Koen Marien1,2, Andrew Reynolds3, Kelly Schats1,2, Laure-Anne Teuwen2,4, Pieter-Jan van Dam4, Luc Dirix4, Mark Kockx2, Steven Van Laere4, Peter Vermeulen4

1Laboratory of Physiopharmacology, University of Antwerp; 2HistoGeneX NV; 3Tumour Biology Team, The Institute of Cancer Research (ICR); 4Translation Cancer

Research Unit, GZA Hospitals St. Augustinus

Discussion It is proposed that whilst DGP liver metastases utilise sprouting

angiogenesis, the RGP liver metastases co-opt pre-existing liver

sinusoidal vessels instead. By applying a clustering method to the

blood vessel objects we now have an objective way of confirming

these observations: RGP liver metastases have a vasculature with

a morphology similar to the normal liver sinusoidal system and

without vascular hotspots. Moreover, the proposed method to

quantify vascular hot spots in tissue sections can probably be

applied to detect heterogeneity in sample cohorts of other cancer

types.

Introduction The liver is a well vascularized organ that frequently hosts metastases in patients with colorectal adenocarcinomas (CRC). Different growth patterns at the tumour–liver

interface have been described: desmoplastic (DGP), pushing and replacement (RGP) (Van den Eynden et al., 2013). While the DGP is characterized by desmoplasia,

inflammation and, importantly, sprouting angiogenesis, in the RGP cancer cells “replace” the hepatocytes and co-opt the sinusoidal blood vessels of the liver without eliciting

sprouting angiogenesis. Moreover, our unpublished data suggests that patients with RGP liver metastases respond poorly to bevacizumab, when compared to patients with

DGP liver metastases. This is most likely because bevacizumab can only inhibit sprouting angiogenesis and does not target the co-opted sinusoidal blood vessels. In order to

provide further evidence that the mechanism of tumour vascularisation is different in DGP metastases when compared to RGP metastases, in the current study we

performed unsupervised spatial modelling of blood vessel patterns in patient samples of CRC liver metastases.

Unsupervised spatial modelling of blood vessel patterns in colorectal

cancer liver metastases: additional evidence for non-angiogenic growth

Fig. 1: Overview of the steps done in Definiens to get the coordinates of the segmented vessel objects. Left: Manual region of interest (ROI) delineation by the pathologist in the whole-slide image (WSI) of the liver CRC metastasis. Mid: Threshold-based vessel segmentation with Definiens in the liver (top) and in both the DGP (bottom left) and RGP (bottom right) ROI. Right: Export of the coordinates of the centroids of all vessel objects for post-processing in R (see Fig. 2).

Fig. 3: Unsupervised spatial modelling show similar blood vessel patterns for the RGP of CRC metastases and normal liver. A: Selected ROIs at the tumor-liver interface of normal liver (top), RGP (mid), and DGP (bottom) in CD31-stained tissue. B: Vessel segmentation and classification results in Definiens (Fig. 1). C: Cluster results for the selected ROIs as calculated in R (Fig. 2).

Fig. 4: Normalized number of clusters of blood vessel objects for DGP, RGP and normal liver. The number of clusters was different between DGP and RGP (p < 0.05), but also between DGP and normal liver (p < 0.001). However no difference was found between RGP and normal liver (p = 0.16).

Results There was a statistically significant difference between the growth patterns as determined by one-way

ANOVA (F(2,22) = 10.8, p < 0.001). A post-hoc Tukey test showed that the number of clusters divided

by number of vessel objects (normalization) was significantly different between DGP and RGP (p <

0.05), but also between DGP and normal liver (p < 0.001). However, no difference was found

between RGP and normal liver (p = 0.16).

1. Van den Eynden GG, et al. The multifaceted role of the microenvironment in liver metastasis. Cancer Res. 2013.

2. Lopez XM, et al. Clustering methods applied in the detection of Ki67 hot-spots in whole tumor slide images. Cytom A. 2012.

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Fig. 2: Overview of the steps done in R to get clustered vessel objects. Creating a cluster is an iterative process. A vessel object is a core-object (red) when it has a least three neighbours in its spherical neighbourhood (black) (radius = maximum nonoutlier value). A density-reachable object (green) is a vessel object that is one of the minimum three neighbours of a core-object, but is not a core-object itself. Together with the core-object, the density-reachable objects belonging to the same neighbourhood define a specific cluster. (Lopez et al., 2012).

Materials and methods

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