The development of targeted anti-cancer therapies through the study of cancer genomes is intended to increase survival rates and decrease treatment-related toxicity. We treated a transposon–driven, functional genomic mouse model of medulloblastoma with ‘humanized’ in vivo therapy (microneurosurgical tumour resection followed by multi-fractionated, image-guided radiotherapy). Genetic events in recurrent murine medulloblastoma exhibit a very poor overlap with those in matched murine diagnostic samples (<5%). Whole-genome sequencing of 33 pairs of human diagnostic and post-therapy medulloblastomas demonstrated substantial genetic divergence of the dominant clone after therapy (<12% diagnostic events were retained at recurrence). In both mice and humans, the dominant clone at recurrence arose through clonal selection of a pre-existing minor clone present at diagnosis. Targeted therapy is unlikely to be effective in the absence of the target, therefore our results offer a simple, proximal, and remediable explanation for the failure of prior clinical trials of targeted therapy.

Background With the decreasing cost of sequencing and the rapid developments in genomics technologies and protocols, the need for validated bioinformatics software that enables efficient large-scale data processing is growing. Findings Here we present GenPipes, a flexible Python-based framework that facilitates the development and deployment of multi-step workflows optimized for high-performance computing clusters and the cloud. GenPipes already implements 12 validated and scalable pipelines for various genomics applications, including RNA sequencing, chromatin immunoprecipitation sequencing, DNA sequencing, methylation sequencing, Hi-C, capture Hi-C, metagenomics, and Pacific Biosciences long-read assembly. The software is available under a GPLv3 open source license and is continuously updated to follow recent advances in genomics and bioinformatics. The framework has already been configured on several servers, and a Docker image is also available to facilitate additional installations. Conclusions GenPipes offers genomics researchers a simple method to analyze different types of data, customizable to their needs and resources, as well as the flexibility to create their own workflows.

Dysfunction of virus-specific CD4 + T cells in chronic human infections is poorly understood. We performed genome-wide transcriptional analyses and functional assays of CD4 + T cells specific for human immunodeficiency virus (HIV) from HIV-infected people prior and after initiation of antiretroviral therapy (ART). A follicular helper T cell (T FH cell)–like profile characterized HIV-specific CD4 + T cells in viraemic infection. HIV-specific CD4 + T cells from people spontaneously controlling the virus (elite controllers) robustly expressed genes associated with the T H 1, T H 17 and T H 22 subsets of helper T cells. Viral suppression by ART resulted in a distinct transcriptional landscape, with a reduction in the expression of genes associated with T FH cells but persistently low expression of genes associated with T H 1, T H 17 and T H 22 cells compared to the elite controller profile. Thus, altered differentiation is central to the impairment of HIV-specific CD4 + T cells and involves both gain of function and loss of function.

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