In recent years, there has been increasing interest in personalized medicine for cancer, considering the unique biology of each tumor and patient to optimize therapeutic approaches. Sex differences play a role in patient outcomes, and Yang et al. determined that in the case of the brain tumor glioblastoma, these go beyond hormonal influences and appear to be intrinsic to the tumor cells themselves. The authors found that the sex of the patient correlates not only with prognosis but also with responses to different treatments, suggesting that it may be an important factor to consider when optimizing the therapeutic regimen for each patient.

Sex differences in the incidence and outcome of human disease are broadly recognized but, in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging–based measure of response, we found that standard therapy is more effective in female compared with male patients with GBM. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical relevance of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together, these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.

Here, we performed quantitative analyses of therapeutic responses in male and female patients with GBM using a validated magnetic resonance imaging (MRI)–based method for calculating tumor growth velocities. We also applied a computational algorithm to male and female GBM transcriptome data to gain insights into the relevance and biological basis of sex differences in GBM. Our studies indicate that standard treatment is more effective for females than for males with GBM and that, for the current standard of care—surgery, radiation, and temozolomide (TMZ) chemotherapy—survival in males is correlated with the expression of cell cycle regulators, whereas in females it is correlated with the expression of integrin signaling pathway components. These studies provide a coherent view of sex differences in GBM biology and their clinical ramifications. They support the development of diagnostics and treatments that incorporate sex differences in GBM biology.

In addition to sex differences in incidence, emerging analyses suggest that patient outcomes may also differ between males and females in the pediatric ( 19 ) and adult patient populations with GBM ( 20 ). In a study analyzing more than 27,000 patients, Trifiletti et al. ( 10 ) found that female sex was associated with longer survival, as did Ostrom et al. ( 20 ) in an analysis of 5372 GBM cases from the National Cancer Institute’s Surveillance, Epidemiology, and End Results (SEER) program and an additional 228 GBM cases from the Ohio Brain Tumor Study. Similarly, female patients exhibited longer survival from gliosarcoma ( 8 ), and being female was associated with better outcome in a nomogram for predicting survival in patients with GBM ( 9 ). Thus, the elucidation of sex-specific mechanisms in GBM has the potential to improve outcome for all patients by refining our understanding of disease causation and treatment response.

Whereas low-grade glioma incidence is nearly identical in males and females, malignant brain tumors in general occur more commonly in males, regardless of patient age or geographical location ( 5 , 11 , 15 , 16 ). As shown in recent reports, GBM occurs with a male-to-female ratio of 1.6:1 ( 5 , 8 – 10 ). In particular, although the understanding of molecular subtypes of GBM is still evolving ( 17 ), three of the four originally described transcriptional subtypes of GBM—mesenchymal, proneural, and neural GBM—exhibit a 2:1 male-to-female incidence ratio, whereas classical GBM occurs with equal incidence ( 15 , 18 ). To date, analyses of the transcriptome data from which these molecular subtypes were derived have been performed with merged male-female data and have not yielded insights into the molecular basis for sex differences in GBM incidence.

Identifying the basis for sex differences in cancer biology cannot be accomplished by analysis of merged male and female datasets. Instead, it requires comparison of results from parallel analyses of male and female data. The importance of this was recently highlighted in a study of asthma, a disease driven by both genetic and environmental factors, which occurs in twice as many boys as girls. Mersha et al. ( 2 ) examined the influence of genetic variants on asthma, including an analysis of shared and sex-specific variant effects. Of 47 variants that correlated with asthma risk in the sex-specific analyses, only 21 were detected in the combined analysis, suggesting that biologically important mechanisms of disease were obscured by a “net canceling effect” that arose from opposing effects of genetic variation in sexes. A similar effect was observed in neurofibromatosis 1 (NF1)–associated low-grade gliomas. Despite equal tumor incidence in males and females, polymorphisms in AC8 in patients with NF1 increased the risk of low-grade glioma in female patients but reduced the risk in male patients ( 12 ). The effect of AC8 polymorphisms, which may be related to the mechanistic role of cAMP (cyclic adenosine 3′,5′-monophosphate) in NF1-assoiated glioma ( 13 , 14 ), was unapparent without a sex-specific analysis because of the net canceling effect.

