Moore, Raymond

CD36 expression in both immune and non-immune cells is known to be directly involved in cancer metastasis. Extracellular vesicles (EVs) secreted by malignant melanocytes play a vital role in developing tumor-promoting microenvironments, but it is unclear whether this is mediated through CD36. To understand the role of CD36 in melanoma, we first analyzed the SKCM dataset for clinical prognosis, evaluated the percentage of CD36 in lymphatic fluid-derived EVs (LEVs), and tested whether melanoma-derived EVs increase CD36 expression and induce M2-macrophage-like characteristics. Furthermore, we performed a multiplex immunofluorescence (MxIF) imaging analysis to evaluate the CD36 expression and its colocalization with various other cells in the lymph node (LN) of patients and control subjects. Our findings show that cutaneous melanoma patients have a worse clinical prognosis with high CD36 levels, and a higher percentage of CD36 in total LEVs were found at baseline in melanoma patients compared to control. We also found that monocytic and endothelial cells treated with melanoma EVs expressed more CD36 than untreated cells. Furthermore, melanoma-derived EVs can regulate immunosuppressive macrophage-like characteristics by upregulating CD36. The spatial imaging data show that cells in tumor-involved sentinel LNs exhibit a higher probability of CD36 expression than cells from control LNs, but this was not statistically significant. Conclusively, our findings demonstrated that CD36 plays a vital role in controlling the immunosuppressive microenvironment in the LN, which can promote the formation of a protumorigenic niche.

Multiplex immunofluorescence (MxIF) images provide detailed information of cell composition and spatial context for biomedical research. However, compromised data quality could lead to research biases. Comprehensive image quality checking (QC) is essential for reliable downstream analysis. As a reliable and specific staining of cell nuclei, 4′,6-diamidino-2-phenylindole (DAPI) signals were used as references for tissue localization and auto-focusing across MxIF staining–scanning–bleaching iterations and could potentially be reused for QC. To confirm the feasibility of using DAPI as QC reference, pixel-level DAPI values were extracted to calculate signal fluctuations and tissue content similarities in staining–scanning–bleaching iterations for identifying quality issues. Concordance between automatic quantification and human experts’ annotations were evaluated on a data set consisting of 348 fields of view (FOVs) with 45 immune and tumor cell markers. Cell distribution differences between subsets of QC-pass vs QC-failed FOVs were compared to investigate the downstream effects. Results showed that 87.3% FOVs with tissue damage and 73.4% of artifacts were identified. QC-failed FOVs showed elevated regional gathering in cellular feature space compared with the QC-pass FOVs. Our results supported that DAPI signals could be used as references for MxIF image QC, and low-quality FOVs identified by our method must be cautiously considered for downstream analyses.

Abstract The prevalence of germline pathogenic mutations in a comprehensive panel of cancer predisposition genes is not well-defined for patients with pancreatic ductal adenocarcinoma (PDAC). To estimate the frequency of mutations in a panel of 22 cancer predisposition genes, 96 patients unselected for a family history of cancer who were recruited to the Mayo Clinic Pancreatic Cancer patient registry over a 12-month period were screened by next-generation sequencing. Fourteen pathogenic mutations in 13 patients (13.5%) were identified in eight genes: four in ATM, two in BRCA2, CHEK2, and MSH6, and one in BARD1, BRCA1, FANCM, and NBN. These included nine mutations (9.4%) in established pancreatic cancer genes. Three mutations were found in patients with a first-degree relative with PDAC, and 10 mutations were found in patients with first- or second-degree relatives with breast, pancreas, colorectal, ovarian, or endometrial cancers. These results suggest that a substantial proportion of patients with PDAC carry germline mutations in predisposition genes associated with other cancers and that a better understanding of pancreatic cancer risk will depend on evaluation of families with broad constellations of tumors. These findings highlight the need for recommendations governing germline gene-panel testing of patients with pancreatic cancer. Cancer Epidemiol Biomarkers Prev; 25(1); 207–11. ©2015 AACR.

Abstract Motivation: The Biological Reference Repository (BioR) is a toolkit for annotating variants. BioR stores public and user-specific annotation sources in indexed JSON-encoded flat files (catalogs). The BioR toolkit provides the functionality to combine and retrieve annotation from these catalogs via the command-line interface. Several catalogs from commonly used annotation sources and instructions for creating user-specific catalogs are provided. Commands from the toolkit can be combined with other UNIX commands for advanced annotation processing. We also provide instructions for the development of custom annotation pipelines. Availability and implementation: The package is implemented in Java and makes use of external tools written in Java and Perl. The toolkit can be executed on Mac OS X 10.5 and above or any Linux distribution. The BioR application, quickstart, and user guide documents and many biological examples are available at http://bioinformaticstools.mayo.edu . Contact: Kocher.JeanPierre@mayo.edu Supplementary information: Supplementary data are available at Bioinformatics online.