How machine learning model from IIT-Madras team could boost personalised cancer therapy

IIT-Madras study, published in the journal ‘Frontier in Genetics’, dwells on algorithm to identify personalised genes that have the potential to form and drive cancer in individuals.


Researchers at Indian Institute of Technology (IIT)-Madras have developed a machine learning (ML) algorithm to identify personalised genes that have the potential to form and drive cancer in individuals. The model uses a ‘multiomic’ approach, the combined study of intersectional studies that end with the suffix ‘-omics’.

Details of the algorithm were published in a peer-reviewed paper in the journal Frontier in Genetics last month. The findings are expected to help in devising more personalised cancer therapies, contributing to the growing field of targeted therapy and immunotherapy trials.

Called ‘Personalized Identification of driVer OGs and TSGs’, or PIVOT, the model identifies personalised drivers of cancer genes and classifies them as either tumour suppressor genes (TSG) or oncogenes (OG) — the two types of genes involved in cancer.

The algorithm has also identified rarer driver genes that have not been studied enough to be associated with some cancers in large pan-cancer databases.ML works by consuming large datasets and understanding previously identified patterns. It then applies logic to identify new patterns in new (or existing) data.

For this study, the IIT-Madras researchers worked on datasets that contained genomes of individuals with four kinds of cancers — breast cancer, colorectal adenocarcinoma, lower grade glioma (brain tumours), and lung adenocarcinoma — and their identified driver genes and mutations.


The model classified genes as neutral or drivers, and further labelled them into TSGs and OGs.

It also identified other trends in data and newer driver genes from medical literature.

“It is difficult to predict how well we can train and predict genes in unseen cancer types,” said Malvika Sudhakar, lead author of the paper, to ThePrint. “A lot of factors, such as the number of samples, influence the performance of the model.”

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