Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

Brain-penetrant histone deacetylase inhibitor RG2833 improves spatial memory in females of an Alzheimer's disease rat model

BACKGROUND

Nearly two-thirds of Alzheimer's disease (AD) patients are women. Therapeutics for women are critical to lowering their elevated risk of developing this major cause of adult dementia. Moreover, targeting epigenetic processes such as histone acetylation that regulate multiple cellular pathways is advantageous given the multifactorial nature of AD. Histone acetylation takes part in memory consolidation, and its disruption is linked to AD.

OBJECTIVE

Determine whether the investigational drug RG2833 has repurposing potential for AD. RG2833 is a histone deacetylase HDAC1/3 inhibitor that is orally bioavailable and permeates the blood-brain-barrier.

METHODS

RG2833 effects were determined on cognition, transcriptome, and AD-like pathology in 11-month TgF344-AD female and male rats. Treatment started early in the course of pathology when therapeutic intervention is predicted to be most effective.

RESULTS

RG2833-treatment of 11-month TgF344-AD rats: (1) Significantly improved hippocampal-dependent spatial memory in females but not males. (2) Upregulated expression of immediate early genes, such as Arc, Egr1 and c-Fos, and other genes involved in synaptic plasticity and memory consolidation in females. Remarkably, out of 17,168 genes analyzed for each sex, no significant changes in gene expression were detected in males at p < 0.05, false discovery rate <0.05, and fold-change equal or > 1.5. (3) Failed to improve amyloid beta accumulation and microgliosis in female and male TgF344-AD rats.

CONCLUSIONS

Our study highlights the potential of histone-modifying therapeutics such as RG2833 to improve cognitive behavior and drive the expression of immediate early, synaptic plasticity and memory consolidation genes, especially in female AD patients.

Identifying Sex Differences in Lung Adenocarcinoma Using Multi-Omics Integrative Protein Signaling Networks

Lung adenocarcinoma (LUAD) exhibits differences between the sexes in incidence, prognosis, and therapy, suggesting underexplored molecular mechanisms. We conducted an integrative multi-omics analysis using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and The Cancer Genome Atlas (TCGA) datasets to contrast transcriptomes and proteomes between sexes. We used TIGER to analyze TCGA-LUAD expression data and found sex-biased activity of transcription factors (TFs); we used PTM-SEA with CPTAC-LUAD proteomics data and found sex-biased kinase activity. We combined these to construct a kinase-TF signaling network and discovered druggable pathways linked to cancer-related processes. We also found significant sex biases in clinically relevant TFs and kinases, including NR3C1, AR, and AURKA. Using the PRISM drug screening database, we identified potential sex-specific drugs, such as glucocorticoid receptor agonists and aurora kinase inhibitors. Our findings emphasize the importance of considering sex and using multi-omics network methods to discover personalized cancer therapies.

OncoSexome: the landscape of sex-based differences in oncologic diseases

The NIH policy on sex as biological variable (SABV) emphasized the importance of sex-based differences in precision oncology. Over 50% of clinically actionable oncology genes are sex-biased, indicating differences in drug efficacy. Research has identified sex differences in non-reproductive cancers, highlighting the need for comprehensive sex-based cancer data. We therefore developed OncoSexome, a multidimensional knowledge base describing sex-based differences in cancer (https://idrblab.org/OncoSexome/) across four key topics: antineoplastic drugs and responses (SDR), oncology-related biomarkers (SBM), risk factors (SRF) and microbial landscape (SML). SDR covers sex-based differences in 2051 anticancer drugs; SBM describes 12 551 sex-differential biomarkers; SRF illustrates 350 sex-dependent risk factors; SML demonstrates 1386 microbes with sex-differential abundances associated with cancer development. OncoSexome is unique in illuminating multifaceted influences of biological sex on cancer, providing both external and endogenous contributors to cancer development and describing sex-based differences for the broadest oncological classes. Given the increasing global research interest in sex-based differences, OncoSexome is expected to impact future precision oncology practices significantly.

Attenuated sex-related DNA methylation differences in cancer highlight the magnitude bias mediating existing disparities

BACKGROUND

DNA methylation (DNAm) influences both sex differences and cancer development, yet the mechanisms connecting these factors remain unclear.

METHODS

Utilizing data from The Cancer Genome Atlas, we conducted a comprehensive analysis of sex-related DNAm effects in nine non-reproductive cancers, compared to paired normal adjacent tissues (NATs), and validated the results using independent datasets. First, we assessed the extent of sex differential DNAm between cancers and NATs to explore how sex-related DNAm differences change in cancerous tissues. Next, we employed a multivariate adaptive shrinkage approach to model the covariance of cancer-related DNAm effects between sexes, aiming to elucidate how sex impacts aberrant DNAm patterns in cancers. Finally, we investigated correlations between the methylome and transcriptome to identify key signals driving sex-biased DNAm regulation in cancers.

RESULTS

Our analysis revealed a significant attenuation of sex differences in DNAm within cancerous tissues compared to baseline differences in normal tissues. We identified 3,452 CpGs (Pbonf < 0.05) associated with this reduction, with 72% of the linked genes involved in X chromosome inactivation. Through covariance analysis, we demonstrated that sex differences in cancer are predominantly driven by variations in the magnitude of shared DNAm signals, referred to as "amplification." Based on these patterns, we classified cancers into female- and male-biased groups and identified key CpGs exhibiting sex-specific amplification. These CpGs were enriched in binding sites of critical transcription factors, including P53, SOX2, and CTCF. Integrative multi-omics analyses uncovered 48 CpG-gene-cancer trios for females and 380 for males, showing similar magnitude differences in DNAm and gene expression, pointing to a sex-specific regulatory role of DNAm in cancer risk. Notably, several genes regulated by these trios were previously identified as drug targets for cancers, highlighting their potential as sex-specific therapeutic targets.

CONCLUSIONS

These findings advance our understanding of how sex, DNAm, and gene expression interact in cancer, offering insights into the development of sex-specific biomarkers and precision medicine.

Single-Sample Networks Reveal Intra-Cytoband Co-Expression Hotspots in Breast Cancer Subtypes

Breast cancer is a heterogeneous disease comprising various subtypes with distinct molecular characteristics, clinical outcomes, and therapeutic responses. This heterogeneity evidences significant challenges for diagnosis, prognosis, and treatment. Traditional genomic co-expression network analyses often overlook individual-specific interactions critical for personalized medicine. In this study, we employed single-sample gene co-expression network analysis to investigate the structural and functional genomic alterations across breast cancer subtypes (Luminal A, Luminal B, Her2-enriched, and Basal-like) and compared them with normal breast tissue. We utilized RNA-Seq gene expression data to infer gene co-expression networks. The LIONESS algorithm allowed us to construct individual networks for each patient, capturing unique co-expression patterns. We focused on the top 10,000 gene interactions to ensure consistency and robustness in our analysis. Network metrics were calculated to characterize the topological properties of both aggregated and single-sample networks. Our findings reveal significant fragmentation in the co-expression networks of breast cancer subtypes, marked by a change from interchromosomal (TRANS) to intrachromosomal (CIS) interactions. This transition indicates disrupted long-range genomic communication, leading to localized genomic regulation and increased genomic instability. Single-sample analyses confirmed that these patterns are consistent at the individual level, highlighting the molecular heterogeneity of breast cancer. Despite these pronounced alterations, the proportion of CIS interactions did not significantly correlate with patient survival outcomes across subtypes, suggesting limited prognostic value. Furthermore, we identified high-degree genes and critical cytobands specific to each subtype, providing insights into subtype-specific regulatory networks and potential therapeutic targets. These genes play pivotal roles in oncogenic processes and may represent important keys for targeted interventions. The application of single-sample co-expression network analysis proves to be a powerful tool for uncovering individual-specific genomic interactions.

