News

    • Jun 24th, 2016. A loss of function variant in CASP7 protects against Alzheimer’s disease in homozygous APOE ε4 allele carriers. was published on BMC Genomics. In this paper, a novel approach was propsed and aim to uncover protective alleles against AD by analyzing genetic and phenotypic data in Mount Sinai Biobank and Electronic Medical Record (EMR) databases. Discovery from our research shows a likely loss-of-function small deletion variant in the caspase 7 (CASP7) gene associated with significantly reduced incidence of LOAD in carriers of the high-risk APOE ε4 allele. Further investigation of four independent cohorts of European ancestry revealed the protective effect of the CASP7 variant against AD is most significant in homozygous APOE ε4 allele carriers. Meta analysis of multiple datasets shows overall odds ratio = 0.45 (p = 0.004). Analysis of RNA sequencing derived gene expression data indicated the variant correlates with reduced caspase 7 expression in multiple brain tissues we examined.
    • June 14th,2016. Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks. was published on Bioinformatics. Approach in this paper is to utilize data from EMR databases that contain patient data from diverse demographics and ancestries. The implications of this racial underrepresentation of data can be profound regarding effects on the healthcare delivery and actionability in order to perform comparative, population-scale analyses of disease networks across three different populations, namely Caucasian (EA), African American (AA) and Hispanic/Latino (HL). From the analysis, 2158 were significantly comorbid diseases for the EA cohort, 3265 for AA and 672 for HL. Finally, 51 key ‘hub’ diseases that are the focal points in the race-centric networks and of particular clinical importance were identified. Conclusion of this paper is incorporating race-specific disease comorbidity patterns will produce a more accurate and complete picture of the disease landscape overall and could support more precise understanding of disease relationships and patient management towards improved clinical outcomes.
    • May 31,2016. Development and clinical application of an integrative genomic approach to personalized cancer therapy was published on “Genome Medicine”.  On June 1st. 2016 GenomeWeb reported integration of genomic profiling could provide clues to personalized cancer treatment or prognoses in the majority of adult patients with solid tumor types.  In this paper, we implemented a personalized cancer therapy (PCT) program in a clinical setting, using an integrative genomics approach to fully characterize the complexity of each tumor. We carried out whole exome sequencing (WES) and single-nucleotide polymorphism (SNP) microarray genotyping on DNA from tumor and patient-matched normal specimens, as well as RNA sequencing (RNA-Seq) on available frozen specimens, to identify somatic (tumor-specific) mutations, copy number alterations (CNAs), gene expression changes, gene fusions, and also germline variants. To provide high sensitivity in known cancer mutation hotspots, Ion AmpliSeq Cancer Hotspot Panel v2 (CHPv2) was also employed. We integrated the resulting data with cancer knowledge bases and developed a specific workflow for each cancer type to improve interpretation of genomic data. We released the protocol from sample, pipeline,analysis,report to patient survey.
    • Great News! Analysis of 589,306 genomes identifies individuals resilient to severe Mendelian childhood diseases was published on “Nature Biotechnology” in 11 April 2016.  This paper did a comprehensive screen of 874 genes in 589,306 genomes led to the identification of 13 adults harboring mutations for 8 severe Mendelian conditions, with no reported clinical manifestation of the indicated disease. Findings demonstrate the promise of broadening genetic studies to systematically search for well individuals who are buffering the effects of rare, highly penetrant, deleterious mutations. Also indicate that incomplete penetrance for Mendelian diseases is likely more common than previously believed. The identification of resilient individuals may provide a first step toward uncovering protective genetic variants that could help elucidate the mechanisms of Mendelian diseases and new therapeutic strategies. Almost in the very first time when the paper posted online, a number of hits from major media reported this great findings.  Science online reported at 11:AM, April 2016 that ‘buffer genes’ may protect these 13 people from rare genetic diseases.  New York Time refined the findings as people who avoided illness could be key in treating those who didn’t.  Newswise  reported As part of a global collaboration, scientists from the Icahn School of Medicine at Mount Sinai and Sage Bionetworks conducted the largest genome study to date and reported the first systematic search across hundreds of Mendelian disorders in hundreds of thousands of individuals apparently not afflicted with any of these disorders to identify those carrying disease protective factors. This retrospective study of more than 589,000 genomes was a first step for the Resilience Project and was performed with researchers from 23andMe, BGI, the Ontario Institute for Cancer Research, and other institutions.” Daniel MacArthur’s commentary article in Nature like superheroes of disease resistance. Wired was surprised “Genetic Superheroes Walk Among Us, But Shhh! No One Can Tell ‘Em “. Mirro commented ” Genetic superheros could bring life saving treatment to millions” . Population Science said that ” scientists detect which patients are resistant to genetic diseases “. Daily Mail highlighted “Are YOU a genetic ‘superhero? Doctors discover 13 people who are resistant to severe inherited diseases – and there may be more “. NPR News reported “How do genetic superheroes’s overcome their bad DNA?”.  Then Science Daily said  “As part of a global collaboration, scientists from the Icahn School of Medicine at Mount Sinai and Sage Bionetworks conducted the largest genome study to date and reported the first systematic search across hundreds of Mendelian disorders in hundreds of thousands of individuals apparently not afflicted with any of these disorders to identify those carrying disease protective factors”. US News and World Report questions ” Why do some kids escape terrible genetics disorders?”.  The Seattle Times pointed ” Genetic superheroes survive despite devastating mutations, seattle-led study finds”.  The Verge summarized “Scientist could learn a lot from 13 people whose genetic mutations should have made them sick, but didn’t” . Except for these major journal, Nature Biotechnology tweeted the press release to their 60,000 followers:  https://twitter.com/NatureBiotech/status/719598128169689088. Here is a link to the #ResilienceProject hashtag stream on Twitter: https://twitter.com/search?f=tweets&vertical=default&q=%23ResilienceProject&src=typd. This great paper you can find here: Nature Biotechnology study.
