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Identification of potential driver mutations in glioblastoma using machine learning
Medha Pandey1, P Anoosha2, Dhanusha Yesudhas1
1Department of Biotechnology, Bhupat and Jyoti Mehta School of Biosciences, Indian Institute of Technology Madras, Chennai 600036, India.
Abstract:
Glioblastoma is a fast and aggressively growing tumor in the brain and spinal cord. Mutation of amino acid residues in targets proteins, which are involved in glioblastoma, alters the structure and function and may lead to disease. In this study, we collected a set of 9386 disease-causing (drivers) mutations based on the recurrence in patient samples and experimentally annotated as pathogenic and 8728 as neutral (passenger) mutations. We observed that Arg is highly preferred at the mutant sites of drivers, whereas Met and Ile showed preferences in passengers. Inspecting neighboring residues at the mutant sites revealed that the motifs YP, CP and GRH, are preferred in drivers, whereas SI, IQ and TVI are dominant in neutral. In addition, we have computed other sequence-based features such as conservation scores, Position Specific Scoring Matrices (PSSM) and physicochemical properties, and developed a machine learning-based method, GBMDriver (GlioBlastoma Multiforme Drivers), for distinguishing between driver and passenger mutations. Our method showed an accuracy and AUC of 73.59% and 0.82, respectively, on 10-fold cross-validation and 81.99% and 0.87 in a blind set of 1809 mutants. The tool is available at https://web.iitm.ac.in/bioinfo2/GBMDriver/index.html. We envisage that the present method is helpful to prioritize driver mutations in glioblastoma and assist in identifying therapeutic targets.
Insights
Researchers identified distinct amino acid patterns distinguishing glioblastoma driver mutations from passenger mutations. This discovery aids in prioritizing critical mutations for glioblastoma (GBM) and developing targeted therapies.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Glioblastoma (GBM) is an aggressive brain and spinal cord tumor.
- Mutations in GBM-related proteins can alter their structure and function, leading to disease.
- Distinguishing driver mutations (causative) from passenger mutations (neutral) is crucial for understanding GBM pathogenesis.
Purpose of the Study:
- To identify sequence-based features differentiating driver from passenger mutations in glioblastoma.
- To develop a machine learning model for classifying glioblastoma mutations.
- To provide a tool for prioritizing driver mutations and identifying therapeutic targets.
Main Methods:
- Collected and analyzed 9386 driver and 8728 passenger mutations in glioblastoma.
- Examined amino acid preferences and neighboring residue motifs at mutation sites.
- Computed sequence-based features including conservation scores and Position Specific Scoring Matrices (PSSM).
- Developed and validated a machine learning classifier, GBMDriver.
Main Results:
- Specific amino acids (e.g., Arg) and motifs (e.g., YP, CP, GRH) are preferred in glioblastoma driver mutations.
- Distinct amino acid preferences (Met, Ile) and motifs (SI, IQ, TVI) characterize passenger mutations.
- The GBMDriver tool achieved 73.59% accuracy and 0.82 AUC on cross-validation, and 81.99% accuracy and 0.87 AUC on a blind test set.
Conclusions:
- Sequence-based features effectively distinguish glioblastoma driver from passenger mutations.
- The developed GBMDriver tool can aid in prioritizing driver mutations for glioblastoma.
- This approach assists in identifying potential therapeutic targets for glioblastoma treatment.
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