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bioRxiv2017

Characterizing Cancer Drug Response andBiological Correlates: A Geometric NetworkApproach

In the present work, we consider a geometric network approach to study common biological features of anticancer drug response. We use for this purpose the panel of 60 human cell lines (NCI-60) provided by the National Cancer Institute. Our study suggests that utilization of mathematical tools for network-based analysis can provide novel insights into drug response and cancer biology. We adopted a discrete notion of Ricci curvature to measure the robustness of biological networks constructed with a pre-treatment gene expression dataset and coupled the results with the GI50 response of the cell lines to the drugs. The link between network robustness and Ricci curvature was implemented using the theory of optimal mass transport. Our hypothesis behind this idea is that robustness in the biological network contributes to tumor drug resistance, thereby enabling us to predict the effectiveness and sensitivity of drugs in the cell lines. Based on the resulting drug response ranking, we assessed the impact of genes that are likely associated with individual drug response. For important genes identified, we performed a gene ontology enrichment analysis using a curated bioinformatics database which resulted in very plausible biological processes associated with drug response across cell lines and cell types from the biological and literature viewpoint. These results demonstrate the potential of using the mathematical network analysis in assessing drug response and in identifying relevant genomic biomarkers and biological processes for precision medicine.

cancer biology↗

bioRxiv2018

Network-aware mutation clustering of cancer

The grouping of cancers across tissue boundaries is central to precision oncology, but remains a difficult problem. Here we present EPICC (Experimental Protein Interaction Clustering of Cancer), a novel technique to cluster cancer patients based on DNA mutation profile, that leverages knowledge of protein-protein interactions to reduce noise and amplify biological signal. We applied EPICC to data from The Cancer Genome Atlas (TCGA), and both recapitulated known cancer clusterings, and identified new cross-tissue cancer groups that may indicate novel cancer molecular subtypes. Investigation of EPICC clusters revealed new protein modules which were recurrently mutated across cancers, and indicate new avenues for research into cancer biology. EPICC leveraged the Vodafone DreamLab citizen science platform, and we provide our results as a resource for researchers to investigate the role of protein modules in cancer.

bioinformatics↗

bioRxiv2018

Directed Bayesian Networks established functional differences between breast cancer subtypes

Breast cancer is a heterogeneous disease. In clinical practice, tumors are classified as hormonal receptor positive, Her2 positive and triple negative tumors. In previous works, our group defined a new hormonal receptor positive subgroup, the TN-like subtype, which has a prognosis and a molecular profile more similar to triple negative tumors. In this study, proteomics and Bayesian networks were used to characterize protein relationships in 106 breast tumor samples. Components obtained by these methods had a clear functional structure. The analysis of these components suggested differences in processes such as metastasis or proliferation between breast cancer subtypes, including our new subtype TN-like. In addition, one of the components, mainly related with metastasis, had prognostic value in this cohort. Functional approaches allow to build hypotheses about regulatory mechanisms and to establish new relationships among proteins in the breast cancer context.\n\nAuthor SummaryBreast cancer classification in the clinical practice is defined by three biomarkers (estrogen receptor, progesterone receptor and HER2) into hormone receptor positive, HER2+ and triple negative breast cancer (TNBC). Our group recently described a new ER+ subtype with molecular characteristics and prognosis similar to TNBC. In this study we propose a mathematical method, the Bayesian networks, as a useful tool to study protein interactions and differential biological processes in breast cancer subtypes, characterizing differences in relevant processes such as proliferation or metastasis and associated them with patient prognosis.