Current epidemiological data indicate that sex differences exist in the incidence of cardiovascular disease, disorders of the immune system, depression, addiction, asthma, and cancers ( 1 – 4 ), including glioblastoma (GBM) ( 5 ). Although sex differences in disease incidence and severity may parallel variation in circulating sex hormone concentrations, in many cases, sex differences exist across all stages of life, indicating some independence from acute hormone action ( 3 , 6 ). Sex differences in GBM are evident in all age groups and therefore cannot be solely the consequence of activational effects of sex hormones ( 5 , 7 – 11 ). Enumerating the molecular bases for sex differences in GBM is likely to reveal fundamental modulators of cancer risk and outcome as well as guide sex-specific components of precision medicine approaches to cancer treatment.

RESULTS

Standard treatment is more effective in female compared with male patients with GBM Sex differences in GBM incidence have been repeatedly reported (5, 7–11), and recent studies have suggested that being female is associated with better outcome from GBM in both adults and children (8–10, 19, 20). The introduction of TMZ as a component of trimodal care for adults with GBM has improved outcomes and highlighted factors, such as O6-methylguanine DNA methyltransferase (MGMT) promoter methylation, that affect response and survival (21, 22). Thus, we wondered whether sex differences in GBM survival are a consequence of differential treatment effects on male versus female patients. To answer this question, we used an MRI data–based analysis, with which the velocity of radial tumor expansion can be determined (23–26). Growth velocity, which correlates with outcome (27, 28), was measured approximately every 2 months in a cohort of 63 patients with GBM (40 males and 23 females) treated with standard-of-care surgery, focal irradiation (XRT), and systemic TMZ chemotherapy (fig. S1) (21, 22). Analysis of serial magnetic resonance (MR) images obtained during postradiation maintenance TMZ treatment indicated that female patients exhibited a greater response to treatment than male patients. Although the initial tumor growth velocities were similarly distributed in male and female patients (P = 0.3985, Wilcoxon rank sum test; Fig. 1A), a steady and significant decline in growth velocity during TMZ treatment was evident only for female patients [P = 0.02569 (female trend test), P = 0.1186 (male trend test); Fig. 1A]. To determine whether the initial TMZ velocity correlated equivalently with survival across sexes, we fit a Cox proportional hazard model with the main effect of velocity (in continuous scale) for the male and female populations separately and found that the velocity had a sex-specific impact on survival (male, P = 0.302; female, P = 0.0161). To visualize the sex-specific effect, we performed an iterative Kaplan-Meier (KM) analysis in male and female GBMs separately to divide male and female patients into high versus low velocity groups and tested the survival difference using the log rank test. For female patients, lower first TMZ velocity was associated with a significantly longer survival compared with higher velocity [median survival, 3090 days versus 681 days (P = 0.00817); Fig. 1B]. In contrast, male patients exhibited no statistically significant correlation between survival and velocity [median survival, 1111 days versus 533 days (P = 0.263); Fig. 1B]. To determine whether first TMZ velocities correlated with Revised Assessment in Neuro-Oncology (RANO) clinical response criteria, we compared survival, growth velocity, and RANO measures for this patient cohort. No significant correlations were detected (fig. S2). Although these data cannot distinguish between the therapeutic effects of radiation versus TMZ, they do suggest that females with GBM may benefit more from standard treatment than males with GBM and that this difference in response, which is detectable using tumor growth velocity measures, may contribute to their survival advantage. Fig. 1 Sex differences in MRI-based metrics of therapeutic responses and their correlation with survival. (A) Tumor growth velocities calculated from serial T1 gadolinium (Gd)–enhanced MR images exhibit progressive decline for female (n = 23) but not for male (n = 40) patients with GBM. (B) Velocity of tumor growth (low velocity, green line; high velocity, purple line) over the first TMZ imaging interval (1 to 3, 28-day cycles of TMZ) stratifies female survival (P = 0.00817, log rank) but not male survival (P = 0.263). (C) Histograms of pretreatment D and rho values in all available MRI cases (independent 53 and 318 GBM case series) for male (n = 227) and female (n = 144) patients. (D) Pretreatment D significantly stratifies survival among females (n = 144; P = 0.0071, log rank) and not among males (n = 227, P = 0.61). (E) High pretreatment rho is associated with worse survival outcomes for both females (n = 144; P = 0.032, log rank) and males (n = 227, P = 0.0037). To validate and further investigate the basis for this difference in response, we applied an established mathematical model of glioma proliferation and invasion (25, 26, 29). We examined the presurgical MRIs (T1 gadolinium and T2 sequences) of 53 patients from the original growth velocity cohort combined with an additional independent cohort of 318 patients, for a total of 371 newly diagnosed patients with GBM (227 males and 144 females). We found that the distribution of estimated net infiltration rates (D, in square millimeters per year) and net proliferation rates [rho (ρ), 1/year] did not differ between males and females before surgery (Fig. 1C). Next, we sought to determine which of the two components (24, 26, 30) was predictive of survival in male and female patients, separately. The sex-specific median of each component was used to unbiasedly dichotomize the patients into low and high groups. Female patients with low D (≤23.03 mm2/year) had significantly longer survival [median overall survival (OS), 589 days versus 390 days (P = 0.0071)] compared with female patients with high D (>23.03 mm2/year; Fig. 1D, left). This was in contrast to male patients for whom survival did not differ [median OS, 540 days versus 450 days (P = 0.614)] as a function of D (≤28.993 mm2/year versus >28.993 mm2/year; Fig. 1D, right). Rho, in contrast to D, stratified survival for both males and females. Females with low rho (≤18.25 per year) exhibited median OS of 542 days as compared with those with high rho (>18.25 per year), who exhibited median OS of 415 days (P = 0.032; Fig. 1E, left). Males with low rho (≤18.25 per year) exhibited median OS of 596 days as compared with those with high rho (>18.25 per year), who exhibited median OS of 410 days (P = 0.0037; Fig. 1E, right). In the independent analysis of the expansion cohort of 318 patients (195 males and 123 females), D and rho had effects on survival in female patients that were similar to those described in the discovery cohort (227 males and 144 females), but neither D nor rho stratified survival for male patients (fig. S3). Together with the established sex differences in incidence, these data suggest that the biology of male and female GBM may be distinct and that outcomes for all patients might be improved if therapies were better tailored to patient sex.

