Search

Background and Introduction: Over the last decade, treatment of patients with advanced non-small cell lung cancer (NSCLC) has become dependent on tissue and/or blood biomarkers to guide treatment decisions. Timely access to comprehensive biomarker and tumor signature information is crucial for diagnostic testing. With the rapid development and implementation of complex biomarker testing, comprehensive molecular profiling with in-depth analysis of DNA, RNA, and proteins can be easily performed. Initial data from the MedStar Health system showed considerable disparities in the use of next generation sequencing (NGS) between hospitals, and there is a clear need to improve education and understanding regarding which cases are appropriate for NGS, as well as the use of protocols to make those decisions in an expeditious manner., Materials and Methods: Clinical pathways are systems-based tools that aim to create greater transparency around care decision making, therapeutic selection, and care delivery. They enhance quality and efficiency by reducing non-value-added intra-provider variability in care. We aimed to create a comprehensive clinical pathway system, the Targetable Molecular Algorithm (TMA), to increase the understanding and use of NGS in NSCLC by physicians in training, oncology nurse navigators, nurse practitioners, and general oncologists., Discussion: We provide an overview of the implementation of the platform along with navigation guide. A realistic case study-a typical clinical workflow for a patient requiring NGS testing with an EGFR mutation-is also reviewed, demonstrating how the TMA platform can be applied. Additionally, we highlight the importance of the resource, and discuss its strengths, weaknesses, and potential future applications., Results and Conclusion: This new and innovative pathway system will make decision-making easier for clinicians trying to understand the appropriate tests and treatment algorithms for their patients. Our aim is to increase the appropriate and timely use of NGS among health-system providers with the hope that this system will empower physicians to provide better care by providing a quick, simple, user-friendly tool for comprehensive patient care., Competing Interests: J.R. is Advisory Board/Consultant at Genentech/Roche, Sanofi/Genzyme, Personalis, Guardant, Astrazeneca, BMS, Arcus, Abbvie, Daiichi Sankyo, Catalym. He receives research funding (to institution) from: Genentech/Roche, Verastem, Nuvalent, Mesothelioma Applied Research Foundation, LUNGevity Foundation. He has received honoraria/Speaking Fees from Astrazeneca, Merck. C.K. is Consultant/advisory board member at Novartis, Janssen, AstraZeneca, Sanofi, PierianDx, Diffuse pharmaceuticals, Mirati, Jazz Pharmaceuticals, Arcus Biosciences, Daiichi Sankyo, Eisai, Regeneron. He receives research funding (to institution) from: AstraZeneca, Bristol-Myers Squibb, Novartis, Genentech, Janssen, Regeneron, Debiopharm, Karyopharm, Daiichi-Sankyo, Lyell Immunopharma., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

The coronavirus disease 2019 (COVID-19) pandemic caused by the SARS-CoV-2 virus has affected over 700 million people, and caused over 7 million deaths throughout the world as of April 2024, and continues to affect people through seasonal waves. While over 675 million people have recovered from this disease globally, the lingering effects of the disease are still under study. Long term effects of SARS-CoV-2 infection, known as 'long COVID,' include a wide range of symptoms including fatigue, chest pain, cellular damage, along with a strong innate immune response characterized by inflammatory cytokine production. Three years after the pandemic, data about long covid studies are finally emerging. More clinical studies and clinical trials are needed to understand and determine the factors that predispose individuals to these long-term side effects. In this methodology paper, our goal was to apply data driven approaches in order to explore the multidimensional landscape of infected lung tissue microenvironment to better understand complex interactions between viral infection, immune response and the lung microbiome of patients with (a) SARS-CoV-2 virus and (b) NL63 coronavirus. The samples were analyzed with several machine learning tools allowing simultaneous detection and quantification of viral RNA amount at genome and gene level; human gene expression and fractions of major types of immune cells, as well as metagenomic analysis of bacterial and viral abundance. To contrast and compare specific viral response to SARS-COV-2, we analyzed deep sequencing data from additional cohort of patients infected with NL63 strain of corona virus. Our correlation analysis of three types of RNA-seq based measurements in patients i.e. fraction of viral RNA (at genome and gene level), Human RNA (transcripts and gene level) and bacterial RNA (metagenomic analysis), showed significant correlation between viral load as well as level of specific viral gene expression with the fractions of immune cells present in lung lavage as well as with abundance of major fractions of lung microbiome in COVID-19 patients. Our methodology-based proof-of-concept study has provided novel insights into complex regulatory signaling interactions and correlative patterns between the viral infection, inhibition of innate and adaptive immune response as well as microbiome landscape of the lung tissue. These initial findings could provide better understanding of the diverse dynamics of immune response and the side effects of the SARS-CoV-2 infection and demonstrates the possibilities of the various types of analyses that could be performed from this type of data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Background: Intestinal transplant (ITx) rejection is associated with memory T helper type 17 cell (Th17) infiltration of grafted tissues. Modulation of Th17 effector cell response is facilitated by T regulatory (Treg) cells, but a phenotypic characterization of this process is lacking in the context of allograft rejection., Methods: Flow cytometry was performed to examine the expression of surface receptors, cytokines, and transcription factors in Th17 and Treg cells in ITx control (n = 34) and rejection patients (n = 23). To elucidate key pathways guiding the rejection biology, we utilized RNA sequencing (RNAseq) and assessed epigenetic stability through pyrosequencing of the Treg-specific demethylated region (TSDR)., Results: We found that intestinal allograft rejection is characterized by Treg cellular infiltrates, which are polarized toward Th17-type chemokine receptor, ROR-γt transcription factor expression, and cytokine production. These Treg cell subsets have maintained epigenetic stability, as defined by FoxP3-TSDR methylation status, but displayed upregulation of functional Treg and purinergic signaling genes by RNAseq analysis such as CD39, in keeping with suppressor Th17 properties., Conclusion: We show that ITx rejection is associated with increased polarized cells that express a Th17-like phenotype concurrent with regulatory purinergic markers., (Copyright © 2024 American Society for Histocompatibility and Immunogenetics. Published by Elsevier Inc. All rights reserved.)

