Vidya Pundalik Kamath

Metastasis-Associated Gene Expression Changes Predict Poor Outcomes in Patients with Dukes Stage B and C Colorectal Cancer.

Purpose: Colorectal cancer prognosis is currently predicted from pathologic staging, providing limited discrimination for Dukes stage B and C disease. Additional markers for outcome are required to help guide therapy selection for individual patients. Experimental Design: A multisite single-platform microarray study was done on 553 colorectal cancers. Gene expression changes were identified between stage A and D tumors (three training sets) and assessed as a prognosis signature in stage B and C tumors (independent test and external validation sets). Results: One hundred twenty-eight genes showed reproducible expression changes between three sets of stage A and D cancers. Using consistent genes, stage B and C cancers clustered into two groups resembling early-stage and metastatic tumors. A Prediction Analysis of Microarray algorithm was developed to classify individual intermediate-stage cancers into stage A–like/good prognosis or stage D–like/poor prognosis types. For stage B patients, the treatment adjusted hazard ratio for 6-year recurrence in individuals with stage D–like cancers was 10.3 (95% confidence interval, 1.3-80.0; P = 0.011). For stage C patients, the adjusted hazard ratio was 2.9 (95% confidence interval, 1.1-7.6; P = 0.016). Similar results were obtained for an external set of stage B and C patients. The prognosis signature was enriched for downregulated immune response genes and upregulated cell signaling and extracellular matrix genes. Accordingly, sparse tumor infiltration with mononuclear chronic inflammatory cells was associated with poor outcome in independent patients. Conclusions: Metastasis-associated gene expression changes can be used to refine traditional outcome prediction, providing a rational approach for tailoring treatments to subsets of patients. (Clin Cancer Res 2009;15(24):7642–51)

Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue

Limitations on the number of unique protein and DNA molecules that can be characterized microscopically in a single tissue specimen impede advances in understanding the biological basis of health and disease. Here we present a multiplexed fluorescence microscopy method (MxIF) for quantitative, single-cell, and subcellular characterization of multiple analytes in formalin-fixed paraffin-embedded tissue. Chemical inactivation of fluorescent dyes after each image acquisition round allows reuse of common dyes in iterative staining and imaging cycles. The mild inactivation chemistry is compatible with total and phosphoprotein detection, as well as DNA FISH. Accurate computational registration of sequential images is achieved by aligning nuclear counterstain-derived fiducial points. Individual cells, plasma membrane, cytoplasm, nucleus, tumor, and stromal regions are segmented to achieve cellular and subcellular quantification of multiplexed targets. In a comparison of pathologist scoring of diaminobenzidine staining of serial sections and automated MxIF scoring of a single section, human epidermal growth factor receptor 2, estrogen receptor, p53, and androgen receptor staining by diaminobenzidine and MxIF methods yielded similar results. Single-cell staining patterns of 61 protein antigens by MxIF in 747 colorectal cancer subjects reveals extensive tumor heterogeneity, and cluster analysis of divergent signaling through ERK1/2, S6 kinase 1, and 4E binding protein 1 provides insights into the spatial organization of mechanistic target of rapamycin and MAPK signal transduction. Our results suggest MxIF should be broadly applicable to problems in the fields of basic biological research, drug discovery and development, and clinical diagnostics.

Loss of Rab25 promotes the development of intestinal neoplasia in mice and is associated with human colorectal adenocarcinomas

Transformation of epithelial cells is associated with loss of cell polarity, which includes alterations in cell morphology as well as changes in the complement of plasma membrane proteins. Rab proteins regulate polarized trafficking to the cell membrane and therefore represent potential regulators of this neoplastic transition. Here we have demonstrated a tumor suppressor function for Rab25 in intestinal neoplasia in both mice and humans. Human colorectal adenocarcinomas exhibited reductions in Rab25 expression independent of stage, with lower Rab25 expression levels correlating with substantially shorter patient survival. In wild-type mice, Rab25 was strongly expressed in cells luminal to the proliferating cells of intestinal crypts. While Rab25-deficient mice did not exhibit gross pathology, ApcMin/+ mice crossed onto a Rab25-deficient background showed a 4-fold increase in intestinal polyps and a 2-fold increase in colonic tumors compared with parental ApcMin/+ mice. Rab25-deficient mice had decreased beta1 integrin staining in the lateral membranes of villus cells, and this pattern was accentuated in Rab25-deficient mice crossed onto the ApcMin/+ background. Additionally, Smad3+/- mice crossed onto a Rab25-deficient background demonstrated a marked increase in colonic tumor formation. Taken together, these results suggest that Rab25 may function as a tumor suppressor in intestinal epithelial cells through regulation of protein trafficking to the cell surface.