Bioinformatics approaches for studying molecular sex differences in complex diseases

Many complex diseases exhibit pronounced sex differences that can affect both the initial risk of developing the disease, as well as clinical disease symptoms, molecular manifestations, disease progression, and the risk of developing comorbidities. Despite this, computational studies of molecular data for complex diseases often treat sex as a confounding variable, aiming to filter out sex-specific effects rather than attempting to interpret them. A more systematic, in-depth exploration of sex-specific disease mechanisms could significantly improve our understanding of pathological and protective processes with sex-dependent profiles. This survey discusses dedicated bioinformatics approaches for the study of molecular sex differences in complex diseases. It highlights that, beyond classical statistical methods, approaches are needed that integrate prior knowledge of relevant hormone signaling interactions, gene regulatory networks, and sex linkage of genes to provide a mechanistic interpretation of sex-dependent alterations in disease. The review examines and compares the advantages, pitfalls and limitations of various conventional statistical and systems-level mechanistic analyses for this purpose, including tailored pathway and network analysis techniques. Overall, this survey highlights the potential of specialized bioinformatics techniques to systematically investigate molecular sex differences in complex diseases, to inform biomarker signature modeling, and to guide more personalized treatment approaches.

Current and future directions in network biology

Summary

Network biology is an interdisciplinary field bridging computational and biological sciences that has proved pivotal in advancing the understanding of cellular functions and diseases across biological systems and scales. Although the field has been around for two decades, it remains nascent. It has witnessed rapid evolution, accompanied by emerging challenges. These stem from various factors, notably the growing complexity and volume of data together with the increased diversity of data types describing different tiers of biological organization. We discuss prevailing research directions in network biology, focusing on molecular/cellular networks but also on other biological network types such as biomedical knowledge graphs, patient similarity networks, brain networks, and social/contact networks relevant to disease spread. In more detail, we highlight areas of inference and comparison of biological networks, multimodal data integration and heterogeneous networks, higher-order network analysis, machine learning on networks, and network-based personalized medicine. Following the overview of recent breakthroughs across these five areas, we offer a perspective on future directions of network biology. Additionally, we discuss scientific communities, educational initiatives, and the importance of fostering diversity within the field. This article establishes a roadmap for an immediate and long-term vision for network biology.

Availability and implementation

Not applicable.

Gene regulatory networks reveal sex difference in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) has been observed to have significant sex differences in incidence, prognosis, and response to therapy. However, the molecular mechanisms responsible for these disparities have not been investigated extensively.

METHODS

Sample-specific gene regulatory network methods were used to analyze RNA sequencing data from non-cancerous human lung samples from The Genotype Tissue Expression Project (GTEx) and lung adenocarcinoma primary tumor samples from The Cancer Genome Atlas (TCGA); results were validated on independent data.

RESULTS

We found that genes associated with key biological pathways including cell proliferation, immune response and drug metabolism are differentially regulated between males and females in both healthy lung tissue and tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also discovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including AKT2 and KRAS. Using differentially regulated genes between healthy and tumor samples in conjunction with a drug repurposing tool, we identified several small-molecule drugs that might have sex-biased efficacy as cancer therapeutics and further validated this observation using an independent cell line database.

CONCLUSIONS

These findings underscore the importance of including sex as a biological variable and considering gene regulatory processes in developing strategies for disease prevention and management.

Sex-biased Regulation of Extracellular Matrix Genes in COPD

Compared to men, women often develop COPD at an earlier age with worse respiratory symptoms despite lower smoking exposure. However, most preventive, and therapeutic strategies ignore biological sex differences in COPD. Our goal was to better understand sex-specific gene regulatory processes in lung tissue and the molecular basis for sex differences in COPD onset and severity. We analyzed lung tissue gene expression and DNA methylation data from 747 individuals in the Lung Tissue Research Consortium (LTRC), and 85 individuals in an independent dataset. We identified sex differences in COPD-associated gene regulation using gene regulatory networks. We used linear regression to test for sex-biased associations of methylation with lung function, emphysema, smoking, and age. Analyzing gene regulatory networks in the control group, we identified that genes involved in the extracellular matrix (ECM) have higher transcriptional factor targeting in females than in males. However, this pattern is reversed in COPD, with males showing stronger regulatory targeting of ECM-related genes than females. Smoking exposure, age, lung function, and emphysema were all associated with sex-specific differential methylation of ECM-related genes. We identified sex-based gene regulatory patterns of ECM-related genes associated with lung function and emphysema. Multiple factors including epigenetics, smoking, aging, and cell heterogeneity influence sex-specific gene regulation in COPD. Our findings underscore the importance of considering sex as a key factor in disease susceptibility and severity.

Treating sex and gender differences as a continuous variable can improve precision cancer treatments

BACKGROUND

The significant sex and gender differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. One barrier to translation is that cancer phenotypes cannot be segregated into distinct male versus female categories. Instead, within this convenient but contrived dichotomy, male and female cancer phenotypes are highly overlapping and vary between female- and male- skewed extremes. Thus, sex and gender-specific treatments are unrealistic, and our translational goal should be adaptation of treatment to the variable effects of sex and gender on targetable pathways.

METHODS

To overcome this obstacle, we profiled the similarities in 8370 transcriptomes of 26 different adult and 4 different pediatric cancer types. We calculated the posterior probabilities of predicting patient sex and gender based on the observed sexes of similar samples in this map of transcriptome similarity.

RESULTS

Transcriptomic index (TI) values were derived from posterior probabilities and allowed us to identify poles with local enrichments for male or female transcriptomes. TI supported deconvolution of transcriptomes into measures of patient-specific activity in sex and gender-biased, targetable pathways. It identified sex and gender-skewed extremes in mechanistic phenotypes like cell cycle signaling and immunity, and precisely positioned each patient's whole transcriptome on an axis of continuously varying sex and gender phenotypes.

CONCLUSIONS

Cancer type, patient sex and gender, and TI value provides a novel and patient- specific mechanistic identifier that can be used for realistic sex and gender-adaptations of precision cancer treatment planning.

Sex reporting of cells used in cancer research: A systematic review

Sex and gender disparities in biomedical research have been emphasized to improve scientific knowledge applied for the health of both men and women. Despite sex differences in cancer incidence, prognosis, and responses to therapeutic agents, mechanistic explanations at molecular levels are far from enough. Recent studies suggested that cell sex is an important biological variable due to differences in sex chromosome gene expression and differences in events associated with developmental biology. The objective of this study was to analyze the reporting of sex of cells used in cancer research using articles published in Cancer Cell, Molecular Cancer, Journal of Hematology & Oncology, Journal for ImmunoTherapy of Cancer, and Cancer Research in 2020, and to examine whether there exists any sex bias. We found that the percentage of cells with sex notation in the article was 36.5%. Primary cells exhibited higher sex notation compared to cell lines. A higher percentage of female cells were used in cell cultures with sex notation. Also, sex-common cells omitted sex description more often compared to sex-specific cells. None of the cells isolated from embryo and esophagus reported the cell sex in the article. Our results indicate cell sex report in cancer research is limited to a small proportion of cells used in the study. These results call for acknowledging the sex of cells to increase the applicability of biomedical research discoveries.

