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Robert L. Sheridan

Bio: Robert L. Sheridan is an academic researcher from Harvard University. The author has contributed to research in topics: Poison control & Burn injury. The author has an hindex of 69, co-authored 350 publications receiving 28679 citations. Previous affiliations of Robert L. Sheridan include Bristol Royal Infirmary & University of Cincinnati.


Papers
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Journal ArticleDOI
Debra A. Bell1, Andrew Berchuck2, Michael J. Birrer3, Jeremy Chien1  +282 moreInstitutions (35)
30 Jun 2011-Nature
TL;DR: It is reported that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1,BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes.
Abstract: A catalogue of molecular aberrations that cause ovarian cancer is critical for developing and deploying therapies that will improve patients' lives. The Cancer Genome Atlas project has analysed messenger RNA expression, microRNA expression, promoter methylation and DNA copy number in 489 high-grade serous ovarian adenocarcinomas and the DNA sequences of exons from coding genes in 316 of these tumours. Here we report that high-grade serous ovarian cancer is characterized by TP53 mutations in almost all tumours (96%); low prevalence but statistically recurrent somatic mutations in nine further genes including NF1, BRCA1, BRCA2, RB1 and CDK12; 113 significant focal DNA copy number aberrations; and promoter methylation events involving 168 genes. Analyses delineated four ovarian cancer transcriptional subtypes, three microRNA subtypes, four promoter methylation subtypes and a transcriptional signature associated with survival duration, and shed new light on the impact that tumours with BRCA1/2 (BRCA1 or BRCA2) and CCNE1 aberrations have on survival. Pathway analyses suggested that homologous recombination is defective in about half of the tumours analysed, and that NOTCH and FOXM1 signalling are involved in serous ovarian cancer pathophysiology.

5,878 citations

Journal ArticleDOI
29 Jun 2007-Cell
TL;DR: A relatively small set of miRNAs, many of which are ubiquitously expressed, account for most of the differences in miRNA profiles between cell lineages and tissues.

3,687 citations

Journal ArticleDOI
Katherine A Hoadley1, Christina Yau2, Christina Yau3, Toshinori Hinoue4  +735 moreInstitutions (16)
05 Apr 2018-Cell
TL;DR: Molecular similarities among histologically or anatomically related cancer types provide a basis for focused pan-cancer analyses, such as pan-gastrointestinal, Pan-gynecological, pan-kidney, and pan-squamous cancers, and those related by stemness features, which may inform strategies for future therapeutic development.

1,535 citations

Journal ArticleDOI
13 Jul 2006-Nature
TL;DR: A new class of small RNAs that bind to MILI in mouse male germ cells, where they accumulate at the onset of meiosis are described, and are referred to as ‘Piwi-interacting RNAs’ or piRNAs.
Abstract: Small RNAs bound to Argonaute proteins recognize partially or fully complementary nucleic acid targets in diverse gene-silencing processes. A subgroup of the Argonaute proteins--known as the 'Piwi family'--is required for germ- and stem-cell development in invertebrates, and two Piwi members--MILI and MIWI--are essential for spermatogenesis in mouse. Here we describe a new class of small RNAs that bind to MILI in mouse male germ cells, where they accumulate at the onset of meiosis. The sequences of the over 1,000 identified unique molecules share a strong preference for a 5' uridine, but otherwise cannot be readily classified into sequence families. Genomic mapping of these small RNAs reveals a limited number of clusters, suggesting that these RNAs are processed from long primary transcripts. The small RNAs are 26-31 nucleotides (nt) in length--clearly distinct from the 21-23 nt of microRNAs (miRNAs) or short interfering RNAs (siRNAs)--and we refer to them as 'Piwi-interacting RNAs' or piRNAs. Orthologous human chromosomal regions also give rise to small RNAs with the characteristics of piRNAs, but the cloned sequences are distinct. The identification of this new class of small RNAs provides an important starting point to determine the molecular function of Piwi proteins in mammalian spermatogenesis.

1,404 citations

Journal ArticleDOI
TL;DR: To identify other miRNA genes in pathogenic viruses, a new miRNA gene prediction method with small-RNA cloning from several virus-infected cell types was combined and predicted miRNAs in several large DNA viruses.
Abstract: Epstein-Barr virus (EBV or HHV4), a member of the human herpesvirus (HHV) family, has recently been shown to encode microRNAs (miRNAs). In contrast to most eukaryotic miRNAs, these viral miRNAs do not have close homologs in other viral genomes or in the genome of the human host. To identify other miRNA genes in pathogenic viruses, we combined a new miRNA gene prediction method with small-RNA cloning from several virus-infected cell types. We cloned ten miRNAs in the Kaposi sarcoma-associated virus (KSHV or HHV8), nine miRNAs in the mouse gammaherpesvirus 68 (MHV68) and nine miRNAs in the human cytomegalovirus (HCMV or HHV5). These miRNA genes are expressed individually or in clusters from either polymerase (pol) II or pol III promoters, and share no substantial sequence homology with one another or with the known human miRNAs. Generally, we predicted miRNAs in several large DNA viruses, and we could neither predict nor experimentally identify miRNAs in the genomes of small RNA viruses or retroviruses.

1,208 citations


Cited by
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Journal ArticleDOI
23 Jan 2009-Cell
TL;DR: The current understanding of miRNA target recognition in animals is outlined and the widespread impact of miRNAs on both the expression and evolution of protein-coding genes is discussed.

18,036 citations

Journal ArticleDOI
TL;DR: The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.
Abstract: The cBio Cancer Genomics Portal (http://cbioportal.org) is an open-access resource for interactive exploration of multidimensional cancer genomics data sets, currently providing access to data from more than 5,000 tumor samples from 20 cancer studies. The cBio Cancer Genomics Portal significantly lowers the barriers between complex genomic data and cancer researchers who want rapid, intuitive, and high-quality access to molecular profiles and clinical attributes from large-scale cancer genomics projects and empowers researchers to translate these rich data sets into biologic insights and clinical applications.

11,912 citations

Journal ArticleDOI
TL;DR: A practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics, which makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries.
Abstract: The cBioPortal for Cancer Genomics (http://cbioportal.org) provides a Web resource for exploring, visualizing, and analyzing multidimensional cancer genomics data. The portal reduces molecular profiling data from cancer tissues and cell lines into readily understandable genetic, epigenetic, gene expression, and proteomic events. The query interface combined with customized data storage enables researchers to interactively explore genetic alterations across samples, genes, and pathways and, when available in the underlying data, to link these to clinical outcomes. The portal provides graphical summaries of gene-level data from multiple platforms, network visualization and analysis, survival analysis, patient-centric queries, and software programmatic access. The intuitive Web interface of the portal makes complex cancer genomics profiles accessible to researchers and clinicians without requiring bioinformatics expertise, thus facilitating biological discoveries. Here, we provide a practical guide to the analysis and visualization features of the cBioPortal for Cancer Genomics.

10,947 citations

Journal ArticleDOI
15 Jul 2021-Nature
TL;DR: For example, AlphaFold as mentioned in this paper predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture. But the accuracy is limited by the fact that no homologous structure is available.
Abstract: Proteins are essential to life, and understanding their structure can facilitate a mechanistic understanding of their function. Through an enormous experimental effort1–4, the structures of around 100,000 unique proteins have been determined5, but this represents a small fraction of the billions of known protein sequences6,7. Structural coverage is bottlenecked by the months to years of painstaking effort required to determine a single protein structure. Accurate computational approaches are needed to address this gap and to enable large-scale structural bioinformatics. Predicting the three-dimensional structure that a protein will adopt based solely on its amino acid sequence—the structure prediction component of the ‘protein folding problem’8—has been an important open research problem for more than 50 years9. Despite recent progress10–14, existing methods fall far short of atomic accuracy, especially when no homologous structure is available. Here we provide the first computational method that can regularly predict protein structures with atomic accuracy even in cases in which no similar structure is known. We validated an entirely redesigned version of our neural network-based model, AlphaFold, in the challenging 14th Critical Assessment of protein Structure Prediction (CASP14)15, demonstrating accuracy competitive with experimental structures in a majority of cases and greatly outperforming other methods. Underpinning the latest version of AlphaFold is a novel machine learning approach that incorporates physical and biological knowledge about protein structure, leveraging multi-sequence alignments, into the design of the deep learning algorithm. AlphaFold predicts protein structures with an accuracy competitive with experimental structures in the majority of cases using a novel deep learning architecture.

10,601 citations

Journal ArticleDOI
04 Oct 2012-Nature
TL;DR: The ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity.
Abstract: We analysed primary breast cancers by genomic DNA copy number arrays, DNA methylation, exome sequencing, messenger RNA arrays, microRNA sequencing and reverse-phase protein arrays. Our ability to integrate information across platforms provided key insights into previously defined gene expression subtypes and demonstrated the existence of four main breast cancer classes when combining data from five platforms, each of which shows significant molecular heterogeneity. Somatic mutations in only three genes (TP53, PIK3CA and GATA3) occurred at >10% incidence across all breast cancers; however, there were numerous subtype-associated and novel gene mutations including the enrichment of specific mutations in GATA3, PIK3CA and MAP3K1 with the luminal A subtype. We identified two novel protein-expression-defined subgroups, possibly produced by stromal/microenvironmental elements, and integrated analyses identified specific signalling pathways dominant in each molecular subtype including a HER2/phosphorylated HER2/EGFR/phosphorylated EGFR signature within the HER2-enriched expression subtype. Comparison of basal-like breast tumours with high-grade serous ovarian tumours showed many molecular commonalities, indicating a related aetiology and similar therapeutic opportunities. The biological finding of the four main breast cancer subtypes caused by different subsets of genetic and epigenetic abnormalities raises the hypothesis that much of the clinically observable plasticity and heterogeneity occurs within, and not across, these major biological subtypes of breast cancer.

9,355 citations