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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2016

Novel computational method for predicting polytherapy switching strategies to overcome tumor heterogeneity and evolution

1The success of targeted cancer therapy is limited by drug resistance that can result from tumor genetic heterogeneity. The current approach to address resistance typically involves initiating a new treatment after clinical/radiographic disease progression, ultimately resulting in futility in most patients. Towards a potential alternative solution, we developed a novel computational framework that uses human cancer profiling data to systematically identify dynamic, pre-emptive, and sometimes non-intuitive treatment strategies that can better control tumors in real-time. By studying lung adenocarcinoma clinical specimens and preclinical models, our computational analyses revealed that the best anti-cancer strategies addressed existing resistant subpopulations as they emerged dynamically during treatment. In some cases, the best computed treatment strategy used unconventional therapy switching while the bulk tumor was responding, a prediction we confirmed in vitro. The new framework presented here could guide the principled implementation of dynamic molecular monitoring and treatment strategies to improve cancer control.

cancer biology↗

bioRxiv2016

Differential protein expression marks the transition from infection with Opisthorchis viverrini to cholangiocarcinoma

Parts of Southeast Asia have the highest incidence of intrahepatic cholangiocarcinoma (CCA) in the world due to infection by the liver fluke Opisthorchis viverrini (Ov). Ov-associated CCA is the culmination of chronic Ov-infection, with the persistent production of the growth factors and cytokines associated with persistent inflammation, which can endure for years in Ov-infected individuals prior to transitioning to CCA. Isobaric labelling and tandem mass spectrometry of liver tissue from a hamster model of CCA was used to compare protein expression profiles from inflammed tissue (Ov-infected but not cancerous) versus cancerous tissue (Ov-induced CCA). Immunohistochemistry and immunoblotting were used to verify dysregulated proteins in the animal model and in human tissue. We identified 154 dysregulated proteins that marked the transition from Ov-infection to Ov-induced CCA, i.e. proteins dysregulated during carcinogenesis but not Ov-infection. The verification of dysregulated proteins in resected liver tissue from humans with Ov-associated CCA showed the numerous parallels in protein dysregulation between human and animal models of Ov-induced CCA. To identify potential circulating markers for CCA, dysregulated proteins were compared to proteins isolated from exosomes secreted by a human CCA cell line (KKU055) and 27 proteins were identified as dysregulated in CCA and present in exosomes. These data form the basis of potential diagnostic biomarkers for human Ov-associated CCA. The profile of protein dysregulation observed during chronic Ov-infection and then in Ov-induced CCA provides insight into the etiology of an infection-induced inflammation-related cancer.\n\nAbbreviations

cancer biology↗

bioRxiv2016

Time-resolved proteomics vs. ribosome profiling reveals translation dynamics under stress

Many small molecule chemotherapeutics induce stresses that globally inhibit mRNA translation, remodeling the cancer proteome and governing response to treatment. Here we measured protein synthesis in multiple myeloma cells treated with low-dose bortezomib by coupling pulsed-SILAC (pSILAC) with high-accuracy targeted quantitative proteomics. We found that direct measurement of protein synthesis by pSILAC correlated well with the indirect measurement of protein synthesis by ribosome profiling under conditions of robust translation. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate as a function of therapy-induced stress after prolonged bortezomib exposure. Finally, the model we develop here, in combination with our experimental data including both protein synthesis and degradation, predicts changes in proteome remodeling under a variety of cellular perturbations. pSILAC therefore provides an important complement to ribosome profiling in directly measuring proteome dynamics under conditions of cellular stress.

cancer biology↗

bioRxiv2016

A lottery simulation model for predicting cancer risk based on the strength and abundance of individual mutations

Models for predicting an individuals risk of developing cancer are complicated by a lack of well-conserved mutations present in most types of disease. To address this challenge we developed a simple lottery simulation of cancer development based solely on the abundance and relative survival advantage of mutations present within individual stem or progenitor cells. In simulations requiring few mutations or that involve mutations exhibiting a strong survival advantage cancers develop in our simulation in a progressive manner amenable to oncogene-based risk prediction. In contrast, in simulations involving numerous mutations that lack a strong survival advantage, a stochastic process of neutral drift and punctuated equilibrium determines eventual tumour formation. In these situations, the development of cancer is largely attributable to the rate of mutational variability amongst progenitor cells rather than the identity and relative survival advantage of the mutations present at any single time point. These results suggest that measuring rates of stem cell mutational variability represents an important component of predicting an individuals risk of developing cancer.

