SEARCH · Search bioRxiv

Results for “Cancer Biology”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,783 records · Page 99Linked to original sources

bioRxiv2015

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk, and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves LD-pruning markers and applying a P-value threshold to association statistics, but this discards information and may reduce predictive accuracy. We introduce a new method, LDpred, which infers the posterior mean causal effect size of each marker using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the pruning/thresholding approach, particularly at large sample sizes. Accordingly, prediction R2 increased from 20.1% to 25.3% in a large schizophrenia data set and from 9.8% to 12.0% in a large multiple sclerosis data set. A similar relative improvement in accuracy was observed for three additional large disease data sets and when predicting in non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase.

Bioinformatics↗

bioRxiv2019

dSreg: A bayesian model to integrate changes in splicing and RNA binding protein activity

Alternative splicing (AS) is an important mechanism in the generation of transcript diversity across mammals. AS patterns are dynamically regulated during development and in response to environmental changes. Defects or perturbations in its regulation may lead to cancer or neurological disorders, among other pathological conditions. The regulatory mechanisms controlling AS in a given biological context are typically inferred using a two step-framework: differential AS analysis followed by enrichment methods. These strategies require setting rather arbitrary thresholds and are prone to error propagation along the analysis. To overcome these limitations, we propose dSreg, a Bayesian model that integrates RNAseq with data from regulatory features, e.g. binding sites of RNA binding proteins (RBPs). dSreg identifies the key underlying regulators controlling AS changes and quantifies their activity while simultaneously estimating the changes in exon inclusion rates. dSreg increased both the sensitivity and the specificity of the identified alternative splicing changes in simulated data, even at low read coverage. dSreg also showed improved performance when analyzing a collection of knock-down RBPs experiments from ENCODE, as opposed to traditional enrichment methods such as Over-representation Analysis (ORA) and Gene Set Enrichment Analysis (GSEA). dSreg opens the possibility to integrate a large amount of readily available RNA-seq datasets at low coverage for AS analysis and allows more cost-effective RNA-seq experiments. dSreg was implemented in python using stan and is freely available to the community at https://bitbucket.org/cmartiga/dsreg.

bioinformatics↗

bioRxiv2017

Causal inference in cancer epidemiology: what is the role of Mendelian randomization?

Observational epidemiological studies are prone to confounding, measurement error, and reverse causation, undermining their ability to generate reliable causal estimates of the effect of risk factors to inform cancer prevention and treatment strategies. Mendelian randomization (MR) is an analytical approach that uses genetic variants to proxy potentially modifiable exposures (e.g. environmental factors, biological traits, and druggable pathways) to permit robust causal inference of the effects of these exposures on diseases and their outcomes. MR has seen widespread adoption within population health research in cardio-metabolic disease, but also holds much promise for identifying possible interventions (e.g., dietary, behavioural, or pharmacological) for cancer prevention and treatment. However, some methodological and conceptual challenges in the implementation of MR are particularly pertinent when applying this method to cancer aetiology and prognosis, including reverse causation arising from disease latency and selection bias in studies of cancer progression. These issues must be carefully considered to ensure appropriate design, analysis, and interpretation of such studies.\n\nIn this review, we provide an overview of the key principles and assumptions of MR focusing on applications of this method to the study of cancer aetiology and prognosis. We summarize recent studies in the cancer literature that have adopted a MR framework to highlight strengths of this approach compared to conventional epidemiological studies. Lastly, limitations of MR and recent methodological developments to address them are discussed, along with the translational opportunities they present to inform public health and clinical interventions in cancer.

