear differences in time to adverse events between patients with primary prostate tumors manifesting the PRAD metastasis signature as

compared to the rest of the were observable, when applying the signature to profiles from multiple external cohorts (Figure 5B). In addition, in another prostate cancer dataset, consisting of primary prostate cancer samples from patients for which the early onset of metastasis following radical prostatectomy was recorded(37), PRAD metastasis signature scores were significantly elevated (p<1E-9) in the early onset group (Figure 5C).

Molecular patterns associated with metastasis involving protein, microRNAs, and methylation

We went on to examine protein, microRNA, and DNA methylation datasets in TCGA, in order to define differentially expressed features between primary and metastasis samples for each cancer type. RPPA proteomic data involved 218 features and four cancer types (BRCA, PCPG,

SKCM, THCA) with metastasis profiles. For SKCM, a large portion of RPPA features examined were differentially expressed in metastasis (94 features at FDR<10%, Pearson’s correlation on log-transformed data, 83 features significant after corrections for tumor purity, Supplementary

Data S5), analogous to results from mRNA expression. No RPPA features with globally significant (FDR<10%) were found for PCPG or THCA, likely in part due to limited sample power. For BRCA, one protein feature, transglutaminase 2, was elevated in metastasis and globally significant at FDR<10% (FDR<1E-12), corresponding to mRNA-level differences

(Figure 6A). Transglutaminase 2 protein and mRNA were also elevated in SKCM (Figure 6A), and the protein is known to promote metastasis(38). For most cancer types, widespread differences in microRNA (miRNA) expression between metastasis and primary were observed

(Figure 6B and Supplementary Data S5). Most of the significant miRNAs detected were over- expressed versus under-expressed in metastasis, with 85 over-expressed miRNAs and 12 under-expressed miRNAs significant (FDR<10%) in two or more cancer types, and with 17 over- expressed miRNAs significant in three or more cancer type (Figure 6C). For a number of cancer types, mRNA:microRNA pairings, as defined by both a previously identified miRNA-target interaction (as cataloged by miRTarBase(39)) and significant differential expression in

metastasis for both mRNA and microRNA (in opposite directions), could also be identified

(Supplementary Data S5).

Using TCGA data from DNA methylation arrays, we examined 150,253 CpG Island probes, finding widespread differences in methylation between metastasis and primary samples for each cancer type studied (Figure 6D and Supplementary Data S6). The numbers of top significant methylation features (FDR<10%, Pearson’s on logit-transformed data) ranged from 163 for

THCA to 27,530 for SKCM (after corrections for tumor purity), with the other cancer types having between 441 and 6,611 top features. As increased methylation of regulatory regions in proximity to genes can lead to epigenetic silencing, we integrated DNA methylation results with mRNA expression results, defining sets of gene associated with both altered methylation and expression (Figure 6E and Supplementary Data S6). For all cancer types except SARC and

THCA, significant inverse correspondences between methylation and expression results were observed (p<0.05, one-sided Fisher’s exact test or chi-squared test), either involving genes over-expressed and with lower associated methylation in metastasis or involving genes under- expressed and with higher associated methylation in metastasis. The significantly overlapping results involved, for example, 2730 genes for SKCM (both over-expressed and under-expressed genes, with inverse patterns of DNA methylation), 66 genes for PRAD, 43 genes for HNSC, and

33 genes for CESC.

Discussion

Our study of TCGA data on cancer metastasis samples had three overall objectives: 1) to obtain a preliminary global view of metastasis versus primary molecular differences across several cancer types; 2) to provide a resource for future studies investigating the role of specific genes in metastasis; and 3) to help provide direction for future genomics studies of metastasis, e.g. by showcasing the utility of examining molecular differences across cancer types and across other

molecular profiling platforms in addition to RNA-sequencing. A clear limitation of the present study involves the limited number of metastasis samples profiled as part of TCGA consortium, as the main focus of TCGA was to examine genomic and molecular patterns of primary rather than of metastatic cases. For several of the cancer types examined, this limitation is mitigated somewhat by comparing the results from TCGA transcriptomic data to existing data from other studies, thereby demonstrating the relevance of differential gene patterns as observed across sample cohorts. Our results would support the need for future multiplatform-based and pan- cancer genomics studies profiling larger numbers of metastasis with primary samples, which would allow us to further define and refine the molecular signatures of metastasis as put forth in this present study. Nevertheless, our study demonstrates that, even on the basis of a single metastasis sample, there would be molecular information contained here representing real biological differences that may involve at least some metastasis cases for a given cancer type.

Our study has identified widespread molecular differences in metastasis versus primary tumors for 11 different cancer types, with each cancer type having a signature of metastasis that is distinct from that of the other cancer types. This would suggest that there are different molecular pathways to metastasis involved in different cancers. Our findings would seemingly differ with those of two early studies of gene expression patterns of metastasis, one from Ramaswamy et al. (36), which defined a single 128-gene signature of metastasis across multiple cancer types

(lung, breast, prostate, colorectal, uterus, ovary, etc.), and one from Weigelt et al.(40), which could not find any global significant differences over chance expected between breast cancer primary and metastasis samples. Studies subsequent to the Weigelt study have been able to define widespread differences associated with breast cancer metastasis versus primary tumors(5, 8, 9). Interestingly, when surveying TCGA data, none of the Ramaswamy signature genes showed consistent high or low expression patterns in metastasis across the different cancer types (Supplementary Data S2). While the Ramaswamy study found that a subset of

primary tumors from various cancer types expressing the 128-gene metastasis signature were associated with worse outcome, we find in our present study that aggressive prostate cancers in particular appear to express a metastasis signature pattern, but other cancer types such as breast cancer do not show a similar phenomenon. One salient feature of this present study was to survey available data from multiple external sources in addition to TCGA data. Where genes are found to show consistent patterns across multiple datasets and studies, we may place the most confidence in these gene patterns, at least given the currently available data.

The results of this present study (e.g. as provided in the supplementary materials) may serve as a resource for future studies investigating the role of specific genes in metastasis. The various gene signatures of metastasis, as identified in each cancer type by TCGA data, may be mined to help identify candidates for functional studies. For cancer types with only a single metastasis sample with TCGA, there would be potential limitations with the associated metastasis signature, including questions as to the generalizability of the signature to other metastasis cases. Integration of TCGA results with results of external public datasets can considerably strengthen the metastasis associations as identified for specific genes. For example, the TCGA

PRAD metastasis signature was based on a single metastasis profile, but this signature showed highly significant overlap with results with each of three independent profiling datasets of prostate cancer metastasis versus primary disease(4, 6, 7), and the TCGA PRAD signature could also define a subset of aggressive primary prostate cancer. Integration between mRNA data and data from other platforms in TCGA may also be used to select genes of particular interest, such as genes showing concordant alterations involving both expression and DNA methylation. Genes that appear significant in multiple cancer types, including genes encoding cell receptors, may also be of interest for further investigation.

While successfully defining molecular signatures of metastasis across several different cancer types, our present study points to the need for more molecular data on metastasis in human

tumors. Much could be gained by generating molecular data on larger numbers of metastasis and primary cancers, using multiple “omics” data platforms in addition to mRNA expression profiling. The global molecular patterns involved in metastasis would entail proteomic and DNA methylation levels in addition to transcriptomic levels. For many cancer types with metastasis data in TCGA, few or no relevant external molecular profiling datasets were found to be available. For cancer types where a large number of expression outliers could be associated with a single metastasis sample profile, profiling more metastasis cases would enable us to define more robust molecular signatures that would presumably be generalizable to the disease as a whole. Profiling larger numbers of cases would also allow for paired analyses by patient between primary and metastasis samples, as well as offering the possibility of subtype discovery within metastatic tumors according to differential patterns being found within some but not all metastasis cases. Molecular data from human tumors may be combined with molecular data from experimental models of metastasis(41), in order to identify genes common to both, which may help pinpoint critical targets relevant in both the laboratory and human disease settings. The top gene correlates of metastasis by and large do not appear to represent canonical oncogenes(32, 42) or frequent targets of point mutation(43), but rather appear indicative of complex processes at work involving multiple internal and external factors. The molecular signatures of metastasis for each cancer type have the potential to lead to new discoveries into the disease process.

Acknowledgements

This work was supported in part by National Institutes of Health (NIH) grant P30CA125123 (C.

Creighton).

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Figure Legends

Figure 1. Top differentially expressed mRNAs in metastasis versus primary samples for each of 11 cancer types in The Cancer Genome Atlas (TCGA). Top genes for each cancer type were selected using Pearson’s correlation (on log-transformed values) with Storey and

Tibshirini estimate of False Discovery Rate (FDR) of <10% (for SKCM, FDR<10% and significant with p<0.05 for linear model incorporating tumor purity as a covariate). Yellow denotes high expression relative to the average of primary samples; blue denotes low expression. Genes listed individually are either over-expressed in metastasis and focally amplified in a previous pan-cancer analysis(44) or under-expressed in metastasis and focally deleted in pan-cancer analysis. For SKCM, hundreds of genes involved regions of focal amplification or deletion, and so these are not listed here. BRCA, Breast invasive carcinoma;

CESC, Cervical squamous cell carcinoma and endocervical adenocarcinoma; CRC, Colorectal adenocarcinoma (combining COAD and READ projects); ESCA, Esophageal carcinoma; HNSC,

Head and Neck squamous cell carcinoma; PAAD, Pancreatic adenocarcinoma; PCPG,

Pheochromocytoma and Paraganglioma; PRAD, Prostate adenocarcinoma; SARC, Sarcoma;

SKCM, Skin Cutaneous Melanoma; THCA, Thyroid carcinoma.

Figure 2. Genes shared among the cancer type-specific metastasis mRNA signatures. (A)

For both the genes over-expressed in metastasis for at least one cancer type (left, genes from

Figure 1) and the genes under-expressed in metastasis for at least one cancer type (right, genes from Figure 1), the numbers of overlapping genes between any two cancer types are indicated, along with the significance of overlap (using colorgram, by one-sided Fisher’s exact test). (B) Heat map of differential t-statistics (Pearson’s correlation on log-transformed data), by cancer type, comparing metastasis versus primary (red, higher in metastasis; white, not significant with p>0.05), for 821 genes significant for two or more cancer types (FDR<10%; for

SKCM, FDR<10% and significant with p<0.05 for linear model incorporating tumor purity as a covariate). Genes significant for three or more cancer types are indicated by name.

