Metabolomics of Intact Tissues:
 Discrimination Between Different Osteolytic Lesion Regions from a Multiple Myeloma Patient
using 1H HRMAS NMR spectra

1,6Silvia Mari, 2Francesca Fontana, 2Jose Garcia Manteiga, 1Edoardo Gaude, 2Simone Cenci,
3Enrico Caneva, 1Giovanna Musco, 4Stanislav Sykora, and 5Carlos Cobas

1Dulbecco Telethon Institute c/o S. Raffaele Scientific Institute, Milano, Italy,
2Protein Transport and Secretion Unit c/o S.Raffaele Scientific Institute, Milano, Italy,
3Centro Interdipartimentale Grandi Apparecchiature c/o Università degli Studi di Milano, Milano, Italy,
4Extra Byte, Castano Primo, Italy, 5Mestrelab Research, Santiago de Compostela, Spain, 6R4R, Rodano, Italy.

Presented at the
53rd Experimental NMR Conference (ENC), April 15-20, 2012, Miami, Florida (USA).

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This topic was also presented as a Talk: see the slides (PDF 2162 kB, DOI: 10.3247/SL4Nmr12.006).


Multiple myeloma (MM) is a neoplastic plasma cell disorder, that results in end-organ damage, and a bone disease. Bone represents in fact a common place for metastasis for other tumours with different primary origins from mesenchymal to epithelial and other haematological malignancies. The study of the bone environment in multiple myeloma not only gives us new biological knowledge about myeloma progression and aggressiveness but also give light to the metastatic processes taking place in the bone in other types of cancer. When a bone lesion is found in the clinical practice, often without a previous diagnostic of the primary tumour, time is precious and finding out the origin of the metastasis that has produced the lesion will influence further clinical interventions. Strategies aimed to help the diagnostic process when studying bone lesions could have a big impact by saving time for therapeutic intervention and orienting the diagnostic process which could also mean saving precious resources.

The metabolic profiling or metabolomics of disease have proven to be useful for the generation of diagnostic markers. Its use in clinics is beginning to increase exponentially; however, it is still largely unexplored and underexploited. Although the potential of metabolomics has already been shown in finding biomarkers in solid tumours as prostate, breast cancer and colon cancer, much less is known about its potential impact on haematological malignancies and, to our knowledge, nothing is known on bone lesions. Bone tissue biopsies coming from the orthopaedic surgery of a MM patient have been collected and analyzed by HR-MAS NMR.

Since the actual nature of all the metabolites is rarely known in advance, metabolomics often uses alternative statistical evaluation methods, such as multivariate factor analysis. Such approaches require integration over predefined intervals (bins) and a meaningful integration of such intricate and artefact-burdened spectra may often be just as arduous as peaks fitting. Recently, a new algorithm called GSD (Global Spectrum Deconvolution) has been developed and made available in the Mnova software package (Mestrelab Research). GSD is capable of identifying even poorly resolved spectral signals and of fitting all recognizable peaks in even very complex 1D spectra in a surprisingly short time (typically a dozen seconds for up to 1000 peaks). Moreover, it is fully automatic and objective (no human intervention is required) and produces a table of all detectable spectral peaks and their parameters. Such a table can be then used for various purposes like generation of artifact-free synthetic spectra (with or without resolution enhancement), stick spectra, artifact-free integrals, as well as accurate binning void of any bin-crossover problems due to the overlapping wings of spectral peaks. Because of these attractive features, GSD is likely to become a very important pre-processing tool for all metabolomic approaches to the evaluation of NMR spectra of whole bio-samples.

Here we present an integrated application based on an R module called package MUMA (Multivariate & Univariate Metabolomic Analysis) and Mnova software to the processing and analysis of bone tissue biopsies. MUMA was developed by our group and is available online for a free download, including a User tutorial. In this approach, the GSD-based binning matrix is used as input to MUMA which then performs a total spectra normalization and applies one of two possible scalings (auto or pareto). Once the data sets are normalized and scaled, MUMA performs both univariate and multivariate analysis. In the first case, it performs Shapiro Wilk's test, Welch test or Mann Whitney-Wilcoxon test amongst all possible class combinations and outputs the Volcano plots, while in the second case it can run algorithms such as PCA, PLSDA or O-PLSDA). It can also carry out further correlation analysis such as STOCSY and RANSY.

Please, cite this online document as:
Mari S., Fontana F., Manteiga J.G., Gaude E., Cenci S., Caneva E., Musco G., Sykora S., Cobas C.,
   Metabolomic of Intact Tissues:
   Discrimination Between Different Osteolytic Lesion Regions from a Multiple Myeloma Patient 1H HRMAS NMR spectra
   Poster at 53rd ENC, Miami, April 15-20, 2012, DOI: 10.3247/SL4Nmr12.002.


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