Ruthenium-Locked Helical Chirality: A Barrier of Inversion and Formation of an Asymmetric Macrocycle

Upon coordination to metal centers, tetradentate ligands based on the 6,6'-bis(2 ''-aminopyridyl)-2,2'-bipyridine (bapbpy) structure form helical chiral complexes due to the steric clash between the terminal pyridines of the ligand. For octahedral ruthenium(II) complexes, the two additional axial ligands bound to the metal center, when different, generate diastereotopic aromatic protons that can be distinguished by NMR. Based on these geometrical features, the inversion barrier of helical [Ru-II(L)(RR'SO)Cl](+) complexes, where L is a sterically hindered bapbpy derivative and RR'SO is a chiral or achiral sulfoxide ligand, was studied by variable-temperature H-1 NMR The coalescence energies for the inversion of the helical chirality of [Ru(bapbpy)(DMSO)(Cl)]Cl and [Ru(bapbpy)(MTSO)(Cl)]Cl (where MTSO is (R)-methyl p-tolylsulfoxide) were found to be 43 and 44 kJ/mol, respectively. By contrast, in [Ru(biqbpy)(DMSO)(Cl)]Cl (biqbpy = 6,6'-bis(aminoquinolyl)-2,2'-bipyridine increased strain caused by the larger terminal quinoline groups resulted in a coalescence temperature higher than 376 K, which pointed to an absence of helical chirality inversion at room temperature. Further increasing the steric strain by introducing methoxy groups ortho to the nitrogen atoms of the terminal pyridyl groups in bapbpy resulted in the serendipitous discovery of a ring-closing reaction that took place upon trying to make [Ru(OMe-bapbpy)(DMSO)Cl](+) (OMe-bapbpy = 6,6'-bis(6-methoxy-aminopyridyl)2,2'-bipyridine). This reaction generated, in excellent yields, a chiral complex [Ru(L '')(DMSO)Cl]Cl, where L '' is an asymmetric tetrapyridyl macrocycle. This unexpected transformation appears to be specific to ruthenium(II) as macrocyclization did not occur upon coordination of the same ligand to palladium(II) or rhodium(III).Macromolecular Biochemistr

Classification of ductal carcinoma in situ by gene expression profiling

INTRODUCTION: Ductal carcinoma in situ (DCIS) is characterised by the intraductal proliferation of malignant epithelial cells. Several histological classification systems have been developed, but assessing the histological type/grade of DCIS lesions is still challenging, making treatment decisions based on these features difficult. To obtain insight in the molecular basis of the development of different types of DCIS and its progression to invasive breast cancer, we have studied differences in gene expression between different types of DCIS and between DCIS and invasive breast carcinomas. METHODS: Gene expression profiling using microarray analysis has been performed on 40 in situ and 40 invasive breast cancer cases. RESULTS: DCIS cases were classified as well- (n = 6), intermediately (n = 18), and poorly (n = 14) differentiated type. Of the 40 invasive breast cancer samples, five samples were grade I, 11 samples were grade II, and 24 samples were grade III. Using two-dimensional hierarchical clustering, the basal-like type, ERB-B2 type, and the luminal-type tumours originally described for invasive breast cancer could also be identified in DCIS. CONCLUSION: Using supervised classification, we identified a gene expression classifier of 35 genes, which differed between DCIS and invasive breast cancer; a classifier of 43 genes could be identified separating between well- and poorly differentiated DCIS samples

The impact of a pathologist's personality on the interobserver variability and diagnostic accuracy of predictive PD-L1 immunohistochemistry in lung cancer

OBJECTIVES: Programmed death-ligand 1 (PD-L1) is the only approved predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). However, predictive PD-L1 immunohistochemistry is subject to interobserver variability. We hypothesized that a pathologist's personality influences the interobserver variability and diagnostic accuracy of PD-L1 immunoscoring. MATERIALS AND METHODS: Seventeen pathologists performed PD-L1 immunoscoring on 50 resected NSCLC tumors in three categories (<1%;1-49%;≥50%). Also, the pathologists completed a certified personality test (NEO-PI-r), assessing five personality traits: neuroticism, extraversion, openness, altruism and conscientiousness. RESULTS: The overall agreement among pathologists for a series of 47 tumors was substantial (kappa = 0.63). Of these, 23/47 (49%) tumors were entirely negative or largely positive, resulting in a kappa value of 0.93. The remaining 24/47 (51%) tumors had a PD-L1 score around the cutoff value, generating a kappa value of 0.32. Pathologists with high scores for conscientiousness (careful, diligent) had the least interobserver variability (r = 0.6, p = 0.009). Also, they showed a trend towards higher sensitivity (74% vs. 68%, p = 0.4), specificity (86% vs. 82%, p = 0.3) and percent agreement (83% vs. 79%, p = 0.3), although not significant. In contrast, pathologists with high scores for neuroticism (sensitive, anxious) had significantly lower specificity (80% vs. 87%, p = 0.03) and percent agreement (78% vs. 85%, p = 0.03). Also, a trend towards high interobserver variability (r = -0.3, p = 0.2) and lower sensitivity (68% vs. 74%, p = 0.3) was observed, although not significant. Pathologists with relatively high scores for conscientiousness scored fewer tumors PD-L1 positive at the ≥ 1% cut-off (r = -0.5, p = 0.03). In contrast, pathologists with relatively high scores for neuroticism score more tumors PD-L1 positive at ≥ 1% (r = 0.6, p = 0.017) and ≥ 50% cut-offs (r = 0.6, p = 0.009). CONCLUSIONS: This study is the first to demonstrate the impact of a pathologist's personality on the interobserver variability and diagnostic accuracy of immunostaining, in the context of PD-L1 in NSCLC. Larger studies are needed for validation of these findings

