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Article: Genome-Wide Sex and Gender Differences in Cancer.

Lopes-Ramos, Camila M / Quackenbush, John / DeMeo, Dawn L

2020 Volume 10, Page(s) 597788

Abstract: Despite their known importance in clinical medicine, differences based on sex and gender are among the least studied factors affecting cancer susceptibility, progression, survival, and therapeutic response. In particular, the molecular mechanisms driving ...

Abstract	Despite their known importance in clinical medicine, differences based on sex and gender are among the least studied factors affecting cancer susceptibility, progression, survival, and therapeutic response. In particular, the molecular mechanisms driving sex differences are poorly understood and so most approaches to precision medicine use mutational or other genomic data to assign therapy without considering how the sex of the individual might influence therapeutic efficacy. The mandate by the National Institutes of Health that research studies include sex as a biological variable has begun to expand our understanding on its importance. Sex differences in cancer may arise due to a combination of environmental, genetic, and epigenetic factors, as well as differences in gene regulation, and expression. Extensive sex differences occur genome-wide, and ultimately influence cancer biology and outcomes. In this review, we summarize the current state of knowledge about sex-specific genetic and genome-wide influences in cancer, describe how differences in response to environmental exposures and genetic and epigenetic alterations alter the trajectory of the disease, and provide insights into the importance of integrative analyses in understanding the interplay of sex and genomics in cancer. In particular, we will explore some of the emerging analytical approaches, such as the use of network methods, that are providing a deeper understanding of the drivers of differences based on sex and gender. Better understanding these complex factors and their interactions will improve cancer prevention, treatment, and outcomes for all individuals.
Language	English
Publishing date	2020-11-23
Publishing country	Switzerland
Document type	Journal Article ; Review
ZDB-ID	2649216-7
ISSN	2234-943X
ISSN	2234-943X
DOI	10.3389/fonc.2020.597788
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Article ; Online: Adjustment of spurious correlations in co-expression measurements from RNA-Sequencing data.

Hsieh, Ping-Han / Lopes-Ramos, Camila Miranda / Zucknick, Manuela / Sandve, Geir Kjetil / Glass, Kimberly / Kuijjer, Marieke Lydia

Bioinformatics (Oxford, England)

2023 Volume 39, Issue 10

Abstract: 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 ...

Abstract	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.
MeSH term(s)	Gene Expression Profiling/methods ; RNA ; Sequence Analysis, RNA/methods ; Algorithms ; Computational Biology
Chemical Substances	RNA (63231-63-0)
Language	English
Publishing date	2023-10-06
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btad610
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Article: Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma.

Saha, Enakshi / Guebila, Marouen Ben / Fanfani, Viola / Fischer, Jonas / Shutta, Katherine H / Mandros, Panagiotis / DeMeo, Dawn L / Quackenbush, John / Lopes-Ramos, Camila M

bioRxiv : the preprint server for biology

2023

Abstract: 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. Sample-specific ...

Abstract	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. 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. We observe 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, as well as in tumor, and that these regulatory differences are further perturbed by tobacco smoking. We also uncovered significant sex bias in transcription factor targeting patterns of clinically actionable oncogenes and tumor suppressor genes, including
Language	English
Publishing date	2023-09-24
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.09.22.559001
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Article ; Online: Regulatory Network of PD1 Signaling Is Associated with Prognosis in Glioblastoma Multiforme.

Lopes-Ramos, Camila M / Belova, Tatiana / Brunner, Tess H / Ben Guebila, Marouen / Osorio, Daniel / Quackenbush, John / Kuijjer, Marieke L

Cancer research

2021 Volume 81, Issue 21, Page(s) 5401–5412

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Biomarkers, Tumor/genetics ; Biomarkers, Tumor/metabolism ; Brain Neoplasms/genetics ; Brain Neoplasms/immunology ; Brain Neoplasms/metabolism ; Brain Neoplasms/pathology ; Cohort Studies ; Female ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Glioblastoma/genetics ; Glioblastoma/immunology ; Glioblastoma/metabolism ; Glioblastoma/pathology ; Humans ; Lymphocytes, Tumor-Infiltrating/immunology ; Male ; Middle Aged ; Mutation ; Prognosis ; Programmed Cell Death 1 Receptor/genetics ; Programmed Cell Death 1 Receptor/metabolism ; Survival Rate
Chemical Substances	Biomarkers, Tumor ; PDCD1 protein, human ; Programmed Cell Death 1 Receptor
Language	English
Publishing date	2021-09-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1432-1
ISSN	1538-7445 ; 0008-5472
ISSN (online)	1538-7445
ISSN	0008-5472
DOI	10.1158/0008-5472.CAN-21-0730
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Article ; Online: An online notebook resource for reproducible inference, analysis and publication of gene regulatory networks.

