Sex Differences in Cancer Driver Genes and Biomarkers Constance H

Cancer Genome and Epigenome Research

Sex Differences in Cancer Driver Genes and Biomarkers Constance H. Li1,2, Syed Haider1, Yu-Jia Shiah1,2, Kevin Thai1, and Paul C. Boutros1,2,3

Abstract

Cancer differs significantly between men and women; even and BAP1. Sex influenced biomarkers of patient outcome, after adjusting for known epidemiologic risk factors, the sexes where different genes were associated with tumor aggression differ in incidence, outcome, and response to therapy. These in each sex. These data call for increased study and consider- differences occur in many but not all tumor types, and their ation of the molecular role of sex in cancer etiology, progres- origins remain largely unknown. Here, we compare somatic sion, treatment, and personalized therapy. mutation profiles between tumors arising in men and in women. We discovered large differences in mutation density Significance: This study provides a comprehensive cata- and sex biases in the frequency of mutation of specific genes; log of sex differences in somatic alterations, including in these differences may be associated with sex biases in DNA cancer driver genes, which influence prognostic biomarkers mismatch repair genes or microsatellite instability. Sex-biased that predict patient outcome after definitive local therapy. genes include well-known drivers of cancer such as b-catenin Cancer Res; 78(19); 5527–37. 2018 AACR.

Introduction advantage declines and disappears during menopause (11). Some of these differences in treatment response may be attributed to Sex differences in cancer have been known at least since 1949 differences in driver mutations between the sexes, and others to (1), with repeated demonstration that males have higher cancer differences in epigenetics or chromatin conformation. risk both in studies using North American (e.g., SEER; ref. 2) and The origins and mechanisms of these sex differences remain a international databases (e.g., IARC; ref. 3). Most, but not all tumor majorunresolvedquestionincancerbiology.Theymaybe types show increased incidence in men: thyroid cancer occurs caused by differences in the expression of genes on the sex 2.5 times more frequently in women. These differences remain chromosomes, in hormone levels, in developmental biology, after controlling for known epidemiologic risk factors (3). At most or in lifestyle features not reflected in current epidemiologic tumor sites, cancers arising in men induce higher mortality (4); for studies. Likely, a mixture of all these components contributes to example, there is a 3-fold increase in lethality from urinary sex differences in patient outcomes. We hypothesized that, bladder carcinomas in men relative to women (4). Further, there independent of their mechanism, sex differences in cancer are significant differences in response to treatment: female would be reflected by differences in somatic mutation profiles. patients with non–small cell lung cancer respond better to both That is, male and female tumors would acquire mutations at surgery (5, 6) and chemotherapy (7, 8), even after accounting for different rates and of different types. Recent intriguing data differences in variables such as subtype. Female patients with on missense mutations in melanoma support this hypothesis colorectal cancer respond better to surgery, and this difference is (12). We, therefore, undertook a systematic evaluation of sex- driven by improved female survival in the rectal cancer subgroup associated biases in mutations in cancer across a broad range (9). Similarly, female patients with colorectal also respond better of tumor types. Our study provides a comprehensive pan- to chemotherapy, which is partially attributed to differences in cancer catalog of sex-biased mutations and a perspective on tumor site and microsatellite instability (10). Finally, a propen- sex-specific prognostic biomarkers. sity-matched study of nasopharyngeal carcinoma found that females have a survival advantage regardless of tumor stage, Materials and Methods radiation technique, and chemotherapy regimen, but that this Data acquisition and processing mRNA abundance, DNA genome-wide somatic copy-number 1Computational Biology Program, Ontario Institute for Cancer Research, Tor- and somatic mutation profiles for the Cancer Genome Atlas 2 onto, Ontario, Canada. Department of Medical Biophysics, University of Tor- (TCGA) datasets were downloaded from Broad GDAC 3 onto, Toronto, Ontario, Canada. Department of Pharmacology and Toxicology, Firehose (https://gdac.broadinstitute.org/), release 2016-01-28. University of Toronto, Toronto, Ontario, Canada. For mRNA abundance, Illumina HiSeq rnaseqv2 level 3 RSEM- Note: Supplementary data for this article are available at Cancer Research normalized profiles were used. Genes with >75% of samples Online (http://cancerres.aacrjournals.org/). having zero reads were removed from the respective data set. Corresponding Author: Paul C. Boutros, Ontario Institute for Cancer Research, GISTIC v2 (13) level 4 data were used for somatic copy-number Toronto, ON M5G0A3, Canada. Phone: 647-258-4321; E-mail: analysis. mRNA abundance data were converted to log2 scale for [email protected] subsequent analyses. Mutational profiles were based on TCGA- doi: 10.1158/0008-5472.CAN-18-0362 reported MutSig v2.0 calls. All preprocessing was performed in R 2018 American Association for Cancer Research. statistical environment (v3.1.3).

