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Báo cáo y học: " Patient-oriented gene set analysis for cancer mutation data"

Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Wertheim cung cấp cho các bạn kiến thức về ngành y đề tài: Patient-oriented gene set analysis for cancer mutation data. | Boca et al. Genome Biology 2010 11 R112 http genomebiology.com 2010 11 11 R112 Genome Biology METHOD Open Access Patient-oriented gene set analysis for cancer mutation data 1 2 2 2 r -3 Simina M Boca Kenneth W Kinzler Victor E Velculescu Bert Vogelstein Giovanni Parmigiani Abstract Recent research has revealed complex heterogeneous genomic landscapes in human cancers. However mutations tend to occur within a core group of pathways and biological processes that can be grouped into gene sets. To better understand the significance of these pathways we have developed an approach that initially scores each gene set at the patient rather than the gene level. In mutation analysis these patient-oriented methods are more transparent interpretable and statistically powerful than traditional gene-oriented methods. Background To date the sequences of all coding exons the exome have been determined in 74 cancers 1-8 . These studies have revealed that advanced cancers each generally harbor between 30 and 80 point mutations or small insertions or deletions. Other genetic alterations such as amplifications and homozygous deletions contribute another ten genes per tumor. These alterations can be categorized into two classes drivers which bestow a growth advantage on the cancer cell inhibiting cell death or promoting cell birth and passengers which coincidentally occurred in a cell that later or concurrently developed a driver mutation but had no effect on cell proliferation. These same studies have defined a landscape consisting of both mountains - drivers which are mutated at high frequency in tumors of the same type -and hills - drivers which are mutated at low frequency in these tumors. Most driver genes appear to be hills making it difficult or impossible to distinguish them from passenger mutations on the basis of frequency alone. A variety of bioinformatic studies based on these data have suggested that the mountains and hills though heterogeneous among tumors can be grouped