IIIT Hyderabad research integrates genomics for cancer care
HYDERABAD: Cancer is no longer viewed as a single genetic error but as a multi-layered disease shaped by DNA mutations, epigenetic changes and imaging patterns. New research at the Centre for Computational Natural Sciences and Bioinformatics (CCNSB) at IIIT Hyderabad is integrating these layers to improve early detection and personalised cancer care.
A century ago, scientists believed cancer began with a single mistake in a cell. In 1914, the somatic mutation theory proposed that abnormalities in DNA could trigger uncontrolled growth. Over time, researchers identified oncogenes that drive cancer and tumour suppressor genes that prevent it. Later work showed that the tissue environment, viruses, carcinogens and cellular stress also influence tumour development.
“People have been talking about the origin of tumours since the early 1900s, but over time we realised that cancer cannot be explained by mutations alone. Today, cancer is understood as a multifactorial disease, shaped by genetics, gene regulation, environment and time,” said Prof Nita Parekh, Professor of Bioinformatics, IIIT-H.
Studying genetic variations in tumour growth
Genomics the study of DNA variations is central to current cancer research. Prof Parekh said her work examines how genetic variations contribute to tumorigenesis. These range from single-letter changes in the genetic code to large alterations such as missing segments, duplications, inversions or gene fusions across chromosomes.
“By analysing cancer genomes in detail, we can identify which mutations matter, which pathways they disrupt, and how different cancers — or even subtypes of the same cancer — behave very differently,” she said.
One focus of the team’s research is Diffuse Large B-Cell Lymphoma (DLBCL), an aggressive blood cancer with two main subtypes and differing outcomes. Prof Parekh said subtype-specific genetic variations explained why some patients respond to treatment while others do not.
By profiling genetic changes in cancer cell lines, the team identified subtype-specific mutations, disrupted pathways and biomarkers for prognosis and therapy. These findings support genome-guided treatment strategies tailored to individual mutational profiles.
Epigenetics and non-coding RNA add new insights
Prof Parekh said genes alone do not explain cancer behaviour. Epigenetic mechanisms chemical changes that switch genes on or off without altering the DNA sequence also play a role.
One such mechanism is DNA methylation. In cancer, tumour-suppressing genes may be silenced or cancer-promoting genes activated through altered chemical tags. The team is studying methylation patterns in gene promoters, enhancers, gene bodies and non-coding RNAs to identify early regulatory changes that may enable early detection.
Non-coding RNAs, including microRNAs and long non-coding RNAs, also regulate gene expression. “These RNAs do not code for proteins, but they play a major role in regulating gene expression,” Prof Parekh said. Mapping these regulatory networks could explain differences across patients and cancer subtypes.
Breast cancer detection using AI
The IIIT-H team is also studying breast cancer, one of the most common cancers globally. “Breast cancer is not one disease; it has several subtypes, and identifying them early makes a critical difference,” Prof Parekh said.
By combining DNA methylation data, RNA expression profiles and machine learning, researchers identified molecular signatures that distinguish subtypes, predict patient risk and survival, and indicate early diagnostic markers. These findings may enable liquid biopsies blood tests that detect cancer signals before symptoms appear.
The research also includes mammography data analysis. The team has curated large datasets of mammograms to train artificial intelligence models to detect abnormalities, 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, particularly in resource-constrained settings.
India faces cancer challenges including genetic diversity, younger age of onset and delayed diagnosis. Prof Parekh said integrating genetics, epigenetics and gene expression data is essential.
“Understanding cancer requires looking at genetics, epigenetics and gene expression data together — not in isolation,” she said.
By combining genomics, epigenetics and AI-driven imaging, the research moves towards precision medicine, where treatment is guided by a patient’s biological profile.