    • October 28th, 2015. Identification of type 2 diabetes subgroups through topological analysis of patient similarity was getting into the cover of Science Translational Medicine. As the journalist reported ‘ Authors first clustered EMR data to identify T2D patients within the larger group. Topological analysis of the T2D group identified three new T2D subtypes on the basis of distinct patterns of clinical characteristics and disease comorbidities. Genetic association analysis identified more than 300 single nucleotide polymorphisms (SNPs) specific to each subtype. The authors found that classical T2D features such as obesity, high blood sugar, kidney disease, and eye disease, were limited to subtype 1, whereas other comorbidities such as cancer and neurological diseases were specific to subtypes 2 and 3, respectively. These distinctions might call for tailored treatment regimens rather than a one-size-fits-all approach for T2D. Although a larger sample size is needed to determine causal relationships, this study demonstrates the potential of precision medicine.’ You can find the paper http://stm.sciencemag.org/content/7/311/311ra174
    • September 16,2015.  DIVAS paper was accepted by Bioinformatics. DIVAS provides a google-like tool to visualize genetic variants from 150,000 individuals across diseased and healthy populations. It is a powerful tool to filter genetic variants for pathogenicity, estimate disease prevalence, and repurpose Mendelian drugs for new indications.The tool is available at http://rongchenlab.org/software/divas/ The paper is available at http://www.ncbi.nlm.nih.gov/pubmed/26363178
    • July 30,2015. ClinLabGeneticist featured on GenomeWeb. GenomeWeb.com interviewed Rong and published a news report (Mount Sinai Team Launches Workflow Management Software for Genetic Testing, Dx Laboratories) on ClinLabGeneticist – The Clinical Genome Informatics group and genetic testing laboratory at the Icahn School of Medicine at Mount Sinai have developed a data management platform called ClinLabGeneticist that is designed to facilitate whole exome sequencing-based testing in clinical genetic laboratory settings. Here is the PDF and the article shared on social media: http://icahn.mssm.edu/departments-and-institutes/genomics/news/media-highlights as well as our Twitter .
    • July 29, 2015.  Software ClinLabGeneticist published on Genome Medicine.  A software tool ClinLabGeneticist developed by our lab in collaboration with Mount Sinai Genetic Testing Laboratory has been published on a special issue “Diagnostic Genomics” of Genome Medicine: ClinLabGeneticist: a tool for clinical management of genetic variants from whole exome sequencing in clinical genetic laboratories. Jinlian Wang, Jun Liao, Jinglan Zhang, Wei-Yi Cheng, Jörg Hakenberg, Meng Ma, Bryn D. Webb, Rajasekar Ramasamudram-chakravarthi, Lisa Karger, Lakshmi Mehta, Ruth Kornreich, George A. Diaz, Shuyu Li, Lisa Edelmann and Rong Chen.  ClinLabGeneticist automates data management and process management for the highly complex genetic testing workflow, significantly improving the efficiency of whole exome sequencing based genetic testing in clinic.
    • July 22, 2015.  Study on disease variants in regulatory regions featured on Science Translational Medicine.  In the July 22 issue of Science Translational Medicine,  a commentary titled “Diverse diseases, diverse variants” in Editor’s Choice section highlighted our recent paper “Disease-associated variants in different categories of disease located in distinct regulatory elements” Meng Ma, Ying Ru, Ling-Shiang Chuang, Nai-Yun Hsu, Li-Song Shi, Jörg Hakenberg, Wei-Yi Cheng, Andrew Uzilov, Wei Ding, Benjamin S Glicksberg and Rong Chen.  The commentary noted that 97% of the genetic variants are located in non-coding regions, signifying the importance of our study on the relationship between disease-associated genetic variants and regulatory elements.
    • Dec 10, 2014. Disease comorbidity study published in PSB.  A paper on study of disease relationship through EMR mining by Ben, Lily, Wei-yi, Shamear, Joerg, Rafi, Meng, Lisong, Hardik, Joel and Rong has been accepted for publication in 2015 Pacific Symposium of Bioinformatics (PSB). Please find the paper An integrative pipeline for multi-modal discovery of disease relationships.
    • October 2014. Dr. Rong Chen presenting at 2014 ASHG conference.  Rong gave a platform presentation entitled: “Databases, genome repositories, and clinical applications to interpret personal genome for precision and preventative therapies” during the “Cloudy with a Chance of Big Data” session at the 2014 American Society for Human Genetics conference. In this presentation, Dr. Chen outlined the immense effort of collecting all publicly available disease-related genetic data into unique Reference Variant Store resource that will eventually be released to the public in the form of a web server. He also introduced information regarding the resilience project to search for genetic “Unexpected Heroes”, or healthy individuals with resilience to deleterious mutations commonly leading to severe childhood diseases. These resources are facilitating clinical applications to interpret personal genome for clinical diagnosis, precision medicine, and preventative therapies.

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