Sex differences in GBM biology are revealed by JIVE decomposition To gain insight into potential sex differences in GBM biology, we examined the transcriptome data available through The Cancer Genome Atlas (TCGA). Using Joint and Individual Variation Explained (JIVE) to integratively decompose the male and the female transcriptome data of the TCGA dataset into three orthogonal components, we identified the joint structure that was common to both sexes, the individual structure that was specific to each sex, and, additionally, the residuals (fig. S4). The heat maps of the male joint structure across the male patients with GBM and the female joint structure across the female patients with GBM indicated that the joint structures extracted by JIVE closely captured the dominant molecular signatures defining the TCGA GBM subtypes (fig. S5 and table S1). However, the joint component only explained ~45% of the total variance in the transcriptomes for each sex, whereas the sex-specific components, independent of the joint components, explained a large proportion of the remaining variability. Specifically, the male-specific component accounted for 38.5% of the total variability in the male transcriptome, and the female-specific component explained 33.6% of the total variability in the female transcriptome (fig. S6). The extracted male and female individual components exhibited distinct patterns compared with their counterpart joint structure, and more importantly, the male-specific component showed distinct patterns compared with the female-specific component (Fig. 2, A and B). We hypothesized that focused analyses of the extracted sex-specific components would reveal which gliomagenic mechanisms are most characteristic of male versus female GBM. Fig. 2 Heat maps of joint and sex-specific expression components of TCGA GBM transcriptome data revealed by JIVE. The heat maps visualize each expression component. Each row represents a gene, and each column a patient sample. For each patient, there are two color codes presented above the heat map. These identify their assignment to sex-specific clusters and to TCGA molecular subtypes (gray indicates unassigned samples). Samples were ordered by sex-specific clusters. The original female (A) and male (B) expression data were decomposed into the shared expression component common to both sexes (“Joint”) and the expression component individual to each sex (“Female specific” and “Male specific”) and residuals as indicated. The female-relevant heat maps (A) show 283 signature genes that define the five female-specific clusters, and the male-relevant heat maps (B) show 293 signature genes that define the five male-specific clusters. (C) The Venn diagram of male and female signature genes indicates that 116 genes are in common.