cancer biology↗

bioRxiv2016

Somatic Mutations and Neoepitope Homology in Melanomas Treated with CTLA-4 Blockade

Immune checkpoint inhibitors are promising treatments for patients with a variety of malignancies. Toward understanding the determinants of response to immune checkpoint inhibitors, it was previously demonstrated that somatic mutation burden is associated with benefit and a hypothesis was posited that neoantigen homology to pathogens may in part explain the link between somatic mutations and response. To further examine this hypothesis, we reanalyzed cancer exome data obtained from a previously published study of 64 melanoma patients treated with CTLA-4 blockade and a new dataset of RNA-Seq data from 24 of those patients. We found that the predictive accuracy does not increase as analysis narrows from somatic mutation burden to predicted MHC Class I neoantigens, expressed neoantigens, or homology to pathogens. Further, the association between somatic mutation burden and response is only found when examining samples obtained prior to treatment. Neoantigen and expressed neoantigen burden are also associated with response, but neither is more predictive than somatic mutation burden. Neither the previously-described tetrapeptide signature nor an updated method to evaluate neoepitope homology to pathogens were more predictive than mutation burden.

cancer biology↗

bioRxiv2016

MYCOPLASMA FAUCIUM AND BREAST CANCER

Viruses and bacteria are the cause of a large number of different human diseases. It is believed that some of them may even contribute to the development of cancer. The present work is dedicated to the identification of mycoplasmas in patients with breast cancer. Mycoplasmas may participate in the development of several human diseases including chronic fatigue syndrome, acquired immunodeficiency syndrome, atypical pneumonia, etc. Moreover, there is a reason to believe that mycoplasma can participate in the development of cancer, leukemia and lymphoma.\n\nDNA samples from blood, saliva and tumor tissues of the Oncology Institute of Moldova patients diagnosed with breast cancer were analyzed. Mycoplasma testing was performed using nested PCR method. For Mycoplasma spp. detection, we used primers from the region of the 16S-23S RNA genes. The identification of Mycoplasma faucium, Mycoplasma salivarium and Mycoplasma orale was performed by nested PCR with primers for RNA polymerase beta subunit gene corresponding to mycoplasma.\n\nM.faucium and M.salivarius was found in saliva at about 100%, and M.orale at a frequency of about 50%. Only M.faucium was found with the frequency of about 60% in the tissue of the patients. Moreover, a fairly high rate of detection of mycoplasma is observed both in the cases when primers for RNA polymerase gene and primers for 16S-23S RNA were used.\n\nWe found M.faucium in tumor tissues of patients diagnosed with breast cancer. It is known that mycoplasmas are able to stimulate the synthesis of certain cytokines, which act as mitogenes on the cell. We assume that M.faucium can stimulate the mitogenes synthesis in breast tissues (e.g., cytokines) which, in turn, stimulate cell division and thus participate in the initiation of breast cancer.

cancer biology↗

bioRxiv2016

Transition into inflammatory cancer-associated adipocytes in breast cancer microenvironment requires microRNA regulatory mechanism

ABSTRACTSO_ST_ABSIntroductionC_ST_ABSThe role of adipocytes in cancer microenvironment has gained focus during the recent years. However, the characteristics of the cancer-associated adipocytes (CAA) in human breast cancer tissues and the underlying regulatory mechanism are not clearly understood.\n\nMethodWe reviewed pathology specimens of breast cancer patients to understand the morphologic characteristics of CAA, and profiled the mRNA and miRNA expression of CAA by using indirect co-culture system in vitro.\n\nResultsThe CAAs in human breast cancers showed heterogeneous topographic relationship with breast cancer cells within the breast microenvironment. The CAAs exhibited the characteristics of de-differentiation determined by their microscopic appearance and the expression levels of adipogenic markers. Additionally, the 3T3-L1 adipocytes co-cultured with breast cancer cells showed up-regulation of inflammation-related genes including Il6 and Ptx3. The up-regulation of IL6 in CAA was further observed in human breast cancer tissues. miRNA array of co-cultured 3T3-L1 cells showed increased expression of mmu-miR-5112 which may target Cpeb1. Cpeb1 is a negative regulator of Il6. The suppressive role of mmu-miR-5112 was confirmed by dual luciferase reporter assay, and mmu-miR-5112-treated adipocytes showed up-regulation of Il6. The transition of adipocytes into more inflammatory CAA resulted in proliferation-promoting effect in ER positive breast cancer cells such as MCF7 and ZR-75-1 but not in ER negative cells.\n\nConclusionIn this study, we have determined the de-differentiated and inflammatory natures of CAA in breast cancer microenvironment. Additionally, we propose a miRNA-based regulatory mechanism underlying the process of acquiring inflammatory phenotypes in CAA.

