Effects of partner proteins on BCA2 RING ligase activity

Abstract Background BCA2 is an E3 ligase linked with hormone responsive breast cancers. We have demonstrated previously that the RING E3 ligase BCA2 has autoubiquitination activity and is a very unstable protein. Previously, only Rab7, tetherin, ubiquitin and UBC9 were known to directly interact with BCA2. Methods Here, additional BCA2 binding proteins were found using yeast two-hybrid and bacterial-II-hybrid screening techniques with Human breast and HeLa cDNA libraries. Co-expression of these proteins was analyzed through IHC of TMAs. Investigation of the molecular interactions and effects were examined through a series of in vivo and in vitro assays. Results Ten unique BCA2 interacting proteins were identified, two of which were hHR23a and 14-3-3sigma. Both hHR23a and 14-3-3sigma are co-expressed with BCA2 in breast cancer cell lines and patient breast tumors (n = 105). hHR23a and BCA2 expression was significantly correlated (P = \u3c 0.0001 and P = 0.0113) in both nucleus and cytoplasm. BCA2 expression showed a statistically significant correlation with tumor grade. High cytoplasmic hHR23a trended towards negative nodal status. Binding to BCA2 by hHR23a and 14-3-3sigma was confirmed in vitro using tagged partner proteins and BCA2. hHR23a and 14-3-3sigma effect the autoubiquitination and auto-degradation activity of BCA2. Ubiquitination of hHR23a-bound BCA2 was found to be dramatically lower than that of free BCA2, suggesting that hHR23a promotes the stabilization of BCA2 by inactivating its autoubiquitination activity, without degradation of hHR23a. On the other hand, phosphorylated BCA2 protein is stabilized by interaction with 14-3-3sigma both with and without proteasome inhibitor MG-132 suggesting that BCA2 is regulated by multiple degradation pathways. Conclusions The interaction between BCA2 and hHR23a in breast cancer cells stabilizes BCA2. High expression of BCA2 is correlated with grade in breast cancer, suggesting regulation of this E3 ligase is important to cancer progression

Clustering Analysis for Semi-supervised Learning Improves Classification Performance of Digital Pathology

This manuscript was presented at the MLMI workshop, MICCAI2015 in Munich, GermanyPurpose: Completely labeled datasets of pathology slides are often difficult and time consuming to obtain. Semi-supervised learning methods are able to learn reliable models from small number of labeled instances and large quantities of unlabeled data. In this paper, we explored the potential of clustering analysis for semi-supervised support vector machine (SVM) classifier. Method: A clustering analysis method was proposed to find regions of high density prior to finding the decision boundary using a supervised SVM and was compared with another stateof-the-art semi-supervised technique. Different percentages of labeled instances were used to train supervised and semi-supervised SVM learners from an image dataset generated from 50 whole-mount images (8 patients) of breast specimen. Their cross-validated classification performances were compared with each other using the area under the ROC curve measure. Result: Our proposed clustering analysis for semisupervised learning was able to produce a reliable classification model from small amounts of labeled data. Comparing the proposed method in this study with a well-known implementation of semi-supervised SVM, our method performed much faster and produced better results.This research is funded by Canadian Cancer Society (grant # 703006). Reference

Gene expression profiling of ductal carcinomas in situ and invasive breast tumors

Comparative and functional genomics are powerful tools to advance the understanding of the molecular basis of cancer. It is believed that genes are epigenetically regulated and, thus, each tumor type and stage will be characterized by a gene expression fingerprint. In this study we identified genes that are differentially expressed in ductal carcinoma in situ and invasive ductal carcinoma of the breast. To isolate genes that are associated with progression of breast cancer we performed differential display and subtractive cloning procedures using matched RNA from normal and tumor tissue. cDNA microarray analysis generated gene expression profiles typical of the transition front in situ to invasive breast cancer when we used mRAA extracted from a case of low-to intermediate-grade DCIS and a case of high-grade DC1S/IDC. cDNAs from these samples were the probes in a cDNA microarray hybridization to 9183 unique cDAAs representing 8507 genes. Signals from both transcriptomes were obtained for 8083 genes, and the balanced differential expression values between pure DCIS and DCIS/invasive tumors revealed 303 distinct cDNAs with a ratio of > 2. Interferon inducible genes were found to be expressed at the highest level in the pure DCIS sample. Genes most abundantly expressed in the invasive tumor were immunoglobulin heavy constant gamma 3 and calgranulin B. Further analysis of RNA and protein expression in breast tumor cell lines and patient tissue samples revealed that: IGFBP-rP1 is down-regulated in invasive tumors whereas cyclin I protein is regulated by ubiquitination and is associated with ER-negative breast cancers. Conclusion: The known and novel genes discussed here represent targets for molecular characterization during breast cancer development as well as,for designing novel strategies for diagnosis and treatment

3D Pathology Volumetric Technique: A Method for Calculating Breast Tumour Volume from Whole-Mount Serial Section Images

Tumour size, most commonly measured by maximum linear extent, remains a strong predictor of survival in breast cancer. Tumour volume, proportional to the number of tumour cells, may be a more accurate surrogate for size. We describe a novel “3D pathology volumetric technique” for lumpectomies and compare it with 2D measurements. Volume renderings and total tumour volume are computed from digitized whole-mount serial sections using custom software tools. Results are presented for two lumpectomy specimens selected for tumour features which may challenge accurate measurement of tumour burden with conventional, sampling-based pathology: (1) an infiltrative pattern admixed with normal breast elements; (2) a localized invasive mass separated from the in situ component by benign tissue. Spatial relationships between key features (tumour foci, close or involved margins) are clearly visualized in volume renderings. Invasive tumour burden can be underestimated using conventional pathology, compared to the volumetric technique (infiltrative pattern: 30% underestimation; localized mass: 3% underestimation for invasive tumour, 44% for in situ component). Tumour volume approximated from 2D measurements (i.e., maximum linear extent), assuming elliptical geometry, was seen to overestimate volume compared to the 3D volumetric calculation (by a factor of 7x for the infiltrative pattern; 1.5x for the localized invasive mass).Peer Reviewe