Circulating tumor DNA sequencing provides comprehensive mutation profiling for pediatric central nervous system tumors
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE210323 | GEO | 2022/08/03
REPOSITORIES: GEO
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Project description:Background In childhood acute lymphoblastic leukemia (ALL), central nervous system (CNS) involvement is rare at diagnosis (1-4%), but more frequent at relapse (~30%). Minimal residual disease diagnostics predict most bone marrow (BM) relapses, but likely cannot predict isolated CNS relapses. Consequently, CNS relapses may become relatively more important. Because of the significant late sequelae of CNS treatment, early identification of patients at risk of CNS relapse is crucial. Methods Gene expression profiles of ALL cells from cerebrospinal fluid (CSF) and ALL cells from BM were compared and differences were confirmed by real-time quantitative PCR. For a selected set of overexpressed genes, protein expression levels of ALL cells in CSF at relapse and of ALL cells in diagnostic BM samples were evaluated by 8-color flow cytometry. Results CSF-derived ALL cells showed a clearly different gene expression profile than BM-derived ALL cells, with differentially-expressed genes (including SCD and OPN) involved in survival and apoptosis pathways and linked to the JAK-STAT pathway. Flowcytometric analysis showed that a subpopulation of ALL cells (>1%) with a â??CNS signatureâ?? (SCD positivity and increased OPN expression) was already present in BM at diagnosis in ALL patients who later developed a CNS relapse, but was <1% or absent in virtually all other patients. Conclusions The presence of a subpopulation of ALL cells with a â??CNS signatureâ?? at diagnosis may predict isolated CNS relapse. Such information can be used to design new diagnostic and treatment strategies that aim at prevention of CNS relapse with reduced toxicity. We compared gene expression profiles of ALL cells obtained from cerebrospinal fluid (n=8) with profiles of ALL cells obtained from bone marrow at diagnosis (n=22) or at relapse (n=20)
2015-12-31 | E-GEOD-60926 | biostudies-arrayexpress
Project description:Acute lymphoblastic leukemia (ALL) has a striking propensity to metastasize to the central nervous system (CNS).1 In contrast to solid tumor brain metastases, ALL seldom involves the parenchyma but is isolated to the leptomeninges, an infrequent site for carcinomatous invasion.2,3 While CNS metastasis is characteristic across ALL subtypes, a unifying mechanism for invasion has not been determined. Here we show that ALL cells in circulation are unable to breach the blood brain barrier; instead they migrate into the CNS along vessels that passage directly between vertebral or calvarial bone marrow (BM) and the subarachnoid space. The basement membrane of these bridging vessels is enriched in laminin, known to coordinate neuronal progenitor cell pathfinding in the CNS.4-6 The laminin receptor a6 integrin is expressed by most ALL.7,8 We found that a6/laminin mediated ALL migration toward cerebrospinal fluid (CSF) in vitro. ALL-xenografted mice treated with either a PI3Kd inhibitor that decreased ALL a6 expression or specific a6 neutralizing antibodies showed significantly less ALL transit along bridging vessels, CSF blast counts and CNS disease symptoms despite minimally decreased BM disease burden. Our data suggest that a6 integrin expression, common in ALL, allows cells to coopt neural migratory pathways to invade the CNS.
2018-07-12 | GSE114627 | GEO
Project description:Background In childhood acute lymphoblastic leukemia (ALL), central nervous system (CNS) involvement is rare at diagnosis (1-4%), but more frequent at relapse (~30%). Minimal residual disease diagnostics predict most bone marrow (BM) relapses, but likely cannot predict isolated CNS relapses. Consequently, CNS relapses may become relatively more important. Because of the significant late sequelae of CNS treatment, early identification of patients at risk of CNS relapse is crucial. Methods Gene expression profiles of ALL cells from cerebrospinal fluid (CSF) and ALL cells from BM were compared and differences were confirmed by real-time quantitative PCR. For a selected set of overexpressed genes, protein expression levels of ALL cells in CSF at relapse and of ALL cells in diagnostic BM samples were evaluated by 8-color flow cytometry. Results CSF-derived ALL cells showed a clearly different gene expression profile than BM-derived ALL cells, with differentially-expressed genes (including SCD and OPN) involved in survival and apoptosis pathways and linked to the JAK-STAT pathway. Flowcytometric analysis showed that a subpopulation of ALL cells (>1%) with a “CNS signature” (SCD positivity and increased OPN expression) was already present in BM at diagnosis in ALL patients who later developed a CNS relapse, but was <1% or absent in virtually all other patients. Conclusions The presence of a subpopulation of ALL cells with a “CNS signature” at diagnosis may predict isolated CNS relapse. Such information can be used to design new diagnostic and treatment strategies that aim at prevention of CNS relapse with reduced toxicity.
2015-12-31 | GSE60926 | GEO
Project description:Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment.
