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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.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 163 records · Page 9Linked to original sources
bioRxiv2015

Stochastic Models for Population Dynamics

Cell growth and division are stochastic processes that exhibit significant amount of cell-to-cell variation and randomness. In order to connect single cell division dynamics with overall cell population, stochastic population models are needed. We summarize the basic concepts, computational approaches and discuss simple applications of this modeling approach to understanding cancer cell population growth as well as population fluctuations in experiments.

Cancer Biology↗
bioRxiv2015

Physics-based multiscale mass transport model in drug delivery and tumor microenvironment

We describe multiscale transport model, which was developed to simulate drug diffusion and convection in tissues and drug vectors. Models rely on material properties and physical laws of transport. Our methods show that drug transport analysis may provide deep insight into mechanisms of pharmacokinetics useful in nanotherapeutics and transport study within tumor microenvironment. Because the method relies on material properties and structures, the approach can help studying phenotypical differences as well.

Cancer Biology↗
bioRxiv2015

Integrating experimental data to calibrate quantitative cancer models

AbstractFor quantitative cancer models to be meaningful and interpretable the number of unknown parameters must be kept minimal. Experimental data can be utilized to calibrate model dynamics rates or rate constants. Proper integration of experimental data, however, depends on the chosen theoretical framework. Using live imaging of cell proliferation as an example, we show how to derive cell cycle distributions in agent-based models and averaged proliferation rates in differential equation models. We focus on a tumor hierarchy of cancer stem and progenitor non-stem cancer cells.

Cancer Biology↗
bioRxiv2015

Genome-wide analysis of microRNA signature in lung adenocarcinoma with EGFR exon 19 deletion

PurposeThe findings of EGFR mutations and the development of targeted therapies have significantly improved the overall survival of lung cancer patients. Still, the prognosis remains poor, so we need to know more about the genetic alterations in lung cancer. MicroRNAs are dysregulated in lung cancer, and microRNAs can regulate EGFR. So it is very important to predict the candidate microRNAs that target mutated EGFR and to investigate the role of these candidate microRNAs in lung cancer.\n\nMaterials and methodsIn this study, we investigated the difference of microRNAs expression between lung adenocarcinoma cell lines with EGFR exon 19 deletion (H1650 and PC9) and its wild-type (H1299 and A549) using the Phalanx Human Whole Genome Microarray. Then the expression of individual microRNAs was validated by qRT-PCR assays. Moreover, we have detected the microRNAs expression in serum of lung adenocarcinoma patients with EGFR exon 19 deletion and wild-type.\n\nResultsThe expression of 1,732 microRNAs was evaluated, and we found that microRNAs expression was different between these two groups. Hsa-miR-141-3p, hsa-miR-200c-3p, hsa-miR-203, hsa-miR-3182, hsa-miR-934 were up-regulated and hsa-miR-3196 was down-regulated in the EGFR exon 19 deletion group compared with wild-type group. The detection of circulating microRNAs showed that miR-3196 was down-regulated in lung adenocarcinoma patients with EGFR exon 19 deletion compared with wild-type.\n\nConclusionsIt is suggested that microRNAs associate with EGFR exon 19 deletion and miR-3196 can be further explored for potential predictor and targeted biomarker when it is difficult to get the tumors.

Cancer Biology↗
bioRxiv2015

LORENZ SYSTEM IN THERMODYNAMICAL MODELLING OF LEUKEMIA

Entropy rising within normal hematopoiesis is the core idea of the proposed thermodynamical model of malignancy in leukemia. Mathematically its description is supposed to be similar to the Lorenz system of ordinary differential equations for simplified processes of heat flow in fluids. The model provides description of remission and relapse in leukemia as two hierarchical and qualitatively different states of normal hematopoiesis with their own phase spaces. Phase space transition is possible through pitchfork bifurcation, which is considered as the common symmetrical scenario for relapse, induced remission and spontaneous remission of leukemia. Cytopenia is regarded as an adaptive reaction of hematopoiesis to entropy increase caused by leukemia clone. The following hypotheses are formulated: a) Percentage of leukemia cells in marrow as criterion of remission or relapse is not necessarily constant but a variable value; b) Probability of getting remission depends upon normal hematopoiesis reaching bifurcation; c) Duration of remission depends upon eradication of leukemia cells in induction or consolidation therapies; d) Excessively high doses of chemotherapy in consolidation might induce relapse.

Cancer Biology↗
bioRxiv2015

Cancer cells have distinct electrical properties that predict a susceptibility to lipophilic anions; a new cancer drug paradigm

I use the Nernst equation, parameterised with experimental data, to predict that cancer cells will accumulate more of a lipophilic anion than normal cells. This effect is correlated to charge number. Model cancer cells accumulate *100 more of an anion, *103 more di-anion, *106 more tri-anion, *108 more tetra-anion and *1010 more penta-anion (>>1 billion times more). The trend endures, conveying even greater specificity, for higher charge numbers. This effect could be leveraged for cancer therapy. Wherein the lipophilic anion is a toxin that targets some vital cellular process, which normal and cancer cells may even share. It delivers a high, lethal dose to cancer cells but a low, safe dose to normal cells. This mathematical finding conveys the prospect of a broad, powerful new front against cancer.

Cancer Biology↗
bioRxiv2015

Agent-based simulation of large tumors in 3-D microenvironments

Multicellular simulations of tumor growth in complex 3-D tissues, where data come from high content in vitro and bioengineered experiments, have gained significant attention by the cancer modeling community in recent years. Agent-based models are often selected for these problems because they can directly model and track cells states and their interactions with the microenvironment. We describe PhysiCell, a specific agent-based model that includes cell motion, cell cycling, and cell volume changes. The model has been performance tested on systems of 105 cells on desktop computers, and is expected to scale to 106 or more cells on single super-computer compute nodes. We plan an open source release of the software in early 2016 at PhysiCell.MathCancer.org

Cancer Biology↗
bioRxiv2016

Identification and characterization of long non-coding RNAs as targets of mammary tumor cell proliferation and migration

Recent genome-wide studies revealed that as much as 80% of the human genome can be transcribed whereas only 2% of this RNA is translated into proteins. Non-coding transcripts can be subdivided into several groups, with long non-coding RNAs (lncRNAs) representing the largest and most diverse class. With breast cancer being the most frequent malignancy in women worldwide, we set out to investigate the potential of lncRNAs as novel therapeutic targets. By performing RNA-Seq on tumor sections and mammary organoids from MMTV-PyMT and MMTV-Neu-NDL mice, modeling the luminal B and HER2/neu-amplified subtypes of human breast cancer respectively, we generated a comprehensive catalog of differentially expressed lncRNAs. We identified several hundred potentially oncogenic lncRNAs that were over-expressed in a subtype-specific manner as well as numerous lncRNAs up-regulated in both models. Among these lncRNA we defined a subset of 30 previously uncharacterized lncRNAs as Mammary Tumor Associated RNAs (MaTARs) and we identified human orthologs. We functionally validated the role of these MaTARs by antisense oligonucleotide (ASO) mediated knockdown in primary mammary tumor cells and 3D ex vivo organoids. Upon independent knockdown of 15 MaTARs, we observed significantly reduced cell proliferation, invasion and/or collective cell migration in a cancer-specific context. Thus, MaTARs are likely key drivers of mammary tumor progression and/or metastasis and represent promising new therapeutic targets.

