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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 145 records · Page 8Linked to original sources

bioRxiv2015

Bringing Down Cancer Aircraft: Searching For Essential Hypomutated Proteins In Skin Melanoma

Abstract\n\nBackgroundWe propose an approach to detection of essential proteins required for cancer cell survival. Gene is considered essential if mutation with high functional impact upon function of encoded protein causes death of cancer cell. We draw an analogy between essential cancer proteins and well-known Abraham Walds work on estimating the plane critical areas using data on survivability of aircraft encountering enemy fire. Wald reasoned that parts hit least on the returned planes are critical and should be protected more. Similarly we propose that genes essential for tumor cell should carry less high-impact mutations in cancer compared to polymorphisms found in normal cells.\n\nResultsWe used data on mutations from the Cancer Genome Atlas and polymorphisms found in healthy humans (from 1000 Genomes Project) to predict 91 protein-coding genes essential for melanoma. These genes were selected according to several criteria including negative selection, expression in melanocytes and decrease in the proportion of high-impact mutations in cancer compared with normal cells.\n\nGene ontology analysis revealed enrichment of essential proteins related to membrane and cell periphery. We speculate that this could be a sign of immune system-driven negative selection of cancer neo-antigens. Another finding is overrepresentation of semaphorin receptors, which can mediate distinctive signaling cascades and are involved in various aspects of tumor development. Cytokine receptors CCR5 and CXCR1 were also identified as cancer essential proteins and this is confirmed by other studies.\n\nConclusionsOverall our goal was to illustrate the idea of detecting proteins whose sequence integrity and functioning is important for cancer cell survival. Hopefully, this prediction of essential cancer proteins may point to new targets for anti-tumor therapies.

Cancer Biology↗

bioRxiv2015

A New Mutation-Profile-Based Method for Understanding the Evolution of Cancer Somatic Mutations

Human genes exhibit different effects on fitness in cancer and normal cells. Here, we present an evolutionary approach to measure the selection pressure on human genes, using the well-known ratio of the nonsynonymous to synonymous substitution rate in both cancer genomes (CN/CS) and normal populations (pN/pS). A new mutation-profile-based method that adopts sample-specific mutation rate profiles instead of conventional substitution models was developed. We found that cancer-specific selection pressure is quite different from the selection pressure at the species and population levels. Both the relaxation of purifying selection on passenger mutations and the positive selection of driver mutations may contribute to the increased CN/CS values of human genes in cancer genomes compared with the pN/pS values in human populations. The CN/CS values also contribute to the improved classification of cancer genes and a better understanding of the onco-functionalization of cancer genes during oncogenesis. The use of our computational pipeline to identify cancer-specific positively and negatively selected genes may provide useful information for understanding the evolution of cancers and identifying possible targets for therapeutic intervention.

Cancer Biology↗

bioRxiv2015

Comprehensive nucleosome mapping of the human genome in cancer progression

Altered chromatin structure is a hallmark of cancer, and inappropriate regulation of chromatin structure may represent the origin of transformation. Important studies have mapped human nucleosome distributions genome wide, but the role of chromatin structure in cancer progression has not been addressed. We developed a MNase-Transcription Start Site Sequence Capture method (mTSS-seq) to map the nucleosome distribution at human transcription start sites genome-wide in primary human lung and colon adenocarcinoma tissue. Here, we confirm that nucleosome redistribution is an early, widespread event in lung (LAC) and colon (CRC) adenocarcinoma. These altered nucleosome architectures are consistent between LAC and CRC patient samples indicating that they may serve as important early adenocarcinoma markers. We demonstrate that the nucleosome alterations are driven by the underlying DNA sequence and potentiate transcription factor binding. We conclude that DNA-directed nucleosome redistributions are widespread early in cancer progression. We have proposed an entirely new hierarchical model for chromatin-mediated genome regulation.\n\nSignificanceThis is the first report of human nucleosome distribution in cancer progression using sequence capture and HiSeq. We show in lung and colorectal adenocarcinoma patients that nucleosome distribution is a widespread, early response driven by genetically-encoded signals and potentiate regulatory factor binding. We present a model for chromatin-based hierarchical regulation.

