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The detection of gene fusions by RNA-based next-generation sequencing (NGS) is an emerging method in clinical genetic laboratories for oncology biomarker testing to direct targeted therapy selections. A recent Canadian study (CANTRK study) comparing the detection of NTRK gene fusions on different NGS assays to determine subjects' eligibility for tyrosine kinase TRK inhibitor therapy identified the need for recommendations for best practices for laboratory testing to optimize RNA-based NGS gene fusion detection. To develop consensus recommendations, representatives from 17 Canadian genetic laboratories participated in working group discussions and the completion of survey questions about RNA-based NGS. Consensus recommendations are presented for pre-analytic, analytic and reporting aspects of gene fusion detection by RNA-based NGS. [ABSTRACT FROM AUTHOR]

Medulloblastoma (MB) is the most common primary malignant pediatric brain cancer. We recently identified novel roles for the MEK/MAPK pathway in regulating human Sonic Hedgehog (SHH) MB tumorigenesis. The MEK inhibitor, selumetinib, decreased SHH MB growth while extending survival in mouse models. However, the treated mice ultimately succumbed to disease progression. Here, we perform RNA sequencing on selumetinib-treated orthotopic xenografts to identify molecular pathways that compensate for MEK inhibition specifically in vivo. Notably, the JAK/STAT3 pathway exhibits increased activation in selumetinib-treated tumors. The combination of selumetinib and the JAK/STAT3 pathway inhibitor, pacritinib, further reduces growth in two xenograft models and also enhances survival. Multiplex spatial profiling of proteins in drug-treated xenografts reveals shifted molecular dependencies and compensatory changes following combination drug treatment. Our study warrants further investigation into MEK and JAK/STAT3 inhibition as a novel combinatory therapeutic strategy for SHH MB. RNA sequencing of MEK inhibitor (selumetinib)-treated tumors reveals an upregulation of the JAK/STAT3 pathway, with combinatorial therapeutic strategies of JAK/STAT3 inhibitors and selumetinib investigated for the SHH subgroup of medulloblastoma. [ABSTRACT FROM AUTHOR]

Motivation Current fusion detection tools use diverse calling approaches and provide varying results, making selection of the appropriate tool challenging. Ensemble fusion calling techniques appear promising; however, current options have limited accessibility and function. Results MetaFusion is a flexible metacalling tool that amalgamates outputs from any number of fusion callers. Individual caller results are standardized by conversion into the new file type Common Fusion Format. Calls are annotated, merged using graph clustering, filtered and ranked to provide a final output of high-confidence candidates. MetaFusion consistently achieves higher precision and recall than individual callers on real and simulated datasets, and reaches up to 100% precision, indicating that ensemble calling is imperative for high-confidence results. MetaFusion uses FusionAnnotator to annotate calls with information from cancer fusion databases and is provided with a Benchmarking Toolkit to calibrate new callers. Availability and implementation MetaFusion is freely available at https://github.com/ccmbioinfo/MetaFusion. Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]