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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2017

GDSCTools for Mining Pharmacogenomic Interactions in Cancer

MotivationLarge pharmacogenomic screenings integrate heterogeneous cancer genomic data sets as well as anti-cancer drug responses on thousand human cancer cell lines. Mining this data to identify new therapies for cancer sub-populations would benefit from common data structures, modular computational biology tools and user-friendly interfaces.\n\nResultsWe have developed GDSCTools: a software aimed at the identification of clinically relevant genomic markers of drug response. The Genomics of Drug Sensitivity in Cancer (GDSC) database (www.cancerRxgene.org) integrates heterogeneous cancer genomic data sets as well as anti-cancer drug responses on a thousand cancer cell lines. Including statistical tools (ANOVA) and predictive methods (Elastic Net), as well as common data structures, GDSCTools allows users to reproduce published results from GDSC, to analyse their own drug responses or genomic datasets, and to implement new analytical methods.\n\nContactthomas.cokelaer@pasteur.fr

cancer biology↗

bioRxiv2018

Integrated Genomic Analysis of Hypoxia Genes across Cancer Types Identifies Significant Associations with Cancer Hallmarks

Hypoxia is a generic micro-environmental factor in most solid tumours. While most published literature focused on in vitro or single tumour type investigations, we carried out the first multi-omics pan cancer analysis of hypoxia with the aim of gaining a comprehensive understanding of its implication in tumour biology. A core set of 52 mRNAs were curated based on experimentally validated hypoxia gene sets from multiple cancer types. The 52 mRNAs collectively stratified high- and low-hypoxia tumours from The Cancer Genome Atlas (TCGA) database (9698 primary tumours) in each of the 32 cancer types available. High- hypoxia tumours had high expression of not only mRNA but also protein and microRNA markers of hypoxia. In a pan cancer transcriptomic analysis, [≥]70% of the known cancer hallmark pathways were enriched in high-hypoxia tumours, most notably epithelial mesenchymal transition potential, proliferation (G2M checkpoint, E2F targets, MYC targets) and immunology response. In a multi-omics analysis, gene expression-determined high- hypoxia tumours had a higher non-silent mutation rate, DNA damage repair deficiency and leukocyte infiltration. The associations largely remained significant after correcting for confounding factors, showing a profound impact of hypoxia in tumour evolution across cancer types. High-hypoxia tumours determined using the core gene set had a poor prognosis in 16/32 cancer types, with statistical significances remaining in five after adjusting for tumour stage and omics biomarkers. In summary, this first comprehensive in vivo map of hypoxia in cancers highlights the importance of this micro-environmental factor in driving tumour progression.

cancer biology↗

bioRxiv2018

Identification of microRNA clusters cooperatively acting on Epithelial to Mesenchymal Transition in Triple Negative Breast Cancer

MicroRNAs play important roles in many biological processes. Their aberrant expression can have oncogenic or tumor suppressor function directly participating to carcinogenesis, malignant transformation, invasiveness and metastasis. Indeed, miRNA profiles can distinguish not only between normal and cancerous tissue but they can also successfully classify different subtypes of a particular cancer. Here, we focus on a particular class of transcripts encoding polycistronic miRNA genes that yields multiple miRNA components. We describe clustered MiRNA Master Regulator Analysis (ClustMMRA), a fully redesigned release of the MMRA computational pipeline (MiRNA Master Regulator Analysis), developed to search for clustered miRNAs potentially driving cancer molecular subtyping. Genomically clustered miRNAs are frequently co-expressed to target different components of pro-tumorigenic signalling pathways. By applying ClustMMRA to breast cancer patient data, we identified key miRNA clusters driving the phenotype of different tumor subgroups. The pipeline was applied to two independent breast cancer datasets, providing statistically concordant results between the two analysis. We validated in cell lines the miR-199/miR-214 as a novel cluster of miRNAs promoting the triple negative subtype phenotype through its control of proliferation and EMT.

