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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 181 records · Page 10Linked to original sources

bioRxiv2016

The Cancer Epitope Trees of 23 Early Cervical Cancers in Chinese Women

Emerging evidences suggest the heterogeneity of cancers limits the efficacy of immunotherapy. To search for optimal therapeutic targets, we used whole-exome sequencing data from 23 early cervical tumors from Chinese women to investigate the hierarchical structure of the somatic mutations and the predicted neo-epitopes based on their strong binding with major histocompatibility complex class I molecules. We found each tumor carried 117 mutations and 61 neo-epitopes in average and displayed a unique phylogenic tree and \"cancer neo-epitope tree\" comprising different compositions of mutations or neo-epitopes. Conceivably, the neo-epitopes at the top of the tree shared by all cancer cells are the optimal therapeutic targets that might lead to a cure. Human papillomavirus can be used as therapeutic target in only a proportion of cases where the integrated genome exits without active infection. Therefore, the \"cancer neo-epitope tree\" will serve as an important source to determine of the optimal immunotherapeutic target.

Cancer Biology↗

bioRxiv2016

Abrupt transitions to tumor extinction: A quasispecies phenotypic model

BackgroundThe dynamics of heterogeneous tumor cell populations competing with healthy cells is an important topic in cancer research with deep implications in biomedicine. Multitude of theoretical and computational models have addressed this issue, especially focusing on the nature of the transitions governing tumor clearance as some relevant model parameters are tuned. In this contribution, we analyze a mathematical model of unstable tumor progression using the quasispecies framework. Our aim is to define a minimal model incorporating the dynamics of competition between healthy cells and a heterogeneous population of cancer cell phenotypes involving changes in replication-related genes (i.e., proto-oncogenes and tumor suppressor genes), in genes responsible for genomic stability, and in house-keeping genes. Such mutations or loss of genes result into different phenotypes with increased proliferation rates and/or increased genomic instabilities. Also, lethal phenotypes with mutations or loss of house-keeping genes are present in our model.\n\nResultsDespite bifurcations in the classical deterministic quasispecies model are typically given by smooth, continuous shifts (i.e., transcritical bifurcations), we here identify an novel type of abrupt transition causing tumor extinction. Such a bifurcation, named as trans-heteroclinic, is characterized by the exchange of stability between two distant fixed points (that do not collide) involving, respectively, tumor persistence and tumor clearance. The increase of mutation and/or the decrease of the replication rate of tumor cells involves this catastrophic shift of tumor cell populations. The transient times near bifurcation thresholds are also characterized, showing a power law dependence of exponent -1 of the transients as mutation is changed near the bifurcation value.\n\nConclusionsAn abrupt transition involving tumor clearance has been identified with a phenotypic quasispecies cancer model. This result is discussed in the context of targeted cancer therapy as a possible therapeutic strategy to force a catastrophic shift by delivering mutagenic and cytotoxic drugs inside tumor cells. Our model also reveals a novel mechanism causing a discontinuous transition given by the stability exchange of two distant fixed points, which we name as a trans-heteroclinic bifurcation.

Cancer Biology↗

bioRxiv2016

Extent of EMT promotes invasive, contact-induced sliding on progressively narrower fiber-like tracks

Epithelial-mesenchymal transition (EMT) is a complex process by which cells acquire invasive properties that enable escape from the primary tumor. Complete EMT, however, is not required for metastasis: circulating tumor cells exhibit hybrid epithelial-mesenchymal states, and genetic perturbations promoting partial EMT induce metastasis in vivo. An open question is whether and to what extent intermediate stages of EMT promote invasiveness. Here, we investigate this question, building on recent observation of a new invasive property. Migrating cancer cell lines and cells transduced with prometastatic genes slide around other cells on spatially-confined, fiber-like micropatterns. We show here that low-dosage/short-duration exposure to TGF{beta} induces partial EMT and enables sliding on narrower (26 {micro}m) micropatterns than untreated counterparts (41 {micro}m). High-dosage/long-duration exposure induces more complete EMT, including disrupted cell-cell contacts and reduced E-cadherin expression, and promotes sliding on the narrowest (15 {micro}m) micropatterns. These results demonstrate that EMT is a potent inducer of cell sliding, even under significant spatial constraints, and EMT-mediated invasive sliding is progressive, with partial EMT promoting intermediate sliding behavior. Our findings suggest a model in which fiber maturation and partial EMT work synergistically to promote invasiveness during cancer progression.

