DIAGNOSIS AND PROGNOSIS OF ADRENAL BENIGN AND MALIGNANT TUMORS: NEW INSIGHTS FROM A COMPUTERIZED OPERATOR-INDEPENDENT MORPHOMETRIC MODEL AND PROTEOMIC EXPRESSION
Creators: name: DALINO, PAOLO
Publisher: name: Universit� degli Studi di Milano
Year: 2015

description: Background: High mitotic index, high nuclear grade and reticulin distruption are part of representative hallmarks of adrenocortical cancer (ACC). A characteristic neutrophil/T-lymphocytes infiltrate ratio has been often implicated in carcinogenesis, progression and clinical outcome of several cancer types. However, its role in adrenal cortical tumors is unclear. Histology-based diagnosis may also suffer of a moment of subjectivity due to inter- and intra-observer variations. Proteomic studies of malignant tumors represent the future both for possible diagnostic and prognostic implication; whole proteome analysis of adrenocortical tumors from fresh tissues may represent the lacking piece of puzzling management of this tumor. Aim: to assess by computerized morphometry morphological features, vascular, inflammatory, reticulin and proliferative index pattern in adrenocortical adenomas (ACAs) and carcinomas and to assess proteomic profiles from adrenocortical tumors fresh tissues. Methods: A single Institution series of 11 ACAs and 18 ACCs samples was analyzed using a Kontron-Zeiss KS400 image analyzer. Four consecutive sections 4 �m thick were obtained with a total of 250?300 HPF examined for each case. Immunohistochemistry for Ki67, reticulin and CD8/CD15 was obtained. To minimize subjectivity, particularly relevant when quantitative results are expected, we generated a morphometric model based on analysis of volume fractions occupied by Ki67 positive and negative cells (nuclei, cytoplasm) and inflammatory compartments (CD15+ granulocytes, CD8+ lymphocytes) and reticulin framework surface. Lastly, the assessment of Ki-67 by computerized morphometry was compared with pathologist?s evaluation. After sample preparation protocol of 7 ACCs, 5 ACAs and 5 normal adrenal tissue samples, difference In Gel Electrophoresis (DIGE) and following the protein spots individuation and isolation, Mass Spectrometry, were performed to allow protein identification. Results: The volume fraction of Ki-67 positive cells was highest in ACC. The volume fraction of nuclei in unit volume and the nuclear/cytoplasmic ratio in both Ki-67 negative cells and Ki-67 positive cells were prominent in ACC. The surface fraction of reticulin was considerably lower in ACC. Moreover, when comparing morphometric analysis of Ki67+ cells to pathologist?s scores, the data of the point grid analysis revealed significantly lower values compared to conventional histopathology. These values, once statistically analyzed, demonstrated that our morphometric model could improve the sensitivity and specificity of Ki-67 evaluation in ACCs and ACAs (reaching 94% of sensitivity, 100% of specificity) and also that it cold contribute to a better prognosis definition. Proteomics individuated 62% overexpressed proteins in ACCs with respect to ACAs: among them vimentin and vitamin D-binding protein resulted the most varied (3.2 and 3-fold more expressed in ACCs than in ACAs respectively). On the other hand the remaining 38% of proteins resulted under-expressed in ACCs with regard to ACAs, being cathepsin D and aldose reductase both 3-fold less expressed in ACCs than in ACAs. The protein profile of ACCs versus normal adrenal tissue was similar (although with slight differences in terms of fold variation) to that of ACCs versus ACAs; nonetheless a varied new protein (lactate dehydrogenase, 1.8 fold increase in ACCs) with a possible role in tumorigenesis and tumor progression, was detected. Conclusions: Our computerized morphometric model is simple, lacks observer or subjective bias and can be used to supplement objective methods to achieve precise and reader-independent quantification of morphological characteristics and histological biomarkers of adrenocortical tumors. We speculate that the assessment of inflammatory infiltrate may found a place in the diagnostic algorithm of adrenal benign and malignant tumors. The promising preliminary results obtained by the proteomic study of ACCs and ACAs could contribute to the identification of new histological biomarkers. These data, once integrated into a complex algorithm including histological assessment, morphometric analysis and clinical data evaluation would easily contribute to create a prognostic stratification of ACCs with clear advantages for the clinical management of the disease.