Ben Guebila, Marouen / Weighill, Deborah / Lopes-Ramos, Camila M / Burkholz, Rebekka / Pop, Romana T / Palepu, Kalyan / Shapoval, Mia / Fagny, Maud / Schlauch, Daniel / Glass, Kimberly / Altenbuchinger, Michael / Kuijjer, Marieke L / Platig, John / Quackenbush, John

Nature methods

2022 Volume 19, Issue 5, Page(s) 511–513

MeSH term(s)	Algorithms ; Gene Expression Profiling ; Gene Regulatory Networks ; Software
Language	English
Publishing date	2022-04-20
Publishing country	United States
Document type	Letter ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2169522-2
ISSN	1548-7105 ; 1548-7091
ISSN (online)	1548-7105
ISSN	1548-7091
DOI	10.1038/s41592-022-01479-2
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Article ; Online: X chromosome associations with chronic obstructive pulmonary disease and related phenotypes: an X chromosome-wide association study.

Hayden, Lystra P / Hobbs, Brian D / Busch, Robert / Cho, Michael H / Liu, Ming / Lopes-Ramos, Camila M / Lomas, David A / Bakke, Per / Gulsvik, Amund / Silverman, Edwin K / Crapo, James D / Beaty, Terri H / Laird, Nan M / Lange, Christoph / DeMeo, Dawn L

Respiratory research

2023 Volume 24, Issue 1, Page(s) 38

Abstract: Background: The association between genetic variants on the X chromosome to risk of COPD has not been fully explored. We hypothesize that the X chromosome harbors variants important in determining risk of COPD related phenotypes and may drive sex ... ...

Abstract	Background: The association between genetic variants on the X chromosome to risk of COPD has not been fully explored. We hypothesize that the X chromosome harbors variants important in determining risk of COPD related phenotypes and may drive sex differences in COPD manifestations. Methods: Using X chromosome data from three COPD-enriched cohorts of adult smokers, we performed X chromosome specific quality control, imputation, and testing for association with COPD case-control status, lung function, and quantitative emphysema. Analyses were performed among all subjects, then stratified by sex, and subsequently combined in meta-analyses. Results: Among 10,193 subjects of non-Hispanic white or European ancestry, a variant near TMSB4X, rs5979771, reached genome-wide significance for association with lung function measured by FEV Conclusions: This investigation identified loci influencing lung function, COPD, and emphysema in a comprehensive genetic association meta-analysis of X chromosome genetic markers from multiple COPD-related datasets. Sex differences play an important role in the pathobiology of complex lung disease, including X chromosome variants that demonstrate differential effects by sex and variants that may be relevant through escape from X chromosome inactivation. Comprehensive interrogation of the X chromosome to better understand genetic control of COPD and lung function is important to further understanding of disease pathology. Trial registration Genetic Epidemiology of COPD Study (COPDGene) is registered at ClinicalTrials.gov, NCT00608764 (Active since January 28, 2008). Evaluation of COPD Longitudinally to Identify Predictive Surrogate Endpoints Study (ECLIPSE), GlaxoSmithKline study code SCO104960, is registered at ClinicalTrials.gov, NCT00292552 (Active since February 16, 2006). Genetics of COPD in Norway Study (GenKOLS) holds GlaxoSmithKline study code RES11080, Genetics of Chronic Obstructive Lung Disease.
MeSH term(s)	Female ; Male ; Humans ; Genetic Predisposition to Disease/genetics ; Genome-Wide Association Study ; Pulmonary Disease, Chronic Obstructive/diagnosis ; Pulmonary Disease, Chronic Obstructive/epidemiology ; Pulmonary Disease, Chronic Obstructive/genetics ; Pulmonary Emphysema ; Phenotype ; Emphysema ; X Chromosome
Language	English
Publishing date	2023-02-01
Publishing country	England
Document type	Meta-Analysis ; Journal Article
ZDB-ID	2041675-1
ISSN	1465-993X ; 1465-993X
ISSN (online)	1465-993X
ISSN	1465-993X
DOI	10.1186/s12931-023-02337-1
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Article: Bayesian Optimized sample-specific Networks Obtained By Omics data (BONOBO).

Saha, Enakshi / Fanfani, Viola / Mandros, Panagiotis / Ben-Guebila, Marouen / Fischer, Jonas / Hoff-Shutta, Katherine / Glass, Kimberly / DeMeo, Dawn Lisa / Lopes-Ramos, Camila / Quackenbush, John

bioRxiv : the preprint server for biology

2023

Abstract: Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical ... ...

Abstract	Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring co-expression networks is a critical element of GRN inference as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate co-expression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. To address these concerns, we introduce BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data), a scalable Bayesian model for deriving individual sample-specific co-expression networks by recognizing variations in molecular interactions across individuals. For every sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific co-expression matrix constructed from all other samples in the data. Combining the sample-specific gene expression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific co-expression matrices, thus making the method extremely scalable. We demonstrate the utility of BONOBO in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other sample-specific co-expression network inference methods and provides insight into individual differences in the drivers of biological processes.
Language	English
Publishing date	2023-11-17
Publishing country	United States
Document type	Preprint
DOI	10.1101/2023.11.16.567119
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Article ; Online: Correction: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism.

Lopes-Ramos, Camila M / Kuijjer, Marieke L / Ogino, Shuji / Fuchs, Charles S / DeMeo, Dawn L / Glass, Kimberly / Quackenbush, John

Cancer research

2019 Volume 79, Issue 8, Page(s) 2084

Language	English
Publishing date	2019-04-12
Publishing country	United States
Document type	Journal Article ; Published Erratum
ZDB-ID	1432-1
ISSN	1538-7445 ; 0008-5472
ISSN (online)	1538-7445
ISSN	0008-5472
DOI	10.1158/0008-5472.CAN-19-0678
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Article ; Online: GRAND: a database of gene regulatory network models across human conditions.

Ben Guebila, Marouen / Lopes-Ramos, Camila M / Weighill, Deborah / Sonawane, Abhijeet Rajendra / Burkholz, Rebekka / Shamsaei, Behrouz / Platig, John / Glass, Kimberly / Kuijjer, Marieke L / Quackenbush, John

Nucleic acids research

2021 Volume 50, Issue D1, Page(s) D610–D621

Abstract: 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. ... ...

Abstract	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.
MeSH term(s)	Databases, Genetic ; Databases, Pharmaceutical ; Gene Expression Regulation/genetics ; Gene Regulatory Networks/genetics ; Genome, Human/genetics ; Humans ; MicroRNAs/classification ; MicroRNAs/genetics ; Software ; Transcription Factors/classification ; Transcription Factors/genetics
Chemical Substances	MicroRNAs ; Transcription Factors
Language	English
Publishing date	2021-09-10
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	186809-3
ISSN	1362-4962 ; 1362-4954 ; 0301-5610 ; 0305-1048
ISSN (online)	1362-4962 ; 1362-4954
ISSN	0301-5610 ; 0305-1048
DOI	10.1093/nar/gkab778
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Article ; Online: Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism.

Lopes-Ramos, Camila M / Kuijjer, Marieke L / Ogino, Shuji / Fuchs, Charles S / DeMeo, Dawn L / Glass, Kimberly / Quackenbush, John

Cancer research

2018 Volume 78, Issue 19, Page(s) 5538–5547

Abstract: Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these ...

Abstract	Understanding sex differences in colon cancer is essential to advance disease prevention, diagnosis, and treatment. Males have a higher risk of developing colon cancer and a lower survival rate than women. However, the molecular features that drive these sex differences are poorly understood. In this study, we use both transcript-based and gene regulatory network methods to analyze RNA-seq data from The Cancer Genome Atlas for 445 patients with colon cancer. We compared gene expression between tumors in men and women and observed significant sex differences in sex chromosome genes only. We then inferred patient-specific gene regulatory networks and found significant regulatory differences between males and females, with drug and xenobiotics metabolism via cytochrome P450 pathways more strongly targeted in females. This finding was validated in a dataset of 1,193 patients from five independent studies. While targeting, the drug metabolism pathway did not change overall survival for males treated with adjuvant chemotherapy, females with greater targeting showed an increase in 10-year overall survival probability, 89% [95% confidence interval (CI), 78-100] survival compared with 61% (95% CI, 45-82) for women with lower targeting, respectively (
MeSH term(s)	Aged ; Aged, 80 and over ; Antineoplastic Agents/pharmacology ; Biomarkers, Tumor/genetics ; Chemotherapy, Adjuvant ; Colonic Neoplasms/drug therapy ; Colonic Neoplasms/genetics ; Cytochrome P-450 Enzyme System/metabolism ; Female ; Gene Expression Profiling ; Gene Expression Regulation, Neoplastic ; Gene Regulatory Networks ; Genome, Human ; Humans ; Kaplan-Meier Estimate ; Male ; Middle Aged ; Principal Component Analysis ; Sex Factors ; Treatment Outcome
Chemical Substances	Antineoplastic Agents ; Biomarkers, Tumor ; Cytochrome P-450 Enzyme System (9035-51-2)
Language	English
Publishing date	2018-09-25
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1432-1
ISSN	1538-7445 ; 0008-5472
ISSN (online)	1538-7445
ISSN	0008-5472
DOI	10.1158/0008-5472.CAN-18-0454
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