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OncodriveROLE classifies cancer driver genes in loss of function and activating mode of action
Michael P Schroeder1, Carlota Rubio-Perez1, David Tamborero1
1Research Unit on Biomedical Informatics, Department of Experimental and Health Sciences, Universitat Pompeu Fabra, E08003 Barcelona and Institució Catalana de Recerca i Estudis Avançats (ICREA), E08010 Barcelona, Spain.
Motivation:
Several computational methods have been developed to identify cancer drivers genes-genes responsible for cancer development upon specific alterations. These alterations can cause the loss of function (LoF) of the gene product, for instance, in tumor suppressors, or increase or change its activity or function, if it is an oncogene. Distinguishing between these two classes is important to understand tumorigenesis in patients and has implications for therapy decision making. Here, we assess the capacity of multiple gene features related to the pattern of genomic alterations across tumors to distinguish between activating and LoF cancer genes, and we present an automated approach to aid the classification of novel cancer drivers according to their role.
Result:
OncodriveROLE is a machine learning-based approach that classifies driver genes according to their role, using several properties related to the pattern of alterations across tumors. The method shows an accuracy of 0.93 and Matthew's correlation coefficient of 0.84 classifying genes in the Cancer Gene Census. The OncodriveROLE classifier, its results when applied to two lists of predicted cancer drivers and TCGA-derived mutation and copy number features used by the classifier are available at http://bg.upf.edu/oncodrive-role.
Availability And Implementation:
The R implementation of the OncodriveROLE classifier is available at http://bg.upf.edu/oncodrive-role.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
Insights
This study introduces OncodriveROLE, a machine learning tool that accurately distinguishes between cancer-driving genes causing loss-of-function (LoF) and those increasing gene activity. This classification aids in understanding cancer development and guiding therapy decisions.
Area of Science:
- Genomics
- Computational Biology
- Cancer Research
Background:
- Identifying cancer driver genes is crucial for understanding tumorigenesis and therapeutic strategies.
- Cancer driver genes can function through loss-of-function (LoF) or activating alterations.
- Distinguishing between these roles is essential for personalized cancer treatment.
Purpose of the Study:
- To assess gene features for distinguishing between activating and LoF cancer genes.
- To develop an automated approach for classifying novel cancer drivers based on their functional role.
Main Methods:
- Utilized machine learning to classify driver genes based on genomic alteration patterns.
- Incorporated features related to mutation and copy number alterations across tumors.
Main Results:
- The OncodriveROLE classifier achieved 0.93 accuracy and 0.84 Matthew's correlation coefficient.
- Successfully classified genes within the Cancer Gene Census.
Conclusions:
- OncodriveROLE provides an accurate method for classifying cancer driver genes.
- The tool aids in understanding the specific roles of genes in cancer development.
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