cancer biology↗

bioRxiv2018

Chemotaxis model for human breast cancer cells based on signal-to-noise ratio

Chemotaxis, a biased migration of cells under a chemical gradient, plays a significant role in diverse biological phenomena including cancer metastasis. Stromal cells release signaling proteins to induce chemotaxis, which further causes organ-specific metastasis. Epidermal growth factor (EGF) is an example of the chemical attractants, and its gradient stimulates metastasis of breast cancer cells. Hence, the interactions between EGF and breast cancer cells have long been a subject of interest for oncologists and clinicians. However, most current approaches do not systematically separate the effects of gradient and absolute concentration of EGF on chemotaxis of breast cancer cells. In this work, we develop a theoretical model based on signal-to-noise ratio to represent stochastic properties and report our microfluidic experiments to verify the analytical predictions from the model. The results demonstrate that even under the same EGF concentration gradients, breast cancer cells can reveal distinct chemotaxis patterns at different absolute concentrations. Moreover, we found that addition of EGF receptor antibody can promote chemotaxis at a low EGF level. This apparently counterintuitive finding suggests that EGF receptor-targeted therapy may stimulate metastasis of breast cancer at a particular condition, which should be considered in anticancer drug design.

biophysics↗

bioRxiv2015

Estimating cell cycle model parameters using systems identification

A current challenge in data-driven mathematical modeling of cancer is identifying biologically-relevant parameters of mathematical models from sparse and often noisy experimental data of mixed types. We describe a cell cycle model and outline how to use the Optimization Toolbox in Matlab to estimate its timescale parameters, given flow cytometry and cell viability (synthetic) data, and illustrate the technique with simulated data. This technique can be similarly applied to a variety of cell cycle models, particularly as more laboratories begin to use high-content, quantitative cell screening and imaging platforms. An advanced version of this work (CellPD: cell line phenotype digitizer) will be released as open source in early 2016 at MultiCellDS.org.

Cancer Biology↗

bioRxiv2018

Moonlight: a tool for biological interpretation and driver genes discovery

Cancer is a complex and heterogeneous disease. It is crucial to identify the key driver genes and their role in cancer mechanisms with attention to different cancer stages, types or subtypes. Cancer driver genes are elusive and their discovery is complicated by the fact that the same gene can play a diverse role in different contexts. Key biological processes, such as cell proliferation and cell death, have been linked to cancer progression. Thus, in principle, they can be exploited to classify the cancer genes and unveil their role. Here, we present a new method, Moonlight, that exploit expression data to classify cancer genes. Moonlight relies on the integration of functional enrichment analysis, gene regulatory networks and upstream regulator analysis from expression data to score the importance of biological cancer-related processes taking into account either the inter- or intra-tumor heterogeneity. We then employed these scores to predict if each gene acts as a tumor suppressor gene (TSG) or as an oncogene (OCG). Our methodology also allow to predict genes with dual role, i.e. the moonlight genes (TSG in one cancer type or stage and OCG in another), as well as to elucidate the underlying biological processes. Availability: https://bioconductor.org/packages/MoonlightR & https://github.com/ibsquare/MoonlightR/

bioinformatics↗

bioRxiv2016

The E3 ubiquitin ligase UBR5 regulates centriolar satellite stability and primary cilia formation via ubiquitylation of CSPP-L.

Primary cilia are crucial for signal transduction in a variety of pathways, including Hedgehog and Wnt. Disruption of primary cilia formation (ciliogenesis) is linked to numerous developmental disorders (known as ciliopathies) and diseases, including cancer. The Ubiquitin-Proteasome System (UPS) component UBR5 was previously identified as a putative modulator of ciliogenesis in a functional genomics screen. UBR5 is an E3 Ubiquitin ligase that is frequently deregulated in tumours, but its biological role in cancer is largely uncharacterised, partly due to a lack of understanding of interacting proteins and pathways. We validated the effect of UBR5 depletion on primary cilia formation using a robust model of ciliogenesis, and identified CSPP1, a centrosomal and ciliary protein required for cilia formation, as a UBR5-interacting protein. We show that UBR5 ubiquitylates CSPP1, and that UBR5 is required for cytoplasmic organization of CSPP1-comprising centriolar satellites in centrosomal periphery. Hence, we have established a key role for UBR5 in ciliogenesis that may have important implications in understanding cancer pathophysiology.

cell biology↗

bioRxiv2018

Comparative Proteogenomic Analysis of Right-sided Colon Cancer, Left-sided Colon Cancer and Rectal Cancers Reveal Distinct Mutational Profiles.

