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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2018

PDX Finder: A Portal for Patient-Derived tumor Xenograft Model Discovery

Patient-derived tumor xenograft (PDX) mouse models are a versatile oncology research platform for studying tumor biology and for testing chemotherapeutic approaches tailored to genomic characteristics of individual patients tumors. PDX models are generated and distributed by a diverse group of academic labs, research organizations, multi-institution consortia, and contract research organizations. The distributed nature of PDX repositories and the use of different standards in the associated metadata presents a significant challenge to finding PDX models relevant to specific cancer research questions. The Jackson Laboratory and EMBL-EBI are addressing these challenges by co-developing PDX Finder, a comprehensive open global catalog of PDX models and their associated datasets. Within PDX Finder, model attributes are harmonized and integrated using a previously developed community minimal information standard to support consistent searching across the originating resources. Links to repositories are provided from the PDX Finder search results to facilitate model acquisition and/or collaboration. The PDX Finder resource currently contains information for more than 1900 PDX models of diverse cancers including those from large resources such as the Patient-Derived Models Repository, PDXNet, and EurOPDX. Individuals or organizations that generate and distribute PDXs are invited to increase the \"findability\" of their models by participating in the PDX Finder initiative at www.pdxfinder.org.

cancer biology↗

bioRxiv2018

An unsupervised deep learning framework with variational autoencoders for genome-wide DNA methylation analysis and biologic feature extraction applied to breast cancer

Recent advances in deep learning, particularly unsupervised approaches, have shown promise for furthering our biological knowledge through their application to gene expression datasets, though applications to epigenomic data are lacking. Here, we employ an unsupervised deep learning framework with variational autoencoders (VAEs) to learn latent representations of the DNA methylation landscape from three independent breast tumor datasets. Through interrogation of methylation-based learned latent dimension activation values, we demonstrate the feasibility of VAEs to track representative differential methylation patterns among clinical subtypes of tumors. CpGs whose methylation was most correlated VAE latent dimension activation values were significantly enriched for CpG sparse regulatory regions of the genome including enhancer regions. In addition, through comparison with LASSO, we show the utility of the VAE approach for revealing novel information about CpG DNA methylation patterns in breast cancer.

bioinformatics↗

bioRxiv2018

Collective radioresistance of T47D breast carcinoma cells is mediated by a Syncytin-1 homologous protein

It is generally accepted that radiotherapy must target clonogenic cells, i.e., those cells in a tumour that have self-renewing potential. Focussing on isolated clonogenic cells, however, may lead to an underestimate or even to an outright neglect of the importance of biological mechanisms that regulate tumour cell sensitivity to radiation. We develop a new statistical and experimental approach to quantify the effects of radiation on cell populations as a whole. In our experiments, we change the proximity relationships of the cells by culturing them in wells with different shapes, and we find that the radiosensitivity of T47D human breast carcinoma cells in tight clusters is different from that of isolated cells. Molecular analyses show that T47D cells express a Syncytin-1 homologous protein (SyHP). We observe that SyHP translocates to the external surface of the plasma membrane of cells killed by radiation treatment. The data support the fundamental role of SyHP in the formation of intercellular cytoplasmic bridges and in the enhanced radioresistance of surviving cells. We conclude that complex and unexpected biological mechanisms of tumour radioresistance take place at the cell population level. These mechanisms may significantly bias our estimates of the radiosensitivity of breast carcinomas in vivo and thereby affect treatment plans, and they call for further investigations.

cancer biology↗

bioRxiv2017

Clinically Important sex differences in GBM biology revealed by analysis of male and female imaging, transcriptome and survival data

Sex differences in the incidence and outcome of human disease are broadly recognized but in most cases not adequately understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence, and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level, or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that temozolomide chemotherapy is more effective in female compared to male GBM patients. We then applied a novel computational algorithm to linked GBM transcriptome and outcome data, and identified novel sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling were identified as the critical determinants of survival for male and female patients, respectively. The clinical utility of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses, and that improved outcome for all patients might be accomplished via tailoring treatment to sex differences in molecular mechanisms.\n\nOne Sentence SummaryMale and female glioblastoma are biologically distinct and maximal chances for cure may require sex-specific approaches to treatment.

