The Experts below are selected from a list of 23163 Experts worldwide ranked by ideXlab platform
Dimitrios H Roukos - One of the best experts on this subject based on the ideXlab platform.
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Primary liver Cancer Genome Sequencing: translational implications and challenges.
Expert review of gastroenterology & hepatology, 2017Co-Authors: Demosthenes E Ziogas, Ioannis D Kyrochristos, Georgios K. Glantzounis, Dimitrios K. Christodoulou, Evangelos Felekouras, Dimitrios H RoukosAbstract:Introduction: The prognosis of primary liver Cancer (PLC) remains poor and is explained by the slow progress in understanding the molecular pathways driving tumorigenesis, therapeutic resistance an...
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Clinical relevance of Cancer Genome Sequencing.
World journal of gastroenterology, 2013Co-Authors: David Neil Cooper, Dimitrios H RoukosAbstract:The arrival of both high-throughput and bench-top next-generation Sequencing technologies and sequence enrichment methods has revolutionized our approach to dissecting the genetic basis of Cancer. These technologies have been almost invariably employed in whole-Genome Sequencing (WGS) and whole-exome Sequencing (WES) studies. Both WGS and WES approaches have been widely applied to interrogate the somatic mutational landscape of sporadic Cancers and identify novel germline mutations underlying familial Cancer syndromes. The clinical implications of Cancer Genome Sequencing have become increasingly clear, for example in diagnostics. In this editorial, we present these advances in the context of research discovery and discuss both the clinical relevance of Cancer Genome Sequencing and the challenges associated with the adoption of these genomic technologies in a clinical setting.
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Research and clinical applications of Cancer Genome Sequencing
Current opinion in obstetrics & gynecology, 2013Co-Authors: David Neil Cooper, Demosthenes E Ziogas, Eugenia Halkia, Margaret Tzaphlidou, Dimitrios H RoukosAbstract:PURPOSE OF REVIEW: To highlight the recent advances in Cancer Genome research and its clinical applications made possible by next-generation Sequencing (NGS), with particular emphasis on gynecological and breast Cancers is the purpose of the review. RECENT FINDINGS: Through advances in NGS technologies, whole-exome Sequencing and whole-Genome Sequencing (WGS) have been performed on various Cancers, identifying in the process numerous recurrent mutations and highly mutated genes. These Cancers include uterine serous carcinomas, high-grade serous ovarian adenocarcinomas and breast Cancer. In contrast to identifying somatic mutations in sporadic Cancers, a far smaller number of studies using NGS have been conducted to identify new causal mutations or genes for hereditary Cancer syndromes. In addition to research discovery, diagnostic applications of NGS have also become increasingly evident. Thus, WGS has been applied in a diagnostic context to identify a complex chromosomal rearrangement in a patient with acute myeloid leukemia of unclear subtype. Similarly, the targeted Sequencing of panels of known Cancer genes using NGS has demonstrated its robustness in the context of identifying known pathological mutations. SUMMARY: The research and clinical applications of Cancer Genome Sequencing have progressed at an unprecedented pace over the last few years, and this promises to be accelerated with new developments of high-throughput NGS technologies and robust analytical tools.
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Cancer Genome Sequencing and functional genomics: from translational to clinical medicine
Pharmacogenomics, 2011Co-Authors: Dimitrios H RoukosAbstract:Dulbecco noted that ‘‘If we wish to learn more about Cancer, we must now concentrate on the cellular Genome’’, and he advocated Sequencing ‘‘the whole Genome of a selected animal species’’, specifically, the human Genome [2]. The Human Genome Project (HGP) began in 1989 with the goal to identify and map the human genes and understand human biology. In 2000, President Bill Clinton announced the completion of the two first drafts of human Genome sequence [3,4] and in a White House press statement articulated that genomics would “lead to a new era of molecular medicine, an era that will bring new ways to prevent, diagnose, treat and cure disease” [5]. Now, a decade later, we know that despite the major advances in genomics research, neither the revolution in genomic medicine for improving health [6], nor Genome heterogeneitybased personalized clinical decisions on prevention or treatment of Cancer have yet arrived. Does genomic medicine remain a first priority to understanding Cancer biology and discovering novel therapeutics? In a paper on global Cancer statistics published in 2011 [7], the increase in Cancer incidence and mortality is particularly growing in the developing world. A small decrease in Cancer death rates in the economically developed parts of the world is mostly attributable to early detection, for example, of the most common type of breast Cancer and improved treatment with tamoxifen or aromatase inhibitors and tobacco control for reducing lung Cancer incidence. But modest progress has been achieved in improving overall survival rates of patients with advanced stages of disease for which a cure still remains elusive.
Zhongming Zhao - One of the best experts on this subject based on the ideXlab platform.
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SGDriver: a novel structural genomics-based approach to prioritize Cancer related and potentially druggable somatic mutations
BMC Bioinformatics, 2015Co-Authors: Junfei Zhao, Feixiong Cheng, Zhongming ZhaoAbstract:Background A huge volume of somatic mutations have been generated through large Cancer Genome Sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). However, understanding the functional consequences of somatic mutations in Cancer and translating the results into clinical use remains a major challenge in Cancer genomic studies. Thanks to the rapid development of structural genomic technologies, such as X-ray and NMR, large amounts of protein structure data have been generated during the past decade, which enables us to map somatic mutations to protein functional features (i.e., protein-ligand binding sites) and investigate their potential impacts[1,2].
