Classification and Prediction of Survival in Patients with the Leukemic Phase of Cutaneous T Cell Lymphoma

We have used cDNA arrays to investigate gene expression patterns in peripheral blood mononuclear cells from patients with leukemic forms of cutaneous T cell lymphoma, primarily Sezary syndrome (SS). When expression data for patients with high blood tumor burden (Sezary cells >60% of the lymphocytes) and healthy controls are compared by Student's t test, at P < 0.01, we find 385 genes to be differentially expressed. Highly overexpressed genes include Th2 cells–specific transcription factors Gata-3 and Jun B, as well as integrin β1, proteoglycan 2, the RhoB oncogene, and dual specificity phosphatase 1. Highly underexpressed genes include CD26, Stat-4, and the IL-1 receptors. Message for plastin-T, not normally expressed in lymphoid tissue, is detected only in patient samples and may provide a new marker for diagnosis. Using penalized discriminant analysis, we have identified a panel of eight genes that can distinguish SS in patients with as few as 5% circulating tumor cells. This suggests that, even in early disease, Sezary cells produce chemokines and cytokines that induce an expression profile in the peripheral blood distinctive to SS. Finally, we show that using 10 genes, we can identify a class of patients who will succumb within six months of sampling regardless of their tumor burden

A phase I trial of the γ-secretase inhibitor MK-0752 in combination with gemcitabine in patients with pancreatic ductal adenocarcinoma.

BACKGROUND: The Notch pathway is frequently activated in cancer. Pathway inhibition by γ-secretase inhibitors has been shown to be effective in pre-clinical models of pancreatic cancer, in combination with gemcitabine. METHODS: A multi-centre, non-randomised Bayesian adaptive design study of MK-0752, administered per os weekly, in combination with gemcitabine administered intravenously on days 1, 8 and 15 (28 day cycle) at 800 or 1000 mg m-2, was performed to determine the safety of combination treatment and the recommended phase 2 dose (RP2D). Secondary and tertiary objectives included tumour response, plasma and tumour MK-0752 concentration, and inhibition of the Notch pathway in hair follicles and tumour. RESULTS: Overall, 44 eligible patients (performance status 0 or 1 with adequate organ function) received gemcitabine and MK-0752 as first or second line treatment for pancreatic cancer. RP2Ds of MK-0752 and gemcitabine as single agents could be combined safely. The Bayesian algorithm allowed further dose escalation, but pharmacokinetic analysis showed no increase in MK-0752 AUC (area under the curve) beyond 1800 mg once weekly. Tumour response evaluation was available in 19 patients; 13 achieved stable disease and 1 patient achieved a confirmed partial response. CONCLUSIONS: Gemcitabine and a γ-secretase inhibitor (MK-0752) can be combined at their full, single-agent RP2Ds

Classification and biomarker identification using gene network modules and support vector machines

<p>Abstract</p> <p>Background</p> <p>Classification using microarray datasets is usually based on a small number of samples for which tens of thousands of gene expression measurements have been obtained. The selection of the genes most significant to the classification problem is a challenging issue in high dimension data analysis and interpretation. A previous study with SVM-RCE (Recursive Cluster Elimination), suggested that classification based on groups of correlated genes sometimes exhibits better performance than classification using single genes. Large databases of gene interaction networks provide an important resource for the analysis of genetic phenomena and for classification studies using interacting genes.</p> <p>We now demonstrate that an algorithm which integrates network information with recursive feature elimination based on SVM exhibits good performance and improves the biological interpretability of the results. We refer to the method as SVM with Recursive Network Elimination (SVM-RNE)</p> <p>Results</p> <p>Initially, one thousand genes selected by t-test from a training set are filtered so that only genes that map to a gene network database remain. The Gene Expression Network Analysis Tool (GXNA) is applied to the remaining genes to form <it>n </it>clusters of genes that are highly connected in the network. Linear SVM is used to classify the samples using these clusters, and a weight is assigned to each cluster based on its importance to the classification. The least informative clusters are removed while retaining the remainder for the next classification step. This process is repeated until an optimal classification is obtained.</p> <p>Conclusion</p> <p>More than 90% accuracy can be obtained in classification of selected microarray datasets by integrating the interaction network information with the gene expression information from the microarrays.</p> <p>The Matlab version of SVM-RNE can be downloaded from <url>http://web.macam.ac.il/~myousef</url></p

Molecular Insights into the Pathogenesis of Alzheimer's Disease and Its Relationship to Normal Aging