cancer biology↗

bioRxiv2016

Relationships between chromosomal aberration breakpoints and chromosomal contacts: biophysical modeling study

Radiation-induced chromosomal exchange aberrations (CAs) are formed presumably at contacts of damaged chromosomal loci. For sparsely ionizing radiation, distribution of aberrations along the chromosome should depend on distribution of contacts, i.e. on 3D organization of the chromosome. The progress in experimental techniques for study of chromosomal contacts and for precise localization of aberration breakpoints allows to verify contacts-based mechanisms of CA formation experimentally. In the present work, the polymer model of mouse interphase chromosome 18 is developed. On this basis the experimental data on contacts and aberrations are jointly analyzed. We demonstrate high correlation between chromosomal contacts and aberrations breakpoint distributions. Possible factors and alternative mechanisms which could modify breakpoint distributions are discussed.

cancer biology↗

bioRxiv2016

Chemotherapy weakly contributes to predicted neoantigen expression in ovarian cancer

BackgroundPatients with highly mutated tumors, such as melanoma or smoking-related lung cancer, have higher rates of response to immune checkpoint blockade therapy, perhaps due to increased neoantigen expression. Many chemotherapies including platinum compounds are known to be mutagenic, but the impact of standard treatment protocols on mutational burden and resulting neoantigen expression in most human cancers is unknown.\n\nMethodsWe sought to quantify the effect of chemotherapy treatment on computationally predicted neoantigen expression for 12 high grade serous ovarian carcinoma (HGSC) patients with pre- and post-chemotherapy samples collected in the Australian Ovarian Cancer Study. We additionally analyzed 16 patients from the cohort with post-treatment samples only, including five primary surgical samples exposed to neoadjuvant chemotherapy. Our approach integrates tumor whole genome and RNA sequencing with class I MHC binding prediction and mutational signatures of chemotherapy exposure extracted from two preclinical studies.\n\nResultsThe mutational signatures for cisplatin and cyclophosphamide identified in a preclinical model had significant but inexact associations with the relevant exposure in the clinical samples. In an analysis stratified by tissue type (solid tumor or ascites), relapse samples collected after chemotherapy harbored a median of 90% more expressed neoantigens than untreated primary samples, a figure that combines the effects of chemotherapy and other mutagenic processes operative during relapse. Neoadjuvant-treated primary samples showed no detectable increase over untreated samples. The contribution from chemotherapy-associated signatures was small, accounting for a mean of 5% (range 0-16) of the expressed neoantigen burden in relapse samples. In both treated and untreated samples, most neoantigens were attributed to COSMIC Signature (3), associated with BRCA disruption, Signature (1), associated with a slow mutagenic process active in healthy tissue, and Signature (8), of unknown etiology.\n\nConclusionRelapsed HGSC tumors harbor nearly double the predicted expressed neoantigen burden of primary samples, but mutations associated with chemotherapy signatures account for only a small part of this increase. The mutagenic processes responsible for most neoantigens are similar between primary and relapse samples. Our analyses are based on mutations detectable from whole genome sequencing of bulk samples and do not account for neoantigens present in small populations of cells.

cancer biology↗

bioRxiv2017

Mining human prostate cancer datasets: The "camcAPP" shiny app

Obtaining access to robust, well-annotated human genomic datasets is an important step in demonstrating relevance of experimental findings and, often, in generating the hypotheses that led to those experiments being conducted in the first place. We recently published data from two cohorts of men with prostate cancer who had undergone prostatectomy, men from Cambridge, UK and Stockholm, Sweden.1 We considered how we might best share our output with those who wish to interrogate the data with their own ideas, gene lists and clinical questions. We recognised that finding, down-loading, processing and assimilating any such dataset into a usable format is daunting and may put off even the more persistent researcher. We also felt that interrogation tools generated to date (e.g. cBioPortal) lack functionality as they either cover too many organ types, or fall short in terms of clinical annotation. We therefore determined to produce an accessible web-based platform ...

cancer biology↗

bioRxiv2016

Familial heterogeneity in breast cancer predisposition: a study of 22 Utah families