Cancer Biology↗

bioRxiv2016

Recognizing the Continuous Nature of Expression Heterogeneity and Clinical Outcomes in Clear Cell Renal Cell Carcinoma

PURPOSEEvaluation of 12 ccRCC publicly-available ccRCC gene expression datasets showed that previously proposed discrete molecular subtypes are unstable. To reflect the continuous nature of gene expression observed, we developed a quantitative score (Continuous Linear Enhanced Assessment of Renal cell carcinoma, or CLEAR) using expression analysis founded on pathologic parameters.\n\nMATERIALS AND METHODS265 ccRCC gene expression profiles were used to develop the CLEAR score, representing a genetic correlate of the continuum of morphological tumor grade. A signature derivation method based on correlation of CLEAR score with gene expression ranking was used to derive an 18-transcript signature. External validation was conducted in multiple public expression datasets.\n\nResultsAs a measure of intertumoral gene expression heterogeneity, the CLEAR score demonstrated both superior prognostic estimates (94% vs 83% adequacy index in TCGA dataset) and inverse correlation with anti-angiogenic tyrosine-kinase inhibition (65% vs 55% adequacy index) in comparison to previously proposed discrete subtyping classifications. Inverse correlation with high-dose interleukin-2 outcomes was also observed for the CLEAR score (p=0.05). Multiple somatic mutations (VHL, PBRM1, SETD2, KDM5C, TP53, BAP1, PTEN, MTOR) were associated with the CLEAR score. Application of the CLEAR score to independent expression profiling of intratumoral ccRCC regions demonstrated its ability to reflect intratumoral expression heterogeneity and further analysis showed average intertumoral heterogeneity exceeded intratumoral heterogeneity.\n\nConclusionsThe CLEAR score, a gene expression signature developed on histopathology, outperformed discrete subtype-classification in prognostic estimates and correlated better with treatment outcomes. Recognizing cancer as a continuum has important implications for laboratory and clinical research.

Cancer Biology↗

bioRxiv2016

Mechanical signals inhibit growth of a grafted tumor in vivo: Proof of Concept

In the past ten years, many studies have shown that malignant tissue has been \"normalized\" in vitro using mechanical signals. We apply the principles of physical oncology (or mechanobiology) in vivo to show the effect of a \"constraint field\" on tumor growth. The human breast cancer cell line, MDA MB 231, admixed with ferric nanoparticles was grafted subcutaneously in Nude mice. The magnetizable particles rapidly surrounded the growing tumor. Two permanent magnets located on either side of the tumor created a gradient of magnetic field. Magnetic energy is transformed into mechanical energy by the particles acting as \"bioactuators\", applying a constraint field and, by consequence, biomechanical stress to the tumor. This biomechanical treatment was applied 2 hours/day during 21 days, from Day 18 to Day 39 following tumor implantation. The study lasted 74 days. Palpable tumor was measured two times a week. There was a significant in vivo difference between the median volume of treated tumors and untreated controls in the mice measured up to D 74 (D 59 + population): (529 [346; 966] mm3 vs 1334 [256; 2106] mm3; p=0.015), treated mice having smaller tumors. The difference was not statistically significant in the group of mice measured at least to D 59 (D 59 population). On ex vivo examination, the surface of the tumor mass, measured on histologic sections, was less in the treated group, G1, than in the control groups: G2 (nanoparticles, no magnetic field), G3 (magnetic field, no nanoparticles), G4 (no nanoparticles, no magnetic field) in the D 59 population (Median left surface was significantly lower in G1 (5.6 [3.0; 42.4] mm2, p=0.005) than in G2 (20.8 [4.9; 34.3]), G3 (16.5 [13.2; 23.2]) and G4 (14.8 [1.8; 55.5]); Median right surface was significantly lower in G1 (4.7 [1.9; 29.2] mm2, p=0.015) than in G2 (25.0 [5.2; 55.0]), G3 (18.0 [14.6; 35.2]) and G4 (12.5 [1.5; 51.8]). There was no statistically significant difference in the day 59+ population. This is the first demonstration of the effect of stress on tumor growth in vivo suggesting that biomechanical intervention may have a high translational potential as a therapy in locally advanced tumors like pancreatic cancer or primary hepatic carcinoma for which no effective therapy is currently available.

