Researchers at the Indian Institute of Technology in Madras (IIT-Madras) have developed an artificial intelligence (AI)-based mathematical model, NBDriver, to identify cancer-causing alterations in cells. This will help identify the most appropriate treatment strategy for a patient. The study was funded by the Department of Biotechnology and has been published in the peer-reviewed journal Cancers.
The algorithm uses a relatively unexplored technique of leveraging DNA composition to pinpoint genetic alterations responsible for cancer progression, according to a news report. 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 involves differentiating between the relatively small number of ‘driver’ mutations that enable cancer cells to grow and the large number of ‘passenger’ mutations that do not have any effect on the progress of the disease, one of the researchers explained. The study found that the neighbouring gene sequences of driver mutations are significantly different from that of passengers. The researchers designed NBDriver to identify the pathogenic variants of mutations that can cause cancer.
A report noted that the model will identify these driver mutations by looking at genome data around the mutations. “Think of it like looking for a spelling error in a sentence. We are feeding a sentence into the model and by looking at the words before and after a word, the model can identify the erroneous word,” explained one of the researchers.
The research will help identify the most appropriate treatment strategy for a patient. Further, it will help tailor the treatments to a specific illness and person’s genetic make-up. 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.”
While global researchers have developed computational methods for distinguishing between driver and passenger mutations, limited literature exists on using raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. The researchers at IIT-Madras aimed 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. The underlying hypothesis was that these patterns would be unique to individual types of mutations – drivers and passengers. Therefore, these patterns could be modelled mathematically to distinguish between the two classes.
The performance of NBDriver was tested on several open-source cancer mutation datasets, the report said. The model could distinguish between well-studied drivers and passenger mutations from cancer genes with an accuracy of 89%. Furthermore, combining the predictions from NBDriver and three others commonly used driver prediction algorithms resulted in an accuracy of 95%, significantly outperforming existing models. NBDriver could accurately identify 85% of the rare driver mutations from patients diagnosed with Glioblastoma Multiforme (GBM), a particularly aggressive type of cancer affecting the brain or spine. NBDriver is available publicly and can be used to obtain predictions on any user-defined set of mutations.
