Sylvia Grünewald

Novel TIE-2 Inhibitor BAY-826 Displays In Vivo Efficacy in Experimental Syngeneic Murine Glioma Models

Targeting the vascular endothelial growth factor signaling axis in glioblastoma inevitably leads to tumor recurrence and a more aggressive phenotype. Therefore, other angiogenic pathways, like the angiopoietin/tunica interna endothelial cell kinase (TIE) signaling axis, have become additional targets for therapeutic intervention. Here, we explored whether targeting the receptor tyrosine kinase TIE‐2 using a novel, highly potent, orally available small molecule TIE‐2 inhibitor (BAY‐826) improves tumor control in syngeneic mouse glioma models. BAY‐826 inhibits TIE‐2 phosphorylation in vitro and in vivo as demonstrated by suppression of Angiopoietin‐1‐ or Na3VO4‐induced TIE‐2 phosphorylation in glioma cells or extracts of lungs from BAY‐826‐treated mice. There was a trend toward prolonged survival upon single‐agent treatment in two of four models (SMA‐497 and SMA‐540) and there was a significant survival benefit in one model (SMA‐560). Co‐treatment with BAY‐826 and irradiation was ineffective in one model (SMA‐497), but provided synergistic prolongation of survival in another (SMA‐560). Decreased vessel densities and increased leukocyte infiltration were observed, but might be independent processes as the effect was also observed in single treatment modalities. These data demonstrate that TIE‐2 inhibition may improve tumor response to treatment in highly vascularized tumors such as glioblastoma.

Abstract 5036: Correlation of preclinical antitumor activity of regorafenib in CRC-PDX xenografts with gene expression and clinical parameters of the primary tumor

Regorafenib is a small molecule inhibitor of multiple transmembrane and intracellular kinases involved in normal cellular functions and in pathologic processes such as oncogenesis, tumor angiogenesis, metastasis, and tumor immunity. Regorafenib is approved for the treatment of patients with metastatic colorectal cancer (CRC) who have been previously treated with fluoropyrimidine-, oxaliplatin- and irinotecan-based chemotherapy, an anti-VEGF therapy, and, if RAS wild-type, an anti-EGFR therapy or with locally advanced, unresectable or metastatic gastrointestinal stromal tumor (GIST) who have been previously treated with imatinib mesylate and sunitinib malate. Recently an overall survival benefit has been shown in patients with hepatocellular carcinoma (HCC) who had previously been treated with sorafenib (RESORCE). OncoTrack is an Innovative Medicines Initiative (IMI) sponsored project with the goal to improve the basis for identification of biomarkers based on the mechanisms of action of therapies approved for this indication. For this purpose, a panel of CRC-PDX xenografts was generated by the OncoTrack project. At the time of the analysis reported here fifty xenografts had been treated with regorafenib at a dose of 10 mg/kg/d or with vehicle for 24 days. The analysis of tumor growth rates (TGR) showed pronounced differences between different tumors and between vehicle and regorafenib treated models. The relative antitumor activity (relative TGR) of regorafenib varied between -0,13 (good response) and 0.0 (no response). Investigations of relative TGR in relationship to (non-) clinical parameters of the primary tumor such as age, gender, sidedness and tumor histology identified a marginally significant (p= 0.04) better response in tumors from younger patients. No other correlations were detected to this end, which may be due to the small sample number. To correlate antitumor activity of regorafenib with gene expression, RNA was isolated from sections of selected vehicle and regorafenib treated xenografts and hybrized on Affymetrix HuGene-2.1_st human transcriptome arrays. Expression profiles were subsequently analyzed using the Random Forests algorithm to identify gene expression signatures predicting response to regorafenib. The best signatures did not perform better than signatures derived after randomizing responses, i.e. no predictive signature could be identified. Further studies with larger samples sizes are necessary to improve the outcome of such an approach; however one should acknowledge that to date no predictive gene signatures could be identified for multikinase inhibitors, which may be intrinsic to their complex mechanism of action. The research reported here received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115234 (OncoTrack). Citation Format: Henrik Seidel, Jens Hoffmann, Ralf Lesche, Sylvia Grunewald, David Henderson, Dieter Zopf. Correlation of preclinical antitumor activity of regorafenib in CRC-PDX xenografts with gene expression and clinical parameters of the primary tumor [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 5036. doi:10.1158/1538-7445.AM2017-5036

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

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 Saila, Katja Valimaki, Ruusu-Maria Kovanen, Outi Monni, Panu Kovanen, Emma Davies, Kristin Stock, Marta Estrada, Georgios Sflomos, Sylvia Grunewald, Catarina Brito, Julia Schuler, Ronald de Hoogt, Cathrin Brisken, Heiko van der Kuip, Wytske van Weerden, Simon Barry, Wolgang Sommergruber, Elizabeth Anderson, Matthias Nees, Juha Klefstrom, 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