Cancer Biology↗

bioRxiv2016

Tree inference for single-cell data

Understanding the mutational heterogeneity within tumours is a keystone for the development of efficient cancer therapies. Here, we present SCITE, a stochastic search algorithm to identify the evolutionary history of a tumour from noisy and incomplete mutation profiles of single cells. SCITE comprises a exible MCMC sampling scheme that allows the user to compute the maximum-likelihood mutation history, to sample from the posterior probability distribution, and to estimate the error rates of the underlying sequencing experiments. Evaluation on real cancer data and on simulation studies shows the scalability of SCITE to present-day single-cell sequencing data and improved reconstruction accuracy compared to existing approaches.

Cancer Biology↗

bioRxiv2016

The mutational landscape of EGFR-, MYC-, and Kras- driven genetically-engineered mouse models of lung adenocarcinoma

Genetically-engineered mouse models (GEMMs) of cancer are increasingly being utilized to assess putative driver mutations identified by large scale sequencing of human cancer genomes. In order to accurately interpret experiments that introduce additional mutations, an understanding of the somatic genetic profile and evolution of GEMM tumors is necessary. Here, we performed whole exome sequencing of tumors from three GEMMs of lung adenocarcinoma driven by mutant EGFR, mutant Kras or by overexpression of MYC. Tumors from EGFR- and Kras- driven models exhibited respectively 0.02 and 0.07 non-synonymous mutations/megabase, a dramatically lower average mutational frequency than observed in human lung adenocarcinomas. Tumors from models driven by strong cancer drivers (mutant EGFR and Kras) harbored few mutations in known cancer genes, whereas tumors driven by MYC, a weaker initiating oncogene in the murine lung, acquired recurrent clonal oncogenic Kras mutations. In addition, although EGFR- and Kras- driven models both exhibited recurrent whole chromosome DNA copy number alterations, the specific chromosomes altered by gain or loss were different in each model. These data demonstrate that GEMM tumors exhibit relatively simple somatic genotypes compared to human cancers of a similar type, making these autochthonous model systems useful for additive engineering approaches to assess the potential of novel mutations on tumorigenesis, cancer progression, and drug sensitivity.

Cancer Biology↗

bioRxiv2016

Wild-type U2AF1 antagonizes the splicing program characteristic of U2AF1-mutant tumors and is required for cell survival

We have asked how the common S34F mutation in the splicing factor U2AF1 regulates alternative splicing in lung cancer, and why wild-type U2AF1 is retained in cancers with this mutation. A human lung epithelial cell line was genetically modified so that U2AF1S34F is expressed from one of the two endogenous U2AF1 loci. By altering levels of mutant or wild-type U2AF1 in this cell line and by analyzing published data on human lung adenocarcinomas, we show that S34F-associated changes in alternative splicing are proportional to the ratio of S34F:wild-type gene products and not to absolute levels of either the mutant or wild-type factor. Preferential recognition of specific 3' splice sites in S34F-expressing cells is largely explained by differential in vitro RNA-binding affinities of mutant versus wild-type U2AF1 for those same 3' splice sites. Finally, we show that lung adenocarcinoma cell lines bearing U2AF1 mutations do not require the mutant protein for growth in vitro or in vivo. In contrast, wild-type U2AF1 is required for survival, regardless of whether cells carry the U2AF1S34F allele. Our results provide mechanistic explanations of the magnitude of splicing changes observed in U2AF1-mutant cells and why tumors harboring U2AF1 mutations always retain an expressed copy of the wild-type allele.\n\nAuthor SummaryLarge-scale genomics studies have identified recurrent mutations in many genes that fall outside the conventional domain of proto-oncogenes. They include genes encoding factors that mediate RNA splicing; mutations affecting four of these genes are present in up to half of proliferative myeloid disorders and in a significant number of solid tumors, including lung adenocarcinoma. Here we have characterized several properties of a common mutant version of the U2AF1 splicing factor, a component of the U2 auxiliary factor complex, in lung cells. We have found that mutant-associated changes in splice site selection are primarily influenced by the ratio of mutant and wild-type U2AF1 gene products; thus increasing wild-type U2AF1 levels represses the mutant-induced splicing program. We show that the altered splice site preferences of mutant U2AF1 can be attributed to changes in its binding to relevant 3' splice sites. We also show that mutant U2AF1 is different from some oncogenes: the growth properties of lung cancer cell lines carrying the mutant allele are unaffected by loss of the mutant gene, while the wild-type allele is absolutely required for survival. These results advance our understanding of the molecular determinants of the mutant-associated splicing program, and they highlight previously unappreciated roles of wild-type U2AF1 in the presence of the recurrent U2AF1S34F mutation.

