Clinical Evolution of Epithelial-Mesenchymal Transition in Human Carcinomas.

The significance of the phenotypic plasticity afforded by epithelial-mesenchymal transition (EMT) for cancer progression and drug resistance remains to be fully elucidated in the clinic. We evaluated epithelial-mesenchymal phenotypic characteristics across a range of tumor histologies using a validated, high-resolution digital microscopic immunofluorescence assay (IFA) that incorporates ß-catenin detection and cellular morphology to delineate carcinoma cells from stromal fibroblasts and that quantitates the individual and colocalized expression of the epithelial marker E-cadherin (E) and the mesenchymal marker vimentin (V) at subcellular resolution ("EMT-IFA"). We report the discovery of ß-catenin+ cancer cells that coexpress E-cadherin and vimentin in core-needle biopsies from patients with various advanced metastatic carcinomas, wherein these cells are transitioning between strongly epithelial and strongly mesenchymal-like phenotypes. Treatment of carcinoma models with anticancer drugs that differ in their mechanism of action (the tyrosine kinase inhibitor pazopanib in MKN45 gastric carcinoma xenografts and the combination of tubulin-targeting agent paclitaxel with the BCR-ABL inhibitor nilotinib in MDA-MB-468 breast cancer xenografts) caused changes in the tumor epithelial-mesenchymal character. Moreover, the appearance of partial EMT or mesenchymal-like carcinoma cells in MDA-MB-468 tumors treated with the paclitaxel-nilotinib combination resulted in upregulation of cancer stem cell (CSC) markers and susceptibility to FAK inhibitor. A metastatic prostate cancer patient treated with the PARP inhibitor talazoparib exhibited similar CSC marker upregulation. Therefore, the phenotypic plasticity conferred on carcinoma cells by EMT allows for rapid adaptation to cytotoxic or molecularly targeted therapy and could create a form of acquired drug resistance that is transient in nature. SIGNIFICANCE: Despite the role of EMT in metastasis and drug resistance, no standardized assessment of EMT phenotypic heterogeneity in human carcinomas exists; the EMT-IFA allows for clinical monitoring of tumor adaptation to therapy.

Survival outcomes post percutaneous coronary intervention: Why the hype about stent type? Lessons from a healthcare system in India.

A prospective, multicenter study was initiated by the Government of Maharashtra, India, to determine predictors of long-term outcomes of percutaneous coronary intervention (PCI) for coronary artery disease, and to compare the effectiveness of drug-eluting stents (DESs) and bare-metal stents (BMSs) in patients undergoing PCI under government-funded insurance. The present analysis included 4595 patients managed between August 2012 and November 2016 at any of 110 participating centers. Using the classical multivariable regression and propensity-matching approach, we found age to be the most important predictor of 1-year mortality and target lesion revascularization at 1 year post-PCI. However, using machine learning methods to account for unmeasured confounders and bias in this large observational study, we determined total stent length and number of stents deployed as the most important predictors of 1-year survival, followed by age and employment status. The unadjusted death rates were 5.0% and 3.8% for the BMS and DES groups, respectively (p = 0.185, log-rank test). The rate of re-hospitalization (p<0.001) and recurrence of unstable angina (p = 0.08) was significantly lower for DESs than for BMSs. Increased use of DES after 2015 (following establishment of a price cap on DESs) was associated with a sharp decrease in adjusted hazard ratios of DESs versus BMSs (from 0.94 in 2013 to 0.58 in 2016), suggesting that high price was limiting DES use in some high-risk patients. Since stented length and stent number were the most important predictors of survival outcomes, adopting an ischemia-guided revascularization strategy is expected to help improve outcomes and reduce procedural costs. In the elderly, PCI should be reserved for cases where the benefits outweigh the higher risk of the procedure. As unemployed patients had poorer long-term outcomes, we expect that implementation of a post-PCI cardiovascular rehabilitation program may improve long-term outcomes.

The Bayesian basket design for genomic variant-driven phase II trials.

Basket clinical trials are a new category of early clinical trials in which a treatment is evaluated in a population of patients with tumors of various histologic types and primary sites selected for containing specific genomic abnormalities. The objective of such studies is generally to discover histologic types in which the treatment is active. Basket trials are early discovery trials whose results should be confirmed in expanded histology specific cohorts. In this report, we develop a design for planning, monitoring, and analyzing basket trials. A website for using the new design is available at https://brbnci.shinyapps.io/BasketTrials/ and the software is available at GitHub in the "Basket Trials" repository of account brbnci.

Comparison of effects of anti-angiogenic agents in the zebrafish efficacy-toxicity model for translational anti-angiogenic drug discovery.