Translational bioinformatics and data science play a crucial role in biomarker discovery as it enables translational research and helps to bridge the gap between the bench research and the bedside clinical applications. Thanks to newer and faster molecular profiling technologies and reducing costs, there are many opportunities for researchers to explore the molecular and physiological mechanisms of diseases. Biomarker discovery enables researchers to better characterize patients, enables early detection and intervention/prevention and predicts treatment responses. Due to increasing prevalence and rising treatment costs, mental health (MH) disorders have become an important venue for biomarker discovery with the goal of improved patient diagnostics, treatment and care. Exploration of underlying biological mechanisms is the key to the understanding of pathogenesis and pathophysiology of MH disorders. In an effort to better understand the underlying mechanisms of MH disorders, we reviewed the major accomplishments in the MH space from a bioinformatics and data science perspective, summarized existing knowledge derived from molecular and cellular data and described challenges and areas of opportunities in this space., (© The Author(s) 2024. Published by Oxford University Press.)

Although machine learning (ML) has shown promise across disciplines, out-of-sample generalizability is concerning. This is currently addressed by sharing multi-site data, but such centralization is challenging/infeasible to scale due to various limitations. Federated ML (FL) provides an alternative paradigm for accurate and generalizable ML, by only sharing numerical model updates. Here we present the largest FL study to-date, involving data from 71 sites across 6 continents, to generate an automatic tumor boundary detector for the rare disease of glioblastoma, reporting the largest such dataset in the literature (n = 6, 314). We demonstrate a 33% delineation improvement for the surgically targetable tumor, and 23% for the complete tumor extent, over a publicly trained model. We anticipate our study to: 1) enable more healthcare studies informed by large diverse data, ensuring meaningful results for rare diseases and underrepresented populations, 2) facilitate further analyses for glioblastoma by releasing our consensus model, and 3) demonstrate the FL effectiveness at such scale and task-complexity as a paradigm shift for multi-site collaborations, alleviating the need for data-sharing., (© 2022. The Author(s).)

Malignancy of the brain and CNS is unfortunately a common diagnosis. A large subset of these lesions tends to be high grade tumors which portend poor prognoses and low survival rates, and are estimated to be the tenth leading cause of death worldwide. The complex nature of the brain tissue environment in which these lesions arise offers a rich opportunity for translational research. Magnetic Resonance Imaging (MRI) can provide a comprehensive view of the abnormal regions in the brain, therefore, its applications in the translational brain cancer research is considered essential for the diagnosis and monitoring of disease. Recent years has seen rapid growth in the field of radiogenomics, especially in cancer, and scientists have been able to successfully integrate the quantitative data extracted from medical images (also known as radiomics) with genomics to answer new and clinically relevant questions. In this paper, we took raw MRI scans from the REMBRANDT data collection from public domain, and performed volumetric segmentation to identify subregions of the brain. Radiomic features were then extracted to represent the MRIs in a quantitative yet summarized format. This resulting dataset now enables further biomedical and integrative data analysis, and is being made public via the NeuroImaging Tools & Resources Collaboratory (NITRC) repository ( https://www.nitrc.org/projects/rembrandt_brain/ )., (© 2022. The Author(s).)

Thymic carcinoma is rare and has a poorer prognosis than thymomas. The treatment options are limited after failure of platinum-based chemotherapy. We previously performed a single-center phase II study of pembrolizumab in patients with advanced thymic carcinoma, showing a 22.5% response rate. Here, we characterize the genomic and transcriptomic profile of thymic carcinoma samples from 10 patients (5 non-responders versus 5 responders) in this cohort, with the main aim of identifying potential predictors of response to immunotherapy. We find that expression of PDL1 and alterations in genes or pathways that correlated with PD-L1 expression ( CYLD and BAP1 ) could be potential predictors for response or resistance to immunotherapy in patients with advanced thymic carcinoma. Our study provides insights into potential predictive markers/pathways to select patients with thymic carcinoma for anti-PD-1 immunotherapy., Competing Interests: The authors have declared that no competing interests exist., (© 2021 The Author(s).)