To understand the molecular differences between right-sided colon cancer (RCC), left-sided colon cancer (LCC) and rectal cancer, we analyzed colorectal tumors at the DNA, RNA, miRNA and protein levels using previously sequenced data from The Cancer Genome Atlas and Memorial Sloan Kettering Cancer Center. Clonal evolution analysis identified the same tumor-initiating events involving APC, KRAS and TP53 genes in RCC, LCC and rectal cancers. However, the individual role-played by each event, their order in tumor dynamics and selection of downstream mutations were distinct in all three anatomical locations, with some similarities noted between LCC and rectal cancer. We found a potentially targetable alteration APC R1450* specific to RCC that has not been previously described. Differential gene expression analysis revealed multiple genes within the homeobox, G-protein coupled receptor binding and transcription regulation families were dysregulated in RCC, LCC, and rectal cancers and may have a pathological role in these cancers. Further, using a novel in silico proteomic analytic tool developed by our research group, we found distinct central or hub proteins with unique interactomes in each location. Protein expression signatures were not necessarily concordant with the tumor profiles obtained at the DNA and RNA levels, underscoring the relevance of post-transcriptional events in defining the biology of these cancers beyond molecular changes at the DNA and/or RNA level. Ultimately, not only tumor location and the respective genomic profile but also protein-protein interactions will need to be taken into account to improve treatment outcomes of colorectal cancers. Further studies that take into account the alterations found in this study may help in developing more tailored, and perhaps more effective, treatment strategies.\n\nAuthor summaryPatients with right-sided colon cancer (RCC) has a worse prognosis compared to left-sided colon cancer (LCC). Recent data has also shown that wild-type RAS metastatic RCCs have poor outcomes when treated with the combination of chemotherapy and anti-EGFR therapy compared to LCC and rectal cancers. Therefore, There is an urgent unmet need to understand the molecular differences between RCC, LCC, and rectal cancers. In this study, we demonstrate clonal evolutionary trajectory and the order of mutations in RCC, LCC, and rectal cancers are distinct with some similarities between LCC and rectal cancers. The order of the mutations that lead to the acquisition of crucial driver alterations may have prognostic and therapeutic implications. We also discovered a novel targetable alteration, APC R1450* to be significantly enriched in early, late and metastatic RCC but not in LCC and rectal cancers. Amazingly, proteomic signatures were discordant with DNA and RNA levels. These distinct differences in DNA, RNA and post-transcriptional events may contribute to their unique clinicopathological features.\n\nConflict of Interest StatementAshiq Masood Advisory board and speaker Bureau Bristol-Myers Squibb and Boehringer Ingelheim\n\nJanakiraman Subramanian Advisory board - Astra Zeneca, Pfizer, Boehringer Ingelheim, Alexion, Paradigm, Bristol-Myers Squibb Speakers Bureau - Astra Zeneca, Boehringer Ingelheim, Lilly Research Support - Biocept and Paradigm\n\nArif Hussain Advisory board - Novartis, Bayer, Astra Zeneca Consultant - Bristol-Myers-Squibb All other authors have no conflict of interest.

genomics↗

bioRxiv2017

Stromal Reactivity Differentially Drives Tumor Cell Evolution and Prostate Cancer Progression

We implemented a hybrid multiscale model of carcinogenesis that merges data from biology and pathology on the microenvironmental regulation of prostate cancer (PCa) cell behavior. It recapitulates the biology of stromal influence in prostate cancer progression. Our data indicate that the interactions between the tumor cells and reactive stroma shape the evolutionary dynamics of PCa cells and explain overall tumor aggressiveness. We show that the degree of stromal reactivity, when coupled with the current clinical biomarkers, significantly improves PCa prognostication, both for death and recurrence, that may alter treatment decisions. We also show that stromal reactivity correlates directly with tumor growth but inversely modulates tumor evolution. This suggests that the aggressive stromal independent PCa may be an inevitable evolutionary result of poor stromal reactivity. It also suggests that purely tumor centric metrics of aggressiveness may be misleading in terms on clinical outcome.

cancer biology↗

bioRxiv2018

High resolution profile of body wide pathological changes induced by abnormal elastin metabolism in Loxl1 knockout mice