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Patients younger than 18, older than 85 or lacking sex anno- used to reduce false positives that may arise from unbalanced tation were excluded from analysis, resulting in a sample size of tumor type subsets of the pan-cancer data. Multivariate logistic 7,131 across all tumor types for copy-number alterations (CNA; regression (MLR) was used to adjust ternary CNA data for sex, age, 1.5% excluded, Supplementary Table S1) and 6,073 for somatic race, and tumor type. The MLR sex term was tested for significance single-nucleotide variants (SNV; 1.5% excluded; Supplementary and FDR corrected to identify bins with pan-cancer sex biases (q < Table S1). Genes were excluded if they were mutated in fewer than 0.05). 20 patients (for CNAs) or 5% of patients (for SNVs). Gene filters The same approach was applied to each tumor type individ- were applied independently for pan-cancer and per individual ually. Proportions tests were used to select bins for multivariate tumor type data set. All analyses excluded genes on the X and Y analysis (q value < 0.1). MLR was again used to adjust ternary chromosomes. copy-number call for clinical variables. MLR modeling for each tumor type varies based on available clinical data. Tumor type– Mutation load specific models were fit independently per univariately significant Mutation load per patient was calculated as the sum of SNVs bin and variable significance for each bin was extracted from the across all genes on the autosomes. Mutation load was Box–Cox fitted models. FDR correction was used and an FDR threshold of transformed, and transformed values were compared between the 0.05 was used. A description of pan-cancer and tumor type– sexes using unpaired two-sided t tests for both pan-cancer and specific models, along with a breakdown of the data for each tumor type–specific analysis. A linear regression model was used group, can be found in Supplementary Table S1 and results can be to adjust mutation load for tumor type for the pan-cancer com- found in Supplementary Tables S3–S5. parison. Tumor type–specific P values were adjusted using the Benjamini–Hochberg false discovery rate procedure. Tumor types CNA-mRNA functional analysis with q values meeting an FDR threshold of 10% were further Genes in bins altered by sex-biased CNAs after multivariate analyzed using linear regression to adjust for tumor type–specific adjustment for kidney clear cell and kidney papillary cell cancers variables described in Supplementary Table S1. A multivariate were further investigated to determine sex-biased functional q value threshold of 0.05 was then used to determine statistical effects. Available mRNA samples were matched to those used in significance. Full results are in Supplementary Table S2. CNA analysis. For each gene affected by a sex-biased loss, its mRNA abundance was modeled against sex, copy-number loss Genome instability status, and a sex–copy-number loss interaction term. The inter- Genome instability was calculated as the percentage of the action term was used to identify genes with sex-biased mRNA genome affected by copy-number alterations. The number of base changes. FDR-adjusted P values and fold changes were extracted pairs for each CNA segment was summed to obtain a total number for visualization. A q value threshold of 0.05 was used for of base pairs altered per patient. The total number of base pairs statistical significance. For genes affected by sex-biased gains, the was divided by the number of assayed bases excluding the sex same procedure was applied using copy-number gains. chromosomes (7.8 million bp) to obtain the percentage of the genome altered (PGA). Box–Cox transformed PGA was treated as CNA-mRNA survival analysis a continuous variable and compared by sex using two-sided Genes found to have significant or trending (FDR threshold of unpaired t tests for all tumor types combined (pan-cancer) and 10%) sex biases in the CNA-mRNA functional analysis were separately (tumor type–specific). Linear regression models were further analyzed using Cox proportional hazards modeling. That used to adjust PGA for tumor type, age, and race for the pan-cancer is, we focused on genes that were both altered by sex-biased CNAs comparison. Tumor types where univariate testing indicated (MLR q value < 0.05) and showed mRNA abundance differences putative sex biases in PGA (FDR threshold of 10%) were also between the copy-number neutral and loss/gain groups for either adjusted for tumor type–specific variables (Supplementary sex (sex–loss interaction q < 0.1). For each gene, the mRNA Table S1). A q value threshold of 0.05 was used to determine abundance was median dichotomized over all samples to identify statistical significance for multivariate results and full results are low- and high-expression groups. Cox proportional hazard regres- presented in Supplementary Table S2. sion models incorporating sex, mRNA group, and a sex–mRNA group interaction were fit for overall survival after checking the Genome-spanning CNA analysis proportional hazards assumption. FDR-adjusted interaction Adjacent genes whose copy-number profiles across patients P values and log2 hazard ratios were extracted for visualization. were highly correlated (Pearson r > 95%) were binned. The copy- A q value threshold of 0.1 was used to identify genes with sex- number call for each patient was taken to be the majority call influenced survival. across all genes in each bin. Copy-number calls were collapsed to ternary (loss, neutral, gain) representation by combining loss Genome-spanning SNV analysis groups (monoallelic and biallelic) and gain groups (low and We focused on genes mutated in at least 5% of patients. All high). The number of loss, neutral, and gain calls was summed per genes tested are listed in Supplementary Table S6. Mutation data bin and sex, and assessed using univariate and multivariate were binarized to indicate presence or absence of SNV in each gene techniques. For univariate analysis, proportional differences per patient. Proportions of mutated genes were compared between the sexes for gains and losses were tested for each bin between the sexes using proportions tests for univariate analysis. using proportions tests. To account for multiple testing, FDR FDR correction was used to adjust P values and a q value threshold correction was performed and an FDR threshold of 10% was of 0.1 used to select genes for multivariate analysis. used to select bins for further multivariate analysis. After identifying pan-cancer univariately significant genes from After identifying candidate pan-cancer significant bins from proportions testing, binary logistic regression (LR) was used to univariate proportions testing, generalized linear modeling was reduce false positives that may arise from unbalanced tumor type

