Multiple Myeloma (MM) is the second most prevalent blood cancer (10%) after non-Hodgkin's lymphoma and represents approximately 1% of all cancers and 2% of all cancer deaths. MM is a complex disease characterized by numerous genetic alterations and recent mRNA profiling studies have attempted to subclassify the disease to build pathogenetic and prognostic models Correct classification of cancer patients into subtypes is a prerequisite for acute diagnosis and effective treatment. Here we use high accuracy, quantitative proteomics to segregate cancer subtypes directly at the level of expressed proteins. Multiple myeloma is a heterogeneous disease in its initial clinical features as well as its outcome. We investigated two subtypes of Multiple Myeloma: multiple myeloma associated with t(4;14) chromosomal translocation as well as t(4;14)-negative MM subtype. The t(4;14) translocation, found in 15% of multiple myeloma cases, indicates a poor prognosis. Super-SILAC mix was combined of cell lysates from 8 diverse cell lines labelled with heavy amino acids (Lys8 and Arg10). The Super-SILAC library is mixed with samples (lysates), and quantitative mass spectrometric analysis is performed using a setup consisting of LC and on a linear ion trap Orbitrap mass spectrometer with high mass accuracy at the MS and MS/MS levels. This way we have analysed 20 patient samples from present MM. Shotgun proteomic analysis yielded a proteome of more than 5300 quantified proteins overall (3000 on an average per individual sample). High accuracy of quantification allowed robust separation of subtypes by hierarchical clustering on the protein level.