Sex-specific clusters are identified using the TCGA sex-specific transcriptome expression To identify sex-specific patient subgroups, we performed independent hierarchical clustering on the male- and female-specific components from the JIVE decomposition. Weighted and unweighted consensus clustering was applied to the sex-specific expression to evaluate the robustness of sex-specific clustering (fig. S7, A and B). To determine the optimal number of sex-specific clusters, we varied the total number of clusters from two to six for each sex (fig. S7, C and D) and examined the cumulative density function (CDF) curves for the consensus matrices (fig. S7, E and F). We compared the resultant increase in area under the CDF curves (fig. S7, G and H) when the total number of clusters increased by one. The similarity among samples from each sex-specific cluster was examined to remove samples with great dissimilarity to the majority of samples in the cluster based on the Silhouette scores [fig. S7, A and B, right panels]. Five male (mc1–5) and five female (fc1–5) clusters were thus identified as optimally capturing the transcriptomic subtypes within male and female TCGA data. The five male and five female clusters were defined by sets of 293 and 283 genes (Fig. 2C), respectively, with 116 in common but 177 unique to the male clusters and 167 unique to the female clusters (table S2). Cases from multiple TCGA molecular subtypes (18) were distributed to each of the five male or five female clusters (Fig. 2), indicating successful separation of the individual components from the joint structure components and increasing the likelihood that this approach could reveal sex effects on gliomagenic mechanisms. The one exception was fc3, 70% of which were proneural subtype tumors with IDH1 mutations (seven IDH1 mutants and three wild types (WTs)]. In contrast, male proneural subtype tumors with IDH1 mutations were distributed across three of the male clusters, suggesting that IDH1 mutations may have sex-specific effects in GBM.

Sex-specific clusters are robust to excluding IDH1 mutant cases Current diagnostic criteria indicate that IDH1 mutant and WT GBM are two separate diseases (31). Thus, we examined whether the definition of the sex-specific clusters was robust to excluding IDH1 mutant cases. We removed the IDH1 mutant and glioma CpG island methylator phenotype (G-CIMP) cases from the TCGA, GSE13041, GSE16011, and Rembrandt datasets. We followed the same procedure (independent JIVE analysis, consensus clustering, and determination of optimal total number of sex-specific clusters) to identify sex-specific clusters in IDH1 WT cases. Most of the samples (65 to 96.2%) were in agreement with their cluster assignments from the initial analysis, and mc5 was rediscovered in the IDH1 WT cases (Fig. 3). Because fc3 was predominantly composed of IDH1 mutant cases, it was substantially diminished in this analysis. Fig. 3 Sex-specific survival effects of IDH mutation. (A) OS benefit of fc3 and mc5 is demonstrated in the combined TCGA, GSE13041, GSE16011, and REMBRANDT datasets. See table S3 for P values and hazard ratios (HRs). (B) OS for IDH1 WT cases indicates that both fc3 and mc5 exert effects on survival in the absence of IDH1 mutation. (C) OS in IDH1 mutant cases indicates that male-specific clusters are still associated with an effect on survival. The numbers of female IDH1 mutant cases not assigned to fc3 are n = 3, 2, 3, and 6 in fc1, fc2, fc4, and fc5, respectively, using TCGA and GSE16011 samples in combination (see table S7). (D) IDH1 mutation confers a similar survival benefit in males and females with GBM. (E) The survival benefit of fc3 is independent of IDH1 status. In contrast, IDH1 status exerts a significant effect on survival in mc5 cases. P = 4.3 × 10−4 for the comparison between mc5 cases with and without IDH1 mutation. Overall log rank test P value is shown comparing across all the groups presented in each panel (table S8 shows the P values and HRs for all pairwise comparisons).