systems biology↗

bioRxiv2016

Reconstructing phylogenies of metastatic cancers

Reconstructing the evolutionary history of metastases is critical for understanding their basic biological principles and has profound clinical implications1-3. Genome-wide sequencing data has enabled modern phylogenomic methods to accurately dissect subclones and their phylogenies from noisy and impure bulk tumor samples at unprecedented depth4-7. However, existing methods are not designed to infer metastatic seeding patterns. We have developed a tool, called Treeomics, that utilizes Bayesian inference and Integer Linear Programming to reconstruct the phylogeny of metastases. Treeomics allowed us to infer comprehensive seeding patterns for pancreatic8, ovarian9, and prostate cancers10,11. Moreover, Treeomics correctly disambiguated true seeding patterns from sequencing artifacts; 7% of variants were misclassified by conventional statistical methods. These artifacts can skew phylogenies by creating illusory tumor heterogeneity among distinct samples. Last, we performed in silico benchmarking on simulated tumor phylogenies across a wide range of sample purities (30-90%) and sequencing depths (50-800x) to demonstrate the high accuracy of Treeomics compared to existing methods.

Cancer Biology↗

bioRxiv2018

Epigenetic analysis identifies factors driving racial disparity in prostate cancer

Prostate cancer (PCa) is the second most leading cause of death in men worldwide. African American men (AA) represent more aggressive form of PCa as compared to Caucasian (CA) counterparts. Evidence suggests that genetic and other biological factors could account for the observed racial disparity. We analyzed the cancer genome atlas (TCGA) dataset (2015) for existing epigenetic variation in AA and CA prostate cancer patients, and carried out Reduced Representation Bisulphite Sequencing (RRBS) analysis to identify global methylation changes in AA and CA prostate cancer patients. The TCGA dataset analysis revealed that the epigenetic heterogeneity could be categorized into 4 classes, where AA associated primarily to methylation cluster 1 (p value 0.048), and CA associated to methylation cluster 3 (p value 0.000146). We identified enrichment of Wnt signaling genes in both AA and CA, however they were differentially activated in terms of canonical and non-canonical Wnt signaling pathway activation. This was further validated using the GenomeDx expression data. Our RRBS data also suggested distinct methylation patterns in AA compared to CA, and in part validated our TCGA findings. Survival analysis using the RRBS data suggested hypomethylated genes to be significantly associated with recurrence of prostate cancer in CA (p=6.07x10-6) as well as in AA (p=0.0077). Overall, the observed racial disparity in the molecular mechanism involved in the pathogenesis of prostate cancer suggests diverse heterogeneity that potentially could affect survival and should be considered during prognosis and treatment.

cancer biology↗

bioRxiv2018

Infer related genes from large gene expression dataset with embedding

Artificial neural networks (ANNs) have been utilized for classification and prediction task with remarkable accuracy. However, its implications for unsupervised data mining using molecular data is under-explored. We adopted a method of unsupervised ANN, namely word embedding, to extract biologically relevant information from TCGA gene expression dataset. Ground truth relationship, such as cancer types of the input sample and semantic meaning of genes, were showed to retain in the resulting entity matrices. We also demonstrated the interpretability and usage of these matrices in shortlisting candidates from a long gene list. This method is feasible to mine big volume of biological data, and would be a valuable tool to discover novel knowledge from omics data. The resulting embedding matrices mined from TCGA gene expression data are interactively explorable online (http://bit.ly/tcga-embedding-cancer) and could serve as an informative reference.

cancer biology↗

bioRxiv2017

A multi-center study on factors influencing the reproducibility of in vitro drug-response studies