cancer biology↗

bioRxiv2017

Melanoma proteomics unravels major differences related to mutational status

The aim of the study was to explore the molecular differences between melanoma tumor subtypes, based on BRAF and NRAS mutational status. Fourteen formalin-fixed, paraffin- embedded melanoma samples were analyzed using a high-throughput proteomics approach, coupled with probabilistic graphical models and Flux Balance Analysis, to characterize these differences. Proteomics analyses showed differences in expression of proteins related with fatty acid metabolism, melanogenesis and extracellular space between BRAF mutated and BRAF non-mutated melanoma tumors. Additionally, probabilistic graphical models showed differences between melanoma subgroups at biological processes such as melanogenesis or metabolism. On the other hand, Flux Balance Analysis predicts a higher tumor growth rate in BRAF mutated melanoma samples. In conclusion, differential biological processes between melanomas showing a specific mutational status can be detected using combined proteomics and computational approaches.

cancer biology↗

bioRxiv2018

The functional genomic circuitry of human glioblastoma stem cells

SummarySuccessful glioblastoma (GBM) therapies have remained elusive due to limitations in understanding mechanisms of growth and survival of the tumorigenic population. Using CRISPR-Cas9 approaches in patient-derived GBM stem cells to interrogate function of the coding genome, we identify diverse actionable pathways responsible for growth that reveal the gene-essential circuitry of GBM stemness. In particular, we describe the Sox developmental transcription factor family; H3K79 methylation by DOT1L; and ufmylation stress responsiveness programs as essential for GBM stemness. Additionally, we find mechanisms of temozolomide resistance and sensitivity that could lead to combination strategies with this standard of care treatment. By reaching beyond static genome analysis of bulk tumors, with a genome wide functional approach, we dive deep into a broad range of biological processes to provide new understanding of GBM growth and treatment resistance.\n\nSignificanceGlioblastoma (GBM) remains an incurable disease despite an increasingly thorough depth of knowledge of the genomic and epigenomic alterations of bulk tumors. Evidence from multiple approaches support that GBM reflects an aberrant developmental hierarchy, with GBM stem cells (GSCs), fueling tumor growth and invasion. The properties of this tumor subpopulation may also in part explain treatment resistance and disease recurrence. Unfortunately, we still have a limited knowledge of the molecular circuitry of these cells and progress has been slow as we have not been able, until recently, to interrogate function at the genome-wide scale. Here, using parallel genome-wide CRISPR-Cas9 screens, we identify the essential genes for GSC growth. Further, by screening in the presence of low and high dose temozolomide, we identify mechanisms of drug resistance and sensitivity. These functional screens in patient derived cells reveal new aspects of GBM biology and identify a diversity of actionable targets such as genes governing stem cell traits, epigenome regulation and the response to stress stimuli.

cancer biology↗

bioRxiv2016

Exploiting Homeostatic Repopulation to Increase DC Vaccine Efficacy in Multiple Myeloma

Standard of care for multiple myeloma involves autologous hematopoietic cell transplant (AHCT), which can extend life by a year; however the disease remains incurable. A dendritic cell vaccine developed at Moffitt will be used both immediately before and after AHCT, with the aim of achieving complete response. Data will be collected during the trial to test the biological activity of the vaccine. This data will parameterize a model that facilitates the exploration of outcomes when varying the timing of vaccination. This calibrated model will also inform the design of a follow-up trial, which will include vaccination in conjunction with other immunotherapies.

Cancer Biology↗

bioRxiv2018

Connecting Patterns of Tumor Growth with Sex Differences in Extreme Survivorship for Primary Glioblastoma Patients

BackgroundSex is recognized as a significant determinant of outcome among glioblastoma patients, but the relative prognostic importance of glioblastoma features has not been thoroughly explored for sex differences.\n\nMethodsCombining multi-modal MR images, biomathematical models, and patient clinical information, this investigation assesses which pretreatment variables have a sex-specific impact on the survival of glioblastoma patients. Pretreatment MR images of 494 glioblastoma patients (299 males and 195 females) were segmented to quantify tumor volumes. Cox proportional hazard (CPH) models and Students t-tests were used to assess which variables were associated with survival outcomes.\n\nResultsAmong males, tumor (T1Gd) radius was a predictor of overall survival (HR=1.027, p=0.044). Among females, higher tumor cell net invasion rate was a significant detriment to overall survival (HR=1.011, p<0.001). Female extreme survivors had significantly smaller tumors (T1Gd) (p=0.010 t-test), but tumor size was not correlated with female overall survival (p=0.955 CPH). Both male and female extreme survivors had significantly lower tumor cell net proliferation rates than other patients (M p=0.004, F p=0.001, t-test). Additionally, extent of resection, tumor laterality, and IDH1 mutation status were also found to have sex-specific effects on overall survival.\n\nConclusionDespite similar distributions of the MR imaging parameters between males and females, there was a sex-specific difference in how these parameters related to outcomes, which emphasizes the importance of considering sex as a biological factor when determining patient prognosis and treatment approach.