Survival differences exist among sex-specific clusters in the TCGA GBM cohort To establish the importance of the sex-specific clusters, we next determined whether the sex-specific clusters in the TCGA data were associated with differences in survival outcomes. KM analyses of male and female clusters confirmed that survival differences exist among both male and female clusters (Fig. 4, A and B). Not surprisingly, fc3, in which 70% of the cases are IDH1 mutant, exhibited significantly better disease-free survival (DFS) with a median time to progression (TTP) of 1758 days compared with each of the other four female clusters [fc1, 259 days (P = 3.3 × 10−5); fc2, 289 days (P = 5 × 10−4); fc4, 182 days (P = 1.64 × 10−4; fc5, 350 days (P = 9.6 × 10−5); Fig. 4A and table S3]. In contrast, although IDH1 mutant cases segregated nearly equally to mc2, mc3, and mc5, only mc3 (median TTP, 408 days) and mc5 (median TTP, 262 days) were associated with prolonged DFS compared with other male clusters [mc1, 240 days (P = 1.2 × 10−2 versus mc3); mc2, 186 days (P = 7.1 × 10−3 versus mc3, P = 2.8 × 10−2 versus mc5); mc4, 158 days (P = 7.3 × 10−3 versus mc3, P = 1.6 × 10−2 versus mc5; Fig. 4B and table S3]. This finding suggested that an interaction may exist between IDH1 mutation and sex-specific cluster features in males but not in females in the determination of survival. To further evaluate this possibility, we separated the IDH1 mutant patients from the sex-specific clusters (Fig. 4, C and D). Only three cases in fc3 were IDH WT, and each of them was alive at 5 years [median DFS for fc3 was not calculable; fc1, 256 days (P = 1.4 × 10−2); fc2, 274 days (P = 1.7 × 10−2); fc4, 182 days (P = 3.4 × 10−2); fc5, 350 days (P = 1.2 × 10−2); Fig. 4C and table S4]. Similarly, the DFS benefit of mc3 and mc5 remained intact after removal of the IDH1 mutant cases [mc3, 408 days; mc5, 262 days; mc1, 240 days (P = 4.2 × 10−3 versus mc3); mc2, 176 days (P = 1.5 × 10−3 versus mc3, P = 1.5 × 10−2 versus mc5); mc4, 158 days (P = 3.1 × 10−3 versus mc3, P = 2.1 × 10−2 versus mc5); Fig. 4D and table S5]. These results suggest that the survival effects of fc3, mc3, and mc5 may be independent of IDH1 mutational status. Fig. 4 DFS of sex-specific clusters in TCGA GBM dataset and OS of sex-specific clusters in three independent datasets combined. (A) DFS in TCGA-derived female clusters (fc1–5). (B) DFS in TCGA-derived male clusters (mc1–5). (C) DFS in TCGA-derived female clusters (fc1–5) in which IDH1 mutant cases are plotted as an independent cluster. (D) DFS in TCGA-derived male clusters (mc1–5) in which IDH1 mutant cases are plotted as an independent cluster. Independent samples combining the GSE13041, GSE16011, and REMBRANDT datasets were assigned to sex-specific clusters, and the superiority of OS of fc3 (E) and mc5 (F) was validated in the independent samples. Overall log rank test P value is shown comparing across all the groups presented in each panel (see tables S3 and S4 for the P values and HRs for all pairwise comparisons).

Survival patterns of sex-specific clusters were independently validated The transcriptome data of GSE13041, GSE16011, and REMBRANDT were decomposed with the JIVE principal components from the TCGA data analysis (fig. S4), and the independent samples were assigned to the TCGA-derived sex-specific clusters on the basis of the nearest-neighbor algorithm. We then sought to validate the male and female cluster-specific survival profiles using all the independent samples. We were limited to an analysis of OS by data availability. The OS benefit of fc3 and mc5 was validated in these datasets (Fig. 4, E and F, and table S3). Using all the samples of the datasets under analyses (TCGA, GSE13041, GSE16011, and REMBRANDT), median OS for fc3 was 1172 days, as compared with 416 days for fc1 (P = 5.6 × 10−5), 378 days for fc2 (P = 1.2 × 10−7), 423 days for fc4 (P = 8.3 × 10−8), and 359 days for fc5 (P = 4.2 × 10−7) (Fig. 3A, left, and table S3). Median OS for mc5 was 620 days, as compared with 422 days for mc1 (P = 1.4 × 10−6), 360 days for mc2 (P = 8.8 × 10−9), 398 days for mc3 (P = 7.9 × 10−5), and 387 days for mc4 (P = 6.2 × 10−6) (Fig. 3A, right). Of the three validation datasets, only GSE16011 specified IDH mutational status. In this dataset, IDH1 mutant tumors were disproportionately distributed to fc3 but more broadly to multiple male clusters (tables S6 and S7), similar to IDH1 distribution in the TCGA samples.