Abnormal ECM caused serious body wide diseases and elastin is one of the important ECM components. But its systemic function still has not yet been thoroughly illustrated due to limitations related to novel research technologies. To uncover the functions of elastin, a new method for body-wide organ transcriptome profiling, combined with single-cell mass cytometry of the blood, was developed. A body-wide organ transcriptomic (BOT) map was created by performing RNA-seq of 17 organs from both Loxl1 knockout (KO) and wide type (WT) mice. The BOT results showed a systematic up-regulation of genes related to immune response and proliferation process in multiple tissues of the KO mice; histological and immune staining also confirmed the hyperplasia and infiltration of local immune cells in the vagina, small intestine, and liver tissues of KO mice. Furthermore, using 32 markers, CYTOF mass cytometry analysis of the immune cell subpopulations from the peripheral blood revealed apparent systemic immune changes in the KO mice; data showed an activated NK cells and T cells with a higher expression of CD44 and CD38, and a suppressed B cells, macrophages and neutrophils with lower expressions of CD62L, CD44 and IL6. More interestingly, these findings also correlated well with the data obtained from cancer patient databases; tumor patients had higher mutation frequency of Loxl1, and the Loxl1-mutant tumor patients had up-regulated immune process, cell proliferation and decreased survival rate. Thus, this research provided a powerful strategy to screen body-wide organ functions of a particular gene; the findings also illustrated the important biological roles of elastin on multiple organ cells and systemic immunity. These strategy and discoveries are both of important value for the understanding of ECM biology and multi-organ cancer pathology.

cancer biology↗

bioRxiv2017

Mass-spectrometry of single mammalian cells quantifies proteome heterogeneity during cell differentiation

Cellular heterogeneity is important to biological processes, including cancer and development. However, proteome heterogeneity is largely unexplored because of the limitations of existing methods for quantifying protein levels in single cells. To alleviate these limitations, we developed Single Cell ProtEomics by Mass Spectrometry (SCoPE-MS), and validated its ability to identify distinct human cancer cell types based on their proteomes. We used SCoPE-MS to quantify over a thousand proteins in differentiating mouse embryonic stem (ES) cells. The single-cell proteomes enabled us to deconstruct cell populations and infer protein abundance relationships. Comparison between single-cell proteomes and transcriptomes indicated coordinated mRNA and protein covariation. Yet many genes exhibited functionally concerted and distinct regulatory patterns at the mRNA and the protein levels, suggesting that post-transcriptional regulatory mechanisms contribute to proteome remodeling during lineage specification, especially for developmental genes. SCoPE-MS is broadly applicable to measuring proteome configurations of single cells and linking them to functional phenotypes, such as cell type and differentiation potentials.

systems biology↗

bioRxiv2018

De Novo Mutational Signature Discovery in Tumor Genomes using SparseSignatures

Cancer is the result of mutagenic processes that can be inferred from tumor genomes by analyzing rate spectra of point mutations, or "mutational signatures". Here we present SparseSignatures, a novel framework to extract signatures from somatic point mutation data. Our approach incorporates a user-specified background signature, employs regularization to reduce noise in non-background signatures, uses cross-validation to identify the number of signatures, and is scalable to large datasets. We show that SparseSignatures outperforms current state-of-the-art methods on simulated data using a variety of standard metrics. We then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms and are strongly associated with patient clinical features. Authors SummaryCancer is a genetic disease, occurring as a result of mutagenic processes causing DNA somatic mutations in genes controlling cellular growth and division. These somatic mutations arise from processes such as defective DNA repair and environmental mutagens, which massively increase the rate of somatic variants. As a result, due to the specificity of molecular lesions caused by such processes, and the specific repair mechanisms deployed by the cell to mitigate the damage, mutagenic processes generate characteristic point mutation rate spectra which are called mutational signatures. These signatures can indicate which mutagenic processes are active in a tumor, reveal biological differences between cancer subtypes, and may be useful markers for therapeutic response. Here, we develop SparseSignatures, a novel framework for mutational signature discovery capable of both identifying the active signatures in a dataset of point mutations and calculating their exposure values, i.e., the number of mutations originating from each signature in each patient. We show that our approach outperforms current state-of-the-art methods on simulated data using a variety of standard metrics and then apply SparseSignatures to whole genome sequences of pancreatic and breast tumors, discovering well-differentiated signatures that are linked to known mutagenic mechanisms.