Figure 3. Functional gene classes shared among the cancer type-specific metastasis mRNA signatures. (A) Left, Gene Ontology (GO) terms significantly enriched for at least three cancer types (enrichment for cancer type defined as FDR<10% using one-sided Fisher’s exact test) within the respective sets of genes over-expressed in metastasis (based on the gene sets represented in Figure 1); right, GO terms significantly enriched for at least three cancer types within the respective sets of genes under-expressed in metastasis. For both sets of enriched

GO terms, the numbers of genes involved for each cancer type and overall significance of enrichment (by colorgram; black, highly significant) are indicated. (B) Protein-protein interaction network involving genes over-expressed in metastasis, with focus on genes involved in receptor activity. Nodes represent genes with GO annotation “receptor activity” and which were found over-expressed in metastasis for at least one cancer type (FDR<10%; for SKCM, FDR<10% and significant with p<0.05 for linear model incorporating tumor purity as a covariate). Nodes are colored according to the individual cancer types in which a trend (p<0.05, Pearson’s correlation on log-transformed data) of higher expression in metastasis versus primary samples was observed. A line between two nodes signifies that the corresponding protein products of the genes can physically interact (according to the literature, from Entrez gene interactions database).

Figure 4. Significance of overlap between TCGA metastasis mRNA signatures and metastasis mRNA signatures from datasets external to TCGA. (A) For both the genes over- expressed in metastasis for a given cancer type (left) and the genes under-expressed in metastasis for a given cancer type (right), the numbers of overlapping genes between the TCGA mRNA signatures (rows, signatures from Figure 1) and the genes over- or under-expressed in metastasis (p<0.05, t-test) in the indicated external datasets from previously published gene

expression profiling studies (columns), along with the corresponding significances of overlap

(using colorgram, by one-sided Fisher’s exact test, chi-squared test for TCGA SKCM gene sets). (B) For each indicated cancer type, numbers of genes overlapping between the TCGA metastasis signature genes (left, genes over-expressed in metastasis; right, genes under- expressed in metastasis) and the genes significantly high or low in metastasis (p<0.05, t-test) in the published external datasets corresponding to the given cancer type. Significance of overlap

(by one-sided Fisher’s exact test; chi-squared test for SKCM genes) is indicated for TCGA genes found in one or more external datasets (blue bars) and in two or more external datasets

(red bars). Selected top genes overlapping between TCGA and results from other datasets are listed (BRCA over-expressed: TCGA p<1E-6 and p<1E-6 for E-MTAB-4003 dataset; BRCA under-expressed: TCGA p<1E-6 and p<1E-6 for E-MTAB-4003 dataset; CRC under-expressed:

TCGA FDR<10% and p<0.05 for two or more external datasets; PAAD over-expressed: TCGA

FDR<10% and p<0.01 for GSE42952 dataset; PAAD under-expressed: TCGA FDR<10% and p<0.05 for one or more external datasets; PRAD over-expressed: TCGA FDR<10% and p<0.01 for all three external datasets; PRAD under-expressed: TCGA FDR<10% and p<0.05 for all three external datasets; SKCM over-expressed: TCGA FDR<10% and p<0.05 for all three external datasets; SKCM under-expressed: TCGA FDR<10% and p<0.05 for all three external datasets; THCA over-expressed: TCGA FDR<10% and p<0.001 for GSE60542 dataset; P- values by Pearson’s correlation or t-test on log-transformed data). (C) TCGA-BRCA metastasis gene expression signature similarity score (t-statistic as derived from the “t-score” metric(21,

45)), as applied to the sample profiles in the GSE110590 breast cancer metastases RNA-seq dataset(5). For selected groups of metastasis according to site, comparisons with the primary group are indicated (t-test as applied to the signature t-scores).

Figure 5. For specific cancer types, gene expression signatures of metastasis found present within a subset of primary samples and associated with more aggressive

disease. (A) For each of the indicated cancer types, the corresponding TCGA metastasis gene signature was applied to the primary sample mRNA profiles for that cancer type; across the primary samples, the metastasis signature similarity scores (t-statistic as derived from the “t- score” metric(21, 45)) were correlated with the cancer stage or grade (Gleason grade for PRAD, pathologic stage for the other cancer types). One-sided p-values indicate the Pearson’s correlation between the signature score and stage or grade (numerical 1-4 for stage, 6-10 for

Gleason grade). (B) For each of three independent mRNA expression profiling datasets of primary prostate cancer(7, 30, 31), differences in survival between patients with tumors manifesting the TCGA-PRAD metastasis signature (top third of signature similarity scores across the samples) and the other patients. P-values by log-rank test. (C) The TCGA PRAD metastasis gene signature was applied to the primary sample mRNA profiles for the GSE46691 prostate cancer dataset (37), consisting of primary prostate cancer samples from patients for which the early onset metastasis following radical prostatectomy was recorded. Box plot represents 5%, 25%, 50%, 75%, and 95%. P-value for differences in signature scores between groups with or without metastasis by t-test.