Intestinal transplantation (ITx) can be life-saving for patients with advanced intestinal failure experiencing complications of parenteral nutrition. New surgical techniques and conventional immunosuppression have enabled some success, but outcomes post-ITx remain disappointing. Refractory cellular immune responses, immunosuppression-linked infections, and posttransplant malignancies have precluded widespread ITx application. To shed light on the dynamics of ITx allograft rejection and treatment resistance, peripheral blood samples and intestinal allograft biopsies from 51 ITx patients with severe rejection, alongside 37 stable controls, were analyzed using immunohistochemistry, polychromatic flow cytometry, and reverse transcription-PCR. Our findings inform both immunomonitoring and treatment. In terms of immunomonitoring, we found that while ITx rejection is associated with proinflammatory and activated effector memory T cells in the blood, evidence of treatment efficacy can only be found in the allograft itself, meaning that blood-based monitoring may be insufficient. In terms of treatment, we found that the prominence of intra-graft memory TNF-α and IL-17 double-positive T helper type 17 (Th17) cells is a leading feature of refractory rejection. Anti-TNF-α therapies appear to provide novel and safer treatment strategies for refractory ITx rejection; with responses in 14 of 14 patients. Clinical protocols targeting TNF-α, IL-17, and Th17 warrant further testing., (© 2020 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background: Spectrin, beta, non-erythrocytic 1 (SPTBN1), an adapter protein for transforming growth factor beta (TGF-β) signaling, is recognized as a tumor suppressor in the development of hepatocellular carcinoma (HCC); however, the underlying molecular mechanisms of this tumor suppression remain obscure. Methods: The effects on expression of pro-inflammatory cytokines upon the inhibition or impairment of SPTBN1 in HCC cell lines and liver tissues of Sptbn1 +/- and wild-type (WT) mice were assessed by analyses of quantitative real-time reverse-transcription polymerase chain reaction (QRT-PCR), enzyme linked immunosorbent assay (ELISA), Western blotting and gene array databases from HCC patients. We investigated the detailed molecular mechanisms underlying the inflammatory responses by immunoprecipitation-Western blotting, luciferase reporter assay, chromatin immunoprecipitation quantitative real time PCR (ChIP-qPCR), immunohistochemistry (IHC) and electrophoretic mobility shift assay (EMSA). The proportion of myeloid-derived suppressor cells in liver, spleen, bone marrow and peripheral blood samples from WT and Sptbn1 +/- mice were measured by fluorescence-activated cell sorting (FACS) analysis. Further, the hepatocacinogenesis and its correlation with inflammatory microenvironment by loss of SPTBN1/SOCS1 and induction of p65 were analyzed by treating WT and Sptbn1 +/- mice with 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC). Results: Loss of SPTBN1 in HCC cells upregulated the expression of pro-inflammatory cytokines including interleukin-1α (IL-1α), IL-1β, and IL-6, and enhanced NF-κB transcriptional activation. Mechanistic analyses revealed that knockdown of SPTBN1 by siRNA downregulated the expression of suppressor of cytokine signaling 1 (SOCS1), an E3 ligase of p65, and subsequently upregulated p65 accumulation in the nucleus of HCC cells. Restoration of SOCS1 abrogated this SPTBN1 loss-associated elevation of p65 in HCC cells. In human HCC tissues, SPTBN1 gene expression was inversely correlated with gene expression of IL-1α, IL-1β and IL-6. Furthermore, a decrease in the levels of SPTBN1 gene, as well as an increase in the gene expression of IL-1β or IL-6 predicted shorter relapse free survival in HCC patients, and that HCC patients with low expression of SPTBN1 or SOCS1 protein is associated with poor survival. Heterozygous loss of SPTBN1 ( Sptbn1 +/- ) in mice markedly upregulated hepatic expression of IL-1α, IL-1β and IL-6, and elevated the proportion of myeloid-derived suppressor cells (MDSCs) and CD4 + CD25 + Foxp3 + regulatory T cells (Foxp3 + Treg) cells in the liver, promoting hepatocarcinogenesis of mouse fed by DDC. Conclusions : Our findings provided evidence that loss of SPTBN1 in HCC cells increases p65 protein stability via the inhibition of SOCS1 and enhances NF-κB activation, stimulating the release of inflammatory cytokines, which are critical molecular mechanisms for the loss of SPTBN1-induced liver cancer formation. Reduced SPTBN1 and SOCS1 predict poor outcome in HCC patients., Competing Interests: Competing Interests: The authors have declared that no competing interest exists., (© The author(s).)

Pancreatic cancer ranks one of the worst in overall survival outcome with a 5 year survival rate being less than 10%. Pancreatic cancer faces unique challenges in its diagnosis and treatment, such as the lack of clinically validated biomarkers and the immensely immunosuppressive tumor microenvironment. Recently, the LY6 gene family has received increasing attention for its multi-faceted roles in cancer development, stem cell maintenance, immunomodulation, and association with more aggressive and hard-to-treat cancers. A detailed study of mRNA expression of LY6 gene family and its association with overall survival (OS) outcome in pancreatic cancers is lacking. We used publicly available clinical datasets to analyze the mRNA expression of a set of LY6 genes and its effect on OS outcome in the context of the tumor microenvironment and immunomodulation. We used web-based tools Kaplan-Meier Plotter, cBioPortal, Oncomine and R-programming to analyze copy number alterations, mRNA expression and its association with OS outcome in pancreatic cancer. These analyses demonstrated that high expression of LY6 genes is associated with OS and disease free survival (DFS) outcome. High expression of LY6 genes and their association with OS outcome is dependent on the composition of tumor microenvironment. Considering that LY6 proteins are anchored to the outer cell membrane or secreted, making them readily accessible, these findings highlight the potential of LY6 family members in the future of pancreatic cancer diagnosis and treatment., Competing Interests: CONFLICTS OF INTEREST The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. The opinions expressed herein are those of the authors and are not necessarily representative of those of the government of the United States, the Uniformed Services University, the Department of Defense, NIH, or any other US federal agency., (Copyright: © 2021 Russ et al.)