Alzheimer's disease (AD) is a complex neurodegenerative disorder that diverges from the process of normal brain aging by unknown mechanisms. We analyzed the global structure of age- and disease-dependent gene expression patterns in three regions from more than 600 brains. Gene expression variation could be almost completely explained by four transcriptional biomarkers that we named BioAge (biological age), Alz (Alzheimer), Inflame (inflammation), and NdStress (neurodegenerative stress). BioAge captures the first principal component of variation and includes genes statistically associated with neuronal loss, glial activation, and lipid metabolism. Normally BioAge increases with chronological age, but in AD it is prematurely expressed as if some of the subjects were 140 years old. A component of BioAge, Lipa, contains the AD risk factor APOE and reflects an apparent early disturbance in lipid metabolism. The rate of biological aging in AD patients, which cannot be explained by BioAge, is associated instead with NdStress, which includes genes related to protein folding and metabolism. Inflame, comprised of inflammatory cytokines and microglial genes, is broadly activated and appears early in the disease process. In contrast, the disease-specific biomarker Alz was selectively present only in the affected areas of the AD brain, appears later in pathogenesis, and is enriched in genes associated with the signaling and cell adhesion changes during the epithelial to mesenchymal (EMT) transition. Together these biomarkers provide detailed description of the aging process and its contribution to Alzheimer's disease progression

Variational self -consistent estimates for the effective behavior of viscoplastic polycrystals

A fundamental problem in mechanics of materials is the computation of the macroscopic response of polycrystalline aggregates from the properties of their constituent single-crystal grains and the microstructure. In this work, the nonlinear homogenization method of deBotton and Ponte Castañeda (1995) was used to compute “variational” self-consistent estimates for the effective behavior of different types of viscoplastic polycrystals, including a two-dimensional model, as well as cubic and hexagonal polycrystals. In contrast with the “incremental” and “tangent” self-consistent estimates, the new results are found to satisfy all known bounds, even in the strongly nonlinear, rate-insensitive limit. The new results also exhibit a more realistic scaling law for the macroscopic tensile flow stress at large grain anisotropy, depending only on the number (less than five) of independent slip systems, but not on the strain rate sensitivity. The predictions of other nonlinear extensions of the self-consistent method were found to be inconsistent with this scaling law, suggesting that they may be less accurate than the variational self-consistent estimates proposed here. One additional advantage of the variational procedure over earlier self-consistent models is that it is not restricted to pure power law behavior and can be applied to polycrystals with various hardening rules. In this context three cases of hexagonal polycrystals in which slip systems obey power laws with different exponents were considered: creep of ice, recrystallized Zircaloy-4, and the simultaneous thermal and irradiation creep of Zr-2.5Nb. The macroscopic loading curves, predicted by the variational procedure, suggest that the slope transition associated with different values of creep exponent occurs only if the grains have at least four independent systems available for each exponent value. Also, unlike the Taylor and Reuss upper and lower bounds, the new self-consistent estimates are able to account for grain shape in a rigorous statistical sense. For these reasons, they can be shown to be significantly more accurate than earlier estimates. For example, for ionic and hexagonal polycrystals with highly anisotropic “flat” grains the new self-consistent estimates can be less than half of the corresponding Taylor predictions

Variational self -consistent estimates for the effective behavior of viscoplastic polycrystals

A fundamental problem in mechanics of materials is the computation of the macroscopic response of polycrystalline aggregates from the properties of their constituent single-crystal grains and the microstructure. In this work, the nonlinear homogenization method of deBotton and Ponte Castañeda (1995) was used to compute “variational” self-consistent estimates for the effective behavior of different types of viscoplastic polycrystals, including a two-dimensional model, as well as cubic and hexagonal polycrystals. In contrast with the “incremental” and “tangent” self-consistent estimates, the new results are found to satisfy all known bounds, even in the strongly nonlinear, rate-insensitive limit. The new results also exhibit a more realistic scaling law for the macroscopic tensile flow stress at large grain anisotropy, depending only on the number (less than five) of independent slip systems, but not on the strain rate sensitivity. The predictions of other nonlinear extensions of the self-consistent method were found to be inconsistent with this scaling law, suggesting that they may be less accurate than the variational self-consistent estimates proposed here. One additional advantage of the variational procedure over earlier self-consistent models is that it is not restricted to pure power law behavior and can be applied to polycrystals with various hardening rules. In this context three cases of hexagonal polycrystals in which slip systems obey power laws with different exponents were considered: creep of ice, recrystallized Zircaloy-4, and the simultaneous thermal and irradiation creep of Zr-2.5Nb. The macroscopic loading curves, predicted by the variational procedure, suggest that the slope transition associated with different values of creep exponent occurs only if the grains have at least four independent systems available for each exponent value. Also, unlike the Taylor and Reuss upper and lower bounds, the new self-consistent estimates are able to account for grain shape in a rigorous statistical sense. For these reasons, they can be shown to be significantly more accurate than earlier estimates. For example, for ionic and hexagonal polycrystals with highly anisotropic “flat” grains the new self-consistent estimates can be less than half of the corresponding Taylor predictions