Biology subjects

Mansfield, A.

Publications and source records attributed to Mansfield, A..

2 recordsLinked to original sources

bioRxiv2018

B7-H1(PD-L1) confers chemoresistance through ERK and p38 MAPK pathway in tumor cells

Development of resistance to chemotherapy and immunotherapy is a major obstacle in extending the survival of patients with cancer. Although several molecular mechanisms have been identified that can contribute to chemoresistance, the role of immune checkpoint molecules in tumor chemoresistance remains underestimated. It has been recently observed that overexpression of B7-H1(PD-L1) confers chemoresistance in human cancers, however the underlying mechanisms are unclear. Here we show that the development of chemoresistance depends on the increased activation of ERK pathway in tumor cells overexpressing B7-H1. Conversely, B7-H1 deficiency renders tumor cells susceptible to chemotherapy in a cell-context dependent manner through activation of the p38 MAPK pathway. B7-H1 in tumor cells associates with the catalytic subunit of a DNA-dependent serine / threonine protein kinase (DNA-PKcs). DNA-PKcs is required for the activation of ERK or p38 MAPK in tumors expressing B7-H1, but not in B7-H1 negative or B7-H1 deficient tumors. Ligation of B7-H1 by anti-B7-H1 monoclonal antibody (H1A) increased the sensitivity of human triple negative breast tumor cells to cisplatin therapy in vivo. Our results suggest that B7-H1(PD-L1) expression in cancer cells modifies their chemosensitivity towards certain drugs and targeting B7-H1 intracellular signaling pathway is a new way to overcome cancer chemoresistance.

cancer biology↗

bioRxiv2018

xAtlas: Scalable small variant calling across heterogeneous next-generation sequencing experiments

MotivationThe rapid development of next-generation sequencing (NGS) technologies has lowered the barriers to genomic data generation, resulting in millions of samples sequenced across diverse experimental designs. The growing volume and heterogeneity of these sequencing data complicate the further optimization of methods for identifying DNA variation, especially considering that curated highconfidence variant call sets commonly used to evaluate these methods are generally developed by reference to results from the analysis of comparatively small and homogeneous sample sets.\n\nResultsWe have developed xAtlas, an application for the identification of single nucleotide variants (SNV) and small insertions and deletions (indels) in NGS data. xAtlas is easily scalable and enables execution and retraining with rapid development cycles. Generation of variant calls in VCF or gVCF format from BAM or CRAM alignments is accomplished in less than one CPU-hour per 30x short-read human whole-genome. The retraining capabilities of xAtlas allow its core variant evaluation models to be optimized on new sample data and user-defined truth sets. Obtaining SNV and indels calls from xAtlas can be achieved more than 40 times faster than established methods while retaining the same accuracy.\n\nAvailabilityFreely available under a BSD 3-clause license at https://github.com/jfarek/xatlas.\n\nContactfarek@bcm.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.

bioinformatics↗