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subsets of the pan-cancer data. Age and race were also included in Results the pan-cancer model. FDR correction was again applied and Sex biases in mutation burden genes with significant pan-cancer sex terms were extracted from We leveraged data from TCGA studies comprising 7,131 the models (q value < 0.05). matched tumor–normal pairs of 18 tumor types: 4,265 from LR was also used for multivariate analysis of each individual males and 2,866 from females (Supplementary Table S1). We tumor type to adjust for clinical variables. The same model focused on somatic CNAs and SNVs in protein-coding genes variables from the CNA MLR models were used. Tumor type– as they are well-established driver events. These data are specific models were fitted independently per univariately select- well powered to detect differences in driver-gene mutation ed gene and variable significance for each gene was extracted from frequencies between tumors arising in men and those arising in the fitted models as P values. FDR correction was used to adjust women (Supplementary Fig. S1). We excluded genes and P values and a LR q value threshold of 0.05 was used. A description regions of the X and Y chromosomes and analyzed autosomal of pan-cancer and tumor type–specific models can be found in differences (19). Supplementary Table S1. A summary of results can be found in We first compared pan-cancer mutational burden between Supplementary Table S5. tumors arising in men and those arising in women. Male-derived tumors exhibited a higher density of somatic-coding SNVs than Validation of sex biases female-derived tumors in univariate analysis (difference in Copy-number data for tumor types with sex-biased CNAs were means ¼ 0.17; 95% CI, 0.14–0.20, P ¼ 1.0 10 29, unpaired downloaded from the Progenetix database (14) as a meta-analysis Welch t test on Box–Cox transformed mutation load; Supple- data set. Matching genomic regions were analyzed using propor- mentary Fig. S2). This sex bias persisted even after multivariate tions tests to validate genes in sex-biased CNAs. Similarly, somatic analysis adjusting for imbalances in sample numbers across SNV data were obtained from cBioPortal and the ICGC Data tumor type, race, and age (linear regression P ¼ 4.5 10 6; Portal and analyzed to validate sex-biased somatic SNV load and Supplementary Table S2). After finding sex differences on the pan- genes with sex-biased mutations frequencies. A description of cancer level, we asked if there were such differences within validation data, data sources, and results are available in Supple- individual cancer types and focused our analysis on each tumor mentary Table S7. type. Six of these showed univariate sex biases in mutation density (10% FDR threshold; Supplementary Fig. S2) and were further Multigene prognostic models investigated using tumor type–specific multivariate modeling. Computationally purified tumor mRNA profiles for the Direc- Again, we used Box–Cox transformation and linear modeling to tor's Challenge data were downloaded (15). The training and determine whether sex remained a significant variable after adjust- validation cohorts were processed and split as previously ing for possible confounders (linear regression q values given described and were checked for balance between male and female in Fig. 1A; model-specific variables described in Supplementary samples. Colon transcriptomic data were downloaded (16, 17) Table S1). Finally, because the association between sex and and reprocessed and normalized. Colon training and validation mutation load may be biased by later stage male-derived tumors cohorts were balanced for data source, sex, and survival status. (Supplementary Table S1), we created a sub–pan-cancer model Survival modeling was performed using overall survival as the using only tumor types with stage data and found that higher clinical endpoint for both datasets. mutation prevalence in male-derived samples persisted after To identify genes univariately associated with survival, puri- accounting for stage. A summary of univariate and multivariate fied mRNA abundance was median dichotomized for each results can be found in Supplementary Table S2. gene identify low- and high-expression groups. Cox propor- Of the six tumor types with univariate sex differences (Fig. 1A), tional hazard regression models included variables for sex, males exhibited more somatic-coding SNVs in bladder urothelial mRNA–group and the sex–mRNA group interaction, and cancer (BLCA: difference in Box–Cox means ¼ 0.55; 95% CI, P values and log hazard ratios were extracted for visualization. 2 0.20–0.90; multivariate q ¼ 3.6 10 3), melanoma (SKCM: A P value threshold of 0.01 was used to determine statistical difference in Box–Cox means ¼ 0.78; 95% CI, 0.29–1.3; multi- significance. variate q ¼ 0.037), renal papillary cell cancer (KIRP: difference in Ridge regression models were used to train 50,000 randomly Box–Cox means ¼ 2.2; 95% CI, 0.81–3.6; multivariate q ¼ 0.019), generated 100-gene prognostic signatures. The glmnet package and liver hepatocellular cancer (LIHC: difference in Box–Cox (v2.0-5) was used to run 10-fold cross-validation using means ¼ 0.16; 95% CI, 0.049–0.27; multivariate q ¼ 0.019). glmnetcv (a ¼ 0.1) and AUC as the type measure. Signatures There was an opposite trend in glioblastoma where female- were trained using the training cohort and validated in the derived samples had higher mutation burden (GBM: difference validation cohort. Signatures were then run on male- and in Box–Cox means ¼ 1.6; 95% CI, 0.14–3.0; multivariate q ¼ female-only validation patients, and Cox proportional hazards 0.094). Using independent sequencing datasets, we validated the modeling was performed. Signatures that failed the propor- male biases seen in bladder, liver, lung adenocarcinoma, and skin tional hazards assumption were removed from analysis. The cancers (Supplementary Table S7). same approach was used to train a signature using the top 100 To see if these sex differences affected multiple mutation types, univariately significant genes. we also compared the load of CNAs across tumor types based on the percentage of genome altered, which is a prognostic marker in Statistical analysis and data visualization several tumor types (20–22). A putative univariate sex bias in pan- All statistical analyses and data visualization were performed in cancer PGA was not significant after multivariate adjustment the R statistical environment (v3.2.1) using the BPG (v5.3.4; (Supplementary Fig. S2); however, 4/18 individual tumor types ref. 18), mlogit (v0.2-4), glmnet (v2.0-5), and pROC (v1.8) showed univariate sex differences in PGA (Supplementary packages.

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Figure 1. Mutation burden is sex biased. We found sex differences in somatic mutation load (A) and genome instability (B). Each point represents a sample (male-derived, blue; female-derived, pink). We focused on tumor types with univariately signiﬁcant sex differences in mutation and show q values from multivariate modeling here. Red lines show mean mutation burden for each group. C, Mosaic map showing the relationship between microsatellite instability and sex in stomach and esophageal cancer. D, Higher male mutation prevalence emerges after adjusting for microsatellite instability. Adjusted Box–Cox transformed data are shown.