Evidence that some influential biomedical results cannot be repeated has increased interest in practices that generate data meeting findable, accessible, interoperable and reproducible (FAIR) standards. Multiple papers have identified examples of irreproducibility, but practical steps for increasing reproducibility have not been widely studied. Here, seven research centers in the NIH LINCS Program Consortium investigate the reproducibility of a prototypical perturbational assay: quantifying the responsiveness of cultured cells to anti-cancer drugs. Such assays are important for drug development, studying cell biology, and patient stratification. While many experimental and computational factors have an impact on intra- and inter-center reproducibility, the factors most difficult to identify and correct are those with a strong dependency on biological context. These factors often vary in magnitude with the drug being analyzed and with growth conditions. We provide ways of identifying such context-sensitive factors, thereby advancing the conceptual and practical basis for greater experimental reproducibility.

cancer biology↗

bioRxiv2018

O-GlcNAc modification of oncogenic transcription factor Sox2 promotes protein stability and regulates self-renewal in pancreatic cancer

Pancreatic cancer is among the 3rd leading cause of cancer related deaths in the United States along with a 5-year survival rate of 7%. The aggressive biology of the disease is responsible for such dismal outcome and is manifested by an increase in self-renewal capacity of the cancer cells, which leads to an increased rate of tumor-recurrence, contributing to poor prognosis. Transcription factor SOX2 maintains a critical balance between differentiation and \"stemness\" and is thus tightly regulated within a cell. In cancer, SOX2 is aberrantly \"turned-on\" leading to activation of self-renewal pathways in cancer. Regulation of Sox2 in cancer is poorly understood. In the current study, we show for the first time that in pancreatic cancer, Sox2 is modified by addition of O-GlcNAc moiety, catalyzed by OGT (O-GlcNAc Transferase) at S246. This activates Sox2 transcriptional activity by stabilizing the protein in the nucleus. A CRISPR-OGT knockout in pancreatic cancer cell line S2VP10 resulted in a delayed tumor initiation. We further showed that mutation of this site (S246A) prevents the modification of Sox2 and its downstream activity. Our study also demonstrated that targeting OGT in vivo with a small molecule inhibitor OSMI, results in decreased tumor burden, delayed tumor progression and a decreased expression of SOX2 in pancreatic cancer cells. Our study highlights for the first time that that the O-GlcNAc transferase dependent SOX2 glycosylation has a profound effect on the transcriptional activity of SOX2 and is instrumental in determining self-renewal in pancreatic cancer.\n\nSignificanceOur study highlights for the first time that that the O-GlcNAc transferase dependent SOX2 glycosylation determines self-renewal in pancreatic cancer which is responsible for tumor initiation.

cancer biology↗

bioRxiv2016

Homeostasis Back and Forth: An Eco-Evolutionary Perspective of Cancer

The role of genetic mutations in cancer is indisputable: they are a key source of tumor heterogeneity and drive its evolution to malignancy. But the success of these new mutant cells relies on their ability to disrupt the homeostasis that characterizes healthy tissues. Mutated clones unable to break free from intrinsic and extrinsic homeostatic controls will fail to establish a tumor. Here we will discuss, through the lens of mathematical and computational modeling, why an evolutionary view of cancer needs to be complemented by an ecological perspective in order to understand why cancer cells invade and subsequently transform their environment during progression. Importantly, this ecological perspective needs to account for tissue homeostasis in the organs that tumors invade, since they perturb the normal regulatory dynamics of these tissues, often co-opting them for its own gain. Furthermore, given our current lack of success in treating advanced metastatic cancers through tumor centric therapeutic strategies, we propose that treatments that aim to restore homeostasis could become a promising venue of clinical research. This eco-evolutionary view of cancer requires mechanistic mathematical models in order to both integrate clinical with biological data from different scales but also to detangle the dynamic feedback between the tumor and its environment. Importantly, for these models to be useful, they need to embrace a higher degree of complexity than many mathematical modelers are traditionally comfortable with.