epidemiology↗

bioRxiv2017

Phenotype-Driven Transitions In Regulatory Network Structure

Complex traits and diseases like human height or cancer are often not caused by a single mutation or genetic variant, but instead arise from multiple factors that together functionally perturb the underlying molecular network. Biological networks are known to be highly modular and contain dense \"communities\" of genes that carry out cellular processes, but these structures change between tissues, during development, and in disease. While many methods exist for inferring networks, we lack robust methods for quantifying changes in network structure. Here, we describe ALPACA (ALtered Partitions Across Community Architectures), a method for comparing two genome-scale networks derived from different phenotypic states to identify condition-specific modules. In simulations, ALPACA leads to more nuanced, sensitive, and robust module discovery than currently available network comparison methods. We used ALPACA to compare transcriptional networks in three contexts: angiogenic and non-angiogenic subtypes of ovarian cancer, human fibroblasts expressing transforming viral oncogenes, and sexual dimorphism in human breast tissue. In each case, ALPACA identified modules enriched for processes relevant to the phenotype. For example, modules specific to angiogenic ovarian tumors were enriched for genes associated with blood vessel development, interferon signaling, and flavonoid biosynthesis. In comparing the modular structure of networks in female and male breast tissue, we found that female breast has distinct modules enriched for genes involved in estrogen receptor and ERK signaling. The functional relevance of these new modules indicate that not only does phenotypic change correlate with network structural changes, but also that ALPACA can identify such modules in complex networks.\n\nSignificance statementDistinct phenotypes are often thought of in terms of unique patterns of gene expression. But the expression levels of genes and proteins are driven by networks of interacting elements, and changes in expression are driven by changes in the structure of the associated networks. Because of the size and complexity of these networks, identifying functionally significant changes in network topology has been an ongoing challenge. We describe a new method for comparing networks derived from related conditions, such as healthy and disease tissue, and identifying emergent modules associated with the phenotypic differences between the conditions. We show that this method can find both known and previously unreported pathways involved in three contexts: ovarian cancer, tumor viruses, and breast tissue development.

systems biology↗

bioRxiv2016

Distinct Responses to Reduplicated Chromosomes Require Distinct Mad2 Responses

Duplicating chromosomes once each cell cycle produces sister chromatid pairs, which separate accurately at anaphase. In contrast, reduplicating chromosomes without separation frequently produces polytene chromosomes, a barrier to accurate mitosis. Chromosome reduplication occurs in many contexts, including: polytene tissue development, polytene tumors, and following treatment with mitosis-blocking chemotherapeutics. However, mechanisms responding to or resolving polyteny during mitosis are poorly understood. Here, using Drosophila, we uncover two distinct reduplicated chromosome responses. First, when reduplicated polytene chromosomes persist into metaphase, an anaphase delay prevents tissue malformation and apoptosis. Second, reduplicated polytene chromosomes can also separate prior to metaphase through a spindlePindependent mechanism termed Separation-Into-Recent-Sisters (SIRS). Both reduplication responses require the spindle assembly checkpoint protein Mad2. While Mad2 delays anaphase separation of metaphase polytene chromosomes, Mad2s control of overall mitotic timing ensures efficient SIRS. Our results pinpoint mechanisms enabling continued proliferation after genome reduplication, a finding with implications for cancer progression and prevention.

Cell Biology↗

bioRxiv2018

Regulation of DVL2 ubiquitylation by USP9X biases participation in canonical or non-canonical WNT signalling

The WNT signaling network is comprised of multiple receptors that relay various input signals via distinct transduction pathways to execute multiple complex and context-specific output processes. Integrity of the WNT signaling network relies on proper specification between canonical and non-canonical pathways, which presents a regulatory challenge given that several signal transducing elements are shared between pathways. Here, we report that USP9X, a deubiquitylase, and WWP1, an E3 ubiquitin ligase, interact physically to regulate a ubiquitin switch on DVL2, a WNT signaling protein. Our findings indicate that USP9X-mediated deubiquitylation of DVL2 is required for canonical WNT activation, while DVL2 ubiquitylation promotes its localization to actin-rich projections and increased cellular motility. We propose that a WWP1-USP9X axis regulates a ubiquitin switch on DVL2 that specifies its participation in either canonical WNT or WNT-PCP pathways. These findings have important implications for therapeutic targeting of USP9X in human cancer.

cell biology↗

bioRxiv2018

Partial reprogramming induces a steady decline in epigenetic age before loss of somatic identity

Induced pluripotent stem cells (IPSCs), with their unlimited regenerative capacity, carry the promise for tissue replacement to counter age-related decline. However, attempts to realise in vivo iPSC have invariably resulted in the formation of teratomas. Partial reprogramming in prematurely aged mice has shown promising results in alleviating age-related symptoms without teratoma formation. Does partial reprogramming lead to rejuvenation (i.e. \"younger\" cells), rather than dedifferentiation, which bears the risk of cancer? Here we analyse the dynamics of cellular age during human iPSC reprogramming and find that partial reprogramming leads to a reduction in the epigenetic age of cells. We also find that the loss of somatic gene expression and epigenetic age follow different kinetics, suggesting that they can be uncoupled and there could be a safe window where rejuvenation can be achieved with a minimised risk of cancer.

developmental biology↗

bioRxiv2018

A novel approach to modeling transcriptional heterogeneity identifies the oncogene candidate CBX2 in invasive breast carcinoma