Fig. S2). These were further analyzed with multivariate modeling the mutation rates of DNA MMR genes, we observed significantly to examine the influence of sex (Fig. 1B; Supplementary Table S2). lower mRNA abundance in female-derived tumors for MLH1 Males showed elevated genomic instability in stomach and (male mean ¼ 8.89, female mean ¼ 8.5, 95% CI, 0.19, 0.62, esophageal cancer (STES: difference in Box–Cox means ¼ 1.7; t test q ¼ 0.0011) and PMS2 (male mean ¼ 9.0, female mean ¼ 95% CI, 0.92–2.4; multivariate q ¼ 9.7 10 3), head and neck 8.87, 95% CI, 0.05–0.21, t test q ¼ 0.0060). Taken together, this cancer (HNSC: difference in Box–Cox means ¼ 1.9; 95% CI, 1.0– suggests that differential mRNA abundance may form a link 2.8; multivariate q ¼ 0.016), and kidney clear cell cancer (KIRC: between MMR and sex biases in mutation load in stomach and difference in Box–Cox means ¼ 0.40; 95% CI, 0.14–0.67; mul- esophageal cancer. We did not find novel sex biases in colorectal tivariate q ¼ 0.019). A strong opposite trend was seen in sarcoma, or pancreatic mutation burden after accounting for MSI (Supple- where PGA was higher in female-derived tumors (SARC: differ- mentary Fig. S3). ence in Box–Cox means ¼ 1.5; 95% CI, 0.41–2.7; multivariate q ¼ To investigate whether sex-biased mutation load is generally 0.021). associated with DNA MMR, we also looked specifically at MMR Measures of mutation burden such as SNV load and PGA may genes in all tumor types with sex-biased mutation load. We found be correlated with defects in DNA mismatch repair (MMR). For decreased MSH2 (male mean ¼ 8.45, female mean ¼ 8.83, 95% example, microsatellite instability (MSI), a marker of defective CI, 0.22–0.53, t test q ¼ 3.98 10 6), MSH3 (male mean ¼ 8.50, DNA MMR, is more common in some tumor types (23) and could female mean ¼ 8.71, 95% CI, 0.082–0.34, t test q ¼ 1.51 10 3), be a confounder in the relationship between mutation burden MSH6 (male mean ¼ 9.12, female mean ¼ 9.65, 95% CI, 0.37– and sex. We further examined three tumor types with available 0.67, t test q ¼ 4.57 10 10) and PMS1 (male mean ¼ 7.71, MSI-monodinucleotide assay data: colorectal, pancreatic, and female mean ¼ 8.01, 95% CI, 0.14–0.46, t test q ¼ 2.26 10 4) stomach and esophageal cancers. In samples with MSI data mRNA abundance in male kidney papillary tumors, correspond- (Supplementary Table S1), we found an association between MSI ing with higher male mutation prevalence. Similarly, male mRNA and sex in stomach and esophageal cancer (Pearson c2 P ¼ 1.4 abundance of PMS2 (male mean ¼ 8.84, female mean ¼ 8.97, 10 5; 40% of female-derived samples vs. 26% of male-derived 95% CI, 0.025–0.24, t test q ¼ 0.055) and MLH3 (male mean ¼ samples; Fig. 1C) and colorectal cancer (Pearson c2 P ¼ 0.025; 8.70, female mean ¼ 8.87, 95% CI, 0.039–0.30, t test q ¼ 0.055) 33% of female-derived samples vs. 25% of male-derived samples; was also lower than that of female-derived tumors in liver cancer. Supplementary Fig. S3). By contrast, MSI status was not sex This suggests that for some tumor types, differences in mutation associated in pancreatic cancer (Pearson c2 P ¼ 0.63). Incorpo- load may be explained by sex biases in the efficiency of MMR. rating MSI into our analyses of SNV burden and PGA, we first Taken together, this analysis of mutation burden identified sex noted that MSI was associated with increased SNV burden but not biases across several tumor types even after adjusting for race, PGA in all three tumor types. We then used multivariate models tumor stage, and smoking history, among others. Indeed, a sex including MSI to examine the interplay between sex, MSI, and bias in stomach and esophageal cancer was only discovered after mutation burden. Intriguingly, though there was no univariate adjusting for MSI status, highlighting its importance. Finally, relationship between sex and SNV burden in stomach and esoph- changes in the abundances of DNA MMR mRNA form a candidate ageal cancer, a novel sex bias emerged after adjusting for MSI (MV mechanism for sex biases in mutation density. P ¼ 0.023; Fig. 1D). We observed the same effect in an independent data set (Supplementary Table S7). The association between Sex biases in somatic CNAs sex and PGA persisted in this new model, enforcing the sex bias in Differences in mutation density might reflect changes in specific PGA for this tumor type. Because MSI is thought to result from driver genes, or alternatively global changes as might be induced defective DNA MMR, we also looked for sex biases specifically in a by differences in DNA damage or repair. To distinguish these set of seven MMR genes (24). Though we did not find sex biases in possibilities, we compared male- and female-derived tumors in

