Genomic and proteomic approaches for studying human cancer: Prospects for true patient-tailored therapy
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Genomic and proteomic approaches for studying human cancer: Prospects for true patient-tailored therapy Kristen M. Carr,* Kevin Rosenblatt, Emanuel F. Petricoin and Lance A. Liotta Laboratory of Pathology, National Cancer Institute/National Institutes of Health, 10 Center Drive, MSC 1500, Bethesda, MD 20892-1500, USA *Correspondence to: Tel: þ 1 301 496 4333; Fax: þ1 301 480 9488; E-mail: [email protected] Date received (in revised form): 16th October 2003
Abstract Global gene expression analysis is beginning to move from the laboratories of basic investigators to large-scale clinical trials. The potential of this technology to improve diagnosis and tailored treatment of human disease may soon be realised, now that several comprehensive studies have demonstrated the utility of gene expression profiles for the classification of tumours into distinct, clinically relevant subtypes and the prediction of clinical outcomes. In addition, new data from the emerging proteomics platforms add another layer of molecular information to the study of human disease, as scientists attempt to catalogue a complete inventory of the proteins encoded by the genome and to establish a ‘biosignature’ profile of human health and disease. As a result, it is anticipated that, together, these technologies will facilitate the comprehensive study of genes, gene products and signalling pathways so that the objective of personalised molecular medicine can be achieved. This paper will review the studies that best demonstrate how genomics and proteomics technologies can be used to improve cancer diagnosis and treatment it will specifically highlight the important work being incorporated into clinical trials. Keywords: molecular medicine, gene expression analysis, proteomics, clinical trials
Introduction Cancer researchers have made significant progress in identifying a new ‘molecular taxonomy’ of cancer through the use of genomics technologies. Specifically, the use of DNA microarrays has created robust molecular phenotypes for many tumours, including brain,1,2 breast,3 – 10 colon,11,12 gastric,13 kidney,14 leukaemia,15 – 17 lymphoma,18 – 20 lung,21 – 23 melanoma,24 ovary,25 – 28 prostate29 – 32 and small, round blue-cell tumours of childhood.33 In a subset of these studies, the gene expression profiles strongly suggest that this information would improve diagnosis and predict clinical outcome when compared with the standardised prognostic criteria, such as tumour grade, tumour size, patient age and patient performance status.6,7,19,20 These recent breakthroughs in the laboratory have been qualified successes. The lack of a standardised method for data collection, data analysis and validation, however, has made it difficult to rigorously compare studies from different laboratories, and has thus hampered the introduction of this type of data into clinical medicine. Fortunately, the microarray field has proposed universal standardisation guidelines to help scientists and clinicians accurately compare the results from different laboratories,
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