← Home Research

Research

The mission of the group is to improve the diagnosis, prognostication and monitoring of blood cancers. Almost all of our work uses cutting edge next-generation sequencing and computational methods for genomics. We are also developing machine learning and deep learning approaches to further our mission.

We actively collaborate with adult and pediatric hematolymphoid disease management groups and the bone marrow transplantation team at the Tata Memorial Centre and ACTREC.

We also collaborate with leading public cancer hospitals in India for multicenter studies and are also involved in international collaborations

Our Research Areas

1. Measurable Residual Disease Detection for Acute Myeloid Leukemia

Our group has led pioneering clinical work comparing multiparameter flow cytometry and error-corrected panel-based next-generation sequencing for the detection of MRD in acute myeloid leukemia. We are exploring ways to improve MRD detection in AML. This includes development of high efficiency duplex sequencing. It also includes combined protein and DNA based single-cell genomics to study chemotherapy resistance and clonal evolution in AML.

2. Development of Novel Next-Generation Sequencing Methods for Genomics of Blood Cancers

We develop NGS-based molecular methods for diagnosis, prognostication and monitoring of blood cancers. We are developing adaptive whole genome sequencing as well as third generation whole genome sequencing assays for blood cancers. Adaptive nanopore whole-genome sequencing will enable rapid and comprehensive leukemia diagnosis. To make some of our technologies accessible, we are standardizing targeted nanopore RNA-sequencing workflows for public hospitals across India, aiming to deliver affordable, high-quality molecular diagnostics nationwide. Our efforts also extend to developing ultra-sensitive minimal residual disease (MRD) assays for real-time monitoring of treatment response in children with B-cell precursor acute lymphoblastic leukemia (BCP-ALL).

3. Artificial Intelligence and its Application to Blood Cancers

We are developing machine learning and deep learning approaches to transform leukemia diagnostics: This includes the development and validation of methylation-based classifiers for ultrarapid diagnosis of leukemia and prediction of biological subclasses. It also involves development of image-based classification using whole-slide imaging, and development of neural networks for drug response prediction in AML.

4. Genomics of Acute Leukemia and its Germline Predisposition

We are mapping the molecular landscape of childhood leukemia through large-scale whole-transcriptome sequencing to identify key driver events and develop transcriptomic classifiers for precise disease subtyping. We are also investigating the true incidence of germline predisposition to AML in a cohort of pediatric and adult AML. We are also developing functional validation assays for some variants.

A complete & updated list of our papers can be seen on PubMed.

Research Funding