Nuclear Kaiso Expression Is Associated with High Grade and Triple-Negative Invasive Breast Cancer

Kaiso is a BTB/POZ transcription factor that is ubiquitously expressed in multiple cell types and functions as a transcriptional repressor and activator. Little is known about Kaiso expression and localization in breast cancer. Here, we have related pathological features and molecular subtypes to Kaiso expression in 477 cases of human invasive breast cancer. Nuclear Kaiso was predominantly found in invasive ductal carcinoma (IDC) (p = 0.007), while cytoplasmic Kaiso expression was linked to invasive lobular carcinoma (ILC) (p = 0.006). Although cytoplasmic Kaiso did not correlate to clinicopathological features, we found a significant correlation between nuclear Kaiso, high histological grade (p = 0.023), ERα negativity (p = 0.001), and the HER2-driven and basal/triple-negative breast cancers (p = 0.018). Interestingly, nuclear Kaiso was also abundant in BRCA1-associated breast cancer (p<0.001) and invasive breast cancer overexpressing EGFR (p = 0.019). We observed a correlation between nuclear Kaiso and membrane-localized E-cadherin and p120-catenin (p120) (p<0.01). In contrast, cytoplasmic p120 strongly correlated with loss of E-cadherin and low nuclear Kaiso (p = 0.005). We could confirm these findings in human ILC cells and cell lines derived from conditional mouse models of ILC. Moreover, we present functional data that substantiate a mechanism whereby E-cadherin controls p120-mediated relief of Kaiso-dependent gene repression. In conclusion, our data indicate that nuclear Kaiso is common in clinically aggressive ductal breast cancer, while cytoplasmic Kaiso and a p120-mediated relief of Kaiso-dependent transcriptional repression characterize ILC

Tracing differences between male and female breast cancer : Both diseases own a different biology

Aims: Male breast cancer (MBC) is a rare and poorly characterized disease. In the present study we used a novel biomathematical model to further characterize MBC and to identify differences between male and female breast cancer (FBC). Methods and results: A total of 134 cases of MBC were stained immunohistochemically for 13 key oncoproteins, and staining percentages were used in a mathematical model to identify dependency patterns between these proteins. The results were compared with a large group of FBC (n = 728). MBC and FBC clearly differed on the molecular level. In detail, the results suggest a different role for progesterone receptor (PR) compared to oestrogen receptor (ER) in MBC, while in FBC ER and PR show a similar pattern. In addition, Androgen receptor (AR) seems to be a more powerful effector in MBC. Grades 1 and 2 tumours were clearly separated from grade 3 tumours, and luminal types A and B tumours also showed a different pattern. Conclusions: Defined morphological and molecular phenotypes can be identified in MBC, but these seem to be the result of different molecular mechanisms and perhaps multiple genetic pathways, as characterized previously in FBC, emphasizing the rising concept that MBC and FBC should be regarded as different and unique diseases

Plot of the performance measures.

<p>A–C) Performance measures referring to subset A. D–F) Performance measures referring to subset B.</p

Expression of membrane markers in male and female invasive breast cancer.

*<p>Correction for age, histology, ERα expression, and tumor size, Confidence Interval (CI), Odds Ratio (OR). OR >1 indicates higher expression in male.</p

Automatic Nuclei Segmentation in H&E Stained Breast Cancer Histopathology Images

<div><p>The introduction of fast digital slide scanners that provide whole slide images has led to a revival of interest in image analysis applications in pathology. Segmentation of cells and nuclei is an important first step towards automatic analysis of digitized microscopy images. We therefore developed an automated nuclei segmentation method that works with hematoxylin and eosin (H&E) stained breast cancer histopathology images, which represent regions of whole digital slides. The procedure can be divided into four main steps: 1) pre-processing with color unmixing and morphological operators, 2) marker-controlled watershed segmentation at multiple scales and with different markers, 3) post-processing for rejection of false regions and 4) merging of the results from multiple scales. The procedure was developed on a set of 21 breast cancer cases (subset A) and tested on a separate validation set of 18 cases (subset B). The evaluation was done in terms of both detection accuracy (sensitivity and positive predictive value) and segmentation accuracy (Dice coefficient). The mean estimated sensitivity for subset A was 0.875 (±0.092) and for subset B 0.853 (±0.077). The mean estimated positive predictive value was 0.904 (±0.075) and 0.886 (±0.069) for subsets A and B, respectively. For both subsets, the distribution of the Dice coefficients had a high peak around 0.9, with the vast majority of segmentations having values larger than 0.8.</p> </div

Examples of automated nuclei segmentation in breast cancer sections (all images are shown at the same scale; the nuclear pleomorphism grades are III, II, II and I respectively).

<p>A–D) Original images. E–H) Intermediate results prior to the rejection of spurious regions based on solidity, boundary salience and mass displacement. I–L) Intermediate results prior to the merging of contours from multiple scales. M–P) Final segmentation results.</p