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Results for “Cancer Biology”

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Hybrid Discrete-Continuum Cellular Automaton (HCA) model of Prostate to Bone Metastasis

Prostate to bone metastases induce a \"vicious cycle\" by promoting excessive osteoclast and osteoblast mediated bone degradation and formation that in turn yields factors that drive cancer growth. Recent advances defining the molecular mechanisms that control the vicious cycle have revealed new therapeutic targeting opportunities. However, given the complex temporal and simultaneous cellular interactions occurring in the bone microenvironment, assessing the impact of putative therapies is challenging. To this end, we have integrated biological and computational approaches to generate an accurate model of normal bone matrix homeostasis and the prostate cancer-bone microenvironment. The model faithfully reproduces the basic multicellular unit (BMU) bone coupling process and introduction of a single prostate cancer cell yields a vicious cycle that is similar in cellular composition and pathophysiology to models of prostate to bone metastasis.

Cancer Biology

Calculating biological module enrichment or depletion and visualizing data on large-scale molecular maps with ACSNMineR and RNaviCell R packages

Biological pathways or modules represent sets of interactions or functional relationships occurring at the molecular level in living cells. A large body of knowledge on pathways is organized in public databases such as the KEGG, Reactome, or in more specialized repositories, such as the Atlas of Cancer Signaling Network (ACSN). All these open biological databases facilitate analyses, improving our understanding of cellular systems. We hereby describe the R package ACSNMineR for calculation of enrichment or depletion of lists of genes of interest in biological pathways. ACSNMineR integrates ACSN molecular pathways, but can use any molecular pathway encoded as a GMT file, for instance sets of genes available in the Molecular Signatures Database (MSigDB). We also present the R package RNaviCell, that can be used in conjunction with ACSNMineR to visualize different data types on web-based, interactive ACSN maps. We illustrate the functionalities of the two packages with biological data taken from large-scale cancer datasets.

Bioinformatics

The value of prior knowledge in machine learning of complex network systems

Our overall goal is to develop machine learning approaches based on genomics and other relevant accessible information for use in predicting how a patient will respond to a given proposed drug or treatment. Given the complexity of this problem, we begin by developing, testing, and analyzing learning methods using data from simulated systems, which allows us access to a known ground truth. We examine the benefits of using prior system knowledge and investigate how learning accuracy depends on various system parameters as well as the amount of training data available. The simulations are based on Boolean networks - directed graphs with 0/1 node states and logical node update rules - which are the simplest computational systems that can mimic the dynamic behavior of cellular systems. Boolean networks can be generated and simulated at scale, have complex yet cyclical dynamics, and as such provide a useful framework for developing machine learning algorithms for modular and hierarchical networks such as biological systems in general and cancer in particular. We demonstrate that utilizing prior knowledge (in the form of network connectivity information), without detailed state equations, greatly increases the power of machine learning algorithms to predict network steady state node values (\"phenotypes\") and perturbation responses (\"drug effects\").

bioinformatics

Pathway-Level Integration of Proteogenomic data in Breast Cancer Using Independent Component Analysis

Recent advances in the multi-omics characterization necessitate pathway-level abstraction and knowledge integration across different data types. In this study, we apply independent component analysis (ICA) to human breast cancer proteogenomics data to retrieve mechanistic information. We show that as an unsupervised feature extraction method, ICA was able to construct signatures with known biological relevance on both transcriptome and proteome levels. Moreover, proteome and transcriptome signatures can be associated by their respective correlation with patient clinical features, providing an integrated description of phenotype-related biological processes. Our results demonstrate that the application of ICA to proteogenomics data could lead to pathway-level knowledge discovery. Potential extension of this approach to other data and cancer types may contribute to pan-cancer integration of multi-omics information.