2023-12-31 | GSE241396 | GEO
Project description:Central nervous system (CNS) tumors are the leading cause of pediatric cancer death, and these patients have an increased risk for developing secondary neoplasms. Due to the low prevalence of pediatric CNS tumors, major advances in targeted therapies have been lagging compared to other adult tumors. We collected single nuclei RNA-seq data from 35 pediatric CNS tumors and three non-tumoral pediatric brain tissues (84,700 nuclei) and characterized tumor heterogeneity and transcriptomic alterations. We distinguished cell subpopulations associated with specific tumor types including radial glial cells in ependymomas and oligodendrocyte precursor cells in astrocytomas. In tumors, we observed pathways important in neural stem cell-like populations, a cell type previously associated with therapy resistance. Lastly, we identified transcriptomic alterations among pediatric CNS tumor types compared to non-tumor tissues, while accounting for cell type effects on gene expression. Our results suggest potential tumor type and cell type-specific targets for pediatric CNS tumor treatment.
2023-08-08 | GSE211362 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-14 | GSE90496 | GEO
Project description:Background: Modern neuropathology is challenged by an increasing number of clinically-relevant CNS tumor subgroups that require assessment of a multitude of molecular markers for classification, as well a highly trained medical staff. Failure to meet this challenge leads to tumor misclassification, which can have severe consequences for affected patients. Methods: We compiled a cohort of genome-wide DNA methylation profiles of 2,682 tumors from 82 histologically and/or molecularly distinct CNS tumor classes across all ages and histologies that served as reference for a Random Forest-based diagnostic classifier. This classifier was used to prospectively investigate a further 1,104 CNS tumor samples in order to determine its clinical utility. Results: The classifier was able to reliably assign tumor samples to a given diagnostic category with a misclassification rate of less than 2%. The system functioned robustly across laboratories and using different DNA methylation profiling techniques. Prospective application to clinical samples resulted in a reclassification of 12% of tumors compared with standard practice alone. A further 12% could not be classified by methylation profiling – this subset was highly enriched for unusual syndrome-associated tumors and likely novel entities. Conclusion: This study represents a proof-of-concept for the application of machine learning approaches in molecular diagnostics using a single, easy-to-use assay. The reference cohort and Random Forest-based classifier are available online as a valuable community tool for improving precision in brain tumor diagnostics. We expect that approaches similar to the one presented herein will rapidly restructure diagnostic practice in neurooncology and across tumor pathology.
2018-01-18 | GSE109379 | GEO
Project description:In this study we identified genes differentially expressed in the central nervous system (CNS) of mice during infection with mouse-adapted scrapie agents. We used cDNA microarrays to examine gene expression profiles at early, mid (pre-clinical) and late (clinical) time points after inoculation. Keywords = scrapie Keywords = prion Keywords = CNS Keywords = BMAP
2004-10-13 | E-GEOD-1840 | biostudies-arrayexpress
Project description:Using a quantitative alternative splicing (AS) microarray platform, we have identified a large number of widely-expressed mouse genes containing single or coordinated-pairs of alternative exons that are spliced in a tissue-regulated fashion. The majority of these AS events display differential regulation in central nervous system (CNS) tissues. Approximately half of the corresponding genes have neural-specific functions and operate in common processes and interconnected pathways. Differential regulation of AS in the CNS tissues correlates strongly with a set of mostly new motifs that are predominantly located in the intron and constitutive exon sequences neighboring CNS-regulated alternative exons. Different subsets of these motifs are correlated with either increased inclusion or increased exclusion of alternative exons in CNS-tissues, relative to the other profiled tissues. Keywords: Alternative splicing
2007-08-01 | GSE8081 | GEO
Project description:Embryonal tumours of the central nervous system (CNS) represent a heterogeneous group of tumours about which little is known biologically, and whose diagnosis, on the basis of morphologic appearance alone, is controversial. Medulloblastomas, for example, are the most common malignant brain tumour of childhood, but their pathogenesis is unknown, their relationship to other embryonal CNS tumours is debated, and patients' response to therapy is difficult to predict. We approached these problems by developing a classification system based on DNA microarray gene expression data derived from 99 patient samples. Here we demonstrate that medulloblastomas are molecularly distinct from other brain tumours including primitive neuroectodermal tumours (PNETs), atypical teratoid/rhabdoid tumours (AT/RTs) and malignant gliomas. Previously unrecognized evidence supporting the derivation of medulloblastomas from cerebellar granule cells through activation of the Sonic Hedgehog (SHH) pathway was also revealed. We show further that the clinical outcome of children with medulloblastomas is highly predictable on the basis of the gene expression profiles of their tumours at diagnosis. golub-00460 Assay Type: Gene Expression Provider: Affymetrix Array Designs: Hu6800 Organism: Homo sapiens (ncbitax) Material Types: synthetic_RNA, organism_part, whole_organism, total_RNA Disease States: synthetic_RNA, organism_part, whole_orMedulloblastoma, renal rhabdoid tumor, Atypical Teratoid/Rhabdoid Tumor, Supratentorial PNET, Supratentorial PNET (pineoblastoma), Normal, Malignant Glioma, Extrarenal Rhabdoid Tumorganism, total_RNA
2015-05-16 | E-GEOD-68956 | biostudies-arrayexpress