cancer biology↗

bioRxiv2018

Molecular phenotyping using networks, diffusion, and topology: soft tissue sarcoma

Many biological datasets are high-dimensional yet manifest an underlying order. In this paper, we describe an unsupervised data analysis methodology that operates in the setting of a multivariate dataset and a network which expresses influence between the variables of the given set. The technique involves network geometry employing the Wasserstein distance, global spectral analysis in the form of diffusion maps, and topological data analysis using the Mapper algorithm. The prototypical application is to gene expression profiles obtained from RNA-Seq experiments on a collection of tissue samples, considering only genes whose protein products participate in a known pathway or network of interest. Employing the technique, we discern several coherent states or signatures displayed by the gene expression profiles of the sarcomas in the Cancer Genome Atlas along the p53 signaling network. The signatures substantially recover the leiomyosarcoma, dedifferentiated liposarcoma (DDLPS), and synovial sarcoma histological subtype diagnoses, but they also include a new signature defined by simultaneous activation and inactivation of about a dozen genes, including activation of fibrinolysis inhibitor SERPINE1/PAI and inactivation of p53-family tumor suppressor gene P73 along with cyclin dependent kinase inhibitor 2A CDKN2A/P14ARF.

cancer biology↗

bioRxiv2018

Chemical characterization, biological assessment and molecular docking studies of essential oil of Ocimum viride

Ocimum viride (family: Lamiaceae) is a medicinally important aromatic plant that grows widely in north western Himalayan range of Indian subcontinent. Essentials oils (EOs) and purified aromatic compounds derived from plants of genus Ocimum have long been used in traditional system of medicine to treat various chronic disorders. In this study we made an attempt to assess the chemical composition of essential oil (EO) obtained from Ocimum viride for potential antimicrobial and anticancer properties. Gas chromatography-mass spectrometry (GCMS) analysis revealed that EOs of aerial parts (leaves) of Ocimum viride contain high amounts of oxygenated monoterpenes, thymol and gamma terpinene. Notably, thymol (~50%) and {gamma}-terpinene (~18%) were identified as the most abundant components of the oil. EOs showed most prominent antibacterial effect against Bacillus subtilis and in silico molecular docking analyses of antibacterial action against bacterial cell wall of Bacillus subtilis showed interaction of thymol with Sec A protein of Bacillus subtilis (binding energy of -15 kcal/mol) with active site Lys284, Trp275, Leu269, Arg19, Glu277, pro270. While, in vitro cytotoxic effect of EO against six human cancer cell lines showed maximum effect with IC50 value of ~0.034 {+/-} 0.001L/ mL against HT-29 colon cancer cell line. DNA fragmentation analysis and cell cycle analysis revealed that EO inhibits the growth of HT-29 colon cancer cells probably through induction of unrepairable DNA damage and subsequent cell death. Taken together, our results indicate that EO possesses potent antimicrobial and anticancer properties, and may find applications in bacterial growth inhibition and cancer therapeutics.

cancer biology↗

bioRxiv2018

Computational enhancement of single-cell sequences for inferring tumor evolution

Motivation: Tumor sequencing has entered an exciting phase with the advent of single-cell techniques that are revolutionizing the assessment of single nucleotide variation (SNV) at the highest cellular resolution. However, state-of-the-art single-cell sequencing technologies produce data with many missing bases (MBs) and incorrect base designations that lead to false-positive (FP) and false-negative (FN) detection of somatic mutations. While computational methods are available to make biological inferences in the presence of these errors, the accuracy of the imputed MBs and corrected FPs and FNs remains unknown.\n\nResults: Using computer simulated datasets, we assessed the robustness performance of four existing methods (OncoNEM, SCG, SCITE, and SiFit) and one new method (BEAM). BEAM is a Bayesian evolution-aware method that improves the quality of single-cell sequences by using the intrinsic evolutionary information in the single-cell data in a molecular phylogenetic framework. Overall, BEAM and SCITE performed the best. Most of the methods imputed MBs with high accuracy, but effective detection and correction of FPs and FNs require sampling a large number of SNVs. Analysis of an empirical dataset shows that computational methods can improve both the quality of tumor single-cell sequences and their utility for biological inference.\n\nConclusions: Tumor cells descend from pre-existing cells, which creates evolutionary continuity in single-cell sequencing datasets. This information enables BEAM and other methods to correctly impute missing data and incorrect base assignments, but correction of FPs and FNs remains challenging when the number of SNVs sampled is small relative to the number of cells sequenced.\n\nAvailability: BEAM is available on the web at https://github.com/SayakaMiura/BEAM.\n\nContact: s.kumar@temple.edu

