{"title":"利用大规模分子数据集改善乳腺癌治疗。","authors":"Chad J Creighton","doi":"10.2217/BMT.12.14","DOIUrl":null,"url":null,"abstract":"<p><p>The treatment of breast cancer patients could potentially be advanced by having a more complete understanding of breast cancer biology, including a catalog of recurrently altered genes. Over the last decade, thousands of human breast tumors have been profiled for gene expression and DNA copy number alterations, and ongoing efforts in DNA sequencing are establishing the set of somatically mutated genes. Much of the molecular data being generated resides in the public domain, available as a resource for further research. The challenge then becomes how to best utilize all these data, to mine them for candidate biomarkers and targets, which is the subject of this review. Some examples of integrative analysis and combining results from diverse data sets are also presented.</p>","PeriodicalId":43086,"journal":{"name":"Breast Cancer Management","volume":"1 1","pages":"57-64"},"PeriodicalIF":0.4000,"publicationDate":"2012-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.2217/BMT.12.14","citationCount":"3","resultStr":"{\"title\":\"Using large-scale molecular data sets to improve breast cancer treatment.\",\"authors\":\"Chad J Creighton\",\"doi\":\"10.2217/BMT.12.14\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The treatment of breast cancer patients could potentially be advanced by having a more complete understanding of breast cancer biology, including a catalog of recurrently altered genes. Over the last decade, thousands of human breast tumors have been profiled for gene expression and DNA copy number alterations, and ongoing efforts in DNA sequencing are establishing the set of somatically mutated genes. Much of the molecular data being generated resides in the public domain, available as a resource for further research. The challenge then becomes how to best utilize all these data, to mine them for candidate biomarkers and targets, which is the subject of this review. Some examples of integrative analysis and combining results from diverse data sets are also presented.</p>\",\"PeriodicalId\":43086,\"journal\":{\"name\":\"Breast Cancer Management\",\"volume\":\"1 1\",\"pages\":\"57-64\"},\"PeriodicalIF\":0.4000,\"publicationDate\":\"2012-05-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://sci-hub-pdf.com/10.2217/BMT.12.14\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Breast Cancer Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2217/BMT.12.14\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"ONCOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Breast Cancer Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2217/BMT.12.14","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ONCOLOGY","Score":null,"Total":0}
Using large-scale molecular data sets to improve breast cancer treatment.
The treatment of breast cancer patients could potentially be advanced by having a more complete understanding of breast cancer biology, including a catalog of recurrently altered genes. Over the last decade, thousands of human breast tumors have been profiled for gene expression and DNA copy number alterations, and ongoing efforts in DNA sequencing are establishing the set of somatically mutated genes. Much of the molecular data being generated resides in the public domain, available as a resource for further research. The challenge then becomes how to best utilize all these data, to mine them for candidate biomarkers and targets, which is the subject of this review. Some examples of integrative analysis and combining results from diverse data sets are also presented.
期刊介绍：
Breast Cancer Management (ISSN: 1758-1923) addresses key issues in disease management by exploring the best patient-centered clinical research and presenting this information both directly, as clinical findings, and in practice-oriented formats of direct relevance in the clinic. The journal also highlights significant advances in basic and translational research, and places them in context for future therapy. Breast Cancer Management provides oncologists and other health professionals with the latest findings and opinions on reducing the burden of this widespread disease. Recent research findings and advances clinical practice in the field are reported and analyzed by international experts. The journal presents this information in clear, accessible formats. All articles are subject to independent review by a minimum of three independent experts. Unsolicited article proposals are welcomed and authors are required to comply fully with the journal’s Disclosure & Conflict of Interest Policy as well as major publishing guidelines, including ICMJE and GPP3. Coverage includes: Diagnosis and imaging, Surgical approaches, Radiotherapy, Systemic therapies, Cancer clinical trials, Genetic aspects of disease, Personalized medicine, Translational research and biomarker studies, Management of psychological distress, Epidemiological studies, Pharmacoeconomics, Evidence-based treatment guidelines.
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