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The MAP3-kinase TGF-beta-activated kinase 1 (TAK1) critically modulates innate and adaptive immune responses and connects cytokine stimulation with activation of inflammatory signaling pathways. Here, we report that conditional ablation of TAK1 in liver parenchymal cells (hepatocytes and cholangiocytes) causes hepatocyte dysplasia and early-onset hepatocarcinogenesis, coinciding with biliary ductopenia and cholestasis. TAK1-mediated cancer suppression is exerted through activating NF-kappaB in response to tumor necrosis factor (TNF) and through preventing Caspase-3-dependent hepatocyte and cholangiocyte apoptosis. Moreover, TAK1 suppresses a procarcinogenic and pronecrotic pathway, which depends on NF-kappaB-independent functions of the I kappaB-kinase (IKK)-subunit NF-kappaB essential modulator (NEMO). Therefore, TAK1 serves as a gatekeeper for a protumorigenic, NF-kappaB-independent function of NEMO in parenchymal liver cells.

A key barrier to the realization of personalized medicine for cancer is the identification of biomarkers. Here we describe a two-stage strategy for the discovery of serum biomarker signatures corresponding to specific cancer-causing mutations and its application to prostate cancer (PCa) in the context of the commonly occurring phosphatase and tensin homolog (PTEN) tumor-suppressor gene inactivation. In the first stage of our approach, we identified 775 N-linked glycoproteins from sera and prostate tissue of wild-type and Pten-null mice. Using label-free quantitative proteomics, we showed that Pten inactivation leads to measurable perturbations in the murine prostate and serum glycoproteome. Following bioinformatic prioritization, in a second stage we applied targeted proteomics to detect and quantify 39 human ortholog candidate biomarkers in the sera of PCa patients and control individuals. The resulting proteomic profiles were analyzed by machine learning to build predictive regression models for tissue PTEN status and diagnosis and grading of PCa. Our approach suggests a general path to rational cancer biomarker discovery and initial validation guided by cancer genetics and based on the integration of experimental mouse models, proteomics-based technologies, and computational modeling.serum biomarkers | mass spectrometry | Pten conditional knockout mouse model

The purpose of this research was to implement a deep learning network to overcome two of the major bottlenecks in improved image reconstruction for clinical positron emission tomography (PET). These are the lack of an automated means for the optimization of advanced image reconstruction algorithms, and the computational expense associated with these state-of-the art methods. We thus present a novel end-to-end PET image reconstruction technique, called DeepPET, based on a deep convolutional encoder–decoder network, which takes PET sinogram data as input and directly and quickly outputs high quality, quantitative PET images. Using simulated data derived from a whole-body digital phantom, we randomly sampled the configurable parameters to generate realistic images, which were each augmented to a total of more than 291,000 reference images. Realistic PET acquisitions of these images were simulated, resulting in noisy sinogram data, used for training, validation, and testing the DeepPET network. We demonstrated that DeepPET generates higher quality images compared to conventional techniques, in terms of relative root mean squared error (11%/53% lower than ordered subset expectation maximization (OSEM)/filtered back-projection ( FBP), structural similarity index (1%/11% higher than OSEM/FBP), and peak signal-to-noise ratio (1.1/3.8 dB higher than OSEM/FBP). In addition, we show that DeepPET reconstructs images 108 and 3 times faster than OSEM and FBP, respectively. Finally, DeepPET was successfully applied to real clinical data. This study shows that an end-to-end encoder–decoder network can produce high quality PET images at a fraction of the time compared to conventional methods.