The MALDI-TOF spectra of peptides from the sera of normal and myocardial infarction patients produced patterns that provided an accurate diagnostic of MI. In myocardial infarction, the spectral pattern originated from the cleavage of complement C3 alpha chain to release the C3f peptide and cleavage of fibrinogen to release peptide A. The fibrinogen peptide A and complement C3f peptide were in turn progressively truncated by aminopeptidases to produce two families of fragments that formed the characteristic spectral pattern of MI. Time course and inhibitor studies demonstrated that the peptide patterns in the serum reflect the balance of disease-specific-protease and aminopeptidase activity ex vivo.

One of the major difficulties in mining low abundance biomarkers from serum or plasma is due to the fact that a small number of proteins such as albumin, alpha2-macroglobulin, transferrin, and immunoglobulins, may represent as much as 80% of the total serum protein. The large quantity of these proteins makes it difficult to identify low abundance proteins in serum using traditional 2-dimensional electrophoresis. We recently used a combination of multidimensional liquid chromatography and gel electrophoresis coupled to matrix-assisted laser desorption/ionization-quadrupole-time of flight and Ion Trap liquid chromatography-tandem mass spectrometry to identify protein markers in sera of Alzheimer's disease (AD), insulin resistance/type-2 diabetes (IR/D2), and congestive heart failure (CHF) patients. We identified 8 proteins that exhibit higher levels in control sera and 36 proteins that exhibit higher levels in disease sera. For example, haptoglobin and hemoglobin are elevated in sera of AD, IR/D2, and CHF patients. The levels of several other proteins including fibrinogen and its fragments, alpha 2-macroglobulin, transthyretin, pro-platelet basic protein, protease inhibitors clade A and C, as well as proteins involved in the classical complement pathway such as complement C3, C4, and C1 inhibitor, were found to differ between IR/D2 and control sera. The sera levels of proteins, such as the 10 kDa subunit of vitronectin, alpha 1-acid glycoprotein, apolipoprotein B100, fragment of factor H, and histidine-rich glycoprotein were observed to be different between AD and controls. The differences observed in these biomarker candidates were confirmed by Western blot and the enzyme-linked immunosorbent assay. The biological meaning of the proteomic changes in the disease states and the potential use of these changes as diagnostic tools or for therapeutic intervention will be discussed.

Sunitinib is a multitargeting tyrosine kinase inhibitor used for metastatic renal cancer. There are no biomarkers that can predict sunitinib response. Such markers are needed to avoid administration of costly medication with side effects to patients who would not benefit from it. We compared global miRNA expression between patients with a short (≤12 months) versus prolonged (>12 months) progression-free survival (PFS) under sunitinib as first-line therapy for metastatic renal cell carcinoma. We identified a number of differentially expressed miRNAs and developed miRNA statistical models that can accurately distinguish between the two groups. We validated our models in the discovery set and an independent set of 57 patients. Target prediction and pathway analysis showed that these miRNAs are involved in vascular endothelial growth factor (VEGF), TGFβ, and mammalian target of rapamycin (mTOR)-mediated signaling and cell-cell communication. We tested the effect of these miRNAs on cellular proliferation and angiogenesis. We validated the negative correlation between miR-221 and its target, VEGFR2.miR-221 overexpression was associated with a poor PFS while its target, VEGFR2 was associated with longer survival. Gain of function experiments showed that miR-221 and miR-222 decreased angiogenesis and cellular proliferation in human umbilical vein endothelial cells (HUVEC) while increasing cellular proliferation in ACHN cells. miRNAs represent potential predictive markers for sunitinib response.

BackgroundBreast cancer is the most common malignancy among women worldwide in terms of incidence and mortality. About 10% of North American women will be diagnosed with breast cancer during their lifetime and 20% of those will die of the disease. Breast cancer is a heterogeneous disease and biomarkers able to correctly classify patients into prognostic groups are needed to better tailor treatment options and improve outcomes. One powerful method used for biomarker discovery is sample screening with mass spectrometry, as it allows direct comparison of protein expression between normal and pathological states. The purpose of this study was to use a systematic and objective method to identify biomarkers with possible prognostic value in breast cancer patients, particularly in identifying cases most likely to have lymph node metastasis and to validate their prognostic ability using breast cancer tissue microarrays.Methods and FindingsDifferential proteomic analyses were employed to identify candidate biomarkers in primary breast cancer patients. These analyses identified decorin (DCN) and endoplasmin (HSP90B1) which play important roles regulating the tumour microenvironment and in pathways related to tumorigenesis. This study indicates that high expression of Decorin is associated with lymph node metastasis (p<0.001), higher number of positive lymph nodes (p<0.0001) and worse overall survival (p = 0.01). High expression of HSP90B1 is associated with distant metastasis (p<0.0001) and decreased overall survival (p<0.0001) these patients also appear to benefit significantly from hormonal treatment.ConclusionsUsing quantitative proteomic profiling of primary breast cancers, two new promising prognostic and predictive markers were found to identify patients with worse survival. In addition HSP90B1 appears to identify a group of patients with distant metastasis with otherwise good prognostic features.

PURPOSE Prostate-specific antigen testing has led to overtreatment of prostate cancer (PCa). Only a small subset of PCa patients will have an aggressive disease that requires intensive therapy, and there is currently no biomarker to predict disease aggressiveness at the time of surgery. MicroRNAs (miRNAs) are reported to be involved in PCa pathogenesis. METHODS This study involved 105 participants. For the discovery phase, prostatectomy samples were dichotomized to high-risk (n = 27, biochemical failure &lt;36 months after prostatectomy) and low-risk groups (n = 14, ≥36 months without biochemical failure). Expression of 754 mature miRNAs was compared between the 2 groups. Linear regression models were built to accurately predict biochemical failure risk. miRNA mimics were transfected into PCa model cell lines to test effects on proliferation and to deduce responding signaling pathways. RESULTS We identified 25 differentially expressed miRNAs between the biochemical failure risk groups. Based on the expression of 2–3 miRNAs, 3 logistic regression models were developed, each with a high positive predictive value. Candidate miRNAs and the best-performing model were also verified on an independent PCa set. miRNA-152, featured in the models, was further investigated by using cell line models and was shown to affect cell proliferation. Predicted interaction between miR-152 and (mRNA)ERBB3 (erythroblastic leukemia viral oncogene homolog 3) was experimentally validated in vitro. CONCLUSIONS miRNAs can help to predict biochemical failure risk at the time of prostatectomy.