cancer biology

Pan-cancer characterisation of microRNA with hallmarks of cancer reveals role of microRNA-mediated downregulation of tumour suppressor genes

microRNA are key regulators of the human transcriptome across a number of diverse biological processes, such as development, aging, and cancer, where particular miRNA have been identified as tumour suppressive and oncogenic. In this work, we sought to elucidate, in a comprehensive manner, across 15 epithelial cancer types comprising 7,316 clinical samples from the Cancer Genome Atlas, the association of miRNA expression and target regulation with the pheno-typic hallmarks of cancer. Utilising penalized regression techniques to integrate transcriptomic, methylation and mutation data, we find evidence for a complex map of interactions underlying the relationship of miRNA regulation and the hallmarks of cancer. This highlighted high redundancy for the oncomiR-1 cluster of oncogenic miRNAs, in particular hsa-miR-17-5p. In addition, we reveal extensive miRNA regulation of tumour suppressor genes such as PTEN, FAT4, and CDK12, uncovering an alternative mechanism of repression in the absence of mutation, methylation or copy number changes.

genomics

Estimation of the cancer risk induced by rejuvenation therapy with young blood and treatment recommendations

In recent years the transfer of blood from young to old individuals was shown to bear the potential of rejuvenation of stem cell activity. While this process might increase life expectancy by prolonging functionality of organs, higher cell replication rates bear also the risk of cancer. The extent of this risk is not known.\n\nWhile it is difficult to evaluate this cancer risk in experiments, this is possible with a mathematical model for tissue homeostasis by stem cell replication and associated cancer risk. The model suggests that young blood treatments can induce a substantial delay of organ failure with only minor increase in cancer risk. The benefit of rejuvenation therapy as well as the impact on cancer risk depend on the biological age at the time of treatment and on the overall cell turnover rate of the organs. Different organs have to be considered separately in the planning of the systemic treatment. In particular, the model predicts that the treatment schedules successfully applied in mice are not directly transferable to humans and guidelines for successful protocols are proposed. The model presented here may be used as a guidance for the development of treatment protocols.\n\nAdditional informationThere is NO competing interests.

systems biology

Integrated sequence and gene expression analysis of mouse models of breast cancer reveals critical events with human parallels

Mouse models have an essential role in cancer research, yet little is known about how various models resemble human cancer at a genomic level. However, the shared genomic alterations in each model and corresponding human cancer are critical for translating findings in mice to the clinic. We have completed whole genome sequencing and transcriptome profiling of two widely used mouse models of breast cancer, MMTV-Neu and MMTV-PyMT. This genomic information was integrated with phenotypic data and CRISPR/Cas9 studies to understand the impact of key events on tumor biology. Despite the engineered initiating transgenic event in these mouse models, they contain similar copy number alterations, single nucleotide variants, and translocation events as human breast cancer. Through integrative in vitro and in vivo studies, we identified copy number alterations in key extracellular matrix proteins including Collagen 1 Type 1 alpha 1 (Col1a1) and Chondroadherin (CHAD) that drive metastasis in these mouse models. Importantly this amplification is also found in 25% of HER2+ human breast cancer and is associated with increased metastasis. In addition to copy number alterations, we observed a propensity of the tumors to modulate tyrosine kinase mediated signaling through mutation of phosphatases. Specifically, we found that 81% of MMTV-PyMT tumors have a mutation in the EGFR regulatory phosphatase, PTPRH. Mutation in PTPRH led to increased phospho-EGFR levels and decreased latency. Moreover, PTPRH mutations increased response to EGFR kinase inhibitors. Analogous PTPRH mutations are present in lung cancer patients and together this data suggests that a previously unidentified population of human lung cancer patients may respond to EGFR targeted therapy. These findings underscore the importance of understanding the complete genomic landscape of a mouse model and illustrate the utility this has in understanding human cancers.

cancer biology

Identification of DISE-inducing shRNAs by monitoring cellular responses

Off-target effects (OTE) are an undesired side effect of RNA interference (RNAi) caused by partial complementarity between the targeting siRNA and mRNAs other than the gene to be silenced. The death receptor CD95 and its ligand CD95L contain multiple sequences that when expressed as either si-or shRNAs kill cancer cells through a defined OTE that targets critical survival genes. Death induced by survival gene elimination (DISE) is characterized by specific morphological changes such as elongated cell shapes, senescence-like enlarged cells, appearance of large intracellular vesicles, release of mitochondrial ROS followed by activation of caspase-2, and induction of a necrotic form of mitotic catastrophe. Using genome-wide shRNA lethality screens with eight different cancer cell lines, we recently identified 651 genes as critical for the survival of cancer cells. To determine whether the toxic shRNAs targeting these 651 genes contained shRNAs that kill cancer cell through DISE rather than by silencing their respective target genes, we tested all shRNAs in the TRC library derived from a subset of these genes targeting tumor suppressors (TS). We now report that only by monitoring the responses of cancer cells following expression of shRNAs derived from these putative TS it was possible to identify DISE-inducing shRNAs in five of the genes. These data indicate that DISE in general is not an undefined toxic response of cells caused by a random OTE but rather a specific cellular response with shared features that points at a specific biological function involving multiple genes in the genome.

