Genome research using appropriately collected pathological tissue samples is expected to yield breakthroughs in the development of biomarkers and identification of therapeutic targets for diseases such as cancers. In this connection, the Japanese Society of Pathology (JSP) has developed “The JSP Guidelines on the Handling of Pathological Tissue Samples for Genomic Research” based on an abundance of data from empirical analyses of tissue samples collected and stored under various conditions. Tissue samples should be collected from appropriate sites within surgically resected specimens, without disturbing the features on which pathological diagnosis is based, while avoiding bleeding or necrotic foci. They should be collected as soon as possible after resection: at the latest within about 3 h of storage at 4°C. Preferably, snap‐frozen samples should be stored in liquid nitrogen (about −180°C) until use. When intending to use genomic DNA extracted from formalin‐fixed paraffin‐embedded tissue, 10% neutral buffered formalin should be used. Insufficient fixation and overfixation must both be avoided. We hope that pathologists, clinicians, clinical laboratory technicians and biobank operators will come to master the handling of pathological tissue samples based on the standard operating procedures in these Guidelines to yield results that will assist in the realization of genomic medicine.

In addition to conventional cytology, liquid-based cytology (LBC) is also used for immunocytochemistry and gene analysis. However, an appropriate method to obtain high quality DNA for next-generation sequencing (NGS) using LBC specimens remains controversial. We determined the optimal conditions for fixation with an alcohol-based fixative for LBC and DNA extraction using cultured cancer cell lines and clinical specimens. The extracted DNA was processed for NGS after the DNA quality was confirmed based on the DNA concentration and degree of degradation. The optimal conditions for cultured cells to obtain high quality DNA were to fix the cells at a density of 6 × 10 3 or 2 × 10 4 cells/mL and to use the magnetic bead-based DNA extraction method. Even after storing the fixed cells for 90 days, DNA extracted using the above and other extraction kits, including membrane-based methods, did not undergo degradation. Furthermore, 5-year-old residual LBC samples demonstrated high DNA quality that was suitable for NGS. Furthermore, a cancer genome panel analysis was successfully performed with DNA extracted from cultured cells fixed at 6 × 10 3 cells/mL for 90 days, and with DNA from residual LBC samples even after 1 year of storage. Residual LBC samples may be a useful source of DNA for clinical NGS to promote genome-based cancer medicine.

Purpose: Pancreatic cancer remains a disease of high mortality despite advanced diagnostic techniques. Mucins (MUC) play crucial roles in carcinogenesis and tumor invasion in pancreatic cancers. MUC1 and MUC4 expression are related to the aggressive behavior of human neoplasms and a poor patient outcome. In contrast, MUC2 is a tumor suppressor, and we have previously reported that MUC2 is a favorable prognostic factor in pancreatic neoplasia. This study investigates whether the methylation status of three mucin genes from postoperative tissue specimens from patients with pancreatic neoplasms could serve as a predictive biomarker for outcome after surgery.Experimental Design: We evaluated the methylation status of MUC1, MUC2, and MUC4 promoter regions in pancreatic tissue samples from 191 patients with various pancreatic lesions using methylation-specific electrophoresis. Then, integrating these results and clinicopathologic features, we used support vector machine-, neural network-, and multinomial-based methods to develop a prognostic classifier.Results: Significant differences were identified between the positive-and negative-prediction classifiers of patients in 5-year overall survival (OS) in the cross-validation test. Multivariate analysis revealed that these prognostic classifiers were independent prognostic factors analyzed by not only neoplastic tissues but also nonneoplastic tissues. These classifiers had higher predictive accuracy for OS than tumor size, lymph node metastasis, distant metastasis, and age and can complement the prognostic value of the TNM staging system.Conclusions: Analysis of epigenetic changes in mucin genes may be of diagnostic utility and one of the prognostic predictors for patients with pancreatic ductal adenocarcinoma.

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