Unknown,Transcriptomics,Genomics,Proteomics

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Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

ABSTRACT: Background: The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable treating physicians to decide when to intervene more aggressively and to plan clinical trials more accurately. Methods: In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. Results: We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days (error rate 0.079, p< 0.001). If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The error rate of this predictor was 2.3 fold lower than the error rate of random predictions (error rate = 0.35, p<0.001). The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing (the error rate of the first level predictor was < 0.18 with p<0.001 for all the patient groups). Conclusions: We conclude that gene expression analysis is a valuable tool that can be used in clinical practice to predict future MS disease activity. Similar approach can be also useful for dealing with other autoimmune diseases that characterized by relapsing-remitting nature Keywords: Disease prediction Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 patients with definite MS and 32 patients with clinically isolated syndrome (CIS). The dataset included the expression levels of 10,594 genes and annotated sequences corresponding to 22,215 gene-transcripts that appear in the microarray. We designed a two stage predictor. The first stage predictor was based on the expression level of 10 genes, and predicted the time to next relapse with a resolution of 500 days. If the predicted relapse was to occur in less than 500 days, a second stage predictor based on an additional different set of 9 genes was used, resulting in a prediction with a resolution of 50 days as to the timing of the next relapse. The predictors were further evaluated and found effective not only in untreated patients but were also valid for MS patients which subsequently received immunomodulatory treatments after the initial testing.

ORGANISM(S): Homo sapiens

SUBMITTER: Michael Gurevich

PROVIDER: E-GEOD-15245 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells.

Gurevich Michael M Tuller Tamir T Rubinstein Udi U Or-Bach Rotem R Achiron Anat A

BMC medical genomics 20090722

<h4>Background</h4>The ability to predict the spatial frequency of relapses in multiple sclerosis (MS) would enable physicians to decide when to intervene more aggressively and to plan clinical trials more accurately.<h4>Methods</h4>In the current study our objective was to determine if subsets of genes can predict the time to the next acute relapse in patients with MS. Data-mining and predictive modeling tools were utilized to analyze a gene-expression dataset of 94 non-treated patients; 62 pat ...[more]

PMID: 19624813

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Project description:Background: We have previously shown that the Gene expression Grade Index (GGI) was able to identify two subtypes of estrogen receptor (ER)-positive tumors that were associated with statistically distinct clinical outcomes in both untreated and tamoxifen-treated patients. Here, we aim to investigate the ability of the GGI to predict relapses in postmenopausal women who were treated with tamoxifen (T) or letrozole (L) within the BIG 1-98 trial. Methods: We generated gene expression profiles (Affymetrix) and computed the GGI for a matched, case-control sample of patients enrolled in the BIG 1-98 trial from the two hospitals where frozen samples were available. All relapses (cases) were identified from patients randomized to receive monotherapy or from the switching treatment arms for whom relapse occurred before the switch. Each case was randomly matched with four controls based upon nodal status and treatment (T or L). The prognostic value of GGI was assessed as a continuous predictor and divided at the median. Predictive accuracy of GGI was estimated using time-dependent area under the curve (AUC) of the ROC curves. Results: Frozen samples were analyzable for 48 patients (10 cases and 38 controls). Seven of the 10 cases had been assigned to receive L. Cases and controls were comparable with respect to menopausal and nodal status, local and chemotherapy, and HER2 positivity. Cases were slightly older than controls and had a larger proportion of large, poorly differentiated ER+/PgR- tumors. The GGI was significantly and linearly related to risk of relapse: each 10-unit increase in GGI resulted in an increase of approximately 11% in the hazard rate (p=0.02). Within the subgroups of patients with node-positive disease or who were treated with L, the hazard of relapse was significantly greater for patients with GGI at or above the median. AUC reached a maximum of 78% at 27 months. Conclusions: This analysis supports the GGI as a good predictor of relapse for ER-positive patients, even among patients who receive L. Validation of these results, in a larger series from BIG 1-98, is planned using the simplified GGI represented by a smaller set of genes and tested by qRT-PCR on paraffin-embedded tissues. Test whether the Gene expression Grade Index (GGI) is a potential predictor of relapse for endocrine-treated breast cancer patients in the BIG 1-98 trial. 55 microarray experiments from primary breast tumors of endocrine-treated patients. No replicate, no reference sample.