Validation of a Radiosensitivity Molecular Signature in Breast Cancer

Purpose: Previously, we developed a radiosensitivity molecular signature [radiosensitivity index (RSI)] that was clinically validated in 3 independent datasets (rectal, esophageal, and head and neck) in 118 patients. Here, we test RSI in radiotherapy (RT)-treated breast cancer patients. Experimental Design: RSI was tested in 2 previously published breast cancer datasets. Patients were treated at the Karolinska University Hospital (n = 159) and Erasmus Medical Center (n = 344). RSI was applied as previously described. Results: We tested RSI in RT-treated patients (Karolinska). Patients predicted to be radiosensitive (RS) had an improved 5-year relapse-free survival when compared with radioresistant (RR) patients (95% vs. 75%, P = 0.0212), but there was no difference between RS/RR patients treated without RT (71% vs. 77%, P = 0.6744), consistent with RSI being RT-specific (interaction term RSI × RT, P = 0.05). Similarly, in the Erasmus dataset, RT-treated RS patients had an improved 5-year distant metastasis-free survival over RR patients (77% vs. 64%, P = 0.0409), but no difference was observed in patients treated without RT (RS vs. RR, 80% vs. 81%, P = 0.9425). Multivariable analysis showed RSI is the strongest variable in RT-treated patients (Karolinska, HR = 5.53, P = 0.0987, Erasmus, HR = 1.64, P = 0.0758) and in backward selection (removal α of 0.10), RSI was the only variable remaining in the final model. Finally, RSI is an independent predictor of outcome in RT-treated ER+ patients (Erasmus, multivariable analysis, HR = 2.64, P = 0.0085). Conclusions: RSI is validated in 2 independent breast cancer datasets totaling 503 patients. Including prior data, RSI is validated in 5 independent cohorts (621 patients) and represents, to our knowledge, the most extensively validated molecular signature in radiation oncology. Clin Cancer Res; 18(18); 5134–43. ©2012 AACR.

Quantitative single cell analysis of cell population dynamics during submandibular salivary gland development and differentiation

Summary Epithelial organ morphogenesis involves reciprocal interactions between epithelial and mesenchymal cell types to balance progenitor cell retention and expansion with cell differentiation for evolution of tissue architecture. Underlying submandibular salivary gland branching morphogenesis is the regulated proliferation and differentiation of perhaps several progenitor cell populations, which have not been characterized throughout development, and yet are critical for understanding organ development, regeneration, and disease. Here we applied a serial multiplexed fluorescent immunohistochemistry technology to map the progressive refinement of the epithelial and mesenchymal cell populations throughout development from embryonic day 14 through postnatal day 20. Using computational single cell analysis methods, we simultaneously mapped the evolving temporal and spatial location of epithelial cells expressing subsets of differentiation and progenitor markers throughout salivary gland development. We mapped epithelial cell differentiation markers, including aquaporin 5, PSP, SABPA, and mucin 10 (acinar cells); cytokeratin 7 (ductal cells); and smooth muscle &agr;-actin (myoepithelial cells) and epithelial progenitor cell markers, cytokeratin 5 and c-kit. We used pairwise correlation and visual mapping of the cells in multiplexed images to quantify the number of single- and double-positive cells expressing these differentiation and progenitor markers at each developmental stage. We identified smooth muscle &agr;-actin as a putative early myoepithelial progenitor marker that is expressed in cytokeratin 5-negative cells. Additionally, our results reveal dynamic expansion and redistributions of c-kit- and K5-positive progenitor cell populations throughout development and in postnatal glands. The data suggest that there are temporally and spatially discreet progenitor populations that contribute to salivary gland development and homeostasis.

Excess PLAC8 promotes an unconventional ERK2-dependent EMT in colon cancer.

The epithelial-to-mesenchymal transition (EMT) transcriptional program is characterized by repression of E-cadherin (CDH1) and induction of N-cadherin (CDH2), and mesenchymal genes like vimentin (VIM). Placenta-specific 8 (PLAC8) has been implicated in colon cancer; however, how PLAC8 contributes to disease is unknown, and endogenous PLAC8 protein has not been studied. We analyzed zebrafish and human tissues and found that endogenous PLAC8 localizes to the apical domain of differentiated intestinal epithelium. Colon cancer cells with elevated PLAC8 levels exhibited EMT features, including increased expression of VIM and zinc finger E-box binding homeobox 1 (ZEB1), aberrant cell motility, and increased invasiveness. In contrast to classical EMT, PLAC8 overexpression reduced cell surface CDH1 and upregulated P-cadherin (CDH3) without affecting CDH2 expression. PLAC8-induced EMT was linked to increased phosphorylated ERK2 (p-ERK2), and ERK2 knockdown restored cell surface CDH1 and suppressed CDH3, VIM, and ZEB1 upregulation. In vitro, PLAC8 directly bound and inactivated the ERK2 phosphatase DUSP6, thereby increasing p-ERK2. In a murine xenograft model, knockdown of endogenous PLAC8 in colon cancer cells resulted in smaller tumors, reduced local invasion, and decreased p-ERK2. Using MultiOmyx, a multiplex immunofluorescence-based methodology, we observed coexpression of cytosolic PLAC8, CDH3, and VIM at the leading edge of a human colorectal tumor, supporting a role for PLAC8 in cancer invasion in vivo.

