Christhunesa Christudass

EGFR-Mediated Beclin 1 Phosphorylation in Autophagy Suppression, Tumor Progression, and Tumor Chemoresistance

Cell surface growth factor receptors couple environmental cues to the regulation of cytoplasmic homeostatic processes, 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 the autophagy protein Beclin 1, leading to its multisite tyrosine phosphorylation, enhanced binding to inhibitors, and decreased Beclin 1-associated VPS34 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.

Acquisition of paclitaxel resistance is associated with a more aggressive and invasive phenotype in prostate cancer.

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. J. Cell. Biochem. 114: 1286–1293, 2013.

Creatine kinase brain overexpression protects colorectal cells from various metabolic and non‐metabolic stresses

Creatine kinase brain (CKB) is one of three cytosolic isoforms of creatine kinase that is predominantly expressed in the brain. The enzyme is overexpressed in a wide variety of cancers, with the exception of colon cancer, where it is downregulated. The significance of this downregulation remains poorly understood. Here, we demonstrate that overexpression of CKB‐C283S, a dominant‐negative construct that lacks the kinase function but retains its ability to dimerize, causes remarkable changes in cell shape, adhesion, and invasion. Furthermore, it results in increased expression of stromal cell markers such as PAGE4 and SNAIL, suggesting an epithelial‐to‐mesenchymal transition (EMT) in these cells. In cells transfected with a CKB‐expressing construct, CKB localizes not only to the cytosol but also to the nucleus, indicating a structural or kinase role unrelated to ATP storage. Furthermore, overexpression of CFP‐tagged wild‐type (WT) CKB in Caco‐2 colon cancer cells dramatically increased the number of cells in G2/M but had little effect on cell proliferation. Taken together, these data demonstrate that the downregulation of CKB may play an important role in colon cancer progression by promoting EMT. J. Cell. Biochem. 112: 1066–1075, 2011.

Cell Orientation Entropy (COrE): Predicting Biochemical Recurrence from Prostate Cancer Tissue Microarrays

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.

Adaptive energy selective active contour with shape priors for nuclear segmentation and gleason grading of prostate cancer

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.

Selective invocation of shape priors for deformable segmentation and morphologic classification of prostate cancer tissue microarrays

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 three 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 from 40 patient studies. Nuclear shape based, architectural and textural features extracted from these segmentations were extracted and found to able to discriminate different Gleason grade patterns with a classification accuracy of 86% via a quadratic discriminant analysis (QDA) classifier. On average the AdACM model provided 60% savings in computational times compared to a non-optimized hybrid active contour model involving a shape prior.

Cell cluster graph for prediction of biochemical recurrence in prostate cancer patients from tissue microarrays

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. Quantitative histomorphometry is a relatively new field which attempt to develop and apply novel advanced computerized image analysis and feature extraction methods for the quantitative characterization of tumor morphology on digitized pathology slides. Recently, 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 several extant graph based algorithms (Voronoi, Delaunay, Minimum Spanning Tree) is that they do not allow for extraction of local spatial attributes from complex networks such as those that emerges from large histopathology images with potentially thousands of nuclei. In this paper, we define a cluster of cells as a node and construct a novel graph called Cell Cluster Graph (CCG) to characterize local spatial architecture. CCG is constructed by first identifying the cell clusters to use as nodes for the construction of the graph. Pairwise spatial relationship between nodes is translated into edges of the CCG, each of which are assigned certain probability, i.e. each edge between any pair of a nodes has a certain probability to exist. Spatial constraints are employed to deconstruct the entire graph into subgraphs and we then extract global and local graph based features from the CCG. We evaluated the ability of the CCG to predict 5 year biochemical failures in men with CaP and who had previously undergone radical prostatectomy. Extracted features from CCG constructed using nuclei as nodal centers on tissue microarray (TMA) images obtained from the surgical specimens of 80 patients allowed us to train a support vector machine classifier via a 3 fold randomized cross validation procedure which yielded a classification accuracy of 83:1±1:2%. By contrast the Voronoi, Delaunay, and Minimum spanning tree based graph classifiers yielded corresponding classification accuracies of 67:1±1:8% and 60:7±0:9% respectively.

