IIIT-H researchers decoding genetic drivers of cancer progression
Researchers at IIIT-Hyderabad are advancing cancer research by integrating genomics, epigenetics, gene regulation and artificial intelligence to identify tumour-driving mutations, cancer subtypes and early diagnostic markers, paving the way for personalised therapies and early detection
Hyderabad: The International Institute of Information Technology (IIIT) – Hyderabad researchers are advancing their cancer research by studying how genetic mutation, epigenetic changes and gene regulation together drive tumour development.
At the centre of modern cancer research is genomics – the study of variations in DNA. “One part of my work looks at the role of genetic variations in tumorigenesis,” remarked Nita Parekh, Professor of Bioinformatics, IIIT-H, referring to some small changes such as a single-letter swap in the genetic code.
With cancer genomes analysis the IIIT Hyderabad team identified, which mutations are critical, which biological pathways are disrupted and how different cancers or even subtypes of the same cancer behave differently.
One focus of Prof. Parekh’s research has been on Diffuse Large B-Cell Lymphoma (DLBCL), an aggressive blood cancer. DLBCL has two main subtypes, each with very different outcomes. “When we looked at subtype-specific genetic variations, we could clearly see why some patients respond well to treatment while others do not,” she said.
By profiling genetic variations across cancer cell lines, Prof. Parekh’s team identified subtype-specific mutations, disrupted pathways and novel biomarkers for prognosis and therapy.
One other type of cancer that the IIIT-H team is working on is breast cancer, one of the most common cancers worldwide. By combining DNA methylation data, RNA expression profiles and machine learning, the researchers identified molecular signatures that can distinguish cancer subtypes, predict patient risk and survival and also point to early diagnostic markers. These findings pave the way for liquid biopsies – simple blood tests that detect cancer signals long before symptoms appear.
The research also extended beyond molecular data into medical imaging. Prof. Parekh’s team developed large, curated datasets of mammograms to train AI models that can detect abnormalities early, segment suspicious regions, classify tumours as benign or malignant and generate preliminary clinical reports. This work aims to support radiologists, reduce diagnostic delays, and improve screening accuracy – especially in resource-constrained settings.