Automatic evaluation of tumor budding in immunohistochemically stained colorectal carcinomas and correlation to clinical outcome

Background: Tumor budding, meaning a detachment of tumor cells at the invasion front of colorectal carcinoma (CRC) into single cells or clusters (<=5 tumor cells), has been shown to correlate to an inferior clinical outcome by several independent studies. Therefore, it has been discussed as a complementary prognostic factor to the TNM staging system, and it is already included in national guidelines as an additional prognostic parameter. However, its application by manual evaluation in routine pathology is hampered due to the use of several slightly different assessment systems, a time-consuming manual counting process and a high inter-observer variability. Hence, we established and validated an automatic image processing approach to reliably quantify tumor budding in immunohistochemically (IHC) stained sections of CRC samples. Methods: This approach combines classical segmentation methods (like morphological operations) and machine learning techniques (k-means and hierarchical clustering, convolutional neural networks) to reliably detect tumor buds in colorectal carcinoma samples immunohistochemically stained for pan-cytokeratin. As a possible application, we tested it on whole-slide images as well as on tissue microarrays (TMA) from a clinically well-annotated CRC cohort. Results: Our automatic tumor budding evaluation tool detected the absolute number of tumor buds per image with a very good correlation to the manually segmented ground truth (R2 value of 0.86). Furthermore the automatic evaluation of whole-slide images from 20 CRC-patients, we found that neither the detected number of tumor buds at the invasion front nor the number in hotspots was associated with the nodal status. However, the number of spatial clusters of tumor buds (budding hotspots) significantly correlated to the nodal status (p-value = 0.003 for N0 vs. N1/N2). TMAs were not feasible for tumor budding evaluation, as the spatial relationship of tumor buds (especially hotspots) was not preserved. Conclusions: Automatic image processing is a feasible and valid assessment tool for tumor budding in CRC on whole-slide images. Interestingly, only the spatial clustering of the tumor buds in hotspots (and especially the number of hotspots) and not the absolute number of tumor buds showed a clinically relevant correlation with patient outcome in our data

Guidelines for the use of flow cytometry and cell sorting in immunological studies (third edition)

The third edition of Flow Cytometry Guidelines provides the key aspects to consider when performing flow cytometry experiments and includes comprehensive sections describing phenotypes and functional assays of all major human and murine immune cell subsets. Notably, the Guidelines contain helpful tables highlighting phenotypes and key differences between human and murine cells. Another useful feature of this edition is the flow cytometry analysis of clinical samples with examples of flow cytometry applications in the context of autoimmune diseases, cancers as well as acute and chronic infectious diseases. Furthermore, there are sections detailing tips, tricks and pitfalls to avoid. All sections are written and peer‐reviewed by leading flow cytometry experts and immunologists, making this edition an essential and state‐of‐the‐art handbook for basic and clinical researchers.DFG, 389687267, Kompartimentalisierung, Aufrechterhaltung und Reaktivierung humaner Gedächtnis-T-Lymphozyten aus Knochenmark und peripherem BlutDFG, 80750187, SFB 841: Leberentzündungen: Infektion, Immunregulation und KonsequenzenEC/H2020/800924/EU/International Cancer Research Fellowships - 2/iCARE-2DFG, 252623821, Die Rolle von follikulären T-Helferzellen in T-Helferzell-Differenzierung, Funktion und PlastizitätDFG, 390873048, EXC 2151: ImmunoSensation2 - the immune sensory syste

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

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

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

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

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