Abstract 4896: A high throughput method of identifying cells with increased genomic content across diverse cancer tissues

Abstract Cancer cells with increased genomic content have been associated with recurrence and poor patient outcomes. The current manual identification of these rare cells by pathologists is time-consuming and labor intensive, given their scarcity within tissue samples. This study introduces a robust methodology for the automated identification of cancer cells with increased genomic content within prostate cancer tissues. Leveraging Qupath software integrated with the Stardist extension, our approach combines precise nuclear segmentation with threshold setting using a Python script. This methodology enables the specific identification of noncancerous small cells, cancerous cells, and those exhibiting genomic content significantly surpassing the average cancer cell (3x to 5x), complemented by thresholds based on hematoxylin intensity for enhanced result accuracy. Thus, our methodology offers an efficient solution to streamline the identification processes for these challenging cells. We have extensively validated our method across diverse models, including animal and human models, transgenic and subcutaneous models, and Human Tissue Microarrays (TMAs), to identify cancer cells with increased genomic content. The rarity of these cells within tissue samples underscores the practical significance of our research, providing a solution to the current difficulties faced by researchers and clinicians in their identification. The integration of this methodology aims to improve efficiency in cancer tissue analysis. Ongoing efforts are directed towards the evaluation of this high-throughput methodology using animal models and TMAs with known outcomes, further substantiating its applicability and potential clinical relevance. Citation Format: Shilpa Priyadarsini Nair, Kenneth J. Pienta, Sarah R. Amend. A high throughput method of identifying cells with increased genomic content across diverse cancer tissues 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 (6Suppl): Abstract nr 4896.