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WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts.
Jiang, Lin; Zheng, Jingjing; Kwan, Johnny S H; Dai, Sheng; Li, Cong; Li, Mulin Jun; Yu, Bolan; To, Ka F; Sham, Pak C; Zhu, Yonghong; Li, Miaoxin.
Afiliação
  • Jiang L; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
  • Zheng J; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China.
  • Kwan JSH; Center for Genome Research, Sun Yat-sen University, Guangzhou 510080, China.
  • Dai S; First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China.
  • Li C; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
  • Li MJ; Center for Precision Medicine, Sun Yat-sen University, Guangzhou 510080, China.
  • Yu B; Center for Genome Research, Sun Yat-sen University, Guangzhou 510080, China.
  • To KF; Departmelnt of Anatomical and Cellular Pathology, The Chinese University of Hong Kong, New Territories, Hong Kong.
  • Sham PC; State Key Laboratory in Oncology in South China, The Chinese University of Hong Kong, New Territories, Hong Kong.
  • Zhu Y; Li Ka-Shing Institute of Health Sciences, The Chinese University of Hong Kong, New Territories, Hong Kong.
  • Li M; Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
Nucleic Acids Res ; 47(16): e96, 2019 09 19.
Article em En | MEDLINE | ID: mdl-31287869
ABSTRACT
Genomic identification of driver mutations and genes in cancer cells are critical for precision medicine. Due to difficulty in modelling distribution of background mutation counts, existing statistical methods are often underpowered to discriminate cancer-driver genes from passenger genes. Here we propose a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER, http//grass.cgs.hku.hk/limx/witer or KGGSeq,http//grass.cgs.hku.hk/limx/kggseq/), to detect cancer-driver genes showing an excess of somatic mutations. By fitting the distribution of background mutation counts properly, this approach works well even in small or moderate samples. Compared to alternative methods, it detected more significant and cancer-consensus genes in most tested cancers. Applying this approach, we estimated 229 driver genes in 26 different types of cancers. In silico validation confirmed 78% of predicted genes as likely known drivers and many other genes as very likely new drivers for corresponding cancers. The technical advances of WITER enable the detection of driver genes in TCGA datasets as small as 30 subjects and rescue of more genes missed by alternative tools in moderate or small samples.
Assuntos

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oncogenes / Software / Regulação Neoplásica da Expressão Gênica / Genômica / Proteínas de Neoplasias / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China País de publicação: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Oncogenes / Software / Regulação Neoplásica da Expressão Gênica / Genômica / Proteínas de Neoplasias / Neoplasias Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: Nucleic Acids Res Ano de publicação: 2019 Tipo de documento: Article País de afiliação: China País de publicação: ENGLAND / ESCOCIA / GB / GREAT BRITAIN / INGLATERRA / REINO UNIDO / SCOTLAND / UK / UNITED KINGDOM