Project description:The gene expression patterns of favorable histology Wilms tumors (FHWT) that relapsed were compared with those that did not relapse using oligonucleotide arrays; Description: 250 FHWT of all stages enriched for relapses treated on National Wilms Tumor Study 5 passed quality parameters and were suitable for analysis using oligonucleotide arrays. Relapse risk stratification utilized Support Vector Machine; two and ten fold cross-validation was applied. The number of genes associated with relapse was less than that predicted by chance alone for 106 patients (32 relapses) with stages I and II FHWT and no further analyses were performed. This number was greater than expected by chance for 76 local stage III patients. Cross validation including an additional 68 local stage III patients (total 144 patients, 53 relapses) demonstrated that classifiers for relapse composed of 50 genes were associated with a median sensitivity of 47%, specificity 70%, and total error rate of 38%. Analysis of genes differentially expressed in relapse patients revealed apoptosis, Wnt signaling, IGF pathway, and epigenetic modification to be mechanisms important in relapse. Potential therapeutic targets include FRAP/MTOR and CD40. Experiment Overall Design: 144 stage 3 FHWT, with fifty-three relapses (cases) and ninety-one non-relapses (controls) with a minimum of three years follow-up, included all relapses and a 30% random selection of non-relapses for which frozen tumor tissue was available who passed all quality control parameters. The NWTS-5 protocol was approved by the review boards of institutions that registered patients. Histological diagnosis and local stage were confirmed by central review. Experiment Overall Design: Quality control steps taken: Experiment Overall Design: 1. Samples were snap frozen immediately following surgery and were mailed on dry ice to the Tumor Bank and retained at -80C. Experiment Overall Design: 2. Frozen sections were evaluated histologically and tumors with less than 80% viable tumor cellularity were excluded. Experiment Overall Design: 3. Array images were assessed by eye to confirm scanner alignment and the absence of significant bubbles or scratches. Experiment Overall Design: 4. Samples for which the 3'/5' ratios for GAPDH were greater than 3.2 were excluded. Experiment Overall Design: 5. The BioB spike controls were confirmed as present; BioC, BioD and cre were confirmed as increasing intensity. Experiment Overall Design: 6. When scaled to a target intensity of 2500, scaling factors were between 12 and 53; background levels were 34 â 115: Q values were 1.3 â 3.7 and mean intensities were within acceptable limits. Experiment Overall Design: 7. The range of percent present calls was from 38% to 52%. Experiment Overall Design: 8. Verification of gene expression was performed utilizing quantitative RT-PCR for five genes. Â Â Experiment Overall Design: Statistical Analysis: Positional-dependent-nearest-neighbor model (PDNN) software was used to translate the scanned images into expression analysis files and to normalize the data across all arrays (http://odin.mdacc.tmc.edu/~zhangli/PerfectMatch/). Genes with maximum expression less than a log scale of 6 across all tumors and Affymetrix control genes were excluded, resulting in 20,931 probe sets for analysis. Support Vector Machine (SVM) as developed by Chang and Lin (http://www.csie.ntu.edu.tw/~cjlin/libsvm) and implemented in an R software package, e1071 was chosen for relapse risk stratification using the p-value of the t-test comparison between case and control to select the genes. Using all 144 tumors, 109 genes were identified with p-value <0.001 and are provided in the data table labelled "t-test comparison between case and control" (ie, Supplemental table 2, in related publication)." Two and ten fold cross validations were utilized to investigate the ability of classifiers established in randomly selected training set to predict relapse in an independent test set comprised of the remaining tumors. For two-fold cross validation, the dataset was randomly divided 500 times into training and corresponding test sets of equal size, each including half the patients who relapsed. A classifier for relapse was identified for each training set and used to assign tumors in the corresponding test set to low and high risk categories. The training and test sets were then swapped. The number of top K genes in each classifier evaluated ranged from 1-150. Therefore, a total of 150,000 different classifiers were developed, one for each value of K from 1-150, for each of the 1,000 (500*2) training sets. For ten-fold cross validation the dataset was randomly divided 500 times into ten groups of approximately equal size. Each group included approximately the same number of relapses. For each such group, a classifier was built with the remaining 9/10 of the samples and then used to categorize tumors in the group as low or high risk; the process was repeated until all tumor samples were categorized as low or high risk. For all the cross validation procedures, to avoid gene-selection bias, classifiers were completely rebuilt in each cross validation iteration.

Dataset Information

Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells

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Prediction of acute multiple sclerosis relapses by transcription levels of peripheral blood cells.

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