BACKGROUND: Anti-angiogenic therapy in certain cancers has been associated with improved control of tumor growth and metastasis. Development of anti-angiogenic agents has, however, been saddled with higher attrition rate due to suboptimal efficacy, narrow therapeutic windows, or development of organ-specific toxicities. The aim of this study was to evaluate the translational ability of the zebrafish efficacy-toxicity model to stratify anti-angiogenic agents based on efficacy, therapeutic windows, and off-target effects to streamline the compound selection process in anti-angiogenic discovery. METHODS: The embryonic model of zebrafish was employed for studying angiogenesis and toxicity. The zebrafish were treated with anti-angiogenic compounds to evaluate their effects on angiogenesis and zebrafish-toxicity parameters. Angiogenesis was measured by scoring the development of subintestinal vessels. Toxicity was evaluated by calculating the median lethal concentration, the lowest observed effect concentration, and gross morphological changes. Results of efficacy and toxicity were used to predict the therapeutic window. RESULTS: In alignment with the clinical outcomes, the zebrafish assays demonstrated that vascular endothelial growth factor receptor (VEGFR) inhibitors are the most potent anti-angiogenic agents, followed by multikinase inhibitors and inhibitors of endothelial cell proliferation. The toxicity assays reported cardiac phenotype in zebrafish treated with VEGFR inhibitors and multikinase inhibitors with VEGFR activity suggestive of cardiotoxic potential of these compounds. Several other pathological features were reported for multikinase inhibitors suggestive of off-target effects. The predicted therapeutic window was translational with the clinical trial outcomes of the anti-angiogenic agents. The zebrafish efficacy-toxicity approach could stratify anti-angiogenic agents based on the mechanism of action and delineate chemical structure-driven biological activity of anti-angiogenic compounds. CONCLUSION: The zebrafish efficacy-toxicity approach can be used as a predictive model for translational anti-angiogenic drug discovery to streamline compound selection, resulting in safer and efficacious anti-angiogenic agents entering the clinics.

Overfitting in prediction models - is it a problem only in high dimensions?

The growing recognition that human diseases are molecularly heterogeneous has stimulated interest in the development of prognostic and predictive classifiers for patient selection and stratification. In the process of classifier development, it has been repeatedly emphasized that in situations where the number of candidate predictor variables is much larger than the number of observations, the apparent (training set, resubstitution) accuracy of the classifiers can be highly optimistically biased and hence, classification accuracy should be reported based on evaluation of the classifier on a separate test set or using complete cross-validation. Such evaluation methods have however not been the norm in the case of low-dimensional, p

An evaluation of resampling methods for assessment of survival risk prediction in high-dimensional settings.

Resampling techniques are often used to provide an initial assessment of accuracy for prognostic prediction models developed using high-dimensional genomic data with binary outcomes. Risk prediction is most important, however, in medical applications and frequently the outcome measure is a right-censored time-to-event variable such as survival. Although several methods have been developed for survival risk prediction with high-dimensional genomic data, there has been little evaluation of the use of resampling techniques for the assessment of such models. Using real and simulated datasets, we compared several resampling techniques for their ability to estimate the accuracy of risk prediction models. Our study showed that accuracy estimates for popular resampling methods, such as sample splitting and leave-one-out cross validation (Loo CV), have a higher mean square error than for other methods. Moreover, the large variability of the split-sample and Loo CV may make the point estimates of accuracy obtained using these methods unreliable and hence should be interpreted carefully. A k-fold cross-validation with k = 5 or 10 was seen to provide a good balance between bias and variability for a wide range of data settings and should be more widely adopted in practice.

Using cross-validation to evaluate predictive accuracy of survival risk classifiers based on high-dimensional data.

Developments in whole genome biotechnology have stimulated statistical focus on prediction methods. We review here methodology for classifying patients into survival risk groups and for using cross-validation to evaluate such classifications. Measures of discrimination for survival risk models include separation of survival curves, time-dependent ROC curves and Harrell's concordance index. For high-dimensional data applications, however, computing these measures as re-substitution statistics on the same data used for model development results in highly biased estimates. Most developments in methodology for survival risk modeling with high-dimensional data have utilized separate test data sets for model evaluation. Cross-validation has sometimes been used for optimization of tuning parameters. In many applications, however, the data available are too limited for effective division into training and test sets and consequently authors have often either reported re-substitution statistics or analyzed their data using binary classification methods in order to utilize familiar cross-validation. In this article we have tried to indicate how to utilize cross-validation for the evaluation of survival risk models; specifically how to compute cross-validated estimates of survival distributions for predicted risk groups and how to compute cross-validated time-dependent ROC curves. We have also discussed evaluation of the statistical significance of a survival risk model and evaluation of whether high-dimensional genomic data adds predictive accuracy to a model based on standard covariates alone.

What should physicians look for in evaluating prognostic gene-expression signatures?

