This AI-based algorithm will help identify cancer-causing cell alterations

Researchers at the Indian Institute of Technology (IIT), Madras have developed an artificial intelligence-based mathematical model to identify cancer-causing alterations in cells.

The algorithm uses a relatively unexplored technique of leveraging DNA composition to pinpoint genetic alterations responsible for cancer progression. The results have been recently published in the reputed peer-reviewed International Journal of Cancer.

According to the team, cancer is caused due to the uncontrolled growth of cells driven mainly by genetic alterations. In recent years, high-throughput DNA sequencing has revolutionised the area of cancer research by enabling the measurement of these alterations.

However, due to the complexity and size of these sequencing datasets, pinpointing the exact changes from the genomes of cancer patients is notoriously difficult.

 

"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," said B Ravindran, head of Robert Bosch Centre for Data Science and AI (RBCDSAI), IIT Madras.

The model will help in identifying the most appropriate treatment strategy for a patient through an approach known as "precision oncology" and "tailoring treatments" not only to a specific illness but also to a person's genetic make-up is challenging and requires extensive cataloguing of the 'driver' variants of interest.

The researchers hope that the 'driver' mutations predicted through their mathematical model will ultimately help discover potentially novel drug targets and 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," said Karthik Raman, associate professor, IIT Madras.

"Detecting 'driver' mutations, particularly rare ones, is an exceptionally difficult task, and the development of such methods can ultimately accelerate early diagnoses and the development of personalised therapies."

Developing an easy-to-use web interface that will enable cancer researchers to get predictions on their preferred set of variants, studying the context of these mutations, and how they impact the evolution of cancer are among the next steps the team will be working on.