Oncogenes promote the development of and serve as therapeutic targets against subsets of cancers. Here, a new statistical approach that captures transcriptional heterogeneity in tumor and adjacent normal (i.e. tumor-free) mRNA expression profiles was developed to identify oncogene candidates that were overexpressed in a subset of breast tumors. Intronic DNA methylation was strongly associated with the overexpression of chromobox 2 (CBX2), an oncogene candidate that was identified using our method but not through prior analytical approaches. CBX2 overexpression in breast tumors was associated with the upregulation of genes involved in cell cycle progression and is associated with poorer 5-year survival. The predicted function of CBX2 was confirmed in vitro providing the first experimental evidence that CBX2 promotes breast cancer cell growth. Modeling mRNA expression heterogeneity in tumors is a novel powerful approach with the potential to uncover therapeutic targets that benefit subsets of cancer patients.

systems biology↗

bioRxiv2018

CTP synthase regulation by miR-975 controls cell proliferation and differentiation in Drosophila melanogaster

CTP synthase (CTPsyn) is an essential metabolic enzyme. As a key regulator of the nucleotide pool, the protein has been found to be elevated in cancer models. In many organisms, CTPsyn compartmentalizes into filaments termed cytoophidia. For D. melanogaster, it is only its Isoform C i.e. CTPsynIsoC which forms the structure. The fruit flys testis is home to somatic and germline stem cells. Both micro and macro-cytoophidia are normally seen in the transit amplification regions close to its apical tip, where the stem-cell niche is located and development is at its most rapid. Here, we report that CTPsynIsoC overexpression causes the lengthening of cytoophidia throughout the entirety of the testicular body. A bulging apical tip is found in approximately one-third of like-genotyped males. Immunostaining shows that the cause of this tumour-like phenotype is most likely due to increased numbers of both germline cells and spermatocytes. We also report that under conditions whereby miR-975 is overexpressed, greater incidences of the same bulged-phenotype coincides with induced upregulation of CTPsynIsoC. However, RT-qPCR assays reveal that either overexpression genotype provokes a differential response in expression of a number of genes concurrently associated with CTPsyn and cancer, showing that the pathways CTPsynIsoC affect and miR-975 regulate may be completely independent of each other. This study presents the first instance of consequences of miRNA-asserted regulation upon CTPsyn in D. melanogaster, and further reaffirms the enzymes close ties to cancer and carcinogenesis.

cell biology↗

bioRxiv2018

Basolateral localization of MMP14 drives apicobasal polarity change during EMT independently of its catalytic activity

The transmembrane Matrix Metalloproteinase MMP14/MT1-MMP is known to promote cell migration by cleavage of the extracellular matrix. To initiate migration, epithelial cells need to gain mesenchymal attributes. They reduce cell-cell junctions and apicobasal polarity and gain migratory capabilities. This process is named epithelial-mesenchymal transition (EMT). MMP14s implication in EMT is still ill-defined. We used chick neural crest (NC) cells as a model to explore the function of MMP14 in physiological EMT. Our results show that MMP14 is expressed by chick NC cells. However, it is its subcellular localization, rather than its expression, that correlates with EMT. MMP14 is first apical and switches to basolateral domains during EMT. Loss of function and rescue experiments show that MMP14 is involved in EMT independently of its catalytic activity. It lies downstream of pro-EMT genes and upstream of cell polarity. We found that basolateral localization of MMP14 is required and sufficient to induce polarity change in NC cells and neuroepithelial cells, respectively. These effects on polarity occur without impact on cell-cell adhesion or the extracellular matrix. Overall, our data points to a new function of MMP14 in EMT that will need to be further explored in other systems such as cancer cells.

developmental biology↗

bioRxiv2019

Computational Analysis of Insulin-Glucagon Signalling Network: Implications of Bistability in Metabolic Homeostasis and Disease states

Insulin and glucagon control plasma macronutrient homeostasis through their signalling network composed of multiple feedback and crosstalk mechanisms. To understand how these interactions contribute to metabolic homeostasis and disease states, we analysed the steady state response of metabolic regulation (catabolic or anabolic) with respect to structural and input perturbations in the integrated signalling network, for varying levels of plasma glucose. Structural perturbations revealed: the positive feedback of AKT on IRS is responsible for the bistability in anabolic zone (glucose >5.5 mmol); the positive feedback of calcium on cAMP is responsible for ensuring ultrasensitive response in catabolic zone (glucose <4.5 mmol); the crosstalk between AKT and PDE3 is responsible for efficient catabolic response under low glucose condition; the crosstalk between DAG and PKC regulates the span of anabolic bistable region with respect to plasma glucose levels. The macronutrient perturbations revealed: varying plasma amino acids and fatty acids from normal to high levels gradually shifted the bistable response towards higher glucose range eventually making the response catabolic or unresponsive to increasing glucose levels. The analysis reveals that certain macronutrient composition may be more conducive to homeostasis than others. The network perturbations that may contribute to disease states such as diabetes, obesity and cancer are discussed.

