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bioRxiv2017

Population-level distribution and putative immunogenicity of cancer neoepitopes

BackgroundTumor neoantigens are a driver of cancer immunotherapy response; however, current neoantigen prediction tools produce many candidates that require further prioritization for research/clinical applications. Additional filtration criteria and population-level understanding may help to produce refined lists of putative neoantigens. Herein, we show neoepitope immunogenicity is likely related to measures of peptide novelty and report population-level behavior of these and other metrics.\n\nMethodsWe propose four peptide novelty metrics to refine predicted neoantigenicity: tumor vs. paired normal peptide binding affinity difference, tumor vs. paired normal peptide sequence similarity, tumor vs. closest human peptide sequence similarity, and tumor vs. closest microbial peptide sequence similarity. We apply these metrics to tumor neoepitopes predicted from somatic missense mutations in The Cancer Genome Atlas (TCGA) and a cohort of melanoma patients, as well as to a group of peptides with neoepitope-specific immune response data using an extension of pVAC-Seq [1].\n\nResultsWe show neoepitope burden varies across TCGA disease sites and HLA alleles, with surprisingly low repetition of neoepitope sequences across patients or neoepitope preferences among sets of HLA alleles. Only 20.3% of predicted neoepitopes across TCGA patients displayed novel binding change based on our binding affinity difference criteria. Similarity of amino acid sequence was typically high between paired tumor-normal epitopes, but in 24.6% of cases, neoepitopes were more similar to other human peptides, or even to bacterial (56.8% of cases) or viral peptides (15.5% of cases), than their paired normal counterparts. Applied to peptides with neoepitope-specific immune response, a linear model incorporating neoepitope binding affinity, protein sequence similarity between neoepitopes and their closest viral peptides, and paired binding affinity difference was able to predict immunogenicity with an AUROC of 0.66.\n\nConclusionsOur proposed neoepitope prioritization criteria emphasize neoepitope novelty and refine patient neoepitope predictions for focus on biologically meaningful candidate neoantigens. We have demonstrated that neoepitopes should be considered not only with respect to their paired normal epitope, but with respect to the entire human proteome, as well as bacterial and viral peptides, with potential implications for neoepitope immunogenicity and personalized vaccines for cancer treatment. We conclude that putative neoantigens are highly variable across individuals as a function of both cancer genetics and personalized HLA repertoire, while the overall behavior of filtration criteria reflects predictable patterns.

cancer biology↗

bioRxiv2016

Consensus Gene Expression Subtypes Of Epithelial Tumors

While the heterogeneity of cancer is increasingly appreciated, it is also clear that recurrently deregulated pathways are shared between tumors across tissue types. To identify similarities in subsets of tumors from multiple epithelial organs, we performed transcriptome profile analysis of different epithelial tumors and cell lines (n=2928) from 17 different organs. We identified three to seven consensus gene expression subtypes in epithelial tumors and cell lines, irrespective of the organ-specific origin. These consensus subtypes showed biological significance. Moreover, we identified specific gene signatures that could be used to develop clinical assays for future application of this classification system. Overall, our study identifies consensus transcriptome subtypes across organ-specific tumors and highlights new avenues for precision therapy irrespective of the organ of origin.

Cancer Biology↗

bioRxiv2018

Mesothelioma Interactome with 367 Novel Protein-Protein Interactions

Malignant pleural mesothelioma (MPM) is an aggressive cancer of the thorax with a median survival of one year. We constructed an MPM interactome with over 300 computationally predicted PPIs and over 1300 known PPIs of 62 literature-curated genes whose activity affects MPM. Known PPIs of the 62 MPM associated genes were derived from BioGRID and HPRD databases. Novel PPIs were predicted by applying the HiPPIP algorithm, which computes features of protein pairs such as cellular localization, molecular function, biological process membership, genomic location of the gene, gene expression in microarray experiments, protein domains and tissue membership, and classifies the pairwise features as interacting or non-interacting based on a random forest model. To our satisfaction, the interactome is significantly enriched with genes differentially expressed in MPM tumors compared with normal pleura, and with other thoracic tumors. The interactome is also significantly enriched with genes whose high expression has been correlated with unfavorable prognosis in lung cancer, and with genes differentially expressed on crocidolite exposure. 28 of the interactors of MPM proteins are targets of 147 FDA-approved drugs. By comparing differential expression profiles induced by drug to profiles induced by MPM, potentially repurposable drugs are identified from this drug list. Development of PPIs of disease-specific set of genes is a powerful approach with high translational impact - the interactome is a vehicle to piece together an integrated view on how genes associated with MPM through various high throughput studies are functionally linked, leading to clinically translatable results such as clinical trials with repurposed drugs. The PPIs are made available on a webserver, called Wiki-Pi MPM at http://severus.dbmi.pitt.edu/wiki-MPM with advanced search capabilities.\n\nOne Sentence SummaryMesothelioma Interactome with 367 novel protein-protein interactions may shed light on the mechanisms of cancer genesis and progression

