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This study aimed to compare the performance of MilliSect dissection and manual dissection. Twenty-five formalin-fixed paraffin-embedded (FFPE) breast cancer tissue blocks were selected for comparison. Specific areas of interest (AOIs) in invasive carcinoma on tissue sections were transferred to dissection slides by manual macrodissection or the MilliSect instrument. The comparison criteria were 1) the time required for dissection; 2) RNA concentration and purity; 3) RNA quantity of 5 housekeeping genes (by RT-qPCR); and 4) ER, PR, HER2, Ki-67 and recurrence score (RS) values (by the 21-gene assay). Then, tumor-adjacent tissues, including fibrocollagenous and epithelial tissues, from the same selected tissue blocks of 8 of 25 patients were scraped using the mesodissection method, and their RS values were assessed to evaluate the influence of tumor-adjacent tissues on the target AOIs. Ultimately, 4 AOIs of invasive ductal carcinoma (IDC) from 1 tissue block of another 4 patients with lymph node (LN) metastases each, LN tissue and a mixture of IDC and LN tissue from the other tissue block of the same 4 patients were mesodissected to evaluate the influence of infiltrating lymphocyte levels on the RS values of AOIs. In our experience, the MilliSect instrument, which provides process management documentation, required more time than manual macrodissection (on average, approximately 9.1 min per sample versus 5.8 min per sample, respectively). The RNA yield and quality of the dissected tissues were comparable for the 2 methods. However, the tumor-adjacent tissues of the AOIs may influence the RS to some extent. Tumor-infiltrating lymphocytes (TILs) can dramatically increase RSs, far exceeding the influence of tumor-adjacent fibrocollagenous and epithelial tissues. In conclusion, MilliSect mesodissection is comparable to manual dissection. This mesodissection tool may facilitate AOI alignment and the dissection process for the 21-gene RS assay. Samples whose adjacent tissues are intermixed with TILs warrant special attention.

Abstract Background Liver cancer is the second leading cause of cancer-related deaths and characterized by heterogeneity and drug resistance. Patient-derived xenograft (PDX) models have been widely used in cancer research because they reproduce the characteristics of original tumors. However, the current studies of liver cancer PDX mice are scattered and the number of available PDX models are too small to represent the heterogeneity of liver cancer patients. To improve this situation and to complement available PDX models related resources, here we constructed a comprehensive database, PDXliver, to integrate and analyze liver cancer PDX models. Description Currently, PDXliver contains 116 PDX models from Chinese liver cancer patients, 51 of them were established by the in-house PDX platform and others were curated from the public literatures. These models are annotated with complete information, including clinical characteristics of patients, genome-wide expression profiles, germline variations, somatic mutations and copy number alterations. Analysis of expression subtypes and mutated genes show that PDXliver represents the diversity of human patients. Another feature of PDXliver is storing drug response data of PDX mice, which makes it possible to explore the association between molecular profiles and drug sensitivity. All data can be accessed via the Browse and Search pages. Additionally, two tools are provided to interactively visualize the omics data of selected PDXs or to compare two groups of PDXs. Conclusion As far as we known, PDXliver is the first public database of liver cancer PDX models. We hope that this comprehensive resource will accelerate the utility of PDX models and facilitate liver cancer research. The PDXliver database is freely available online at: http://www.picb.ac.cn/PDXliver/