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WITER: a powerful method for estimation of cancer-driver genes using a weighted iterative regression modelling
Lin Jiang1,2,3,4, Jingjing Zheng1,2,3, Johnny S H Kwan5,6,7
1Zhongshan School of Medicine, Sun Yat-sen University, Guangzhou 510080, China.
Nucleic Acids Research
|July 10, 2019
Summary
A new statistical method, WITER, accurately identifies cancer-driver genes by properly modeling mutation counts. This approach improves precision medicine by detecting more significant driver genes, even in small cancer sample sizes.
Area of Science:
- Genomics
- Cancer Biology
- Statistical Genetics
Background:
- Identifying cancer driver genes is crucial for precision medicine.
- Existing statistical methods struggle to distinguish driver from passenger mutations due to challenges in modeling background mutation distributions.
- This limitation often leads to underpowered detection of driver genes, especially in smaller sample sizes.
Purpose of the Study:
- To introduce a novel statistical approach, weighted iterative zero-truncated negative-binomial regression (WITER), for detecting cancer driver genes with an excess of somatic mutations.
- To improve the power of driver gene detection, particularly in small or moderate sample sizes.
- To provide a robust tool for genomic identification of driver mutations in cancer.
Main Methods:
- Developed a new statistical method, WITER (Weighted Iterative Zero-Truncated Negative-Binomial Regression).
- Properly fitted the distribution of background mutation counts to enhance detection accuracy.
- Applied WITER to analyze somatic mutation data from various cancer types, including TCGA datasets.
Main Results:
- WITER demonstrated superior performance compared to alternative methods, detecting more significant and cancer-consensus driver genes.
- Identified an estimated 229 driver genes across 26 different cancer types.
- In silico validation confirmed 78% of predicted genes as known drivers and highlighted many potential new drivers.
- WITER successfully detected driver genes in small TCGA datasets (as few as 30 subjects) and identified genes missed by other tools.
Conclusions:
- WITER offers a powerful and accurate statistical approach for identifying cancer driver genes.
- The method enhances precision medicine by enabling more comprehensive driver gene discovery, even with limited sample sizes.
- WITER represents a significant technical advancement in cancer genomics research and driver mutation detection.
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