Computational Systems Biology Lab

by Aditi Jain

Although, there does exist tools that identify personalized cancer genes, however, they use unsupervised learning method and predicts based on the presence and absence of mutations in cancer-related genes. This study, however, is the first one to use the supervised learning method by applying gene-based labelling that is cancer-specific. This new model also takes into account the functional impact of the mutations while making predictions.

“The research area of precision medicine is still at a nascent stage. PIVOT helps push these boundaries and presents prospects for experimental research based on the genes identified,” says Malvika Sudhakar, lead author of the study.

Read the whole blog at https://rbcdsai.iitm.ac.in/blogs/towards-personalized-cancer-treatment/

Access the PIVOT tool at https://github.com/RamanLab/PIVOT/

Original Paper: 

• M. Sudhakar, R. Rengaswamy, and K. Raman, “Multi-Omic Data Helps Improve Prediction of Personalised Tumor Suppressors and Oncogenes,” Frontiers in Genetics, vol. 13, p. 854190, 2022.
  [bibtex]

  @article{Sudhakar2022Multiomic,
    title = {Multi-Omic Data Helps Improve Prediction of Personalised Tumor Suppressors and Oncogenes},
    author = {Sudhakar, Malvika and Rengaswamy, Raghunathan and Raman, Karthik},
    year = {2022},
    doi = {10.3389/fgene.2022.854190},
    pmid = {35620468},
    journal = {Frontiers in Genetics},
    volume = {13},
    pages = {854190},
    issn = {1664-8021},
    abstract = {The progression of tumorigenesis starts with a few mutational and structural driver events in the cell. Various cohort-based computational tools exist to identify driver genes but require multiple samples to identify less frequently mutated driver genes. Many studies use different methods to identify driver mutations/genes from mutations that have no impact on tumour progression; however, a small fraction of patients show no mutational events in any known driver genes. Current unsupervised methods map somatic and expression data onto a network to identify personalised driver genes based on changes in expression. Our method is the first machine learning model to classify genes as tumour suppressor gene (TSG), oncogene (OG) or neutral, thus assigning the functional impact of the gene in the patient. In this study, we develop a multi-omic approach, PIVOT (Personalised Identification of driVer OGs and TSGs), to train on experimentally or computationally validated mutational and structural driver events. Given the lack of any gold standards for the identification of personalised driver genes, we label the data using four strategies and, based on classification metrics, show gene-based labelling strategies perform best. We build different models using SNV, RNA, and multi-omic features to be used based on the data available. Our models trained on multi-omic data improved predictions compared to mutation and expression data, achieving an accuracy {$\geq$} 0.99 for BRCA, LUAD and COAD datasets. We show network and expression-based features contribute the most to PIVOT. Our predictions on BRCA, COAD and LUAD cancer types reveal commonly altered genes such as TP53, and PIK3CA, which are predicted drivers for multiple cancer types. Along with known driver genes, our models also identify new driver genes such as PRKCA, SOX9 and PSMD4. Our multi-omic model labels both CNV and mutations with a more considerable contribution by CNV alterations. While predicting labels for genes mutated in multiple samples, we also label rare driver events occurring in as few as one sample. We also identify genes with dual roles within the same cancer type. Overall, PIVOT labels personalised driver genes as TSGs and OGs and also identifies rare driver genes. PIVOT is available at https://github.com/RamanLab/PIVOT.},
  }