Dataset Information

Molecular biomarkers and integrated pathological diagnosis in the reclassification of gliomas.

ABSTRACT: The present study aimed to evaluate the impact caused by the 2016 World Health Organization (WHO) diagnostic classification of gliomas in 139 patients studied in Argentina. Formalin-fixed paraffin-embedded tissues were used for histological and immunohistochemical analysis [glial fibrillary acidic protein, KI67, synaptophysin and isocitrate dehydrogenase (IDH)1-R132H]. DNA from formalin-fixed paraffin-embedded tissues was used for molecular analysis: 1p/19q co-deletion and mutation status of the IDH gene. These experiments were performed by direct Sanger sequencing and multiplex ligation-dependent probe amplification. According to the new classification, diagnoses included oligodendroglioma IDH-mutant and 1p/19q co-deletion (4.20%), anaplastic oligodendroglioma IDH-mutant and 1p/19q co-deletion (2.52%), diffuse astrocytoma IDH-mutant (6.72%), diffuse astrocytoma IDH-wild type (1.68%), anaplastic astrocytoma IDH-mutant (5.04%), anaplastic astrocytoma IDH-wild type (8.40%), glioblastoma IDH-mutant (5.88%) and glioblastoma IDH-wild type (65.56%). Regarding tumor histology, 60% of oligodendrogliomas, 35% of astrocytoma and 100% of unclassified gliomas were re-classified, while glioblastomas maintained their initial classification. Additionally, the present study evaluated the prognostic value of the histological grade for the 2007 and 2016 WHO classifications of gliomas. The histological subgroup associated with longer overall survival (OS) was grade II glioma (OS-2007WHO, 35.6 months; and OS-2016WHO, 47.7 months). Glioblastoma was the subgroup associated with a poor outcome (OS-2007WHO, 10.4 months; and OS-2016WHO, 11.1 months). The present study evaluated the OS of tumor grade subgroups with respect to their IDH status. For all subgroups, IDH-mutant tumors were associated with an improved prognosis compared with IDH-wild type tumors. The results suggested that the incorporation of molecular biomarkers in the new WHO classification improves tumor characterization and prognostic value of the subgroups.

SUBMITTER: Ruiz MF

PROVIDER: S-EPMC8204142 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Integrated bioinformatics and statistical approaches are now playing the vital role in identifying potential molecular biomarkers more accurately in presence of huge number of alternatives for disease diagnosis, prognosis and therapies by reducing time and cost compared to the wet-lab based experimental procedures. Breast cancer (BC) is one of the leading causes of cancer related deaths for women worldwide. Several dry-lab and wet-lab based studies have identified different sets of molecular biomarkers for BC. But they did not compare their results to each other so much either computationally or experimentally. In this study, an attempt was made to propose a set of molecular biomarkers that might be more effective for BC diagnosis, prognosis and therapies, by using the integrated bioinformatics and statistical approaches. At first, we identified 190 differentially expressed genes (DEGs) between BC and control samples by using the statistical LIMMA approach. Then we identified 13 DEGs (AKR1C1, IRF9, OAS1, OAS3, SLCO2A1, NT5E, NQO1, ANGPT1, FN1, ATF6B, HPGD, BCL11A, and TP53INP1) as the key genes (KGs) by protein-protein interaction (PPI) network analysis. Then we investigated the pathogenetic processes of DEGs highlighting KGs by GO terms and KEGG pathway enrichment analysis. Moreover, we disclosed the transcriptional and post-transcriptional regulatory factors of KGs by their interaction network analysis with the transcription factors (TFs) and micro-RNAs. Both supervised and unsupervised learning's including multivariate survival analysis results confirmed the strong prognostic power of the proposed KGs. Finally, we suggested KGs-guided computationally more effective seven candidate drugs (NVP-BHG712, Nilotinib, GSK2126458, YM201636, TG-02, CX-5461, AP-24534) compared to other published drugs by cross-validation with the state-of-the-art alternatives top-ranked independent receptor proteins. Thus, our findings might be played a vital role in breast cancer diagnosis, prognosis and therapies.

