Biology subjects

DeMeo, D. L.

Publications and source records attributed to DeMeo, D. L..

At least 19 recordsLinked to original sources

bioRxiv2025

Sex differences in gene regulation and its impact on cancer incidence

Sex differences in the incidence rates of many cancer types are well-documented. While differences in lifestyle, environmental exposures, and hormone levels can influence disease risk and incidence, there is limited understanding of how cancer driver genes are differentially regulated between males and females in normal tissues, and how these differences may contribute to the observed sex differences in cancer epidemiology. We studied 8,279 gene regulatory networks across 29 non-cancerous tissues in the GTEx dataset, referred here as normal tissues. Using network centrality measures, we compared the networks between males and females, focusing on interactions between 644 transcription factors and 476 cancer genes (COSMIC Cancer Gene Census). Cancer genes were differentially targeted by transcription factors in males and females across normal tissues. We found an overrepresentation of sex-biased cancer genes on the X chromosome, particularly among genes escaping X chromosome inactivation, with higher regulatory targeting of tumor suppressor genes in females compared to males. Key signaling pathways, including WNT, NOTCH, and p53, showed differential transcriptional targeting by sex. We observed higher targeting of cancer-related pathways in females for tissues that have higher tumor incidence in females (breast, lung, and thyroid) and higher targeting in males for tissues with increased tumor incidence in males (stomach, colon, and liver). Sex-biased transcription factors that were consistently observed across multiple tissues are enriched for sex hormone response elements in their promoters. Our findings showed that normal tissues have sex-biased regulation of genes implicated in tumorigenesis, helping to explain the molecular basis of sex differences observed in cancer incidence rates and in identifying targets for cancer prevention in a sex-aware manner.

systems biology↗

bioRxiv2025

DNA-methylation markers associated with lung function at birth and childhood reveal early life programming of inflammatory pathways

RationaleLung function deficits may be caused by early life epigenetic programming. Early childhood studies are necessary to understand life-course trends in lung diseases. ObjectivesWe aimed to examine whether DNA-methylation at birth and childhood is associated with lung function growth. MethodsWe measured DNA-methylation in leukocytes from participants in two childhood asthma cohorts (CAMP [n=703, mean-age 12.9 years] and GACRS [n=788, mean-age 9.3 years]) and cord blood from participants in the VDAART study (n=572) to identify CpGs and pathways associated with lung function. ResultsWe identified 1,049 consistent differentially methylated CpGs (608 relatively hypermethylated) across all three studies (FDR-P<0.05). Relatively hypomethylated CpGs were enriched for gluconeogenesis, cell adhesion and VEGF signaling. Relatively hypermethylated CpGs were enriched for Hippo, B-cell and growth hormone receptor signaling. Functional enrichment suggested potential regulatory roles for active enhancers and histone modifications. Additionally, enrichment in PI3K/AKT and Notch pathways in males and enrichment in hormonal pathways in females was identified. Gaussian graphical models identified sex-differential DNA-methylation nodes and hub scores at birth and childhood. Integrating with previously identified polygenic risk scores for asthma and drug-target enrichment identified seven robust genes including MPO, CHCHD3, CACNA1S, PI4KA, EP400, CREBBP and KCNA10 with known associations as biomarkers for asthma severity and drug targets for airway inflammation. ConclusionsEpigenetic variability from birth through puberty provides mechanistic insights into fetal programming of developmental and immune pathways associated with lung function. These early life observations reveal potential targets for mitigating risk for lung function decline and asthma progression in later life. Key messagesO_LIWe identified consistent DNA methylation signatures between birth and childhood in critical metabolic, lung development and immune pathways that were associated with lung function and may be influenced by sex and genetics. C_LIO_LIOur integrative findings provide a deeper understanding for accelerated lung function decline across the life-course and could pave the way for translational interventions for lung diseases based on epigenetic plasticity. C_LI

genomics↗

bioRxiv2024

Selective loss of Y chromosomes in lung adenocarcinoma modulates the tumor immune environment through cancer/testis antigens

There is increasing recognition that the allosomes, X and Y, play an important role in health and disease beyond the determination of biological sex. A loss of the Y chromosome (LOY) occurs in most solid tumors in males and is often associated with worse survival, suggesting that LOY may give tumor cells a growth or survival advantage. We here use an expression-based continuous measure of LOY that allows us to investigate LOY in lung adenocarcinoma (LUAD) using both bulk and single-cell expression data. We find evidence suggesting that LOY affects the tumor immune environment by altering cancer/testis antigen expression and consequently facilitating tumor immune evasion, also reflected in inferred gene regulatory networks. In immunotherapy data, we further show that LOY and changes in expression of particular cancer/testis antigens are associated with response to pembrolizumab treatment and outcome. This computational study provides new insights into the mechanisms behind LOY in LUAD and a powerful biomarker for predicting immunotherapy response in LUAD tumors in males.

cancer biology↗

bioRxiv2024

Aging-associated Alterations in the Gene Regulatory Network Landscape Associate with Risk, Prognosis and Response to Therapy in Lung Adenocarcinoma

Aging is the primary risk factor for many individual cancer types, including lung adenocarcinoma (LUAD). To understand how aging-related alterations in the regulation of key cellular processes might affect LUAD risk and survival outcomes, we built individual (person)-specific gene regulatory networks integrating gene expression, transcription factor protein-protein interaction, and sequence motif data, using PANDA/LIONESS algorithms, for both non-cancerous lung tissue samples from the Genotype Tissue Expression (GTEx) project and LUAD samples from The Cancer Genome Atlas (TCGA). In GTEx, we found that pathways involved in cell proliferation and immune response are increasingly targeted by regulatory transcription factors with age; these aging-associated alterations are accelerated by tobacco smoking and resemble oncogenic shifts in the regulatory landscape observed in LUAD and suggests that dysregulation of aging pathways might be associated with an increased risk of LUAD. Comparing normal adjacent samples from individuals with LUAD with healthy lung tissue samples from those without LUAD, we found that aging-associated genes show greater aging-biased targeting patterns in younger individuals with LUAD compared to their healthy counterparts of similar age, a pattern suggestive of age acceleration. This implies that an accelerated aging process may be responsible for tumor incidence in younger individuals. Using drug repurposing tool CLUEreg, we found small molecule drugs with potential geroprotective effects that may alter the accelerating aging profiles we found. We also observed that, in contrast to chronological age, a network-informed aging signature was associated with survival and response to chemotherapy in LUAD. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=113 SRC="FIGDIR/small/601689v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1a8c735org.highwire.dtl.DTLVardef@1ae8833org.highwire.dtl.DTLVardef@16fd65dorg.highwire.dtl.DTLVardef@16287e5_HPS_FORMAT_FIGEXP M_FIG C_FIG

cancer biology↗

bioRxiv2023

Bayesian Optimized sample-specific Networks Obtained ByOmics data (BONOBO)

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. Graphical Abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=191 SRC="FIGDIR/small/567119v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@f97de5org.highwire.dtl.DTLVardef@1b864a9org.highwire.dtl.DTLVardef@d8a384org.highwire.dtl.DTLVardef@d73091_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Gene regulatory Networks Reveal Sex Difference in Lung Adenocarcinoma

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 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. 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.

cancer biology↗

bioRxiv2022

Using methylation data to improve transcription factor binding prediction

Scanning the genome for the sequence patterns defined by Position Weight Matrices (PWM) is commonly used to estimate transcription factor (TF) binding "motif" locations. However, the PWM-based scores assigned to these inferred locations are associated with only modest accuracy when benchmarked against in vivo TF binding. One reason for this limited performance may be because PWMs do not incorporate information regarding the epigenetic context necessary for TF binding. To investigate this, we developed a framework to score inferred TF binding locations using CpG methylation data. We intersected motif locations identified using PWMs with methylation information captured in both whole genome bisulfite sequencing and Illumina EPIC array data for six cell lines, scored motif locations based on these data, and compared with experimental data characterizing TF binding (ChIP-seq). We found that, for most TFs, binding is better predicted using methylation-based scoring compared to standard PWM scores. In addition, our analysis shows that, while most TFs do not bind to methylated promoter regions, there are several exceptions to this rule, indicating that the role of methylation in TF binding may be cell-type and context specific. Finally, we also illustrate that our approach can be generalized to infer TF binding when methylation information is only proximally available, i.e. measured for nearby CpGs that do not directly overlap with a motif location. Overall, our approach provides a framework for inferring context specific TF binding using methylation data, a crucial initial step for understanding the impact of methylation on gene regulatory processes.

bioinformatics↗

bioRxiv2021

Clonal hematopoiesis is driven by aberrant activation of TCL1A

A diverse set of driver genes, such as regulators of DNA methylation, RNA splicing, and chromatin remodeling, have been associated with pre-malignant clonal expansion of hematopoietic stem cells (HSCs). The factors mediating expansion of these mutant clones remain largely unknown, partially due to a paucity of large cohorts with longitudinal blood sampling. To circumvent this limitation, we developed and validated a method to infer clonal expansion rate from single timepoint data called PACER (passenger-approximated clonal expansion rate). Applying PACER to 5,071 persons with clonal hematopoiesis accurately recapitulated the known fitness effects due to different driver mutations. A genome-wide association study of PACER revealed that a common inherited polymorphism in the TCL1A promoter was associated with slower clonal expansion. Those carrying two copies of this protective allele had up to 80% reduced odds of having driver mutations in TET2, ASXL1, SF3B1, SRSF2, and JAK2, but not DNMT3A. TCL1A was not expressed in normal or DNMT3A-mutated HSCs, but the introduction of mutations in TET2 or ASXL1 by CRISPR editing led to aberrant expression of TCL1A and expansion of HSCs in vitro. These effects were abrogated in HSCs from donors carrying the protective TCL1A allele. Our results indicate that the fitness advantage of multiple common driver genes in clonal hematopoiesis is mediated through TCL1A activation. PACER is an approach that can be widely applied to uncover genetic and environmental determinants of pre-malignant clonal expansion in blood and other tissues.

genetics↗

bioRxiv2021

A robust and adaptive framework for interaction testing in quantitative traits between multiple genetic loci and exposure variables

The identification and understanding of gene-environment interactions can provide insights into the pathways and mechanisms underlying complex diseases. However, testing for gene-environment interaction remains a challenge since statistical power is often limited, the specification of environmental effects is nontrivial, and such misspecifications can lead to false positive findings. To address the lack of statistical power, recent methods aim to identify interactions on an aggregated level using, for example, polygenic risk scores. While this strategy increases power to detect interactions, identifying contributing key genes and pathways is difficult based on these global results. Here, we propose RITSS (Robust Interaction Testing using Sample Splitting), a gene-environment interaction testing framework for quantitative traits that is based on sample splitting and robust test statistics. RITSS can incorporate multiple genetic variants and/or multiple environmental factors. Using sample splitting, a screening step enables the selection and combination of potential interactions into scores with improved interpretability, based on the users unrestricted choices for statistical/machine learning approaches. In the testing step, the application of robust test statistics minimizes the susceptibility of the results to main effect misspecifications. Using extensive simulation studies, we demonstrate that RITSS controls the type 1 error rate in a wide range of scenarios. In an application to lung function phenotypes and human height in the UK Biobank, RITSS identified genome-wide significant interactions with subcomponents of genetic risk scores. While the contributing single variant interactions are moderate, our analysis results indicate interesting interaction patterns that result in strong aggregated signals that provide further insights into gene-environment interaction mechanisms.

bioinformatics↗

bioRxiv2021

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

Gene regulatory network inference allows for the study of transcriptional control to identify the alteration of cellular processes in human diseases. Our group has developed several tools to model a variety of regulatory processes, including transcriptional (PANDA, SPIDER) and post-transcriptional (PUMA) gene regulation, and gene regulation in individual samples (LIONESS). These methods work by performing repeated operations on data matrices in order to integrate information across multiple lines of biological evidence. This limits their use for large-scale genomic studies due to the associated high computational burden. To address this limitation, we developed gpuZoo, which includes GPU-accelerated implementations 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 the multi-core CPU implementation of the same methods. gpuZoo takes advantage of the modern multi-GPU device architecture to build a population of sample-specific gene regulatory networks with similar runtime and cost improvements by combining GPU acceleration with an efficient on-line derivation. Taken together, gpuZoo allows parallel and on-line gene regulatory network inference in large-scale genomic studies with cost-effective performance. 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.

systems biology↗

bioRxiv2020

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 cells epigenetic state plays an important role in mediating these interactions, primarily by influencing chromatin accessibility. However, effectively leveraging epigenetic information when constructing regulatory networks remains a challenge. We developed SPIDER, which incorporates epigenetic information (DNase-Seq) into a message passing framework in order to estimate gene regulatory networks. We validated SPIDERs predictions using ChlP-Seq data from ENCODE and found that SPIDER networks were more accurate than other publicly available, epigenetically informed regulatory networks as well as networks based on methods that leverage epigenetic data to predict transcription factor binding sites. SPIDER was also able to improve the detection of cell line specific regulatory interactions. Notably, SPIDER can recover ChlP-seq verified transcription factor binding events in the regulatory regions of genes that do not have a corresponding sequence motif. Constructing biologically interpretable, epigenetically informed networks using SPIDER will allow us to better understand gene regulation as well as aid in the identification of cell-specific drivers and biomarkers of cellular phenotypes.

systems biology↗

bioRxiv2018

New Statistical Methods for Constructing Robust Differential Correlation Networks

The interplay among microRNAs (miRNAs) plays an important role in the developments of complex human diseases. Co-expression networks can characterize the interactions among miRNAs. Differential correlation network is a powerful tool to investigate the differences of co-expression networks between cases and controls. To construct a differential correlation network, the Fishers Z-transformation test is usually used. However, the Fishers Z-transformation test requires the normality assumption, the violation of which would result in inflated Type I error rate. Several bootstrapping-based improvements for Fishers Z test have been proposed. However, these methods are too computationally intensive to be used to construct differential correlation networks for high-throughput genomic data. In this article, we proposed six novel robust equal-correlation tests that are computationally efficient. The systematic simulation studies and a real microRNA data analysis showed that one of the six proposed tests (ST5) overall performed better than other methods.

bioinformatics↗

bioRxiv2018

Expanded genetic landscape of chronic obstructive pulmonary disease reveals heterogeneous cell type and phenotype associations

Chronic obstructive pulmonary disease (COPD) is the leading cause of respiratory mortality worldwide. Genetic risk loci provide novel insights into disease pathogenesis. To broaden COPD genetic risk loci discovery and identify cell type and phenotype associations we performed a genome-wide association study in 35,735 cases and 222,076 controls from the UK Biobank and additional studies from the International COPD Genetics Consortium. We identified 82 loci with P value < 5x10-8; 47 were previously described in association with either COPD or population-based lung function. Of the remaining 35 novel loci, 13 were associated with lung function in 79,055 individuals from the SpiroMeta consortium. Using gene expression and regulation data, we identified enrichment for loci in lung tissue, smooth muscle and alveolar type II cells. We found 9 shared genomic regions between COPD and asthma and 5 between COPD and pulmonary fibrosis. COPD genetic risk loci clustered into groups of quantitative imaging features and comorbidity associations. Our analyses provide further support to the genetic susceptibility and heterogeneity of COPD.

genetics↗

bioRxiv2018

Detecting Differential Variable microRNAs via Model-Based Clustering

Identifying genomic probes (e.g., DNA methylation marks) is becoming a new approach to detect novel genomic risk factors for complex human diseases. The F test is the standard equal-variance test in Statistics. For high-throughput genomic data, the probe-wise F test has been successfully used to detect biologically relevant DNA methylation marks that have different variances between two groups of subjects (e.g., cases vs. controls). In addition to DNA methylation, microRNA is another mechanism of epigenetics. However, to the best of our knowledge, no studies have identified differentially variable (DV) microRNAs. In this article, we proposed a novel model-based clustering to improve the power of the probe-wise F test to detect DV microRNAs. We imposed special structures on covariance matrices for each cluster of microRNAs based on the prior information about the relationship between variance in cases and variance in controls and about the independence among cases and controls. To the best of our knowledge, the proposed method is the first clustering algorithm that aims to detect DV genomic probes. Simulation studies showed that the proposed method outperformed the probe-wise F test and had certain robustness to the violation of the normality assumption. Based on two real datasets about human hepatocellular carcinoma (HCC), we identified 7 DV-only microRNAs (hsa-miR-1826, hsa-miR-191, hsa-miR-194-star, hsa-miR-222, hsa-miR-502-3p, hsa-miR-93, and hsa-miR-99b) using the proposed method, one (hsa-miR-1826) of which has not yet been reported to relate to HCC in the literature.

