GliomaPredict: a clinically useful tool for assigning glioma patients to specific molecular subtypes

<p>Abstract</p> <p>Background</p> <p>Advances in generating genome-wide gene expression data have accelerated the development of molecular-based tumor classification systems. Tools that allow the translation of such molecular classification schemas from research into clinical applications are still missing in the emerging era of personalized medicine.</p> <p>Results</p> <p>We developed GliomaPredict as a computational tool that allows the fast and reliable classification of glioma patients into one of six previously published stratified subtypes based on sets of extensively validated classifiers derived from hundreds of glioma transcriptomic profiles. Our tool utilizes a principle component analysis (PCA)-based approach to generate a visual representation of the analyses, quantifies the confidence of the underlying subtype assessment and presents results as a printable PDF file. GliomaPredict tool is implemented as a plugin application for the widely-used GenePattern framework.</p> <p>Conclusions</p> <p>GliomaPredict provides a user-friendly, clinically applicable novel platform for instantly assigning gene expression-based subtype in patients with gliomas thereby aiding in clinical trial design and therapeutic decision-making. Implemented as a user-friendly diagnostic tool, we expect that in time GliomaPredict, and tools like it, will become routinely used in translational/clinical research and in the clinical care of patients with gliomas.</p

Conversion of a molecular classifier obtained by gene expression profiling into a classifier based on real-time PCR: a prognosis predictor for gliomas

<p>Abstract</p> <p>Background</p> <p>The advent of gene expression profiling was expected to dramatically improve cancer diagnosis. However, despite intensive efforts and several successful examples, the development of profile-based diagnostic systems remains a difficult task. In the present work, we established a method to convert molecular classifiers based on adaptor-tagged competitive PCR (ATAC-PCR) (with a data format that is similar to that of microarrays) into classifiers based on real-time PCR.</p> <p>Methods</p> <p>Previously, we constructed a prognosis predictor for glioma using gene expression data obtained by ATAC-PCR, a high-throughput reverse-transcription PCR technique. The analysis of gene expression data obtained by ATAC-PCR is similar to the analysis of data from two-colour microarrays. The prognosis predictor was a linear classifier based on the first principal component (PC1) score, a weighted summation of the expression values of 58 genes. In the present study, we employed the delta-delta Ct method for measurement by real-time PCR. The predictor was converted to a Ct value-based predictor using linear regression.</p> <p>Results</p> <p>We selected <it>UBL5 </it>as the reference gene from the group of genes with expression patterns that were most similar to the median expression level from the previous profiling study. The number of diagnostic genes was reduced to 27 without affecting the performance of the prognosis predictor. PC1 scores calculated from the data obtained by real-time PCR showed a high linear correlation (r = 0.94) with those obtained by ATAC-PCR. The correlation for individual gene expression patterns (r = 0.43 to 0.91) was smaller than for PC1 scores, suggesting that errors of measurement were likely cancelled out during the weighted summation of the expression values. The classification of a test set (n = 36) by the new predictor was more accurate than histopathological diagnosis (log rank p-values, 0.023 and 0.137, respectively) for predicting prognosis.</p> <p>Conclusion</p> <p>We successfully converted a molecular classifier obtained by ATAC-PCR into a Ct value-based predictor. Our conversion procedure should also be applicable to linear classifiers obtained from microarray data. Because errors in measurement are likely to be cancelled out during the calculation, the conversion of individual gene expression is not an appropriate procedure. The predictor for gliomas is still in the preliminary stages of development and needs analytical clinical validation and clinical utility studies.</p

Histopathological grading of pediatric ependymoma: reproducibility and clinical relevance in European trial cohorts

<p>Abstract</p> <p>Background</p> <p>Histopathological grading of ependymoma has been controversial with respect to its reproducibility and clinical significance. In a 3-phase study, we reviewed the pathology of 229 intracranial ependymomas from European trial cohorts of infants (2 trials - SFOP/CNS9204) and older children (2 trials - AIEOP/CNS9904) to assess both diagnostic concordance among five neuropathologists and the prognostic utility of histopathological variables, particularly tumor grading.</p> <p>Results</p> <p>In phase 1, using WHO criteria and without first discussing any issue related to grading ependymomas, pathologists assessed and independently graded ependymomas from 3 of 4 trial cohorts. Diagnosis of grade II ependymoma was less frequent than grade III, a difference that increased when one cohort (CNS9204) was reassessed in phase 2, during which the pathologists discussed ependymoma grading, jointly reviewed all CNS9204 tumors, and defined a novel grading system based on the WHO classification. In phase 3, repeat independent review of two cohorts (SFOP/CNS9904) using the novel system was associated with a substantial increase in concordance on grading. Extent of tumor resection was significantly associated with progression-free survival (PFS) in SFOP and AIEOP, but not in CNS9204 and CNS9904. Strength of consensus on grade was significantly associated with PFS in only one trial cohort (AIEOP). Consensus on the scoring of individual histopathological features (necrosis, angiogenesis, cell density, and mitotic activity) correlated with PFS in AIEOP, but in no other trial.</p> <p>Conclusions</p> <p>We conclude that concordance on grading ependymomas can be improved by using a more prescribed scheme based on the WHO classification. Unfortunately, this appears to have utility in limited clinical settings.</p

Demographic variation in incidence of adult glioma by subtype, United States, 1992-2007