Cancer Biology↗

bioRxiv2015

Gibbs Free Energy of Protein-Protein Interactions Reflects Tumor Stage

The sequential changes occurring with cancer progression are now being harnessed with therapeutic intent. Yet, there is no understanding of the chemical thermodynamics of proteomic changes associated with cancer progression/cancer stage. This manuscript reveals a strong correlation of a chemical thermodynamic measure (known as Gibbs free energy) of protein-protein interaction networks for several cancer types and 5-year overall survival and stage in patients with cancer. Earlier studies have linked degree entropy of signaling networks to patient survival data, but not with stage. It appears that Gibbs free energy is a more general metric and accounts better for the underlying energetic landscape of protein expression in cells, thus correlating with stage as well as survival.\n\nThis is an especially timely finding because of improved ability to obtain and analyze genomic/proteomic information from individual patients. Yet, at least at present, only candidate gene imaging (FISH or immunohistochemistry) can be used for entropy computations. With continually expanding use of genomic information in clinical medicine, there is an ever-increasing need to understand the thermodynamics of protein-protein interaction networks.

Cancer Biology↗

bioRxiv2015

Cancer evolution simulation identifies possible principles underlying intratumor heterogeneity

Cancer arises from accumulation of somatic mutations and accompanying evolutionary selection for growth advantage. During the evolutionary process, an ancestor clone branches into multiple clones, yielding intratumor heterogeneity. However, principles underlying intratumor heterogeneity have been poorly understood. Here, to explore the principles, we built a cellular automaton model, termed the BEP model, which can reproduce the branching cancer evolution in silico. We then extensively searched for conditions leading to high intratumor heterogeneity by performing simulations with various parameter settings on a supercomputer. Our result suggests that multiple driver genes of moderate strength can shape subclonal structures by positive natural selection. Moreover, we found that high mutation rate and a stem cell hierarchy can contribute to extremely high intratumor heterogeneity, which is characterized by fractal patterns, through neutral evolution. Collectively, This study identified the possible principles underlying intratumor heterogeneity, which provide novel insights into the origin of cancer robustness and evolvability.

Cancer Biology↗

bioRxiv2015

Large-scale analysis of genome and transcriptome alterations in multiple tumors unveils novel cancer-relevant splicing networks

Alternative splicing is regulated by multiple RNA-binding proteins and influences the expression of most eukaryotic genes. However, the role of this process in human disease, and particularly in cancer, is only starting to be unveiled. We systematically analyzed mutation, copy number and gene expression patterns of 1348 RNA-binding protein (RBP) genes in 11 solid tumor types, together with alternative splicing changes in these tumors and the enrichment of binding motifs in the alternatively spliced sequences. Our comprehensive study reveals widespread alterations in the expression of RBP genes, as well as novel mutations and copy number variations in association with multiple alternative splicing changes in cancer drivers and oncogenic pathways. Remarkably, the altered splicing patterns in several tumor types recapitulate those of undifferentiated cells. These patterns are predicted to be mainly controlled by MBNL1 and involve multiple cancer drivers, including the mitotic gene NUMA1. We show that NUMA1 alternative splicing induces enhanced cell proliferation and centrosome amplification in non-tumorigenic mammary epithelial cells. Our study uncovers novel splicing networks that potentially contribute to cancer development and progression.

Cancer Biology↗

bioRxiv2015

An integrated transcriptomics and proteomics study of Head and Neck Squamous Cell Carcinoma – methodological and analytical considerations.