Although innate lymphoid cells (ILCs) play fundamental roles in mucosal barrier functionality and tissue homeostasis, ILC-related mechanisms underlying intestinal barrier function, homeostatic regulation, and graft rejection in intestinal transplantation (ITx) patients have yet to be thoroughly defined. We found protective type 3 NKp44 + ILCs (ILC3s) to be significantly diminished in newly transplanted allografts, compared to allografts at 6 months, whereas proinflammatory type 1 NKp44 - ILCs (ILC1s) were higher. Moreover, serial immunomonitoring revealed that in healthy allografts, protective ILC3s repopulate by 2-4 weeks postoperatively, but in rejecting allografts they remain diminished. Intracellular cytokine staining confirmed that NKp44 + ILC3 produced protective interleukin-22 (IL-22), whereas ILC1s produced proinflammatory interferon-gamma (IFN-γ) and tumor necrosis factor-alpha (TNF-α). Our findings about the paucity of protective ILC3s immediately following transplant and their repopulation in healthy allografts during the first month following transplant were confirmed by RNA-sequencing analyses of serial ITx biopsies. Overall, our findings show that ILCs may play a key role in regulating ITx graft homeostasis and could serve as sentinels for early recognition of allograft rejection and be targets for future therapies., (© 2020 The American Society of Transplantation and the American Society of Transplant Surgeons.)

Background & Aims: Chromosomal instability (CIN) is a carcinogenesis event that promotes metastasis and resistance to therapy by unclear mechanisms. Expression of the colon cancer-associated transcript 2 gene (CCAT2), which encodes a long noncoding RNA (lncRNA), associates with CIN, but little is known about how CCAT2 lncRNA regulates this cancer enabling characteristic., Methods: We performed cytogenetic analysis of colorectal cancer (CRC) cell lines (HCT116, KM12C/SM, and HT29) overexpressing CCAT2 and colon organoids from C57BL/6N mice with the CCAT2 transgene and without (controls). CRC cells were also analyzed by immunofluorescence microscopy, γ-H2AX, and senescence assays. CCAT2 transgene and control mice were given azoxymethane and dextran sulfate sodium to induce colon tumors. We performed gene expression array and mass spectrometry to detect downstream targets of CCAT2 lncRNA. We characterized interactions between CCAT2 with downstream proteins using MS2 pull-down, RNA immunoprecipitation, and selective 2'-hydroxyl acylation analyzed by primer extension analyses. Downstream proteins were overexpressed in CRC cells and analyzed for CIN. Gene expression levels were measured in CRC and non-tumor tissues from 5 cohorts, comprising more than 900 patients., Results: High expression of CCAT2 induced CIN in CRC cell lines and increased resistance to 5-fluorouracil and oxaliplatin. Mice that expressed the CCAT2 transgene developed chromosome abnormalities, and colon organoids derived from crypt cells of these mice had a higher percentage of chromosome abnormalities compared with organoids from control mice. The transgenic mice given azoxymethane and dextran sulfate sodium developed more and larger colon polyps than control mice given these agents. Microarray analysis and mass spectrometry indicated that expression of CCAT2 increased expression of genes involved in ribosome biogenesis and protein synthesis. CCAT2 lncRNA interacted directly with and stabilized BOP1 ribosomal biogenesis factor (BOP1). CCAT2 also increased expression of MYC, which activated expression of BOP1. Overexpression of BOP1 in CRC cell lines resulted in chromosomal missegregation errors, and increased colony formation, and invasiveness, whereas BOP1 knockdown reduced viability. BOP1 promoted CIN by increasing the active form of aurora kinase B, which regulates chromosomal segregation. BOP1 was overexpressed in polyp tissues from CCAT2 transgenic mice compared with healthy tissue. CCAT2 lncRNA and BOP1 mRNA or protein were all increased in microsatellite stable tumors (characterized by CIN), but not in tumors with microsatellite instability compared with nontumor tissues. Increased levels of CCAT2 lncRNA and BOP1 mRNA correlated with each other and with shorter survival times of patients., Conclusions: We found that overexpression of CCAT2 in colon cells promotes CIN and carcinogenesis by stabilizing and inducing expression of BOP1 an activator of aurora kinase B. Strategies to target this pathway might be developed for treatment of patients with microsatellite stable colorectal tumors., (Copyright © 2020 AGA Institute. Published by Elsevier Inc. All rights reserved.)

