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The survival rate of pancreatic cancer patients is the lowest among those with common solid tumors, and early detection is one of the most feasible means of improving outcomes. We compared plasma proteomes between pancreatic cancer patients and sex-and age-matched healthy controls using surface-enhanced laser desorption/ionization coupled with hybrid quadrupole time-of-flight mass spectrometry. Proteomic spectra were generated from a total of 245 plasma samples obtained from two institutes. A discriminating proteomic pattern was extracted from a training cohort (71 pancreatic cancer patients and 71 healthy controls) using a support vector machine learning algorithm and was applied to two validation cohorts. We recognized a set of four mass peaks at 8,766, 17,272, 28,080, and 14,779 m/z, whose mean intensities differed significantly (Mann-Whitney U test, P < 0.01), as most accurately discriminating cancer patients from healthy controls in the training cohort [sensitivity of 97.2% (69 of 71), specificity of 94.4% (67 of 71), and area under the curve value of 0.978]. This set discriminated cancer patients in the first validation cohort with a sensitivity of 90.9% (30 of 33) and a specificity of 91.1% (41 of 45), and its discriminating capacity was further validated in an independent cohort at a second institution. When combined with CA19-9, 100% (29 of 29 patients) of pancreatic cancers, including early-stage (stages I and II) tumors, were detected. Although a multi-institutional large-scale study will be necessary to confirm clinical significance, the biomarker set identified in this study may be applicable to using plasma samples to diagnose pancreatic cancer. (Cancer Res 2005; 65(22): 10613-22)

The E-cadherin/catenin system acts as an invasion suppressor of epithelial malignancies. This invasion suppressive activity seems be mediated not only by the cell adhesive activity of E-cadherin but by other undetermined signaling pathways elicited by B-catenin. In fact, cancer cells that have infiltrated the stroma reduce the expression of E-cadherin and accumulate B-catenin. We attempted to identify the alternative partner proteins that make complexes with Bcatenin in the absence of E-cadherin. An f100-kDa protein was constantly coimmunoprecipitated with B-catenin from SW480 colorectal cancer cells, which lack the expression of E-cadherin, and was identified as actinin-4 by mass spectrometry. Transfection of E-cadherin cDNA suppressed the association between B-catenin and actinin-4. Inhibition of E-cadherin by RNA interference transferred the B-catenin and actinin-4 proteins into the membrane protrusions of DLD-1 cells. Immunofluorescence histochemistry of clinical colorectal cancer specimens showed that the B-catenin and actinin-4 proteins were colocalized in colorectal cancer cells infiltrating the stroma. We reported previously that overexpression of actinin-4 induces cell motility and specifically promotes lymph node metastasis by colorectal cancer. The association between B-catenin and actinin-4 and its regulation by E-cadherin may represent a novel molecular link connecting cell adhesion and motility. Shutting down the signals mediating this association may be worth considering as a therapeutic approach to cancer invasion and metastasis.

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Purpose: Establishment of a reliable method of predicting the efficacy of chemotherapy and radiotherapy is necessary to provide the most suitable treatment for each cancer patient. We investigated whether proteomic profiles of serum samples obtained from untreated patients were capable of being used to predict the efficacy of combined preoperative chemoradiotherapy against esophageal cancer. Experimental Design: Proteomic spectra were obtained from a training set of 27 serum samples (15 pathologically diagnosed responders to preoperative chemoradiotherapy and12 nonresponders) by surface-enhanced laser desorption and ionization coupled with hybrid quadrupole time-of-flight mass spectrometry. A proteomic pattern prediction model was constructed from the training set by machine learning algorithms, and it was then tested with an independent validation set consisting of serum samples from 15 esophageal cancer patients in a blinded manner. Results:We selected a set of four mass peaks, at 7,420, 9,112,17,123, and12,867 m/z, from a total of 859 protein peaks, as perfectly distinguishing responders from nonresponders in the training set with a support vector machine algorithm. This set of peaks (i.e., the classifier) correctly diagnosed chemoradiosensitivity in 93.3% (14 of 15) of the cases in the validation set. Conclusions: Recent mass spectrometric approaches have revealed that serum contains a large volume of information that reflects the microenvironment of diseased organs. Although a multi-institutional large-scale study will be necessary to confirm each component of the classifier, there is a subtle but definite difference in serum proteomic profile between responders and nonresponders to chemoradiotherapy.