cancer biology↗

bioRxiv2015

Simulating multi-substrate diffusive transport in 3-D tissues with BioFVM

To simulate the spatiotemporal distribution of chemical compounds, we present BioFVM, an open-source reaction-diffusion equation solver using finite volume methods with motivation for biological applications. With various numerical solvers, we can simulate the interaction of dozens of compounds, including growth substrates, drugs, and signaling compounds in 3-D tissues, with cells by treating them as various source/sink terms. BioFVM has linear computational cost scalings and demonstrates first-order accuracy in time and second-order accuracy in space. Beyond simulating the transport of drugs and growth substrates in tissues, the ability to simulate dozens of compounds should make 3-D simulations of multicellular secretomics feasible.

Cancer Biology↗

bioRxiv2018

Study on tumor cell RNA cross-anticancer mechanism in non-tumor mice experimental model

To report the experiments of fractal structure and self-similar function in biological coupled oscillation brings life system waving growth in life organism. At the same time by self-organization the system openness come into being to maintain stability features of growth survival. From this, the orderly oscillation of tumor cell RNA was recognized that this process could lead to system sequentiality openness. According to waving growth of basic research the procedure could be sure to turn into a model as out of control of growing in tumor. Then in complete contrast that derived from the same component which have cross resistance effect. It shows that its biological behavior is converted as only a single physical power to play action by inactivated processing. Subsequent further confirms the essential roles whether essential roles link up with recovery of physiological function. The result in that it could exhibit feature of anticancer owing to be endowed tissue embryonization response. So that it also exerts preventing on senile chronic diseases. In theory, the concept of this tissue response in life organism closely link up with the single physical power of RNA component so as to bring up specific procedure of growing and subsisting that deal with keeping life vigor generalized explanatory.

cancer biology↗

bioRxiv2018

A second generation IL-2 receptor-targeted diphtheria fusion toxin exhibits anti-tumor activity and synergy with anti-PD-1 in melanoma

Denileukin diftitox (DAB1-389-IL-2, Ontak(R)) is a diphtheria toxin-based fusion protein that depletes CD25-positive cells including regulatory T cells (Tregs) and was approved for the treatment of persistent or recurrent cutaneous T cell lymphoma. However, the clinical use of denileukin diftitox was limited by vascular leak toxicity and production issues related to drug aggregation and purity. We found that a single amino acid substitution (V6A) in a motif associated with vascular leak induction yields a fully active, second-generation biologic, s-DAB1-386-IL-2(V6A), which elicits 50-fold less HUVEC monolayer permeation and is 3.7-fold less lethal to mice by LD50 analysis than s-DAB1-386-IL-2 Additionally, to overcome aggregation problems, we developed a novel production method for the fusion toxin using Corynebacterium diphtheriae that secretes fully-folded, biologically active, monomeric s-DAB1-386-IL-2 into the culture medium. Using the poorly immunogenic mouse B16F10 melanoma model, we initiated treatment 7 days after tumor challenge and observed that, while both s-DAB1-386-IL-2(V6A) and s-DAB1-386-IL-2 are inhibitors of tumor growth, the capacity to treat with higher doses of s-DAB1-386-IL-2(V6A) could provide a superior activity window. In a sequential dual therapy study in tumors that have progressed for 10 days both s-DAB1-386-IL-2(V6A) and s-DAB1-386-IL-2 given prior to checkpoint inhibition with anti-PD-1 antibodies inhibited tumor growth, while either drug given as monotherapy had less effect. s-DAB1-386-IL-2(V6A), a fully monomeric protein with reduced vascular leak, is a second-generation diphtheria toxin-based fusion protein with promise as a cancer immunotherapeutic both alone and in conjunction with PD-1 blockade.\n\nSignificance StatementRegulatory T cells (Tregs) infiltrate tumors in various cancers and promote an immunosuppressive microenvironment that hinders anti-tumor immunity. Denileukin diftitox, a diphtheria toxin-based fusion protein that depletes Tregs, was approved for the treatment of T cell malignancies, but its clinical use was limited due to the presence of protein aggregates and toxicity associated with vascular leakage. Here we report the production of a second generation IL-2 receptor-targeted, fully-folded, monomeric diphtheria fusion toxin, and a V6A mutant variant which showed reduced vascular leak in vitro and reduced lethality in mice. In a mouse model of melanoma, we found significant decrease in tumor growth associated with reduction in Tregs when the protein was tested as monotherapy or in combination with checkpoint blockade.

