International Institute of Information Technology Hyderabad’s Centre for Computational Natural Sciences and Bioinformatics (CCNSB) is advancing cancer detection and treatment by integrating genomics, epigenetics and artificial intelligence to build more precise and personalised care models.
Researchers at CCNSB are examining cancer as a complex, multi-layered disease shaped not only by DNA mutations but also by epigenetic regulation, gene expression patterns and imaging signatures. The work reflects a broader scientific shift from viewing cancer as a single genetic error to understanding it as a multifactorial condition influenced by genetic alterations, regulatory mechanisms, environmental factors and time.
The team’s genomic research focuses on identifying variations in DNA that drive tumorigenesis. These include single nucleotide changes as well as large-scale alterations such as deletions, duplications, inversions and gene fusions. By analysing cancer genomes in detail, researchers are mapping mutations that disrupt key biological pathways and distinguishing differences across cancer types and subtypes. This approach is helping to identify clinically relevant mutations and biomarkers that can guide targeted therapies.
A major area of investigation has been Diffuse Large B-Cell Lymphoma (DLBCL), an aggressive blood cancer with two primary subtypes that show markedly different treatment responses and outcomes. Through subtype-specific genomic profiling across cancer cell lines, the team has identified distinct mutation patterns, altered pathways and potential prognostic biomarkers. The findings support genome-guided treatment strategies in which therapies are tailored to a patient’s specific mutational profile.
Beyond DNA sequence changes, the research also examines epigenetic modifications that regulate gene activity without altering the genetic code. DNA methylation, one of the most studied epigenetic mechanisms, can silence tumour-suppressor genes or activate oncogenes in cancer. Investigations at CCNSB extend beyond gene promoters to include enhancers, gene bodies and non-coding regions, revealing regulatory shifts that may occur before tumours fully develop. Such early molecular changes are being explored as candidates for early detection.
The team is also studying non-coding RNAs, including microRNAs and long non-coding RNAs, which regulate gene expression through complex molecular networks. Alterations in these regulatory molecules can influence cancer progression, subtype behaviour and treatment response. Mapping these networks is expected to improve understanding of tumour heterogeneity across patients.
In breast cancer research, scientists are integrating DNA methylation data, RNA expression profiles and machine learning models to identify molecular signatures associated with specific subtypes. The integrated approach has enabled the identification of markers linked to risk stratification, survival prediction and early diagnosis. The findings may contribute to the development of liquid biopsy techniques capable of detecting cancer-related signals through minimally invasive blood tests.
The research programme also incorporates artificial intelligence-driven analysis of medical imaging. Large curated mammography datasets are being used to train models capable of detecting abnormalities, segmenting suspicious regions, classifying tumours as benign or malignant and generating preliminary reports. These tools are intended to assist radiologists, reduce diagnostic delays and enhance screening accuracy, particularly in resource-constrained settings.
With India facing challenges such as genetic diversity, earlier age of onset and delayed diagnosis, locally generated data and context-specific models are considered critical. By combining genomics, epigenetics, transcriptomics and AI-enabled imaging, the work at CCNSB is contributing to the broader shift toward precision oncology, where diagnosis and treatment are guided by the unique biological profile of each patient.
Also Read