cancer biology

Personalized Regression Enables Sample-Specific Pan-Cancer Analysis

In many applications, inter-sample heterogeneity is crucial to understanding the complex biological processes under study. For example, in genomic analysis of cancers, each patient in a cohort may have a different driver mutation, making it difficult or impossible to identify causal mutations from an averaged view of the entire cohort. Unfortunately, many traditional methods for genomic analysis seek to estimate a single model which is shared by all samples in a population, ignoring this inter-sample heterogeneity entirely. In order to better understand patient heterogeneity, it is necessary to develop practical, personalized statistical models. To uncover this inter-sample heterogeneity, we propose a novel regularizer for achieving patient-specific personalized estimation. This regularizer operates by learning two latent distance metrics - one between personalized parameters and one between clinical covariates - and attempting to match the induced distances as closely as possible. Crucially, we do not assume these distance metrics are already known. Instead, we allow the data to dictate the structure of these latent distance metrics. Finally, we apply our method to learn patient-specific, interpretable models for a pan-cancer gene expression dataset containing samples from more than 30 distinct cancer types and find strong evidence of personalization effects between cancer types as well as between individuals. Our analysis uncovers sample-specific aberrations that are overlooked by population level methods, suggesting a promising new path for precision analysis of complex diseases such as cancer.

bioinformatics

Comparing cancer cell lines and tumor samples by genomic profiles

Cancer cell lines are often used in laboratory experiments as models of tumors, although they can have substantially different genetic and epigenetic profiles compared to tumors. We have developed a general computational method - TumorComparer - to systematically quantify similarities and differences between tumor material when detailed genetic and molecular profiles are available. The comparisons can be flexibly tailored to a particular biological question by placing a higher weight on functional alterations of interest ( weighted similarity). In a first pan-cancer application, we have compared 260 cell lines from the Cancer Cell Line Encyclopaedia (CCLE) and 1914 tumors of six different cancer types from The Cancer Genome Atlas (TCGA), using weights to emphasize genomic alterations that frequently recur in tumors. We report the potential suitability of particular cell lines as tumor models and identify apparently unsuitable outlier cell lines, some of which are in wide use, for each of the six cancer types. In future, this weighted similarity method may be generalized for use in a clinical setting to compare patient profiles consisting of genomic patterns combined with clinical attributes, such as diagnosis, treatment and response to therapy.

Cancer Biology

Discovery and optimization of piperazine-1-thiourea-based human phosphoglycerate dehydrogenase inhibitors

Proliferating cells, including cancer cells, obtain serine both exogenously and via the metabolism of glucose. By catalyzing the first, rate-limiting step in the synthesis of serine from glucose, phosphoglycerate dehydrogenase (PHGDH) controls flux through the biosynthetic pathway for this important amino acid and represents a putative target in oncology. To discover inhibitors of PHGDH, a coupled biochemical assay was developed and optimized to enable high-throughput screening for inhibitors of human PHGDH. Feedback inhibition was minimized by coupling PHGDH activity to two downstream enzymes (PSAT1 and PSPH), providing a significant improvement in enzymatic turnover. Further coupling of NADH to a diaphorase/resazurin system enabled a red-shifted detection readout, minimizing interference due to compound autofluorescence. With this protocol, over 400,000 small molecules were screened for PHGDH inhibition, and following hit validation and triage work, a piperazine-1-thiourea was identified. Following rounds of medicinal chemistry and SAR exploration, two probes (NCT-502 and NCT-503) were identified. These molecules demonstrated improved target activity and encouraging ADME properties, enabling both in vitro and in vivo assessment of the biological importance of PHGDH, and its role in the fate of serine in PHGDH-dependent cancer cells.