systems biology↗

bioRxiv2018

Kinetic modelling of quantitative proteome data predicts metabolic reprogramming of liver cancer

Metabolic alterations can serve as targets for diagnosis and therapy of cancer. Due to the highly complex regulation of cellular metabolism, definite identification of metabolic pathway alterations remains challenging and requires sophisticated experimentation. Here, we applied a comprehensive kinetic model of the central carbon metabolism (CCM) to characterize metabolic reprogramming in murine liver cancer. We show that relative differences of protein abundances of metabolic enzymes obtained by mass spectrometry can be used to scale maximal enzyme capacities. Model simulations predicted tumor - specific alterations of various components of the CCM, a selected number of which were subsequently verified by in vitro and in vivo experiments. Furthermore, we demonstrate the ability of the kinetic model to identify metabolic pathways whose inhibition results in selective tumor cell killing. Our systems biology approach establishes that combining cellular experimentation with computer simulations of physiology-based metabolic models enables a comprehensive understanding of deregulated energetics in cancer.

systems biology↗

bioRxiv2018

Methionine metabolism influences the genomic architecture of H3K4me3 with the link to gene expression encoded in peak width

Nutrition and metabolism are known to influence chromatin biology and epigenetics by modifying the levels of post-translational modifications on histones, yet how changes in nutrient availability influence specific aspects of genomic architecture and connect to gene expression is unknown. To investigate this question we considered, as a model, the metabolically-driven dynamics of H3K4me3, a histone methylation mark that is known to encode information about active transcription, cell identity, and tumor suppression. We analyzed the genome-wide changes in H3K4me3 and gene expression in response to alterations in methionine availability under conditions that are known to affect the global levels of histone methylation in both normal rodent physiology and in human cancer cells. Surprisingly, we found that the location of H3K4me3 peaks at specific genomic loci was largely preserved under conditions of methionine restriction. However, upon examining different geometrical features of peak shape, it was found that the response of H3K4me3 peak width encoded almost all aspects of H3K4me3 biology including changes in expression levels, and the presence of cell identity and cancer associated genes. These findings reveal simple yet new and profound principles for how nutrient availability modulates specific aspects of chromatin dynamics to mediate key biological features.

genetics↗

bioRxiv2018

A comprehensive analysis of single-cell transcriptome network underlying gastric premalignant lesions and early gastric cancer

Intestinal-type gastric cancer is preceded by premalignant lesions including chronic atrophic gastritis (CAG) and intestinal metaplasia (IM), which are characterized as changes in cell types. In this study, for the first time, we systematically constructed a single-cell atlas for a total of 31,164 high-quality cells from gastric mucosa biopsies of patients spanning a cascade of gastric premalignant lesions and early gastric cancer (EGC) using single-cell RNA sequencing (scRNA-seq). Based on the atlas, we construct a network underlying the changes of cellular and molecular characteristics of gastric epithelial cells across different lesions. We found the conversion of gland mucous cells (GMCs) toward a more intestinal-like stem cell phenotype during metaplasia, and identified OR51E1 as a novel marker for early-malignant enteroendocrine cells. We also found that HES6 might mark a goblet cell subset that precede morphologically identifiable goblet cells in IM mucosa, potentially aiding the identification of metaplasia at the early stage. Finally, we identified a panel of EGC-related specific signature, with clinical implications for the precise diagnosis of EGC. Our study offers unparalleled insights into the human gastric cellulome in premalignant and early-malignant lesions and provides an important data resource that will facilitate studies in gastritis-induced tumourigenesis and gastric cell biology.\n\nSignificance StatementUnderstanding cellular characteristics in gastric premalignant and malignant lesions would help us better understand the gastric cancer (GC) pathogenesis. In this paper, for the first time, we systematically constructed a single-cell transcriptome network of human premalignant gastric mucosa and early GC (EGC) and derived novel findings from it. We identified OR51E1 as a novel marker for early-malignant enteroendocrine cells and a panel of genes as the EGC-specific signature, with clinical implications for the precise diagnosis of EGC. We also found HES6 might mark a goblet cell subset that precede morphologically identifiable goblet cells in IM mucosa, potentially aiding the identification of metaplasia at the early stage. Our study provided an unprecedented data resource that will facilitate studies underlying gastritis-induced tumorigenesis.