Figure 6. Molecular correlates of metastasis by protein, microRNA, or DNA methylation profiling. (A) Transglutaminase 2 protein and mRNA (TGM2 gene) were significantly elevated in TCGA BRCA metastases as well as in TCGA SKCM metastases. Box plots represent 5%,

25%, 50%, 75%, and 95%. P-values by t-test on log-transformed data. (B) Numbers of significant microRNAs between metastasis (“met.”) and primary samples for each cancer type

(FDR<10%, based on Pearson’s correlation using log-transformed values; for SKCM, FDR<10% and significant with p<0.05 for linear model incorporating tumor purity as a covariate), along with the numbers or microRNAs significant (FDR<10%) for two or three cancer types. (C) Heat map of differential t-statistics (Pearson’s correlation on log-transformed data), by cancer type, comparing metastasis versus primary (red, higher in metastasis; white, not significant with

p>0.05), for 29 microRNAs that were either significantly over-expressed for three or more cancer types (FDR<10%) or significantly under-expressed for two or more cancer types. (D)

Numbers of significant DNA methylation array probes located within CpG Islands (by Illumina

450K array, ~150K CpG Island probes) between metastasis (“met.”) and primary samples for each cancer type (FDR<10%, based on Pearson’s correlation using logit-transformed values; for SKCM, FDR<10% and significant with p<0.05 for linear model incorporating tumor purity as a covariate). (E) For each cancer type, numbers of genes overlapping between the RNA-seq and DNA methylation results (FDR<10% for each platform, with top features in SKCM corrected for tumor purity as described above). Left plot represents genes over-expressed in metastases

(“met.”), and right plot represents genes under-expressed in metastases. Significance of overlap by one-sided Fisher’s exact test (chi-squared test for SKCM results).

Figure 1

BRCA BRCA CESC CESC CRC CRC primary metastasis AFM primary metastasis primary metastasis n=1095 n=7 AKR1C4 n=304 n=2 n=379 n=1 differential expression C7 (change from primary) CASC1 C9 C7 1/3-fold 3-fold CRIP3 GABRB2 GRK1 DUXA KIF26B CCNE1 KRAS F10 MMP14 HTR5A LYRM5 OR2T35 MXRA5 ADAM6 Genes over-expressed in metastasis LOC150185 CD2 PCID2 PTPRD and focally amplified in pan-cancer analysis PROZ RNF144A NLRP4 FNTA SERPIND1RSAD2 OR2G3 GZMB Genes under-expressed in metastasis NEGR1 SLC22A7 SH3PXD2B and focally deleted in pan-cancer analysis COL10A1 SOX11 OR5AU1 NKX2-3 COL17A1 VCAN ZIM2 PDE4D 547 genes (FDR<0.1) 271 genes (FDR<0.1) 180 genes (FDR<0.1) RHOBTB3 CYS1 VSNL1 HAPLN1 F2RL2 XG SLC16A10 FGF18 ZNF544 PDE4D

AIFM3 UBE2M MAP3K7 B4GALT7 UFD1L MCC AARS2 WDR70 ESCA ESCA HNSC HNSC CAPSL MCTP1 PAAD PAAD SKCM SKCM ABCC10 SLC27A5 CDC45 ARID1A MDN1 primary metastasis CAPN11 SLC35B2 primary metastasis ARL10 primary metastasis primary metastasis n=184 n=1 n=520 n=2 CHMP2A NT5C2 n=178 n=1 n=104 n=367 CNPY3 SRF CNPY3 ATG2B ODZ2 CUL7 TBCC COMT BDKRB1 PAM CUL9 TJAP1 DDX41 BDKRB2 PAPOLA HSP90AB1 TMEM151B DGCR6L BTRC PPP1R2P3 KLHDC3 TMEM63B DGCR6 BVES RASA1 MEA1 TRIM28 EDDM3B C19orf26 RCOR1 MGC2752 TRIT1 FBXO4 CASP8AP2 REV3L INSM2 MYCL1 XPO5 FLJ35024 CCNK ROCK2 KCTD1 YIPF3 NKX2-1 ISOC2 CDC42BPB SIK3 ZIM2 NSD1 ZBTB45 MRPL40 CPEB4 SLC16A1 OR2M4 ZNF318 NLRP9 DENND2C SLK ZNF805 POLH ZNF324B PRODH DICER1 SYNCRIP F2RL2 POLR1C ZNF324 RNF32 ENTPD7 TECPR2 NKX2-3 173 genes (FDR<0.1) 178 genes (FDR<0.1) ZNF496 1205 genes (FDR<0.1) 8038 genes (FDR<0.1) PPP2R5D RPL37 F2RL2 TJP1 NUDCD2 RPL7L1 ZNF497 RTDR1 GTF3C4 TTC37 SFTA3 ZNF551 SPEF2 HOMER1 WDR20 STXBP6 THAP7 HSP90AA1 YY1 TMEM191A LDB1 ZMYND11 TRMT2A MAN2A1

