DriverML: a machine learning algorithm for identifying driver genes in cancer sequencing studies

Abstract

Although rapid progress has been made in computational approaches for prioritizing cancer driver genes, research is far from achieving the ultimate goal of discovering a complete catalog of genes truly associated with cancer. Driver gene lists predicted from these computational tools lack consistency and are prone to false positives. Here, we developed an approach (DriverML) integrating Rao’s score test and supervised machine learning to identify cancer driver genes. The weight parameters in the score statistics quantified the functional impacts of mutations on the protein. To obtain optimized weight parameters, the score statistics of prior driver genes were maximized on pan-cancer training data. We conducted rigorous and unbiased benchmark analysis and comparisons of DriverML with 20 other existing tools in 31 independent datasets from The Cancer Genome Atlas (TCGA). Our comprehensive evaluations demonstrated that DriverML was robust and powerful among various datasets and outperformed the other tools with a better balance of precision and sensitivity. In vitro cell-based assays further proved the validity of the DriverML prediction of novel driver genes. In summary, DriverML uses an innovative, machine learning-based approach to prioritize cancer driver genes and provides dramatic improvements over currently existing methods. Its source code is available at https://github.com/HelloYiHan/DriverML .

pDriver: A novel method for unravelling personalised coding and miRNA cancer drivers

“…In this section, we compare the performance of pDriver with eight existing methods with different approaches for discovering cancer driver genes, including three methods for identifying personalised cancer drivers (DawnRank (Hou and Ma, 2014), PNC (Guo et al, 2019), and SCS (Guo et al, 2018)) and five methods for identifying cancer drivers at the population level (ActiveDriver (Reimand and Bader, 2013), DriverML (Han et al, 2019), DriverNet (Bashashati et al, 2012), MutSigCV (Lawrence et al, 2013), and OncodriveFM (Gonzalez-Perez and Lopez-Bigas, 2012)). Besides, ActiveDriver, DriverML, MutSigCV, and OncodriveFM are mutation-based methods while DawnRank, DriverNet, PNC, and SCS are network-based methods.…”

“…Mutation-based methods discover cancer drivers by investigating the characteristics of mutations. For instance, MutSi-gCV (Lawrence et al, 2013) evaluates the significance of mutations in genes, OncodriveFM (Gonzalez-Perez and Lopez-Bigas, 2012) and Dri-verML (Han et al, 2019) examine the functional impact of mutations, OncodriveCLUST (Tamborero et al, 2013) uses recurrence of mutations, ActiveDriver (Reimand and Bader, 2013) looks at enrichment in externally defined regions, and CoMEt (Leiserson et al, 2015) uses mutual exclusivity. Network-based methods detect cancer drivers by evaluating the role of genes in biological networks like DriverNet (Bashashati et al, 2012), MEMo (Ciriello et al, 2012), HotNet (Reyna et al, 2018), NetSig (Horn et al, 2018), and CBNA .…”

“…We apply pDriver to five TCGA cancer datasets and validate the results with the Cancer Gene Census (CGC), in comparison with the state-ofthe-art cancer driver prediction methods, including 3 personalised driver prediction methods (DawnRank (Hou and Ma, 2014), PNC (Guo et al, 2019), and SCS (Guo et al, 2018)) and 5 population level driver prediction methods (ActiveDriver (Reimand and Bader, 2013), DriverML (Han et al, 2019), DriverNet (Bashashati et al, 2012), MutSigCV (Lawrence et al, 2013), and OncodriveFM (Gonzalez-Perez and Lopez-Bigas, 2012)). Since there is no ground truth available for individual patients' cancer drivers, following the same approach in existing literature on personalised driver prediction, we aggregate the results of personalised coding drivers discovered by pDriver (and the other 3 personalised driver prediction methods) for the comparison.…”

A New TTZ Feature Extracting Algorithm to Decipher Tobacco Related Mutation Signature Genes for the Personalized Lung Adenocarcinoma Treatment

“…2) Subnetwork methods: identifying signature genes based on prior knowledge of pathways, proteins or genetic interactions [16], [17]. 3) Hotspot-based methods [18]- [20]. The term hotspot refers to hotspot mutation regions, which are driven by positive selection and especially located in functional domains or important residues for three-dimensional protein structures [21], [22].…”

“…However, despite the rapid progress in computational approaches to prioritize cancer mutational signature genes with the advent of next generation sequencing technologies, the ultimate goal of discovering a complete catalog of genes truly associated with TEP is far from being achieved. Signature gene lists predicted from these tools lack consistency [18]. Many tools are not optimally balanced between precision and sensitivity [23].…”