Project description:The "integrated diagnosis" for infiltrating gliomas in the 2016 revised World Health Organization (WHO) classification of tumors of the central nervous system requires assessment of the tumor for IDH mutations and 1p/19q codeletion. Since TERT promoter mutations and ATRX alterations have been shown to be associated with prognosis, we analyzed whether these tumor markers provide additional prognostic information within each of the five WHO 2016 categories. We used data for 1206 patients from the UCSF Adult Glioma Study, the Mayo Clinic and The Cancer Genome Atlas (TCGA) with infiltrative glioma, grades II-IV for whom tumor status for IDH, 1p/19q codeletion, ATRX, and TERT had been determined. All cases were assigned to one of 5 groups following the WHO 2016 diagnostic criteria based on their morphologic features, and IDH and 1p/19q codeletion status. These groups are: (1) Oligodendroglioma, IDH-mutant and 1p/19q-codeleted; (2) Astrocytoma, IDH-mutant; (3) Glioblastoma, IDH-mutant; (4) Glioblastoma, IDH-wildtype; and (5) Astrocytoma, IDH-wildtype. Within each group, we used univariate and multivariate Cox proportional hazards models to assess associations of overall survival with patient age at diagnosis, grade, and ATRX alteration status and/or TERT promoter mutation status. Among Group 1 IDH-mutant 1p/19q-codeleted oligodendrogliomas, the TERT-WT group had significantly worse overall survival than the TERT-MUT group (HR: 2.72, 95% CI 1.05-7.04, p = 0.04). In both Group 2, IDH-mutant astrocytomas and Group 3, IDH-mutant glioblastomas, neither TERT mutations nor ATRX alterations were significantly associated with survival. Among Group 4, IDH-wildtype glioblastomas, ATRX alterations were associated with favorable outcomes (HR: 0.36, 95% CI 0.17-0.81, p = 0.01). Among Group 5, IDH-wildtype astrocytomas, the TERT-WT group had significantly better overall survival than the TERT-MUT group (HR: 0.48, 95% CI 0.27-0.87), p = 0.02). Thus, we present evidence that in certain WHO 2016 diagnostic groups, testing for TERT promoter mutations or ATRX alterations may provide additional useful prognostic information.

Project description:BACKGROUND:The subtyping of breast cancer based on features of tumour biology such as hormonal receptor and HER2 status has led to increasingly patient-specific treatment and thus improved outcomes. However, such subgroups may not be sufficiently informed to best predict outcome and/or treatment response. The incorporation of multi-modal data may identify unexpected and actionable subgroups to enhance disease understanding and improve outcomes. METHODS:This retrospective cross-sectional study used the cancer registry Surveillance, Epidemiology and End Results (SEER), which represents 28% of the U.S. population. We included adult female patients diagnosed with breast cancer in 2010. Latent class analysis (LCA), a data-driven technique, was used to identify clinically homogeneous subgroups ("endophenotypes") of breast cancer from receptor status (hormonal receptor and HER2), clinical, and demographic data and each subgroup was explored using Bayesian networks. RESULTS:Included were 44,346 patients, 1257 (3%) of whom had distant organ metastases at diagnosis. Four endophenotypes were identified with LCA: 1) "Favourable biology" had entirely local disease with favourable biology, 2) "HGHR-" had the highest incidence of HR- receptor status and highest grade but few metastases and relatively good outcomes, 3) "HR+ bone" had isolated bone metastases and uniform receptor status (HR+/HER2-), and 4) "Distant organ spread" had high metastatic burden and poor survival. Bayesian networks revealed clinically intuitive interactions between patient and disease features. CONCLUSIONS:We have identified four distinct subgroups of breast cancer using LCA, including one unexpected group with good outcomes despite having the highest average histologic grade and rate of HR- tumours. Deeper understanding of subgroup characteristics can allow us to 1) identify actionable group properties relating to disease biology and patient features and 2) develop group-specific diagnostics and treatments.

Dataset Information

Molecular biomarkers and integrated pathological diagnosis in the reclassification of gliomas.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets