On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue.

BACKGROUND: Adenocarcinoma (ADC) and squamous cell carcinoma (SCC) are the most prevalent histological types among lung cancers. Distinguishing between these subtypes is critically important because they have different implications for prognosis and treatment. Normally, histopathological analyses are used to distinguish between the two, where the tissue samples are collected based on small endoscopic samples or needle aspirations. However, the lack of cell architecture in these small tissue samples hampers the process of distinguishing between the two subtypes. Molecular profiling can also be used to discriminate between the two lung cancer subtypes, on condition that the biopsy is composed of at least 50 % of tumor cells. However, for some cases, the tissue composition of a biopsy might be a mix of tumor and tumor-adjacent histologically normal tissue (TAHN). When this happens, a new biopsy is required, with associated cost, risks and discomfort to the patient. To avoid this problem, we hypothesize that a computational method can distinguish between lung cancer subtypes given tumor and TAHN tissue. METHODS: Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. Then, we evaluated the classification performance of our models by measuring the area under the receiver operating characteristic curve (AUC). Finally, we analyzed the relevance of the selected genes using hierarchical clustering and IPA® software for gene functional analysis. RESULTS: All Bayesian models achieved high classification performance (AUC > 0.94), which were confirmed by hierarchical cluster analysis. From the genes selected, 25 (93 %) were found to be related to cancer (19 were associated with ADC or SCC), confirming the biological relevance of our method. CONCLUSIONS: The results from this study confirm that computational methods using tumor and TAHN tissue can serve as a prognostic tool for lung cancer subtype classification. Our study complements results from other studies where TAHN tissue has been used as prognostic tool for prostate cancer. The clinical implications of this finding could greatly benefit lung cancer patients.

DNA Methylation Signature of Post-injury Neointimal Cells During Vascular Remodeling in the Rat Balloon Injury Model.

Vascular smooth muscle cell (VSMC) accumulation in the neointimal is a common feature in vascular diseases such as atherosclerosis, transplant arteriosclerosis and restenosis. In this study, we isolated the neointimal cells and uninjured residential vascular smooth muscle cells by laser micro dissection and carried out single-cell whole-genome methylation sequencing. We also sequenced the bisulfite converted genome of circulating bone-marrow-derived cells such as peripheral blood mononuclear cells (PBMC) and bone marrow mononuclear cells (BMMC). We found totally 2,360 differential methylation sites (DMS) annotated to 1,127 gene regions. The majority of differentially methylated regions (DMRs) were located in intergenic regions, outside those CpG islands and island shores. Interestingly, exons have less DMRs than promotors and introns, and CpG islands contain more DMRs than islands shores. Pearson correlation analysis showed a clear clustering of neointimal cells with PBMC/BMMC. Gene set enrichment analysis of differentially methylated CpG sites revealed that many genes were important for regulation of VSMC differentiation and stem cell maintenance. In conclusion, our results showed that neointimal cells are more similar to the progenitor cells in methylation profile than the residential VSMCs at the 30th day after the vascular injury.