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the entire pan-cancer cohort (Fig. 2A). We binned adjacent genes tumors but only 35% of female-derived tumors (q < 10 3 for all across all samples so that all genes within a bin had highly genes; MLR). correlated sample CNA profiles (Pearson r > 0.95). We then To determine if these sex-biased CNAs influence the tumor calculated the average bin CNA profile per sample and compared transcriptome, we evaluated mRNA abundances in matched the rates of bin gain and loss between the sexes using proportions patient samples. We first focused on genes within large segments tests. Bins that were significant in univariate analysis (10% FDR of sex-biased losses. We used linear regression to model mRNA as threshold) were further analyzed using MLR. Bins with MLR a function of sex, copy-number loss vs. no copy-number loss, and q values < 0.05 contain genes lost or gained at significantly the interaction between sex and copy-number loss status. This different rates between the sexes. allowed us to identify not only mRNA changes associated with In pan-cancer analysis, we discovered sex-associated differen- copy-number loss alone, but also interactions where sex and copy- tial CNAs in broad genomic segments covering 3,442 of the number loss synergize for an additional effect on mRNA. Approx- 23,693 genes annotated to autosomes. The vast majority of these imately half of genes in regions affected by sex-biased copy- (94.5%; 3,251 genes) were amplifications. Concordant with PGA number losses were associated with changes in mRNA abundance observations (Fig. 1B), most were more prevalent in male-derived (Supplementary Fig. S12), indicating that sex-biased CNAs have tumors (Supplementary Tables S3 and S4). Numerous cancer transcriptional consequences. In addition, there were multiple driver genes were sex biased in their CNA profile. For example, genes with interaction effects (10% FDR threshold) on chromo- the MYC oncogene was amplified in 48% of male-derived tumors somes 3, 6, and 9 (Supplementary Fig. S12, red lines), including vs. 37% of female-derived tumors (q ¼ 0.037, MLR). Hence, sex genes where sex and copy-number loss together changed mRNA biases are seen in both genome-wide and in pan-cancer gene- abundance over 2-fold relative to their effects in isolation. These specific CNA mutation profiles. sex-copy-number interactions suggested that sex-biased copy- To evaluate if these large-scale pan-cancer differences in CNAs number changes induce transcriptional changes, and in some of specific genes also occurred in individual tumor types, we cases these changes vary by sex. applied the same methodology to each tumor type. We created Next, we extended our focus to all genes affected by sex-biased tumor type–specific gene bins and again used multivariate model- losses (proportions test q < 0.1 and MLR q < 0.05) whose mRNA ing to control for tumor type–specific factors (Methods; Supple- was repressed across samples with the loss. Applying the same mentary Table S1). Sex-biased CNAs affecting thousands of genes linear regression model, we examined the effect of copy-number were detected in eight tumor types: kidney clear cell, kidney loss in the sexes and again extracted both the copy-number loss papillary, head and neck, stomach and esophageal, liver, bladder, and the sex-copy-number loss interaction terms. Of the 2,165 and both lung adenocarcinoma and squamous cell cancer (Fig. genes, 74% showed associations between copy-number loss and 2B; Supplementary Figs. S4–S10; Supplementary Table S5). Some decreased mRNA abundance (Supplementary Fig. S13, black sex-biased events were highly focal, such as female-biased loss of points). In addition, copy-number loss affected mRNA abun- NCKAP5 in head and neck cancer (19% of male-derived vs. 37% dance differently between the sexes in 36 genes that showed of female-derived tumors; q ¼ 0.046, MLR; Supplementary Fig. S5; significant interactions between sex and copy-number loss (sex- Supplementary Table S3). Other sex-biased events covered broad loss interaction, q < 0.1; Fig. 2C, red points). Thus, sex-biased genomic segments, such as whole-chromosome arms. CNAs are associated with divergent transcriptomes in male- and We performed pathway enrichment analysis for each tumor female-derived tumors. type to investigate functional consequences of sex-biased CNAs. Finally, to demonstrate that these transcriptomic divergences Significant gene ontology terms related to genes in sex-biased are functional and clinically relevant, we evaluated the associ- gains and losses were found using g:Profiler (25) and interaction ation of the 36 genes with sex biases in both CNAs and mRNA networks were visualized in Cytoscape (26) using Enrichment abundance (sex–loss interaction q < 0.1) with overall patient Map (Supplementary Fig. S11; ref. 27). The largest perturbed survival. Using univariate Cox proportional hazards modeling, networks included metabolic processes in liver cancer, as well as we identified 16 sex-biased genes associated with outcome in nuclear organization and regulation processes in kidney clear cell both male and female tumors (Fig. 2D). Several genes showed cancer. In head and neck cancer, sex-biased CNAs affect genes strikingly divergent clinical associations, and all 16 with sex- related to lipoprotein and sterol activity. Immune-related pro- biased survival were more prognostic in female-derived sam- cesses were significant in several tumor types including stomach ples than male. For instance, loss of LATS1 was a marker of poor and esophageal and both kidney clear cell and papillary cancers. prognosis in women (HR ¼ 0.39; 95% CI, 0.17–0.85, q ¼ 0.03), These pathway results suggest sex-biased CNAs may lead to but not men (HR ¼ 1.2; 95% CI, 0.80–1.8, q ¼ 0.67; Fig. 2E). downstream biases in biological processes. Conversely, UBAC1 loss was a marker of good overall survival To further characterize the consequences of sex-biased CNAs, in women (HR ¼ 2.64; 95% CI, 1.5–4.6, q ¼ 0.0037) but not we focused on kidney clear cell tumors (KIRC), a tumor type with men (HR ¼ 1.4; 95% CI, 0.95–2.1, q ¼ 0.34; Supplementary robust statistical power (nmale ¼ 336; nfemale ¼ 185; Supplemen- Fig. S14). Similar patterns of sex-associated CNAs inducing tary Fig. S1) and strong evidence of sex-biased PGA (Fig. 1B). After divergent transcriptomes associated with clinical aggressivity multivariate adjustment for age, race, stage, and grade, we iden- were observed for KIRP (Supplementary Fig. S15), demonstrat- tified 3,581 genes contained in sex-biased losses and 138 genes ing the generality of this phenomenon. contained in sex-biased gains. All of these were more commonly Taken together, these data demonstrate that the frequency of mutated in male-derived tumors (Fig. 2B; Supplementary Tables somatic CNAs in specific genes is sex biased in many, but not all S4 and S5). All sex-biased CNAs were broad events, with large tumor types. These differences do not appear to be a result of well- losses of chromosomes 3, 6p, 8q, and 9 (covering the driver genes known clinical or epidemiologic factors. Sex-biased CNAs are TSC1 and CDKN2A (28)). Most prominent of these was a large associated with sex biases in the transcriptome (and presumably region from 3p11.1 to 3p12.3 deleted in 60% of male-derived the proteome as a well), and these transcriptomic differences are