cancer biology↗

bioRxiv2017

Whole-exome sequencing of plasma cell-free DNA portrays the somatic mutation landscape of refractory metastatic colorectal cancer and enables the discovery of mutated KDR/VEGFR2 receptors as modulators of anti-angiogenic therapies.

The non-invasive detection of cancer mutations is a breakthrough in oncology. Here, we applied whole-exome sequencing of matched germline and basal plasma cell-free DNA samples (WES-cfDNA) on a RAS/BRAF/PIK3CA wild-type metastatic colorectal cancer patient with primary resistance to standard treatment regimens including VEGFR inhibitors. Using WES-cfDNA, we could detect 73% (54/74) of the somatic mutations uncovered by WES-tumor including a variety of mutation types: frameshift (indels), missense, noncoding (splicing), and nonsense mutations. Additionally, WES-cfDNA discovered 14 high-confidence somatic mutations not identified by WES-tumor. Importantly, in the absence of the tumor specimen, WES-cfDNA could identify 68 of the 88 (77.3%) total mutations that could be identified by both techniques. Of tumor biology relevance, we identified the novel KDR/VEGFR2 L840F somatic mutation, which we showed was a clonal mutation event in this tumor. Comprehensive in vitro and in vivo functional assays confirmed that L840F causes strong resistance to anti-angiogenic drugs, whereas the KDR/VEGFR2 hot-spot mutant R1032Q confers sensitivity to cabozantinib. Moreover, we found a 1-3% of recurrent KDR somatic mutations across large and non-overlapping cancer sequencing projects, and the majority of these mutations were located in protein residues frequently mutated in other cancer-relevant kinases, such as EGFR, ABL1, and ALK, suggesting a functional role.\n\nIn summary, the current study highlights the capability of exomic sequencing of cfDNA from plasma of cancer patients as a powerful platform for somatic landscape analysis and discovery of resistance-associated cancer mutations. Because of its advantage to generate results highly concordant to those of tumor sequencing without the hurdle of conventional tumor biopsies, we anticipate that WES-cfDNA will become frequently used in oncology. Moreover, our study identified for the first-time KDR/VEGFR2 somatic mutations as potential genetic biomarkers of response to anti-angiogenic cancer therapies and will serve as reference for further studies on the topic.

cancer biology↗

bioRxiv2017

Imaging specific cellular glycan structures using glycosyltransferases via click chemistry

Heparan sulfate (HS) is a polysaccharide fundamentally important for biologically activities. T/Tn antigens are universal carbohydrate cancer markers. Here, we report the specific imaging of these carbohydrates using a mesenchymal stem cell line and human umbilical vein endothelial cells (HUVEC). The staining specificities were demonstrated by comparing imaging of different glycans and validated by either removal of target glycans, which results in loss of signal, or installation of target glycans, which results in gain of signal. As controls, representative key glycans including O-GlcNAc, lactosaminyl glycans and hyaluronan were also imaged. HS staining revealed novel architectural features of the extracellular matrix (ECM) of HUVEC cells. Results from T/Tn antigen staining suggest that O-GalNAcylation is a rate-limiting step for O-glycan synthesis. Overall, these highly specific approaches for HS and T/Tn antigen imaging should greatly facilitate the detection and functional characterization of these biologically important glycans.

molecular biology↗

bioRxiv2018

Comprehensive 2D and 3D phenotypic characterization of human invasive lobular carcinoma cell lines