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Detecting somatic point mutations in Cancer Genome Sequencing data: a comparison of mutation callers.
Genome medicine, 2013Co-Authors: Qingguo Wang, Peilin Jia, Haiquan Chen, Donald Hucks, Kimberly B. Dahlman, William Pao, Zhongming ZhaoAbstract:Driven by high throughput next generation Sequencing technologies and the pressing need to decipher Cancer Genomes, computational approaches for detecting somatic single nucleotide variants (sSNVs) have undergone dramatic improvements during the past 2 years. The recently developed tools typically compare a tumor sample directly with a matched normal sample at each variant locus in order to increase the accuracy of sSNV calling. These programs also address the detection of sSNVs at low allele frequencies, allowing for the study of tumor heterogeneity, Cancer subclones, and mutation evolution in Cancer development. We used whole Genome Sequencing (Illumina Genome Analyzer IIx platform) of a melanoma sample and matched blood, whole exome Sequencing (Illumina HiSeq 2000 platform) of 18 lung tumor-normal pairs and seven lung Cancer cell lines to evaluate six tools for sSNV detection: EBCall, JointSNVMix, MuTect, SomaticSniper, Strelka, and VarScan 2, with a focus on MuTect and VarScan 2, two widely used publicly available software tools. Default/suggested parameters were used to run these tools. The missense sSNVs detected in these samples were validated through PCR and direct Sequencing of genomic DNA from the samples. We also simulated 10 tumor-normal pairs to explore the ability of these programs to detect low allelic-frequency sSNVs. Out of the 237 sSNVs successfully validated in our Cancer samples, VarScan 2 and MuTect detected the most of any tools (that is, 204 and 192, respectively). MuTect identified 11 more low-coverage validated sSNVs than VarScan 2, but missed 11 more sSNVs with alternate alleles in normal samples than VarScan 2. When examining the false calls of each tool using 169 invalidated sSNVs, we observed >63% false calls detected in the lung Cancer cell lines had alternate alleles in normal samples. Additionally, from our simulation data, VarScan 2 identified more sSNVs than other tools, while MuTect characterized most low allelic-fraction sSNVs. Our study explored the typical false-positive and false-negative detections that arise from the use of sSNV-calling tools. Our results suggest that despite recent progress, these tools have significant room for improvement, especially in the discrimination of low coverage/allelic-frequency sSNVs and sSNVs with alternate alleles in normal samples.
Elaine R. Mardis - One of the best experts on this subject based on the ideXlab platform.
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Insights from Large-Scale Cancer Genome Sequencing
Annual Review of Cancer Biology, 2018Co-Authors: Elaine R. MardisAbstract:The basic catalog of DNA-based alterations that contribute to the onset and progression of Cancer has been largely elucidated due to the results obtained from the combination of massively parallel Sequencing and computational analysis methods applied to thousands of Cancer samples. These combined approaches have provided novel and surprising insights into the myriad ways that DNA-level disruptions lead to activation and inactivation of cellular pathways, thereby altering the carefully controlled growth and division of normal cells and rendering them Cancerous. This review presents genomic insights gained from these large-scale studies and highlights how this new knowledge will be translated in the future into improved clinical care and monitoring of Cancer patients.
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Abstract S5-6: Activating HER2 mutations in HER2 gene amplification negative breast Cancers.
General Session Abstracts, 2012Co-Authors: Ron Bose, Adam C. Searleman, Shyam M. Kavuri, Wei Shen, Dong Shen, Daniel C. Koboldt, John Monsey, Li Ding, Elaine R. MardisAbstract:Background: Breast Cancer Genome Sequencing projects, performed by the Genome Sequencing centers in the U.S., Canada, and the U.K., are elucidating the somatic mutations and other genomic alterations that occur in human breast Cancer. These studies recently identified somatic HER2 mutations in breast Cancers lacking HER2 gene amplification. Results: Compilation of data from seven Sequencing studies documented 22 patients with somatic HER2 mutations. These mutations clustered in three regions. The first cluster was at amino acid (aa) 309–310 (exon 8), located in the extracellular domain. These aa residues form part of the HER2 dimerization interface. The second cluster was at aa 755–781, located in the kinase domain (exons 19–20). This was the most common location for HER2 mutations, with 17 out of 22 patients having somatic mutations here. The third region was at aa 835–896, also in the kinase domain (exons 21–22). Using multiple experimental approaches (cell line experiments, in vitro kinase assays, protein structure modeling, and xenograft experiments), we tested seven of these HER2 mutations and showed that 4 of them are activating mutations that are sensitive to lapatinib and trastuzumab. Another 2 mutations were found to be lapatinib resistant and we determined their sensitivity to neratinib, canertinib, and gefitinib. Conclusions: These findings biologically validate somatic HER2 mutations as good targets for breast Cancer treatment, but the appropriate choice of targeted drug is dependent on the precise mutation present. This study is among the first to functionally characterize mutations identified by breast Cancer Genome Sequencing. A prospective, multi-institutional clinical trial has been launched to screen for HER2 mutation positive patients and determine the clinical outcome of treatment with HER2 targeted drugs. Citation Information: Cancer Res 2012;72(24 Suppl):Abstract nr S5-6.
