Arun Sreekumar Lab


About the Lab


Development and progression of cancer has long been known to be multifactorial. To begin to understand cancer progression with a systems perspective, we need to characterize and integrate the various molecular components involved across the various biospecimens analyzed, defining the new area of Systems Biology or Omics. The emergence of the “Omics” era has shifted the focus from the assessment of individual genes and proteins to examination of a substantial component of the expressed genome and proteome. Deciphering the molecular networks that distinguish subsets of tumors that progress to advanced disease will delineate the etiology of cancer, as well as lead to the identification of biomarkers that will aid in the identification of patients that should be treated. Although such studies have been carried out using genomics, global transcriptomics and to some extent proteomics platforms, few studies have examined metabolomic alterations.

Information Gathering

Like the other Omics-style sciences, metabolomics seeks to gather information in an unbiased manner for the purpose of generating new hypotheses through a thorough examination of the gathered data. Where genomics is best understood as defining the genetic potential, transcriptomics is a window into the future (desired) direction of the cellular activity, and proteomics is a window to the functional potential of the cell; metabolomics, the Omics science of metabolism, is the only window into the current and actual state of the cell (or by extension, organism) at a specific point in time. It therefore seeks to capture physiological status as a function of biochemical (or metabolic) activity. It’s important to emphasize that subtle alterations in the genomic, epigenomic and proteomic functions in a cell can lead to enormous changes in the concentration of specific metabolites. This downstream amplification could allow for a more sensitive detection of the perturbation. Since metabolite expression is a direct indicator of changes in protein activity, the metabolomic profile is considered to be a distal read-out of cellular phenotype. Importantly, unlike other Omics datasets, metabolomics data is less complex and hence computationally approachable, more amenable to interpretation and yet as part of a larger whole, easily integrated with other Omics datasets in a systems-based analytical approach.

Our Approach and Work

Our laboratory has used mass spectrometry-based metabolomic profiling approach to study biochemical alterations in prostate, breast and bladder cancer. Our work on prostate cancer revealed the importance of sarcosine in tumor progression while our studies on bladder cancer revealed methylation-associated perturbation of phase I metabolism.