<p>Abstract</p> <p>Background</p> <p>We hypothesized that race/ethnic group, sex, age, and/or calendar period variation in adult glioma incidence differs between the two broad subtypes of glioblastoma (GBM) and non-GBM. Primary GBM, which constitute 90-95% of GBM, differ from non-GBM with respect to a number of molecular characteristics, providing a molecular rationale for these two broad glioma subtypes.</p> <p>Methods</p> <p>We utilized data from the Surveillance, Epidemiology, and End Results Program for 1992-2007, ages 30-69 years. We compared 15,088 GBM cases with 9,252 non-GBM cases. We used Poisson regression to calculate adjusted rate ratios and 95% confidence intervals.</p> <p>Results</p> <p>The GBM incidence rate increased proportionally with the 4^thpower of age, whereas the non-GBM rate increased proportionally with the square root of age. For each subtype, compared to non-Hispanic Whites, the incidence rate among Blacks, Asians/Pacific Islanders, and American Indians/Alaskan Natives was substantially lower (one-fourth to one-half for GBM; about two-fifths for non-GBM). Secondary to this primary effect, race/ethnic group variation in incidence was significantly less for non-GBM than for GBM. For each subtype, the incidence rate was higher for males than for females, with the male/female rate ratio being significantly higher for GBM (1.6) than for non-GBM (1.4). We observed significant calendar period trends of increasing incidence for GBM and decreasing incidence for non-GBM. For the two subtypes combined, we observed a 3% decrease in incidence between 1992-1995 and 2004-2007.</p> <p>Conclusions</p> <p>The substantial difference in age effect between GBM and non-GBM suggests a fundamental difference in the genesis of primary GBM (the driver of GBM incidence) versus non-GBM. However, the commonalities between GBM and non-GBM with respect to race/ethnic group and sex variation, more notable than the somewhat subtle, albeit statistically significant, differences, suggest that within the context of a fundamental difference, some aspects of the complex process of gliomagenesis are shared by these subtypes as well. The increasing calendar period trend of GBM incidence coupled with the decreasing trend of non-GBM incidence may at least partly be due to a secular trend in diagnostic fashion, as opposed to real changes in incidence of these subtypes.</p

Inhibition of Dehydration-Induced Water Intake by Glucocorticoids Is Associated with Activation of Hypothalamic Natriuretic Peptide Receptor-A in Rat

Atrial natriuretic peptide (ANP) provides a potent defense mechanism against volume overload in mammals. Its primary receptor, natriuretic peptide receptor-A (NPR-A), is localized mostly in the kidney, but also is found in hypothalamic areas involved in body fluid volume regulation. Acute glucocorticoid administration produces potent diuresis and natriuresis, possibly by acting in the renal natriuretic peptide system. However, chronic glucocorticoid administration attenuates renal water and sodium excretion. The precise mechanism underlying this paradoxical phenomenon is unclear. We assume that chronic glucocorticoid administration may activate natriuretic peptide system in hypothalamus, and cause volume depletion by inhibiting dehydration-induced water intake. Volume depletion, in turn, compromises renal water excretion. To test this postulation, we determined the effect of dexamethasone on dehydration-induced water intake and assessed the expression of NPR-A in the hypothalamus. The rats were deprived of water for 24 hours to have dehydrated status. Prior to free access to water, the water-deprived rats were pretreated with dexamethasone or vehicle. Urinary volume and water intake were monitored. We found that dexamethasone pretreatment not only produced potent diuresis, but dramatically inhibited the dehydration-induced water intake. Western blotting analysis showed the expression of NPR-A in the hypothalamus was dramatically upregulated by dexamethasone. Consequently, cyclic guanosine monophosphate (the second messenger for the ANP) content in the hypothalamus was remarkably increased. The inhibitory effect of dexamethasone on water intake presented in a time- and dose-dependent manner, which emerged at least after 18-hour dexamethasone pretreatment. This effect was glucocorticoid receptor (GR) mediated and was abolished by GR antagonist RU486. These results indicated a possible physiologic role for glucocorticoids in the hypothalamic control of water intake and revealed that the glucocorticoids can act centrally, as well as peripherally, to assist in the normalization of extracellular fluid volume

A Novel Semi-Supervised Methodology for Extracting Tumor Type-Specific MRS Sources in Human Brain Data

BackgroundThe clinical investigation of human brain tumors often starts with a non-invasive imaging study, providing information about the tumor extent and location, but little insight into the biochemistry of the analyzed tissue. Magnetic Resonance Spectroscopy can complement imaging by supplying a metabolic fingerprint of the tissue. This study analyzes single-voxel magnetic resonance spectra, which represent signal information in the frequency domain. Given that a single voxel may contain a heterogeneous mix of tissues, signal source identification is a relevant challenge for the problem of tumor type classification from the spectroscopic signal.Methodology/Principal FindingsNon-negative matrix factorization techniques have recently shown their potential for the identification of meaningful sources from brain tissue spectroscopy data. In this study, we use a convex variant of these methods that is capable of handling negatively-valued data and generating sources that can be interpreted as tumor class prototypes. A novel approach to convex non-negative matrix factorization is proposed, in which prior knowledge about class information is utilized in model optimization. Class-specific information is integrated into this semi-supervised process by setting the metric of a latent variable space where the matrix factorization is carried out. The reported experimental study comprises 196 cases from different tumor types drawn from two international, multi-center databases. The results indicate that the proposed approach outperforms a purely unsupervised process by achieving near perfect correlation of the extracted sources with the mean spectra of the tumor types. It also improves tissue type classification.Conclusions/SignificanceWe show that source extraction by unsupervised matrix factorization benefits from the integration of the available class information, so operating in a semi-supervised learning manner, for discriminative source identification and brain tumor labeling from single-voxel spectroscopy data. We are confident that the proposed methodology has wider applicability for biomedical signal processing