Hierarchical Normalized Cuts: Unsupervised Segmentation of Vascular Biomarkers from Ovarian Cancer Tissue Microarrays

Research has shown that turner vascular markers (TVMs) may serve as potential OCa biomarkers for prognosis prediction. One such TVM is ESM-1, which can be visualized by staining ovarian Tissue Microarrays (TMA) with in antibody to ESM-1. The ability to quickly and quantitatively estimate vascular stained regions may yield an image based metric linked to disease survival and outcome. Automated segmentation of the vascular stained regions on the TMAs. however, is hindered by the presence of spuriously stained false positive regions. In this paper, we present a general, robust and efficient unsupervised segmentation algorithm, termed Hierarchical Normalized Cuts (HNCut), and show its application in precisely quantifying the presence and extent of a TVM on OCa TMAs. The strength of HNCut is in the use of a hierarchically represented data structure that bridges the mean shift (MS) and the normalized cuts (NCut,) algorithms. This allows HNCut to efficiently traverse a pyramid of the input image at various color resolutions, efficiently and accurately segmenting the object class of interest (in this case ESM-1. vascular stained regions) by simply annotating half a, dozen pixels belonging to the target; class. Quantitative and qualitative analysis of our results, using 100 pathologist annotated samples across multiple studies, prove the superiority of our method (sensitivity 81%, Positive predictive value (PPV), 80%) versus a popular supervised learning technique, Probabilistic Boosting Trees (sensitivity, PPV of 76% and 66%)

Identification of novel vascular targets in lung cancer

Background: Lung cancer remains the leading cause of cancer-related death, largely owing to the lack of effective treatments. A tumour vascular targeting strategy presents an attractive alternative; however, the molecular signature of the vasculature in lung cancer is poorly explored. This work aimed to identify novel tumour vascular targets in lung cancer. Methods: Enzymatic digestion of fresh tissue followed by endothelial capture with Ulex lectin-coated magnetic beads was used to isolate the endothelium from fresh tumour specimens of lung cancer patients. Endothelial isolates from the healthy and tumour lung tissue were subjected to whole human genome expression profiling using microarray technology. Results: Bioinformatics analysis identified tumour endothelial expression of angiogenic factors, matrix metalloproteases and cellsurface transmembrane proteins. Predicted novel tumour vascular targets were verified by RNA-seq, quantitative real-time PCR analysis and immunohistochemistry. Further detailed expression profiling of STEAP1 on 82 lung cancer patients confirmed STEAP1 as a novel target in the tumour vasculature. Functional analysis of STEAP1 using siRNA silencing implicates a role in endothelial cell migration and tube formation. Conclusions: The identification of cell-surface tumour endothelial markers in lung is of interest in therapeutic antibody and vaccine development