PCPG PCPG PRAD PRAD AFP PAIP1 GABRG3 SARC SARC THCA THCA primary metastasis primary metastasis AKR1C1 SKP2 GLB1L3 primary metastasis primary metastasis n=179 n=2 n=497 n=1 AKR1C3 SPEF2 GLOD4 n=259 n=1 C13orf35 n=503 n=8 AKR1C4 WDR70 KCNMB1 CD274 AKR1C4 C1orf229 ZNF124 LOC100128239 EHF C5orf28 ZNF280A LOXL4 FLJ44054 C6 C5orf34 ZNF324B MTERFD2 LOC338588 CCNE1 ZNF669 NCAM1 NKX2-1 DNMT1 ZNF695 NDN OR1C1 E2F3 ANO7 NSA2 OR2L2 ENSA BOK PDE8B OR2M1P FBXO4 CECR6 PER3 OR2M3 OR2L8 G6PD CNNM1 PPP1R7 OR2T8 POTEG KPNA2 OR2W5 CNTNAP3 SCAMP1 OR6F1 LETM2 COX15 SEMA4G PGC 43 genes (FDR<0.1) TMEM190 SBK2 LGSN 185 genes (FDR<0.1) 192 genes (FDR<0.1) 1013 genes (FDR<0.1) DMGDH SLC7A8 RFPL4A MRPS30 FAM49A DUSP1 TMEM18 SBK2 OPLAH EPHA7 TRIM8 SFTA3 tAKR OR4K15 FAM20C TSLP OR4L1 FOXC1 WDR41 ZNF658

Figure 2 A significance of overlap BRCA CESC CRC ESCA HNSC PAAD PCPG PRAD SARC SKCM THCA BRCA CESC CRC ESCA HNSC PAAD PCPG PRAD SARC SKCM THCA # genes # genes

BRCA 342 17 12 2 5 15 0 23 7 53 18 BRCA 205 2 4 0 9 2 0 10 0 70 0 p-value (-log10) CESC 244 17 7 8 3 12 3 9 6 31 10 CESC 27 2 3 0 3 0 0 1 0 8 0 0 CRC 84 12 7 1 0 4 1 2 2 13 0 CRC 96 4 3 0 13 4 0 3 0 12 0 ESCA 171 2 8 1 10 7 1 3 11 32 0 ESCA 2 0 0 0 1 0 0 0 0 1 0 HNSC 537 5 3 0 10 2 2 11 1 20 6 HNSC 668 9 3 13 1 1 0 5 0 55 1 5 PAAD 154 15 12 4 7 2 0 4 2 18 4 PAAD 24 2 0 4 0 1 0 1 0 6 0 PCPG 37 0 3 1 1 2 0 0 1 0 1 PCPG 6 0 0 0 0 0 0 0 0 2 0 PRAD 693 23 9 2 3 11 4 0 4 210 19 PRAD 320 10 1 3 0 5 1 0 0 86 0 10 SARC 182 7 6 2 11 1 2 1 4 14 1 SARC 3 0 0 0 0 0 0 0 0 2 0 SKCM 4088 53 31 13 32 20 18 0 210 14 42 SKCM 3950 70 8 12 1 55 6 2 86 2 0 THCA 184 18 10 0 0 6 4 1 19 1 42 THCA 8 0 0 0 0 1 0 0 0 0 0 FDR>0.1 genes over-expressed in metastasis (FDR<0.1) genes under-expressed in metastasis (FDR<0.1)

B 821 genes (significant for two or more cancer types) differential expression BRCA t-statistic CESC low met CRC -6 ESCA -4 HNSC -2 0 PAAD 2 PCPG 4 PRAD 6 SARC high met SKCM THCA p>0.05 F3 DIO2 RTP1 ARSJ PRM2 MMP3 F2RL2 KRT17 KRT14 PTPLA TPSB2 CELA1 FOXF1 FOXF2 AZGP1 PRSS8 AP1M2 CNTN6 OR7C1 LHCGR NKX2-1 FAM89A TPSAB1 CYP1A2 OR7E5P FAM180A DCAF8L2 KRTAP5-7 ADCYAP1R1 VPS13A-AS1 C7 SPIC ELP3 DLK1 CD5L STAR RAD1 GRID2 ATP4A SPEF2 SSTR3 NR5A1 FCER2 GP1BA CFHR1 BEND4 AMHR2 FCAMR CT45A6 ADIPOQ AKR1C4 HSD3B2 LRRTM3 CLEC4G CLEC4M GABRA2 C13orf30 GABRG1 FAM151A FAM163B MYCNOS CYP17A1 FAM129C SIGLEC11 LOC647309

Figure 3

genes over-expressed in metastasis genes under-expressed in metastasis

A BRCA 4 2 14 6 2 2 1 7 0 29 0 0 2 0 2 50 44 36 4 46 44 29 3 23 23 16 28 24 42 20 85 4 GO term enrichment CESC 4 3 8 7 3 4 3 4 1 8 1 1 1 2 5 37 34 34 4 5 2 5 3 0 0 3 2 0 5 3 6 1 0 p-value (-log10) CRC 5 2 3 2 0 1 0 5 2 3 2 2 2 2 7 13 12 11 0 24 17 11 0 6 6 8 17 8 23 11 36 9 ESCA 5 6 11 8 3 5 7 8 4 4 0 0 2 1 6 18 18 18 0 0 0 0 0 0 0 0 0 0 0 0 0 0 HNSC 35 36 61 34 24 43 10 40 16 41 19 19 22 22 41 18 17 14 3 38 31 11 1 33 33 25 67 68 108 42 166 35 5 PAAD 2 3 10 2 2 0 1 2 1 18 0 0 1 2 6 24 23 20 0 5 3 2 0 3 3 1 0 3 3 1 4 0 PCPG 0 0 0 0 0 0 7 0 0 0 0 0 0 0 0 8 8 8 0 0 1 0 0 1 1 0 1 1 1 1 3 0 PRAD 41 75 101 83 56 40 2 45 35 37 27 48 41 29 28 1 28 27 5 1 10 9 9 29 18 48 18 89 7 27 28 18 10 SARC 5 2 4 3 2 3 6 9 1 3 4 3 7 3 6 26 18 17 0 0 0 0 0 0 0 0 0 0 1 1 1 0 SKCM 187 226 408 236 155 213 1 215 72 144 78 77 97 73 284 267 211 178 16 400 307 68 9 152 146 88 244 264 474 147 784 142 THCA 1 2 6 5 2 1 3 1 0 8 0 0 0 0 0 28 25 24 0 1 0 0 0 0 0 0 2 0 2 0 4 2