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Figure 2. Functional sex differences in CNAs are associated with outcome. Sex differences in CNAs for pan-cancer (A) and kidney clear cell cancer (B). Each plot shows, from top to bottom, the q value showing significance of sex from multivariate modeling, with yellow (green) points corresponding to 0.05 < q < 0.01 and deep blue (red) points corresponding to q < 0.01; the proportion of samples with aberration; the difference in proportion between male and female groups for amplifications; the same repeated for deletions; and the CNA profile heat map. The columns represent genes ordered by chromosome. Light blue and pink points represent data for male- and female-derived samples, respectively. C, Transcriptome differences between the sexes are seen in the interaction between sex and copy-number loss status in mRNA abundance modeling. Red points are genes with significant sex–copy-number loss interaction terms (q < 0.05). D, Genes with sex-biased copy-number loss and mRNA changes are associated with differential overall survival outcomes between the sexes. Again, the interaction term estimate and q values were used to determine genes with sex biases in survival. E, LATS1 is a marker of poor overall survival in women, but not in men.

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associated with differences in clinical outcome within and 29). Comparison of mRNA abundance between hepatocellular between the sexes. carcinoma samples with mutated and wild-type BAP1 revealed striking sex differences: female-derived tumors with mutated Sex biases in somatic SNVs BAP1 had 1.4-fold decreased mRNA abundance compared with We next asked whether sex differences were specific to somatic those with wild-type BAP1, compared with a 4-fold decrease in CNAs or if they also occurred in other mutation types. We male-derived samples (Supplementary Fig. S17). Indeed, linear compared the proportions of male-derived (nmale ¼ 3,591) and modeling confirmed the significant interaction between sex and 5 female-derived (nfemale ¼ 2,482) samples with SNVs in pan-cancer BAP1 mutation status (P ¼ 5.8 10 ). The same sex-associated univariate analysis. Similar to our CNA analysis, we adjusted for mRNA differences were not observed in kidney clear cell cancer unequal sample numbers of the tumor types and other factors (Fig. 3E), but we did observe a striking interaction in survival using LR, here with a binary response variable indicating whether modeling. BAP1 mutation was associated with poor prognosis in the gene harbored SNVs or not. In total, we tested 103 genes that female patients (HR ¼ 2.59; 95% CI, 1.40–4.81, P ¼ 0.0025; Fig. were mutated in at least 5% of samples (Supplementary Table S6). 3F) but not male patients (HR ¼ 0.80; 95% CI, 0.32–1.97, P ¼ Of these, four genes showed sex biases after adjustment for tumor 0.62). Indeed, the interaction between sex and BAP1 mutation type, age, and race, and all four showed elevated mutation rates in was significant in Cox proportional hazards survival modeling male-derived samples (Fig. 3A). Some of these mutations may be (interaction q ¼ 0.0025). Mutation of BAP1 is known to be passengers and reflect increased DNA damage in male-derived associated with worse prognosis in kidney clear cell cancer, but tumors. evidence on its sex-biased prognostic value is conflicting (30). Similarly to our CNA analysis, we next evaluated each of the 18 We extended this mRNA and survival analysis to other sex- tumor types independently. We screened for candidate mutations biased SNVs in liver, kidney papillary, and stomach and esoph- using univariate analyses and FDR adjustment and then per- ageal cancer but did not find additional sex–SNV interactions in formed multivariable modeling. We excluded genes mutated in these data (Supplementary Fig. S18). However, we noted that less than 5% of samples, meaning many lower-frequency sex- EP400 encodes a chromatin remodeling protein thought to be biased genes have not yet been uncovered and our results repre- involved in ATM-mediated DNA damage response (31). We sent a lower bound of sex biases in somatic SNVs. Of the 18 tumor returned to the mutation prevalence data to investigate whether types evaluated, four exhibited sex-biased mutations in specific sex-biased EP400 mutation in stomach and esophageal cancer was genes: stomach and esophageal, hepatocellular carcinoma, and associated with sex biases in mutation burden. Not only is both kidney clear cell and kidney papillary cell cancers (Fig. 3B–D; mutated EP400 itself associated with higher SNV load (mutated Supplementary Fig. S16; Supplementary Table S6). In stomach EP400 mean SNV load ¼ 4.82, wild EP400 mean ¼ 3.82, 95% CI, and esophageal cancer, all 10 sex-biased genes were mutated in a 0.80–1.20, t test P ¼ 4.70 10 13; Supplementary Fig. S19), there greater fraction of female-derived samples, including a number of is an interaction between EP400 mutation and sex where muta- transcription factors such as ZFHX3 (95% CI of the difference, tion of this gene is associated with a greater increase in mutation 2.5%–15%, q ¼ 0.018, LR), ZBTB20 (95% CI of the difference, burden in female-derived samples than in male-derived samples 2.2%–14%, q ¼ 0.034, LR), and GTF3C1 (95% CI of the differ- (Supplementary Fig. S19, interaction P ¼ 0.009). This indicates ence, 4.0%–16%, q ¼ 0.012, LR; Fig. 3B; Supplementary Table S6). that not only is EP400 mutation associated with increased muta- The largest differences in mutation frequency were seen in liver tion load, there is a greater effect in female-derived samples than carcinoma, where two genes showed dramatic sex biases in male. Further, given the relationship between MSI, mutation mutation frequency (Fig. 3C; Supplementary Table S6). Male burden and sex we described previously, we examined whether tumors were strongly enriched for mutations in b-catenin there was a relationship between EP400 mutation and MSI- (CTNNB1), with 33% of male-derived tumors harboring a muta- positive samples and found no association (P > 0.05). Finally, tion compared with 12% of female-derived tumors (95% CI for we also validated sex-biased EP400 mutation in an independent the difference, 12%–30%, q ¼ 0.0014, LR). These large differences data set (Supplementary Table S7). Overall, our analysis of suggest mutational associations with etiologic factors. For exam- somatic SNVs revealed that sex-biased mutation frequency is ple, CTNNB1 mutations occur more frequently in tumors asso- associated with impacts on mRNA abundance, survival, and ciated with Hepatitis B (95% CI for the difference, 1.9 to 27%, mutation burden in several tumor types. P ¼ 0.07), and sex remains significant even after accounting for viral and alcohol risk factors. We validated this higher female Clinical relevance of transcriptomic sex differences mutation frequency in CTNNB1 in an independent patient cohort The differential clinical impact of sex-biased kidney renal cell from the Liver Cancer—NCC, JP project on the ICGC Data Portal genes (Figs. 2B–E, 3E and F) suggested that sex may influence the (17% higher; 95% CI for the difference; 9.7%–25%, P ¼ 2.7 accuracy of biomarkers used to personalize therapy. We asked if 10 5; Supplementary Table S7). sex-na€ve approaches to prognostic biomarker development result The deubiquitinating enzyme BAP1 was almost exclusively in biomarkers that can predict survival accurately well across all mutated in female-derived hepatocellular tumors, occurring in samples, but better in one sex than the other. The sex biases in 14% of female-derived tumors and 1.6% of male-derived tumors mutational profiles and transcriptional changes suggest that bio- (95% CI of difference, 5.6%–20%, q ¼ 0.017, LR). This enrich- markers developed using data from both sexes without annotation ment of BAP1 mutations was also seen in 15% of female-derived may contain predictive features biased toward the sex in which that kidney clear cell tumors compared with 6.1% of male-derived tumor type most frequently occurs. We focused on multigene tumors (95% CI of difference, 1.7%–15%, q ¼ 0.001, LR; Fig. 3D; prognostic mRNA signatures, such as those developed for non– Supplementary Table S6)—these tumors are not thought to be small cell lung cancer to identify early-stage patients who might virally associated. BAP1 has been implicated as a tumor suppres- benefit from intensification of therapy (32, 33). We used the sor and is frequently inactivated in kidney clear cell cancer (28, benchmark Director's Challenge data set of 443 tumor samples

