Abstract 2273: Development of a prognostic biomarker for ovarian cancer based on a high-resolution digital pathology platform for spatial statistical analysis of platelets and blood vessels
Authors: Ju Young Ahn, Wendolyn Carlos-Alcalde, Matthew Vasquez, Min Soon Cho, Vahid Afshar-Kharghan, Stephen T. Wong
Journal: Cancer Research (2024)

Abstract Purpose: A third of ovarian cancer patients have elevated platelet counts that are associated with poor prognosis. However, the exact mechanism by which platelets preferentially extravasate into tumor microenvironment is unknown, partly owing to lack of effective tools that can accurately segment and measure platelet counts found in tumor tissues. In this study, we developed a high-resolution digital pathology (DP) platform with whole tissue confocal imaging and deep learning model that can be used to automatically segment out platelets and blood vessels to perform spatial statistics (SA) of platelets with high precision and consistency. We utilized our SADP platform to study an interaction between tumor secreted SDF-1 and CXCR4 on platelets which initiates platelet chemotaxis towards tumor microenvironment. Methods: A deep learning algorithm to accurately segment out platelets was developed based on the U-Net structure using our original datasets from the murine ovarian cancer model. Tumor-bearing mice were injected with 5 mg/kg of Plerixafor, a CXCR4 inhibitor, daily for 4 weeks. Then, tumor nodules were resected, stained with CD41a and CD31 antibodies, and imaged using a whole slide confocal microscopy. We performed manual annotation of these images to generate the mask dataset and performed further data augmentation to increase the diversity of datasets. We trained our model using 85% of these images and performed validation using the remaining 15%. Results: Using our SADP platform, the validation dataset achieved pixel-wise accuracy of 98.8%. A receiver operating characteristic (ROC) curve demonstrated an area under the ROC curve (AUC) of 0.99 with high sensitivity and specificity. Intersection over union (IoU) score of 0.765 was obtained. A statistically significant (p=0.0467), 7.03% reduction in the platelet density was shown in the Plerixafor-treated mouse ovarian tumor tissues (n=8) compared to the control tumor tissues (n=9). We also found that platelets are spatially concentrated near blood vessels in the control tumor tissues compared to the Plerixafor-treated tumor tissues (p=0.0377). Conclusions: We developed a SADP platform to segment and quantitate tens of thousands of platelets from whole slide mouse ovarian cancer tissues automatically with high accuracy and consistency and incorporated spatial statistics to understand their spatial relationships and patterns that would not be apparent in non-spatial analyses. Applying the SADP platform, we demonstrated that treatment with Plerixafor leads to overall reduction in platelet infiltration within ovarian murine tumors. Our ongoing effort is to utilize our SADP platform on human tumor tissue images from a whole slide digital scanner to further characterize spatial distribution of platelets as a potential prognostic marker for ovarian cancer. Citation Format: Ju Young Ahn, Wendolyn Carlos-Alcalde, Matthew Vasquez, Min Soon Cho, Vahid Afshar-Kharghan, Stephen T. Wong. Development of a prognostic biomarker for ovarian cancer based on a high-resolution digital pathology platform for spatial statistical analysis of platelets and blood vessels [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2273.