bioinformatics↗

bioRxiv2017

Personal Cancer Genome Reporter: Variant Interpretation Report For Precision Oncology

SummaryIndividual tumor genomes pose a major challenge for clinical interpretation due to their unique sets of acquired mutations. There is a general scarcity of tools that can i) systematically interrogate cancer genomes in the context of diagnostic, prognostic, and therapeutic biomarkers, ii) prioritize and highlight the most important findings, and iii) present the results in a format accessible to clinical experts. We have developed a stand-alone, open-source software package for somatic variant annotation that integrates a comprehensive set of knowledge resources related to tumor biology and therapeutic biomarkers, both at the gene and variant level. Our application generates a tiered report that will aid the interpretation of individual cancer genomes in a clinical setting.\n\nAvailability and ImplementationThe software is implemented in Python/R, and is freely available through Docker technology. Documentation, example reports, and installation instructions are accessible via the project GitHub page: https://github.com/sigven/pcgr)\n\nContactsigven@ifi.uio.no

bioinformatics↗

bioRxiv2017

The Landscape Of Human Mutually Exclusive Splicing

Mutually exclusive splicing of exons is a mechanism of functional gene and protein diversification with pivotal roles in organismal development and diseases such as Timothy syndrome, cardiomyopathy and cancer in humans. In order to obtain a first genome-wide estimate of the extent and biological role of mutually exclusive splicing in humans we predicted and subsequently validated mutually exclusive exons (MXEs) using 515 publically available RNA-seq datasets. Here, we provide evidence for the expression of over 855 MXEs, 42% of which represent novel exons, increasing the annotated human mutually exclusive exome more than five-fold. The data provides strong evidence for the existence of large and multi-cluster MXEs in higher vertebrates and offers new insights into MXE splicing mechanics and evolution. Finally, MXEs are significantly enriched in pathogenic mutations and their spatio-temporal expression predicts human disease pathology.

bioinformatics↗

bioRxiv2018

High-resolution mapping of cancer cell networks using co-functional interactions

Powerful new technologies for perturbing genetic elements have expanded the study of genetic interactions in model systems ranging from yeast to human cell lines. However, technical artifacts can confound signal across genetic screens and limit the immense potential of parallel screening approaches. To address this problem, we devised a novel PCA-based method for eliminating these artifacts and bolstering sensitivity and specificity for detection of genetic interactions. Applying this strategy to a set of >300 whole genome CRISPR screens, we report ~1 million pairs of correlated \"co-functional\" genes that provide finer-scale information about cell compartments, biological pathways, and protein complexes than traditional gene sets. Lastly, we employed a gene community detection approach to implicate core genes for cancer growth and compress signal from functionally related genes in the same community into a single score. This work establishes new algorithms for probing cancer cell networks and motivates the acquisition of further CRISPR screen data across diverse genotypes and cell types to further resolve the complexity of cell signaling processes.

genomics↗

bioRxiv2018

Pan-cancer analysis of transcriptional metabolic dysregulation using The Cancer Genome Atlas

Understanding the levels of metabolic dysregulation in different disease settings is vital for the safe and effective incorporation of metabolism-targeted therapeutics in the clinic. Using transcriptomic data from 10,704 tumor and normal samples from The Cancer Genome Atlas, across 26 disease sites, we developed a novel bioinformatics pipeline that distinguishes tumor from normal tissues, based on differential gene expression for 114 metabolic pathways. This pathway dysregulation was confirmed in separate patient populations, further demonstrating the robustness of this approach. A bootstrapping simulation was then applied to assess whether these alterations were biologically meaningful, rather than expected by chance. We provide distinct examples of the types of analysis that can be accomplished with this tool to understand cancer specific metabolic dysregulation, highlighting novel pathways of interest in both common and rare disease sites. Utilizing a pathway mapping approach to understand patterns of metabolic flux, differential drug sensitivity, can accurately be predicted. Further, the identification of Master Metabolic Transcriptional Regulators, whose expression was highly correlated with pathway gene expression, explains why metabolic differences exist in different disease sites. We demonstrate these also have the ability to segregate patient populations and predict responders to different metabolism-targeted therapeutics.

bioinformatics↗