Prof. Ritu Gupta's research lab is focused on translational hemato-oncology and precision medicine, integrating advanced hematopathology, flow cytometry, molecular genomics, single-cell technologies, artificial intelligence, and computational approaches to address clinically relevant challenges in hematological malignancies.Her research programme brings together laboratory and clinical expertise to improve the diagnosis, risk stratification, disease monitoring, and treatment of patients with acute myeloid leukemia (AML), multiple myeloma (MM), and other hematological malignancies. A major emphasis is placed on understanding disease heterogeneity, clonal evolution, therapeutic resistance, and measurable residual disease, with the goal of translating technological advances into clinically useful diagnostic and prognostic tools.
Major Research Areas
Acute Myeloid Leukemia and Leukemic Stem Cells
Research focuses on the characterization of leukemic stem cells and their molecular, epigenetic, immunological, and microenvironmental features. Single-cell and multi-omics approaches are being used to understand cellular heterogeneity, clonal evolution, disease progression, relapse, and mechanisms of therapeutic resistance in AML.
Multiple Myeloma and Clonal Evolution
Research in multiple myeloma focuses on genomic characterization, clonal evolution, subclonal architecture, and genetic determinants of treatment response and disease progression. Advanced genomic approaches are being applied to identify biomarkers with prognostic and therapeutic relevance and to support individualized patient management.
Minimal Residual Disease and Advanced Diagnostics
The laboratory is developing and validating highly sensitive approaches for measurable residual disease (MRD) assessment using multiparametric flow cytometry, molecular methods such as next-generation sequencing, ddPCR and q-PCR, and image-based analysis. These efforts aim to improve the sensitivity, reproducibility, and accessibility of disease monitoring in haematological malignancies.
Artificial Intelligence and Computational Hematopathology
Artificial intelligence, machine learning, deep learning, and computational genomics are increasingly integrated into the research programme. These approaches are being explored for risk prediction, biomarker discovery, image-based diagnosis, genomic data interpretation, disease classification, and clinical outcome prediction, with a particular focus on developing clinically relevant and resource-sensitive solutions for Indian patients.
Precision Hemato-Oncology and Translational Genomics
The research programme integrates next-generation sequencing, single-cell analysis, molecular profiling, and high-dimensional flow cytometry to develop a deeper understanding of hematological malignancies. The overarching goal is to translate these findings into precision diagnostics, prognostic models, treatment-response assessment, and personalized therapeutic strategies.
