Thomas J. Fuchs

TAK1 Suppresses a NEMO-Dependent but NF-κB-Independent Pathway to Liver Cancer

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.

Cancer genetics-guided discovery of serum biomarker signatures for diagnosis and prognosis of prostate cancer

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.

Computational pathology: Challenges and promises for tissue analysis

The histological assessment of human tissue has emerged as the key challenge for detection and treatment of cancer. A plethora of different data sources ranging from tissue microarray data to gene expression, proteomics or metabolomics data provide a detailed overview of the health status of a patient. Medical doctors need to assess these information sources and they rely on data driven automatic analysis tools. Methods for classification, grouping and segmentation of heterogeneous data sources as well as regression of noisy dependencies and estimation of survival probabilities enter the processing workflow of a pathology diagnosis system at various stages. This paper reports on state-of-the-art of the design and effectiveness of computational pathology workflows and it discusses future research directions in this emergent field of medical informatics and diagnostic machine learning.

Neuron geometry extraction by perceptual grouping in ssTEM images

In the field of neuroanatomy, automatic segmentation of electron microscopy images is becoming one of the main limiting factors in getting new insights into the functional structure of the brain. We propose a novel framework for the segmentation of thin elongated structures like membranes in a neuroanatomy setting. The probability output of a random forest classifier is used in a regular cost function, which enforces gap completion via perceptual grouping constraints. The global solution is efficiently found by graph cut optimization. We demonstrate substantial qualitative and quantitative improvement over state-of the art segmentations on two considerably different stacks of ssTEM images as well as in segmentations of streets in satellite imagery. We demonstrate that the superior performance of our method yields fully automatic 3D reconstructions of dendrites from ssTEM data.

Prognostic relevance of Wnt-inhibitory factor-1 (WIF1) and Dickkopf-3 (DKK3) promoter methylation in human breast cancer

BackgroundSecreted Wnt signaling antagonists have recently been described as frequent targets of epigenetic inactivation in human tumor entities. Since gene silencing of certain Wnt antagonists was found to be correlated with adverse patient survival in cancer, we aimed at investigating a potential prognostic impact of the two Wnt antagonizing molecules WIF1 and DKK3 in breast cancer, which are frequently silenced by promoter methylation in this disease.MethodsWIF1 and DKK3 promoter methylation were assessed by methylation-specific PCR with bisulfite-converted DNA from 19 normal breast tissues and 150 primary breast carcinomas. Promoter methylation was interpreted in a qualitative, binary fashion. Statistical evaluations included two-sided Fishers exact tests, univariate log-rank tests of Kaplan-Meier curves as well as multivariate Cox regression analyses.ResultsWIF1 and DKK3 promoter methylation were detected in 63.3% (95/150) and 61.3% (92/150) of breast carcinoma samples, respectively. In normal breast tissues, WIF1 methylation was present in 0% (0/19) and DKK3 methylation in 5.3% (1/19) of samples. In breast carcinomas, WIF1 methylation was significantly associated with methylation of DKK3 (p = 0.009). Methylation of either gene was not associated with clinicopathological parameters, except for DKK3 methylation being associated with patient age (p = 0.007). In univariate analysis, WIF1 methylation was not associated with clinical patient outcome. In contrast, DKK3 methylation was a prognostic factor in patient overall survival (OS) and disease-free survival (DFS). Estimated OS rates after 10 years were 54% for patients with DKK3-methylated tumors, in contrast to patients without DKK3 methylation in the tumor, who had a favorable 97% OS after 10 years (p < 0.001). Likewise, DFS at 10 years for patients harboring DKK3 methylation in the tumor was 58%, compared with 78% for patients with unmethylated DKK3 (p = 0.037). Multivariate analyses revealed that DKK3 methylation was an independent prognostic factor predicting poor OS (hazard ratio (HR): 14.4; 95% confidence interval (CI): 1.9–111.6; p = 0.011), and short DFS (HR: 2.5; 95% CI: 1.0–6.0; p = 0.047) in breast cancer.ConclusionAlthough the Wnt antagonist genes WIF1 and DKK3 show a very similar frequency of promoter methylation in human breast cancer, only DKK3 methylation proves as a novel prognostic marker potentially useful in the clinical management of this disease.

Nuclear karyopherin α2 expression predicts poor survival in patients with advanced breast cancer irrespective of treatment intensity

Intensive lymph node involvement indicates poor prognosis in breast cancer patients. The significance of other molecular prognostic factors in this subgroup is unclear. Karyopherin α2 (KPNA2) has been reported as an important factor of tumorgenesis and progression of breast cancer. The aim of present study was to evaluate the impact of KPNA2 expression on prognosis of patients with high risk breast cancer (HRBC) and response intensive chemotherapy within the randomized WSG‐AM‐01 trial. KPNA2 nuclear expression (>10% vs. <10% of nuclei) was measured by immunohistochemistry on tissue arrays of 191 patients randomized to tandem high dose vs. conventional dose‐dense chemotherapy in HRBC with >9 positive lymph nodes and correlated with clinical outcome (median follow‐up of 63.3 months) by Kaplan–Meier and multivariate Cox hazard model analysis, including, molecular subtypes determined by k‐clustering (k = 5). KPNA2 overexpression (n = 74, 39%) significantly correlated with shorter event‐free and overall survival (OS) in both therapy arms by univariate analysis. Multivariate analysis showed that the overexpression of KPNA2 was an independent prognostic factor of decreased OS HR = 1.86 [95% CI: 1.07–3.23, p = 0.03]. This predictive value was independent of basal‐like/Her‐2/neu subtypes, significantly associated with KPNA2 and was addressed particularly to G2 tumors. Our data suggest the use of KPNA2 nuclear expression as novel prognostic marker in node‐positive patients, especially in determination of G2 tumors in 2 subgroups of different prognosis. KPNA2 expression may be also considered as a marker for global chemoresistance, which can not be overcome by conventional dose‐modification of chemotherapy in advanced breast cancer.

The Bayesian group-Lasso for analyzing contingency tables

Group-Lasso estimators, useful in many applications, suffer from lack of meaningful variance estimates for regression coefficients. To overcome such problems, we propose a full Bayesian treatment of the Group-Lasso, extending the standard Bayesian Lasso, using hierarchical expansion. The method is then applied to Poisson models for contingency tables using a highly efficient MCMC algorithm. The simulated experiments validate the performance of this method on artificial datasets with known ground-truth. When applied to a breast cancer dataset, the method demonstrates the capability of identifying the differences in interactions patterns of marker proteins between different patient groups.

Aerosols Transmit Prions to Immunocompetent and Immunodeficient Mice

Prions, the agents causing transmissible spongiform encephalopathies, colonize the brain of hosts after oral, parenteral, intralingual, or even transdermal uptake. However, prions are not generally considered to be airborne. Here we report that inbred and crossbred wild-type mice, as well as tga20 transgenic mice overexpressing PrPC, efficiently develop scrapie upon exposure to aerosolized prions. NSE-PrP transgenic mice, which express PrPC selectively in neurons, were also susceptible to airborne prions. Aerogenic infection occurred also in mice lacking B- and T-lymphocytes, NK-cells, follicular dendritic cells or complement components. Brains of diseased mice contained PrPSc and transmitted scrapie when inoculated into further mice. We conclude that aerogenic exposure to prions is very efficacious and can lead to direct invasion of neural pathways without an obligatory replicative phase in lymphoid organs. This previously unappreciated risk for airborne prion transmission may warrant re-thinking on prion biosafety guidelines in research and diagnostic laboratories.

A Seven-Marker Signature and Clinical Outcome in Malignant Melanoma: A Large-Scale Tissue-Microarray Study with Two Independent Patient Cohorts

Background Current staging methods such as tumor thickness, ulceration and invasion of the sentinel node are known to be prognostic parameters in patients with malignant melanoma (MM). However, predictive molecular marker profiles for risk stratification and therapy optimization are not yet available for routine clinical assessment. Methods and Findings Using tissue microarrays, we retrospectively analyzed samples from 364 patients with primary MM. We investigated a panel of 70 immunohistochemical (IHC) antibodies for cell cycle, apoptosis, DNA mismatch repair, differentiation, proliferation, cell adhesion, signaling and metabolism. A marker selection procedure based on univariate Cox regression and multiple testing correction was employed to correlate the IHC expression data with the clinical follow-up (overall and recurrence-free survival). The model was thoroughly evaluated with two different cross validation experiments, a permutation test and a multivariate Cox regression analysis. In addition, the predictive power of the identified marker signature was validated on a second independent external test cohort (n = 225). A signature of seven biomarkers (Bax, Bcl-X, PTEN, COX-2, loss of β-Catenin, loss of MTAP, and presence of CD20 positive B-lymphocytes) was found to be an independent negative predictor for overall and recurrence-free survival in patients with MM. The seven-marker signature could also predict a high risk of disease recurrence in patients with localized primary MM stage pT1-2 (tumor thickness ≤2.00 mm). In particular, three of these markers (MTAP, COX-2, Bcl-X) were shown to offer direct therapeutic implications. Conclusions The seven-marker signature might serve as a prognostic tool enabling physicians to selectively triage, at the time of diagnosis, the subset of high recurrence risk stage I–II patients for adjuvant therapy. Selective treatment of those patients that are more likely to develop distant metastatic disease could potentially lower the burden of untreatable metastatic melanoma and revolutionize the therapeutic management of MM.

End-to-end dexterous manipulation with deliberate interactive estimation

This paper presents a model based approach to autonomous dexterous manipulation, developed as part of the DARPA Autonomous Robotic Manipulation (ARM) program. The developed autonomy system uses robot, object, and environment models to identify and localize objects, and well as plan and execute required manipulation tasks. Deliberate interaction with objects and the environment increases system knowledge about the combined robot and environmental state, enabling high precision tasks such as key insertion to be performed in a consistent framework. This approach has been demonstrated across a wide range of manipulation tasks, and in independent DARPA testing archived the most successfully completed tasks with the fastest average task execution of any evaluated team.