Evaluation of single-sample network inference methods for precision oncology

A major challenge in precision oncology is to detect targetable cancer vulnerabilities in individual patients. Modeling high-throughput omics data in biological networks allows identifying key molecules and processes of tumorigenesis. Traditionally, network inference methods rely on many samples to contain sufficient information for learning, resulting in aggregate networks. However, to implement patient-tailored approaches in precision oncology, we need to interpret omics data at the level of individual patients. Several single-sample network inference methods have been developed that infer biological networks for an individual sample from bulk RNA-seq data. However, only a limited comparison of these methods has been made and many methods rely on 'normal tissue' samples as reference, which are not always available. Here, we conducted an evaluation of the single-sample network inference methods SSN, LIONESS, SWEET, iENA, CSN and SSPGI using transcriptomic profiles of lung and brain cancer cell lines from the CCLE database. The methods constructed functional gene networks with distinct network characteristics. Hub gene analyses revealed different degrees of subtype-specificity across methods. Single-sample networks were able to distinguish between tumor subtypes, as exemplified by node strength clustering, enrichment of known subtype-specific driver genes among hubs and differential node strength. We also showed that single-sample networks correlated better to other omics data from the same cell line as compared to aggregate networks. We conclude that single-sample network inference methods can reflect sample-specific biology when 'normal tissue' samples are absent and we point out peculiarities of each method.

Sex-biased gene expression and gene-regulatory networks of sex-biased adverse event drug targets and drug metabolism genes

BACKGROUND

Previous pharmacovigilance studies and a retroactive review of cancer clinical trial studies identified that women were more likely to experience drug adverse events (i.e., any unintended effects of medication), and men were more likely to experience adverse events that resulted in hospitalization or death. These sex-biased adverse events (SBAEs) are due to many factors not entirely understood, including differences in body mass, hormones, pharmacokinetics, and liver drug metabolism enzymes and transporters.

METHODS

We first identified drugs associated with SBAEs from the FDA Adverse Event Reporting System (FAERS) database. Next, we evaluated sex-specific gene expression of the known drug targets and metabolism enzymes for those SBAE-associated drugs. We also constructed sex-specific tissue gene-regulatory networks to determine if these known drug targets and metabolism enzymes from the SBAE-associated drugs had sex-specific gene-regulatory network properties and predicted regulatory relationships.

RESULTS

We identified liver-specific gene-regulatory differences for drug metabolism genes between males and females, which could explain observed sex differences in pharmacokinetics and pharmacodynamics. In addition, we found that ~ 85% of SBAE-associated drug targets had sex-biased gene expression or were core genes of sex- and tissue-specific network communities, significantly higher than randomly selected drug targets. Lastly, we provide the sex-biased drug-adverse event pairs, drug targets, and drug metabolism enzymes as a resource for the research community.

CONCLUSIONS

Overall, we provide evidence that many SBAEs are associated with drug targets and drug metabolism genes that are differentially expressed and regulated between males and females. These SBAE-associated drug metabolism enzymes and drug targets may be useful for future studies seeking to explain or predict SBAEs.

Sex Differences in Colon Cancer: Genomic and Nongenomic Signalling of Oestrogen

Colon cancer (CRC) is a prevalent malignancy that exhibits distinct differences in incidence, prognosis, and treatment responses between males and females. These disparities have long been attributed to hormonal differences, particularly the influence of oestrogen signalling. This review aims to provide a comprehensive analysis of recent advances in our understanding of the molecular mechanisms underlying sex differences in colon cancer and the protective role of membrane and nuclear oestrogen signalling in CRC development, progression, and therapeutic interventions. We discuss the epidemiological and molecular evidence supporting sex differences in colon cancer, followed by an exploration of the impact of oestrogen in CRC through various genomic and nongenomic signalling pathways involving membrane and nuclear oestrogen receptors. Furthermore, we examine the interplay between oestrogen receptors and other signalling pathways, in particular the Wnt/β-catenin proliferative pathway and hypoxia in shaping biological sex differences and oestrogen protective actions in colon cancer. Lastly, we highlight the potential therapeutic implications of targeting oestrogen signalling in the management of colon cancer and propose future research directions to address the current gaps in our understanding of this complex phenomenon.

Statistical normalization methods in microbiome data with application to microbiome cancer research

Mounting evidence has shown that gut microbiome is associated with various cancers, including gastrointestinal (GI) tract and non-GI tract cancers. But microbiome data have unique characteristics and pose major challenges when using standard statistical methods causing results to be invalid or misleading. Thus, to analyze microbiome data, it not only needs appropriate statistical methods, but also requires microbiome data to be normalized prior to statistical analysis. Here, we first describe the unique characteristics of microbiome data and the challenges in analyzing them (Section 2). Then, we provide an overall review on the available normalization methods of 16S rRNA and shotgun metagenomic data along with examples of their applications in microbiome cancer research (Section 3). In Section 4, we comprehensively investigate how the normalization methods of 16S rRNA and shotgun metagenomic data are evaluated. Finally, we summarize and conclude with remarks on statistical normalization methods (Section 5). Altogether, this review aims to provide a broad and comprehensive view and remarks on the promises and challenges of the statistical normalization methods in microbiome data with microbiome cancer research examples.

Understanding blood development and leukemia using sequencing-based technologies and human cell systems

Our current understanding of human hematopoiesis has undergone significant transformation throughout the years, challenging conventional views. The evolution of high-throughput technologies has enabled the accumulation of diverse data types, offering new avenues for investigating key regulatory processes in blood cell production and disease. In this review, we will explore the opportunities presented by these advancements for unraveling the molecular mechanisms underlying normal and abnormal hematopoiesis. Specifically, we will focus on the importance of enhancer-associated regulatory networks and highlight the crucial role of enhancer-derived transcription regulation. Additionally, we will discuss the unprecedented power of single-cell methods and the progression in using in vitro human blood differentiation system, in particular induced pluripotent stem cell models, in dissecting hematopoietic processes. Furthermore, we will explore the potential of ever more nuanced patient profiling to allow precision medicine approaches. Ultimately, we advocate for a multiparameter, regulatory network-based approach for providing a more holistic understanding of normal hematopoiesis and blood disorders.

Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data

MOTIVATION

Gene co-expression measurements are widely used in computational biology to identify coordinated expression patterns across a group of samples. Coordinated expression of genes may indicate that they are controlled by the same transcriptional regulatory program, or involved in common biological processes. Gene co-expression is generally estimated from RNA-Sequencing data, which are commonly normalized to remove technical variability. Here, we demonstrate that certain normalization methods, in particular quantile-based methods, can introduce false-positive associations between genes. These false-positive associations can consequently hamper downstream co-expression network analysis. Quantile-based normalization can, however, be extremely powerful. In particular, when preprocessing large-scale heterogeneous data, quantile-based normalization methods such as smooth quantile normalization can be applied to remove technical variability while maintaining global differences in expression for samples with different biological attributes.

RESULTS

We developed SNAIL (Smooth-quantile Normalization Adaptation for the Inference of co-expression Links), a normalization method based on smooth quantile normalization specifically designed for modeling of co-expression measurements. We show that SNAIL avoids formation of false-positive associations in co-expression as well as in downstream network analyses. Using SNAIL, one can avoid arbitrary gene filtering and retain associations to genes that only express in small subgroups of samples. This highlights the method's potential future impact on network modeling and other association-based approaches in large-scale heterogeneous data.

AVAILABILITY AND IMPLEMENTATION

The implementation of the SNAIL algorithm and code to reproduce the analyses described in this work can be found in the GitHub repository https://github.com/kuijjerlab/PySNAIL.

