Lung cancer is the most frequent cause of cancer death worldwide 1 . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint Main Consistent sex differences in lifetime risk and survival are recognised across many common cancers 3 . In the UK, in 2010, all-cancer incidence and mortality following age adjustment were 14% and 37% higher in males respectively rising to 46% and 53% when considering lung cancer alone 4 . In line with a convergence of smoking habits, the gap between male and female lung cancer incidence rates is narrowing. Nevertheless, males continue to demonstrate an excess of cases and a relative survival disadvantage. Males with lung cancer have an increased risk of death at 5 years compared with females irrespective of stage, age, period of diagnosis and histologic type 2,5 . The mechanisms responsible for worse outcomes in males have not been established but appear to be independent of cigarette smoking, co-morbidities and treatment type 6 .
An abundance of gene expression changes accompanies lung cancer. The scale and diversity of these changes however, have made it difficult to discern central pathogenic processes and their relationship with prognosis. In the present study we therefore analysed gene expression at a system level, comparing transcriptome organisation between NSCLC (non-small-cell lung cancer) tumours and matched unaffected pulmonary tissues through Weighted Gene Coexpression Network Analysis (WGCNA) 7 . We observed a strongly modular organisation amongst expressed genes, including a combination of shared (pulmonary consensus) and divergent (tumour or normal tissue-specific) co-expression networks. We were then able to relate these co-ordinated gene cliques to inter-individual differences in patient attributes including sex.
Human whole transcriptome data were generated from pulmonary tumours and, with few exceptions, matched unaffected tissue (referred to hereon as 'normal') using the Affymetrix . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint HuGene 1.1 ST microarray. Tumour samples and adjacent normal lung tissue were donated from surgical resections, undertaken with curative intent. Following quality control, a total of 18,717 transcripts and 237 samples were available for analysis (Table 1) . These samples originate from 126 patients and two major NSCLC histological subtypes; lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC).
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By convention, shared and divergent components of transcriptome organisation are specified through the construction of consensus gene co-expression networks, derived from all samples and common across tissues ('C') 8 . Comparisons can then be made against networks derived separately in each tissue, allowing identification of tissue-specific networks.
We report 46 networks that demonstrate similar patterns of co-ordination in tumour ('T') and histologically normal ('N') lung tissue (containing 35 -881 transcript clusters [TC]), as well as relative conservancy in their higher-order organisation (D(Preserve tumour, normal ) 0.84). More than a third of transcripts (43.4%, n = 8,129) however were not assigned to any consensus network, indicating relative independence or inconsistent patterns of co-ordination between tumour and histologically normal tissues. . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
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One network specific to histologically normal tissue (Normal: lavenderblush3) featured a highly significant relationship with biological sex (bicor 0.82 P = 3.72x10 -28 , n Obs = 113, see Extended Data Figure 3 ). Modest relationships with both FEV1 (bicor 0.31, P = 3.60x10 -03 , n Obs = 85) and BMI (bicor 0.23, P = 2.74x10 -02 , n Obs = 91) were also observed but did not retain significance when males and females were examined separately, indicating that these associations were mediated by sex. The transcripts comprising this network were significantly enriched for gonosomal (sex chromosome) inheritance (HP:0010985, Padj 1.080x10 -08 ) followed by histone demethylase activity (GO:0032452, Padj 1.059x10 -05 ). The majority of its 39 members (detailed in Supplementary Table S1) mapped to the sex chromosomes (15 X, 16 Y), and its autosomal members (n = 8) also showed prior evidence of sex-biased expression (e.g. DDX43, NOX5, NLRP2) 9,10 . These data indicate a theme of sex bias or specificity in normal pulmonary gene expression, in keeping with the known impact of gonadal sex on pulmonary development and physiology.
Almost 95% of this network's members (37/39 transcripts) lacked assignment to a consensus network, indicating near-complete divergence in co-expression patterning between tumour and histologically normal tissues. Moreover, over 41% of these transcripts (n = 16), in particular those mapped to the Y chromosome (n = 12, 75%), lacked assignment to a tumour network indicating a specific loss rather than restructuring of co-ordination amongst Y chromosome genes in tumour tissue.
