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

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

Publication date: Jul 09, 2019

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.

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Jiang, L., Zheng, J., Kwan, J.S.H., Dai, S., Li, C., Li, M.J., Yu, B., To, K.F., Sham, P.C., Zhu, Y., and Li, M. WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling background mutation counts. 04968. 2019 Nucleic Acids Res.

Concepts Keywords
Cancer Regression analysis
Cgs Actuarial science
Grass Branches of biology
Mutation Evolutionary biology
Negative Binomial Regression Estimation theory
Nucleic Driver cancers
Precision Medicine Significant cancer
Regression Drivers drivers cancers
Somatic Cancer
Mutation
Academic disciplines
Http

Semantics

Type Source Name
pathway BSID Methylation
gene UNIPROT MDFIC
gene UNIPROT IDS
drug DRUGBANK Esomeprazole
gene UNIPROT ELK3
gene UNIPROT EPHB1
gene UNIPROT SLC6A2
gene UNIPROT XCL1
gene UNIPROT ALYREF
gene UNIPROT EMD
drug DRUGBANK Ademetionine
drug DRUGBANK Aspartame
gene UNIPROT PTPN5
gene UNIPROT SNAP25
gene UNIPROT HERPUD1
gene UNIPROT MAX
drug DRUGBANK Aminosalicylic Acid
gene UNIPROT FBN1
gene UNIPROT TNF
disease MESH dif
gene UNIPROT ANXA13
gene UNIPROT LARGE1
gene UNIPROT FOXD3
disease MESH multiple
disease MESH diagnosis
pathway BSID Reproduction
gene UNIPROT NFKBIZ
gene UNIPROT ETV6
gene UNIPROT PIK3CA
gene UNIPROT TP53
disease MESH growth
gene UNIPROT GOPC
gene UNIPROT FBLIM1
drug DRUGBANK Coenzyme M
gene UNIPROT JUN
gene UNIPROT CPSF4
disease DOID cancer
disease MESH cancer

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