Abstract 1698: Systems pathology for characterization of cancer model systems in a multicenter IMI-PREDECT project

Abstract

Abstract Despite of our increased understanding of cancer biology, the majority of anti-cancer therapies fail at late-stage clinical trials. Thus, there is an urgent need to develop and validate novel preclinical models that could predict drug efficacy in humans. For this purpose, as a part of IMI-PREDECT public-private research consortium, this study describes methodology and infrastructure for characterization and comparison of preclinical models for drug target validation applying a systems pathology approach. Formalin-fixed paraffin-embedded (FFPE) samples from 1050 different in vitro and in vivo models of breast, prostate and lung cancers, as well as 364 clinical tumors from the same origin, were collected from 26 PREDECT collaborators across the EU. We established standard operating procedures for central processing of FFPE samples, including tissue microarray (TMA) construction and immunohistochemistry (IHC) with 15 different antibodies (CK8, Ki67, p-histone H3, ER, AR, p-AKT, p-ERK, p-p38, γH2AX, cleaved caspase 3, p-MET, HIF1α, p63, vimentin, E-cadherin). We constructed 50 TMA blocks, from which sections were cut and stained as well as digitized using whole slide imaging. Images were hosted on a WebMicroscope digital pathology platform and sample metadata on a PREDECT Metadata database (MBase). We developed image analysis methods for the detection and quantification of IHC biomarkers in the 48,800 stained TMA spots. As a proof-of-concept, we compared MCF-7 on several preclinical platforms including cell cultures, xenografts and xenograft tissue slices. Our results of the integrated biomarker phenotype suggest that of the various MCF-7 in vivo and ex vivo complex cell culture models, the xenograft tissue slice model was the most similar model platform to human clinical samples. In summary, we established a systems pathology approach to analyse and compare novel preclinical cancer models with IHC and digital imaging. The intention is that this large database will be made publicly available on the web as images and summary data that could be broadly useful to the community of cancer researchers and drug developers in comparing cancer model systems. The established infrastructure and workflow integrating molecular and digital pathology in a large-scale consortium setting could be applied to quantitative characterisation of consortium data in collaborations similar to PREDECT. Citation Format: Sami Blom, Yinhai Wang, Tauno Metsalu, Tiina Vesterinen, Teijo Pellinen, Anne Grote, Nina Linder, Jenni Säilä, Katja Välimäki, Ruusu-Maria Kovanen, Outi Monni, Panu Kovanen, Emma Davies, Kristin Stock, Marta Estrada, Georgios Sflomos, Sylvia Grünewald, Catarina Brito, Julia Schüler, Ronald de Hoogt, Cathrin Brisken, Heiko van der Kuip, Wytske van Weerden, Simon Barry, Wolgang Sommergruber, Elizabeth Anderson, Matthias Nees, Juha Klefström, Jaak Vilo, Emmy Verschuren, Ralph Graeser, John Hickman, Johan Lundin, Olli Kallioniemi. Systems pathology for characterization of cancer model systems in a multicenter IMI-PREDECT project. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 1698. doi:10.1158/1538-7445.AM2015-1698