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Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

ABSTRACT: Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

PROVIDER: PRJNA889441 | ENA |

REPOSITORIES: ENA

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Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology

Project description:To assess their utility in routine neuropathology, we prospectively integrated DNA methylation-based CNS tumor classification and targeted gene panel sequencing of tumor and constitutional DNA with blinded neuropathological reference diagnostics for a population-based cohort of > 1,200 newly-diagnosed pediatric patients.

2023-01-30 | GSE215240 | GEO

Multiomic neuropathology improves diagnostic accuracy in pediatric neuro-oncology.

Project description:The large diversity of central nervous system (CNS) tumor types in children and adolescents results in disparate patient outcomes and renders accurate diagnosis challenging. In this study, we prospectively integrated DNA methylation profiling and targeted gene panel sequencing with blinded neuropathological reference diagnostics for a population-based cohort of more than 1,200 newly diagnosed pediatric patients with CNS tumors, to assess their utility in routine neuropathology. We show that the multi-omic integration increased diagnostic accuracy in a substantial proportion of patients through annotation to a refining DNA methylation class (50%), detection of diagnostic or therapeutically relevant genetic alterations (47%) or identification of cancer predisposition syndromes (10%). Discrepant results by neuropathological WHO-based and DNA methylation-based classification (30%) were enriched in histological high-grade gliomas, implicating relevance for current clinical patient management in 5% of all patients. Follow-up (median 2.5 years) suggests improved survival for patients with histological high-grade gliomas displaying lower-grade molecular profiles. These results provide preliminary evidence of the utility of integrating multi-omics in neuropathology for pediatric neuro-oncology.

| S-EPMC10115638 | biostudies-literature

Advanced molecular neuropathology to increase diagnostic accuracy in pediatric neurooncology

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Analysis of Histological, Genomic, Molecular, and Clinical Factors in CNS Cancer: The Neuro-Oncology Group

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Analysis of Histological, Genomic, Molecular, and Clinical Factors in CNS Cancer: The Neuro-Oncology Group

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Diagnostic accuracy of liquid biopsy in endometrial cancer

Project description:Diagnostic accuracy of liquid biopsy in endometrial cancer

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Diagnostic accuracy of liquid biopsy in endometrial cancer

Project description:INTRODUCTION. Liquid biopsies are a minimally invasive collection of a patient body fluid sample. In oncology, they offer several advantages compared to traditional tissue biopsies. However, potential of this method in endometrial cancer (EC) remains poorly explored. We studied the utility of tumor educated platelets (TEPs) and circulating tumor DNA (ctDNA) for preoperative EC diagnosis, including histology determination. MATERIALS AND METHODS. TEPs from 297 subjects (53 EC patients, 40 patients with benign gynecologic conditions and 204 healthy women) were RNA-sequenced. DNA sequencing was performed in 519 primary tumor tissue samples and in16 plasma samples. Artificial intelligence was applied to sample classification. RESULTS. Platelet-dedicated classifier yielded AUC of 93.1% in test set when discriminating between healthy subjects and cancer patients. However, the discrimination between endometrial cancer and benign gynecologic conditions was relatively low, with AUC of 60.7%. ctDNA-dedicated classifier discriminated primary tumor tissue samples with AUC of 91.4% and ctDNA blood samples with AUC of 87.5%. CONCLUSIONS Liquid biopsies show potential in EC diagnosis. Both TEPs and ctDNA profiles coupled with artificial intelligence constitute a source of useful information. Further work, involving more cases, is warranted.

2021-12-01 | GSE184904 | GEO

Assessment of Spatial Genomics for Oncology Discovery

Project description:Tumor heterogeneity is a major challenge for oncology drug discovery and development. Understanding of the spatial tumor landscape is key to identifying new targets and impactful model systems. Here, we test the utility of spatial transcriptomics (ST) for Oncology Discovery by profiling 40 tissue sections and 80,024 capture spots across a diverse set of tissue types, sample formats, and RNA capture chemistries. We verify the accuracy and fidelity of ST by leveraging matched pathology analysis that provide a ground truth for tissue section composition. We then use spatial data to demonstrate the capture of key tumor depth features, identifying hypoxia, necrosis, vasculature, and extracellular matrix variation. We also leverage spatial context to identify relative cell type locations showing the anti-correlation of tumor and immune cells in syngeneic cancer models. Lastly, we demonstrate target identification approaches in clinical pancreatic adenocarcinoma samples, highlighting tumor intrinsic biomarkers and paracrine signaling.

2022-11-01 | GSE211895 | GEO

Beta‑blockers prolong response to androgen deprivation therapy in prostate cancer through modulation of the neuro‑immuno‑oncology axis

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