IDH1 mutation status interacts with sex-specific clusters IDH1 mutation confers a better prognosis in GBM (32). The survival advantage of mc5 and fc3 was observed, irrespective of IDH1 status, and for males, IDH1 mutation distributed more equally across the clusters without consistent survival benefits (Fig. 3, B and C). In IDH1 WT cases from the combined data (TCGA, GSE16011, and REMBRANDT), mc5 was still correlated with the longest survival among the male clusters (HRs, 0.61 to 0.65 and P = 0.0039 to 0.022; Fig. 3B and table S5), and fc3 had HRs ranging from 0.26 to 0.29 compared with the other female clusters (P = 0.0032 to 0.0093; Fig. 3B and table S4). In IDH1 mutant cases from the combined data (TCGA and GSE16011), the sample size was too small and lacked sufficient power to render statistical significance on survival comparisons between mc5 and fc3 versus all the other male (mc1–4) and female (fc1, fc2, fc4, and fc5) clusters, respectively, but the estimated HRs for mc5 compared with the other male clusters, and fc3 compared with the other female clusters were always below 1 [for mc5: HRs, 0.15 to 0.54, P = 0.017 versus mc1, P = 6.3 × 10−5 versus mc4, P = 0.12 versus mc2, and P = 0.16 versus mc3; for fc3: HRs, 0.19 to 0.91, P = 0.013 versus fc2; tables S6 and S7]. Thus, IDH1 mutation was validated as a good prognostic feature for both males and females (Fig. 3D). However, IDH1 mutation interacts with fc3 and mc5 cluster features differently (interaction P = 0.07; Fig. 3E and table S8). Fc3 conferred longer survival regardless of the IDH1 mutation status. In contrast, IDH1 mutation further stratified survival differences among mc5 cases (Fig. 3E), such that IDH1 mutant mc5 GBM showed comparable or even slightly better survival than IDH1 mutant fc3 GBM [HR = 0.79, confidence interval = 0.34 to 1.9 (P = 0.59); table S8], although statistically not significant. Together with the broader distribution of IDH1 mutation cases across all male sex–specific clusters, these findings indicate that IDH1 mutation interacts with sex in the determination of survival.

Sex-specific clusters show differing survival patterns by TCGA molecular subtype To gain further insights into cluster-specific effects on survival, we compared the survival differences of the male- and female-specific clusters when stratified by the original Verhaak subtypes (18). We found a consistent cluster effect in which neural, mesenchymal, and proneural specimens in mc5 and fc3 exhibited better survival than tumors of these same Verhaak subtypes that had clustered to mc1–4 or fc1, fc2, fc4, and fc5 (fig. S8). Neither male nor female cluster effects were evident for the classical subtype tumors, the only subtype for which there is no sex difference in incidence (15). These data suggest that for those molecular subtypes of GBM in which sex affects tumor incidence, sex also affects patient survival. In addition, these findings indicate that sex can modulate the impact of specific gliomagenic mechanisms on survival but that not all mechanisms, such as those underlying classical subtype tumors, will be sensitive to the effects of sex.