bioinformatics↗

bioRxiv2018

Gene regulatory network analysis identifies sex-linked differences in colon cancer drug metabolism processes

Significant sex differences are observed in colon cancer, and understanding these differences 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. We used 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 found no significant sex differences except for sex-chromosome genes. 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 that included 1,193 patients from five independent studies. While targeting of the drug metabolism pathway did not change the overall survival for males treated with adjuvant chemotherapy, females with greater targeting had an increase in 10-year overall survival probability, with 89% (95% CI: 78%-100%) survival compared to 61% (95% CI: 45%-82%) for women with lower targeting, respectively (p=0.034). Our network analysis uncovered patterns of transcriptional regulation that differentiate male and female colon cancer. Most importantly, targeting of the drug metabolism pathway was predictive of survival in women who received adjuvant chemotherapy. This network-based approach can be used to investigate the molecular features that drive sex differences in other cancers and complex diseases.

systems biology↗

bioRxiv2017

Integrative genomics analysis identifies ACVR1B as a candidate causal gene of emphysema distribution in non-alpha 1-antitrypsin deficient smokers

BackgroundSeveral genetic risk loci associated with emphysema apico-basal distribution (EABD) have been identified through genome-wide association studies (GWAS), but the biological functions of these variants are unknown. To characterize gene regulatory functions of EABD-associated variants, we integrated EABD GWAS results with 1) a multi-tissue panel of expression quantitative trait loci (eQTL) from subjects with COPD and the GTEx project and 2) epigenomic marks from 127 cell types in the Roadmap Epigenomics project. Functional validation was performed for a variant near ACVR1B.\n\nResultsSNPs from 168 loci with P-values<5x10-5 in the largest GWAS meta-analysis of EABD (Boueiz A. et al, AJRCCM 2017) were analyzed. 54 loci overlapped eQTL regions from our multi-tissue panel, and 7 of these loci showed a high probability of harboring a single, shared GWAS and eQTL causal variant (colocalization posterior probability[≥]0.9). 17 cell types exhibited greater than expected overlap between EABD loci and DNase-I hypersensitive peaks, DNaseI hotspots, enhancer marks, or digital DNaseI footprints (permutation P-value < 0.05), with the strongest enrichment observed in CD4+, CD8+, and regulatory T cells. A region near ACVR1B demonstrated significant colocalization with a lung eQTL and overlapped DNase-I hypersensitive regions in multiple cell types, and reporter assays in human bronchial epithelial cells confirmed allele-specific regulatory activity for the lead variant, rs7962469.\n\nConclusionsIntegrative analysis highlights candidate causal genes, regulatory variants, and cell types that may contribute to the pathogenesis of emphysema distribution. These findings will enable more accurate functional validation studies and better understanding of emphysema distribution biology.

genomics↗

bioRxiv2017

Understanding Tissue-specific Gene Regulation

Although all human tissues carry out common processes, tissues are distinguished by gene expres-sion patterns, implying that distinct regulatory programs control tissue-specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue-specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue-network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue-specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes.

genomics↗

bioRxiv2016

Transcriptional landscape of cell lines and their tissues of origin

Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. An important question is how well a cell lines transcriptional and regulatory processes reflect those of its tissue of origin. We analyzed RNA-Seq data from GTEx for 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines and whole blood samples; and 244 paired fibroblast cell lines and skin biopsies. A combination of gene expression and network analyses shows that while cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, they also exhibit changes in their patterns of transcription factor regulation. Cell cycle genes are over-expressed in cell lines compared to primary tissue, and they have a reduction of repressive transcription factor targeting. Our results provide insight into the expression and regulatory alterations observed in cell lines and suggest that these changes should be considered when using cell lines as models.\n\nHighlightsO_LICell lines differ from their source tissues in gene expression and regulation\nC_LIO_LIDistinct cell lines share altered patterns of cell cycle regulation\nC_LIO_LICell cycle genes are less strongly targeted by repressive TFs in cell lines\nC_LIO_LICell lines share expression with their source tissue, but at reduced levels\nC_LI

genomics↗