11 p>0.05 86 # genes 40 697 703 759 472 780 846 261 895 294 287 376 264 955 289 616 597 420 792 579 1139 1448 1572 1313 1205 1703 1312 1424 2471 4238 proteolysis DNA repair DNA keratin filament receptor activity peptidase activity extracellular space extracellular region protein deubiquitination DNA metabolic process DNA regulation of locomotion regulation of localization protein catabolic process signaling receptor activity cellular response to stress hemidesmosome assembly oxidation-reduction process cell-substrate junction assembly macromolecule catabolic process extracellular structure organization protein modification by small protein... proteasomal protein catabolic process intracellular ribonucleoprotein complex ubiquitin/proteasome catabolic process single-organism developmental process negative regulation of cell cycle process single-organism membrane organization positive reg. of cellular comp. movement positive reg. of multicellular organismal... cellular macromolecule catabolic process transmembrane signaling receptor activity cellular response to DNA damage stimulus cellular response to DNA peptidase activity, acting on L-amino acid... peptidase activity,

B Protein-protein interaction network involving genes associated with GO:receptor activity

TGFBR3 ACVR2A NR2C2 NR2F1 AHR HNF4G HNF4A GRIK5 ARNT CALCRL CRCP ASGR1 TLR4 TLR5 GRID2 DLG3 MED13 CFI TSHR ASGR2 TNFRSF4 CD226 TLR1 ERBB4 MED17 SEC63 TLR10 AGER GRIA2 NR0B2 MED30 GLP1R LHCGR DERL1 TNFRSF9 ITGB2

KCNJ1 ESRRG NR5A2 SEC62 SORL1 MERTK BMPR2 GPR183 TRAM1 BMPR1A CFTR KCNH2 GPC6 NRP1 PTPRR EPHB6 CD80 MED14 MED1 PPARA CELSR2 NCAM1 RYK CD86 PPARG STRA6 EPNA3 LRP2 EFNB2 INTS6 CD28 PIGR ESRRB NR4A2 THRAP3 TFRC GRM5 CD79B FLT1 ATP6AP2 NTRK1 INSRR NR3C1 XPR1 FCGR2C KDR ITGB1 CD79A CD3G AMOT ITGA4 IL2RA INSR PTPRC NR2F2 CD4 FZD4 DDR2 CXCR5 CD36 CD46 ITGAV ITGB6 VTN DLG1 CXCR4 CD1D IFNAR1 CALM3 GRM7 CNR1 FZD5 CD55 HCRTR1 ADRB1 GABRR1 CSF1R NPC1 CR2 GABRR2 F2R F2 KCNQ5 CD47 LIFR ANTXR2 MET CD97 GP1BA LRP6 CR1 P2RX2 ABCC9 IL6ST FLT4 OSMR PTPRH P2RX4 KCNJ8 CUL5 GHR PTPRB Higher expression in metastasis versus primary for given cancer type

BRCA CESC CRC ESCA HNSC PAAD PCPG PRAD SARC SKCM THCA

Figure 4

significance of overlap p>0.05 p-value (-log10) 0 30 15

A genes over-expressed in metastasis genes under-expressed in metastasis BRCA CRC PAAD PRAD SKCM THCA BRCA CRC PAAD PRAD SKCM THCA external external datasets datasets # genes E-MTAB-4003 GSE100534 GSE110590 GSE50760 GSE22834 GSE41258 GSE42952 GSE19281 GSE21034 GSE3933 GSE6099 GSE65904 GSE17275 GSE46517 GSE60542 # genes E-MTAB-4003 GSE100534 GSE110590 GSE50760 GSE22834 GSE41258 GSE42952 GSE19281 GSE21034 GSE3933 GSE6099 GSE65904 GSE17275 GSE46517 GSE60542 BRCA 234 20 26 158 127 135 128 84 54 21 47 20 3 65 50 342 BRCA 144 30 101 60 61 63 83 47 42 40 16 55 8 54 27 205 CRC 34 5 1 6 24 12 9 6 17 0 4 3 0 12 4 84 CRC 45 13 34 25 31 41 35 25 32 29 13 11 4 22 5 96 PAAD 80 16 8 17 48 32 37 14 26 11 12 10 1 30 13 154 PAAD 13 2 7 11 6 7 15 8 7 6 2 6 0 5 5 24 PRAD 296 112 162 45 263 95 133 112 354 213 106 39 22 245 167 693 PRAD 116 33 74 67 92 62 79 60 178 101 41 40 8 67 63 320 TCGA SKCM 1174 453 203 246 1785 809 595 597 648 608 366 745 172 1647 1026 4088 TCGA SKCM 1180 253 317 300 1128 681 425 580 387 412 283 1106 135 1124 705 3950 THCA 102 9 6 35 51 46 37 18 44 6 14 17 1 34 69 184 THCA 3 1 4 2 1 2 3 0 3 5 0 0 1 0 2 8 # genes # genes 432 398 2113 6871 2313 3921 1369 6580 4037 3102 2350 4873 2866 1990 1549 4076 2681 6079 1927 4609 1551 4679 3656 2620 2744 2990 3363 1876 2607 2533

