Predicting Relapse in Patients With Medulloblastoma by Integrating Evidence From Clinical and Genomic Feature
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PROVIDER: PRJNA832260 | ENA |
REPOSITORIES: ENA
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Project description:Despite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis. The training set consisted of 96 samples for which relapse status at 30 months post-treatment was known. Matched normal blood samples were collected through the COG Tumor Bank (protocol ACNS02B3) and from Children's Hospital Boston under institutional review board approval. The test set included 78 samples: 47 samples from our original study6 not used for training, 16 samples from Kool et al,33 and 15 samples from the COG Tumor Bank. All training and test samples correspond to patients at least 3 years old treated with conventional chemotherapy, surgical resection, and craniospinal irradiation. In the test set, 15 samples (Children's Oncology Group Tumor Bank) were processed as described above. Of those 15, a total of six have DNA copy number data. Another set of 47 samples was taken from the data set from Pomeroy et al. (Affymetrix Hu6800; Affymetrix). None of those samples had DNA copy number data. The remaining 16 samples, all with DNA copy number data, came from Kool et al. (Affymetrix U133; Affymetrix).
2022-04-28 | GSE201583 | GEO
Project description:PurposeDespite significant progress in the molecular understanding of medulloblastoma, stratification of risk in patients remains a challenge. Focus has shifted from clinical parameters to molecular markers, such as expression of specific genes and selected genomic abnormalities, to improve accuracy of treatment outcome prediction. Here, we show how integration of high-level clinical and genomic features or risk factors, including disease subtype, can yield more comprehensive, accurate, and biologically interpretable prediction models for relapse versus no-relapse classification. We also introduce a novel Bayesian nomogram indicating the amount of evidence that each feature contributes on a patient-by-patient basis.Patients and methodsA Bayesian cumulative log-odds model of outcome was developed from a training cohort of 96 children treated for medulloblastoma, starting with the evidence provided by clinical features of metastasis and histology (model A) and incrementally adding the evidence from gene-expression-derived features representing disease subtype-independent (model B) and disease subtype-dependent (model C) pathways, and finally high-level copy-number genomic abnormalities (model D). The models were validated on an independent test cohort (n = 78).ResultsOn an independent multi-institutional test data set, models A to D attain an area under receiver operating characteristic (au-ROC) curve of 0.73 (95% CI, 0.60 to 0.84), 0.75 (95% CI, 0.64 to 0.86), 0.80 (95% CI, 0.70 to 0.90), and 0.78 (95% CI, 0.68 to 0.88), respectively, for predicting relapse versus no relapse.ConclusionThe proposed models C and D outperform the current clinical classification schema (au-ROC, 0.68), our previously published eight-gene outcome signature (au-ROC, 0.71), and several new schemas recently proposed in the literature for medulloblastoma risk stratification.
| S-EPMC3082982 | biostudies-literature
Project description:Gene expression profiles were analyzed from 60 children with first bone marrow relapse. Genes and pathways differentiating early vs. late relapse were identified. Signatures predicting early response to therapy were also identified. Keywords: ordered
2006-03-20 | GSE3911 | GEO
Project description:Gene expression profiles were analyzed from 60 children with first bone marrow relapse. Genes and pathways differentiating early vs. late relapse were identified. Signatures predicting early response to therapy were also identified. Experiment Overall Design: 60 patients at relapse were analyzed
2008-06-13 | E-GEOD-3911 | biostudies-arrayexpress
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. Keywords: Classification by microarray analysis
2009-01-30 | GSE10320 | GEO
Project description:Objective: A subset of Crohns disease (CD) patients experiences long-term remission after infliximab withdrawal. Biomarkers are needed to identify those patients.Design: New biomarkers of relapse were searched in the baseline serum of CD patients stopping infliximab when they were under combined therapy (antimetabolite and infliximab) and stable clinical remission (STORI cohort, n=102). From shotgun proteomics experiment (discovery step), biomarker candidates were identified and further targeted by selected reaction monitoring (verification step). The dataset was stratified to search for markers of short- (<6 months) or long-term relapse (>6 months). The risk of relapse and the predicting capacity associated with biomarker candidates were evaluated using univariate Cox model and log-rank statistic, respectively. To test their complementary predicting capacity, biomarker candidates were systematically combined in pairs. Results: Distinct biomarker candidates were associated with the risk (hazard ratio: HR) of short- (15 proteins, 2.9<HR<16.1, p<0.05) and long-term (17 proteins, 2<HR<4.4, p<0.05) relapse, they reflect different pathophysiological processes. In stratified and non-stratified datasets, novel marker combinations exhibited a high predicting capacity as shown by their higher Z-scores (FDR<0.001) than CRP and faecal calprotectin (current references in predicting relapse). Conclusion: We identified for the first time circulating biomarker candidates associated with the risk of long-term relapse in CD patients stopping infliximab. We also highlight a sequence of pathophysiological processes leading to relapse, this could help to better understand the disease progression. Our findings may pave the way for a better non-invasive evaluation of the risk of relapse when contemplating anti-TNFα withdrawal in CD patients.