Pathway analysis indicates that survival in males and females with GBM may be dependent on different mechanisms The unequal effects of sex on survival for tumors of different molecular subtypes suggest that the effects of sex are not mediated solely by factors such as sex hormones, whose actions would distribute equivalently across patients of a given sex regardless of their molecular subtype. Instead, these findings indicate that either tumor cell–intrinsic sex differences or an interaction between tumor cell–intrinsic and microenvironmental sex differences determines responsiveness to treatment and patient survival. To gain insight into possible mechanisms underlying sex-specific survival benefits, we compared the survival and transcriptome expression of fc3 and mc5. Median survival for fc3 was 1172 days compared with 620 days for mc5 (Fig. 5A). To test whether similar or distinct mechanisms accounted for these sex differences in survival, we asked what distinguished fc3 and mc5 from the other female and male clusters, respectively. One hundred ninety-seven transcripts distinguished mc5 from the other male clusters, and 123 transcripts distinguished fc3 from the other female clusters (table S2). Using the Genomatix Suite for pathway analysis, we found that 13 transcripts belonging to calcium/calmodulin signaling, synaptic, and other neuronal function pathways were shared between mc5 and fc3 (Fig. 5B). Examination of the female-specific transcripts revealed the integrin signaling pathway as the most significant pathway (adjusted P < 0.001) that distinguished fc3 from other female clusters (Fig. 5C and table S9), with nine transcripts in the pathway (labeled fc3.9). Six of the nine transcripts from this pathway [PLAT (33), CHL1 (34, 35), FERMT1 (36), PCDH8 (37), IGFBP2 (38, 39), and POSTN (40)] have known roles in glioma, and three (PLAT, IGFBP2, and POSTN) can distinguish proneural from classical high-grade glioma gene signatures (41). Six of the nine genes (AK5, AMIGO2, PLAT, CHL1, PCDH8, and IGFBP2) were down-regulated in fc3 compared with other female clusters, suggesting that better survival in fc3 patients is favored by tumors with reduced integrin signaling (fig. S9). Fig. 5 Analysis of genes and pathways that mediate better survival. (A) In the combined dataset, the survival of females assigned to fc3 (median survival, 1172 days) was compared with the survival of males assigned to mc5 (median survival, 620 days). (B to D) Genes that distinguished fc3 and mc5 from other female and male clusters, respectively, were compared (see table S2). Pathways in all analyses were prioritized by the combination of the numbers of genes from the pathway involved and the corrected P value for the relevance of the pathway. (B) Calcium/calmodulin signaling was the most significantly involved shared pathway between fc3 and mc5 (adjusted P < 0.001). (C) The integrin signaling pathway was the most significant female-specific pathway (adjusted P < 0.001; table S9). Genes that were up- and down-regulated in fc3 compared with the other female clusters are in red and blue boxes, respectively. (D) Cell cycle regulation was the most significant male-specific pathway (adjusted P < 0.001; table S9). Genes that were up- and down-regulated in mc5 compared with the other male clusters are in red and blue boxes, respectively. See table S2 for the complete gene lists and statistics for each analysis. Better outcome in mc5 was significantly (adjusted P < 0.001) associated with cell cycle regulation pathways (Fig. 5D). Seventeen transcripts (labeled mc5.17) were components of this pathway, and they included known critical regulators of mitosis such as CDC20 (37, 38), CKS2 (39), PRC1 (40), NUSAP1 (41), PBK (42), cyclin B1 and B2 (43), and KIF20A (44). Fifteen of the 17 transcripts were significantly down-regulated in mc5 compared with the other male clusters (P ≤ 0.0061 for differences in the original expression data; P ≤ 1.7 × 10−6 for difference in male-specific expression data) and approached the expression observed in fc3 (Fig. 6, A and B, and fig. S10, A, A’, B, and B’). NEFH and NEFM were the exceptions, with each exhibiting greater expression in mc5 compared with each of the other male clusters. This suggests that treatment response and survival in males are determined by lower activity in factors that promote cell cycle progression. Fig. 6 mc5-defining genes and OS in the merged TCGA, GSE16011, and GSE13041 datasets. (A) Density plots for sex-specific expression of male (in blue) and female (in red) GBM specimens of three mc5-defining genes (BIRC5, KIF20A, and CCNB2). The overlay in male and female plots indicates near-identical expression in the populations. (B) Expression of each gene by sex and sex-specific clusters is presented as boxplots. (C) High and low expression groups for each gene were defined relative to the level of expression that distinguished mc5 from the other male clusters (see the “Overall sex-specific survival effects” section in the Supplementary Materials). The survival effects of differences in expression were determined for males and females. Each gene exerted a greater effect on survival in males compared with females. P values from the Cox regression model are labeled in red for comparisons between survival curves of female patients with GBM with low versus high expression of each and labeled in blue for the same survival analysis of male patients with GBM. The P value labeled in green refers to the interaction of sex and expression of a gene in the Cox regression models. Parallel analyses of the fc3-defining genes and the other mc5-defining genes are presented in figs. S9 and S10, respectively. Each of the 9 genes that distinguished fc3 (fc3.9) and each of the 17 genes that distinguished mc5 (mc5.17) from other female and male clusters were similarly expressed in male and female patients with GBM overall (Fig. 6A and figs. S9 and S10, A and A’). Thus, we wondered whether these genes might exert sex-specific effects on survival. For each transcript, we separated all male and female cases into low and high expression groups based on the amount of expression that distinguished mc5 or fc3 from the other male or female clusters. We then determined the effect on OS for each transcript in each sex separately. Last, we compared the effect of the whole gene set on OS between males and females in the combined dataset. None of the distinguishing genes of fc3 exhibited a differential effect on survival in males compared with females (fig. S9). In contrast, although each of the down-regulated cell cycle pathway genes in mc5 affected OS in both males and females, they exhibited a greater effect, as evidenced by smaller HRs, in males compared with females (Fig. 6C and fig. S10, C and C’). Comparing the survival effects of the gene set in males and females, the HRs of the 17 genes were significantly higher in males than in their female counterparts (P = 4.6 × 10−5, Wilcoxon signed-rank test), indicating that the gene set as a whole exerted a greater effect in males than in females, despite almost overlapping expression density of each gene in males and females (Fig. 6A and figs. S9 and S10).