cancer biology↗

bioRxiv2017

Transposable Element Expression Predicts Prognosis in Acute Myeloid Leukemia

Over half of the human genome is comprised of transposable elements (TE). TE have been implicated in cancer pathogenesis. Despite large-scale studies of the transcriptome in cancer, a comprehensive look at TE expression investigating its relationship to various mutations and its role in predicting prognosis has not been performed. We characterized TE expression in 178 adult acute myeloid leukemia (AML) patients using transcriptome data from The Cancer Genome Atlas (TCGA). We identified significant dysregulation of TE, with distinct patterns of TE expression correlated to specific mutations and distinct coding gene networks. TP53 mutated AML had a unique TE expression signature and was associated with significantly suppressed expression of TE and various classes of non-coding RNA. We identified 17 candidate prognostic TE transcripts that can classify AML subtypes as either high or low risk. These 17 TE were able to further sub-stratify low risk AML (based on mutational profile and coding gene expression) into favorable and unfavorable prognostic categories. The expression signature of the 17 TE was able to predict prognosis in an independent cohort of 284 pediatric AML patients, and was also able to predict time to relapse in an independent dataset of relapsed adult cases. This first comprehensive study of TE expression in AML demonstrates that TE expression can be used as a biomarker for predicting prognosis in AML and also provides novel insights into the biology of TP53 mutated AML. Studies characterizing its role in other cancers are warranted.

cancer biology↗

bioRxiv2017

Application of Atlas of Cancer Signalling Network in pre-clinical studies

Initiation and progression of cancer involve multiple molecular mechanisms. The knowledge on these mechanisms is expanding and should be converted into guidelines for tackling the disease. We discuss here formalization of biological knowledge into a comprehensive resource Atlas of Cancer Signalling Network (ACSN) and Google Maps-based tool NaviCell that supports map navigation. The application of maps for omics data visualisation in the context of signalling maps is possible using NaviCell Web Service module and NaviCom tool for generation of network-based molecular portraits of cancer using multi-level omics data. We review how these resources and tools are applied for cancer pre-clinical studies among others for rationalizing synergistic effect of drugs and designing complex disease stage-specific druggable interventions following structural analysis of the maps together with omics data. Modules and maps of ACSN as signatures of biological functions, can help in cancer data analysis and interpretation. In addition, they can also be used to find association between perturbations in particular molecular mechanisms to the risk of a specific cancer type development. These approaches and beyond help to study interplay between molecular mechanisms of cancer, deciphering how gene interactions govern hallmarks of cancer in specific context. We discuss a perspective to develop a flexible methodology and a pipeline to enable systematic omics data analysis in the context of signalling network maps, for stratifying patients and suggesting interventions points and drug repositioning in cancer and other human diseases.

systems biology↗

bioRxiv2017

Aging in a relativistic biological space-time

Here we present a theoretical and mathematical perspective on the process of aging. We extend the concepts of physical space and time to an abstract, mathematically-defined space, which we associate with a concept of \"biological space-time\" in which biological clocks operate. We hypothesize that biological dynamics, represented as trajectories in biological space-time, may be used to model and study different rates of biological aging. As a consequence of this hypothesis, we show how the dilation or contraction of time resulting from accelerated or decelerated biological dynamics may be used to study precipitous or protracted aging. We show specific examples of how these principles may be used to model different rates of aging, with an emphasis on cancer in aging. We discuss the implications of this theory, including novel concepts that may improve our interpretation of biological aging.

cancer biology↗

bioRxiv2017

FiloQuant reveals increased filopodia density during DCIS progression

Filopodia are commonly observed cellular protrusions in vitro and in vivo. Defective filopodia formation is linked to several pathologies including cancer, wherein actively protruding filopodia, at the invasive front, and filopodia-mediated probing of the microenvironment accompanies cancer cell dissemination. Despite wide biological significance, delineating the function of these finger-like protrusions in more complex systems remains technically challenging, particularly hindered by lack of compatible methods to quantify filopodia properties. Here, we present FiloQuant, a freely available ImageJ plugin, to detect filopodia and filopodia-like protrusions in both fixed and live-cell microscopy data. We demonstrate that FiloQuant can extract quantifiable information including protrusion dynamics, density and length from multiple cell types and in a range of microenvironments, such as during collective or single cancer cell migration in 2D and 3D, in fixed neuronal cultures, in activated natural killer cells and in sprouting endothelial cells in vivo. In cellular models of breast ductal carcinoma in situ (DCIS) we reveal a link between filopodia formation at the cell-matrix interface, during collective invasion and in 3D tumour spheroids, with the previously reported local invasive potential of these breast cancer models in vivo. Finally, using intravital microscopy, we observed that tumour spheroids display prominent filopodia in vivo, supporting a potential role for these protrusions during tumorigenesis.

