Histological aspect of the vascular belt zone in CRC tissue.

<p>CRC primary tumor sample stained for CD34 (brown), (A) primary tumor near the invasion front contains few blood vessels, (B) liver metastasis near the invasion front contains many small blood vessels, (C) primary tumor at the intestinal lumen contains many dilated blood vessels, (D) liver metastasis tumor center contains few blood vessels.</p

Identification of a characteristic vascular belt zone in human colorectal cancer

<div><p>Blood vessels in cancer</p><p>Intra-tumoral blood vessels are of supreme importance for tumor growth, metastasis and therapy. Yet, little is known about spatial distribution patterns of these vessels. Most experimental or theoretical tumor models implicitly assume that blood vessels are equally abundant in different parts of the tumor, which has far-reaching implications for chemotherapy and tumor metabolism. In contrast, based on histological observations, we hypothesized that blood vessels follow specific spatial distribution patterns in colorectal cancer tissue. We developed and applied a novel computational approach to identify spatial patterns of angiogenesis in histological whole-slide images of human colorectal cancer.</p><p>A characteristic spatial pattern of blood vessels in colorectal cancer</p><p>In 33 of 34 (97%) colorectal cancer primary tumors blood vessels were significantly aggregated in a sharply limited belt-like zone at the interface of tumor tissue to the intestinal lumen. In contrast, in 11 of 11 (100%) colorectal cancer liver metastases, a similar hypervascularized zone could be found at the boundary to surrounding liver tissue. Also, in an independent validation cohort, we found this vascular belt zone: 22 of 23 (96%) samples of primary tumors and 15 of 16 (94%) samples of liver metastases exhibited the above-mentioned spatial distribution.</p><p>Summary and implications</p><p>We report consistent spatial patterns of tumor vascularization that may have far-reaching implications for models of drug distribution, tumor metabolism and tumor growth: luminal hypervascularization in colorectal cancer primary tumors is a previously overlooked feature of cancer tissue. In colorectal cancer liver metastases, we describe a corresponding pattern at the invasive margin. These findings add another puzzle piece to the complex concept of tumor heterogeneity.</p></div

Magnitude of angiogenic zones for all analyzed samples in the validation cohort, shown as waterfall plots.

<p>This Fig shows the result of the analysis of the independent validation cohort of N = 39 samples and is organized identically to <b><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0171378#pone.0171378.g005" target="_blank">Fig 5</a></b>. (A) Analysis of blood vessel aggregation in the tumor parts next to the intestinal lumen. (B) Blood vessel excess at the invasion front. The first sample was cropped at -1200, but the true value of -2077 is overlaid on the bar. (A-B) Error bars indicate the 95% confidence interval that was calculated by a Monte Carlo method. All measurements are statistically significant except if labeled “ns” (for “not significant”).</p

Characteristic vascular belt zones in CRC tissue.

<p>(A) Histological whole slide images of a representative primary tumor (B) and a liver metastasis stained for blood vessels (CD34). (C) The corresponding angiogenic hotspot probability map shows that angiogenic hotspots are preferably located close to the intestinal lumen in primary tumors. (D) Blood vessel distribution is more heterogeneous, but generally close to the invasion front in metastases (dark = low density, bright = high density, * in the color bar marks the level of significance).</p

Graphical explanation of the “vessel excess”.

<p>(A) The distance of each blood vessel to the intestinal lumen was measured. A histogram of these values is plotted in red (1). The red curve peaks at approx. 0.2 mm. This peak corresponds to an accumulation of blood vessels close to the intestinal lumen. We then assessed whether this peak is due to chance or due to a non-random effect. To this end, a random point pattern was simulated and the experiment was repeated with these random points. This was repeated 100 times. The results of these experiments are plotted in black (3), the mean distance histogram of the random points is shown in blue (2). The peak of the observed curve (1) is far outside the range of random fluctuations. This shows that the spatial accumulation of blood vessels close to the intestinal lumen is very likely not due to a random effect. (B) To quantify the blood vessel excess, the difference of (1) and (2) is plotted as a histogram. The area under the curve until the first x-intersection is regarded as the blood vessel excess close to the intestinal lumen. This Fig shows representative data for one sample (C2-Smp008).</p

Physiological blood vessel distribution in colon mucosa.

<p>Blood vessel architecture in normal colon mucosa where blood vessels run along the crypt wall at the barrier to the intestinal lumen. (A) Original CD34-stained image. This image is representative of five independent whole slide images. (B) For better clarity, the original image was processed by color deconvolution and only the CD34-staining is shown. (C) Schematic illustration of the physiological blood vessel architecture in colon mucosa. A blood vessel runs closely to the surface between two adjacent crypts.</p

Graphical summary of the model.

<p>(A-B) Based on experimental data, we propose a new model for angiogenesis that is schematically shown in A for primary tumors and B for metastasis. The model states that blood vessels are preferentially located close to the intestinal lumen in primary tumors and close to the invasion front in liver metastases.</p

Magnitude of angiogenic zones for all analyzed samples in the first cohort, shown as waterfall plots.

<p>(A) Analysis of blood vessel aggregation in the tumor parts next to the intestinal lumen. Statistically significant positive blood vessel excess at the luminal side was detected in 33 of 34 untreated CRC primary tumors (“prim”, blue) and 2 of 4 neoadjuvant CRC primary tumors (“neo”, green). (B) Blood vessel excess at the invasion front in N = 36 untreated CRC primary tumors (“prim”, blue), N = 4 neoadjuvant CRC primary tumors (“neo”, green), and N = 11 CRC liver metastases (“met”, red). (A-B) Error bars indicate the 95% confidence interval that was calculated by a Monte Carlo method. All measurements are statistically significant except if labeled “ns” (for “not significant”).</p

Validation of the segmentation procedure.

<p>Blood vessels in N = 100 image patches were counted by three blinded human observers. In each image patch, each observer manually determined the number of blood vessels. Then, for each image patch, these numbers were compared in a pair-wise manner between the three observers. The mean count calculated from the three observers was compared to the result of the automatic method. The experimental data are shown as scatter plots. (A-C) Inter-observer variability, (D) Correlation of the automatic count to the mean of human observers.</p

KRAS mutations identified by sanger sequencing compared to deep amplicon sequencing analyzed with different variant calling tools.

<p>UG, Unified Genotyper pipeline; SAM, Samtools mpileup/Bcftools pipeline, SVC, Somatic Variant Caller; NA, not available</p><p>KRAS mutations identified by sanger sequencing compared to deep amplicon sequencing analyzed with different variant calling tools.</p