The tumour-specific disturbance in sex-related gene co-expression was visualised through hierarchical clustering (Figure 1a) . The disturbance could be detected as a discrete branch characterised by a loss or substantial curtailment of male-specific gene expression, comprising . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint more than a quarter of all male tumour samples (n = 18, 28%) including tumours of both an LUAD and LUSC histology. This branch most prominently featured low expression across a cluster of eight Y-chromosome transcripts, as encoded by seven genes (DDX3Y, EIF1AY, KDM5D, RPS4Y1, TXLNGY, USP9Y and UTY).
Data from 34 of the 39 TC comprising the normal-specific sex-associated network were available in an independent sample of 69 lung cancer patients with either LUAD or LUSC, providing 64 tumour and 59 unaffected samples (Table 1) 
Mosaic loss of the Y chromosome in peripheral blood, concomitant with aging and tobacco smoke exposure 11 , is associated with increased risk for disease and mortality in men 12 and represents a risk factor for cancer-related mortality 13 . Previous analyses of sex-chromosome aneuploidies have specified six core genes that show obligate Y chromosome dosage sensitivity in their expression 14 . Of these, all 5 available in the discovery dataset (represented on the array and meeting the described filtration criteria) were assigned to the sex-associated network in normal tissue (TXLNGY also known as CYorf15B, DDX3Y, USP9Y, UTY and ZFY) but lacked network assignment in either the tumour-specific or consensus datasets. This indicates a tumour-specific disruption consistent with abnormal Y chromosome dosage.
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Within the sex-associated gene co-expression network, network membership (MM, a metric closely related to intra-network connectivity) was highest for the gene KDM5D (MM 0.99, P = 6.21x10 -94 ) (Supplementary Table S1 ). KDM5D is a male-specific demethylase with established roles in the epigenetic modification of sexually dimorphic histone methylation marks 16,17 . Specifically, KDM5D demethylates trimethylated H3K4 (H3K4me3). This chromatin landmark is generally detected near the start site of transcriptionally active genes 18 and is anti-correlated with DNA methylation 19 . KDM5D-mediated H3K4 demethylation is required for sex-dependent regulation of gene expression in mouse embryonic fibroblasts 20 .
Consistent with this role, male tumours exhibiting the low Y gene expression phenotype were accompanied by a distinctive autosomal DNA hypomethylation signature ( Figure 3 ).
Specifically, median autosomal DNA methylation levels were significantly reduced in these tumours relative to paired unaffected tissues (n 17, P = 3.116 x10 -6 ). This relative reduction was not reproduced in male tumours lacking the low Y gene expression feature (n 5, P=0.0625) indicating that extensive hypomethylation is a specific characteristic of the low Y pulmonary tumour state and potentially therefore also a latent factor contributing to lung cancer-related the methylation changes reported elsewhere 21 . Autosomal DNA methylation levels were also significantly lower in male tumours exhibiting low Y gene expression as compared with other male tumours lacking this feature (P=0.0082). These results demonstrate coincidence between reduced Y chromosome gene expression and widespread autosomal DNA hypomethylation and suggest deficiency of the epigenetic modifier KDM5D as a potential mechanism.
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint Examination of individual regions showing significant differential methylation between low-Y expressing tumours and unaffected paired tissues confirmed cancer-associated changes in DNA methylation strongly biased in favour of hypomethylation. Promoter regions 1Kb upstream of 1,728 genes were hypomethylated in low Y expressing tumours with methylation differences exceeding 20%. These regions showed significant enrichment for multiple motifs relating to the dimeric AP-1 (activating protein 1) transcription factor complex (Supplementary Table 5 ) which has established roles in malignant transformation and invasion 22 . Nevertheless, hypomethylation was not universal and a total of 473 promoter regions were significantly hypermethylated in low Y expressing tumours. These sites showed significant enrichment for an X-box motif, recognised by RFX transcription factors, and functioning in cellular specialization and terminal differentiation with particular relevance to ciliogenesis 23 .
