Automated image analysis algorithm development for cell surface-binding therapeutic agent target & its comparison to manual pathology scoring

Standard pathology scoring of cell surface targets in tumor tissue is done routinely by semi-quantitative scoring and is usually performed by a pathologist assessing a given number of fields of view (FOVs) from an immunohistochemistry (IHC) stained slide. In clinical studies evaluating cell surface-binding therapeutic agents, a tissue based patient selection assay with pathology semi-quantitative scoring such as H-score or percent positive is frequently used to enroll patients. Image analysis has become a powerful tool to evaluate and quantify IHC assays including those used for patient enrollment. It allows one to leverage the power of computer software and machine learning approaches to develop algorithms for the detection of specific markers and to run these solutions across entire stained tumor sections. Here, we developed an automated image analysis solution using the Definiens Developer Software specifically to detect a cell surface target in human gastrointestinal tumors. This algorithm is able to detect and quantify apical/membrane staining of the target. We used an initial set of colorectal cancer (CRC) samples (n=30) stained for the target. Tumor and non-tumor compartments were identified using the algorithm; target expression was then assessed in the tumor compartment only. Multiple outputs were generated with the image analysis tool including area of apical stain over the total area of interior luminal border (H-score luminal ), intensity of apical stain over total apical stain area (H-score apical ) and apical stain over the total area of the tumor region (H-score total tumor ). H-score luminal was the output most similar to the semi-quantitative pathology scoring previously performed on these samples. H-score total tumor was the only output that used the total area of the tumor in its composite score. The various image analysis parameters showed good correlation with each other. This use case is an example of applying digital pathology to an existing IHC assay to quantitatively assess marker expression across entire tumor sections. Implementation of quantitative image analysis allows clinical development programs including that assessing cell surface-binding therapeutic agents the ability to incorporate a variety of otherwise difficult-to-measure parameters such as total tumor area and other cellular parameters from an IHC stain into a more robust biomarker analysis package. In addition, this quantitative approach could help in establishing or fine tuning an IHC assay9s cut off when used prospectively in clinical trials. Citation Format: Brittany Bahamon, Andrzej Cholewinski, Ruben Cardenes, Aleksandra Zuraw, Israel Barragan, Marise B. McNeeley, Ronald D. Luff, Adnan Abu-Yousif, Hadi Danaee. Automated image analysis algorithm development for cell surface-binding therapeutic agent target u0026 its comparison to manual pathology scoring [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4535.