[in Japanese]

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BACKGROUND: The survival of non-small cell lung cancer (NSCLC) patients who receive potentially curative surgical treatment is hampered in part because of the lack of effective strategies for selecting cases with fatal prognosis. METHODS: Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry (MALDI MS) was here used to analyze protein profiles of more than lung (NL) specimens. MS peaks associated with relapse-related death were selected using three distinct statistical methods, and used to build an individualized weighted-voting–based prediction classifier of prognosis in a training cohort. We further applied multiple proteomic approaches to identify a set of discriminatory proteins that might be associated with clinical behavior of NSCLC. RESULTS: We based our bioinformatic analyses upon more than 2000 MS signals obtained from frozen lung specimens in a training cohort. The resulting individualized classifier based on the expression profile could accurately predict both relapse-free and overall survival in the training cohort, and, more importantly, its utility could be validated in an independent blinded test cohort. CONCLUSIONS: We have discovered a relapse-related death signature that may distinguish NSCLC patients with a high risk of relapse and death from those with a favorable prognosis, judging from our findings with both training and independent test cohorts. The present individualized prediction classifier should have an impact on choice of optimal clinical management of NSCLC.

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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Objective: To find the biomarker of SSc at peptide level and to investigate the biological function of the molecule.
Methods: Sera form 40 patients with SSc, 30 SLE, 21 RA, 30 OA, and 26 healthy donors were used in peptidomic analysis. The biofunctions of complement DRC3f and C3f was testified by stimulating the cultured skin fibroblasts, skin and lung microvascular endothelial cells (MEC) with both recombinant peptides and the 3000 Dalton-filtered DRC3f-positive sera. The cell proliferation was observed on bioreduction of MTS into formazan by living cells. The production of TGF-beta, VEGF and EGF were measured by ELISA.
Results: A group of peptides with m/z of 1865, 1778, 1691, 1563, and 1450 were detected dominantly in SSc sera. These peptides were identified to be DRC3f and its derivatives. The levels of DRC3f was related to the age of the onset, frequency of interstitial pulmonary fibrosis, sicca complaints, and the severity of vascular impairment, as well as to the levels of Complement C3, C4, CH50, IgG, IgA, CRP, ESR and ANA. Synthesized DRC3f and C3f enhanced the MEC proliferation independent on growth factors. Furthermore, both the whole and filtered sera containing DRC3f enhanced proliferation of endothelial cells. DRC3f and C3f increased the production of TGF-beta by skin fibroblasts and MEC but not by lung MEC.
Conclusions: DRC3f, predominantly detected in SSc and related to disease severity and activity and functioning as growth factors, is a useful marker of SSc and may play important roles in pathogenesis of SSc.

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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[in Japanese]

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Gefitinib (Iressa, ZD1839), an inhibitor of the epidermal growth factor receptor (EGFR) tyrosine kinase, has been approved for patients with advanced non-small-cell lung cancer (NSCLC). However, gefitinib benefits only a limited proportion of patients and is associated with the potentially lethal adverse effect of interstitial pneumonia. To develop tumor markers able to predict the response to gefitinib treatment, we used two-dimensional difference gel electrophoresis to conduct a proteomic study on NSCLC patients who relapsed after surgery and received gefitinib monotherapy. Protein expression profiles were created from tumor tissues obtained at the time of surgery and a support vector machine algorithm was used to identify nine proteins by which 31 responders could be distinguished from 16 non-responders. The predictive performance of the nine proteins was validated by an additional six responders and eight non-responders, resulting in positive and negative predictive values of 100% (6/6) and 87.5% (7/8), respectively, for the response to gefitinib. Differential expression of one of the nine proteins, H-FABP, was monitored by an ELISA assay and was consistent with the 2D-DIGE results. We also identified 12 proteins able to distinguish tumors on the basis of EGFR gene mutation status. Study of these proteins will contribute to the development of personalized therapy for patients with NSCLC.

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Peptides play crucial role in many physiological events. In the central nerve system, most neuron contains biologically active peptides together with conventional neurotransmitters. Neuropeptides to act as neurotransmitter, neurohormone or neuromodulator are involved in a wide variety of physiological processes including reproduction, growth, stress, sleep/wake cycles, feeding and many other pathways. Here, we performed the comprehenmsive peptidome analysis of mouse brain using 2D-microHPLC-MALDI-TOF-MS analytical platform developed by us. 2D-microHPLC-MALDI-TOF-MS is composed of ion-exchange (SCX) and reversed-phase (RP) high-performance liquid chromatography, followed by matrix-assisted laser desorption/ionization time of flight mass spectrometry. The peptides from mouse brain were separated into 6 fractions by SCX and then each fractions of SCX were separated into 190 fractions by RP (total 1140 fractions), and each fraction is analyzed with mass spectrometer. More than 4500 ion peaks were observed with m/z from 700 to 7000. These MS data was processed using gel viewer as two-dimensional virtual peptide map where the x-axis displays m/z and the y-axis displays fraction number of RP. MS/MS sequencing and database tools identified over 1000 different peptides including several known neropeptides and its precurcer proteins.