B genes over-expressed in metastasis ** genes under-expressed in metastasis **

2072 1789 2072 1789 ** one or more external datasets (p<0.05) one or more external datasets (p<0.05) 524 two or more external datasets (p<0.05) ** two or more external datasets (p<0.05) 500 ** 500 overlap p<=0.0001 465 overlap p<=0.0001 * 440 * ** overlap p<1E-15 ** overlap p<1E-15 400 400

300 ** 300 ** 249 ** ** 220 184 200 200 165 ** ** ** 96 * Number of overlapping genes Number of overlapping genes 84 100 ** 69 100 46 61 * 30 32 ** 27 16 * 4 5 7 2 0 0 BRCA CRC PAAD PRAD SKCM THCA BRCA CRC PAAD PRAD SKCM THCA TCGA TCGA # genes 342 81 154 693 4088 184 # genes 205 90 24 320 3950 8 A1CF F11 AGXT2L1 ADAM12 ABI2 ADH1B C11orf41 ADAMDEC1 C7orf60 ALDH1A3 AKR1B10 LAD1 ACSM2B F9 APC2 AURKA APPBP2 BEND4 COL11A1 ARSJ CPA3 APH1B CD207 LAMA3 ADRA1A FAM151A ARSF AURKB BRWD1 BLK CTHRC1 COL12A1 CRISPLD1 ATAD1 CDA LCN2 APOA2 FAM9B ASB4 C7orf49 C1orf25 C4orf7 F2RL2 COL14A1 F2RL2 CTBS CDS1 MAP7 APOC3 FSHR ATP2B3 CDCA8 C1orf9 CCR7 FOXF2 CSRP2 FOXF1 CTSO CELSR2 MAPK13 ART4 G6PC CLRN1 CENPF CD86 CDK5R1 GJB2 CYTIP HIGD1B DHRS7 CLCA4 MYO1C ASPDH HRG DHCR7 CIT ELK4 CR2 GRP DIO2 ITIH5 ESR1 COL17A1 NDEL1 ASPG KNG1 FCN2 DNMT3B EPB41L2 FAM129C KLK4 FBLN1 MS4A2 FOXC1 CRABP2 NMU C14orf105 MBOAT4 FDPS DYRK2 FNDC3A FCER2 LRRC15 FOXF1 MYLIP MEIS2 CSTAOTUB2 C3orf27 NR1H4 GRIK1 FANCD2 NNT FCRL1 MFAP5 FOXF2 MYO1B NR4A1 DHRS1 PDZK1IP1 C3P1 OR5AK2 KCTD1 FGD1 NUCB2 GP1BA MMP13 HOXB5 NKX2-3 PDE8B DIO2 PI3 C8A PDILT SRRM4 GDAP1 OGT HOTAIR MMP3 HOXB7 SHISA2 RPS27L DSC3 POF1B C9 PLG ST8SIA5 GPI P4HA1 HOXC10 PDGFRL IGFBP6 STXBP6 SEC62 EHF PPP4R1 CA5A PPY SULT2A1 GTSE1 PIK3CA HOXC6 POSTN LTBP1 TPSAB1 SPCS3 EPHA1 S100A14 CASR REG3G HNRNPUL1 PLOD2 HOXC9 PPAPDC1A MAP2K4 TPSB2 ZMAT1 EPS8L1 S100A9 CFHR4 SHISA3 IPO9 PLS1 LILRA4 SFRP2 PDE4D VOPP1 EVPL SCEL CLEC4M SLC17A2 KIF23 PPM1B LOC283663 SGIP1 PRR16 EXPH5 SERPINB13 Selected top overlapping genes CRP SLC2A2 LIG1 PTPRO MS4A1 Selected top overlapping genes TNFAIP6 RAC2 FGFBP1 SFN CRYGD TCF21 NCAPH RARB P2RX5 VSNL1 SGIP1 GJA1 SLPI CYP11B1 TEX13B NHSL1 RBM26 PAX5 WNT2 SULF1 IMPA2 SNAI2 CYP3A4 TMEM225 PEG10 SENP6 PDE3B SVIL KCNK1 SPINT2 DAO ZDHHC19 SKP2 SFRS7 RASGRP2 TACC1 KLK13 SPRR3 DBH TMEM65 SLC33A1 STAP1 TNFSF11 KLK6 ST14 TOP2A SMARCAL1 TCL1A TPSAB1 KLK7 STMN2 TPX2 UBA5 TIMD4 TPSB2 KLK8 TP63 UBE2T ZMYM2 TXK TWSG1 KRT16 TTC15 ZMYM4 VPREB3 WLS ZCCHC18 C GSE110590 dataset, breast cancer metastases ȗȗ ȗȗ ȗ ȗȗȗ 15