Purpose: In this work, we introduce CDGnet (Cancer-Drug-Gene Network), an evidence-based network approach for recommending targeted cancer therapies. CDGnet represents a user-friendly informatics tool that expands the range of targeted therapy options for patients with cancer who undergo molecular profiling by including the biologic context via pathway information., Methods: CDGnet considers biologic pathway information specifically by looking at targets or biomarkers downstream of oncogenes and is personalized for individual patients via user-inputted molecular alterations and cancer type. It integrates a number of different sources of knowledge: patient-specific inputs (molecular alterations and cancer type), US Food and Drug Administration-approved therapies and biomarkers (curated from DailyMed), pathways for specific cancer types (from Kyoto Encyclopedia of Genes and Genomes [KEGG]), gene-drug connections (from DrugBank), and oncogene information (from KEGG). We consider 4 different evidence-based categories for therapy recommendations. Our tool is delivered via an R/Shiny Web application. For the 2 categories that use pathway information, we include an interactive Sankey visualization built on top of d3.js that also provides links to PubChem., Results: We present a scenario for a patient who has estrogen receptor (ER)-positive breast cancer with FGFR1 amplification. Although many therapies exist for patients with ER-positive breast cancer, FGFR1 amplifications may confer resistance to such treatments. CDGnet provides therapy recommendations, including PIK3CA, MAPK, and RAF inhibitors, by considering targets or biomarkers downstream of FGFR1., Conclusion: CDGnet provides results in a number of easily accessible and usable forms, separating targeted cancer therapies into categories in an evidence-based manner that incorporates biologic pathway information.

The mammalian target of rapamycin (mTOR) and associated phosphatidylinositol 3-kinase/AKT/mTOR signaling pathway is commonly up-regulated in cancer, including bladder cancer. mTOR complex 2 (mTORC2) is a major regulator of bladder cancer cell migration and invasion, but the mechanisms by which mTORC2 regulates these processes are unclear. A discovery mass spectrometry and reverse-phase protein array-based proteomics dual approach was used to identify novel mTORC2 phosphoprotein targets in actively invading cancer cells. mTORC2 targets included focal adhesion kinase, proto-oncogene tyrosine-protein kinase Src, and caveolin-1 (Cav-1), among others. Functional testing shows that mTORC2 regulates Cav-1 localization and dynamic phosphorylation of Cav-1 on Y14. Regulation of Cav-1 activity by mTORC2 also alters the abundance of caveolae, which are specialized lipid raft invaginations of the plasma membrane associated with cell signaling and membrane compartmentalization. Our results demonstrate a unique role for mTORC2-mediated regulation of caveolae formation in actively migrating cancer cells., (Copyright © 2019 American Society for Investigative Pathology. Published by Elsevier Inc. All rights reserved.)

Mental illness is increasingly recognized as both a significant cost to society and a significant area of opportunity for biological breakthrough. As -omics and imaging technologies enable researchers to probe molecular and physiological underpinnings of multiple diseases, opportunities arise to explore the biological basis for behavioral health and disease. From individual investigators to large international consortia, researchers have generated rich data sets in the area of mental health, including genomic, transcriptomic, metabolomic, proteomic, clinical and imaging resources. General data repositories such as the Gene Expression Omnibus (GEO) and Database of Genotypes and Phenotypes (dbGaP) and mental health (MH)-specific initiatives, such as the Psychiatric Genomics Consortium, MH Research Network and PsychENCODE represent a wealth of information yet to be gleaned. At the same time, novel approaches to integrate and analyze data sets are enabling important discoveries in the area of mental and behavioral health. This review will discuss and catalog into an organizing framework the increasingly diverse set of MH data resources available, using schizophrenia as a focus area, and will describe novel and integrative approaches to molecular biomarker discovery that make use of mental health data., (© The Author 2017. Published by Oxford University Press.)

Background: Osteosarcoma is the most common malignant bone tumor in children. Survival remains poor among histologically poor responders, and there is a need to identify them at diagnosis to avoid delivering ineffective therapy. Genetic variation contributes to a wide range of response and toxicity related to chemotherapy. The aim of this study is to use sequencing of blood cells to identify germline haplotypes strongly associated with drug resistance in osteosarcoma patients., Methods: We used sequencing data from two patient datasets, from Inova Hospital and the NCI TARGET. We explored the effect of mutation hotspots, in the form of haplotypes, associated with relapse outcome. We then mapped the single nucleotide polymorphisms (SNPs) in these haplotypes to genes and pathways. We also performed a targeted analysis of mutations in Drug Metabolizing Enzymes and Transporter (DMET) genes associated with tumor necrosis and survival., Results: We found intronic and intergenic hotspot regions from 26 genes common to both the TARGET and INOVA datasets significantly associated with relapse outcome. Among significant results were mutations in genes belonging to AKR enzyme family, cell-cell adhesion biological process and the PI3K pathways; as well as variants in SLC22 family associated with both tumor necrosis and overall survival. The SNPs from our results were confirmed using Sanger sequencing. Our results included known as well as novel SNPs and haplotypes in genes associated with drug resistance., Conclusion: We show that combining next generation sequencing data from multiple datasets and defined clinical data can better identify relevant pathway associations and clinically actionable variants, as well as provide insights into drug response mechanisms.

The Rembrandt brain cancer dataset includes 671 patients collected from 14 contributing institutions from 2004-2006. It is accessible for conducting clinical translational research using the open access Georgetown Database of Cancer (G-DOC) platform. In addition, the raw and processed genomics and transcriptomics data have also been made available via the public NCBI GEO repository as a super series GSE108476. Such combined datasets would provide researchers with a unique opportunity to conduct integrative analysis of gene expression and copy number changes in patients alongside clinical outcomes (overall survival) using this large brain cancer study.