Cancer Biology↗
bioRxiv2016

The prognostic potential of alternative transcript isoforms across human tumors

BackgroundPhenotypic changes during cancer progression are associated to alterations in gene expression, which can be exploited to build molecular signatures for tumor stage identification and prognosis. However, it is not yet known whether the relative abundance of transcript isoforms may be informative for clinical stage and survival.\n\nMethodsUsing information theory and machine learning methods, we integrated RNA sequencing and clinical data from The Cancer Genome Atlas project to perform the first systematic analysis of the prognostic potential of transcript isoforms in 12 solid tumors to build new predictive signatures for stage and prognosis. This study was also performed in breast tumors according to estrogen receptor status and melanoma tumors with proliferative and invasive phenotypes.\n\nResultsTranscript isoform signatures accurately separate early from late stage and metastatic from non-metastatic tumors, and are predictive of the survival of patients with undetermined lymph node invasion or metastatic status. These signatures show similar, and sometimes better, accuracies compared with known gene expression signatures, and are largely independent of gene expression changes. Furthermore, we show frequent transcript isoform changes in breast tumors according to estrogen receptor status, and in melanoma tumors according to the invasive or proliferative phenotype, and derive accurate predictive models of stage and survival within each patient subgroup.\n\nConclusionsOur analyses reveal new signatures based on transcript isoform abundances that characterize tumor phenotypes and their progression independently of gene expression. Transcript isoform signatures appear especially relevant to determine lymph node invasion and metastasis, and may potentially contribute towards current strategies of precision cancer medicine.

Cancer Biology↗
bioRxiv2016

Non-linear tumor-immune interactions arising from spatial metabolic heterogeneity

A hybrid multiscale mathematical model of tumor growth is used to investigate how tumoral and microenvironmental heterogeneity affect the response of the immune system. The model includes vascular dynamics and evolution of metabolic tumor phenotypes. Cytotoxic T cells are simulated, and their effect on tumor growth is shown to be dependent on the structure of the microenvironment and the distribution of tumor phenotypes. Importantly, no single immune strategy is best at all stages of tumor growth.

Cancer Biology↗
bioRxiv2016

Reaction-Diffusion Model of PDGF-driven Recruitment in Experimental Glioblastoma

Platelet-derived growth factor (PDGF) drives the formation of gliomas in an experimental animal model, which notably involves the recruitment of large numbers of glial progenitor cells. (Assanah 2006). In order to understand the underlying mechanism, particularly what factors influence the degree of recruitment, and how varied amounts of PDGF would affect the gross characteristics and overall appearance of tumors in the brain, we adapted a reaction diffusion model of glioma, which has been used for analyzing clinical data, to model the interactions at play in these experimental models.

Cancer Biology↗
bioRxiv2016

Single-chromosome aneuploidy commonly functions as a tumor suppressor

Whole-chromosome aneuploidy is a hallmark of human malignancies. The prevalence of chromosome segregation errors in cancer - first noted more than 100 years ago - has led to the widespread belief that aneuploidy plays a crucial role in tumor development. Here, we set out to test this hypothesis. We transduced congenic euploid and trisomic fibroblasts with 14 different oncogenes or oncogene combinations, thereby creating genetically-matched cancer cell lines that differ only in karyotype. Surprisingly, nearly all aneuploid cell lines divided slowly in vitro, formed few colonies in soft agar, and grew poorly as xenografts, relative to matched euploid lines. Similar results were obtained when comparing a near-diploid human colorectal cancer cell line with derivatives of that line that harbored extra chromosomes. Only a few aneuploid lines grew at close to wild-type levels, and no aneuploid line exhibited greater tumorigenic capabilities than its euploid counterpart. These results demonstrate that rather than promoting tumorigenesis, aneuploidy, particularly single chromosome gains, can very often function as a tumor suppressor. Moreover, our results suggest one potential way that cancers can overcome the tumor suppressive effects of aneuploidy: rapidly-growing aneuploid cell lines that had evolved in vitro or in vivo demonstrated recurrent karyotype changes that were absent from their euploid counterparts. Thus, the genome-destabilizing effects of single-chromosome aneuploidy may facilitate the development of balanced, high-complexity karyotypes that are frequently found in advanced malignancies.