bioinformatics↗

bioRxiv2026

Spatial Mapping of the Lung Cancer Ecosystem Reveals Distinct Patterns of Intratumoral and Internodular Heterogeneity

The spatial organization of malignant and non-malignant cells within the tumor microenvironment (TME) critically influences tumor evolution and therapeutic response. However, the architecture of micro-niches remains incompletely understood. Leveraging Xenium-based spatial transcriptomics, we comprehensively mapped the spatial ecosystem of an orthotopic murine lung cancer model, identifying distinct spatial domains that form unique, organized cellular neighborhoods. These domains cluster into three major communities: (1) non-tumoral regions that recapitulate canonical normal lung structures; (2) a heterogeneous peri-tumoral region composed of spatial domains characterized by mesenchymal remodeling, active immune checkpoint signaling, and immunosuppressive myeloid populations; and (3) intra-tumoral regions that reveal marked tumor nodule heterogeneity, with unique tumor-specific domains exhibiting hallmark cancer pathways. Furthermore, our analytic approach was applicable to human lung cancer tissue. Notably, spatial domain analysis allowed us to resolve tumor nodules into multiple biologically distinct subtypes, defined by domain composition, hallmark cancer programs, and intercellular communication patterns within the TME.

cancer biology↗

bioRxiv2017

No evidence of MET and HER2 over-expression in non-small cell lung carcinoma and breast cancer, respectively, raises serious doubts on using RNA-seq profiles of tumor-educated platelets as a ‘liquid biopsy’ source

In this detailed critique of the study proposing using RNA-seq from tumor-educated platelets (TEP) as a liquid biopsy source [1], several flawed assumptions leave little biological basis behind the statistical computations. First, there is no supporting evidence provided for the FFPE based classification of METoverexpression and EGFR mutation on tumor-tissues. Considering that raw reads of MET expression in a subset of healthy [N=21, mean=112, sd=77] and NSCLC [N=24, mean=11, sd=12] samples (typically with millions of reads) translates into over-expression in reality, providing the data for such computations is vital for future validation. A similar criticism applies for classifying samples based on EGFR mutations (the study uses only exon 20 and 21 from a wide range of possible mutations) with negligible counts [N=24, mean=3, sd=6]. While Ofner et. al, 2017 faced major problems associated with FFPE DNA, it is also true that Fassunke, et al., 2015 found concordance in 26 out of 26 samples for EGFR mutations in another FFPE-based study. However, Fassunke, et al., 2015 have been meticulous in describing the EGFR amplicons (exon 18 and 19 are missing in the TEP-study). Any error in initial classification renders downstream computations error-prone. The low counts of MET in the RNA-seq firmly establishes that inclusion of genes with such low counts in the set of 1100 discriminatory genes (Table S4) makes no sense as the \"real\" counts could vary wildly. Yet, TRAT1 is an example of one discriminator gene with counts of healthy [N=21, mean=164, sd=375] and NSCLC [N=24, mean=53, sd=176]. There are many such genes which should be excluded. Moving on to a discriminator with high counts (F13A1) in both healthy [N=21, mean=28228, sd=48581] and NSCLC [N=24, mean=98336, sd=74574] samples, a bonafide platelet gene that \"encodes the coagulation factor XIII A subunit\". Platelets do not have a nucleus, and thus the blue-print (chromosomes and related machinery) for making or regulating mRNA. They are boot-strapped with mRNA, like F13A1, during origination and then just go on keep collecting mRNA during circulation (which is the premise of their use in liquid biopsy). The assumption that these genes are differentially spliced in huge numbers is highly speculative without providing experimental proof. The discovery of spliceosomes in anucleate platelets [2] in 2005, 30 years after splicing was discovered in the nucleus by Sharp and Robert, probably indicates that spliceosomes are not dominant in platelets. Zucker, et al., 2017 have shown for another gene F11 that it is present in platelets as pre-mRNA and is spliced upon platelet activation [3]. Any study using the F13A1 gene as a discriminator ought to show the same two things, followed by differential counts in TEP. Ironically, F11 is not present in the discriminator set. Another blood coagulation related gene (TFPI) shows slight over-expression in NSCLC (moderate counts, healthy [N=21, mean=1352, sd=592] and NSCLC [N=24, mean=1854, sd=846]), agreeing with Iversen, et al., 1998 [4], but in contrast to Fei, et al., 2017 [5], demonstrating that the jury is still out on the levels of many such genes. Thus, circulating mRNA from tumor tissues are not discriminatoryif MET is degraded to such levels in platelets educated by NSCLC tumors, why not other possible mRNA that might have been picked during the same class? Furthermore, high count genes can only be bona-fide platelet genes, and have no supporting experimental proof of splicing differences (any one gene would suffice to instill some confidence). In conclusion, looking past the statistical smoke surrounding \"surrogate signatures\", one finds no biological relevance.