cancer biology

IMO-HIP 2015 Report: An Evolutionary Game Theory Approach to evolutionary-enlightened application of chemotherapy in bone metastatic prostate cancer

Prostate cancer metastasis to the bone is predominantly lethal and results from the ability of successful metastatic prostate cancer cells to co-opt microenvironmental cells and processes involved in bone remodelling. Understanding how the interactions between tumour and stromal cells determine successful metastases and how metastatic tumours respond to treatment is an emergent process that is hard to asses biologically and thus can benefit from mathematical models. In this work we describe a mathematical model of bone remodelling and the establishment of a prostate cancer metastasis in the bone using evolutionary game theory. We have mathematically recapitulated the current paradigm of a vicious cycle driving the tumor growth and we have used this tool to investigate the key interactions between the tumour and the bone stroma. Crucially, the model sheds light on the role that the interactions of heterogeneous tumor cells with the bone microenvironment have in the treatment of cancer. Our results show that resistant populations naturally become dominant in the metastases under a number treatment schemes and that schedules designed by an evolutionary game theory approach could be used to better control the tumour and the associated bone growth than the current standard of care.

Cancer Biology

Molecular characterization of breast and lung tumors by integration of multiple data types with sparse-factor analysis

Effective cancer treatment is crucially dependent on the identification of the biological processes that drive a tumor. However, multiple processes may be active simultaneously in a tumor. Clustering is inherently unsuitable to this task as it assigns a tumor to a single cluster. In addition, the wide availability of multiple data types per tumor provides the opportunity to profile the processes driving a tumor more comprehensively.\n\nHere we introduce Functional Sparse-Factor Analysis (funcSFA) to address these challenges. FuncSFA integrates multiple data types to define a lower dimensional space capturing the relevant variation. A tailor-made module associates biological processes with these factors. FuncSFA is inspired by iCluster, which we improve in several key aspects. First, we increase the convergence efficiency significantly, allowing the analysis of multiple molecular datasets that have not been pre-matched to contain only concordant features. Second, FuncSFA does not assign tumors to discrete clusters, but identifies the dominant driver processes active in each tumor. This is achieved by a regression of the factors on the RNA expression data followed by a functional enrichment analysis and manual curation step.\n\nWe apply FuncSFA to the TCGA breast and lung datasets. We identify EMT and Immune processes common to both cancer types. In the breast cancer dataset we recover the known intrinsic subtypes and identify additional processes. These include immune infiltration and EMT, and processes driven by copy number gains on the 8q chromosome arm. In lung cancer we recover the major types (adenocarcinoma and squamous cell carcinoma) and processes active in both of these types. These include EMT, two immune processes, and the activity of the NFE2L2 transcription factor.\n\nIn summary, FuncSFA is a robust method to perform discovery of key driver processes in a collection of tumors through unsupervised integration of multiple molecular data types and functional annotation.\n\nAuthor SummaryIn order to select effective cancer treatment, we need to determine which biological processes are active in a tumor. To this end, tumors have been quantified by high dimensional molecular measurements such as RNA sequencing and DNA copy number profiling. In order to support decision making, these measurements need to be condensed into interpretable summaries. Such summaries can be made interpretable by connecting them to biological processes.\n\nBiological process activity is continuous and multiple biological processes are taking place in a single tumor. Therefore, the biological processes associated with a tumor are misrepresented by clustering, which tries to put every tumor in a single cluster. In the method introduced in this paper (funcSFA), molecular measurements are summarized into a small number factors. A factor is a continuous value per tumor that aims to represent the activity of a biological process.\n\nWhen applied to breast and lung cancer, funcSFA identifies factors covering well known biology of these tumor types. FuncSFA also finds novel factors covering biology whose importance is not yet widely recognized in these tumor types. Some of the factors suggest treatment opportunities that can be further investigated in cell lines and mice.

bioinformatics

C. elegans models of Marfan and Marfan-like Syndromes reveal trafficking defects of the type II TGFβ receptor as a potential novel disease mechanism

