‘Multi-omics’ is changing how India spots and treats TB, cancers Premium

From tuberculosis to cancer, AI, ML, and genomics are transforming disease diagnosis and treatment.

In the last decade, India’s use of genomics has undergone a significant transformation, so much so that the diagnoses, management, and treatment of many diseases — including tuberculosis, cancers, and those caused by antimicrobial resistance — stand on the cusp of a revolution.

Most recently, in January 2024, the Department of Biotechnology said it had completed sequencing 10,000 genomes from 99 ethnic groups under its ‘Genome India’ project. This national initiative aims to develop a reference genome for Indian people, which will help design genome-wide and disease-specific ‘genetic chips’ for low-cost diagnostics and research.

Earlier, in October 2020, the Council for Scientific and Industrial Research (CSIR) had reportedly sequenced the entire genomes of 1,008 individuals in India representing diverse ethnic groups in six months. This effort was part of a mission called ‘IndiGen’ — to create a pilot dataset with which researchers could analyse the epidemiology of genetic diseases and help develop affordable screening approaches, optimise treatment, and minimise adverse events for them.

Other, more disease-specific consortia have also sprung up around the country and efforts are underway to create new datasets to address individual health problems, ranging from the age-old scourge of tuberculosis to cancers, rare genetic disorders in children, and even antimicrobial resistance. Researchers have also been able to extract more value from these using artificial intelligence and machine learning, and by combining their contents with other extensive datasets on proteins (proteomics), gene expression in cells (transcriptomics), and chemical changes that regulate gene expression (epigenomics) to develop a ‘multi-omics’ approach to tackle diseases.

A recent consortium concerns tuberculosis, a disease that continues to pose significant challenges to its eradication, in India and around the world. The Indian Tuberculosis Genomic Surveillance Consortium (InTGS) comprises 10 Report India sites covering eight states for tuberculosis, with the goal of sequencing around 32,000 tuberculosis clinical strains from active patients, and develop a centralised biological repository of clinical Mycobacterium tuberculosis strains in India.

Other major objectives vis-à-vis tuberculosis include mapping the genetic diversity of pulmonary and extra-pulmonary isolates of the tuberculosis bacterium from newly reported active cases in India, the associated treatment outcomes, and correlating mutations with drug resistance patterns, according to Vinay Nandicoori, director of the CSIR-Centre for Cellular and Molecular Biology (CCMB), Hyderabad. The project’s ultimate goal is to validate identified mutations to develop a sequence-based method to determine drug resistance, and to combine the epidemiological data with results from whole-genome sequencing to develop working solutions.

Scientists from a mix of leading research institutes have divided the various parts of the project thus. In the first stage, scientists from the Jawaharlal Institute of Postgraduate Medical Education and Research, Puducherry; the National Institute for Research in Tuberculosis, Bhagwan Mahavir Medical Research Centre, Hyderabad; the Byramjee Jeejeebhoy Government Medical College, Pune; and the P.D. Hinduja Hospital, Mumbai, will collect the clinical samples, including the patients’ metadata. Next, scientists at the International Centre for Genetic Engineering and Biotechnology, New Delhi, will isolate the genetic material from the samples and set up a strain repository. In the third stage, scientists at CCMB and the National Institute of Biomedical Genomics, Kalyani, will conduct whole genome sequencing. In the fourth and final stage, a team at the National Institute of Immunology, New Delhi, will conduct RNA sequencing data analysis, and develop AI and ML models to predict drug resistance and take cognisance of the metadata to detect resistance patterns, according to Dr. Nandicoori.