An estimated 17% of cancers worldwide are associated with infectious causes. The extent and biological significance of viral presence/infection in actual tumor samples is generally unknown but could be measured using human transcriptome (RNA-seq) data from tumor samples. We present an open source bioinformatics pipeline viGEN, which allows for not only the detection and quantification of viral RNA, but also variants in the viral transcripts. The pipeline includes 4 major modules: The first module aligns and filter out human RNA sequences; the second module maps and count (remaining un-aligned) reads against reference genomes of all known and sequenced human viruses; the third module quantifies read counts at the individual viral-gene level thus allowing for downstream differential expression analysis of viral genes between case and controls groups. The fourth module calls variants in these viruses. To the best of our knowledge, there are no publicly available pipelines or packages that would provide this type of complete analysis in one open source package. In this paper, we applied the viGEN pipeline to two case studies. We first demonstrate the working of our pipeline on a large public dataset, the TCGA cervical cancer cohort. In the second case study, we performed an in-depth analysis on a small focused study of TCGA liver cancer patients. In the latter cohort, we performed viral-gene quantification, viral-variant extraction and survival analysis. This allowed us to find differentially expressed viral-transcripts and viral-variants between the groups of patients, and connect them to clinical outcome. From our analyses, we show that we were able to successfully detect the human papilloma virus among the TCGA cervical cancer patients. We compared the viGEN pipeline with two metagenomics tools and demonstrate similar sensitivity/specificity. We were also able to quantify viral-transcripts and extract viral-variants using the liver cancer dataset. The results presented corresponded with published literature in terms of rate of detection, and impact of several known variants of HBV genome. This pipeline is generalizable, and can be used to provide novel biological insights into microbial infections in complex diseases and tumorigeneses. Our viral pipeline could be used in conjunction with additional type of immuno-oncology analysis based on RNA-seq data of host RNA for cancer immunology applications. The source code, with example data and tutorial is available at: https://github.com/ICBI/viGEN/.

The CINdex Bioconductor package addresses an important area of high-throughput genomic analysis. It calculates the chromosome instability (CIN) index, a novel measurement that quantitatively characterizes genome-wide copy number alterations (CNAs) as a measure of CIN. The advantage of this package is an ability to compare CIN index values between several groups for patients (case and control groups), which is a typical use case in translational research. The differentially changed cytobands or chromosomes can then be linked to genes located in the affected genomic regions, as well as pathways. This enables in-depth systems biology-based network analysis and assessment of the impact of CNA on various biological processes or clinical outcomes. This package was successfully applied to analysis of DNA copy number data in colorectal cancer as a part of multi-omics integrative study as well as for analysis of several other cancer types. The source code, along with an end-to-end tutorial, and example data are freely available in Bioconductor at http://bioconductor.org/packages/CINdex/., Competing Interests: Declaration of conflicting interests:The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

About 70% of all breast cancers are estrogen receptor alpha positive (ER+; ESR1). Many are treated with antiestrogens. Unfortunately, de novo and acquired resistance to antiestrogens is common but the underlying mechanisms remain unclear. Since growth of cancer cells is dependent on adequate energy and metabolites, the metabolomic profile of endocrine resistant breast cancers likely contains features that are deterministic of cell fate. Thus, we integrated data from metabolomic and transcriptomic analyses of ER+ MCF7-derived breast cancer cells that are antiestrogen sensitive (LCC1) or resistant (LCC9) that resulted in a gene-metabolite network associated with EGR1 (early growth response 1). In human ER+ breast tumors treated with endocrine therapy, higher EGR1 expression was associated with a more favorable prognosis. Mechanistic studies showed that knockdown of EGR1 inhibited cell growth in both cells and EGR1 overexpression did not affect antiestrogen sensitivity. Comparing metabolite profiles in LCC9 cells following perturbation of EGR1 showed interruption of lipid metabolism. Tolfenamic acid, an anti-inflammatory drug, decreased EGR1 protein levels and synergized with antiestrogens in inhibiting cell proliferation in LCC9 cells. Collectively, these findings indicate that EGR1 is an important regulator of breast cancer cell metabolism and is a promising target to prevent or reverse endocrine resistance., Competing Interests: CONFLICTS OF INTEREST The authors have no conflicts of interest to disclose.

Background: G-DOC Plus is a data integration and bioinformatics platform that uses cloud computing and other advanced computational tools to handle a variety of biomedical BIG DATA including gene expression arrays, NGS and medical images so that they can be analyzed in the full context of other omics and clinical information., Results: G-DOC Plus currently holds data from over 10,000 patients selected from private and public resources including Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and the recently added datasets from REpository for Molecular BRAin Neoplasia DaTa (REMBRANDT), caArray studies of lung and colon cancer, ImmPort and the 1000 genomes data sets. The system allows researchers to explore clinical-omic data one sample at a time, as a cohort of samples; or at the level of population, providing the user with a comprehensive view of the data. G-DOC Plus tools have been leveraged in cancer and non-cancer studies for hypothesis generation and validation; biomarker discovery and multi-omics analysis, to explore somatic mutations and cancer MRI images; as well as for training and graduate education in bioinformatics, data and computational sciences. Several of these use cases are described in this paper to demonstrate its multifaceted usability., Conclusion: G-DOC Plus can be used to support a variety of user groups in multiple domains to enable hypothesis generation for precision medicine research. The long-term vision of G-DOC Plus is to extend this translational bioinformatics platform to stay current with emerging omics technologies and analysis methods to continue supporting novel hypothesis generation, analysis and validation for integrative biomedical research. By integrating several aspects of the disease and exposing various data elements, such as outpatient lab workup, pathology, radiology, current treatments, molecular signatures and expected outcomes over a web interface, G-DOC Plus will continue to strengthen precision medicine research. G-DOC Plus is available at: https://gdoc.georgetown.edu .