Autocrine 17-β-Estradiol/Estrogen Receptor-α Loop Determines the Response to Immune Checkpoint Inhibitors in Non-Small Cell Lung Cancer

PURPOSE

The response to immune checkpoint inhibitors (ICI) often differs between genders in non-small cell lung cancer (NSCLC), but metanalyses results are controversial, and no clear mechanisms are defined. We aim at clarifying the molecular circuitries explaining the differential gender-related response to anti-PD-1/anti-PD-L1 agents in NSCLC.

EXPERIMENTAL DESIGN

We prospectively analyzed a cohort of patients with NSCLC treated with ICI as a first-line approach, and we identified the molecular mechanisms determining the differential efficacy of ICI in 29 NSCLC cell lines of both genders, recapitulating patients' phenotype. We validated new immunotherapy strategies in mice bearing NSCLC patient-derived xenografts and human reconstituted immune system ("immune-PDXs").

RESULTS

In patients, we found that estrogen receptor α (ERα) was a predictive factor of response to pembrolizumab, stronger than gender and PD-L1 levels, and was directly correlated with PD-L1 expression, particularly in female patients. ERα transcriptionally upregulated CD274/PD-L1 gene, more in females than in males. This axis was activated by 17-β-estradiol, autocrinely produced by intratumor aromatase, and by the EGFR-downstream effectors Akt and ERK1/2 that activated ERα. The efficacy of pembrolizumab in immune-PDXs was significantly improved by the aromatase inhibitor letrozole, which reduced PD-L1 and increased the percentage of antitumor CD8+T-lymphocytes, NK cells, and Vγ9Vδ2 T-lymphocytes, producing durable control and even tumor regression after continuous administration, with maximal benefit in 17-β-estradiol/ERα highfemale immune-xenografts.

CONCLUSIONS

Our work unveils that 17-β-estradiol/ERα status predicts the response to pembrolizumab in patients with NSCLC. Second, we propose aromatase inhibitors as new gender-tailored immune-adjuvants in NSCLC. See related commentary by Valencia et al., p. 3832.

Demographic confounders distort inference of gene regulatory and gene co-expression networks in cancer

Gene regulatory networks (GRNs) and gene co-expression networks (GCNs) allow genome-wide exploration of molecular regulation patterns in health and disease. The standard approach for obtaining GRNs and GCNs is to infer them from gene expression data, using computational network inference methods. However, since network inference methods are usually applied on aggregate data, distortion of the networks by demographic confounders might remain undetected, especially because gene expression patterns are known to vary between different demographic groups. In this paper, we present a computational framework to systematically evaluate the influence of demographic confounders on network inference from gene expression data. Our framework compares similarities between networks inferred for different demographic groups with similarity distributions obtained for random splits of the expression data. Moreover, it allows to quantify to which extent demographic groups are represented by networks inferred from the aggregate data in a confounder-agnostic way. We apply our framework to test four widely used GRN and GCN inference methods as to their robustness w. r. t. confounding by age, ethnicity and sex in cancer. Our findings based on more than $ {44000}$ inferred networks indicate that age and sex confounders play an important role in network inference for certain cancer types, emphasizing the importance of incorporating an assessment of the effect of demographic confounders into network inference workflows. Our framework is available as a Python package on GitHub: https://github.com/bionetslab/grn-confounders.

Heterogeneity in the gene regulatory landscape of leiomyosarcoma

Characterizing inter-tumor heterogeneity is crucial for selecting suitable cancer therapy, as the presence of diverse molecular subgroups of patients can be associated with disease outcome or response to treatment. While cancer subtypes are often characterized by differences in gene expression, the mechanisms driving these differences are generally unknown. We set out to model the regulatory mechanisms driving sarcoma heterogeneity based on patient-specific, genome-wide gene regulatory networks. We developed a new computational framework, PORCUPINE, which combines knowledge on biological pathways with permutation-based network analysis to identify pathways that exhibit significant regulatory heterogeneity across a patient population. We applied PORCUPINE to patient-specific leiomyosarcoma networks modeled on data from The Cancer Genome Atlas and validated our results in an independent dataset from the German Cancer Research Center. PORCUPINE identified 37 heterogeneously regulated pathways, including pathways representing potential targets for treatment of subgroups of leiomyosarcoma patients, such as FGFR and CTLA4 inhibitory signaling. We validated the detected regulatory heterogeneity through analysis of networks and chromatin states in leiomyosarcoma cell lines. We showed that the heterogeneity identified with PORCUPINE is not associated with methylation profiles or clinical features, thereby suggesting an independent mechanism of patient heterogeneity driven by the complex landscape of gene regulatory interactions.

Considering Sex and Gender in Therapeutics throughout the Product Life Cycle: A Narrative Review and Case Study of Gilteritinib

Background

Biological sex-related factors influence pharmacokinetic, pharmacodynamic, and disease processes that may affect the predictability of drug dosing and adverse effects, which may in turn have clinical consequences for patients' lives. Nonetheless, sex-related factors are not always taken into account in clinical trial design or clinical decision-making, for multiple reasons, including a paucity of studies that clearly and objectively study and measure sex-disaggregated and sex-related outcomes, as well as gaps in regulatory and policy structures for integrating these considerations.

Objectives

To complete a narrative review and use a case study to understand available evidence, inform future research, and provide policy considerations that incorporate information on sex- and gender-related factors into clinician-facing resources.

Methods

A comprehensive review of available literature was conducted using a sex- and gender-based analysis plus (SGBA Plus) approach to identify sex- and/or gender-disaggregated information for gilteritinib, a chemotherapeutic agent. Systematic searches were performed in MEDLINE (Ovid), Embase (Ovid), CENTRAL (Wiley), International Pharmaceutical Abstracts (Ovid), Scopus, and ClinicalTrials.gov, from inception to March 18, 2021. The information was then summarized and compared with the Canadian product monograph for this drug.

Results

Of 311 records screened, 3 provided SGBA Plus information as a component of outcomes, rather than just as categories or demographic characteristics. Of these, 2 were case studies, and 1 was a clinical trial. No studies from the ClinicalTrials.gov database that were in progress at the time of this review provided details about sex-disaggregated outcomes. The Canadian product monograph did not include sex-disaggregated outcome data.

Conclusions

The available evidence from clinical trials, other published literature, and guidance documents does not provide details about sex-disaggregated outcomes for gilteritinib. This paucity of available evidence may create a challenge for clinicians who are making decisions about the efficacy and safety of prescribed therapies in sex-specific populations that have not been well studied.

Accounting for sex differences as a continuous variable in cancer treatments

The significant sex differences that exist in cancer mechanisms, incidence, and survival, have yet to impact clinical practice. We propose that one barrier to translation is that sex differences in cancer phenotypes resemble sex differences in height: highly overlapping, but distinct, male and female population distributions that vary continuously between female- and male- skewed extremes. A consequence of this variance is that sex-specific treatments are rendered unrealistic, and our translational goal should be adaptation of treatment to the variable sex-effect on targetable pathways. To develop a tool that could advance this goal, we applied a Bayesian Nearest Neighbor (BNN) analysis to 8370 cancer transcriptomes from 26 different adult and 4 different pediatric cancer types to establish patient-specific Transcriptomic Indices (TI). TI precisely positions a patient's whole transcriptome on axes of mechanistic phenotypes like cell cycle signaling and immunity that exhibit skewing in the cancer population relative to sex-identified extremes (poles). Importantly, the TI approach reveals that even when TI values are identical, underlying mechanisms in male and female individuals can differ in identifiable ways. Thus, cancer type, patient sex, and TI value provides a novel and patient- specific mechanistic identifier that can be used for precision cancer treatment planning.

