Pediatric CNS tumor classification by DNA-methylation dataset
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ABSTRACT:
PROVIDER: EGAD00010002599 | EGA |
REPOSITORIES: EGA
Similar Datasets
Project description:In this study nanopore sequencing was applied to obtain sparse DNA methylation profiles from pediatric CNS tumor samples. A neural network was used to classify the tumor based on the obtained methylation profile.
2023-08-08 | GSE237874 | GEO
Project description:In this study nanopore sequencing was applied to obtain sparse DNA methylation profiles from pediatric CNS tumor samples. A neural network was used to classify the tumor based on the obtained methylation profile.
| EGAD00001011303 | EGA
Project description:The primary treatment of CNS tumors starts with a neurosurgical resection in order to obtain tumor tissue for diagnosis and to reduce tumor load and mass effect. The neurosurgeon has to decide between radical resection versus a more conservative strategy to prevent surgical morbidity. The prognostic impact of a radical resection varies between tumor types. However due to a lack of pre-operative tissue-based diagnostics, limited knowledge of the precise tumor type is available at the time of surgery. Current standard practice includes preoperative imaging and intraoperative histological analysis, but these are not always conclusive. After surgery, histopathological and molecular tests are performed to diagnose the precise tumor type. The results may indicate that an additional surgery is needed or that the initial surgery could have been less radical. Using rapid Nanopore sequencing, a sparse methylation profile can be directly obtained during surgery, making it ideally suited to enable intraoperative diagnostics. We developed a state-of-the-art neural-network approach called Sturgeon, to deliver trained models that are lightweight and universally applicable across patients and sequencing depths. We demonstrate our method to be accurate and fast enough to provide a correct diagnosis with as little as 20 to 40 minutes of sequencing data in 45 out of 49 pediatric samples, and inconclusive results in the other four. In four intraoperative cases we achieved a turnaround time of 60-90 minutes from sample biopsy to result; well in time to impact surgical decision making. We conclude that machine-learned diagnosis based on intraoperative sequencing can assist neurosurgical decision making, allowing neurological comorbidity to be avoided or preventing additional surgeries.
| EGAS00001007475 | EGA
Project description:Ultra-fast deep-learned pediatric CNS tumor classification
| PRJNA996977 | ENA
Project description:As part of the advancement in therapeutic decision-making for brain tumor patients at St. Jude Children’s Research Hospital (SJCRH), we developed three robust classifiers, a deep learning neural network (NN), k-nearest neighbor (kNN), and random forest (RF), trained on a reference series DNA-methylation profiles to classify central nervous system (CNS) tumor types. The models’ performance was rigorously validated against 2,054 samples from two independent cohorts. In addition to classic metrics of model performance, we compared the robustness of the three models to reduced tumor purity, a critical consideration in the clinical utility of such classifiers. Our findings revealed that the NN model exhibited the highest accuracy and maintained a balance between precision and recall. The NN model was the most resistant to drops in performance associated with a reduction in tumor purity, showing good performance until the purity fell below 50%. Through rigorous validation, our study emphasizes the potential of DNA-methylation-based deep learning methods to improve precision medicine for brain tumor classification in the clinical setting.
2024-10-10 | GSE276299 | GEO
Project description:Primitive neuroectodermal tumors of the central nervous system (CNS-PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children but also affecting adolescents and adults. Herein we demonstrate that a significant proportion of institutionally diagnosed CNS-PNETs display molecular profiles indistinguishable from those of various other well defined CNS tumor entities, facilitating diagnosis and appropriate therapy for patients with these tumors. From the remaining fraction of CNS-PNETs we identify four new CNS tumor entities, each associated with a recurrent genetic alteration and distinct histopathological and clinical features. These new molecular entities, designated “CNS neuroblastoma with FOXR2 activation (CNS NB-FOXR2)”, “CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT-CIC)”, “CNS high grade neuroepithelial tumor with MN1 alteration (CNS HGNET-MN1)”, and “CNS high grade neuroepithelial tumor with BCOR alteration (CNS HGNET-BCOR)”, will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by poorly differentiated CNS tumors.
| EGAS00001001632 | EGA
Project description:Primitive neuroectodermal tumors of the central nervous system (CNS PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children. Using DNA methylation and gene expression profiling we have demonstrated that a significant proportion of institutionally diagnosed CNS PNETs display molecular profiles indistinguishable from those of various other well defined CNS tumor entities, facilitating diagnosis and appropiate therapy for children with these tumors. From the remaining fraction of CNS PNETs, we have identified four distinct new CNS tumor entities extending to other neuroepithelial tumors, each associated with a recurrent genetic alteration and particular histopathological and clinical features. These molecular entities, designated âCNS Neuroblastoma with FOXR2 activation (CNS NB FOXR2)â, âCNS Ewing sarcoma family tumor with CIC alteration (CNS EFT CIC)â, âCNS high grade neuroepithelial tumor with MN1 alteration (CNS HGNET MN1)â, and âCNS high grade neuroepithelial tumor with BCOR alteration (CNS HGNET BCOR)â, will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by these poorly differentiated CNS tumors. 182 brain tumor samples were selected for RNA extraction and hybridization on Affymetrix Affymetrix Human Genome U133 Plus 2.0 Arrays.
2016-02-25 | E-GEOD-73038 | biostudies-arrayexpress
Project description:Using a public reference data set of 82 unique entities, 382 nanopore-sequenced brain tumor samples were classified based on their methylation status through an ad hoc random forest algorithm. As a measure of confidence, score recalibration was performed and platform-specific thresholds were defined.
2022-07-30 | GSE209865 | GEO
Project description:DNA methylation profiling of CNS tumors of the novel CNS embryonal tumor type ET, PLAGL
| PRJNA876274 | ENA
Project description:Primitive neuroectodermal tumors of the central nervous system (CNS PNETs) are highly aggressive, poorly differentiated embryonal tumors occurring predominantly in young children. Using DNA methylation and gene expression profiling we have demonstrated that a significant proportion of institutionally diagnosed CNS PNETs display molecular profiles indistinguishable from those of various other well defined CNS tumor entities, facilitating diagnosis and appropiate therapy for children with these tumors. From the remaining fraction of CNS PNETs, we have identified four distinct new CNS tumor entities extending to other neuroepithelial tumors, each associated with a recurrent genetic alteration and particular histopathological and clinical features. These molecular entities, designated “CNS Neuroblastoma with FOXR2 activation (CNS NB FOXR2)”, “CNS Ewing sarcoma family tumor with CIC alteration (CNS EFT CIC)”, “CNS high grade neuroepithelial tumor with MN1 alteration (CNS HGNET MN1)”, and “CNS high grade neuroepithelial tumor with BCOR alteration (CNS HGNET BCOR)”, will enable meaningful clinical trials and the development of therapeutic strategies for patients affected by these poorly differentiated CNS tumors.
2016-02-25 | GSE73038 | GEO