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Investigating the Spatial Distribution of Proliferating Cells in Primary Ovarian Cancers.
Hegazi, Mohamed A A A; Pasqualini, Fabio; Taverna, Gianluigi; Bresalier, Robert S; Chiriva-Internati, Maurizio; Grizzi, Fabio.
Afiliación
  • Hegazi MAAA; Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, 20089 Rozzano, Milan, Italy.
  • Pasqualini F; Department of Immunology and Inflammation, IRCCS Humanitas Research Hospital, 20089 Rozzano, Milan, Italy.
  • Taverna G; Department of Biomedical Sciences, Humanitas University, 20072 Pieve Emanuele, Milan, Italy.
  • Bresalier RS; Department of Urology, Humanitas Mater Domini, 21100 Castellanza, Varese, Italy.
  • Chiriva-Internati M; Departments of Gastroenterology, Hepatology & Nutrition, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
  • Grizzi F; Departments of Gastroenterology, Hepatology & Nutrition, Division of Internal Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA.
Discov Med ; 36(182): 632-645, 2024 Mar.
Article en En | MEDLINE | ID: mdl-38531804
ABSTRACT

BACKGROUND:

Ovarian cancer (OC) accounts for about 4% of female cancers globally. While Ki67-immunopositive (Ki67+) cell density is commonly used to assess proliferation in OC, the two-dimensional (2D) distribution pattern of these cells is poorly understood. This study explores the 2D distribution pattern of Ki67+ cells in primary OC tissues and models the proliferation process to improve our understanding of this hallmark of cancer.

METHODS:

A total of 100 tissue cores, included in a tissue microarray (TMA) representing 5 clear cell carcinomas, 62 serous carcinomas, 10 mucinous adenocarcinomas, 3 endometrioid adenocarcinomas, 10 lymph node metastases from OC, and 10 samples of adjacent normal ovary tissue, were stained using a standardized immunohistochemical protocol. The computer-aided image analysis system assessed the 2D distribution pattern of Ki67+ proliferating cells, providing the cell number and density, patterns of randomness, and cell-to-cell closeness. Three computer models were created to simulate behavior and responses, aiming to gain insights into the variations in the proliferation process.

RESULTS:

Significant differences in Ki67+ cell density were found between low- and high-grade serous carcinoma/mucinous adenocarcinomas (p = 0.003 and p = 0.01, respectively). The Nearest Neighbor Index of Ki67+ cells differed significantly between high-grade serous carcinomas and endometrioid adenocarcinomas (p = 0.01), indicating distinct 2D Ki67+ distribution patterns. Proxemics analysis revealed significant differences in Ki67+ cell-to-cell closeness between low- and high-grade serous carcinomas (p = 0.002). Computer models showed varied effects on the overall organization of Ki67+ cells and the ability to preserve the original 2D distribution pattern when altering the location and/or density of Ki67+ cells.

CONCLUSIONS:

Cell proliferation is a hallmark of OCs. This study provides new evidence that investigating the Ki67+ cell density and 2D distribution pattern can assist in understanding the proliferation status of OCs. Moreover, our computer models suggest that changes in Ki67+ cell density and their location are critical for maintaining the 2D distribution pattern.
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Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Carcinoma Endometrioide / Adenocarcinoma Mucinoso Idioma: En Revista: Discov Med Año: 2024 Tipo del documento: Article

Texto completo: 1 Base de datos: MEDLINE Asunto principal: Neoplasias Ováricas / Carcinoma Endometrioide / Adenocarcinoma Mucinoso Idioma: En Revista: Discov Med Año: 2024 Tipo del documento: Article