Invasive lobular carcinoma (ILC) is the second most common subtype of breast cancer following invasive ductal carcinoma (IDC) and characterized by the loss of E-cadherin-mediated adherens junctions. Despite displaying unique histological and clinical features, ILC still remains a chronically understudied disease with limited knowledge on the available laboratory research models. To this end, herein we report a comprehensive 2D and 3D phenotypic characterization of four Estrogen Receptor-positive human ILC cell lines - MDA-MB-134, SUM44, MDA-MB-330 and BCK4. Compared to the IDC cell lines MCF7, T47D and MDA-MB-231, ultra-low attachment culture conditions revealed a remarkable anchorage-independence ability that was unique to the ILC cells, a feature not evident in soft agar gels. 3D Collagen I and Matrigel culture indicated a generally loose morphology for the ILC cell lines, which exhibited differing preferences for adhesion to ECM proteins in 2D. Furthermore, ILC cells had limited migration and invasion ability in wound-scratch and transwell assays with the exception of haptotaxis to Collagen I. Transcriptional comparison of the cell lines confirmed the decreased cell proliferation and E-cadherin-mediated intercellular junctions in ILC, while uncovering the induction of novel pathways related to cyclic nucleotide phosphodiesterase activity, ion channels, drug metabolism and alternative cell adhesion molecules such as N-cadherin, some of which were also differentially regulated in ILC versus IDC tumors. Altogether, these studies will serve as an invaluable resource for the breast cancer research community and facilitate further functional discoveries towards understanding ILC, identifying novel drug targets and ultimately improving the outcome of patients with ILC.\n\nAuthors ContributionsConception and design: N. Tasdemir, NE. Davidson, S. Oesterreich\n\nDevelopment of methodology: N. Tasdemir, L. Zhu, GC. Tseng, S. Oesterreich\n\nAcquisition of data (performed experiments, processed data, etc.): N. Tasdemir, E. Bossart, Z. Li, Z. Li\n\nAnalysis and interpretation of data (e.g. biological interpretation, statistical analysis, computational analysis): N. Tasdemir, Z. Li, KM. Levine, NE. Davidson, S. Oesterreich\n\nWriting, review and/or revision of the manuscript: N. Tasdemir, Z. Li, KM. Levine, BM. Jacobson, GC. Tseng, NE. Davidson, S. Oesterreich\n\nStudy supervision: NE. Davidson and S. Oesterreich

cancer biology↗

bioRxiv2017

A simple rule for the evolution of fast dispersal at the edge of expanding populations

Predicting evolution of expanding populations is critical to control biological threats such as invasive species and cancer metastasis. Expansion is primarily driven by reproduction and dispersal, but nature abounds with examples of evolution where organisms pay a reproductive cost to disperse faster. When does selection favor this survival of the fastest? We searched for a simple rule, motivated by evolution experiments where swarming bacteria evolved into an hy-perswarmer mutant which disperses[~] 100% faster but pays a growth cost of [~] 10% to make many copies of its flagellum. We analyzed a two-species model based on the Fisher equation to explain this observation: the population expansion rate (v) results from an interplay of growth (r) and dispersal (D) and is independent of the carrying capacity: [Formula]. A mutant can take over the edge only if its expansion rate (v2) exceeds the expansion rate of the established species (v1); this simple condition (v2 > v1) determines the maximum cost in slower growth that a faster mutant can pay and still be able to take over. Numerical simulations and time-course experiments where we tracked evolution by imaging bacteria suggest that our findings are general: less favorable conditions delay but do not entirely prevent the success of the fastest. Thus, the expansion rate defines a traveling wave fitness, which could be combined with trade-offs to predict evolution of expanding populations.

evolutionary biology↗

bioRxiv2017

DNAJB1-PRKACA fusion kinase drives tumorigenesis and interacts with β-catenin and the liver regenerative response.