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Cancer Genome Sequencing and its implications for personalized Cancer vaccines.
Cancers, 2011Co-Authors: Peter S. Goedegebuure, Matthew J. Ellis, Elaine R. Mardis, Xiuli Zhang, John M. Herndon, Timothy P. Fleming, Beatriz M. Carreno, Ted H. Hansen, William E. GillandersAbstract:New DNA Sequencing platforms have revolutionized human Genome Sequencing. The dramatic advances in Genome Sequencing technologies predict that the $1,000 Genome will become a reality within the next few years. Applied to Cancer, the availability of Cancer Genome sequences permits real-time decision-making with the potential to affect diagnosis, prognosis, and treatment, and has opened the door towards personalized medicine. A promising strategy is the identification of mutated tumor antigens, and the design of personalized Cancer vaccines. Supporting this notion are preliminary analyses of the epitope landscape in breast Cancer suggesting that individual tumors express significant numbers of novel antigens to the immune system that can be specifically targeted through Cancer vaccines.
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Cancer Genome Sequencing: a review
Human molecular genetics, 2009Co-Authors: Elaine R. Mardis, Richard K. WilsonAbstract:A genomic era of Cancer studies is developing rapidly, fueled by the emergence of next-generation Sequencing technologies that provide exquisite sensitivity and resolution. This article discusses several areas within Cancer genomics that are being transformed by the application of new technology, and in the process are dramatically expanding our understanding of this disease. Although, we anticipate that there will be many exciting discoveries in the near future, the ultimate success of these endeavors rests on our ability to translate what is learned into better diagnosis, treatment and prevention of Cancer.
Cord Brakebusch - One of the best experts on this subject based on the ideXlab platform.
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Rho GTPases in Cancer: friend or foe?
Oncogene, 2019Co-Authors: Julius H. Svensmark, Cord BrakebuschAbstract:The Rho GTPases RhoA, Rac1, and Cdc42 are important regulators of cytoskeletal dynamics. Although many in vitro and in vivo data indicate tumor-promoting effects of activated Rho GTPases, also tumor suppressive functions have been described, suggesting either highly cell-type-specific functions for Rho GTPases in Cancer or insufficient Cancer models. The availability of a large number of Cancer Genome-Sequencing data by The Cancer Genome Atlas (TCGA) allows for the investigation of Rho GTPase function in human Cancers in silico. This information should be used to improve our in vitro and in vivo Cancer models, which are essential for a molecular understanding of Rho GTPase function in malignant tumors and for the potential development of Cancer drugs targeting Rho GTPase signaling.
Xiaohui Xie - One of the best experts on this subject based on the ideXlab platform.
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MixClone: a mixture model for inferring tumor subclonal populations
BMC Genomics, 2015Co-Authors: Xiaohui XieAbstract:Background Tumor Genomes are often highly heterogeneous, consisting of Genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor Genome analysis. With the advancement of next-generation Sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from Cancer Genome Sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy. Results We describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole Genome Sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real Cancer Sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone . Conclusions The probabilistic mixture model proposed here provides a new framework for subclonal analysis based on Cancer Genome Sequencing data. By applying the method to both simulated and real Cancer Sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.
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MixClone: a mixture model for inferring tumor subclonal populations.
BMC genomics, 2015Co-Authors: Xiaohui XieAbstract:Tumor Genomes are often highly heterogeneous, consisting of Genomes from multiple subclonal types. Complete characterization of all subclonal types is a fundamental need in tumor Genome analysis. With the advancement of next-generation Sequencing, computational methods have recently been developed to infer tumor subclonal populations directly from Cancer Genome Sequencing data. Most of these methods are based on sequence information from somatic point mutations, However, the accuracy of these algorithms depends crucially on the quality of the somatic mutations returned by variant calling algorithms, and usually requires a deep coverage to achieve a reasonable level of accuracy. We describe a novel probabilistic mixture model, MixClone, for inferring the cellular prevalences of subclonal populations directly from whole Genome Sequencing of paired normal-tumor samples. MixClone integrates sequence information of somatic copy number alterations and allele frequencies within a unified probabilistic framework. We demonstrate the utility of the method using both simulated and real Cancer Sequencing datasets, and show that it significantly outperforms existing methods for inferring tumor subclonal populations. The MixClone package is written in Python and is publicly available at https://github.com/uci-cbcl/MixClone. The probabilistic mixture model proposed here provides a new framework for subclonal analysis based on Cancer Genome Sequencing data. By applying the method to both simulated and real Cancer Sequencing data, we show that integrating sequence information from both somatic copy number alterations and allele frequencies can significantly improve the accuracy of inferring tumor subclonal populations.