KDM5D has a functional ancestral homolog on the X chromosome, KDM5C, which escapes X-inactivation and shows a male-biased pattern of deleterious mutations which associate with male cancer 24 and multi-locus loss of DNA methylation 25 . We show here that transcript abundance of KDM5C is significantly higher in male tumours exhibiting low KDM5D expression (≥1.5 SD below the overall male mean) as compared with male tumours lacking this feature (n 65, W = 181, P = 0.01). These data indicate active regulation of the dosage balance between these gametologs and suggest that overexpression of KDM5C is unable to fully compensate for deficiency of KDM5D.
Down-regulated expression of KDM5D has previously been reported in the context of renal cell carcinoma 15 , prostate cancer 26 and gastric cancer 27 ; in at least a proportion of tumours due . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint to somatic loss or segmental deletions of the Y chromosome. Clinically, low KDM5D expression is variably associated with a poorer prognosis, more aggressive phenotype and metastasis.
At the time of last follow-up 33 male patients with tumour samples had died. Of these, 8 (24%) had markedly low male tumour KDM5D expression (≥1.5 SD below the overall male mean), meaning that almost two thirds (62%) of all males with low tumour KDM5D had died as opposed to 49% of males lacking this marker.
We sought to isolate the relationship between KDM5D deficiency and prognosis in lung cancer by fitting a multivariate Cox proportional hazards model in the discovery dataset ( Figure 4 ). In silico validation of the association between tumour KDM5D and survival was sought via the online Kaplan-Meier plotter platform (http://kmplot.com/analysis/) accessing 1,100 male tumour samples derived from 11 independent lung cancer mRNA gene chip datasets.
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint Consistent with our observations in the discovery dataset, relatively low tumour KDM5D mRNA expression was associated with an unfavourable prognosis in males (HR 1.67, 95% CI 1.4 to 2.0, P = 1.2x10 -10 , Extended Data Figure 5 ).
Tumour KDM5D abundance, as gauged through RNA-seq, was available through the Kaplan-Meier plotter platform across 14 non-sex-specific cancer types totalling 2,423 male patients.
Survival analysis incorporating low KDM5D as a prognostic indicator yielded nominally significant P-values (P ≤ 0.05) in seven cancers types and most significantly in head-neck squamous cell carcinoma (HR 1.79 [1.3-2.5], P = 0.0003) and liver hepatocellular carcinoma (HR 1.85 [1.16-2.94], P = 0.008). Both head and neck and liver hepatocellular carcinoma have raised incidence in males 28,29 , and within head and neck cancer male sex also carries a significant survival disadvantage. We note that the smallest P-values were observed in cancers where automatic thresholding placed a cut-off below 20% of the maximum recorded in that tissue (Supplementary Table S2 ) suggesting a low natural split in the male abundance spectrum in some cancers. Nevertheless, when KDM5D abundance was alternatively split at the lowest quartile, significance was retained for both head-neck squamous cell carcinoma (HR 1.75 [1.3-2.5], P = 0.0011) and liver hepatocellular carcinoma (HR 1.82 [1.1-2.9], P = 0.0099).
There is a recognised need for biomarkers capable of predicting systemic therapy sensitivity and response. In prostate cancer (PC) cell lines, low expression of KDM5D is already associated with a reduced sensitivity to docetaxel (in the presence of androgen) and cisplatin 30 .
Conversely, PC cells deficient for KDM5D show an increased sensitivity to ATR inhibitors (ATRi) 26 ; compounds that are currently in early phase clinical trials as cancer therapeutics or . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint chemo-sensitizing agents 31 . Specifically, PC cells deficient for KDM5D exhibit replicative stress and ATR activation and, following exposure to ATRi, curtailed proliferation and increased apoptosis. This indicates that at least within the context of PC, the combination of KDM5D deficiency and ATRi constitute a synthetic lethal interaction 26 .
Whilst extension of these findings to NSCLC or SCLC has not been explored, our findings suggest that LOY-mediated curtailment of Y-chromosome gene expression, particularly deficiency of the demethylase KDM5D, may identify a male patient group with distinct progression, mortality and synthetic lethality profiles.
