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Supplementary Table S1. Sequences of artificial miRNAs against TAZ. Supplementary Table S2. Primers used for quantitative RT-PCR. Supplementary Table S3. Correlation between TAZ gene expression (202134_s_at) and clinicopathological parameters of 196 NSCLC patients included in the Uppsala gene microarray. Supplementary Table S4. Correlation between TAZ protein expression and clinicopathological parameters of 345 NSCLC patients included in the Uppsala tissue microarray. Supplementary Table S5. 259 TAZ-regulated genes commonly upregulated by TAZ overexpression or downregulated by TAZ knockdown in at least two cells out of A549, H441, MCF10A, and SW480 cells.

Purpose: TAZ, also known as WWTR1, has recently been suggested as an oncogene in non–small cell lung cancer (NSCLC). We investigated the clinical relevance of TAZ expression and its functional role in NSCLC tumorigenesis.Experimental Design: We characterized TAZ at the DNA (n = 192), mRNA (n = 196), and protein levels (n = 345) in an NSCLC patient cohort. Gene expression analysis was complemented by a meta-analysis of public datasets (n = 1,382). The effects of TAZ on cell proliferation and cell cycle were analyzed in cell cultures and on tumor growth in mice. TAZ-dependent microarray-based expression profiles in NSCLC cells were combined with molecular profiles in human NSCLC tissues for in silico analysis.Results: Higher TAZ mRNA and protein levels were associated with shorter patient survival. Transduction of TAZ enhanced cell proliferation and tumorigenesis in bronchial epithelial cells, whereas TAZ silencing suppressed cell proliferation and induced cell cycle arrest in NSCLC cells. Microarray and cell culture experiments showed that ErbB ligands (amphiregulin, epiregulin, and neuregulin 1) are downstream targets of TAZ. Our in silico analysis revealed a TAZ signature that substantiated the clinical impact of TAZ and confirmed its relationship to the epidermal growth factor receptor signaling pathway.Conclusion: TAZ expression defines a clinically distinct subgroup of patients with NSCLC. ErbB ligands are suggested to mediate the effects of TAZ on lung cancer progression. Our findings emphasize the tumorigenic role of TAZ and may serve as the basis for new treatment strategies. Clin Cancer Res; 20(17); 4660–72. ©2014 AACR.

Background: People with diabetes in general have a higher risk of developing Chronic Kidney Disease (CKD). The early detection of CKD in people with diabetes helps their health care providers to adjust their therapy to slow down the disease progression to higher stages, prevent complications, and reduce cardiovascular-related conditions. It also takes a great financial burden off the insurance companies in the long run by way of future treatment costs. We have developed a model that uses the historical data from the past 2 years of people with diabetes (PwD) and predicts the chance of them having CKD in the next 3 years. Methods: Our model employed the XGBoost (eXtreme Gradient Boosting) algorithm. It used more than 860,000 PwD data from the IBM EHR (Electronic Health Records) database for training and optimizing the model parameters. It was then independently verified using more than 500,000 PwD data from the CPRD (Clinical Praxis Research Datalink) database from the UK, and close to 140,000 PwD data from the INPC (Indiana Network for Patient Care) database from the US. We considered an incident of CKD based on the appearance of a relevant ICD code. The major benefit of the XGBoost algorithm in the context of risk prediction based on EHR data is that there is no need for imputation of the missing values. Results: We evaluated the performance of the Roche XGBoost model for CKD in terms of the area under the receiver operating characteristic curve (AUC). While we obtain comparable results for the US-based data sets (prediction model: AUCExplorys=0.83, AUCINPC=0.84), we observe a performance decrease for the validation on CPRD data (AUCCPRD=0.74). However, on all data sources our XGBoost model shows a superior performance when compared to appropriate benchmark algorithms from literature. Conclusions: The XGBoost model for CKD disease prediction has a superior performance over benchmark models from literature. Disclosure N. Afshar: Employee; Self; Roche Diabetes Care. I. Singh: Employee; Self; Roche Diabetes Care. T. Huschto: Employee; Self; Roche Diabetes Care. M. Andorrà: Advisory Panel; Self; Attune Neurosciences Inc., Employee; Self; Roche Diabetes Care, Stock/Shareholder; Self; Bionure, S. L., Goodgut, S. L. S. Chittajallu: Employee; Self; Roche Diabetes Care. C. Ringemann: Employee; Self; Roche Diabetes Care. J. T. Odegard: Employee; Self; Roche Diabetes Care. H. Mikulski: None. V. N. Babinsky: Employee; Self; Roche Diabetes Care. H. König: Employee; Self; Roche Diabetes Care.