cell biology↗

bioRxiv2018

Model for breast cancer diversity and spatial heterogeneity

We present and analyze a growth model of an avascular tumor that considers the basic biological principles of proliferation, motility, death and genetic mutations of the cell. From a regulatory network analysis and an analysis of genomic data we identify two sets of genes-a set of six genes and a set of sixteen genes-that are believed to play an important role in the evolution of breast cancer. Considering that cancer cells shape the tissue microenvironment and niches to their competitive advantage, the model assumes that cancer and normal cells compete for essential nutrients and that the rate of the \"driver\" mutations depends on nutrient availability. To this end, we propose a coupling between the transport of nutrients and gene mutations dynamics. Gene mutation dynamics are modeled as a Yule-Furry Markovian process, while transport of nutrients is described with a system of reaction-diffusion equations. For each representative tumor we calculate its diversity, represented by the Shannon index, and its spatial heterogeneity, measured by its fractal dimension. These quantities are important in the clinical diagnosis of tumor malignancy. A tumor malignancy diagram, obtained by plotting diversity versus fractal dimension, is calculated for different values of a parameter {beta}, which is related to the occurrence of driver mutations. It is found that when {beta} < 1, tumors show greater diversity and more spatial heterogeneity as compared with {beta} > 1. More importantly, it is found that the results and conclusions are similar when we use the six-gene set versus sixteen-gene set.

cancer biology↗

bioRxiv2018

OMCD: OncoMir Cancer Database

MicroRNAs (miRNAs) are crucially important in the development of cancer. Their dysregulation, commonly observed in various types of cancer, is largely cancer-dependent. Thus, to understand the tumor biology and to develop accurate and sensitive biomarkers, we need to understand pan-cancer miRNA expression. At the University of Minnesota, we developed the OncoMir Cancer Database (OMCD), hosted on a web server, which allows easy and systematic comparative genomic analyses of miRNA sequencing data derived from more than 9,500 cancer patients tissue samples available in the Cancer Genome Atlas (TCGA). OMCD includes associated clinical information and is searchable by organ-specific terms common to the TCGA. Freely available to all users (www.oncomir.umn.edu/omcd/), OMCD enables (1) simple visualization of TCGA miRNA sequencing data, (2) statistical analysis of differentially expressed miRNAs for each cancer type, and (3) exploration of miRNA clusters across cancer types.\n\nDatabase URLwww.oncomir.umn.edu/omcd

bioinformatics↗

bioRxiv2017

Gelsolin regulates proliferation, apoptosis and invasion in NK/T-cell lymphoma cells through the PI3K/Akt signaling pathway

The expression of gelsolin (GSN) is abnormal in many cancers, including extranodal nasal-type natural killer/T-cell lymphoma (NKTCL). However, the biological function of GSN and its mechanism in NKTCL remain unclear. We found GSN overexpression significantly suppressed cell proliferation, colony formationand invasion and promoted apoptosis of YTS cells. Moreover, the upregulation of GSN significantly decreased the protein levels of PI3K and p-AKT. Interestingly, blocking the PI3K/AKT signaling pathway significantly inhibited cell proliferation and invasion and promoted apoptosis of YTS cells. In conclusion, our findings indicate that GSN can suppress cell proliferation and invasion and promote apoptosis of YTS cells, which is likely to be mediated at least partially through inhibition of the PI3K/AKT signaling pathway.