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint Tables   Table 1: . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. TC8173513  TC8166493  TC8168412  TC8166195  TC8171148  TC8171229  TC8171222  TC7943158  TC8165866  TC8166500  TC8172876  TC8166956  TC8172827  TC8176719  TC8176698  TC8176578  TC8176709  TC8176375  TC8177232  TC8176624  TC8177137  TC8176469  TC8177214  TC8177261  TC7975694  TC8166182  TC8120679  TC8176442  TC7984488  TC8031398  TC8171760  TC8171624  TC8153835  TC8036862  TC8044605  TC8176655  TC8177217  TC8176384  TC8176460  TC8173513  TC8166493  TC8171229  TC8171222  TC8171148  TC8165866  TC8166956  TC8166500  TC8172876  TC8172827  TC8176442  TC8176469  TC8177214  TC8176460  TC8177217  TC8176384  TC8176655  TC8031398  TC8171624  TC8171760  TC8153835  TC8036862  TC8044605  TC8168412  TC7943158  TC8166195  TC8176375  TC8176624  TC8177137  TC8176709  TC8176578  TC8176719  TC8176698  TC8177232 a b
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint and males lacking this feature (nd), see Figure 1 . Data is shown for both tumour and histologically normal tissue. Normality was assessed with a Shapiro Wilk test. Differences in DNA methylation between paired tumour and histologically normal tissues were assessed using a two-tailed paired t-test (low Y group), and a Wilcoxon test (non-low Y group). A twotailed unpaired Mann-Whitney test was used to assess differences in DNA methylation between the two tumours groups. Error bars represent standard deviation from the mean. Magnitude of significance is denoted with asterisks (*). Abbreviations: d (deficient chromosome Y gene expression), nd (non-deficient chromosome Y gene expression), ns (non-significant).
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint expression data for the remaining 296 samples were RMA-treated using Oligo (1.38.0) and filtered. Specifically, transcript cluster intensity was required to exceed the data set median in 1 or more sample (genefilter 1.56.0), and be designated within the Affymetrix annotation (netaffx build 36) with a cross-hybridisation potential of 1 (unique), a non-missing mRNA assignment and as part of the main design probe set category. Together these filters yielded 18,717 transcript clusters (TC). Gene annotations were collated from the netaffx build 36, and the Bioconductor package hugene11sttranscriptcluster.db (8.5.0) as assembled from public repositories. Samples derived from patients with a LUAD or LUSC histology were selectively retained for analysis (Table 1) .
Gene expression data from the Affymetrix HuGene 1.1 ST array were available for a total of 123 samples from 69 patients with either a LUAD or LUSC histology. Quality control and data pre-processing were carried out as described for the discovery dataset, yielding a final data dimension of 123 samples and 17,264 TC.
A consensus network analysis of tumour and normal lung expression data was performed using step-by-step unsigned WGCNA (1.51) 32 , employing a soft-thresholding power of 5 (Extended Data Figure 6 ) and scaling topological overlap matrices (TOM) for purposes of comparability (scaling parameter 0.95).
Adaptive branch pruning was performed using dynamicTreeCut (1.63-1), applying a minimum cluster size of 30, a maximum joining height of 0.995 and a deep split parameter of 2 (specifying the sensitivity to cluster splitting). Modules classified as too close in terms of the correlation of their module eigengenes were merged (maximum dissimilarity that qualifies . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint modules for merging 0.25). Consensus modules were related to phenotypic traits through twosided biweight mid-correlation (robustY=FALSE, maxPOutliers=0.05) as per recommended best practice for settings that include binary or ordinal variables, and compared with modules identified in tumour or unaffected tissue alone as calculated using equivalent computational parameters. Pathway enrichment analysis was implemented in g:profiler (https://biit.cs.ut.ee/gprofiler/) 33 based on unique Entrez ID annotations (as determined through hugene11sttranscriptcluster.db 8.5.0) and incorporating the tailor-made g:SCS algorithm for multiple testing correction. 
Sequencing read coverage was analysed for a total of 21 samples (15 patients, described in Supplementary Table S3 ) through the analysis of WES data (McGill University Innovation Centre, Montreal) available as part of a wider study. Sequence read coverage was obtained for all chromosome Y genes using the BEDtools coverage tool and normalised both by gene length . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity.