The problem of \"missing heritability\" in genome-wide analyses of complex diseases is thought to be attributable to some combination of: rare variants of moderate to large effect, common variants of very small effect, and epigenetic, epistatic, or shared environmental effects. Rare variants do not affect large numbers of people by definition, but identified genes and pathways frequently lead to important insights into pathogenesis, and become targets of chemoprevention or therapy. Family studies remain an efficient way to identify rare variants with sizable effects on disease risk. We present a genome-wide study of breast cancer in 22 large high-risk families including 154 women diagnosed with breast cancer. Appropriate marker spacing was achieved by simulation studies of founder haplotypes to reduce the chance that linkage disequilibrium produced spurious linkage peaks. For each family, we generated 100 simulations of null linkage genome-wide to estimate the probability that individual results were due to chance. We identified a total of 12 putative susceptibility regions with per-family genome-wide probability < 0.05. These regions were located on 10 chromosomes; 10 of the 22 families showed linkage at these locations; two or more families showed linkage to 6 regions on 5 chromosomes (4q, 5q, 6p, 14q, 18p, and 18q). These results indicate that there is considerable heterogeneity among families in genomic regions and thus variants predisposing to breast cancer. Moreover, they suggest that uncommon high- or medium-risk genetic variants remain to be found, and that family designs can be an efficient way to identify them.

cancer biology↗

bioRxiv2016

KIF21A influences breast cancer metastasis and survival

Breast cancer pathogenesis is known to be propagated by the differential expression of a group of proteins called the Kinesin Superfamily (KIFs), which are instrumental in the intracellular transport of chromosomes along microtubules during mitosis. During mitosis, KIFs are strictly regulated through temporal synthesis so that they are only present when needed. However, their misregulation may contribute to uncontrolled cell growth due to premature sister chromatid separation, highlighting their involvement in tumorigenesis. One particular KIF, KIF21A, was recently found to promote the survival of human breast cancer cells in vitro. However, how KIF21A influences other cancerous phenotypes is currently unknown. This study therefore aimed to consolidate the in vitro role of KIF21A in breast cancer metastasis, while also analysing KIF21A expression in human breast cancer tissue to determine its prognostic value. This was achieved by silencing KIF21A in MCF-7 and MDA-MB-231 breast cancer cell lines via siRNA transfection. Migration, invasion, proliferation, and adhesion assays were then performed to measure the effects of KIF21A silencing on oncogenic behaviour. Immunohistochemistry was also conducted in 263 breast cancer tissue samples to compare KIF21A expression levels against various prognostic outcomes and clinicopathological parameters. KIF21A knockdown reduced cell migration (by 42.8% [MCF-7] and 69.7% [MDA-MB-231]) and invasion (by 72.5% [MCF-7] and 42.5% [MDA-MB-231]) in both cell lines, but had no effect on adhesion or proliferation, suggesting that KIF21A plays an important role in the early stages of breast cancer metastasis. Unexpectedly however, KIF21A was shown to negatively correlate with various pro-malignant clinicopathological parameters, including tumour size and histological grade, and high KIF21A expression predicted better breast cancer survival (hazard ratio = 0.45), suggesting that KIF21A is a tumour suppressor. The conflicting outcomes of in vitro and in vivo data may be due to the possible multi-functionality of KIF21A or study limitations, and means no definitive conclusions can be drawn about the role of KIF21A in breast cancer. This warrants further investigation, which may prove pivotal to the development of novel chemotherapeutic strategies to mediate KIF21As function and enhance prognostic outcomes.

cancer biology↗

bioRxiv2016

VHL inactivation without hypoxia is sufficient to achieve genome hypermethylation

VHL inactivation is a key oncogenic event for renal carcinomas. In normoxia, VHL suppresses HIF1a-mediated response to hypoxia. It has previously been shown that hypoxic conditions inhibit TET-dependent hydroxymethylation of cytosines and cause DNA hypermethylation at gene promoters. In this work, we performed VHL inactivation by CRISPR/Cas9 and studied its effects on gene expression and DNA methylation. We showed that even without hypoxia, VHL inactivation leads to hypermethylation of the genome which mainly occurred in AP-1 and TRIM28 binding sites. We also observed promoter hypermethylation of several transcription regulators associated with decreased gene expression.

cancer biology↗

bioRxiv2016

BaalChIP: Bayesian analysis of allele-specific transcription factor binding in cancer genomes