A segmental deletion resulting in DNAJB1-PRKACA gene fusion is now recognized as the signature genetic event of fibrolamellar hepatocellular carcinoma (FL-HCC), a rare but lethal liver cancer that primarily affects adolescents and young adults. Here, we implement CRISPR/Cas9 genome editing and transposon-mediated somatic gene transfer to demonstrate that expression of both the endogenous fusion protein or a chimeric cDNA leads to the formation of indolent liver tumors in mice that closely resemble human FL-HCC. Notably, overexpression of the wild type PRKACA was unable to fully recapitulate the oncogenic activity of DNAJB1-PRKACA, implying that FL-HCC does not simply result from enhanced PRKACA expression. Tumorigenesis was significantly enhanced by genetic activation of {beta}-catenin, an observation supported by evidence of recurrent Wnt pathway mutations in human FL-HCC, as well as treatment with hepatotoxin 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC), which causes tissue injury, inflammation and fibrosis. Our study validates the DNAJB1-PRKACA fusion kinase as an oncogenic driver and candidate drug target for FL-HCC and establishes a practical model for preclinical studies to identify strategies to treat this disease.\n\nSignificanceEfforts to understand and treat FL-HCC have been confounded by a lack of models that accurately reflect the genetics and biology of the disease. Here, we demonstrate that the Dnajb1-Prkaca gene fusion drives tumorigenesis in mice, and that fusion to DNAJB1 drives FL-HCC initiation more effectively than wild type PRKACA overexpression. The requirement of the PRKACA kinase domain in tumor initiation establishes the potential utility of kinase inhibitors targeting the fusion. By identifying genetic and environmental factors that can enhance the consistency and aggressiveness of disease progression, we reveal biological characteristics of the disease and advance a robust platform for future pre-clinical studies.

cancer biology↗

bioRxiv2018

Leveraging single cell RNA sequencing experiments to model intra-tumor heterogeneity

PURPOSEMany cancers can be treated with targeted therapy. Almost inevitably, tumors develop resistance to targeted therapy, either from preexistence or by evolving new genotypes and traits. Intra-tumor heterogeneity serves as a reservoir for resistance, which often occurs due to selection of minor cellular sub-clones. On the level of gene expression, the clonal heterogeneity can only be revealed by high-dimensional single cell methods. We propose to use a general diversity index (GDI) to quantify heterogeneity on multiple scales and relate it to disease evolution.\n\nMETHODSWe focused on individual patient samples probed with single cell RNA sequencing to describe heterogeneity. We developed a pipeline to analyze single cell data, via sample normalization, clustering and mathematical interpretation using a generalized diversity measure, and exemplify the utility of this platform using single cell data.\n\nRESULTSWe focused on three sources of RNA sequencing data: two healthy bone marrow (BM) samples, two acute myeloid leukemia (AML) patients, each sampled before and after BM transplant (BMT), four samples of pre-sorted lineages, and six lung carcinoma patients with multi-region sampling. While healthy/normal samples scored low in diversity overall, GDI further quantified in which respect these samples differed. While a widely used Shannon diversity index sometimes reveals less differences, GDI exhibits differences in the number of potential key drivers or clonal richness. Comparing pre and post BMT AML samples did not reveal differences in heterogeneity, although they can be very different biologically.\n\nCONCLUSIONGDI can quantify cellular heterogeneity changes across a wide spectrum, even when standard measures, such as the Shannon index, do not. Our approach offers wide applications to quantify heterogeneity across samples and conditions.

cancer biology↗

bioRxiv2017

Sensitivity analysis based ranking reveals unknown biological hypotheses for down regulated genes in time buffer during administration of PORCN-WNT inhibitor ETC-1922159 in CRC