STGRNS: an interpretable transformer-based method for inferring gene regulatory networks from single-cell transcriptomic data

MOTIVATION

Single-cell RNA-sequencing (scRNA-seq) technologies provide an opportunity to infer cell-specific gene regulatory networks (GRNs), which is an important challenge in systems biology. Although numerous methods have been developed for inferring GRNs from scRNA-seq data, it is still a challenge to deal with cellular heterogeneity.

RESULTS

To address this challenge, we developed an interpretable transformer-based method namely STGRNS for inferring GRNs from scRNA-seq data. In this algorithm, gene expression motif technique was proposed to convert gene pairs into contiguous sub-vectors, which can be used as input for the transformer encoder. By avoiding missing phase-specific regulations in a network, gene expression motif can improve the accuracy of GRN inference for different types of scRNA-seq data. To assess the performance of STGRNS, we implemented the comparative experiments with some popular methods on extensive benchmark datasets including 21 static and 27 time-series scRNA-seq dataset. All the results show that STGRNS is superior to other comparative methods. In addition, STGRNS was also proved to be more interpretable than "black box" deep learning methods, which are well-known for the difficulty to explain the predictions clearly.

AVAILABILITY AND IMPLEMENTATION

The source code and data are available at https://github.com/zhanglab-wbgcas/STGRNS.

Sex-Specific Silica Nanoparticle Protein Corona Compositions Exposed to Male and Female BALB/c Mice Plasmas

As various nanoparticles (NPs) are increasingly being used in nanomedicine products for more effective and less toxic therapy and diagnosis of diseases, there is a growing need to understand their biological fate in different sexes. Herein, we report a proof-of-concept result of sex-specific protein corona compositions on the surface of silica NPs as a function of their size and porosity upon incubation with plasma proteins of female and male BALB/c mice. Our results demonstrate substantial differences between male and female protein corona profiles on the surface of silica nanoparticles. By comparing protein abundances between male and female protein coronas of mesoporous silica nanoparticles and Stöber silica nanoparticles of ∼100, 50, and 100 nm in diameter, respectively, we detected 17, 4, and 4 distinct proteins, respectively, that were found at significantly different concentrations for these constructs. These initial findings demonstrate that animal sex can influence protein corona formation on silica NPs as a function of the physicochemical properties. A more thorough consideration of the role of plasma sex would enable nanomedicine community to design and develop safer and more efficient diagnostic and therapeutic nanomedicine products for both sexes.

Physiological and molecular sex differences in human skeletal muscle in response to exercise training

Sex differences in exercise physiology, such as substrate metabolism and skeletal muscle fatigability, stem from inherent biological factors, including endogenous hormones and genetics. Studies investigating exercise physiology frequently include only males or do not take sex differences into consideration. Although there is still an underrepresentation of female participants in exercise research, existing studies have identified sex differences in physiological and molecular responses to exercise training. The observed sex differences in exercise physiology are underpinned by the sex chromosome complement, sex hormones and, on a molecular level, the epigenome and transcriptome. Future research in the field should aim to include both sexes, control for menstrual cycle factors, conduct large-scale and ethnically diverse studies, conduct meta-analyses to consolidate findings from various studies, leverage unique cohorts (such as post-menopausal, transgender, and those with sex chromosome abnormalities), as well as integrate tissue and cell-specific -omics data. This knowledge is essential for developing deeper insight into sex-specific physiological responses to exercise training, thus directing future exercise physiology studies and practical application.

Deep learning identifies explainable reasoning paths of mechanism of action for drug repurposing from multilayer biological network

The discovery and repurposing of drugs require a deep understanding of the mechanism of drug action (MODA). Existing computational methods mainly model MODA with the protein-protein interaction (PPI) network. However, the molecular interactions of drugs in the human body are far beyond PPIs. Additionally, the lack of interpretability of these models hinders their practicability. We propose an interpretable deep learning-based path-reasoning framework (iDPath) for drug discovery and repurposing by capturing MODA on by far the most comprehensive multilayer biological network consisting of the complex high-dimensional molecular interactions between genes, proteins and chemicals. Experiments show that iDPath outperforms state-of-the-art machine learning methods on a general drug repurposing task. Further investigations demonstrate that iDPath can identify explicit critical paths that are consistent with clinical evidence. To demonstrate the practical value of iDPath, we apply it to the identification of potential drugs for treating prostate cancer and hypertension. Results show that iDPath can discover new FDA-approved drugs. This research provides a novel interpretable artificial intelligence perspective on drug discovery.

De novo serine biosynthesis from glucose predicts sex-specific response to antifolates in non-small cell lung cancer cell lines

Lung cancer is the leading cause of cancer-related death. Intriguingly, males with non-small cell lung cancer (NSCLC) have a higher mortality rate than females. Here, we investigated the role of serine metabolism as a predictive marker for sensitivity to the antifolate pemetrexed in male and female NSCLC cell lines. Using [13C6] glucose tracing in NSCLC cell lines, we found that a subset of male cells generated significantly more serine from glucose than female cells. Higher serine biosynthesis was further correlated with increased sensitivity to pemetrexed in male cells only. Concordant sex differences in metabolic gene expression were evident in NSCLC and pan-cancer transcriptome datasets, suggesting a potential mechanism with wide-reaching applicability. These data were further validated by integrating antifolate drug cytotoxicity and metabolic pathway transcriptome data from pan-cancer cell lines. Together, these findings highlight the importance of considering sex differences in cancer metabolism to improve treatment for all patients.

Western-Style Diet, pks Island-Carrying Escherichia coli, and Colorectal Cancer: Analyses From Two Large Prospective Cohort Studies

BACKGROUND & AIMS

Evidence supports a carcinogenic role of Escherichia coli carrying the pks island that encodes enzymes for colibactin biosynthesis. We hypothesized that the association of the Western-style diet (rich in red and processed meat) with colorectal cancer incidence might be stronger for tumors containing higher amounts of pks+E coli.

METHODS

Western diet score was calculated using food frequency questionnaire data obtained every 4 years during follow-up of 134,775 participants in 2 United States-wide prospective cohort studies. Using quantitative polymerase chain reaction, we measured pks+E coli DNA in 1175 tumors among 3200 incident colorectal cancer cases that had occurred during the follow-up. We used the 3200 cases and inverse probability weighting (to adjust for selection bias due to tissue availability), integrated in multivariable-adjusted duplication-method Cox proportional hazards regression analyses.

RESULTS

The association of the Western diet score with colorectal cancer incidence was stronger for tumors containing higher levels of pks+E coli (Pheterogeneity = .014). Multivariable-adjusted hazard ratios (with 95% confidence interval) for the highest (vs lowest) tertile of the Western diet score were 3.45 (1.53-7.78) (Ptrend = 0.001) for pks+E coli-high tumors, 1.22 (0.57-2.63) for pks+E coli-low tumors, and 1.10 (0.85-1.42) for pks+E coli-negative tumors. The pks+E coli level was associated with lower disease stage but not with tumor location, microsatellite instability, or BRAF, KRAS, or PIK3CA mutations.

CONCLUSIONS

The Western-style diet is associated with a higher incidence of colorectal cancer containing abundant pks+E coli, supporting a potential link between diet, the intestinal microbiota, and colorectal carcinogenesis.

