Validation of a genomic classifier that predicts metastasis following radical prostatectomy in an at risk patient population.

PURPOSE: Patients with locally advanced prostate cancer after radical prostatectomy are candidates for secondary therapy. However, this higher risk population is heterogeneous. Many cases do not metastasize even when conservatively managed. Given the limited specificity of pathological features to predict metastasis, newer risk prediction models are needed. We report a validation study of a genomic classifier that predicts metastasis after radical prostatectomy in a high risk population. MATERIALS AND METHODS: A case-cohort design was used to sample 1,010 patients after radical prostatectomy at high risk for recurrence who were treated from 2000 to 2006. Patients had preoperative prostate specific antigen greater than 20 ng/ml, Gleason 8 or greater, pT3b or a Mayo Clinic nomogram score of 10 or greater. Patients with metastasis at diagnosis or any prior treatment for prostate cancer were excluded from analysis. A 20% random sampling created a subcohort that included all patients with metastasis. We generated 22-marker genomic classifier scores for 219 patients with available genomic data. ROC and decision curves, competing risk and weighted regression models were used to assess genomic classifier performance. RESULTS: The genomic classifier AUC was 0.79 for predicting 5-year metastasis after radical prostatectomy. Decision curves showed that the genomic classifier net benefit exceeded that of clinical only models. The genomic classifier was the predominant predictor of metastasis on multivariable analysis. The cumulative incidence of metastasis 5 years after radical prostatectomy was 2.4%, 6.0% and 22.5% in patients with low (60%), intermediate (21%) and high (19%) genomic classifier scores, respectively (p<0.001). CONCLUSIONS: Results indicate that genomic information from the primary tumor can identify patients with adverse pathological features who are most at risk for metastasis and potentially lethal prostate cancer.

Prospective time-resolved LCA of fully electric supercap vehicles in Germany.

UNLABELLED: The ongoing transition of the German electricity supply toward a higher share of renewable and sustainable energy sources, called Energiewende in German, has led to dynamic changes in the environmental impact of electricity over the last few years. Prominent scenario studies predict that comparable dynamics will continue in the coming decades, which will further improve the environmental performance of Germany's electricity supply. Life cycle assessment (LCA) is the methodology commonly used to evaluate environmental performance. Previous LCA studies on electric vehicles have shown that the electricity supply for the vehicles' operation is responsible for the major part of their environmental impact. The core question of this study is how the prospective dynamic development of the German electricity mix will affect the impact of electric vehicles operated in Germany and how LCA can be adapted to analyze this impact in a more robust manner. The previously suggested approach of time-resolved LCA, which is located between static and dynamic LCA, is used in this study and compared with several static approaches. Furthermore, the uncertainty issue associated with scenario studies is addressed in general and in relation to time-resolved LCA. Two scenario studies relevant to policy making have been selected, but a moderate number of modifications have been necessary to adapt the data to the requirements of a life cycle inventory. A potential, fully electric vehicle powered by a supercapacitor energy storage system is used as a generic example. The results show that substantial improvements in the environmental repercussions of the electricity supply and, consequentially, of electric vehicles will be achieved between 2020 and 2031 on the basis of the energy mixes predicted in both studies. This study concludes that although scenarios might not be able to predict the future, they should nonetheless be used as data sources in prospective LCA studies, because in many cases historic data appears to be unsuitable for providing realistic information on the future. The time-resolved LCA approach improves the assessment's robustness substantially, especially when nonlinear developments are foreseen in the future scenarios. This allows for a reduction of bias in LCA-based decision making. However, a deeper integration of time-resolved data in the life cycle inventory and the implementation of a more suitable software framework are desirable. KEY POINTS: The study describes how life cycle assessment's (LCA) robustness can be improved by respecting prospective fluctuations, like the transition of the German electricity mix, in the modeling of the life cycle inventory. It presents a feasible and rather simple process to add time-resolved data to LCA. The study selects 2 different future scenarios from important German studies and processes their data systematically to make them compatible with the requirements of a life cycle inventory. The use of external scenarios as basis for future-oriented LCA is reflected critically. A case study on electric mobility is presented and used to compare historic, prospective static, and prospective time-resolved electricity mix modeling approaches. The case study emphasizes the benefits of time-resolved LCA in direct comparison with the currently used approaches.

Whole-transcriptome profiling of thyroid nodules identifies expression-based signatures for accurate thyroid cancer diagnosis.

PURPOSE: Due to the limitations of fine-needle aspiration biopsy (FNAB) cytopathology, many individuals who present with thyroid nodules eventually undergo thyroid surgery to diagnose thyroid cancer. The objective of this study was to use whole-transcriptome profiling to develop and validate a genomic classifier that significantly improves the accuracy of preoperative thyroid cancer diagnosis. MATERIALS AND METHODS: Nucleic acids were extracted and amplified for microarray expression analysis on the Affymetrix Human Exon 1.0 ST GeneChips from 1-mm-diameter formalin-fixed and paraffin-embedded thyroid tumor tissue cores. A training group of 60 thyroidectomy specimens (30 cancers and 30 benign lesions) were used to assess differential expression and for subsequent generation of a genomic classifier. The classifier was validated in a blinded fashion on a group of 31 formalin-fixed and paraffin-embedded thyroid FNAB specimens. RESULTS: Expression profiles of the 57 thyroidectomy training and 31 FNAB validation specimens that passed a series of quality control steps were analyzed. A genomic classifier composed of 249 markers that corresponded to 154 genes, had an overall validated accuracy of 90.0% in the 31 patient FNAB specimens and had positive and negative predictive values of 100% and 85.7%, respectively. The majority of the identified markers that made up the classifier represented non-protein-encoding RNAs. CONCLUSIONS: Whole-transcriptome profiling of thyroid nodule surgical specimens allowed for the development of a genomic classifier that improved the accuracy of preoperative thyroid cancer FNAB diagnosis.

Discovery and validation of a prostate cancer genomic classifier that predicts early metastasis following radical prostatectomy.

PURPOSE: Clinicopathologic features and biochemical recurrence are sensitive, but not specific, predictors of metastatic disease and lethal prostate cancer. We hypothesize that a genomic expression signature detected in the primary tumor represents true biological potential of aggressive disease and provides improved prediction of early prostate cancer metastasis. METHODS: A nested case-control design was used to select 639 patients from the Mayo Clinic tumor registry who underwent radical prostatectomy between 1987 and 2001. A genomic classifier (GC) was developed by modeling differential RNA expression using 1.4 million feature high-density expression arrays of men enriched for rising PSA after prostatectomy, including 213 who experienced early clinical metastasis after biochemical recurrence. A training set was used to develop a random forest classifier of 22 markers to predict for cases--men with early clinical metastasis after rising PSA. Performance of GC was compared to prognostic factors such as Gleason score and previous gene expression signatures in a withheld validation set. RESULTS: Expression profiles were generated from 545 unique patient samples, with median follow-up of 16.9 years. GC achieved an area under the receiver operating characteristic curve of 0.75 (0.67-0.83) in validation, outperforming clinical variables and gene signatures. GC was the only significant prognostic factor in multivariable analyses. Within Gleason score groups, cases with high GC scores experienced earlier death from prostate cancer and reduced overall survival. The markers in the classifier were found to be associated with a number of key biological processes in prostate cancer metastatic disease progression. CONCLUSION: A genomic classifier was developed and validated in a large patient cohort enriched with prostate cancer metastasis patients and a rising PSA that went on to experience metastatic disease. This early metastasis prediction model based on genomic expression in the primary tumor may be useful for identification of aggressive prostate cancer.