WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

Lin Jiang; Jingjing Zheng; Johnny S H Kwan; Sheng Dai; Cong Li; Mulin Jun Li; Bolan Yu; Ka F To; Pak C Sham; Yonghong Zhu; Miaoxin Li

doi:10.1093/nar/gkz566

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Journal article

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WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts

Lin Jiang, Jingjing Zheng, Johnny S H Kwan, Sheng Dai, Cong Li, Mulin Jun Li, Bolan Yu, Ka F To, Pak C Sham, Yonghong Zhu, …

Nucleic acids research, Vol.47(16), pp.e96-e96

09/19/2019

DOI: 10.1093/nar/gkz566

PMID: 31287869

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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.

Benchmarking

Computer Simulation

Gene Expression Regulation, Neoplastic

Genomics - methods

Genomics - statistics & numerical data

Humans

Internet

Mutation

Neoplasm Proteins - classification

Neoplasm Proteins - genetics

Neoplasm Proteins - metabolism

Neoplasms - classification

Neoplasms - diagnosis

Neoplasms - genetics

Oncogenes

Regression Analysis

Sample Size

Software

Details

Title: Subtitle: WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts
Creators: Lin Jiang - First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
Jingjing Zheng - Sun Yat-sen University
Johnny S H Kwan - Chinese University of Hong Kong
Sheng Dai - Sun Yat-sen University
Cong Li - Sun Yat-sen University
Mulin Jun Li - Tianjin Medical University
Bolan Yu - Third Affiliated Hospital of Guangzhou Medical University
Ka F To - Chinese University of Hong Kong
Pak C Sham - University of Hong Kong
Yonghong Zhu - First Affiliated Hospital, Sun Yat-sen University, Guangzhou 510080, China
Miaoxin Li - Key Laboratory of Tropical Disease Control (SYSU), Ministry of Education, Guangzhou 510080, China
Resource Type: Journal article
Publication Details: Nucleic acids research, Vol.47(16), pp.e96-e96
DOI: 10.1093/nar/gkz566
PMID: 31287869
ISSN: 0305-1048
eISSN: 1362-4962
Language: English
Date published: 09/19/2019
Academic Unit: Internal Medicine
Record Identifier: 9985177943302771

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