Sex differences in gene regulatory networks during mid-gestational brain development

Neurodevelopmental disorders differ considerably between males and females, and fetal brain development is one of the most critical periods to determine risk for these disorders. Transcriptomic studies comparing male and female fetal brain have demonstrated that the highest difference in gene expression occurs in sex chromosomes, but several autossomal genes also demonstrate a slight difference that has not been yet explored. In order to investigate biological pathways underlying fetal brain sex differences, we applied medicine network principles using integrative methods such as co-expression networks (CEMiTool) and regulatory networks (netZoo). The pattern of gene expression from genes in the same pathway tend to reflect biologically relevant phenomena. In this study, network analysis of fetal brain expression reveals regulatory differences between males and females. Integrating two different bioinformatics tools, our results suggest that biological processes such as cell cycle, cell differentiation, energy metabolism and extracellular matrix organization are consistently sex-biased. MSET analysis demonstrates that these differences are relevant to neurodevelopmental disorders, including autism.

Connections for Matters of the Heart: Network Medicine in Cardiovascular Diseases

Cardiovascular diseases (CVD) are diverse disorders affecting the heart and vasculature in millions of people worldwide. Like other fields, CVD research has benefitted from the deluge of multiomics biomedical data. Current CVD research focuses on disease etiologies and mechanisms, identifying disease biomarkers, developing appropriate therapies and drugs, and stratifying patients into correct disease endotypes. Systems biology offers an alternative to traditional reductionist approaches and provides impetus for a comprehensive outlook toward diseases. As a focus area, network medicine specifically aids the translational aspect of in silico research. This review discusses the approach of network medicine and its application to CVD research.

A novel network based linear model for prioritization of synergistic drug combinations

Drug combination therapies can improve drug efficacy, reduce drug dosage, and overcome drug resistance in cancer treatments. Current research strategies to determine which drug combinations have a synergistic effect rely mainly on clinical or empirical experience and screening predefined pools of drugs. Given the number of possible drug combinations, the speed, and scope to find new drug combinations are very limited using these methods. Due to the exponential growth in the number of drug combinations, it is difficult to test all possible combinations in the lab. There are several large-scale public genomic and phenotypic resources that provide data from single drug-treated cells as well as data from small molecule treated cells. These databases provide a wealth of information regarding cellular responses to drugs and offer an opportunity to overcome the limitations of the current methods. Developing a new advanced data processing and analysis strategy is imperative and a computational prediction algorithm is highly desirable. In this paper, we developed a computational algorithm for the enrichment of synergistic drug combinations using gene regulatory network knowledge and an operational module unit (OMU) system which we generate from single drug genomic and phenotypic data. As a proof of principle, we applied the pipeline to a group of anticancer drugs and demonstrate how the algorithm could help researchers efficiently find possible synergistic drug combinations using single drug data to evaluate all possible drug pairs.

Considerations and challenges for sex-aware drug repurposing

Sex differences are essential factors in disease etiology and manifestation in many diseases such as cardiovascular disease, cancer, and neurodegeneration [33]. The biological influence of sex differences (including genomic, epigenetic, hormonal, immunological, and metabolic differences between males and females) and the lack of biomedical studies considering sex differences in their study design has led to several policies. For example, the National Institute of Health's (NIH) sex as a biological variable (SABV) and Sex and Gender Equity in Research (SAGER) policies to motivate researchers to consider sex differences [204]. However, drug repurposing, a promising alternative to traditional drug discovery by identifying novel uses for FDA-approved drugs, lacks sex-aware methods that can improve the identification of drugs that have sex-specific responses [7, 11, 14, 33]. Sex-aware drug repurposing methods either select drug candidates that are more efficacious in one sex or deprioritize drug candidates based on if they are predicted to cause a sex-bias adverse event (SBAE), unintended therapeutic effects that are more likely to occur in one sex. Computational drug repurposing methods are encouraging approaches to develop for sex-aware drug repurposing because they can prioritize sex-specific drug candidates or SBAEs at lower cost and time than traditional drug discovery. Sex-aware methods currently exist for clinical, genomic, and transcriptomic information [1, 7, 155]. They have not expanded to other data types, such as DNA variation, which has been beneficial in other drug repurposing methods that do not consider sex [114]. Additionally, some sex-aware methods suffer from poorer performance because a disproportionate number of male and female samples are available to train computational methods [7]. However, there is development potential for several different categories (i.e., data mining, ligand binding predictions, molecular associations, and networks). Low-dimensional representations of molecular association and network approaches are also especially promising candidates for future sex-aware drug repurposing methodologies because they reduce the multiple hypothesis testing burden and capture sex-specific variation better than the other methods [151, 159]. Here we review how sex influences drug response, the current state of drug repurposing including with respect to sex-bias drug response, and how model organism study design choices influence drug repurposing validation.

Individual-specific networks for prediction modelling - A scoping review of methods

BACKGROUND

Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space.

METHODS

We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase.

RESULTS

Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction.

CONCLUSION

The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling.

gpuZoo: Cost-effective estimation of gene regulatory networks using the Graphics Processing Unit

Gene regulatory network inference allows for the modeling of genome-scale regulatory processes that are altered during development, in disease, and in response to perturbations. Our group has developed a collection of tools to model various regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, as well as gene regulation in individual samples (LIONESS). These methods work by postulating a network structure and then optimizing that structure to be consistent with multiple lines of biological evidence through repeated operations on data matrices. Although our methods are widely used, the corresponding computational complexity, and the associated costs and run times, do limit some applications. To improve the cost/time performance of these algorithms, we developed gpuZoo which implements GPU-accelerated calculations, dramatically improving the performance of these algorithms. The runtime of the gpuZoo implementation in MATLAB and Python is up to 61 times faster and 28 times less expensive than multi-core CPU implementation of the same methods. gpuZoo is available in MATLAB through the netZooM package https://github.com/netZoo/netZooM and in Python through the netZooPy package https://github.com/netZoo/netZooPy.

Predicting genotype-specific gene regulatory networks

Understanding how each person's unique genotype influences their individual patterns of gene regulation has the potential to improve our understanding of human health and development, and to refine genotype-specific disease risk assessments and treatments. However, the effects of genetic variants are not typically considered when constructing gene regulatory networks, despite the fact that many disease-associated genetic variants are thought to have regulatory effects, including the disruption of transcription factor (TF) binding. We developed EGRET (Estimating the Genetic Regulatory Effect on TFs), which infers a genotype-specific gene regulatory network for each individual in a study population. EGRET begins by constructing a genotype-informed TF-gene prior network derived using TF motif predictions, expression quantitative trait locus (eQTL) data, individual genotypes, and the predicted effects of genetic variants on TF binding. It then uses a technique known as message passing to integrate this prior network with gene expression and TF protein–protein interaction data to produce a refined, genotype-specific regulatory network. We used EGRET to infer gene regulatory networks for two blood-derived cell lines and identified genotype-associated, cell line–specific regulatory differences that we subsequently validated using allele-specific expression, chromatin accessibility QTLs, and differential ChIP-seq TF binding. We also inferred EGRET networks for three cell types from each of 119 individuals and identified cell type–specific regulatory differences associated with diseases related to those cell types. EGRET is, to our knowledge, the first method that infers networks reflective of individual genetic variation in a way that provides insight into the genetic regulatory associations driving complex phenotypes.

The spectrum of sex differences in cancer

Sex differences in cellular and systems biology have been evolutionarily selected to optimize reproductive success in all species with little (sperm) and big (ova) gamete producers. They are evident from the time of fertilization and accrue throughout development through genetic, epigenetic, and circulating sex hormone-dependent mechanisms. Among other effects, they significantly impact on chromatin organization, metabolism, cell cycle regulation, immunity, longevity, and cancer risk and survival. Sex differences in cancer should be expected and accounted for in basic, translational, and clinical oncology research.