The transforming growth factor-{beta} (TGF{beta}) family plays an important role in many developmental processes and when mutated often contributes to various diseases. Marfan syndrome is a genetic disease with an occurrence of approximately 1 in 5,000. The disease is caused by mutations in fibrillin, which lead to an increase in TGF{beta} ligand activity, resulting in abnormalities of connective tissues which can be life-threatening. Mutations in other components of TGF{beta} signaling (receptors, Smads, Schnurri) lead to similar diseases with attenuated phenotypes relative to Marfan syndrome. In particular, mutations in TGF{beta} receptors, most of which are clustered at the C-terminal end, result in Marfan-like (MFS-like) syndromes. Even though it was assumed that many of these receptor mutations would reduce or eliminate signaling, in many cases signaling is active. From our trafficking work in C. elegans, we noticed that many of these receptor mutations that lead to Marfan-like syndromes overlap with mutations that cause mis-trafficking of the receptor, suggesting a link between Marfan-like syndromes and TGF{beta} receptor trafficking. To test this hypothesis, we introduced some of these key MFS and MFS-like mutations into the C. elegans TGF{beta} receptor and asked if receptor trafficking is altered. We find that some of the mutated receptors localize to the apical surface rather than basolateral surface of the polarized intestinal cells. Further, we find that these mutations result in longer animals, a phenotype due to over-stimulation of the pathway and, importantly, indicating that function of the receptor is not abrogated in these mutants. Our nematode models of Marfan syndrome suggest that MFS and MFS-like mutations in the type II receptor lead to mis-trafficking of the receptor and possibly provides an explanation for the disease, a phenomenon which might also occur in some cancers that possess the same mutations within the type II receptor (e.g. colon cancer). Author SummaryThe transforming growth factor-{beta} (TGF{beta}) family plays an important role in many basic biological processes and when mutated often contributes to various diseases. Marfan syndrome (MFS) is a genetic disease with an occurrence of approximately 1 in 5,000. The disease is caused by mutations in fibrillin, which lead to an increase in TGF{beta} ligand activity, resulting in abnormalities of connective tissues which can be life-threatening. However, some patients with normal fibrillin genes also show symptoms and pathologies associated with Marfan syndrome, known collectively as Marfan-like Syndromes (MFS-like). When these patients were assessed for mutations in other components of the TGF{beta} pathway, several mutations clustered in a small region of the receptors, primarily in the type II TGF{beta} receptor were found. We find that mimicking these mutations in the nematode type II TGF{beta} receptor causes the mutant receptor to move to regions of the cell where it is not normally found. Importantly, these mutations do not abrogate the function of the receptor, suggesting that mis-localization of the receptor might be previously unknown cause of disease etiology. We hypothesize that receptor mutations present at the LTA lead to disruptions in interactions with trafficking regulators leading to symptoms of MFS/MFS-like syndromes - a novel disease mechanism of MFS/MFS-like syndromes that might also extend to other cancers bearing similar mutations.

cancer biology

Candidate cancer driver mutations in super-enhancers and long-range chromatin interaction networks

A comprehensive catalogue of the mutations that drive tumorigenesis and progression is essential to understanding tumor biology and developing therapies. Protein-coding driver mutations have been well-characterized by large exome-sequencing studies, however many tumors have no mutations in protein-coding driver genes. Non-coding mutations are thought to explain many of these cases, however few non-coding drivers besides TERT promoter are known. To fill this gap, we analyzed 150,000 cis-regulatory regions in 1,844 whole cancer genomes from the ICGC-TCGA PCAWG project. Using our new method, ActiveDriverWGS, we found 41 frequently mutated regulatory elements (FMREs) enriched in non-coding SNVs and indels (FDR<0.05) characterized by aging-associated mutation signatures and frequent structural variants. Most FMREs are distal from genes, reported here for the first time and also recovered by additional driver discovery methods. FMREs were enriched in super-enhancers, H3K27ac enhancer marks of primary tumors and long-range chromatin interactions, suggesting that the mutations drive cancer by distally controlling gene expression through threedimensional genome organization. In support of this hypothesis, the chromatin interaction network of FMREs and target genes revealed associations of mutations and differential gene expression of known and novel cancer genes (e.g., CNNB1IP1, RCC1), activation of immune response pathways and altered enhancer marks. Thus distal genomic regions may include additional, infrequently mutated drivers that act on target genes via chromatin loops. Our study is an important step towards finding such regulatory regions and deciphering the somatic mutation landscape of the non-coding genome.

