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Peripheral T cell lymphomas (PTCLs) are highly aggressive malignancies with poor prognosis. Their molecular pathogenesis is not well understood and small animal models for the disease are lacking. Recently, the chromosomal translocation t(5;9)(q33;q22) generating the interleukin-2 (IL-2)–inducible T cell kinase (ITK)–spleen tyrosine kinase (SYK) fusion tyrosine kinase was identified as a recurrent event in PTCL. We show that ITK-SYK associates constitutively with lipid rafts in T cells and triggers antigen-independent phosphorylation of T cell receptor (TCR)–proximal proteins. These events lead to activation of downstream pathways and acute cellular outcomes that correspond to regular TCR ligation, including up-regulation of CD69 or production of IL-2 in vitro or deletion of thymocytes and activation of peripheral T cells in vivo. Ultimately, conditional expression of patient-derived ITK-SYK in mice induces highly malignant PTCLs with 100% penetrance that resemble the human disease. Our work demonstrates that constitutively enforced antigen receptor signaling can, in principle, act as a powerful oncogenic driver. Moreover, we establish a robust clinically relevant and genetically tractable model of human PTCL.

Automatic segmentation of the liver and hepatic lesions is an important step towards deriving quantitative biomarkers for accurate clinical diagnosis and computer-aided decision support systems. This paper presents a method to automatically segment liver and lesions in CT and MRI abdomen images using cascaded fully convolutional neural networks (CFCNs) enabling the segmentation of large-scale medical trials and quantitative image analyses. We train and cascade two FCNs for the combined segmentation of the liver and its lesions. As a first step, we train an FCN to segment the liver as ROI input for a second FCN. The second FCN solely segments lesions within the predicted liver ROIs of step 1. CFCN models were trained on an abdominal CT dataset comprising 100 hepatic tumor volumes. Validation results on further datasets show that CFCN-based semantic liver and lesion segmentation achieves Dice scores over 94% for the liver with computation times below 100s per volume. We further experimentally demonstrate the robustness of the proposed method on 38 MRI liver tumor volumes and the public 3DIRCAD dataset.