GRAND: a database of gene regulatory network models across human conditions

Gene regulation plays a fundamental role in shaping tissue identity, function, and response to perturbation. Regulatory processes are controlled by complex networks of interacting elements, including transcription factors, miRNAs and their target genes. The structure of these networks helps to determine phenotypes and can ultimately influence the development of disease or response to therapy. We developed GRAND (https://grand.networkmedicine.org) as a database for computationally-inferred, context-specific gene regulatory network models that can be compared between biological states, or used to predict which drugs produce changes in regulatory network structure. The database includes 12 468 genome-scale networks covering 36 human tissues, 28 cancers, 1378 unperturbed cell lines, as well as 173 013 TF and gene targeting scores for 2858 small molecule-induced cell line perturbation paired with phenotypic information. GRAND allows the networks to be queried using phenotypic information and visualized using a variety of interactive tools. In addition, it includes a web application that matches disease states to potentially therapeutic small molecule drugs using regulatory network properties.

Evaluation of Sex-Aware PrediXcan Models for Predicting Gene Expression

Gene-based methods such as PrediXcan use expression quantitative trait loci to build tissue-specific gene expression models when only genetic data is available. There are known sex differences in tissue-specific gene expression and in the genetic architecture of gene expression, but such differences have not been incorporated into predicted gene expression models to date. We built sex-aware PrediXcan models using whole blood transcriptomic data from the Genotype-Tissue Expression (GTEx) project (195 females and 371 males) and evaluated their performance in an independent dataset. Specifically, PrediXcan models were built following the method described in Gamazon et al. 2015, but we included both whole-sample and sex-specific models. Validation was evaluated leveraging lymphoblast RNA sequencing data from the EUR cohort of the 1000 Genomes Project (178 females and 171 males). Correlations (R2) between observed and predicted expression were evaluated in 5,283 autosomal genes to determine performance of models. In sum, we successfully predicted 1,149 genes in males and 623 in females, while 3,511 genes appeared to be not sex-specific. Of the sex-specific genes, 15% (189 genes in males and 73 genes in females) exhibited higher R2 in sex-specific models compared to whole-sample models, although the overall gain in predictive power was generally minimal and well within measurement error. Nevertheless, two female-specific genes and six male-specific genes showed significantly better prediction when using the sex-specific weights versus the whole-sample weights; furthermore, several of these genes play a role in mitochondrial metabolism, which is known to be influenced by sex hormones. Taken together, these results support previous reports of the small contribution of genetic architecture to sex-specific expression. Still, sex-aware PrediXcan models were able to provide robust sex-specific prediction signals. Future studies exploring the contribution of the X chromosome and tissue specificity on sex-specific genetically regulated expression will clarify the utility of this method.

Effects of novel SGLT2 inhibitors on cancer incidence in hyperglycemic patients: a meta-analysis of randomized clinical trials

Epidemiological evidence shows that diabetic patients have an increased cancer risk and a higher mortality rate. Glucose could play a central role in metabolism and growth of many tumor types, and this possible mechanism is supported by the high rate of glucose demand and uptake in cancer. Thus, growing evidence suggests that hyperglycemia contributes to cancer progression but also to its onset. Many mechanisms underlying this association have been hypothesized, such as insulin resistance, hyperinsulinemia, and increased inflammatory processes. Inflammation is a common pathophysiological feature in both diabetic and oncological patients, and inflammation linked to high glucose levels sensitizes microenvironment to tumorigenesis, promoting the development of malignant lesions by altering and sustaining a pathological condition in tissues. Glycemic control is the first goal of antidiabetic therapy, and glucose level reduction has also been associated with favorable outcomes in cancer. Here, we describe key events in carcinogenesis focusing on hyperglycemia as supporter in tumor progression and in particular, related to the role of a specific hypoglycemic drug class, sodium-glucose linked transporters (SGLTs). We also discuss the use of SGLT2 inhibitors as a novel potential cancer therapy. Our meta-analysis showed that SGLT-2 inhibitors were significantly associated with an overall reduced risk of cancer as compared to placebo (RR = 0.35, CI 0.33-0.37, P = 0. 00) with a particular effectiveness for dapaglifozin and ertuglifozin (RR = 0. 06, CI 0. 06-0. 07 and RR = 0. 22, CI 0. 18-0. 26, respectively). Network Medicine approaches may advance the possible repurposing of these drugs in patients with concomitant diabetes and cancer.

Constructing gene regulatory networks using epigenetic data

The biological processes that drive cellular function can be represented by a complex network of interactions between regulators (transcription factors) and their targets (genes). A cell's epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, how to effectively use epigenetic data when constructing a gene regulatory network remains an open question. Almost all existing network reconstruction approaches focus on estimating transcription factor to gene connections using transcriptomic data. In contrast, computational approaches for analyzing epigenetic data generally focus on improving transcription factor binding site predictions rather than deducing regulatory network relationships. We bridged this gap by developing SPIDER, a network reconstruction approach that incorporates epigenetic data into a message-passing framework to estimate gene regulatory networks. We validated SPIDER's predictions using ChIP-seq data from ENCODE and found that SPIDER networks are both highly accurate and include cell-line-specific regulatory interactions. Notably, SPIDER can recover ChIP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. The networks estimated by SPIDER have the potential to identify novel hypotheses that will allow us to better characterize cell-type and phenotype specific regulatory mechanisms.

Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme

Glioblastoma is an aggressive cancer of the brain and spine. While analysis of glioblastoma 'omics data has somewhat improved our understanding of the disease, it has not led to direct improvement in patient survival. Cancer survival is often characterized by differences in gene expression, but the mechanisms that drive these differences are generally unknown. We therefore set out to model the regulatory mechanisms associated with glioblastoma survival. We inferred individual patient gene regulatory networks using data from two different expression platforms from The Cancer Genome Atlas. We performed comparative network analysis between patients with long- and short-term survival. Seven pathways were identified as associated with survival, all of them involved in immune signaling; differential regulation of PD1 signaling was validated to correspond with outcome in an independent dataset from the German Glioma Network. In this pathway, transcriptional repression of genes for which treatment options are available was lost in short-term survivors; this was independent of mutational burden and only weakly associated with T-cell infiltration. Collectively, these results provide a new way to stratify patients with glioblastoma that uses network features as biomarkers to predict survival. They also identify new potential therapeutic interventions, underscoring the value of analyzing gene regulatory networks in individual patients with cancer. SIGNIFICANCE: Genome-wide network modeling of individual glioblastomas identifies dysregulation of PD1 signaling in patients with poor prognosis, indicating this approach can be used to understand how gene regulation influences cancer progression.

The role of sex as a biological variable in the efficacy and toxicity of therapeutic nanomedicine

Males and females have physiological, hormonal, and genetic differences that can cause different responses to medicinal treatments. The role of sex in the pharmacokinetics and pharmacodynamics of drugs is well established in the literature. However, researchers have yet to robustly and consistently consider the impact of sex differences on the pharmacokinetics and pharmacodynamics of nanomedicine formulations when designing nanomedicine therapeutics and/or constructing clinical trials. In this review, we highlight the physiological and anatomical differences between sexes and discuss how these differences can influence the therapeutic efficacy, side effects, and drug delivery safety of nanomedicine products. A deep understanding of the effects of sex on nano-based drug delivery agents will robustly improve the risk assessment process, resulting in safer formulations, successful clinical translation, and improved therapeutic efficacies for both sexes.

