Abstract: OBJECTIVE: This study investigated the antitumour effects of intermediate frequency alternating electric fields (IF-AEF) in a murine melanoma cell line (B16F10) and a mouse tumour model. METHODS: IF-AEF was applied at 100 kHz. Proliferation of B16F10 cells in vitro was assessed by the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide assay. IF-AEF was applied in vivo to mice bearing B16F10 tumours. Terminal deoxy nucleotidyl transferase-mediated dUTP nick end labelling (TUNEL) assay for apoptosis, and immunohistochemical detection of CD34 and vascular endothelial growth factor (VEGF), were performed. RESULTS: IF-AEF inhibited the proliferation of B16F10 cells in an electrical intensity and time-dependent manner. Treatment with IF-AEF for 7 days significantly inhibited the growth of tumours compared with untreated controls. IF-AEF induced apoptosis in vivo and reduced CD34-positive cell numbers; CD34 is a special marker of microvessel density. CONCLUSION: IF-AEF reduced microvessel density related to tumour growth and may serve as a therapeutic strategy for cancer treatment.

Abstract: MOTIVATION: ChIPseq is rapidly becoming a common technique for investigating protein-DNA interactions. However, results from individual experiments provide a limited understanding of chromatin structure, as various chromatin factors cooperate in complex ways to orchestrate transcription. In order to quantify chromtain interactions, it is thus necessary to devise a robust similarity metric applicable to ChIPseq data. Unfortunately, moving past simple overlap calculations to give statistically rigorous comparisons of ChIPseq datasets often involves arbitrary choices of distance metrics, with significance being estimated by computationally intensive permutation tests whose statistical power may be sensitive to non-biological experimental and post-processing variation. RESULTS: We show that it is in fact possible to compare ChIPseq datasets through the efficient computation of exact P-values for proximity. Our method is insensitive to non-biological variation in datasets such as peak width, and can rigorously model peak location biases by evaluating similarity conditioned on a restricted set of genomic regions (such as mappable genome or promoter regions). Applying our method to the well-studied dataset of Chen et al. (2008), we elucidate novel interactions which conform well with our biological understanding. By comparing ChIPseq data in an asymmetric way, we are able to observe clear interaction differences between cofactors such as p300 and factors that bind DNA directly. AVAILABILITY: Source code is available for download at http://sonorus.princeton.edu/IntervalStats/IntervalStats.tar.gz. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.