cancer biology↗

bioRxiv2026

The Anti-Cancer Effects of Selected Indigenous Medicinal Plants of the Arid Bioregion

Ethnopharmacological relevance: Australian Indigenous medicinal plants represent a valuable yet underexplored source of bioactive compounds with potential therapeutic relevance. The Iningai community of Central Queensland has traditionally used native plants to manage conditions associated with inflammation, pain, infection, and general illness. Scientific evaluation of these plants may provide evidence for their customary applications and identify bioactivities relevant to anticancer biodiscovery. Aim of the study: This study evaluated leaf and stem extracts of seven medicinal plants-Pittosporum angustifolium, Alphitonia excelsa, Calytrix microcoma, Geijera parviflora, Melaleuca uncinata, Gossypium australe, and Eucalyptus similis-traditionally used by the Iningai community, focusing on three biological processes relevant to cancer: oxidative stress, inflammation, and cellular proliferation. Materials and methods: Antioxidant activity was assessed using DPPH radical-scavenging and ferric reducing antioxidant power (FRAP) assays. Anti-inflammatory activity was evaluated in lipopolysaccharide (LPS)-stimulated THP-1 macrophage-like cells by profiling IFN-, TNF-, IL-6, IL-12, IL-18, and IL-23. Antiproliferative activity was assessed using MTT-based viability assays in human and murine liver cancer cell lines (Huh7, Hep3B, Hep-55.1c, and A52). Results: The extracts exhibited distinct biological activity profiles. G. parviflora stem and C. microcoma leaf extracts showed the strongest antioxidant activities, whereas P. angustifolium stem exhibited the weakest radical-scavenging capacity. Cytokine responses were extract-specific, with E. similis leaf extract demonstrating broad and pronounced suppression of multiple LPS-induced pro-inflammatory cytokines. Several extracts produced concentration-dependent reductions in liver cancer cell viability, with P. angustifolium stem exhibiting the most consistent and potent antiproliferative activity across the cell lines tested. Notably, strong antioxidant or anti-inflammatory activity did not necessarily correspond with antiproliferative activity. Conclusion: Australian Indigenous medicinal plant extracts demonstrated distinct antioxidant, immunomodulatory, and antiproliferative activities rather than uniform bioactivity across experimental systems. The divergent activities of G. parviflora, C. microcoma, E. similis, and P. angustifolium highlight the importance of integrated biological screening and support the value of Indigenous knowledge-guided biodiscovery. These plants represent promising sources for further investigation of selective bioactive compounds with potential relevance to anticancer drug discovery.

cancer biology↗

bioRxiv2015

miR-551a and miR-551b target GLIPR2 and promote tumor growth in High-Risk Head and Neck Cancer by modulating autophagy

