Individualized markers optimize class prediction of microarray data

BACKGROUND: Identification of molecular markers for the classification of microarray data is a challenging task. Despite the evident dissimilarity in various characteristics of biological samples belonging to the same category, most of the marker – selection and classification methods do not consider this variability. In general, feature selection methods aim at identifying a common set of genes whose combined expression profiles can accurately predict the category of all samples. Here, we argue that this simplified approach is often unable to capture the complexity of a disease phenotype and we propose an alternative method that takes into account the individuality of each patient-sample. RESULTS: Instead of using the same features for the classification of all samples, the proposed technique starts by creating a pool of informative gene-features. For each sample, the method selects a subset of these features whose expression profiles are most likely to accurately predict the sample's category. Different subsets are utilized for different samples and the outcomes are combined in a hierarchical framework for the classification of all samples. Moreover, this approach can innately identify subgroups of samples within a given class which share common feature sets thus highlighting the effect of individuality on gene expression. CONCLUSION: In addition to high classification accuracy, the proposed method offers a more individualized approach for the identification of biological markers, which may help in better understanding the molecular background of a disease and emphasize the need for more flexible medical interventions

The quest for reliable prediction of chemotherapy-induced delayed nausea among breast cancer patients

Aim: Though female sex is considered a risk factor when predicting chemotherapy-induced nausea, not all women will experience nausea. Therefore, the aim of this pilot study was to evaluate the accuracy, and usefulness, of a blood-based assay for predicting chemotherapy-induced delayed nausea among breast cancer patients.Methods: Whole blood from consented breast cancer patients, determined to benefit from chemotherapy, were used to test each individual for their intrinsic glutathione recycling capacity. Both highly-emetogenic and moderately-emetogenic chemotherapies were included in the study. Test results obtained from chemotherapy naïve patients were used to predict delayed nausea. Predicted outcomes were later compared to reported outcomes documented in medical records. Statistical analyses were used to test the accuracy and efficacy of our blood-based test.Results: Even with current and effective anti-emetics, we report that ~31% of breast cancer patients reported delayed nausea. Using the SAS/STAT classification and regression tree method we were able to show that this assay can be used as a predictive tool with an AUC of 0.71-0.74 depending on treatment regimen.Conclusion: The new predictive assay provides an added value in identifying individual breast cancer patients at high risk of developing moderate or severe delayed nausea after treatment with taxane- based therapies such as docetaxel/cyclophosphamide and docetaxel/carboplatin/trastuzumab/pertuzumab. We believe that this assay could help guide the use of anti-emetics for improved patient-oriented care

Molecular understanding for the adsorption of water and alcohols in hydrophilic and hydrophobic zeolitic metal-organic frameworks

A molecular simulation study is reported for the adsorption of water and alcohols (methanol and ethanol) in two metal-organic frameworks (MOFs) topologically similar to rho-zeolite; one is a hydrophilic Na -exchanged rho-zeolite-like MOF (Na-rho-ZMOF), and the other is a hydrophobic zeolitic-imidazolate framework-71 (ZIF-71). The adsorption isotherms in Na-rho-ZMOF are type I as a consequence of the high affinity of the nonframework Na ions and ionic framework. The adsorption capacity decreases in the order of water > methanol > ethanol. Water is adsorbed more closely in the window region, whereas methanol and ethanol are populated in the α cage due to steric effect. In water/methanol and water/ethanol mixtures, water adsorption increases continuously with increasing pressure and replaces alcohols competitively at high pressures. In ZIF-71, the framework-adsorbate affinity is relatively weaker and type V adsorption is observed. Water has vanishingly small adsorption at low pressures and a sharp increase in adsorption at 22 kPa due to capillary condensation. Methanol and ethanol exhibit cluster-growth adsorption, followed by continuous pore filling. The adsorption in ZIF-71 increases in the order of water < methanol < ethanol at low pressures; however, the opposite order is observed at high pressures because of entropy effect. In water/alcohol mixtures, alcohols are selectively more adsorbed at low pressures but surpassed by water with increasing pressure. The framework charges have a substantial effect on adsorption in Na-rho-ZMOF, but not in ZIF-71. This study provides a molecular understanding for the adsorption of water and alcohols in two zeolitic MOFs with the identical topology and reveals the significantly different adsorption mechanisms