Cancer Metabolomics

MetaboID – a software package for user-guided NMR spectral assignment

    A Matlab™-based user interface explicitly designed to aid in the assignment of complex mixture spectra.  

Current features:

  • Freely editable spectral library, currently consisting of 360 unique compounds collected from the BMRB and HMDB
  • A collection of 3 tools designed as a guide through the assignment process (currently for 1D NMR spectra)
  • Open-source code to encourage development of user-specific processing tools



Clik here to download MetaboID:

Get the MetaboID manual:        view

Link to tutorial videos:               Coming Soon!

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Prostate Cancer Metabolomics (Collaboration with Dr. Arul Chinnaiyan lab in Pathology)

    Prostate cancer (PCa) is among the most common cancers detected in aging men, affecting 1 in 6 with an estimated 192,280 men diagnosed and 27,360 cancer-related deaths in the United States in 2009. Benign prostatic hyperplasia (BPH), an inflammation of the prostate causing urological problems, is estimated to affect up to 50% of men over age 60 and 90% of men over age 70. The current standard test for prostate disease relies on serum PSA levels, however both PCa and BPH can generate elevated PSA levels ( > 4 ng/mL). In addition, the serum PSA test suffers from high false-positive rates for cancer diagnosis (only 25% – 35% are actually cancerous [1]). Thus, this test will not differentiate BPH from localized PCa and biopsy is ultimately necessary for final confirmation and choice of treatment. In order to alleviate patient stress and associated health care costs for biopsies that are often unnecessary, it is imperative that non-invasive biomarkers be identified that will reliably differentiate disease states of the prostate. The goal of our research in the lab is to identify biomarkers in non-invasive biospecimens that will indicate with greater sensitivity and specificity the identification and progression of PCa, alleviating the dependence on PSA testing. Development of such a metabolite biomarker panel based on non-invasive sampling will both reduce health care costs and improve patient quality of life. 

    Our proposed research will utilize cutting-edge NMR and LC-MS experiments. The novelty in our approach lies in the discovery process that we will employ, namely metabolomic profiling utilizing NMR followed by metabolite biomarker nomination and targeted validation utilizing both NMR and MS.

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Head and Neck Squamous Cell Carcinoma: HR-MAS NMR Based Metabolomics: (Read More)

    Head and neck squamous cell carcinoma (HNSCC) is the fifth most common form of cancer worldwide and accounts for approximately 3% of all human malignancies. HNSCC is diagnosed based on physical examination, endoscopy, X-ray, computed tomography (CT), magnetic resonance imaging (MRI), and blood and urine tests. Nevertheless, definitive diagnosis relies on histopathological examination of tissue biopsies, according to the World Health Organization classification of tumors. With objectives to delineate metabolic signatures of HNSCC and also to nominate the potential metabolic biomarkers for the early detection of HNSCC, our metabolomics team explored high-resolution magic angle spinning (HR-MAS) NMR spectroscopy on pathologically proven HNSCC human tissues. This research work was carried out in collaboration with Dr. Yvonne L. Kapila, School of Dentistry, University of Michigan.

Figure: Representative 600 MHz 1H HR-MAS CPMG NMR spectra (area normalized) of matched Normal Adjacent Tissue (NAT) and HNSCC tissues from various anatomical sites of head and neck involving oral cavity, tongue, lip and larynx of four different patients. The triglyceride signals were indicated as ‘TG’. The corresponding H&E photomicrographs of post HR-MAS NMR tissues are shown alongside of each 1H CPMG spectrum. All the NAT are composed of parakeratinized stratified squamous epithelium and underlying fibrovascular connective tissue. However, tumor tissues were composed of squamous cell carcinomas with neoplastic cells demonstrating varying degrees of epithelial differentiation invade the adjacent connective tissue predominantly as nests, sheets or single cells.

Figure: Representative 600 MHz 1H CPMG NMR spectra (area normalized) of (A) NAT, (B) Tumor and (C) LN-Met tissues taken from a single patient. The corresponding H&E photomicrographs of post HR-MAS NMR tissues are shown alongside of each 1H CPMG spectrum. 


Publications Related to NMR Based Metabolomics 

  1. Tripathi P, Somashekar BS, Ponnusamy M, Gursky A, Dailey S, Kunju P, Lee CT, Chinnaiyan AM, Rajendiran TM, Ramamoorthy A. HR-MAS NMR Tissue Metabolomic Signatures Cross-Validated by Mass Spectrometry Distinguish Bladder Cancer from Benign Disease. J Proteome Res. 2013 Jun 3. [Epub ahead of print]. Link
  2. MacKinnon N, Somashekar BS, Tripathi P, Ge W, Rajendiran TM, Chinnaiyan AM, Ramamoorthy A.  MetaboID: A Graphical User Interface Package for Assignment of 1H NMR Spectra of Bodyfluids and Tissues.  J Magn Reson. 2013 Jan;226:93-9. Link
  3. Somashekar BS, Amin AG, Tripathi P, MacKinnon N, Rithner CD, Shanley CA, Basaraba R, Henao-Tamayo M, Kato-
    Maeda M, Ramamoorthy A, Orme IM, Ordway DJ, Chatterjee D. Metabolomic signatures in guinea pigs infected with epidemic associated W-Beijing strains of Mycobacterium tuberculosis.
    J Proteome Res. 2012 Oct 5;11(10):4873-84. Link
  4. Tripathi P, Kamarajan P, Somashekar BS, MacKinnon N, Chinnaiyan AM, Kapila YL, Rajendiran TM, Ramamoorthy A. Delineating Metabolic Signatures of Head and Neck Squamous Cell Carcinoma: Phospholipase A2, a Potential Therapeutic Target. Int J Biochem Cell Biol. 2012 Jun 26;44(11):1852-1861. Link
  5. MacKinnon N, Ge W, Khan A, Somashekar B, Tripathi P, Siddiqui J, Wei J, Chinnaiyan A, Rajendira T, Ramamoorthy A. Variable Reference Alignment: an improved peak alignment protocol for NMR spectral data with large intersample variation. Anal Chem. 2012 Jun 19;84(12):5372-9. Link
  6. MacKinnon N, Khan AP, Chinnaiyan AM, Rajendiran TM, Ramamoorthy A. Androgen receptor activation results in metabolite signatures of an aggressive prostate cancer phenotype: an NMR-based metabonomics study. Metabolomics 2012, DOI: 10.1007/s11306-012-0398-4. Link
  7. Somashekar BS, Kamarajan P, Danciu T, Kapila YL, Chinnaiyan AM, Rajendiran TM, Ramamoorthy A. Magic Angle Spinning NMR-Based Metabolic Profiling of Head and Neck Squamous Cell Carcinoma Tissues. J Proteome Res. 2011 Nov 4;10(11):5232-41. Link