NE HEALTH BUREAU
CHENNAI, JULY 6
The Indian Institute of Technology Madras (IIT Madras) Researchers have developed an Artificial Intelligence-based tool, ‘PIVOT’, that can predict cancer-causing genes in an individual. This tool will ultimately help in devising personalized cancer treatment strategies.
According to World Health Organization, cancer is a leading cause of death worldwide and accounted for nearly one in six deaths in 2020.
Cancer is an uncontrolled growth of cells that can occur due to mutations in oncogenes or by tumor suppressor genes or both. However, not all mutations necessarily result in cancer. Therefore, it is important to identify genes that are causing cancer to devise appropriate personalized cancer treatment strategies.
- According to World Health Organization, cancer is a leading cause of death worldwide and accounted for nearly one in six deaths in 2020.
- Cancer, being a complex disease, cannot be dealt with in a one-treatment-fits-all fashion: Dr Karthik Raman, Core Member, RBCDSAI, IIT Madras
‘PIVOT,’ developed by IIT Madras researchers, is designed to predict genes that are responsible for causing cancer in an individual. The prediction is based on a model that utilizes information on mutations, expression of genes, and copy number variation in genes and perturbations in the biological network due to an altered gene expression.
The research was led by Prof Raghunathan Rengaswamy, Dean (Global Engagement), IIT Madras, and Professor, Department of Chemical Engineering, IIT Madras, Dr. Karthik Raman, Associate Professor, Bhupat and Jyoti Mehta School of Biosciences, IIT Madras and a Core Member, Robert Bosch Centre for Data Science and Artificial Intelligence (RBCDSAI), IIT Madras, and Malvika Sudhakar, a Research Scholar, IIT Madras.
The findings of the research have been published in a peer-reviewed journal Frontier in Genetics (https://doi.org/10.3389/fgene.2022.854190).
Highlighting the significance of the Research, Dr Karthik Raman, Core Member, RBCDSAI, IIT Madras, said, “Cancer, being a complex disease, cannot be dealt with in a one-treatment-fits-all fashion. As cancer treatment increasingly shifts towards personalized medicine, such models that build toward pinpointing differences between patients can be very useful.”
Researchers from @iitmadras have developed an Artificial Intelligence-based tool, ‘PIVOT’, that can predict cancer-causing genes in an individual. This tool will ultimately help in devising personalised #cancer treatment strategies.@rbc_dsai_iitmhttps://t.co/wvIJVF9OS8 pic.twitter.com/T4IDjT7xkV
— IIT Madras (@iitmadras) July 6, 2022
The tool is based on a machine learning model that classifies genes as tumour suppressor genes, oncogenes or neutral genes. The tool was able to successfully predict both the existing oncogenes and tumour-suppressor genes like TP53, and PIK3CA, among others, and new cancer-related genes such as PRKCA, SOX9 and PSMD4.
Speaking on the importance of providing personalized cancer treatment, Malvika Sudhakar, Research Scholar, IIT Madras, said, “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.”
Current cancer treatments are known to be detrimental to the overall health of the patient. Knowledge of the genes responsible for the initiation and progression of cancer in patients can help determine the combination of drugs and therapy most suitable for a patient’s recovery. Although there are tools available to identify personalized cancer genes, they use unsupervised learning and predict based on presence and absence of mutations in cancer-related genes. This study, however, is the first one to use supervised learning and takes into account the functional impact of mutations while making predictions.
IIT Madras researchers have built AI prediction models for three different types of cancer including Breast Invasive Carcinoma, Colon Adenocarcinoma and Lung Adenocarcinoma. They are planning to extend it further to many more cancer types. The team is also working on a list of personalized cancer-causing genes that can help in identifying the suitable drug for patients based on their personalized cancer profile.