The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint and sample sequencing depth. Percentage of loss of chromosome Y was then calculated considering only the captured regions. Normality of the data was examined through Shapiro-Wilk normality tests. Paired and un-paired t-tests were performed as appropriate, to examine between-group differences, and these were plotted using GraphPad Prism (8.3.1).
Whole-Genome Bisulphite Sequencing (WGBS) was used to assess DNA methylation levels in the DSS Bioconductor R package (2.34.0), which takes into account spatial correlation, read depth and biological variation between groups. DMRs were called using the criterion absolute methylation differences >20% and P <0.001.
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The copyright holder for this preprint this version posted June 19, 2020. . https://doi.org/10.1101/2020.06.17.20132951 doi: medRxiv preprint Coordinates 1Kb upstream hg19 Ensembl genes were downloaded from UCSC Table Browser to obtain promoter genomic regions. Proximity of DMRs to promoter regions was analysed with Bedtools' IntersectbED 36 . Then, enriched TF binding motifs in the genomic regions of promoters employed the motif enrichment algorithm in the HOMER tool 37 . CpG normalization and use of the repeat-masked sequence were the options given for finding enriched motif in the genomic regions given.
The Y Chromosome Deletion Detection System assay, Version 2 (Promega, WI, USA) was performed in a total of 16 patients from the low Y tumour group (31 samples, 15 complete tumour-normal tissue pairs), across 20 regions of the Y chromosome as per the manufacturer's instructions and as detailed elsewhere 15 . Briefly, the intensity value for each Y-linked amplicon was normalized to the intensity value of corresponding (non-Y) control amplicon obtained from the same sample. The average of these values across 3 replicates, the Y-index, was used to calculate a patient-specific tumour:normal ratio. Corresponding expression data were available for all but one of these samples.
Survival curves and a multivariate Cox proportional hazards model were fitted using the R package Survival (2.44-1.1). Survival curves and forest plots were drawn using survminer . CC-BY-NC-ND 4.0 International license It is made available under a is the author/funder, who has granted medRxiv a license to display the preprint in perpetuity. (which was not certified by peer review)
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The prognostic value of tumour KDM5D in male cancer was assessed via the Kaplan-Meier plotter (http://kmplot.com/analysis/); an online platform providing access to overall survival data in combination with gene chip or RNA-seq transcriptional data 39,40 .
Arrays designated as biased through the Kaplan-Meier plotter quality control pipeline were excluded. Overall survival was available in 1,100 male patients with lung cancer split across 11 independent cohorts (CaArray, GSE14814, GSE19188, GSE29013, GSE30219, GSE31210, GSE31908, GSE37745, GSE4573, GSE50081 and TCGA). KDM5D was accessed through the Affymetrix ID 206700_s_at (range 3 -3581) with automatic thresholding (applied cut-off 515, 14.38% of maximal).
The wider prognostic value of tumour KDM5D in male cancer outside of the lung was explored via the Kaplan-Meier plotter utilising RNA-seq data available across a total of 2,423 male patients and 14 cancer types, excluding sex-specific cancers and cancers individually represented by ≤20 samples. These included bladder carcinoma (n = 298), esophageal adenocarcinoma (n = 69), esophageal squamous cell carcinoma (n = 69), head-neck squamous cell carcinoma (n = 366), kidney renal clear cell carcinoma (n = 344), kidney renal papillary cell carcinoma (n = 211), liver hepatocellular carcinoma (n = 249), pancreatic ductal adenocarcinoma (n = 97), pheochromocytoma and paraganglioma (n = 77), rectum adenocarcinoma (n = 90), sarcoma (n = 118), stomach adenocarcinoma (n = 238), thymoma (n = 62) and thyroid carcinoma (n = 135). Automatic thresholding was applied.
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Gene expression data have been deposited in NCBI's Gene Expression Omnibus and will be accessible through GEO SuperSeries accession number GSE151103
(https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE151103); comprising SubSeries GSE151101 (discovery) and GSE151102 (replication) upon acceptance for peer-reviewed publication. Sequence data are available upon request.
Network analysis code utilised in this manuscript follows the publicly available WGCNA consensus pipeline 32 . Scripts used for sequence analysis are available upon request.
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