Cancer Biology↗
bioRxiv2016

In-silico analysis of MHC genes in hereditary colorectal cancer shows identical by state SNP sharing affecting HLA-DQB1 binding groove

BackgroundThe role of Human Leukocyte Antigen (HLA) alleles in colorectal cancer susceptibility, development and progression is the focus of ongoing scrutiny. MHC polymorphisms in a Sudanese family with hereditary colorectal cancer were studied using an in silico approach and the results were verified using The Cancer Genome Atlas (TCGA). In this family study, we tested for sharing of nucleotide polymorphisms identified by whole exome capture in major histocompatibility complex region and carried out in-silico prediction of their effects in tumor and control samples. SNPs were analyzed to highlight identical by state sharing, to identify runs of homozygosity, as well as to predict structural and functional effects using homology modeling, damaging effect predictions, and regulatory changes prediction.\n\nResultsMHC II area showed significantly high degree of homozygosity in tumor samples. Non-synonymous SNPs shared identical by state (IBS) between tumor samples were predicted to affect HLA-DQB1 binding groove. A similar haplotype of these SNPs was identified in a TCGA colonic adenocarcinoma tumor sample. No significant regulatory effects (in the form of transcription factor or miRNA binding site variants) were predicted.\n\nConclusionsThe results demonstrate IBS SNP sharing of markers affecting HLA-DQB1 binding specificity and probable loss of heterozygosity in MHC II region in colorectal cancer. The significance of this sharing in cancer pathogenesis remains to be established.

Cancer Biology↗
bioRxiv2016

Effectors and potential targets selectively upregulated in human KRAS mutant lung adenocarcinomas

Genetic and proteomic analysis of human tumor samples can provide an important compliment to information obtained from model systems. Here we examined protein and gene expression from the Cancer Genome and Proteome Atlases (TCGA and TCPA) to characterize proteins and protein-coding genes that are selectively upregulated in KRAS-mutant lung adenocarcinomas. Phosphoprotein activation of several MAPK signaling components was considerably stronger in KRAS-mutants than any other group of tumors, even those with activating mutations in receptor tyrosine kinases (RTKs) and BRAF. Co-occurring mutations in KRAS-mutants were associated with differential activation of PDK1 and PKC-alpha. Genes showing strong activation in RNA-seq data included negative regulators of RTK/RAF/MAPK signaling along with potential oncogenic effectors including activators of Rac and Rho proteins and the receptor protein-tyrosine phosphatase genes PTPRM and PTPRE. These results corroborate RAF/MAPK signaling as an important therapeutic target in KRAS-mutant lung adenocarcinomas and pinpoint new potential targets.

Cancer Biology↗
bioRxiv2016

Dissecting resistance mechanisms in melanoma combination therapy

We present a compartment model that explains melanoma cell response and resistance to mono and combination therapies. Model parameters were estimated by utilizing an optimization algorithm to identify parameters that minimized the difference between predicted cell populations and experimentally measured cell numbers. The model was then validated with in vitro experimental data. Our simulations show that although a specific timing of the combination therapy is effective in controlling tumor cell populations over an extended period of time, the treatment eventually fails. We subsequently predict a more optimal combination therapy that incorporates an additional drug at the right moment.

Cancer Biology↗
bioRxiv2016

A CRISPR screen reveals a WNT7B-FZD5 signaling circuit as a therapeutic opportunity in pancreatic cancer.

AbstractCRISPR-Cas9 genome editing enables high-resolution detection of genetic vulnerabilities of cancer cells. We conducted a genome-wide CRISPR-Cas9 screen in RNF43 mutant pancreatic ductal adenocarcinoma (PDAC) cells, which rely on Wnt signaling for proliferation, and discovered a unique requirement for a WNT7B-FZD5 signaling circuit. Our results highlight an underappreciated level of functional specificity at the ligand-receptor level. We derived a panel of recombinant antibodies that reports the expression of nine out of ten human Frizzled receptors and confirm that WNT7B-FZD5 functional specificity cannot be explained by protein expression patterns. We developed two human antibodies that target FZD5 and robustly inhibited the growth of RNF43 mutant PDAC cells grown in vitro and as xenografts, providing strong orthogonal support for the functional specificity observed genetically. Proliferation of a patient-derived PDAC cell line harboring a RNF43 variant previously associated with PDAC was also selectively inhibited by the FZD5 antibodies, further demonstrating their use as a potential targeted therapy.

Cancer Biology↗