In:
Cancer Research, American Association for Cancer Research (AACR), Vol. 82, No. 12_Supplement ( 2022-06-15), p. 452-452
Abstract:
Antibody-Drug-Conjugates (ADCs) are biopharmaceutical drugs designed for targeted tumor therapy, meant to improve therapeutic index by restricting drug delivery to tumor cells that express the target antigen. ADCs bind to the target molecule on the cell membrane, which triggers internalization, linker cleavage, and ultimately drug release inside the target cell. Prospective patient selection can be done by quantifying the level of target expression in the tumor using immuno-histochemistry (IHC). However, this process typically involves pathologists and is time consuming, expensive, and prone to human bias. We have developed a supervised deep learning algorithm that segments IHC images of invasive tumor epithelium into individual epithelial cells and their membrane, cytoplasm and nucleus with high accuracy. On unseen test data its performance for epithelial cell detection and segmentation is comparable to the inter-pathologist consensus. With our algorithm, we can describe the target molecule distribution of individual cells in a fully quantitative fashion after using standard IHC methods: we call our approach Quantitative Continuous Score (QCS). We applied QCS to interrogate the mechanism of action of AZD8205, a B7-H4 directed ADC incorporating a novel topoisomerase I linker-warhead. Pharmacodynamic effects were evaluated in vivo, using a human tumor xenograft mouse model and the cell line HT29-huB7-H4 Clone 26, engineered to express human B7-H4. After tumors grew in volume to approximately 250 to 300 mm3, animals were randomized and each mouse received an IV injection of either AZD8205 (1.25, 3.5, or 7 mg/kg) or control articles. Tumors were collected at designated timepoints, fixed in 10% neutral buffered formalin and subsequently embedded into paraffin blocks. IHC and QCS were then used to examine human IgG, γH2AX foci, cleaved caspase-3, and epithelial cell density in tumor samples over time. Using our novel approach we could quantitatively measure the level of AZD8205 bound to tumor cells, with the highest level of ADC on the cell membrane detected at 24-48 hrs. Increased dose levels accelerated the binding kinetics of the drug and led to to a 4- and 3-fold excess of γH2AX and CC-3 respectively, as well as more cells being killed, with up to 2/3 of all epithelial cells dead at the highest dose studied. In summary, we here set the basis for future mechanistic investigation of model systems using computational pathology to improve our understanding of ADC effects. Computational pathology has the potential to determine molecule abundance quantitatively, increase throughput and avoid human bias. Our data implies QCS has the potential to identify patients who may respond to AZD8205, which we will interrogate further and integrate into future clinical studies. Citation Format: Philipp Wortmann, Tze Heng Tan, Susanne Haneder, Andrea Ennio Storti, Ansh Kapil, Jon Chesebrough, Daniel Sutton, Michal Sulikowski, Arthur Lewis, Sofia Koch, Steve Sweet, Zifeng Song, David Chain, Yeoun Jin Kim, Nadia Luheshi, Krista Kinneer, Zachary A. Cooper, Marlon Rebelatto, Günter Schmidt, Hadassah Sade, J. Carl Barrett. Development and implementation of image analysis-based Quantitative Continuous Score (QCS) for B7-H4 IHC to understand AZD8205 pharmacodynamics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 452.
Type of Medium:
Online Resource
ISSN:
1538-7445
DOI:
10.1158/1538-7445.AM2022-452
Language:
English
Publisher:
American Association for Cancer Research (AACR)
Publication Date:
2022
detail.hit.zdb_id:
2036785-5
detail.hit.zdb_id:
1432-1
detail.hit.zdb_id:
410466-3
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