Most cancer treatments benefit only a minority of patients. This has led to a widespread interest in the identification of gene-expression-based prognostic signatures. Well-developed and validated genomic signatures can lead to personalized treatment decisions resulting in improved patient management. However, the pace of acceptance of these signatures in clinical practice has been slow. This is because many of the signatures have been developed without clear focus on the intended clinical use, and proper independent validation studies establishing their medical utility have rarely been performed. The practicing physician and the patient are thus left in doubt about the reliability and medical utility of the signatures. We aim to provide guidance to physicians in critically evaluating published studies on prognostic gene-expression signatures so that they are better equipped to decide which signatures, if any, have sufficient merit for use, in conjunction with other factors in helping their patients to make good treatment decisions. A discussion of the lessons to be learned from the successful development of the Oncotype DX genetic test for breast cancer is presented and contrasted with a review of the current status of prognostic gene-expression signatures in non-small-cell lung cancer.

Gene expression-based prognostic signatures in lung cancer: ready for clinical use?

A substantial number of studies have reported the development of gene expression-based prognostic signatures for lung cancer. The ultimate aim of such studies should be the development of well-validated clinically useful prognostic signatures that improve therapeutic decision making beyond current practice standards. We critically reviewed published studies reporting the development of gene expression-based prognostic signatures for non-small cell lung cancer to assess the progress made toward this objective. Studies published between January 1, 2002, and February 28, 2009, were identified through a PubMed search. Following hand-screening of abstracts of the identified articles, 16 were selected as relevant. Those publications were evaluated in detail for appropriateness of the study design, statistical validation of the prognostic signature on independent datasets, presentation of results in an unbiased manner, and demonstration of medical utility for the new signature beyond that obtained using existing treatment guidelines. Based on this review, we found little evidence that any of the reported gene expression signatures are ready for clinical application. We also found serious problems in the design and analysis of many of the studies. We suggest a set of guidelines to aid the design, analysis, and evaluation of prognostic signature studies. These guidelines emphasize the importance of focused study planning to address specific medically important questions and the use of unbiased analysis methods to evaluate whether the resulting signatures provide evidence of medical utility beyond standard of care-based prognostic factors.

Managing protein flexibility in docking and its applications.

Docking, virtual screening and structure-based drug design are routinely used in modern drug discovery programs. Although current docking methods deal with flexible ligands, managing receptor flexibility has proved to be challenging. In this brief review, we present the current state-of-the-art for computationally handling receptor flexibility, including a novel statistical computational approach published recently. We conclude, from a comparison of the different approaches, that a combination of methods is likely to provide the most reliable solution to the problem of finding the right protein conformation for a given ligand.

Modeling and selection of flexible proteins for structure-based drug design: backbone and side chain movements in p38 MAPK.

Receptor rearrangement upon ligand binding (induced fit) is a major stumbling block in docking and virtual screening. Even though numerous studies have stressed the importance of including protein flexibility in ligand docking, currently available methods provide only a partial solution to the problem. Most of these methods, being computer intensive, are often impractical to use in actual drug discovery settings. We had earlier shown that ligand-induced receptor side-chain conformational changes could be modeled statistically using data on known receptor-ligand complexes. In this paper, we show that a similar approach can be used to model more complex changes like backbone flips and loop movements. We have used p38 MAPK as a test case and have shown that a few simple structural features of ligands are sufficient to predict the induced variation in receptor conformations. Rigorous validation, both by internal resampling methods and on an external test set, corroborates this finding and demonstrates the robustness of the models. We have also compared our results with those from an earlier molecular dynamics simulation study on DFG loop conformations of p38 MAPK, and found that the results matched in the two cases. Our statistical approach enables one to predict the final ligand-induced conformation of the active site of a protein, based on a few ligand properties, prior to docking the ligand. We can do this without having to trace the step-by-step process by which this state is arrived at (as in molecular dynamics simulations), thereby drastically reducing computational effort.

A novel computational analysis of ligand-induced conformational changes in the ATP binding sites of cyclin dependent kinases.

Protein kinases in general are known to be very flexible macromolecules. In this article, the conformational plasticity of the ATP binding site in cyclin dependent kinases is analyzed. Movement of the two lysine residues lining the ATP binding site are shown to play a major role in the conformational variability of the site. Linear models are developed to identify and quantify ligand properties that maximally influence the lysine side chain conformations. A few simple properties of the ligands are shown to account for more than 70% of the variation in the lysine conformations. The results are validated using test data and molecular simulation studies. Illustrative applications of the results of this analysis to finding the appropriate crystal structure for molecular docking and binding mode predictions of novel ligands are provided. This work provides a new approach to quantify ligand-induced conformational changes in the active sites of flexible proteins and to find the appropriate crystal structure for docking novel ligands.