This web server called ‘GBMDriver’ is publicly available online as computational techniques need to be developed to properly identify the driver mutations of different types of Cancer
Indian Institute of Technology Madras (IIT Madras) Researchers have developed a machine learning-based computational tool for better detection of cancer-causing tumours in the brain and spinal cord. Called ‘GBMDriver’ (GlioBlastoma Mutiforme Drivers), this tool is publicly available online.
Glioblastoma is a fast and aggressively growing tumour in the brain and spinal cord. Although there has been research undertaken to understand this tumour, therapeutic options remain limited with an expected survival rate of less than two years from the initial diagnosis.
It is important to evaluate the functional consequences of variants in proteins, which are involved in Glioblastoma to advance the therapeutic options for patients. However, functional validations to identify driver mutations (disease-causing mutations) from all the observed variants would be strenuous work.
The GBMDriver was developed specifically to identify driver mutations and passenger mutations (passenger mutations are neutral mutations) in Glioblastoma.
In order to develop this web server, a variety of factors such as amino acid properties, di- and tri-peptide motifs, conservation scores, and Position Specific Scoring Matrices (PSSM) were taken into account.
In this study, 9386 driver mutations and 8728 passenger mutations in glioblastoma were analysed. Driver mutations in glioblastoma were identified with an accuracy of 81.99 percent, in a blind set of 1809 mutants, which is better than existing computational methods. This method is completely dependent on protein sequence.
This research was led by Prof. M. Michael Gromiha, Department of Biotechnology, IIT Madras. His team included Medha Pandey, PhD Student, IIT Madras and two IIT Madras alumni Dr P. Anoosha currently in The Ohio State University, Columbus, U.S., and Dr Dhanusha Yesudhas who is now at the National Institute of Health, U.S.
Explaining the key findings of their research, Prof. Gromiha said, “We have identified the important amino acid features for identifying cancer-causing mutations and achieved the highest accuracy for distinguishing between driver and neutral mutations. We hope that this tool (GBMDriver) could help to prioritise driver mutations in glioblastoma and assist in identifying potential therapeutic targets, thus helping to develop drug design strategies.”
The key applications of this research include:
- The methodology and features are portable to apply for other diseases
- This method could serve as one of the important criteria for disease prognosis
- Valuable resource to identify mutation-specific drug targets to design therapeutic strategies
Pandey said, “Our method showed an accuracy and AUC of 73.59 per cent and 0.82 respectively on 10-fold cross-validation and 81.99 per cent and 0.87 in a blind set of 1809 mutants. We envisage that the present method is helpful to prioritise driver mutations in glioblastoma and assist in identifying therapeutic targets.”
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