Laboratory
of
Sphingolipid Biology
Details of Lab Research Programs
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The theme of Dr Dasgupta’s group is to understand the intricacies of tumour microenvironment (breast, colon and gallbladder cancer) using multi-omics platforms and identify the molecular and therapeutic targets for precision medicine. Dr Dasgupta’s group is specifically interested in underpinning the role of lipid/glycolipid metabolites in cancer cells and how their deregulated metabolism shapes and alters the tumour microenvironment. The aim is to identify key metabolites, their signaling pathways, regulatory mechanisms and molecular interactions essential for tumour progression and therapeutic response. In collaboration with clinicians, her group is identifying liquid biopsy-based lipid biomarkers for early diagnosis of chronic diseases (IBD, NAFL/NASH, GBS) and breast/colon/liver and gallbladder cancers.​
Spatial Multi-omics to elucidate cancer-immune cell crosstalk via lipid metabolites
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The heterogenous tumour microenvironment (TME) comprises cancer cells, stromal cells, immune cells, neurons, and extracellular proteins that continuously cross-talk with each other. The patient response towards any therapy (chemotherapy, radiation therapy, immunotherapy) strongly depends on the TME and the spatial arrangement of immune cells relative to cancer cells. On one hand, the cancer-immune cell-talk through chemokines, cytokines and signalling metabolites build the character of the TME and on the other hand deregulated glucose, lipid, glycolipid, amino acid and other metabolites in cancer cells modify the structure of the TME. The culmination of these metabolic rewiring in the tumor core and TME lead to immune suppression and exhaustion.
In our lab we are doing spatial multi-omics to understand the cellular architecture of tumours and their neighborhoods in the TME by identifying crucial metabolites that mediate the molecular interactions essential for tumour progression and therapeutic response. We propose an integrated spatially resolved multi-omics approach using spatial transcriptomics (ST), spatial metabolomics (SM), spatial lipidomics (SM) and spatial proteomics (SP) to understand the impact of cell-specific metabolic reprogramming on TME and cellular interactions in different cancer types.
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Identifying and Establishing Lipid-based Diagnostic and Prognostic Biomarkers for Multiple Cancer Types, Liver Diseases and Inflammatory Bowel Disease
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In collaboration with clinicians, we use tumor tissues, biopsy, and plasma samples from patients undergoing treatment for breast cancer, liver diseases and Inflammatory Bowel Syndrome for LC-MS/MS based qualitative and quantitative analysis of various types and classes of lipids. We aim to develop and validate lipid-based diagnostic biomarkers for different metabolic diseases that will be robust enough to overcome the heterogeneity of the disease, help in early detection, capable of better prognosis, and provide scope to be developed as therapeutic targets. Correlation of the lipid profile/s with clinicopathological data, disease recurrence, and disease-free survival status will allow us to determine the impact of temporal changes in the lipid signature/s with disease progression and patient response to treatments. The lipid signature/s will then be further validated in a larger multicentric cohort, using multiple platforms and clinicopathological correlations will be established in association with training and building machine-learning models.
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Unravelling the Molecular Mechanism of Sphingolipid and Ganglioside Metabolism in Regulating Cancer Phenotypes
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Highlight of this thematic area is decoding the molecular intricacies of sphingolipid and ganglioside signalling pathways during tumor progression and those targeting the tumor microenvironment in response to cancer chemotherapy. Delving deep into the mechanistic insight, we focus on elucidating the genetic, epigenetic, transcriptional, post-transcriptional regulation for sphingolipid and ganglioside genes that affect multiple growth regulatory pathways and their downstream signalling. We propose to identify candidate chromatin regulators that modulate gene expression, in response to mTORC2/RICTOR pathway during cancer progression. Our aim is to establish multiple gene-signalling-metabolite networks that coordinate execution of hallmark phenotypes of cancer progression and provide multiple nodes for therapeutic intervention.
