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Bienkowska, J. R.

Publications and source records attributed to Bienkowska, J. R..

3 recordsLinked to original sources

GeneTEFlow: A Nextflow-based pipeline for analysing gene and transposable elements expression from RNA-Seq data

Transposable elements (TEs) are mobile genetic elements in eukaryotic genomes. Recent research highlights the important role of TEs in the embryogenesis, neurodevelopment, and immune functions. However, there is a lack of a one-stop and easy to use computational pipeline for expression analysis of both genes and locus-specific TEs from RNA-Seq data. Here, we present GeneTEFlow, a fully automated, reproducible and platform-independent workflow, for the comprehensive analysis of gene and locus-specific TEs expression from RNA-Seq data employing Nextflow and Docker technologies. This application will help researchers more easily perform integrated analysis of both gene and TEs expression, leading to a better understanding of roles of gene and TEs regulation in human diseases. GeneTEFlow is freely available at https://github.com/zhongw2/GeneTEFlow.

bioinformatics↗

Molecular and cellular phenotypic differences distinguish murine syngeneic models from human tumors

AbstractThe clinical success of immune checkpoint inhibitors that target cytotoxic T-lymphocyte associated protein 4 (CTLA4) and programmed cell death protein 1 (PD-1) or programmed death ligand-1 (PD-L1) demonstrates that reactivation of the human immune system delivers durable responses for some patients and represents an exciting approach for cancer treatment. The combination of multiple immunotherapies as well as the combination of immunotherapy with targeted therapy is being pursued vigorously to increase the rate and extend the duration of response. Preclinical in vivo models for immuno-oncology (IO) typically require immunocompetent mice bearing murine syngeneic tumors. To facilitate translation of preclinical studies into human, we characterized the genomic, transcriptomic, and protein expression of a panel of mouse tumor cell lines grown in vitro culture as well as in vivo tumor samples. Our studies identified many genetic and cellular phenotypic differences that distinguish murine syngeneic models from human cancers. For example, only a small fraction of the somatic single nucleotide variants (SNVs) in mouse cell lines directly match SNVs from human actionable cancer genes. At the cellular level, some epithelial tumor models have a more mesenchymal phenotype with relatively low T-lymphocyte infiltration compared to the corresponding human cancers. Furthermore, in contrast to what has been reported for human tumors, we did not observe a correlation between neoantigen load and cytolytic activity in syngeneic models. Finally, the relative immunogenicity of syngeneic tumors does not typically resemble that of human tumors of the same tissue origin. CT26, a colon tumor model, had the highest immunogenicity and was the most responsive model to CTLA4 inhibitor treatment, by contrast to the relatively low immunogenicity and response rate to checkpoint inhibitor therapies in human colon cancers. These differences highlight limitations of syngeneic models for evaluating novel immune therapies and rationalize some of the challenges associated with translating preclinical findings to clinical studies.

cancer biology↗

TNER: A Novel Bayesian Background Error Suppression Method for Mutation Detection in Circulating Tumor DNA

The use of ultra-deep, next generation sequencing of circulating tumor DNA (ctDNA) holds great promise for early detection of cancer as well as a tool for monitoring disease progression and therapeutic responses. However, the low abundance of ctDNA in the bloodstream coupled with technical errors introduced during library construction and sequencing complicates mutation detection. To achieve high accuracy of variant calling via better distinguishing low frequency ctDNA mutations from background errors, we introduce TNER (Tri-Nucleotide Error Reducer), a novel background error suppression method that provides a robust estimation of background noise to reduce sequencing errors. It significantly enhances the specificity for downstream ctDNA mutation detection without sacrificing sensitivity. Results on both simulated and real healthy subjects data demonstrate that the proposed algorithm consistently outperforms a current, state of the art, position-specific error polishing model, particularly when the sample size of healthy subjects is small. TNER is publicly available at https://github.com/ctDNA/TNER.

bioinformatics↗