Cancer Biology↗

bioRxiv2016

Curvature Analysis of Estrogen Receptor Positive Breast Cancer Under PI3K Inhibition

In this note, we re-examine the work of Bosch et al. from a network point of view. In particular, we employ an extended defintion of Ollivier-Ricci curvature that allows us to study graphs with both positive and negative weights. This is done by utilizing a dual formulation of the Wasserstein 1-metric, allowing us to extend the Earth Movers Distance to signed measures. The resulting curvature may be applied study the robustness properties of general networks modelled as weighted graphs. In this note, we apply the theory to elucidate the robustness and therefore possible mechanisms of resistance of estrogen receptor positive breast cancer under PI3K inhibition.

Cancer Biology↗

bioRxiv2016

Inferring expressed genes by whole-genome sequencing of plasma DNA

The analysis of cell-free DNA (cfDNA) in plasma represents a rapidly advancing field in medicine. cfDNA consists predominantly of nucleosome-protected DNA shed into the bloodstream by cells undergoing apoptosis. We performed whole-genome sequencing (WGS) of plasma DNA and identified two discrete regions at transcription start sites (TSS) where the nucleosome occupancy results in different read-depth coverage patterns in expressed and silent genes. By employing machine learning for gene classification, we found that the plasma DNA read depth patterns from healthy donors reflected the expression signature of hematopoietic cells. In cancer patients with metastatic disease, we were able to classify expressed cancer driver genes in regions with somatic copy number gains with high accuracy. We could even determine the expressed isoform of genes with several TSSs as confirmed by RNA-Seq of the matching primary tumor. Our analyses provide functional information about the cells releasing their DNA into the circulation.

Cancer Biology↗

bioRxiv2016

Using Ordinary Differential Equations to Explore Cancer-Immune Dynamics and Tumor Dormancy

Cancer is not solely a disease of the genome, but is a systemic disease that affects the host on many functional levels, including, and perhaps most notably, the function of the immune response, resulting in both tumor-promoting inflammation and tumor-inhibiting cytotoxic action. The dichotomous actions of the immune response induce significant variations in tumor growth dynamics that mathematical modeling can help to understand. Here we present a general method using ordinary differential equations (ODEs) to model and analyze cancer-immune interactions, and in particular, immune-induced tumor dormancy.

Cancer Biology↗

bioRxiv2016

Toxicity Management in CAR T cell therapy for B-ALL: Mathematical modelling as a new avenue for improvement.

Advances in genetic engineering have made it possible to reprogram individual immune cells to express receptors that recognise markers on tumour cell surfaces. The process of re-engineering T cell lymphocytes to express Chimeric Antigen Receptors (CARs), and then re-infusing the CAR-modified T cells into patients to treat various cancers is referred to as CAR T cell therapy. This therapy is being explored in clinical trials - most prominently for B Cell Acute Lymphoblastic Leukaemia (B-ALL), a common B cell malignancy, for which CAR T cell therapy has led to remission in up to 90% of patients. Despite this extraordinary response rate, however, potentially fatal inflammatory side effects occur in up to 10% of patients who have positive responses. Further, approximately 50% of patients who initially respond to the therapy relapse. Significant improvement is thus necessary before the therapy can be made widely available for use in the clinic.\n\nTo inform future development, we develop a mathematical model to explore interactions between CAR T cells, inflammatory toxicity, and individual patients tumour burdens in silico. This paper outlines the underlying system of coupled ordinary differential equations designed based on well-known immunological principles and widely accepted views on the mechanism of toxicity development in CAR T cell therapy for B-ALL - and reports in silico outcomes in relationship to standard and recently conjectured predictors of toxicity in a heterogeneous, randomly generated patient population. Our initial results and analyses are consistent with and connect immunological mechanisms to the clinically observed, counterintuitive hypothesis that initial tumour burden is a stronger predictor of toxicity than is the dose of CAR T cells administered to patients.\n\nWe outline how the mechanism of action in CAR T cell therapy can give rise to such non-standard trends in toxicity development, and demonstrate the utility of mathematical modelling in understanding the relationship between predictors of toxicity, mechanism of action, and patient outcomes.