Unlabelled: βII-Spectrin (SPTBN1) is an adapter protein for Smad3/Smad4 complex formation during transforming growth factor beta (TGF-β) signal transduction. Forty percent of SPTBN1(+/-) mice spontaneously develop hepatocellular carcinoma (HCC), and most cases of human HCC have significant reductions in SPTBN1 expression. In this study, we investigated the possible mechanisms by which loss of SPTBN1 may contribute to tumorigenesis. Livers of SPTBN1(+/-) mice, compared to wild-type mouse livers, display a significant increase in epithelial cell adhesion molecule-positive (EpCAM(+)) cells and overall EpCAM expression. Inhibition of SPTBN1 in human HCC cell lines increased the expression of stem cell markers EpCAM, Claudin7, and Oct4, as well as decreased E-cadherin expression and increased expression of vimentin and c-Myc, suggesting reversion of these cells to a less differentiated state. HCC cells with decreased SPTBN1 also demonstrate increased sphere formation, xenograft tumor development, and invasion. Here we investigate possible mechanisms by which SPTBN1 may influence the stem cell traits and aggressive behavior of HCC cell lines. We found that HCC cells with decreased SPTBN1 express much less of the Wnt inhibitor kallistatin and exhibit decreased β-catenin phosphorylation and increased β-catenin nuclear localization, indicating Wnt signaling activation. Restoration of kallistatin expression in these cells reversed the observed Wnt activation., Conclusion: SPTBN1 expression in human HCC tissues is positively correlated with E-cadherin and kallistatin levels, and decreased SPTBN1 and kallistatin gene expression is associated with decreased relapse-free survival. Our data suggest that loss of SPTBN1 activates Wnt signaling, which promotes acquisition of stem cell-like features, and ultimately contributes to malignant tumor progression., (© 2014 by the American Association for the Study of Liver Diseases.)

Next generation sequencing (NGS) technologies produce massive amounts of data requiring a powerful computational infrastructure, high quality bioinformatics software, and skilled personnel to operate the tools. We present a case study of a practical solution to this data management and analysis challenge that simplifies terabyte scale data handling and provides advanced tools for NGS data analysis. These capabilities are implemented using the "Globus Genomics" system, which is an enhanced Galaxy workflow system made available as a service that offers users the capability to process and transfer data easily, reliably and quickly to address end-to-endNGS analysis requirements. The Globus Genomics system is built on Amazon 's cloud computing infrastructure. The system takes advantage of elastic scaling of compute resources to run multiple workflows in parallel and it also helps meet the scale-out analysis needs of modern translational genomics research.

The cytoskeletal protein Spectrin, beta, non-erythrocytic 1 (SPTBN1), an adapter protein to SMAD3 in TGF-β signaling, may prevent hepatocellular carcinoma (HCC) development by downregulating the expression of signal transducer and activator of transcription 3 (STAT3). To elucidate the as yet undefined mechanisms that regulate this process, we demonstrate that higher levels of STAT3 transcription are found in livers of heterozygous SPTBN1(+/-) mice as compared to that of wild type mice. We also found increased levels of STAT3 mRNA, STAT3 protein, and p-STAT3 in human HCC cell-lines after knockdown of SPTBN1 or SMAD3, which promoted cell colony formation. Inhibition of STAT3 overrode the increase in cell colony formation due to knockdown of SPTBN1 or SMAD3. We also found that inhibition of SPTBN1 or SMAD3 upregulated STAT3 promoter activity in HCC cell-lines, which is dependent upon the cAMP-response element (CRE) and STAT-binding element (SBE) sites of the STAT3 promoter. Mechanistically, suppression of SPTBN1 and SMAD3 augmented the transcription of STAT3 by upregulating the CRE-binding proteins ATF3 and CREB2 and augmented the binding of those proteins to the regions within or upstream of the CRE site of the STAT3 promoter. Finally, in human HCC tissues, SPTBN1 expression correlated negatively with expression levels of STAT3, ATF3, and CREB2; SMAD3 expression correlated negatively with STAT3 expression; and the level of phosphorylated SMAD3 (p-SMAD3) correlated negatively with ATF3 and CREB2 protein levels. SPTBN1 and SMAD3 collaborate with CRE-binding transcription factors to inhibit STAT3, thereby preventing HCC development., (© The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)

Objectives: Response to the oncology drug gemcitabine may be variable in part due to genetic differences in the enzymes and transporters responsible for its metabolism and disposition. The aim of our in-silico study was to identify gene variants significantly associated with gemcitabine response that may help to personalize treatment in the clinic., Methods: We analyzed two independent data sets: (a) genotype data from NCI-60 cell lines using the Affymetrix DMET 1.0 platform combined with gemcitabine cytotoxicity data in those cell lines, and (b) genome-wide association studies (GWAS) data from 351 pancreatic cancer patients treated on an NCI-sponsored phase III clinical trial. We also performed a subset analysis on the GWAS data set for 135 patients who were given gemcitabine+placebo. Statistical and systems biology analyses were performed on each individual data set to identify biomarkers significantly associated with gemcitabine response., Results: Genetic variants in the ABC transporters (ABCC1, ABCC4) and the CYP4 family members CYP4F8 and CYP4F12, CHST3, and PPARD were found to be significant in both the NCI-60 and GWAS data sets. We report significant association between drug response and variants within members of the chondroitin sulfotransferase family (CHST) whose role in gemcitabine response is yet to be delineated., Conclusion: Biomarkers identified in this integrative analysis may contribute insights into gemcitabine response variability. As genotype data become more readily available, similar studies can be conducted to gain insights into drug response mechanisms and to facilitate clinical trial design and regulatory reviews.