cancer biology↗

bioRxiv2013

U2AF1 mutations alter splice site recognition in hematological malignancies

Whole-exome sequencing studies have identified common mutations affecting genes encoding components of the RNA splicing machinery in hematological malignancies. Here, we sought to determine how mutations affecting the 3' splice site recognition factor U2AF1 alter its normal role in RNA splicing. We find that U2AF1 mutations influence the similarity of splicing programs in leukemias, but do not give rise to widespread splicing failure. U2AF1 mutations cause differential splicing of hundreds of genes, affecting biological pathways such as DNA methylation (DNMT3B), X chromosome inactivation (H2AFY), the DNA damage response (ATR, FANCA), and apoptosis (CASP8). We show that U2AF1 mutations alter the preferred 3' splice site motif in patients, in cell culture, and in vitro. Mutations affecting the first and second zinc fingers give rise to different alterations in splice site preference and largely distinct downstream splicing programs. These allele-specific effects are consistent with a computationally predicted model of U2AF1 in complex with RNA. Our findings suggest that U2AF1 mutations contribute to pathogenesis by causing quantitative changes in splicing that affect diverse cellular pathways, and give insight into the normal function of U2AF1s zinc finger domains.

Cancer Biology↗

bioRxiv2014

Perturbation biology models predict c-Myc as an effective co-target in RAF inhibitor resistant melanoma cells

Systematic prediction of cellular response to perturbations is a central challenge in biology, both for mechanistic explanations and for the design of effective therapeutic interventions. We addressed this challenge using a computational/experimental method, termed perturbation biology, which combines high-throughput (phospho)proteomic and phenotypic response profiles to targeted perturbations, prior information from signaling databases and network inference algorithms from statistical physics. The resulting network models are computationally executed to predict the effects of tens of thousands of untested perturbations. We report cell type-specific network models of signaling in RAF-inhibitor resistant melanoma cells based on data from 89 combinatorial perturbation conditions and 143 readouts per condition. Quantitative simulations predicted c-Myc as an effective co-target with BRAF or MEK. Experiments showed that co-targeting c-Myc, using the BET bromodomain inhibitor JQ1, and the RAF/MEK pathway, using kinase inhibitors is both effective and synergistic in this context. We propose these combinations as pre-clinical candidates to prevent or overcome RAF inhibitor resistance in melanoma.

Cancer Biology↗

bioRxiv2018

The Association between Alcohol Consumption and Telomere Length: A Meta-Analysis Focusing on Observational Studies

BackgroundBoth telomere length and alcohol consumption play important roles in carcinogenesis and biological age. Many efforts have been made to investigate the association between alcohol consumption and telomere length. However, no consensus has been reached yet.\n\nMethodsIn this article, we performed a meta-analysis to integrate the investigation results in the literature about the association between alcohol consumption and telomere length. After searching articles published between 2000 and 2016, 21 articles (including 27 analyses, total sample size 35,891) met our eligibility criteria.\n\nResultsWe found a significant association between alcohol consumption and telomere length (Fishers combined p-value = 3.52E-8 and Liptaks weighted p-value = 8.24E-3). We also found that the significance of the association between alcohol consumption and telomere length varies with study type (cohort, case-control, or cross-sectional) and study population (Europe, Asia, American, or Australia).\n\nConclusionsCombined evidence showed that alcohol consumption is associated with telomere length. The consistent quantifications of alcohol consumption and telomere length would benefit the future aggregation of the evidence from different studies.

cancer biology↗

bioRxiv2018

Rapid clearing of biological organs by using phosphoric acid, a hydrophilic solution with high refractive index

Tissue clearing is a fundamental challenge in biology and medicine to achieve high-resolution optical imaging of tissues deep inside intact organs. The clearing methods, reported up to now, require long incubation time or physical/electrical pressure to achieve tissue clearing, which is done by matching the refractive indices of the whole sample and medium to that of the lipid layer. Here we show that phosphoric acid increases the refractive index of the medium and can increase the transparency of formalin-fixed tissue samples rapidly. Immersion of fixed tissues of mice in phosphoric acid solutions increased their transparency within 60 min in the case of 3-mm-thick fixed tissue specimens. While phosphoric acid suppresses bright signals on the boundary of cells in their phase-contrast images, it does not damage the morphology of cell membrane with phospholipid bilayer. The protocol presented herein may contribute to develop better and faster soaking methods for tissue clearing than previously reported protocols.\n\nHighlights{blacksquare} Phosphoric acid can reduce light scattering by tissue samples.\n{blacksquare}Tissue clearing effect of phosphoric acid is fast and needs only 60-min incubation.\n{blacksquare}Cell membrane was preserved during incubation using phosphoric acid.

cancer biology↗