Summary Cell surface growth factor receptors couple environmental cues to the regulation of cytoplasmic homeostatic process including autophagy, and aberrant activation of such receptors is a common feature of human malignancies. Here, we defined the molecular basis by which the epidermal growth factor receptor (EGFR) tyrosine kinase regulates autophagy. Active EGFR binds to the autophagy protein Beclin 1, leading to its multisite tyrosine phosphorylation, enhanced binding to inhibitors, and decreased Beclin 1-associated Class III phosphatidylinositol-3 kinase activity. EGFR tyrosine kinase inhibitor (TKI) therapy disrupts Beclin 1 tyrosine phosphorylation and binding to its inhibitors, and restores autophagy in non-small cell lung carcinoma (NSCLC) cells with a TKI-sensitive EGFR mutation. In NSCLC tumor xenografts, the expression of a tyrosine phosphomimetic Beclin 1 mutant leads to reduced autophagy, enhanced tumor growth, tumor dedifferentiation, and resistance to TKI therapy. Thus, oncogenic receptor tyrosine kinases directly regulate the core autophagy machinery, which may contribute to tumor progression and chemoresistance.

Drug resistance is a major limitation to the successful treatment of advanced prostate cancer (PCa). Patients who have metastatic, castration-resistant PCa (mCRPC) are treated with chemotherapeutics. However, these standard therapy modalities culminate in the development of resistance. We established paclitaxel resistance in a classic, androgen-insensitive mCRPC cell line (DU145) and, using a suite of molecular and biophysical methods, characterized the structural and functional changes in vitro and in vivo that are associated with the development of drug resistance. After acquiring paclitaxel-resistance, cells exhibited an abnormal nuclear morphology with extensive chromosomal content, an increase in stiffness, and faster cytoskeletal remodeling dynamics. Compared with the parental DU145, paclitaxel-resistant (DU145-TxR) cells became highly invasive and motile in vitro, exercised greater cell traction forces, and formed larger and rapidly growing tumors in mouse xenografts. Furthermore, DU145-TxR cells showed a discrete loss of keratins but a distinct gain of ZEB1, Vimentin and Snail, suggesting an epithelial-to-mesenchymal transition. These findings demonstrate, for the first time, that paclitaxel resistance in PCa is associated with a trans-differentiation of epithelial cell machinery that enables more aggressive and invasive phenotype and portend new strategies for developing novel biomarkers and effective treatment modalities for PCa patients.

We introduce a novel feature descriptor to describe cancer cells called Cell Orientation Entropy (COrE). The main objective of this work is to employ COrE to quantitatively model disorder of cell/nuclear orientation within local neighborhoods and evaluate whether these measurements of directional disorder are correlated with biochemical recurrence (BCR) in prostate cancer (CaP) patients. COrE has a number of novel attributes that are unique to digital pathology image analysis. Firstly, it is the first rigorous attempt to quantitatively model cell/nuclear orientation. Secondly, it provides for modeling of local cell networks via construction of subgraphs. Thirdly, it allows for quantifying the disorder in local cell orientation via second order statistical features. We evaluated the ability of 39 COrE features to capture the characteristics of cell orientation in CaP tissue microarray (TMA) images in order to predict 10 year BCR in men with CaP following radical prostatectomy. Randomized 3-fold cross-validation via a random forest classifier evaluated on a combination of COrE and other nuclear features achieved an accuracy of 82.7 ± 3.1% on a dataset of 19 BCR and 20 non-recurrence patients. Our results suggest that COrE features could be extended to characterize disease states in other histological cancer images in addition to prostate cancer.

Prostate cancer (CaP) is evidenced by profound changes in the spatial distribution of cells. Spatial arrangement and architectural organization of nuclei, especially clustering of the cells, within CaP histopathology is known to be predictive of disease aggressiveness and potentially patient outcome. Traditionally, graph theory has been used to characterize the spatial arrangement of these cells by constructing a graph with cell/nuclei as the node. One disadvantage of this approach is the inability to extract local attributes from complex networks that emerges from large histopathology samples. In this paper, we define a cluster of cells as a node and construct a novel graph called Cell Cluster Graph (CCG). CCG is constructed by first identifying the cell clusters to use as nodes for graph construction. Pairwise spatial relation between nodes is translated to the edges (links) of CCG with a certain probability. We then extract global and local features from the CCG that best capture the tumor morphology. We evaluated the ability of the CCG to capture the characteristics of CaP morphology in order to predict 5 year biochemical failures in men with CaP and treated with radical prostatectomy. Extracted features from CCG constructed using nuclei as nodal centers on tissue microarray (TMA) images obtained from the surgical specimens allowed us to predict biochemical recurrence. A randomized 3-fold cross-validation via support Vector Machine classifier achieved an accuracy of 83.1 ± 1.2% in dataset of 80 patients with 20 cases of biochemical recurrence.

Abstract. Shape based active contours have emerged as a natural solution to overlap resolution. However, most of these shape-based methods are computationally expensive. There are instances in an image where no overlapping objects are present and applying these schemes results in significant computational overhead without any accompanying, additional benefit. In this paper we present a novel adaptive active contour scheme (AdACM) that combines boundary and region based energy terms with a shape prior in a multi level set formulation. To reduce the computational overhead, the shape prior term in the variational formulation is only invoked for those instances in the image where overlaps between objects are identified; these overlaps being identified via a contour concavity detection scheme. By not having to invoke all 3 terms (shape, boundary, region) for segmenting every object in the scene, the computational expense of the integrated active contour model is dramatically reduced, a particularly relevant consideration when multiple objects have to be segmented on very large histopathological images. The AdACM was employed for the task of segmenting nuclei on 80 prostate cancer tissue microarray images. Morphological features extracted from these segmentations were found to able to discriminate different Gleason grade patterns with a classification accuracy of 84% via a Support Vector Machine classifier. On average the AdACM model provided 100% savings in computational times compared to a non-optimized hybrid AC model involving a shape prior.