2020-10-19 | PXD019008 | Pride
Project description:Relapse of acute myeloid leukemia (AML) is highly aggressive and often treatment refractory. We analyzed previously published AML relapse cohorts and found that 40% of relapses occur without changes in driver mutations, suggesting that non-genetic mechanisms drive relapse in a large proportion of cases. We therefore characterized epigenetic patterns of AML relapse using 26 matched diagnosis-relapse samples with ATAC-seq. This analysis identified a relapse-specific chromatin accessibility signature for mutationally stable AML, suggesting that AML undergoes epigenetic evolution at relapse independent of mutational changes. Analysis of leukemia stem cell (LSC) chromatin changes at relapse indicated that this leukemic compartment underwent significantly less epigenetic evolution than non-LSCs, while epigenetic changes in non-LSCs reflected overall evolution of the bulk leukemia. Finally, we used single-cell ATAC-seq paired with mitochondrial sequencing (mtscATAC) to map clones from diagnosis into relapse along with their epigenetic features. We found that distinct mitochondrially-defined clones exhibit more similar chromatin accessibility at relapse relative to diagnosis, demonstrating convergent epigenetic evolution in relapsed AML. These results demonstrate that epigenetic evolution is a feature of relapsed AML and that convergent epigenetic evolution can occur following treatment with induction chemotherapy.
2024-04-17 | GSE256495 | GEO
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.
2009-02-09 | E-GEOD-10320 | biostudies-arrayexpress
Project description:We performed genome wide miRNA expression analysis to investigate the ability of miRNAs in predicting leukemia relapse from 33 t(8;21) pediatric AMLs at diagnosis divided into a training and a validation cohort. COX regression analysis and ROC curves were used to identify a powerful miRNA signature able to predict relapse in t(8;21) AML patients. miRNA profiling showed that patients with high- or low- risk of relapse had different expression profiles. We support the role of microRNAs in modulating the prognosis of t(8;21)-AML and the use of a miRNA expression signature at diagnosis to better define standard risk cases in pediatric AML.
2017-09-14 | GSE97135 | GEO
Project description:We undertook a comprehensive clinical and biological investigation of serial medulloblastoma biopsies obtained at diagnosis and relapse. Combined MYC gene family amplifications and P53 pathway defects commonly emerged at relapse, and all patients in this molecular group died of rapidly progressive disease post-relapse. To study this genetic interaction, we investigated a transgenic model of MYCN-driven medulloblastoma and found spontaneous development of Trp53 inactivating mutations. Abrogation of Trp53 function in this model produced aggressive tumors that mimicked the characteristics of relapsed human tumors with combined P53–MYC dysfunction. Restoration of p53 activity, genetic and therapeutic suppression of MYCN all reduced tumor growth and prolonged survival. Our findings identify P53–MYC interactions which emerge at medulloblastoma relapse as biomarkers of clinically aggressive disease that may be targeted therapeutically. There are currently no effective therapies for children with relapsed medulloblastoma. While clinical and biological features of the disease at diagnosis are increasingly well understood, biopsy is rarely performed at relapse and few biological data are available to guide more effective treatments. Here, we show that medulloblastomas develop altered biology at relapse which is predictive of disease course and cannot be detected at diagnosis. We have discovered the emergence of P53–MYC interactions at relapse, as biomarkers of clinically aggressive relapsed disease, which can be modelled and targeted therapeutically in genetically-engineered mice. These data provide clear precedent for the incorporation of biopsy at relapse into routine clinical practice, to direct palliative care and the development of improved treatment strategies. mouse model expression profiles from various mouse Medulloblastoma models MYCN/MYC overexpressing with/without p53 Mutations were compared to human MB expression profiles using Non-negative Matrix Factorization projection in order to sub-type the mouse models
2015-04-23 | E-GEOD-62625 | biostudies-arrayexpress