Measuring the depth of invasion in vulvar squamous cell carcinoma: interobserver agreement and pitfalls

Aims: The depth of invasion is an important prognostic factor for patients with vulvar squamous cell carcinoma (SCC). The threshold of 1 mm distinguishes between FIGO stages IA and ≥IB disease and guides the need for groin surgery. Therefore, high interobserver agreement is crucial. The conventional and the alternative method are described to measure the depth of invasion. The aims of this study were to assess interobserver agreement for classifying the depth of invasion using both methods and to identify pitfalls. Methods and results: Fifty slides of vulvar SCC with a depth of invasion approximately 1 mm were selected, digitally scanned and independently assessed by 10 pathologists working in a referral or oncology centre and four pathologists in training. The depth of invasion was measured using both the conventional and alternative method in each slide and categorised into ≤1 and >1 mm. The percentage of agreement and Light’s kappa for multi-rater agreement were calculated, and 95% confidence intervals were calculated by bootstrapping (1000 runs). The agreement using the conventional method was moderate (κ = 0.57, 95% confidence interval = 0.45–0.68). The percentage of agreement among the participating pathologists using the conventional method was 85.0% versus 89.4% using the alternative method. Six pitfalls were identified: disagreement concerning which invasive nest is deepest, recognition of invasive growth and where it starts, curved surface, carcinoma situated on the edge of the tis

Elastic SCAD as a novel penalization method for SVM classification tasks in high-dimensional data

<p>Abstract</p> <p>Background</p> <p>Classification and variable selection play an important role in knowledge discovery in high-dimensional data. Although Support Vector Machine (SVM) algorithms are among the most powerful classification and prediction methods with a wide range of scientific applications, the SVM does not include automatic feature selection and therefore a number of feature selection procedures have been developed. Regularisation approaches extend SVM to a feature selection method in a flexible way using penalty functions like LASSO, SCAD and Elastic Net.</p> <p>We propose a novel penalty function for SVM classification tasks, Elastic SCAD, a combination of SCAD and ridge penalties which overcomes the limitations of each penalty alone.</p> <p>Since SVM models are extremely sensitive to the choice of tuning parameters, we adopted an interval search algorithm, which in comparison to a fixed grid search finds rapidly and more precisely a global optimal solution.</p> <p>Results</p> <p>Feature selection methods with combined penalties (Elastic Net and Elastic SCAD SVMs) are more robust to a change of the model complexity than methods using single penalties. Our simulation study showed that Elastic SCAD SVM outperformed LASSO (<it>L</it>₁) and SCAD SVMs. Moreover, Elastic SCAD SVM provided sparser classifiers in terms of median number of features selected than Elastic Net SVM and often better predicted than Elastic Net in terms of misclassification error.</p> <p>Finally, we applied the penalization methods described above on four publicly available breast cancer data sets. Elastic SCAD SVM was the only method providing robust classifiers in sparse and non-sparse situations.</p> <p>Conclusions</p> <p>The proposed Elastic SCAD SVM algorithm provides the advantages of the SCAD penalty and at the same time avoids sparsity limitations for non-sparse data. We were first to demonstrate that the integration of the interval search algorithm and penalized SVM classification techniques provides fast solutions on the optimization of tuning parameters.</p> <p>The penalized SVM classification algorithms as well as fixed grid and interval search for finding appropriate tuning parameters were implemented in our freely available R package 'penalizedSVM'.</p> <p>We conclude that the Elastic SCAD SVM is a flexible and robust tool for classification and feature selection tasks for high-dimensional data such as microarray data sets.</p

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice