Chertenko T. Diagnostics and prognosis of astrocytic brain tumors (clinic-morphological and moleculo-biological features)

Українська версія

Thesis for the degree of Candidate of Sciences (CSc)

State registration number

0419U001504

Applicant for

Specialization

  • 14.03.02 - Патологічна анатомія

05-06-2019

Specialized Academic Board

Д 64.600.03

Essay

In this thesis was performed for the first time the complex study of diffuse astrocytic tumors Grade III-IV with investigation of their epidemiological, clinico-morphological, immunohistochemical and cytogenetical features and the determination of their impacts to the aggressive capability of the tumors. Epidemiological study allowed to clarify data about the incidence rates and the place of diffuse astrocytic tumors among the incidence rates of primary CNS tumors and among their ralapses in the Kharkiv region. It was elaborated the data about additional mutations in IDH1: it was found an association between IDH1105GGTSNP mutation and anaplastic astrocytoma phenotype. This mutation was also associated with remoted relapses. It was obtained the new data about molecule-biological features of relapses and criteria of tumor progression such as: the elevation of Ki-67 expression in early relapses compared with their primary tumors, the higher level of EGFR expression in the group of tumors with relapses within a year after surgery. It was found the strong corelation the number of MMP-9-positive cells and the vascularization index in relapses. The correlations between EGFR and p53, EGFR and MMP-9 were found in primary tumors. It was studied the place of immune cell infiltration in tumor progression (the significant evaluation of CD8-positive cell infiltration in relapses, the association of CD68(++/+++) infiltration with relapses within a year after first surgery). The favorable immune pattern was СD3(-/+)/CD4(++/+++)/CD8(++/+++). It was first recommended the mathematical model of the prediction of glioblastoma progression within a year after first surgery. This model helps to predict the relapse with the accuracy of 86,7% using such criteria as age, EGFR expression, CD68 and T cell infiltration (CD3, CD4, CD8) in tumor.

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