IIT-Madras develops AI-based algorithm to identify cancer-causing alterations

The algorithm uses a relatively unexplored technique of leveraging DNA composition to pinpoint genetic alterations that were responsible for progression of cancer

Researchers at the prestigious Indian Institute of Technology-Madras (IIT-M) have developed an Artificial Intelligence (AI)-based mathematical model to identify cancer-causing alterations in cells. The researchers said the new algorithm uses a relatively unexplored technique of leveraging DNA composition to pinpoint genetic alterations responsible for cancer progression.

Results of the research, led by Prof B Ravindran, Head, RBCDSAI, and Mindtree Faculty Fellow IIT-M and Dr Karthik Raman, Faculty Member, Robert Bosch Centre for Data Science and AI (RBCDSAI), IIT-M, and also the Coordinator, Centre for Integrative Biology and Systems Medicine, IIT-M, was published in the reputed peer-reviewed International Journal Cancers.

Cancer is caused due to the uncontrolled growth of cells driven mainly by genetic alterations, and in recent years, high-throughput DNA Sequencing has revolutionised the area of cancer research by enabling the measurement of these alterations.

Explaining the rationale behind this study, Prof B Ravindran said one of the major challenges faced by cancer researchers involves the differentiation between the relatively small number of ‘driver’ mutations that enable the cancer cells to grow and the large number of ‘passenger’ mutations that do not have any effect on the progression of the disease.”

The researchers hope that the driver mutations predicted through their mathematical model will ultimately help discover potentially novel drug targets and will advance the notion of prescribing the “right drug to the right person at the right time.”

“In most of the previously published techniques, researchers typically analysed DNA sequences from large groups of cancer patients, comparing sequences from cancer as well as normal cells and determined whether a particular mutation occurred more often in cancer cells than random. However, this ‘frequentist’ approach often missed out on relatively rare driver mutations,” Dr Karthik Raman said.

In this study, the researchers decided to look at this problem from a different perspective. The main goal was to discover patterns in the DNA sequences – made up of four letters, or bases, A, T, G, and C surrounding a particular site of alteration.

Highlighting the performance of the algorithm, Dr Ravindran said the new model could distinguish between well-studied drivers and passenger mutations from cancer genes with an accuracy of 89 per cent.