Transcriptome analysis of desmoplastic small round cell tumors identifies actionable therapeutic targets: a report from the Children's Oncology Group
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA657254 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR12454775.fastq.gz | Fastqsanger.gz | |||
| SRR12454776.fastq.gz | Fastqsanger.gz | |||
| SRR12454777.fastq.gz | Fastqsanger.gz | |||
| SRR12454778.fastq.gz | Fastqsanger.gz | |||
| SRR12454779.fastq.gz | Fastqsanger.gz |
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Project description:The oncoprotein encoded by EWS-WT1, the hallmark diagnostic alteration in DSRCT, binds to FGFR4 and IGF2.
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Project description:To further understand the molecular pathogenesis of desmoplastic small round cell tumor (DSRCT), a fatal malignancy occurring primarily in adolescent/young adult males, we used next-generation RNA sequencing to investigate the gene expression profiles intrinsic to this disease. RNA from DSRCT specimens obtained from the Children's Oncology Group was sequenced using the Illumina HiSeq 2000 system and subjected to bioinformatic analyses. Validation and functional studies included WT1 ChIP-seq, EWS-WT1 knockdown using JN-DSRCT-1 cells and immunohistochemistry. A panel of immune signature genes was also evaluated to identify possible immune therapeutic targets. Twelve of 14 tumor samples demonstrated presence of the diagnostic EWSR1-WT1 translocation and these 12 samples were used for the remainder of the analysis. RNA sequencing confirmed the lack of full-length WT1 in all fusion positive samples as well as the JN-DSRCT-1 cell line. ChIP-seq for WT1 showed significant overlap with genes found to be highly expressed, including IGF2 and FGFR4, which were both highly expressed and targets of the EWS-WT1 fusion protein. In addition, we identified CD200 and CD276 as potentially targetable immune checkpoints whose expression is independent of the EWS-WT1 fusion gene in cultured DSCRT cells. In conclusion, we identified IGF2, FGFR4, CD200, and CD276 as potential therapeutic targets with clinical relevance for patients with DSRCT.
| S-EPMC7378211 | biostudies-literature
Project description:Gene Expression Classifiers for Minimal Residual Disease and Relapse Free Survival Improve Outcome Prediction and Risk Classification in Children with High Risk Acute Lymphoblastic Leukemia: A Children's Oncology Group Study
2015-05-12 | GSE68735 | GEO
Project description:Gene Expression Classifiers for Minimal Residual Disease and Relapse Free Survival Improve Outcome Prediction and Risk Classification in Children with High Risk Acute Lymphoblastic Leukemia: A Children's Oncology Group Study willm-00140 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133_Plus_2 Organism: Homo sapiens (ncbitax) Tissue Sites: Blood, Bone marrow Material Types: Peripheral Blood, Bone Marrow Disease States: Childhood Precursor B-Lymphoblastic Leukemia
2015-05-12 | E-GEOD-68735 | biostudies-arrayexpress
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.
2009-06-25 | E-GEOD-11877 | biostudies-arrayexpress
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration.
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2015-08-07 | E-GEOD-55150 | biostudies-arrayexpress
Project description:Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n = 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly different survivals (5-year EFS of 16% vs 64%; P < .001). When infants with MLL-AFF1 were analyzed separately, a 7-gene classifier was developed that split them into 2 distinct groups with significantly different outcomes (5-year EFS of 20% vs 65%; P < .001). In this classifier, elevated expression of NEGR1 was associated with better EFS, whereas IRX2, EPS8, and TPD52 expression were correlated with worse outcome. This classifier also predicted EFS in an independent infant ALL cohort from the Interfant-99 trial. When evaluating expression profiles as a continuous variable relative to patient age, we further identified striking differences in profiles in infants less than or equal to 90 days of age and those more than 90 days of age. These age-related patterns suggest different mechanisms of leukemogenesis and may underlie the differential outcomes historically seen in these age groups. EXP-520 Assay Type: Gene Expression Provider: Affymetrix Array Designs: HG-U133_Plus_2 Organism: Homo sapiens (ncbitax)
2015-05-12 | E-GEOD-68720 | biostudies-arrayexpress
Project description:Gene expression profiling was performed on 97 cases of infant ALL from Children's Oncology Group Trial P9407. Statistical modeling of an outcome predictor revealed 3 genes highly predictive of event-free survival (EFS), beyond age and MLL status: FLT3, IRX2, and TACC2. Low FLT3 expression was found in a group of infants with excellent outcome (n = 11; 5-year EFS of 100%), whereas differential expression of IRX2 and TACC2 partitioned the remaining infants into 2 groups with significantly different survivals (5-year EFS of 16% vs 64%; P < .001). When infants with MLL-AFF1 were analyzed separately, a 7-gene classifier was developed that split them into 2 distinct groups with significantly different outcomes (5-year EFS of 20% vs 65%; P < .001). In this classifier, elevated expression of NEGR1 was associated with better EFS, whereas IRX2, EPS8, and TPD52 expression were correlated with worse outcome. This classifier also predicted EFS in an independent infant ALL cohort from the Interfant-99 trial. When evaluating expression profiles as a continuous variable relative to patient age, we further identified striking differences in profiles in infants less than or equal to 90 days of age and those more than 90 days of age. These age-related patterns suggest different mechanisms of leukemogenesis and may underlie the differential outcomes historically seen in these age groups.
2015-05-12 | GSE68720 | GEO