cancer biology

Novel insights into the molecular heterogeneity of hepatocellularcarcinoma

Hepatocellular carcinoma (HCC) is influenced by numerous factors, which results in diverse genetic, epigenetic and transcriptional scenarios, thus posing obvious challenges for disease management. We scrutinized the molecular heterogeneity of HCC with a multi-omics approach in two small cohorts of resected and explanted livers. Whole-genome transcriptomics was conducted, including polyadenylated transcripts and micro (mi)-RNAs. Copy number variants (CNV) were inferred from whole genome low-pass sequencing data. Fifty-six cancer-related genes were screened using an oncology panel assay. HCC was associated with a dramatic transcriptional deregulation of hundreds of protein-coding genes suggesting downregulation of drugs catabolism, induction of inflammatory responses, and increased cell proliferation in resected livers. Moreover, several long non-coding RNAs and miRNAs not reported previously in the context of HCC were found deregulated. In explanted livers, downregulation of genes involved in energy-producing processes and upregulation of genes aiding in glycolysis were detected. Numerous CNV events were observed, with conspicuous hotspots on chromosomes 1 and 17. Amplifications were more common than deletions, and spanned regions containing genes potentially involved in tumorigenesis. CSF1R, FGFR3, FLT3, NPM1, PDGFRA, PTEN, SMO and TP53 were mutated in all tumors, while other 26 cancer-related genes were mutated with variable penetrance. Our results highlight a remarkable molecular heterogeneity between HCC tumors and reinforce the notion that precision medicine approaches are urgently needed for cancer treatment. We expect that our results will serve as a valuable dataset that will generate hypotheses for us or other researchers to evaluate to ultimately improve our understanding of HCC biology.

cancer biology

Integrated single-nucleotide and structural variation signatures of DNA-repair deficient human cancers

Mutation signatures in cancer genomes reflect endogenous and exogenous mutational processes, offering insights into tumour etiology, features for prognostic and biologic stratification and vulnerabilities to be exploited therapeutically. We present a novel machine learning formalism for improved signature inference, based on multi-modal correlated topic models (MMCTM) which can at once infer signatures from both single nucleotide and structural variation counts derived from cancer genome sequencing data. We exemplify the utility of our approach on two hormone driven, DNA repair deficient cancers: breast and ovary (n=755 cases total). Our results illuminate a new age-associated structural variation signature in breast cancer, and an independently identified substructure within homologous recombination deficient (HRD) tumours in breast and ovarian cancer. Together, our study emphasizes the importance of integrating multiple mutation modes for signature discovery and patient stratification, with biological and clinical implications for DNA repair deficient cancers.

cancer biology

Clusterization in head and neck squamous carcinomas based on lncRNA expression: molecular and clinical correlates

BackgroundLong non-coding RNAs (lncRNAs) have emerged as key players in a remarkably variety of biological processes and pathologic conditions, including cancer. Next-generation sequencing technologies and bioinformatics procedures predict the existence of tens of thousands of lncRNAs, from which we know the functions of only a handful of them, and very little is known in cancer types such as head and neck squamous cell carcinomas (HNSCCs).\n\nResultsHere, we use RNA-seq expression data from The Cancer Genome Atlas (TCGA) and various statistic and software tools in order to get insight about the lncRNome in HNSCC. Based on lncRNAs expression across 426 samples, we discover five distinct tumor clusters that we compare with reported clusters based on various genomic/genetic features. Results demonstrate significant associations between lncRNA-based clustering and DNA-methylation, TP53 mutation, and human papillomavirus infection. Using \"guilt by association\" procedures, we infer the possible biological functions of representative lncRNAs of each cluster. Furthermore, we found that lncRNA clustering is correlated with some important clinical and pathologic features, including patient survival after treatment, tumor grade or sub-anatomical location.\n\nConclusionsWe present a landscape of lncRNAs in HNSCC, and provide associations with important genotypic and phenotypic features that may help to understand the disease.

genomics