Dataset Information

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

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.

SUBMITTER: Jiang L

PROVIDER: S-EPMC6895256 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature

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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 L Zheng Jingjing J Kwan Johnny S H JSH Dai Sheng S Li Cong C Li Mulin Jun MJ Yu Bolan B To Ka F KF Sham Pak C PC Zhu Yonghong Y Li Miaoxin M

Nucleic acids research 20190901 16

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 detec ...[more]

PMID: 31287869

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Project description:In positron emission tomography (PET) imaging, the main function of scatter and randoms corrections is to improve contrast and quantitative accuracy. Both corrections are essential and critically important. Several iterative reconstruction schemes incorporating scatter and randoms corrections have been developed over the years. In this work, the authors propose a new method to incorporate the scatter and randoms corrections into the iterative image reconstruction, which has shown promising results in regards to improving reconstruction performance and image quality as compared to the standard methods.The authors describe a scatter and randoms weighted (SRW) iterative PET reconstruction algorithm. The SRW method is based on the estimation of the trues fraction (TF) within the prompts. Once the TF is estimated, it is then incorporated into the weighting component of the system matrix, and the net result is a scatter and randoms weighting in the sensitivity image similar to the attenuation correction weighting. Although using the measured prompts in the TF estimation was demonstrated to achieve the fastest convergence at high statistics, it is not reliable in low counts situations due to the sparse and noisy nature of the measured prompts. Therefore, a mean estimation of the prompts derived from the forward projection of the reconstructed prompts image was introduced into the TF estimation. A contrast phantom was scanned, and the data were reconstructed using the standard and the SRW methods.The contrast vs noise, precision vs accuracy in contrast, absolute error vs number of iterations comparisons, and standard deviation image over different realizations of the same object were evaluated at low counts situations, and it was observed that the SRW method outperforms the standard approaches such as the scatter and randoms data precorrection and the ordinary Poisson methods. The image intensity (activity) outside the object can also be minimized using the SRW method. In addition, further improvement in accuracy, precision, convergence, and noise properties can be achieved by further improving the TF and the prompts estimate.The authors have developed a practical scatter and randoms weighting scheme in the sensitivity image for iterative PET reconstructions. Our proposed SRW method has a number of advantages over the conventional methods, and it has shown promising results with additional optimization for various applications to be further investigated.

Dataset Information

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

Publications

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

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