Cancer types can be classified according to novel antibody-based proteomics using anti-phosphoprotein monoclonal antibodies (PPmAbs) and multiple discriminant analysis. The antibody-based phosphoproteomics using an antibody panel from over 150 uncharacterized PPmAbs combined with multiple discriminant analysis makes it possible to classify cancer cells. To improve the system, new antibody panels need to be developed using characterized PPmAbs for clinical diagnosis. The uncharacterized 154 PPmAbs were tested for reactivity based on immunohistostaining of several cancer tissues. We focused on AKPS288 PPmAb, and the PPmAb-related antigen was localized in the cytoplasm of tumor cells of the colon and stomach but did not react with non-tumor cells in both tissues. Moreover, the AKPS288 PPmAb showed positive staining in the cytoplasm of normal prostate tissue but not cancer tissue. Based on the mass spectrometry (MS), the PPmAb-related antigen was identified as TATA-element modulatory factor 1 (TMF/ARA160), a tumor-associated antigen (TAA). These results indicate that the use of a novel antibody panel consisting of anti-TAA mAbs could have considerably greater utility for cancer classification than the PPmAb panel with unknown specificity identified in our previous study.

The SAINOME-plate consists of a 384-well plate and a cover that contains a cutter, which can cut a polyacrylamide gel into approximately 4.5-mm square pieces following electrophoresis. In this study, we applied SDS-PAGE and the SAINOME-plate to fractionation of protein mixtures from cell extracts or serum for proteomic approaches. Compared with gel-fractionation using a cutter or a scalpel, SAINOME-plate gel-fractionation is simpler and higher-throughput. In terms of reproducibility of proteomic profiling, SAINOME-plate gel-fractionation was comparable to scalpel gel-fractionation. Additionally, human keratin contamination was lower with the SAINOME-plate than with a scalpel. In serum protein fractionation, the number of proteins identified increased approximately 2-fold and 3.7-fold relative to non-fractionation when the gel was divided into 8 and 96 fractions, respectively. The results demonstrate that the SAINOME-plate gel-fractionation will be a useful method in mass spectrometry-based proteomics.

Glioblastoma (GBM) is the most common malignant primary tumor of the central nervous system in adults. Despite advances in GBM treatment, the prognosis of patients with GBM remains poor and novel drugs are urgently required. In this study, we aimed to identify novel drugs for GBM treatment by using a drug screening approach. To this end, we performed high-throughput screening with 118 drugs, including Food and Drug Administration (FDA)-approved anticancer drugs. We found high inhibition rates (more than 90%) for doxorubicin, bortezomib, and cephalomannine in 6 GBM cell lines. Furthermore, we determined the half-maximal inhibitory concentration (IC₅₀) of cephalomannine and found that the drug has a high potential for anti-GBM activity. Moreover, we noted that cephalomannine inhibited cell proliferation by inducing autophagy. Thus, our results indicate that cephalomannine may be an effective drug candidate for GBM treatment.

Sarcomas are rare mesenchymal malignancies and comprise over 50 histological subtypes. Sarcomas are not well studied because the number of cases of individual sarcoma is low. The utilization of public data, such as gene expression data, may allow for improvement in the novel discovery of sarcoma. In this study, to gain insight into histological subtypes of sarcoma from a public database, we performed a meta-analysis of the gene-expression profiles by surveying the data deposited in the Gene Expression Omnibus database from 2001 to 2014. The gene-expression data for 10 sarcoma subtypes and the gene-expression profiles for 1002 cases were selected for comparative analysis. Genes with histology-oriented molecular signatures were identified, and the results were verified by functional validation using gene oncology analysis. Pathway analysis suggested the existence of differential biological processes among sarcoma subtypes. Furthermore, as an application of the sarcoma gene expression datasets used in this study, we investigated the gene expression patterns of the targets of pazopanib to predict the response of sarcoma to pazopanib. We found that the gene expression distribution patterns of targets of pazopanib were without distinction among 10 subtypes of sarcoma. Taken together, we identified the tissue-specific genes of 10 subtypes of sarcoma by bioinformatics analysis; our results demonstrated the utility of sarcoma datasets in public databases and provide valuable information for future rare cancer research.