Sexually dimorphic production of interleukin-6 in respiratory disease

Diverging susceptibility and severity in respiratory diseases is prevalent between males and females. Sex hormones have inconclusively been attributed as the cause of these differences, however, strong evidence exists promoting genetic factors leading to sexual dimorphism. As such, we investigate differential proinflammatory cytokine (interleukin (IL)‐6 and CXCL8) release from TNF‐α stimulated primary human lung fibroblasts in vitro. We present, for the first time, in vitro evidence supporting clinical findings of differential production of IL‐6 between males and females across various respiratory diseases. IL‐6 was found to be produced approximately two times more from fibroblasts derived from females compared to males. As such we demonstrate sexual dimorphism in cytokine production of IL‐6 outside the context of biological factors in the human body. As such, our data highlight that differences exist between males and females in the absence of sex hormones. We, for the first time, demonstrate inherent in vitro differences exist between males and females in pulmonary fibroblasts.

Sex disparities matter in cancer development and therapy

Curing cancer through precision medicine is the paramount aim of the new wave of molecular and genomic therapies. Currently, whether patients with non-reproductive cancers are male or female according to their sex chromosomes is not adequately considered in patient standard of care. This is a matter of consequence because there is growing evidence that these cancer types generally initiate earlier and are associated with higher overall incidence and rates of death in males compared with females. Gender, in contrast to sex, refers to a chosen sexual identity. Hazardous lifestyle choices (notably tobacco smoking) differ in prevalence between genders, aligned with disproportionate cancer risk. These add to underlying genetic predisposition and influences of sex steroid hormones. Together, these factors affect metabolism, immunity and inflammation, and ultimately the fidelity of the genetic code. To accurately understand how human defences against cancer erode, it is crucial to establish the influence of sex. Our Perspective highlights evidence from basic and translational research indicating that including genetic sex considerations in treatments for patients with cancer will improve outcomes. It is now time to adopt the challenge of overhauling cancer medicine based on optimized treatment strategies for females and males.

Sex as an important factor in nanomedicine

Nanomedicine has demonstrated substantial potential to improve the quality and efficacy of healthcare systems. Although the promise of nanomedicine to transform conventional medicine is evident, significant numbers of therapeutic nanomedicine products have failed in clinical trials. Most studies in nanomedicine have overlooked several important factors, including the significance of sex differences at various physiological levels. This report attempts to highlight the importance of sex in nanomedicine at cellular and molecular level. A more thorough consideration of sex physiology, among other critical variations (e.g., health status of individuals), would enable researchers to design and develop safer and more-efficient sex-specific diagnostic and therapeutic nanomedicine products.

Gene Targeting in Disease Networks

Profiling of whole transcriptomes has become a cornerstone of molecular biology and an invaluable tool for the characterization of clinical phenotypes and the identification of disease subtypes. Analyses of these data are becoming ever more sophisticated as we move beyond simple comparisons to consider networks of higher-order interactions and associations. Gene regulatory networks (GRNs) model the regulatory relationships of transcription factors and genes and have allowed the identification of differentially regulated processes in disease systems. In this perspective, we discuss gene targeting scores, which measure changes in inferred regulatory network interactions, and their use in identifying disease-relevant processes. In addition, we present an example analysis for pancreatic ductal adenocarcinoma (PDAC), demonstrating the power of gene targeting scores to identify differential processes between complex phenotypes, processes that would have been missed by only performing differential expression analysis. This example demonstrates that gene targeting scores are an invaluable addition to gene expression analysis in the characterization of diseases and other complex phenotypes.

Female Immunity Protects from Cutaneous Squamous Cell Carcinoma

PURPOSE

Cancer susceptibility and mortality are higher in males, and the mutational and transcriptomic landscape of cancer differs by sex. The current assumption is that men are at higher risk of epithelial cancers as they expose more to carcinogens and accumulate more damage than women. We present data showing women present with less aggressive primary cutaneous squamous cell carcinoma (cSCC) and early strong immune activation.

EXPERIMENTAL DESIGN

We explored clinical and molecular sexual disparity in immunocompetent and immunosuppressed patients with primary cSCC (N = 738, N = 160), advanced-stage cSCC (N = 63, N = 20) and FVB/N mice exposed to equal doses of DMBA, as well as in human keratinocytes by whole-exome, bulk, and single-cell RNA sequencing.

RESULTS

We show cSCC is more aggressive in men, and immunocompetent women develop mild cSCC, later in life. To test whether sex drives disparity, we exposed male and female mice to equal doses of carcinogen, and found males present with more aggressive, metastatic cSCC than females. Critically, females activate cancer immune-related expression pathways and CD4 and CD8 T-cell infiltration independently of mutations, a response that is absent in prednisolone-treated animals. In contrast, males increase the rate of mitosis and proliferation in response to carcinogen. Women's skin and keratinocytes also activate immune-cancer fighting pathways and immune cells at UV radiation-damaged sites. Critically, a compromised immune system leads to high-risk, aggressive cSCC specifically in women.

CONCLUSIONS

This work shows the immune response is sex biased in cSCC and highlights female immunity offers greater protection than male immunity.

Clinical Efficacy and Safety of Angiogenesis Inhibitors: Sex Differences and Current Challenges

Vasoactive molecules, such as vascular endothelial growth factor (VEGF) and endothelins, share cytokine-like activities and regulate endothelial cell (EC) growth, migration and inflammation. Some endothelial mediators and their receptors are targets for currently approved angiogenesis inhibitors, drugs that are either monoclonal antibodies raised towards VEGF, or inhibitors of vascular receptor protein kinases and signaling pathways. Pharmacological interference with the protective functions of ECs results in a similar spectrum of adverse effects. Clinically, the most common side effects of VEGF signaling pathway inhibition include an increase in arterial pressure, left ventricular (LV) dysfunction ultimately causing heart failure, and thromboembolic events, including pulmonary embolism, stroke, and myocardial infarction. Sex steroids such as androgens, progestins, and estrogen and their receptors (ERα, ERβ, GPER; PR-A, PR-B; AR) have been identified as important modifiers of angiogenesis, and sex differences have been reported for anti-angiogenic drugs. This review article discusses the current challenges clinicians are facing with regard to angiogenesis inhibitor treatments, including the need to consider sex differences affecting clinical efficacy and safety. We also propose areas for future research taking into account the role of sex hormone receptors and sex chromosomes. Development of new sex-specific drugs with improved target and cell-type selectivity likely will open the way personalized medicine in men and women requiring antiangiogenic therapy and result in reduced adverse effects and improved therapeutic efficacy.

PUMA: PANDA Using MicroRNA Associations

Motivation

Conventional methods to analyze genomic data do not make use of the interplay between multiple factors, such as between microRNAs (miRNAs) and the messenger RNA (mRNA) transcripts they regulate, and thereby often fail to identify the cellular processes that are unique to specific tissues. We developed PUMA (PANDA Using MicroRNA Associations), a computational tool that uses message passing to integrate a prior network of miRNA target predictions with target gene co-expression information to model genome-wide gene regulation by miRNAs. We applied PUMA to 38 tissues from the Genotype-Tissue Expression project, integrating RNA-Seq data with two different miRNA target predictions priors, built on predictions from TargetScan and miRanda, respectively. We found that while target predictions obtained from these two different resources are considerably different, PUMA captures similar tissue-specific miRNA–target regulatory interactions in the different network models. Furthermore, the tissue-specific functions of miRNAs we identified based on regulatory profiles (available at: https://kuijjer.shinyapps.io/puma_gtex/) are highly similar between networks modeled on the two target prediction resources. This indicates that PUMA consistently captures important tissue-specific miRNA regulatory processes. In addition, using PUMA we identified miRNAs regulating important tissue-specific processes that, when mutated, may result in disease development in the same tissue.

Availability and implementation

PUMA is available in C++, MATLAB and Python on GitHub (https://github.com/kuijjerlab and https://netzoo.github.io/).

Supplementary information

Supplementary data are available at Bioinformatics online.