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Figure 3. Sex biases in driver SNVs. Sex differences in somatic SNVs for pan-cancer (A), stomach and esophageal carcinoma (B), hepatocellular carcinoma (C), and kidney clear cell cancer (D). Each plot shows, from top to bottom, the q value for signiﬁcance of sex from multivariate modeling, with yellow points corresponding to 0.05 < q < 0.01 and green points corresponding to q < 0.01; proportion of samples with aberration; difference in proportion between male and female groups; mutation prevalence across all samples and a heat map showing mutation status for each sample. E, BAP1 mRNA abundance compared across sex and mutation status for kidney clear cell cancer. The P value for the sex–SNV interaction from mRNA modeling is shown. F, Mutated BAP1 is associated with poor prognosis in female patients, but not male patients in kidney clear cell cancer.

(223 men and 220 women) with mRNA abundance proﬁles (34) Overall, 0.8% of genes were prognostic in both sexes (black after deconvolution of tumor and stromal expression (15). points) and 1.5% were prognostic in patients of only one sex Univariate Cox proportional hazards modeling identiﬁed stark (blue and pink points). Strikingly, 79 genes (0.9%) had mRNA- differences between male- and female-derived tumors (Fig. 4A). based groups that interacted with sex for an additional effect on

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Figure 4. Sex differences influence prognostic biomarker accuracy. Comparing female and male hazard ratios from univariate Cox proportional hazards modeling in non–small cell lung cancer (A) and colon cancer (C). Red points, genes with significant interaction terms between sex and risk group. Blue and pink points are genes prognostic only in males and females, respectively. Gray points, genes not significant in either sex. B, SPINK1 is prognostic in females but not males in non–small cell lung cancer. D, Sex-specific receiver operating characteristic curves for a 100-gene non–small cell lung cancer signature fit on the combined sex training cohort and tested on female and male test cohorts. Blue lines, males; pink lines, females.

survival (red points, P < 0.01). These divergences could be of the validation cohort when sex was not considered (HR ¼ 2.3; significant magnitude. For example, elevated tumor abundance of 95% CI, 1.32–4.01, P ¼ 0.0035; Supplementary Fig. S22). SPINK1 was associated with poor outcome in women only However, this overall value hid significant sex bias: the (interaction P ¼ 0.0032; Fig. 4B), while FBXO46 was prognostic signature performed very well in men (AUC ¼ 0.73), but was in males and not females (interaction P ¼ 0.0070; Supplementary indistinguishable from chance in women (AUC ¼ Fig. S20). To assess the generality of these results, we assessed a set 0.54;Fig.4D).Finally,weverified that male- and female- of 783 patients with colorectal cancer with median 3.5-year derived tumors showed fundamentally distinct distributions survival. There were again large differences in the magnitude and using the independent training and validation cohorts defined even direction of association between expression and outcome by the data set generators (34) and empirically estimating the between the sexes (Fig. 4C; Supplementary Fig. S21). null distributions by training 50,000 randomly generated sig- To assess the performance of multigene biomarkers, we natures (Supplementary Fig. S23; ref. 35). Together, these applied ridge regression to the top 100 univariately prognostic results show that large sex differences observed in driver genes genes found in the combined sex training cohort. The multi- lead to differences in the development application of biomar- gene signature attained an AUC of 0.63 and was prognostic in kers for personalized therapy.