The use and benefit of adjuvant chemotherapy to treat stage II colorectal cancer (CRC) patients is not well understood since the majority of these patients are cured by surgery alone. Identification of biological markers of relapse is a critical challenge to effectively target treatments to the ~20% of patients destined to relapse. We have integrated molecular profiling results of several "omics" data types to determine the most reliable prognostic biomarkers for relapse in CRC using data from 40 stage I and II CRC patients. We identified 31 multi-omics features that highly correlate with relapse. The data types were integrated using multi-step analytical approach with consecutive elimination of redundant molecular features. For each data type a systems biology analysis was performed to identify pathways biological processes and disease categories most affected in relapse. The biomarkers detected in tumors urine and blood of patients indicated a strong association with immune processes including aberrant regulation of T-cell and B-cell activation that could lead to overall differences in lymphocyte recruitment for tumor infiltration and markers indicating likelihood of future relapse. The immune response was the biologically most coherent signature that emerged from our analyses among several other biological processes and corroborates other studies showing a strong immune response in patients less likely to relapse.

Approximately 80% of Stage II colon cancer patients are cured by appropriate surgery. However, 20% relapse, and virtually all of these people will die due to metastatic disease. Adjuvant chemotherapy has little or no impact on relapse or survival in Stage II colon cancer, and can only add toxicity without benefit for 80% of the target population that has been cured by surgery. Despite much effort, it is difficult to identify clinical or molecular determinants of outcome in Stage II colon cancer, defeating attempts to target treatments to the 20% of individuals who are destined to relapse. We hypothesized that a multidimensional molecular analysis will identify a combination of factors that serve as prognostic biomarkers in Stage II adenocarcinoma of the colon. The Georgetown informatics team generated and analyzed multi-omics profiling datasets in stage II CRC patients with or without relapse to identify molecular signatures in CRC that may serve both as prognostic markers of recurrence, and also allow for identification of the subgroup of patients who might benefit from adjuvant chemotherapy. The datasets were loaded to GDOC® (Georgetown Database of Cancer) for further mining and analysis. The G-DOC web portal (http://gdoc.georgetown.edu) includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major "omics" types: DNA, mRNA, microRNA, and metabolites. Through technology re-use, the G-DOC infrastructure will accelerate progress for a variety of ongoing programs in need of integrative multi-omics analysis, and advance our opportunities to practice effective personalized oncology in the near future.

The aim of this study was to perform comparative analysis of multiple public datasets of gene expression in order to identify common genes as potential prognostic biomarkers. Additionally, the study sought to identify biological processes and pathways that are most significantly associated with early distant metastases (<5 years) in women with estrogen receptor-positive (ER+) breast tumors. Datasets from three published studies were selected for in silico analysis of gene expression profiles of ER+ breast cancer, using time to distant metastasis as the clinical endpoint. A subset of 44 differently expressed genes (DEGs) was found common to all three studies and characterized by mitotic checkpoint genes and pathways that regulate mitotic spindle and chromosome dynamics. DEG promoter regions were enriched with NFY binding sites. Analysis of miRNA target sites identified significant enrichment of miR-192, miR-193B, and miR-16-1 targets. Aberrant mitotic regulation could drive increased genomic instability leading to a progression towards an early onset metastatic phenotype. The relative importance of mitotic instability may reflect the clinical utility of mitotic poisons in metastatic breast cancer, including poisons such as the taxanes, epothilones, and vinca alkaloids.

Currently, cancer therapy remains limited by a "one-size-fits-all" approach, whereby treatment decisions are based mainly on the clinical stage of disease, yet fail to reference the individual's underlying biology and its role driving malignancy. Identifying better personalized therapies for cancer treatment is hindered by the lack of high-quality "omics" data of sufficient size to produce meaningful results and the ability to integrate biomedical data from disparate technologies. Resolving these issues will help translation of therapies from research to clinic by helping clinicians develop patient-specific treatments based on the unique signatures of patient's tumor. Here we describe the Georgetown Database of Cancer (G-DOC), a Web platform that enables basic and clinical research by integrating patient characteristics and clinical outcome data with a variety of high-throughput research data in a unified environment. While several rich data repositories for high-dimensional research data exist in the public domain, most focus on a single-data type and do not support integration across multiple technologies. Currently, G-DOC contains data from more than 2500 breast cancer patients and 800 gastrointestinal cancer patients, G-DOC includes a broad collection of bioinformatics and systems biology tools for analysis and visualization of four major "omics" types: DNA, mRNA, microRNA, and metabolites. We believe that G-DOC will help facilitate systems medicine by providing identification of trends and patterns in integrated data sets and hence facilitate the use of better targeted therapies for cancer. A set of representative usage scenarios is provided to highlight the technical capabilities of this resource.