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báo cáo khoa học: " Classification of unknown primary tumors with a data-driven method based on a large microarray reference database"

Tuyển tập báo cáo các nghiên cứu khoa học quốc tế ngành y học dành cho các bạn tham khảo đề tài: Classification of unknown primary tumors with a data-driven method based on a large microarray reference database | Ojala et al. Genome Medicine 2011 3 63 http genomemedicine.eom content 3 9 63 Genome Medicine METHOD Open Access Classification of unknown primary tumors with a data-driven method based on a large microarray reference database Kalle A Ojala Sami K Kilpinen and Olli P Kallioniemi Abstract We present a new method to analyze cancer of unknown primary origin CUP samples. Our method achieves good results with classification accuracy 88 leave-one-out cross validation for primary tumors from 56 categories 78 for CUP samples and can also be used to study CUP samples on a gene-by-gene basis. It is not tied to any a priori defined gene set as many previous methods and is adaptable to emerging new information. Background Cancer of unknown primary origin CUP is a classification given to a malignant neoplasm when a metastasis is discovered but the source of the primary tumor remains hidden. If counted together as a single clinical entity CUP is one of the most common cancer types diagnosed in the world. Some 3 to 5 of all newly diagnosed cancers are CUPs which qualifies this disease entity as one of the ten most common cancer types with an incidence that is greater than that of for example leukemia or pancreatic cancers 1 2 . Even at autopsy the location of the primary tumor remains a mystery in up to 70 of CUP cases 1 3 . CUPs present a significant challenge for physicians since many of the current treatment regimes rely on knowledge of the type and origin of the primary tumor. Several methods for identifying CUP samples based on their gene expression profiles have been developed. Talantov et al. 4 and Varadhachary et al. 5 presented an RT-PCR based method that measures the expression of ten signature genes. Ma et al. 6 proposed a similar method based on 92 genes which resulted in an overall accuracy of 82 among 39 cancer types. Tothill et al. 7 presented a support vector machine-based method for classifying cancer types and selected 79 genes for an RT-PCR test reaching a .