Abstract 4707: Quantifying cellular composition using laser microdissection regions of interest via automated digital image analysis of co-registered tissue thin sections
Year: 2019
Type: conference-abstract
Abstract: Low purity tumors often portend poor disease outcome. Understanding the compositional heterogeneity of complex tumors, such as high-grade serous ovarian cancers (HGSOC) is an area of intense study. Laser microdissection affords decoupled molecular profiling analyses of tumor and non-tumor cells not otherwise possible using routine methods. Automated documentation of LMD collection activities are needed to support the development of high stringency quality control and quality assurance (QA/QC) procedures. This study was conducted to demonstrate that histology combined with digital image analysis can be utilized to quantify tumor and stroma cell content within LMD regions of interest (ROI). A single frozen high grade serous ovarian cancer tissue specimen was acquired under an IRB-approved protocol and thin sectioned onto charged glass or polyethylene naphthalate (PEN) membrane slides followed by staining with hematoxylin and eosin HE a difference of ~12% between these cell types. This study highlights the successful use of histology combined with digital image analysis to quantify tumor and stroma cell numbers within HGSOC tissue subjected to LMD. Furthermore, proteogenomic analyses of LMD-recovered tumor and stromal cells will support further understanding of potential correlations between these cell types within the tumor microenvironment of HGSOC. Citation Format: Lorcan Sherry, Mark Anderson, Allison Hunt, Dave Mitchell, Julie Oliver, Thomas Conrads, Nicholas Bateman. Quantifying cellular composition using laser microdissection regions of interest via automated digital image analysis of co-registered tissue thin sections [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 4707.
Source: Tumor Biology
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,500
OpenAlex ID: W2955005823