Cancer Biology↗

bioRxiv2016

Pan-cancer analysis of the metabolic reaction network

Metabolic reprogramming is considered a hallmark of malignant transformation. However, it is not clear whether the network of metabolic reactions expressed by cancers of different origin differ from each other nor from normal human tissues. In this study, we reconstructed functional and connected genome-scale metabolic models for 917 primary tumors based on the probability of expression for 3,765 reference metabolic genes in the sample. This network-centric approach revealed that tumor metabolic networks are largely similar in terms of accounted reactions, despite diversity in the expression of the associated genes. On average, each network contained 4,721 reactions, of which 74% were core reactions (present in >95% of all models). Whilst 99.3% of the core reactions were classified as housekeeping also in normal tissues, we identified reactions catalyzed by ARG2, RHAG, SLC6 and SLC16 family gene members, and PTGS1 and PTGS2 as core exclusively in cancer. The remaining 26% of the reactions were contextual reactions. Their inclusion was dependent in one case (GLS2) on the absence of TP53 mutations and in 94.6% of cases on differences in cancer types. This dependency largely resembled differences in expression patterns in the corresponding normal tissues, with some exceptions like the presence of the NANP-encoded reaction in tumors not from the female reproductive system or of the SLC5A9-encoded reaction in kidney-pancreatic-colorectal tumors. In conclusion, tumors expressed a metabolic network virtually overlapping the matched normal tissues, raising the possibility that metabolic reprogramming simply reflects cancer cell plasticity to adapt to varying conditions thanks to redundancy and complexity of the underlying metabolic networks. At the same time, the here uncovered exceptions represent a resource to identify selective liabilities of tumor metabolism.

Cancer Biology↗

bioRxiv2016

Genomics of primary chemoresistance and remission induction failure in pediatric and adult acute myeloid leukemia

Despite intense efforts, the cure rates of children and adults with AML remain unsatisfactory in large part due to resistance to chemotherapy. Whilst cytogenetic risk stratification proved valuable in identifying causes of therapy failure and disease relapse, cytogenetically normal AML remains the most prevalent disease type, with significant heterogeneity of clinical outcomes, including primary chemoresistance. Using targeted sequencing of 670 genes recurrently mutated in hematologic malignancies, we investigated the genetic basis of primary chemotherapy resistance and remission induction failure of 107 primary cases obtained at diagnosis from children and adults with cytogenetically normal AML. Comparative analysis revealed mutations of SETBP1, ASXL1 and RELN to be significantly enriched at diagnosis in primary induction failure as compared to remission cases. In addition, this analysis revealed novel genomic alterations not previously described in AML, as well as distinct genes that are significantly overexpressed in therapy resistant AML. However, identified gene mutations were sufficient to explain only a minority of cases of primary induction failure. Thus, additional genetic or molecular mechanisms must cause primary chemoresistance in pediatric and adult acute myeloid leukemias.\n\nKey PointsO_LITargeted gene sequencing of 670 genes in adult and pediatric AML\nC_LIO_LIProfiling of 107 primary AML samples identifies new genomic alterations for primary chemoresistance\nC_LI