cancer biology↗

bioRxiv2013

Microenvironmental variables must influence intrinsic phenotypic parameters of cancer stem cells to affect tumourigenicity

Since the discovery of tumour initiating cells (TICs) in solid tumours, studies focussing on their role in cancer initiation and progression have abounded. The biological interrogation of these cells continues to yield volumes of information on their pro-tumourigenic behaviour, but actionable generalised conclusions have been scarce. Further, new information suggesting a dependence of tumour composition and growth on the microenvironment has yet to be studied theoretically. To address this point, we created a hybrid, discrete/continuous computational cellular automaton model of a generalised stem-cell driven tissue with a simple microenvironment. Using the model we explored the phenotypic traits inherent to the tumour initiating cells and the effect of the microenvironment on tissue growth. We identify the regions in phenotype parameter space where TICs are able to cause a disruption in homeostasis, leading to tissue overgrowth and tumour maintenance. As our parameters and model are non-specific, they could apply to any tissue TIC and do not assume specific genetic mutations. Targeting these phenotypic traits could represent a generalizable therapeutic strategy across cancer types. Further, we find that the microenvironmental variable does not strongly effect the outcomes, suggesting a need for direct feedback from the microenvironment onto stem-cell behaviour in future modelling endeavours.\n\nAuthor SummaryIn this paper, we present a mathematical/computational model of a tumour growing according to the canonical cancer stem-cell hypothesis with a simplified microenvironment. We explore the parameters of this model and find good agreement between our model and other theoretical models in terms of the intrinsic cellular parameters, which are difficult to study biologically. We find, however, disagreement between novel biological data and our model in terms of the microenvironmental changes. We conclude that future theoretical models of stem-cell driven tumours must include specific feedback from the microenvironment onto the individual cellular behavior. Further, we identify several cell intrinsic parameters which govern loss of homeostasis into a state of uncontrolled growth.

Cancer Biology↗

bioRxiv2017

Differential Brd4-bound enhancers drive critical sex differences in glioblastoma

Sex can be an important determinant of cancer phenotype, and exploring sex-biased tumor biology holds promise for identifying novel therapeutic targets and new approaches to cancer treatment. In an established isogenic murine model of glioblastoma, we discovered correlated transcriptome-wide sex differences in gene expression, H3K27ac marks, large Brd4-bound enhancer usage, and Brd4 localization to Myc and p53 genomic binding sites. These sex-biased gene expression patterns were also evident in human glioblastoma stem cells (GSCs). These observations led us to hypothesize that Brd4-bound enhancers might underlie sex differences in stem cell function and tumorigenicity in GBM. We found that male and female GBM cells exhibited opposing responses to pharmacological or genetic inhibition of Brd4. Brd4 knockdown or pharmacologic inhibition decreased male GBM cell clonogenicity and in vivo tumorigenesis, while increasing both in female GBM cells. These results were validated in male and female patient-derived GBM cell lines. Furthermore, analysis of the Cancer Therapeutic Response Portal of human GBM samples segregated by sex revealed that male GBM cells are significantly more sensitive to BET inhibitors than are female cells. Thus, for the first time, Brd4 activity is revealed to drive a sex differences in stem cell and tumorigenic phenotype, resulting in diametrically opposite responses to BET inhibition in male and female GBM cells. This has important implications for the clinical evaluation and use of BET inhibitors. SignificanceConsistent sex differences in incidence and outcome have been reported in numerous cancers including brain tumors. GBM, the most common and aggressive primary brain tumor, occurs with higher incidence and shorter survival in males compared to females. Brd4 is essential for regulating transcriptome-wide gene expression and specifying cell identity, including that of GBM. We report that sex-biased Brd4 activity drive sex differences in GBM and render male and female tumor cells differentially sensitive to BET inhibitors. The observed sex differences in BETi treatment strongly indicate that sex differences in disease biology translate into sex differences in therapeutic responses. This has critical implications for clinical use of BET inhibitors further affirming the importance of inclusion of sex as a biological variable.

genomics↗