High throughput molecular profiling and integrated data analysis with tumor tissues require overcoming challenges like tumor heterogeneity and tissue paucity. This study is an attempt to understand and optimize various steps during tissue processing and in establishing pipelines essential for integrated analysis. Towards this effort, we subjected laryngo-pharyngeal primary tumors and the corresponding adjacent normal tissues (n=2) to two RNA and protein isolation methods, one wherein RNA and protein were isolated from the same tissue sequentially (Method 1) and second, wherein the extraction was carried out using two independent methods (Method 2). RNA and protein from both methods were subjected to RNA-seq and iTRAQ based LC-MS/MS analysis. Transcript and peptide identification and quantification was followed by both individual-ome and integrated data analysis. As a result of this analysis, we identified a higher number of total, as well as differentially expressed (DE) transcripts (1329 vs 1134) and proteins (799 vs 408) with fold change [≥] 2.0, in Method 1. Among these, 173 and 86 entities were identified by both transcriptome and proteome analysis in Method 1 and 2, respectively, with higher concordance in the regulation trends observed in the former. The significant cancer related pathways enriched with the individual DE transcript or protein data were similar in both the methods. However, the entities mapping to them were different, allowing enhanced view of the pathways identified after integration of the data and subsequent mapping. The concordant DE transcripts and proteins also revealed key molecules of the pathways with important roles in cancer development. This study thus demonstrates that sequential extraction of the RNA and proteins from the same tissue allows for better profiling of differentially expressed entities and a more accurate integrated data analysis.\n\nAuthor ContributionsARB, MKG, PK and SK contributed final data analysis. KD and JN were involved in the RNASeq experiments while MKG, SHV LB and SC were involved in the iTRAQ MS/MS analysis. RBR and HV contributed towards the standardization of sample collection and processing, and were also involved in obtaining clinical information of the patients along with SD. VK and MAK were involved in study design, providing clinical insights into the analysis and in critical assessment of the manuscript. ARB, MKG and PK were involved in manuscript preparation. AS, PT, BP, MAK and RS were involved in the establishing the study design, overall monitoring of the experimental results and manuscript preparation. PT, MAK, BP and RS are the lead investigators of the project.\n\nSignificance of the studyThe study highlights the need to optimize tissue processing and analytical pipelines to enable accurate integrated analysis of high throughput omics data; a sequential extraction of RNA and protein entities and subsequent integrated analysis was identified to provide a better representation of the molecular profile in terms concordant entities and pathways.\n\n\n\nO_FIG O_LINKSMALLFIG WIDTH=166 HEIGHT=200 SRC=\"FIGDIR/small/024059_ufig1.gif\" ALT=\"Figure 1\">\nView larger version (26K):\norg.highwire.dtl.DTLVardef@1f96bedorg.highwire.dtl.DTLVardef@1cc9eforg.highwire.dtl.DTLVardef@d7e7borg.highwire.dtl.DTLVardef@1eafbd7_HPS_FORMAT_FIGEXP M_FIG C_FIG

Cancer Biology↗

bioRxiv2015

Comment on "Variation in cancer risk among tissues can be explained by the number of stem cell divisions"

Tomasetti and Vogelstein (Science 347, 78-81, 2015) claimed that \"primary prevention measures are not likely to be very effective\" for many cancers because they arise mostly from random mutations fixed during stem cell division, independently of specific genetic or environmental factors. We demonstrate that their calculation for hepatocellular carcinomas overlooked a major subset of tumors proven to be preventable through vaccination. The problem, which is not limited to hepatocellular carcinoma, arises from the general reliance of their analysis on average USA incidences and the omission of incidences in specific risk groups.

Cancer Biology↗

bioRxiv2015

The role of oxygen in avascular tumor growth

The oxygen status of a tumor has significant clinical implications for treatment prognosis, with well-oxygenated subvolumes responding markedly better to radiotherapy than poorly supplied regions. Oxygen is essential for tumor growth, yet estimation of local oxygen distribution can be difficult to ascertain in situ, due to chaotic patterns of vasculature. It is possible to avoid this confounding influence by using avascular tumor models, such as tumor spheroids, a much better approximation of realistic tumor dynamics than monolayers, where oxygen supply can be described by diffusion alone. Similar to in situ tumours, spheroids exhibit an approximately sigmoidal growth curve, often approximated and fitted by logistic and Gompertzian sigmoid functions. These describe the basic rate of growth well, but do not offer an explicitly mechanistic explanation. This work examines the oxygen dynamics of spheroids and demonstrates that this growth can be derived mechanistically with cellular doubling time and oxygen consumption rate (OCR) being key parameters. The model is fitted to growth curves for a range of cell lines and derived values of OCR are validated using clinical measurement. Finally, we illustrate how changes in OCR due to gemcitabine treatment can be directly inferred using this model.

Cancer Biology↗

bioRxiv2015

Why cancer cells have a more hyperpolarised mitochondrial membrane potential and emergent prospects for therapy

Cancer cells have a more hyperpolarised mitochondrial membrane potential ({Psi}IM) than normal cells. {Psi}IM = [~]-220 mV in cancer cells as compared to [~]-140 mV in normal cells. Until now it has not been known why. This paper explains this disparity, in a mathematical framework, and identifies molecular targets and operations unique to cancer cells. These are thence prospective cancer drug targets. BMS-199264 is proposed as an anti-cancer drug. It inhibits the reverse, proton-pumping mode of ATP synthase, which this paper identifies as crucial to cancer cells but not to healthy, normal adult cells. In the cancer cell model, the adenine nucleotide exchanger (ANT) is inversely orientated in the mitochondrial inner membrane as compared to normal cells. This predicts it to have a different drug interaction profile, which can be leveraged for cancer therapy. Uncouplers, which dissipate the proton motive force, are proposed as anti-cancer medicines e.g. 2,4-dinitrophenol.

Cancer Biology↗

bioRxiv2015

Further genetic diversification in multiple tumors and an evolutionary perspective on therapeutics

AbstractsThe genetic diversity within a single tumor can be extremely large, possibly with mutations at all coding sites (Ling et al. 2015). In this study, we analyzed 12 cases of multiple hepatocellular carcinoma (HCC) tumors by sequencing and genotyping several samples from each case. In 10 cases, tumors are clonally related by a process of cell migration and colonization. They permit a detailed analysis of the evolutionary forces (mutation, migration, drift and natural selection) that influence the genetic diversity both within and between tumors. In 23 inter-tumor comparisons, the descendant tumor usually shows a higher growth rate than the parent tumor. In contrast, neutral diversity dominates within-tumor observations such that adaptively growing clones are rarely found. The apparent adaptive evolution between tumors can be explained by the inherent bias for detecting larger tumors that have a growth advantage. Beyond these tumors are a far larger number of clones which, growing at a neutral rate and too small to see, can nevertheless be verified by molecular means. Given that the estimated genetic diversity is often very large, therapeutic strategies need to take into account the pre-existence of many drug-resistance mutations. Importantly, these mutations are expected to be in the very low frequency range in the primary tumors (and become frequent in the relapses, as is indeed reported (1-3). In conclusion, tumors may often harbor a very large number of mutations in the very low frequency range. This duality provides both a challenge and an opportunity for designing strategies against drug resistance (4-8).\n\nOne Sentence SummaryThe total genetic diversity across all tumors of a single patient, with large number of low frequency mutations driven by neutral and adaptive forces, presents both a challenge and an opportunity for new cancer therapeutics.

Cancer Biology↗

bioRxiv2015

The landscape of T cell infiltration in human cancer and its association with antigen presenting gene expression

One sentence summaryIn silico decomposition of the immune microenvironment among common tumor types identified clear cell renal cell carcinoma as the most highly infiltrated by T-cells and further analysis of this tumor type revealed three distinct and clinically relevant clusters which were validated in an independent cohort.\n\nAbstractInfiltrating T cells in the tumor microenvironment have crucial roles in the competing processes of pro-tumor and anti-tumor immune response. However, the infiltration level of distinct T cell subsets and the signals that draw them into a tumor, such as the expression of antigen presenting machinery (APM) genes, remain poorly characterized across human cancers. Here, we define a novel mRNA-based T cell infiltration score (TIS) and profile infiltration levels in 19 tumor types. We find that clear cell renal cell carcinoma (ccRCC) is the highest for TIS and among the highest for the correlation between TIS and APM expression, despite a modest mutation burden. This finding is contrary to the expectation that immune infiltration and mutation burden are linked. To further characterize the immune infiltration in ccRCC, we use RNA-seq data to computationally infer the infiltration levels of 24 immune cell types in a discovery cohort of 415 ccRCC patients and validate our findings in an independent cohort of 101 ccRCC patients. We find three clusters of tumors that are primarily separated by levels of T cell infiltration and APM gene expression. In ccRCC, the levels of Th17 cells and the ratio of CD8+ T/Treg levels are associated with improved survival whereas the levels of Th2 cells and Tregs are associated with negative clinical outcome. Our analysis illustrates the utility of computational immune cell decomposition for solid tumors, and the potential of this method to guide clinical decision-making.