Cancer Biology↗

bioRxiv2016

Mutational signatures associated with tobacco smoking in human cancer

Tobacco smoking increases the risk of at least 15 classes of cancer. We analyzed somatic mutations and DNA methylation in 5,243 cancers of types for which tobacco smoking confers an elevated risk. Smoking is associated with increased mutation burdens of multiple distinct mutational signatures, which contribute to different extents in different cancers. One of these signatures, mainly found in cancers derived from tissues directly exposed to tobacco smoke, is attributable to misreplication of DNA damage caused by tobacco carcinogens. Others likely reflect indirect activation of DNA editing by APOBEC cytidine deaminases and of an endogenous clock-like mutational process. The results are consistent with the proposition that smoking increases cancer risk by increasing the somatic mutation load, although direct evidence for this mechanism is lacking in some smoking-related cancer types.\n\nONE SENTENCE SUMMARYMultiple distinct mutational processes associate with tobacco smoking in cancer reflecting direct and indirect effects of tobacco smoke.

Cancer Biology↗

bioRxiv2016

Quantification of telomere features in tumor tissue sections by an automated 3D imaging-based workflow

The microscopic analysis of telomere features provides a wealth of information on the mechanism by which tumor cells maintain their unlimited proliferative potential. Accordingly, the analysis of telomeres in tissue sections of patient tumor samples provides can be exploited to obtain diagnostic information and to define tumor subgroups. In many instances, however, analysis of the image data is conducted by manual inspection of 2D images at relatively low resolution for only a small part of the sample. As the telomere feature signal distribution is frequently heterogeneous, this approach is prone to a biased selection of the information present in the image and lacks subcellular details. Here we address these issues by using an automated high-resolution imaging and analysis workflow that quantifies individual telomere features on tissue sections for a large number of cells. The approach is particularly suited to assess telomere heterogeneity and low abundant cellular sub-populations with distinct telomere characteristics in a reproducible manner. It comprises the integration of multi-color fluorescence in situ hybridization, immunofluorescence and DNA staining with targeted automated 3D fluorescence microscopy and image analysis. We apply our method to telomeres in glioblastoma and prostate cancer samples, and describe how the imaging data can be used to derive statistically reliable information on telomere length distribution or colocalization with PML nuclear bodies. We anticipate that relating this approach to clinical outcome data will prove to be valuable for pretherapeutic patient stratification.\n\nAbbreviations

Cancer Biology↗

bioRxiv2016

Cancer cells with alternative lengthening of telomeres do not display a general hypersensitivity to ATR inhibition

Telomere maintenance is a hallmark of cancer as it provides cancer cells with cellular immortality. A significant fraction of tumors uses the alternative lengthening of telomeres (ALT) pathway to elongate their telomeres and to gain an unlimited proliferation potential. Since the ALT pathway is unique to cancer cells, it represents a potentially valuable, currently unexploited target for anticancer therapies. Recently, it was proposed that ALT renders cells hypersensitive to ataxia telangiectasia-and RAD3-related (ATR) protein inhibitors (Flynn et al., Science 347, 273). Here, we measured the response of various ALT or telomerase positive cell lines to the ATR inhibitor VE-821. In addition, we compared the effect of the inhibitor on cell viability in an isogenic cell line, in which ALT was active or suppressed. In these experiments a general ATR inhibitor sensitivity of cells with ALT could not be confirmed. We rather propose that the observed variations in sensitivity reflect differences between cell lines that are unrelated to ALT.

Cancer Biology↗

bioRxiv2016

MDM2 case study: Computational protocol utilizing protein flexibility and data mining improves ligand binding mode predictions

Recovery of the P53 tumor suppressor pathway via small molecule inhibitors of oncoprotein MDM2 highlights the critical role of computational methodologies in targeted cancer therapies. Molecular docking programs in particular, have become essential during computer-aided drug design by providing a quantitative ranking of predicted binding geometries of small ligands to proteins based on binding free energy. In this study, we found improved ligand binding mode predictions of small medicinal compounds to MDM2 based on RMSD values using AutoDock and AutoDock Vina employing protein binding site flexibility. Additional analysis suggests a data mining protocol using linear regression can isolate the particular flexible bonds necessary for future optimum docking results. The implementation of a flexible receptor protocol based on a priori knowledge obtained from data mining will improve accuracy and reduce costs of high throughput virtual screenings of potential cancer drugs targeting MDM2.

Cancer Biology↗