Allele-specific measurements of transcription factor binding from ChIP-seq data are key to dissecting the allelic effects of non-coding variants and their contribution to phenotypic diversity. However, most methods to detect allelic imbalance assume diploid genomes. This assumption severely limits their applicability to cancer samples with frequent DNA copy number changes. Here we present a Bayesian statistical approach called BaalChIP to correct for the effect of background allele frequency on the observed ChIP-seq read counts. BaalChIP allows the joint analysis of multiple ChIP-seq samples across a single variant and outperforms competing approaches in simulations. Using 548 ENCODE ChIP-seq and 6 targeted FAIRE-seq samples we show that BaalChIP effectively corrects allele-specific analysis for copy number variation and increases the power to detect putative cis-acting regulatory variants in cancer genomes.

cancer biology↗

bioRxiv2016

Rapid identification of optimal drug combinations for personalized cancer therapy using microfluidics

Functional screening of live patient cancer cells holds great potential for personalized medicine and allows to overcome the limited translatability of results from existing in-vitro and ex-vivo screening models. Here we present a plug-based microfluidics approach enabling the testing of drug combinations directly on cancer cells from patient biopsies. The entire procedure takes less than 48 hours after surgery and does not require ex vivo cultivation. We screened more than 1100 samples for different primary human tumors (each with 56 conditions and at least 20 replicates), and obtained highly specific sensitivity profiles. This approach allowed us to derive optimal treatment options which we further validated in two different pancreatic cancer cell lines. This workflow should pave the way for rapid determination of optimal personalized cancer therapies at assay costs of less than US$ 150 per patient.

cancer biology↗

bioRxiv2016

A Reaction-Diffusion Model for Radiation-InducedBystander Effects

We develop and analyze a reaction-diffusion model to investigate the dynamics of the lifespan of a bystander signal emitted when cells are exposed to radiation. Experimental studies by Mothersill and Seymour 1997, using malignant epithelial cell lines, found that an emitted bystander signal can still cause bystander effects in cells even 60h after its emission. Several other experiments have also shown that the signal can persist for months and even years. Also, bystander effects have been hypothesized as one of the factors responsible for the phenomenon of low-dose hyper-radiosensitivity and increased radioresistance (HRS/IRR). Here, we confirm this hypothesis with a mathematical model, which we fit to Joiners data on HRS/IRR in a T98G glioma cell line. Furthermore, we use phase plane analysis to understand the full dynamics of the signals lifespan. We find that both single and multiple radiation exposure can lead to bystander signals that either persist temporarily or permanently. We also found that, in an heterogeneous environment, the size of the domain exposed to radiation and the number of radiation exposures can determine whether a signal will persist temporarily or permanently. Finally, we use sensitivity analysis to identify those cell parameters that affect the signals lifespan and the signal-induced cell death the most.

cancer biology↗

bioRxiv2016

A statistical test on single-cell data reveals widespread recurrent mutations in tumor evolution

The infinite sites assumption, which states that every genomic position mutates at most once over the lifetime of a tumor, is central to current approaches for reconstructing mutation histories of tumors, but has never been tested explicitly. We developed a rigorous statistical framework to test the assumption with single-cell sequencing data. The framework accounts for the high noise and contamination present in such data. We found strong evidence for recurrent mutations at the same site in 8 out of 9 single-cell sequencing datasets from human tumors. Six cases involved the loss of earlier mutations, five of which occurred at sites unaffected by large scale genomic deletions. Two cases exhibited parallel mutation, including the dataset with the strongest evidence of recurrence. Our results refute the general validity of the infinite sites assumption and indicate that more complex models are needed to adequately quantify intra-tumor heterogeneity.

cancer biology↗

bioRxiv2016

Dissecting cancer resistance to therapies with cell-type-specific dynamic logic models

Therapies targeting specific molecular processes, in particular kinases, are major strategies to treat cancer. Genomic features are commonly used as biomarkers for drug sensitivity, but our ability to stratify patients based on these features is still limited. As response to kinase inhibitors is a dynamic process affecting largely signal transduction, we investigated the association between cell-specific dynamic signaling pathways and drug sensitivity. We measured 14 phosphoproteins under 43 different perturbed conditions (combination of 5 stimuli and 7 inhibitors) for 14 colorectal cancer cell-lines, and built cell-line-specific dynamic logic models of the underlying signaling network. Model parameters, representing pathway dynamics, were used as features to predict sensitivity to a panel of 27 drugs. This analysis revealed associations between cell-specific signaling pathways and drug sensitivity for 14 of the drugs, 9 of which have no genomic biomarker. Following one of these associations, we validated a drug combination predicted to overcome resistance to MEK inhibitors by co-blockade of GSK3. These results underscore the value of perturbation-based studies to find biomarkers and combination therapies complementing those based on a static genomic characterization.

cancer biology↗