Distant metastasis (DM) and local-regional recurrence (LR) after radiation and chemo therapy are major cause of treatment failure for patients with head and neck squamous cell carcinoma. However, detailed underlying mechanisms leading to DM and LR in patients are not fully understood yet. MiRNA have been proposed as biomarkers in a variety of biological and medical conditions such as cancer and stress response. The advantages of miRNA as a biomarker lies in its stability in tissues as well as body fluids, hence the potential for non-invasive diagnosis and prognosis. In this study, towards understanding the molecular mechanism causing DM and LR in HN cancer patients we performed miRNA expression profiling using tumor samples from 118 head and neck cancer patients treated by post-operative radiotherapy (PORT) at M.D. Anderson Cancer Center from 1992 to 1999. All patients were considered to be at high-risk for recurrence having histologically proven advanced squamous cell carcinoma. Amongst these samples, 41 found to have distant metastasis (DM), 53 responded without relapse (no evidence of disease (NED)) to PORT. Comparison of miRNA expression between DM and NED specimens using two-way ANOVA identified 28 miRNAs that were differentially expressed with statistical significance (FDR < 0.2 and fold change > 1.5). Amongst these 28 miRNAs seen in the DM and NED outcome groups, miRNAs 551a and 551b are significantly associated with the DM group. Interestingly these two miRNAs share same seed sequence. Moreover Kaplan-Meir survival analysis in our data set and two other data sets suggested that miR-551a and miR-551b expressions are associated with poor survival in patients. We further performed cell proliferation, migration and invasion assays using the HN5 and UMSCC-17B head and neck cancer cell lines by transfection of either mimic or an inhibitor of miR-551a and miR-551b. The results suggested that miR-551a and miR-551b mimics promote proliferation, migration and invasion whereas the inhibitor decreased. Further studies indicated that these miRNAs target GLIPR2 expression and miR-551a, miR-551b and GLIPR2 axis at least in part plays an important role in tumor progression. Hence we need to further explore miR-551a and miR-551b-3p role in HN cancer progression in detail in in-vivo models to use them as therapeutic targets in future.

Cancer Biology↗

bioRxiv2017

Combined analysis of genome sequencing and RNA-motifs reveals novel damaging non-coding mutations in human tumors

A major challenge in cancer research is to determine the biological and clinical significance of somatic mutations in non-coding regions. This has been studied in terms of recurrence, functional impact, and association to individual regulatory sites, but the combinatorial contribution of mutations to common RNA regulatory motifs has not been explored. We developed a new method, MIRA, to perform the first comprehensive study of significantly mutated regions (SMRs) affecting binding sites for RNA-binding proteins (RBPs) in cancer. Extracting signals related to RNA-related selection processes and using RNA sequencing data from the same samples we identified alterations in RNA expression and splicing linked to mutations on RBP binding sites. We found SRSF10 and MBNL1 motifs in introns, HNRPLL motifs at 5 UTRs, as well as 5 and 3 splice-site motifs, among others, with specific mutational patterns that disrupt the motif and impact RNA processing. MIRA facilitates the integrative analysis of multiple genome sites that operate collectively through common RBPs and can aid in the interpretation of non-coding variants in cancer. MIRA is available at https://github.com/comprna/mira.

cancer biology↗

bioRxiv2017

DNA-dependent protein synthesis exhibited by cancer shed particulates

Genetic heterogeneity in tumours is the bonafide hallmark applicable to all cancer types (Burrell et al, 2013). Furthermore, deregulated ribosome biogenesis and elevated protein biosynthesis have been consistently associated with multiple cancer types (Ruggero, 2012; Ruggero & Pandolfi, 2003). We observed that under cultivation conditions almost all cancer cell types actively shed significant amount of particulates as compared to non-malignant cell lines requiring frequent changing of cultivation media. We therefore asked if cancer cell shed particulates might still retain biological activity associated with protein biosynthesis. Here, we communicate our observations of DNA-dependent protein biosynthetic activity exhibited by the cell-free particulates shed by the cancer cell lines. Using pulsed isotope labelling approach we confirmed the cell-free protein translation activity exhibited by particulates shed by various cancer cell lines. Interestingly, the bioactivity was largely dependent on temperature, pH and on 3-DNA elements. Our results demonstrate that cancer shed particulates are biologically active and may potentially drive expression of tissue non-specific promoters in distant organs.

cancer biology↗