In a recent development of the PORCN-WNT inhibitor ETC-1922159 for colorectal cancer, a list of down-regulated genes were recorded in a time buffer after the administration of the drug. The regulation of the genes were recorded individually but it is still not known which higher ([≥] 2) order interactions might be playing a greater role after the administration of the drug. In order to reveal the priority of these higher order interactions among the down-regulated genes or the likely unknown biological hypotheses, a search engine was developed based on the sensitivity indices of the higher order interactions that were ranked using a support vector ranking algorithm and sorted. For example, LGR family (Wnt signal enhancer) is known to neutralize RNF43 (Wnt inhibitor). After the administration of ETC-1922159 it was found that using HSIC (and rbf, linear and laplace variants of kernel) the rankings of the interaction between LGR5-RNF43 were 61, 114 and 85 respectively. Rankings for LGR6-RNF43 were 1652, 939 and 805 respectively. The down-regulation of LGR family after the drug treatment is evident in these rankings as it takes bottom priorities for LGR5-RNF43 interaction. The LGR6-RNF43 takes higher ranking than LGR5-RNF43, indicating that it might not be playing a greater role as LGR5 during the Wnt enhancing signals. These rankings confirm the efficacy of the proposed search engine design. Conclusion: Prioritized unknown biological hypothesis form the basis of further wet lab tests with the aim to reduce the cost of (1) wet lab experiments (2) combinatorial search and (3) lower the testing time for biologist who search for influential interactions in a vast combinatorial search forest. From in silico perspective, a framework for a search engine now exists which can generate rankings for nth order interactions in Wnt signaling pathway, thus revealing unknown/untested/unexplored biological hypotheses and aiding in understanding the mechanism of the pathway. The generic nature of the design can be applied to any signaling pathway or phenomena under investigation where a prioritized order of interactions among the involved factors need to be investigated for deeper understanding. Future improvements of the design are bound to facilitate medical specialists/oncologists in their respective investigations.\n\nSignificanceRecent development of PORCN-WNT inhibitor enantiomer ETC-1922159 cancer drug show promise in suppressing some types of colorectal cancer. However, the search and wet lab testing of unknown/unexplored/untested biological hypotheses in the form of combinations of various intra/ extracellular factors/genes/proteins affected by ETC-1922159 is not known. Currently, a major problem in biology is to cherry pick the combinations based on expert advice, literature survey or guesses to investigate a particular combinatorial hypothesis. A search engine has be developed to reveal and prioritise these unknown/untested/unexplored combinations affected by the inhibitor. These ranked unknown biological hypotheses facilitate in narrowing down the investigation in a vast combinatorial search forest of ETC-1922159 affected synergistic-factors.

cancer biology↗

bioRxiv2014

Chromothripsis-like patterns are recurring but heterogeneously distributed features in a survey of 22,347 cancer genome screens

BackgroundChromothripsis is a recently discovered phenomenon of genomic rearrangement, possibly arising during a single genome-shattering event. This could provide an alternative paradigm in cancer development, replacing the gradual accumulation of genomic changes with a \"one-off\" catastrophic event. However, the term has been used with varying operational definitions, with the minimal consensus being a large number of locally clustered copy number aberrations. The mechanisms underlying these chromothripsis-like patterns (CTLP) and their specific impact on tumorigenesis are still poorly understood.\n\nResultsHere, we identified CTLP in 918 cancer samples, from a dataset of more than 22,000 oncogenomic arrays covering 132 cancer types. Fragmentation hotspots were found to be located on chromosome 8, 11, 12 and 17. Among the various cancer types, soft-tissue tumors exhibited particularly high CTLP frequencies. Genomic context analysis revealed that CTLP rearrangements frequently occurred in genomes that additionally harbored multiple copy number aberrations (CNAs). An investigation into the affected chromosomal regions showed a large proportion of arm-level pulverization and telomere related events, which would be compatible to a number of underlying mechanisms. We also report evidence that these genomic events may be correlated with patient age, stage and survival rate.\n\nConclusionsThrough a large-scale analysis of oncogenomic array data sets, this study characterized features associated with genomic aberrations patterns, compatible to the spectrum of \"chromothripsis\"-definitions as previously used. While quantifying clustered genomic copy number aberrations in cancer samples, our data indicates an underlying biological heterogeneity behind these chromothripsis-like patterns, beyond a well defined \"chromthripsis\" phenomenon.

Genomics↗

bioRxiv2017

Multi-Omics factor analysis disentangles heterogeneity in blood cancer

Multi-omic studies promise the improved characterization of biological processes across molecular layers. However, methods for the unsupervised integration of the resulting heterogeneous datasets are lacking. We present Multi-Omics Factor Analysis (MOFA), a computational method for discovering the principal sources of variation in multi-omic datasets. MOFA infers a set of (hidden) factors that capture biological and technical sources of variability. It disentangles axes of heterogeneity that are shared across multiple modalities and those specific to individual data modalities. The learnt factors enable a variety of downstream analyses, including identification of sample subgroups, data imputation, and the detection of outlier samples. We applied MOFA to a cohort of 200 patient samples of chronic lymphocytic leukaemia, profiled for somatic mutations, RNA expression, DNA methylation and ex-vivo drug responses. MOFA identified major dimensions of disease heterogeneity, including immunoglobulin heavy chain variable region status, trisomy of chromosome 12 and previously underappreciated drivers, such as response to oxidative stress. In a second application, we used MOFA to analyse single-cell multiomics data, identifying coordinated transcriptional and epigenetic changes along cell differentiation.