Chronic kidney disease (CKD) is one of the most frequent complications faced by people with diabetes mellitus. The ability to accurately predict the onset of this disease at an early stage and adjust the individual therapy and lifestyle appropriately has the potential to prevent or at least slow down the progression of the disease significantly. We have previously published a 3-year CKD risk engine based on longitudinal U.S. EHRs data (Indiana Network for Patient Care and IBM Explorys) from more than 600,000 PwDs and have demonstrated a superior performance compared to previously published risk algorithms developed solely on clinical trial data (AUC = 0.794 vs. AUC = 0.718 for the best benchmark algorithm). Nevertheless, our previous analysis was limited to healthcare data from the U.S. and the risk prediction was only tested for patients at the time point of the initial diabetes diagnosis. To prove that our algorithm has a broader applicability, we have independently validated our risk engine on a dataset of general practitioners from the UK (Clinical Practice Research Datalink) including longitudinal data from more than 140,000 PwDs, thereby showing the transferability of our algorithm into a European context (AUC = 0.763 +/- 0.005 vs. AUC = 0.594 +/- 0.006 for the best benchmark). Moreover, we used this analysis to apply our risk engine at variable time points after the initial diabetes diagnosis within the U.S. and European data and could thereby demonstrate that our algorithm is not limited to the initial time of diagnosis (average AUC = 0.79 irrespective of the time after diagnosis). This validation analysis further corroborates the external validity of our approach to use Real-World-Data from EHRs in the development of CKD risk prediction algorithms and thereby supports a broader use of those algorithms in standard clinical practice. Disclosure T. Huschto: Employee; Self; Roche Diabetes Care. H. König: Employee; Self; Roche Diabetes Care. C.E. Marriott: Employee; Self; Roche Diabetes Care. D. Militz: Employee; Self; Roche Diabetes Care. C. Ringemann: Employee; Self; Roche Diabetes Care.

Diagnostic procedures, therapeutic recommendations, and medical risk stratifications are based on dedicated, strictly controlled clinical trials. However, a plethora of real-world medical data exists, whereupon the increase in data volume comes at the expense of completeness, uniformity, and control. Here, a case-by-case comparison shows that the predictive power of our real world data-based model for diabetes-related chronic kidney disease outperforms published algorithms, which were derived from clinical study data.

Purpose: TAZ, also known as WWTR1, has recently been suggested as an oncogene in non–small cell lung cancer (NSCLC). We investigated the clinical relevance of TAZ expression and its functional role in NSCLC tumorigenesis. Experimental Design: We characterized TAZ at the DNA (n = 192), mRNA (n = 196), and protein levels (n = 345) in an NSCLC patient cohort. Gene expression analysis was complemented by a meta-analysis of public datasets (n = 1,382). The effects of TAZ on cell proliferation and cell cycle were analyzed in cell cultures and on tumor growth in mice. TAZ-dependent microarray-based expression profiles in NSCLC cells were combined with molecular profiles in human NSCLC tissues for in silico analysis. Results: Higher TAZ mRNA and protein levels were associated with shorter patient survival. Transduction of TAZ enhanced cell proliferation and tumorigenesis in bronchial epithelial cells, whereas TAZ silencing suppressed cell proliferation and induced cell cycle arrest in NSCLC cells. Microarray and cell culture experiments showed that ErbB ligands (amphiregulin, epiregulin, and neuregulin 1) are downstream targets of TAZ. Our in silico analysis revealed a TAZ signature that substantiated the clinical impact of TAZ and confirmed its relationship to the epidermal growth factor receptor signaling pathway. Conclusion: TAZ expression defines a clinically distinct subgroup of patients with NSCLC. ErbB ligands are suggested to mediate the effects of TAZ on lung cancer progression. Our findings emphasize the tumorigenic role of TAZ and may serve as the basis for new treatment strategies. Clin Cancer Res; 20(17); 4660–72. ©2014 AACR.