Automatic partitioning of head CTA for enabling segmentation

Radiologists perform a CT Angiography procedure to examine vascular structures and associated pathologies such as aneurysms. Volume rendering is used to exploit volumetric capabilities of CT that provides complete interactive 3-D visualization. However, bone forms an occluding structure and must be segmented out. The anatomical complexity of the head creates a major challenge in the segmentation of bone and vessel. An analysis of the head volume reveals varying spatial relationships between vessel and bone that can be separated into three sub-volumes: “proximal”, “middle”, and “distal”. The “proximal” and “distal” sub-volumes contain good spatial separation between bone and vessel (carotid referenced here). Bone and vessel appear contiguous in the “middle” partition that remains the most challenging region for segmentation. The partition algorithm is used to automatically identify these partition locations so that different segmentation methods can be developed for each sub-volume. The partition locations are computed using bone, image entropy, and sinus profiles along with a rule-based method. The algorithm is validated on 21 cases (varying volume sizes, resolution, clinical sites, pathologies) using ground truth identified visually. The algorithm is also computationally efficient, processing a 500+ slice volume in 6 seconds (an impressive 0.01 seconds / slice) that makes it an attractive algorithm for pre-processing large volumes. The partition algorithm is integrated into the segmentation workflow. Fast and simple algorithms are implemented for processing the “proximal” and “distal” partitions. Complex methods are restricted to only the “middle” partition. The partitionenabled segmentation has been successfully tested and results are shown from multiple cases.

Multivariate Feature Selection using Random Subspace Classifiers for Gene Expression Data

Gene expression analysis techniques identify important genes that predict specified outcomes based on sample characteristics. Given the small sample sizes common to these studies and the large dimensionality of the data, feature selection methods are essential. In addition, cancer-related expression analysis often involves imbalanced datasets due to rare forms of disease. Popular methods of feature selection employ univariate techniques to identify the features most suitable for analysis. We propose a multivariate technique for selecting accurate subsets of features using an approach based on random subspaces. The random subspace method is used to explore random combinations of features and only subspaces that produce accurate classifiers are retained. The method is tested on two independent gene expression datasets and compared with a univariate approach. The multivariate feature selection method resulted in a 33% improvement in classification accuracy overall and 90% improvement in classification accuracy for the minority class.

Integrating Biological Covariates into Gene Expression-Based Predictors of Radiation Sensitivity

The use of gene expression-based classifiers has resulted in a number of promising potential signatures of patient diagnosis, prognosis, and response to therapy. However, these approaches have also created difficulties in trying to use gene expression alone to predict a complex trait. A practical approach to this problem is to integrate existing biological knowledge with gene expression to build a composite predictor. We studied the problem of predicting radiation sensitivity within human cancer cell lines from gene expression. First, we present evidence for the need to integrate known biological conditions (tissue of origin, RAS, and p53 mutational status) into a gene expression prediction problem involving radiation sensitivity. Next, we demonstrate using linear regression, a technique for incorporating this knowledge. The resulting correlations between gene expression and radiation sensitivity improved through the use of this technique (best-fit adjusted R2 increased from 0.3 to 0.84). Overfitting of data was examined through the use of simulation. The results reinforce the concept that radiation sensitivity is not driven solely by gene expression, but rather by a combination of distinct parameters. We show that accounting for biological heterogeneity significantly improves the ability of the model to identify genes that are associated with radiosensitivity.

Toward a measure of classification complexity in gene expression signatures

Gene expression signatures identify important genes that predict a specified outcome. In several notable diseases such as leukemia and breast cancer, the results have been encouraging. In these datasets, many techniques work well when discriminating particular outcomes. However, these same methods, applied to other datasets, are unable to achieve similar levels of success. Given the small sample sizes common to these studies and the large dimensionality of the data, several key issues exist when attempting to construct reliable, reproducible gene signatures. The classifiers may not be sufficient to discriminate classes, or the data itself may not be sufficient to produce effective separation. In this paper, three simple measures of classification complexity are considered to explore a limit to the predictive accuracy that can be achieved in a dataset. Two independent gene expression datasets (lung and colorectal cancer) are considered, using three different outcomes on each dataset. Four different classifiers, using the t-test for feature selection, were tested on these datasets as a representative panel of classifiers. Our results indicate that Fishers discriminant ratio provides a good measure of the complexity of the classification problem, with a high correlation between complexity and best classification accuracy across all problems (R2=0.78). Specifically, predicting gender is a low complexity problem as indicated both by the complexity measure and the classification results. More clinically-oriented endpoints are more complex and have lower classification accuracies. Therefore, classification complexity can be used to estimate maximum attainable accuracy for a problem reducing the need to evaluate many different classifiers.