bioinformatics↗

bioRxiv2017

BayCount: A Bayesian Decomposition Method for Inferring Tumor Heterogeneity using RNA-Seq Counts

Tumors are heterogeneous - a tumor sample usually consists of a set of subclones with distinct transcriptional profiles and potentially different degrees of aggressiveness and responses to drugs. Understanding tumor heterogeneity is therefore critical for precise cancer prognosis and treatment. In this paper, we introduce BayCount, a Bayesian decomposition method to infer tumor heterogeneity with highly over-dispersed RNA sequencing count data. Using negative binomial factor analysis, BayCount takes into account both the between-sample and gene-specific random effects on raw counts of sequencing reads mapped to each gene. For the posterior inference, we develop an efficient compound Poisson based blocked Gibbs sampler. Simulation studies show that BayCount is able to accurately estimate the subclonal inference, including number of subclones, the proportions of these subclones in each tumor sample, and the gene expression profiles in each subclone. For real-world data examples, we apply BayCount to The Cancer Genome Atlas lung cancer and kidney cancer RNA sequencing count data and obtain biologically interpretable results. Our method represents the first effort in characterizing tumor heterogeneity using RNA sequencing count data that simultaneously removes the need of normalizing the counts, achieves statistical robustness, and obtains biologically/clinically meaningful insights. The R package BayCount implementing our model and algorithm is available for download.

bioinformatics↗

bioRxiv2018

CD44v6 expression is a novel predictive marker of therapy response and poor prognosis in gastric cancer patients

Late diagnosis, modest treatment options and lack of predictive markers of therapy response dictate the poor overall survival (OS) of [~]1 year in most gastric cancer (GC) patients. We hypothesized that the level of CD44v6 expression in tumor cells could predict therapy response and prognosis in GC patients.\n\nWe analyzed a surgical tumor series of GC patients for the extension of CD44v6 membranous immuno-expression, clinical-pathological features, patient survival, and response to therapy. By integrating this information, we assessed the value of CD44v6 expression to predict benefit from current treatment regimens and prognosis in GC patients. We used GC cell lines and mouse xenografts to assess and/validate the biological impact of CD44v6 expression in GC cells behavior.\n\nWe demonstrated that GC patients whose tumors present higher levels of CD44v6 membranous expression benefit from adding chemotherapy to surgery as opposed to those without CD44v6 expression. Moreover, patients bearing CD44_high tumors presented worse OS than those bearing CD44_absent/low tumors, consolidating the role of CD44v6 expression as an independent factor of poor prognosis in this disease. Finally, our in vitro and patients data pinpoints the CD44v6+ cell population as the driver of tumor recurrence following conventional chemotherapy, in heterogeneous tumors composed by CD44v6- and CD44v6+ cells.\n\nOur study pioneers the identification of CD44v6 as a potential predictive marker of response to conventional chemotherapy, and consolidates CD44v6 as an independent marker of poor prognosis in GC. Overall, our data strongly supports selection of patients with high CD44v6 expressing tumors for conventional chemotherapy with or without surgery, regardless of the TNM stage.

cancer biology↗

bioRxiv2017

Probabilistic Graphical Models Relate Immune Status With Response To Neoadjuvant Chemo-Therapy In Breast Cancer

Breast cancer is the most frequent tumor in women and its incidence is increasing. Neoadjuvant chemotherapy has become standard of care as a complement to surgery in locally advanced or poor-prognosis early stage disease. The achievement of a complete response to neoadjuvant chemotherapy correlates with prognosis but it is not possible to predict who will obtain an excellent response. The molecular analysis of the tumor offers a unique opportunity to unveil predictive factors. In this work, gene expression profiling in 279 tumor samples from patients receiving neoadjuvant chemotherapy was performed and probabilistic graphical models were used. This approach enables addressing biological and clinical questions from a Systems Biology perspective, allowing to deal with large gene expression data and their interactions. Tumors presenting complete response to neoadjuvant chemotherapy had a higher activity of immune related functions compared to resistant tumors. Similarly, samples from complete responders presented higher expression of lymphocyte cell lineage markers, immune-activating and immune-suppressive markers, which may correlate with tumor infiltration by lymphocytes (TILs). These results suggest that the patients immune system plays a key role in tumor response to neoadjuvant treatment. However, future studies with larger cohorts are necessary to validate these hypotheses.