Nuclear Morphometry, Epigenetic Changes, and Clinical Relevance in Prostate Cancer

Nuclear structure alterations in cancer involve global genetic (mutations, amplifications, copy number variations, translocations, etc.) and epigenetic (DNA methylation and histone modifications) events that dramatically and dynamically spatially change chromatin, nuclear body, and chromosome organization. In prostate cancer (CaP) there appears to be early (<50 years) versus late (>60 years) onset clinically significant cancers, and we have yet to clearly understand the hereditary and somatic-based molecular pathways involved. We do know that once cancer is initiated, dedifferentiation of the prostate gland occurs with significant changes in nuclear structure driven by numerous genetic and epigenetic processes. This review focuses upon the nuclear architecture and epigenetic dynamics with potential translational clinically relevant applications to CaP. Further, the review correlates changes in the cancer-driven epigenetic process at the molecular level and correlates these alterations to nuclear morphological quantitative measurements. Finally, we address how we can best utilize this knowledge to improve the efficacy of personalized treatment of cancer.

Cardinal Multiridgelet-based Prostate Cancer Histological Image Classification for Gleason Grading

Computer-aided Gleason grading of prostate cancer tissue images has been in rapid development during the past decade. Automated classifiers using features derived from multi wavelet transform, fractal dimension and other measurements, and using text on forests have shown considerable successes. This paper presents our study on application of cardinal multiridgelet transform (CMRT) to prostate cancer images to extract texture features in the transform domain. CMRT can provide cardinality, approximate translation invariance and rotation invariance simultaneously. With 32 images of Gleason grade 3 and grade4 as a training set and using texture features extracted there from, a support vector machine with Gaussian kernel has been trained to classify grade 3 and grade 4. The leave-one-outcross-validation showed its accuracy of 93.75% and AUC of0.9651. 10 test images of grade 4 showed 100% accuracy.

Macrophage Inhibitory Cytokine 1 Biomarker Serum Immunoassay in Combination with PSA Is a More Specific Diagnostic Tool for Detection of Prostate Cancer

Background Prostate cancer (PCa) is the most common malignancy among men in the United States. Though highly sensitive, the often-used prostate-specific antigen (PSA) test has low specificity which leads to overdiagnosis and overtreatment of PCa. This paper presents results of a retrospective study that indicates that testing for macrophage inhibitory cytokine 1 (MIC-1) concentration along with the PSA assay could provide much improved specificity to the assay. Methods The MIC-1 serum level was determined by a novel p-Chip-based immunoassay run on 70 retrospective samples. The assay was configured on p-Chips, small integrated circuits (IC) capable of storing in their electronic memories a serial number to identify the molecular probe immobilized on its surface. The distribution of MIC-1 and pre-determined PSA concentrations were displayed in a 2D plot and the predictive power of the dual MIC-1/PSA assay was analyzed. Results MIC-1 concentration in serum was elevated in PCa patients (1.44 ng/ml) compared to normal and biopsy-negative individuals (0.93 ng/ml and 0.88 ng/ml, respectively). In addition, the MIC-1 level was correlated with the progression of PCa. The area under the receiver operator curve (AUC-ROC) was 0.81 providing an assay sensitivity of 83.3% and specificity of 60.7% by using a cutoff of 0.494 for the logistic regression value of MIC-1 and PSA. Another approach, by defining high-frequency PCa zones in a two-dimensional plot, resulted in assay sensitivity of 78.6% and specificity of 89.3%. Conclusions The analysis based on correlation of MIC-1 and PSA concentrations in serum with the patient PCa status improved the specificity of PCa diagnosis without compromising the high sensitivity of the PSA test alone and has potential for PCa prognosis for patient therapy strategies.