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Discussion function on X chromosome genes (19). Our analysis comple- ments these sex chromosome–specific findings with a more The broad and unexplained sex differences in cancer outcomes general methodology by broadly analyzing both SNV and CNA represent a major gap in our understanding of the disease. We mutations using transparent tumor type–specific models to gen- evaluated the molecular origins of these differences by comparing erate a catalog of sex-biased events. We also describe for the first somatic mutation profiles in male- and female-derived tumors time, a relationship between mutation load and sex-biased DNA across a broad range of tumor types. We discovered large differ- repair deficiency. ences in mutation density and sex biases in the frequency of The potential consequences of sex-biased SNVs and CNAs range mutation of specific genes. These differences, however, are not from perturbations of biological pathways such as metabolic uniform across tumor types. Rather, some show very significant processes to changes in mRNA abundance and prognostic bio- sex bias in their mutational profiles, while others show no marker performance. Significant insight into these questions on detectable sex biases. Further, some tumor types show sex bias mechanism will arise from on-going primary tumor whole- in SNV mutation profiles, others in CNA mutational profiles, and genome sequencing and chromatin profiling efforts. Independent still others in both. The mechanisms by which these differences of their origins, these mutational sex biases have significant occur remain to be elucidated. consequences for both preclinical and translational research. Candidate mechanisms include differential chromatin architec- Preclinically, the sex of an experimental model (e.g., cell-line, ture, mutagen exposures and DNA repair efficacy and bias. Indeed, organoid, patient-derived xenograft) may influence the effects of our analysis of stomach and esophageal cancer suggests a complex driver-gene mutations and, therefore, should be explicitly con- relationship between sex and the cancer genome landscape. Our sidered. From a translational perspective, our results suggest that analysis of microsatellite instability in this tumor type posits a in some cases, distinct multigene panels should be used to predict mechanism in dysfunctional DNA repair where baseline somatic prognosis or drug sensitivity in men and women. Overall, these SNV load is lower in female-derived samples. However, the high data call for increased study and consideration of the role of sex in proportion of MSI-positive female-derived samples as well as lower cancer etiology, progression, treatment, and personalized therapy. mRNA abundance of DNA MMR genes MLH1 and PMS2 lead to higher SNV load in these individuals. Independently, EP400 is not Disclosure of Potential Conflicts of Interest only more frequently mutated in female-derived samples, it also No potential conflicts of interest were disclosed. has a greater impact and drives overall female somatic SNV burden higher. As a result, though overall SNV burden appears similar Authors' Contributions between male- and female-derived samples, more female samples Conception and design: C.H. Li, Y.-J. Shiah, P.C. Boutros Development of methodology: C.H. Li, P.C. Boutros harbor defects in DNA repair. Additional work is needed to further Analysis and interpretation of data (e.g., statistical analysis, biostatistics, elucidate the interplay between microsatellite instability, DNA computational analysis): C.H. Li, S. Haider, Y.-J. Shiah, K. Thai repair machinery, mutation load, and sex. Writing, review, and/or revision of the manuscript: C.H. Li, K. Thai, Our statistical modeling incorporate clinical and environmen- P.C. Boutros tal variables to approach the true association of sex with the Administrative, technical, or material support (i.e., reporting or organizing genomic characteristic of interest. However, it is important to note data, constructing databases): C.H. Li, K. Thai, P.C. Boutros Study supervision: P.C. Boutros the limitations of this method in capturing all confounding variables. First, information on environmental variables is incom- Acknowledgments plete and may not be accurately reported. Second, adding vari- This study was conducted with the support of the Ontario Institute for Cancer ables increases model complexity and may decrease overall per- Research to P.C. Boutros through funding provided by the Government of formance if there is insufficient data to support the model. Ontario. This work was supported by the Discovery Frontiers: Advancing Big Nevertheless, the tumor type–specific models in this analysis Data Science in Genomics Research program, which is jointly funded by the represent a foundation for putative sex differences, and our Natural Sciences and Engineering Research Council (NSERC) of Canada, the fi Canadian Institutes of Health Research (CIHR), Genome Canada, and the ndings should be taken in context of each tumor type and its Canada Foundation for Innovation (CFI). P.C. Boutros was supported by a associated risk factors. Another challenge of our study lies in Terry Fox Research Institute New Investigator Award and a CIHR New Inves- validating our findings in datasets with sufficient power and tigator Award. This work was supported by an NSERC Discovery grant and by similar quality survival data. Though we were able to validate a Canadian Institutes of Health Research, grant # SVB-145586, to P.C. Boutros. subset of sex-biased CNAs and SNVs, there remain putative TCGA- The authors thank all the members of the Boutros lab for insightful discussions. specific sex biases. These validation challenges may be due to The results described here are in part based upon data generated by the TCGA Research Network: http://cancergenome.nih.gov/. methodological differences between datasets included in meta- analysis and to the high level of heterogeneity in environmental The costs of publication of this article were defrayed in part by the payment of factors that have yet to be accounted for. page charges. This article must therefore be hereby marked advertisement in Existing literature on sex differences in cancer genomics largely accordance with 18 U.S.C. Section 1734 solely to indicate this fact. focus on individual tumor types and on specific genes or on a single data type (23, 26, 36). A previous pan-cancer study incor- Received February 1, 2018; revised May 18, 2018; accepted June 26, 2018; porating multiple mutation types focused on male-biased loss of published first October 1, 2018.

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Constance H. Li, Syed Haider, Yu-Jia Shiah, et al.

Cancer Res 2018;78:5527-5537.

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