cancer biology↗

bioRxiv2018

Critical comparison of ultracentrifugation and a commercial kit for isolation of exosomes derived from glioblastoma and breast cancer cell lines

Exosomes are small extracellular vesicles around 30-100 nm in diameter that are secreted from cells and can be found in most body fluids. Exosomes can be a vital source of biomarkers as they contain various substances (e.g. lipids, RNAs, metabolites and proteins) that can reflect the cell of origin (e.g. cancer cells). For isolation of exosomes present in biological matrices, ultracentrifugation (UC)-based procedures are most common. Other approaches exist, including commercial kits developed for easy and low sample volume isolation. In this study, differential UC and an isolation kit from a major vendor (Total Exosome Isolation Reagent from Thermo Fisher Scientific) were compared. Exosomes were isolated from cell culture media of two different cell sources (patient derived cells from glioblastoma multiforme and the breast cancer cell line MDA-MB-231). For both isolation methods, transmission electron microscopy, dynamic light scattering and western blotting indicated the presence of exosomes. The kit- and UC isolates contained similar amounts of protein measured by the bicinchoninic acid (BCA) assay with absorbance at 562 nm. Using western blot, positive exosome markers were identified in all isolates, and additional exosome markers were identified using MS-based proteomics. For the glioblastoma exosome isolates, the number of proteins identified with liquid chromatography tandem MS (LC-MS/MS) was higher for the UC isolates than the kit isolates when injecting equal protein amounts, contrary to that for the breast cancer exosome isolates. However, negative exosome markers were also found in glioblastoma isolates using LC-MS/MS. Thus, we would not use the term \"exosome isolation\" as impurities may be present with both isolation methods. Notably, potential biomarkers for both diseases were identified in the isolates using LS-MS/MS. In our opinion, the two isolation methods had rather similar performance, although with some minor differences based on cell of origin.

biochemistry↗

bioRxiv2015

A Bayesian network approach for modeling mixed features in TCGA ovarian cancer data

We propose an integrative framework to select important genetic and epigenetic features related to ovarian cancer and to quantify the causal relationships among these features using a logistic Bayesian network model based on The Cancer Genome Atlas data. The constructed Bayesian network has identified four gene clusters of distinct cellular functions, 13 driver genes, as well as some new biological pathways which may shed new light into the molecular mechanisms of ovarian cancer.

Systems Biology↗

bioRxiv2015

Comprehensive cross-population analysis of high-grade serous ovarian cancer supports no more than three subtypes

BackgroundThree to four gene expression-based subtypes of high-grade serous ovarian cancer (HGSC) have been previously reported. We sought to systematically determine the similarity of HGSC subtypes between populations.\n\nMethodsWe independently clustered (k = 3 and k = 4) five publicly-available HGSC mRNA expression datasets with >130 tumors using k-means and non-negative matrix factorization. Within each population, we summarized differential expression patterns for each cluster as moderated t statistic vectors using Significance Analysis of Microarrays. We calculated Pearsons correlations of these vectors to determine similarities and differences in expression patterns between clusters. We defined syn-clusters (SC) as sets of clusters that were strongly correlated across populations, and associated their expression patterns with biological pathways using geneset overrepresentation analyses.\n\nResultsAcross populations, for k = 3, moderated t score correlations for clusters 1, 2 and 3, respectively, ranged between 0.77-0.85, 0.80-0.90, and 0.65-0.77. For k = 4, correlations for clusters 1-4, respectively, ranged between 0.77-0.85, 0.83-0.89, 0.51-0.76, and 0.61-0.75. Within populations, comparing analogous clusters (k = 3 versus k = 4), correlations were high for clusters 1 and 2 (0.91-1.00), but were lower for cluster 3 (0.22-0.80). Results are similar using non-negative matrix factorization. SC1 corresponds to previously-reported mesenchymal-like, SC2 to proliferative-like, SC3 to immunoreactive-like, and SC4 to differentiated-like subtypes.\n\nConclusionsThe mesenchymal-like and proliferative-like subtypes are remarkably consistent across populations and could be uniquely targeted for treatment. The other two previously described subtypes are considerably less robust, and since cross-population comparison reveals that k = 3 and k = 4 are both consistent with our results, they may not represent clear subtypes.

Cancer Biology↗