Cancer Biology↗

bioRxiv2015

Passenger DNA alterations reduce cancer fitness in cell culture and mouse models

Genomic instability causes cancers to acquire hundreds to thousands of mutations and chromosomal alterations during their somatic evolution. Most of these mutations and alterations are termed passengers because they do not confer cancer phenotypes. Evolutionary simulations and cancer genomic studies suggested that mildly-deleterious passengers accumulate, collectively slow cancer progression, reduce the fitness of cancer cells and enhance the effects of therapeutics. However, these effects of passengers and their impact on clinical variables remain limited to genomic analysis. Here, to assess passengers effect on cell fitness and cancer, we specifically introduced increasing passenger loads into human cell lines and mouse models. We found that passenger load dramatically reduced cancer cells fitness in every model investigated. Passengers average fitness cost of [~]3% per MB, indicates that genomic instability in cancer in patients can slow tumor growth and prevent metastatic progression. We conclude that genomic instability in cancer is a double-edged sword: it accelerates the accumulation of adaptive drivers, yet incurs a harmful passenger load that can outweigh drivers benefit. Passenger load could be a useful biomarker for tumor aggressiveness and response to mutagenic or passenger-exacerbating therapies, including anti-tumor immunity.

Cancer Biology↗

bioRxiv2015

Standard melanoma-associated markers do not identify the MM127 metastatic melanoma cell line

Reliable identification of different melanoma cell lines is important for many aspects of melanoma research. Common markers used to identify melanoma cell lines include: S100; HMB-45; and Melan-A. We explore the expression of these three markers in four different melanoma cell lines: WM35; WM793; SK-MEL-28; and MM127. The expression of these markers is examined at both the mRNA and protein level. Our results show that the metastatic cell line, MM127, cannot be detected using any of the commonly used melanoma-associated markers. This implies that it would be very difficult to identify this particular cell line in a heterogeneous sample, and as a result this cell line should be used with care.

Cancer Biology↗

bioRxiv2015

ZNF191 Inhibits Hepatocellular Carcinoma Metastasis via DLG1-mediated YAP1 Inactivation

Searching targets for hepatocellular carcinoma (HCC) treatment, we identified zinc finger protein 191 (ZNF191) as a suppressor against HCC metastasis. Over-expressing ZNF191 in HCC cells impaired cell motility, while ZNF191 depletion promoted HCC cell migration in vitro and metastasis in vivo through triggering yes-associated protein 1 (YAP1) signaling. Chromatin immunoprecipitation-sequencing (ChIP-seq) revealed that ZNF191 specifically bound to the promoter of Discs, Large homolog 1 (DLG1), a cell polarity maintainer and a negative regulator of YAP1. Double-knockdown experiments showed that DLG1 was not only the mediator of ZNF191s function to suppress migration but also a link between ZNF191 and YAP1 signaling. ZNF191 was down-regulated in metastatic HCCs, correlating positively with DLG1 levels and inversely with YAP1 activation. Our findings indicate ZNF191 functions as a metastasis suppressor via DLG1-mediated YAP1 signaling inactivation.