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The accumulation of molecular damage by ascorbylation products impairs molecules of critical importance for the maintenance of homeostatic functions in the lens epithelium. The purpose of this study was to compare the protein composition of the B-3 line of transformed human lens epithelial cells (HLE B-3) exposed to the stress produced from dehydroascorbic acid (DHA) to that of normal control cells, and to identify protein targets of ascorbylation stress in HLE B-3 cells treated with DHA. Using two-dimensional SDS-polyacrylamide gel electrophoresis (2DE) with 7 cm mini gels and electrospray ion trap mass spectrometry with nano spray (ESI-MS), we could compared four two dimensional (2D) gel protein maps at the same time. As a result, in the identified proteins, mass spectral data revealed that there were specifically modified proteins by DHA treatment in senescent cells. These newly observed modifications are methionine oxidation (alfa-enolase, vimentin, and mutant beta-actin), tryptophane oxidation (calreticulin), deamidation (alfa-enolase and vimentin), C-terminal carbamidomethylation (alfa-enolase, vimentin and mutant beta-actin), and N-terminal carbamylation (mutant beta-actin). Mass-mapping profile also showed the remarkably increment of several beta-B2crystallins in the same cells. These results suggest that epithelial cell senescence is associated with a breakdown of anti-ascorbylation mechanisms.

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Using proteomic technology, we have discovered a novel protein in the serum of patients with bone metastases that is specific for cancer metastases in bone. To analyze this protein we used surface enhanced laser desorption/ionization-time of flight-mass spectrometry (SELDI-TOF) and a hybrid linear ion trap (Q TRAP) mass spectrometry system. Coupling of these types of instruments will enable amino aid sequencing and subsequent protein identification. A prospective study was undertaken in 47 patients with liver cancer, to identify which proteins were characteristic and predictive of bone metastasis. Of these patients, bone metastasis developed in 11 cases, and this protein was identified before bone metastasis was confirmed in bone scintigraphy of all patients. This result yielded a sensitivity of 100%. Also, among these patients, peak values of the protein were found in 10 cases. At present, the follow-up investigation is under way because the results of bone scintigraphy did not reveal bone metastasis. An important characteristic of the protein we identified was that it was detectable at about average at 6-11 months before diagnosis in these patients, much earlier than bone metastases can be imaged by bone scintigraphy. In addition, we quantified this protein from the areas under the curves by mass spectrometry, and found that the protein continued to increase in serum up to the time when bone metastasis was confirmed by bone scintigraphy and then decreased after the start of radiotherapy treatment.

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Poor prognosis of hepatocellular carcinoma (HCC) is mainly attributable to a high incidence of intrahepatic recurrence after surgery. To develop the accurate predictive marker for early intrahepatic recurrence and to identify therapeutic targets for the treatment of HCC patients, we examined protein expression profiles in HCC tissues using two-dimensional difference gel electrophoresis (2D-DIGE) representing approximately 1500 protein spots. An initial sample set (a training set) was comprised of 27 HCC patients; 12 patients who had intrahepatic recurrence within 6 months after curative surgery and 15 patients who did not have recurrence within 2 years. Using a supervised classification method based on support vector machine algorithm, we generated for the first time a proteomic signature, comprising of 23 protein spots, which associated with the early recurrence. The predictive performance of the signature was validated by a test set comprising 13 newly enrolled HCC patients; 6 early recurrence and 7 non-recurrence cases. The signature correctly classified 27 HCC patients in the training set, and predicted early recurrence in 12 (92.3%) of 13 patients in the test set with a positive predict value of 100% and a negative predict value of 85.7%. In conclusion, we identified the proteomic signature, which can be a predictive marker for early intrahepatic recurrence. The proteins in the signature can also be the therapeutic targets for HCC.

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SELDI-TOF-MS (surface-enhanced laser desorption / ionization time-of-flight mass spectrometer) allows to detect peaks in the range of tens to hundreds from a single experiment. It is necessary to select informative feature set in order to construct a simple model for disease classification.
In this research, we developed a method to construct an effective disease classification model in combination with a filter and a wrapper approach. In the filter approach, we extracted features using fold-change threshold between means of two groups, followed by a permutation test. In the wrapper approach, we eliminated features from extracted feature set until no decrease of estimated error rate was obtained. The error rate was estimated using .632 bootstrap with classification methods such as linear support vector machine and weighted voting algorithm.
We evaluated our method using two SELDI-TOF-MS datasets; one was serum samples from excessive alcohol drinkers and non-drinkers, and the other from hepatocellular carcinoma (HCC) patients, liver cirrhosis (LC) patients and normal persons. Each set of samples consisted of about 30 mass spectra (range: from 29 to 32) and was divided into a training dataset and a test dataset. As a result, almost similar sensitivity and specificity compared to the whole feature set was obtained using less than the half number of all features.

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