0 similarity score (t-statistic) -5 p<0.05, versus primary

TCGA-BRCA metastasis gene signature TCGA-BRCA ȗ ȗȗ p<0.01, versus primary -10 skin skin liver liver liver liver liver liver liver liver liver liver liver liver liver liver lung lung lung lung lung lung lung lung lung lung lung lung lung lung lung dura brain brain brain brain brain brain brain brain brain brain bone bone bone chest chest ovary spinal spinal spinal pleura pleura kidney primary primary primary primary primary primary primary primary primary primary primary primary primary primary primary adrenal adrenal adrenal adrenal mediastn pancreas pancreas softtissue lymph node lymph node lymph node lymph node lymph node lymph node

Figure 5

ACGSE46691, prostate cancer 31 * p<=0.05 * 10 Gleason>7 4 Gleason<=7 p<1E-9 5 3 * * 0 2 * similarity score (t-statistic) * -5 1 TCGA-PRAD metastasis gene signature

Association of metastasis signature scoring primary cancers primary cancers that did not develop that developed

with aggressive primary cancer [-Log10 (p-value)] 0 metastases metastases CRC BRCA CESC ESCA HNSC PAAD PRAD SKCM THCA (n=333) (n=212)

B GSE21034, prostate cancer GSE16560, prostate cancer GSE10645, prostate cancer 1 1 1 0.9 other (n=93) 0.9 Log rank p<1E-5 0.9 0.8 0.8 0.8 other (n=397) 0.7 0.7 0.7 0.6 0.6 0.6 0.5 0.5 other (n=187) 0.5 0.4 PRAD 0.4 0.4 PRAD metastasis metastasis PRAD signature high (n=199) 0.3 signature high 0.3 censored 0.3 0.2 censored 0.2 metastasis 0.2 Survival probability (n=47) Survival probability signature high Survival probability 0.1 Log rank p<1E-6 0.1 (n=94) 0.1 Log rank p<1E-16 censored 0 0 0 0 50 100 150 0 50 100 150 200 250 300 0 50 100 150 200 250 Recurrence-free survival (months) Disease-specific survival (months) Disease-specific survival (months)

Figure 6 A 5.0 5.0 p=0.03 p<1E-14 4 p<0.01 p<1E-12 10 2.5 2 2.5

5 0.0 0 0.0 mRNA (normalinzed) mRNA mRNA (normalinzed) mRNA 0 -2 -2.5 -2.5 TGM2 TGM2

transglutaminase 2 protein (norm.) BRCA BRCA BRCA BRCA transglutaminase 2 protein (norm.) SKCM SKCM SKCM SKCM primary metastases primary metastases primary metastases primary metastases

BC 200 higher in met. 180 lower in met. 160 differential expression miR-134-5p miR-127-3p miR-409-3p miR-199b-5p miR-382-5p miR-379-5p miR-200a-3p miR-200a-5p miR-200b-3p miR-200b-5p miR-429 miR-205-5p miR-105-3p miR-1224-5p miR-3923 miR-424-3p miR-202-5p miR-507 miR-506-3p miR-514a-5p miR-508-5p miR-508-3p miR-509-3p miR-514a-3p miR-509-3-5p miR-513a-5p miR-513b-5p miR-513c-5p miR-514b-5p 140 t-statistic BRCA 120 low met CESC -6 100 CRC -4 80 ESCA -2 0 HNSC 60 2 PAAD 4 40 PCPG 6 20 PRAD high met # significant microRNAs (FDR<0.1) 0 SARC SKCM p>0.05

CRC THCA BRCA CESC ESCA HNSC PAAD PCPG PRAD SARC SKCM THCA any two any three

DE 2169 1874 methylation higher in met. 22150 methylation higher in met. methylation higher in met. 22150 2169 1874 **856 **585 methylation lower in met. methylation lower in met. 585 methylation lower in met. 7000 856 ** * overlap p<0.05 50 * overlap p<=0.01 ** 6000 35 ** overlap p<0.01 45 ** overlap p<0.0001 30 40 5000 25 ** 35 4000 ** 30 20 25 3000 * 15 20 ** 2000 10 15 ** ** 10 * 1000 5 * 5 ** ** # significant probes (FDR<0.1) 0 0 * * 0 * * overlapping genes, over-expr. in met. overlapping genes, over-expr. overlapping genes, under-expr. in met. overlapping genes, under-expr.

CRC CRC CRC BRCACESC ESCA HNSC PAAD PCPG PRAD SARCSKCM THCA BRCACESC ESCA HNSC PAAD PCPG PRAD SARCSKCM THCA BRCACESC ESCA HNSC PAAD PCPG PRAD SARCSKCM THCA

Molecular correlates of metastasis by systematic pan-cancer analysis across The Cancer Genome Atlas

Fengju Chen, Yiqun Zhang, Sooryanarayana Varambally, et al.

Mol Cancer Res Published OnlineFirst November 6, 2018.

Updated version Access the most recent version of this article at: doi:10.1158/1541-7786.MCR-18-0601

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