Cancer Biology↗

bioRxiv2015

Convergent architecture of the transcriptome of human cancer

ABSTRACTSDespite large-scale efforts to systematically map the cancer genome, little is known about how the interplay of genetic and epigenetic alternations shapes the architecture of the tumors transcriptome. With the goal of constructing a system-level view of the deregulated pathways in cancer cells, we systematically investigated the functional organization of the transcriptomes of 10 tumor types using data sets generated by The Cancer Genome Atlas project (TCGA). Our analysis indicates that the human cancer transcriptome is organized into well-conserved modules of co-expressed genes. In particular, our analysis identified a set of conserved gene modules with distinct cancer hallmark themes involving cell cycle regulation, angiogenesis, innate and adaptive immune response, differentiation, metabolism and regulation of protein phosphorylation. We applied a network inference approach to nominate candidate drivers of these conserved gene modules. The predicted drivers have consistent cancer-relevant functions related to the specific hallmarks and are enriched with cancer consensus genes and significantly mutated genes. We showed genetic alternations of TP53 and other cell cycle drivers have major downstream transcriptional impact on cell cycle regulation. Collectively, our analysis provided global views of convergent transcriptome architecture of human cancer. The result of our analysis can serve as a foundation to link diverse genomic alternations to common transcriptomic features in human cancer.

Cancer Biology↗

bioRxiv2015

Spatial metrics of tumour vascular organisation predict radiation efficacy in a computational model

Intratumoural heterogeneity is known to contribute to poor therapeutic response. Variations in oxygen tension in particular have been correlated with changes in radiation response in vitro and at the clinical scale with overall survival. Heterogeneity at the microscopic scale in tumour blood vessel architecture has been described, and is one source of the underlying variations in oxygen tension. We endeavour to determine whether histologic scale measures of the erratic distribution of blood vessels within a tumour can be used to predict differing radiation response. Using a two-dimensional hybrid cellular automaton model of tumour growth, we evaluate the effect of vessel distribution on cell survival outcomes of simulated radiation therapy. Using the standard equations for the oxygen enhancement ratio for cell survival probability under differing oxygen tensions, we calculate average radiation effect over a range of different vessel densities and organisations. We go on to quantify the vessel distribution heterogeneity and measure spatial organization using Ripleys L function, a measure designed to detect deviations from spatial homogeneity. We find that under differing regimes of vessel density the correlation coefficient between the measure of spatial organization and radiation effect changes sign. This provides not only a useful way to understand the differences seen in radiation effect for tissues based on vessel architecture, but also an alternate explanation for the vessel normalization hypothesis.\n\nAuthor SummaryIn this paper we use a mathematical model, called a hybrid cellular automaton, to study the effect of different vessel distributions on radiation therapy outcomes at the cellular level. We show that the correlation between radiation outcome and spatial organization of vessels changes signs between relatively low and high vessel density. Specifically, that for relatively low vessel density, radiation efficacy is decreased when vessels are more homogeneously distributed, and the opposite is true, that radiation efficacy is improved, when vessel organisation is normalised in high densities. This result suggests an alteration to the vessel normalization hypothesis which states that normalisation of vascular beds should improve radio- and chemo-therapeutic response, but has failed to be validated in clinical studies. In this alteration, we provide a metric that differentiates between vascular architectures in different density regimes in which the hypothesis holds and does not and that could be used for quantitative histologic analysis of tumours, and for radiation dose personalisation.\n\nAuthor ContributionsConceived and designed the experiments: JGS, AGF, ARAA, PKM\n\nPerformed the experiments: JGS\n\nAnalyzed the data: JGS, AGF, ARAA, PKM\n\nWrote the paper: JGS, AGF, ARAA, PKM

Cancer Biology↗

bioRxiv2015

Agent based models to investigate cooperation between cancer cells

We present a type of agent-based model that uses off-lattice spheres to represent individual cells in a solid tumor. The model calculates chemical gradients and determines the dynamics of the tumor as emergent properties of the interactions between the cells. As an example, we present an investigation of cooperation among cancer cells where cooperators secrete a growth factor that is costly to synthesize. Simulations reveal that cooperation is favored when cancer cells from the same lineage stay in close proximity. The result supports the hypothesis that kin selection, a theory that explains the evolution of cooperation in animals, also applies to cancers.

Cancer Biology↗