The transcriptional repressors Snail and Slug contribute to cancer progression by mediating epithelial-mesenchymal transition (EMT), which results in tumor cell invasion and metastases. We extend this current understanding to demonstrate their involvement in the development of resistance to radiation and paclitaxel. The process is orchestrated through the acquisition of a novel subset of gene targets that is repressed under conditions of stress, effectively inactivating p53-mediated apoptosis, while another subset of targets continues to mediate EMT. Repressive activities are complemented by a concurrent derepression of specific genes resulting in the acquisition of stem celllike characteristics. Such cells are bestowed with three critical capabilities, namely EMT, resistance to p53-mediated apoptosis, and a self-renewal program, that together define the functionality and survival of metastatic cancer stem cells. EMT provides a mechanism of escape to a new, less adverse niche; resistance to apoptosis ensures cell survival in conditions of stress in the primary tumor; whereas acquisition of ''stemness'' ensures generation of the critical tumor mass required for progression of micrometastases to macrometastases. Our findings, besides achieving considerable expansion of the inventory of direct genes targets, more importantly demonstrate that such elegant cooperative modulation of gene regulation mediated by Snail and Slug is critical for a cancer cell to acquire stem cell characteristics toward resisting radiotherapy-or chemotherapy-mediated cellular stress, and this may be a determinative aspect of aggressive cancer metastases.

Biophysical techniques play an important role in detecting physiological alterations during pathogenesis. Raman spectroscopy has shown immense potential in identifying several diseased conditions, including oral cancers. Classification of normal, inflammatory, premalignant and malignant conditions has been demonstrated using ex vivo Raman spectroscopy. Feasibility of recording in vivo spectra in clinically implementable time has also been shown. Translation of this technology to clinics requires extensive validation of methodologies, building of robust models and testing the same under stringent conditions as well as on diverse populations. In this context, the ability of Raman spectroscopy in identifying subtle changes in oral mucosa with increasing age, and the influence of these aging related changes on classification with tobacco-related pathological changes was evaluated. A total of 451 spectra from 62 subjects were recorded from buccal mucosa of healthy subjects of 4 different age groups (aged 20-60 years). Also, 478 spectra from 85 subjects belonging to 4 different categories, tobacco exposed mucosa, contralateral normal (opposite side of tumor), premalignant patches and tumors on buccal mucosa were recorded using fiber optic probe-coupled commercial Raman spectrometer. Differences in spectra were explored by unsupervised Principal Component Analysis (PCA) and supervised Linear Discriminant Analysis (LDA), followed by Leave one out cross validation. Results indicate feasibility of classifying early and late age groups. Also, clear classification is observed between healthy and pathological groups, thus inherent heterogeneity in healthy groups seems to have no bearing on classification of normal with abnormal conditions. Findings of the study indicate high sensitivity of Raman spectroscopy in detecting subtle mucosal changes, further supporting efficacy of Raman spectroscopic approaches in oral cancer applications. Prospectively, more vigorous validation studies of Raman methodology would enable routine clinical applications.

Cancer is now recognized as one of the major causes of morbidity and mortality. Histopathological diagnosis, the gold standard, is shown to be subjective, time consuming, prone to interobserver disagreement, and often fails to predict prognosis. Optical spectroscopic methods are being contemplated as adjuncts or alternatives to conventional cancer diagnostics. The most important aspect of these approaches is their objectivity, and multivariate statistical tools play a major role in realizing it. However, rigorous evaluation of the robustness of spectral models is a prerequisite. The utility of Raman spectroscopy in the diagnosis of cancers has been well established. Until now, the specificity and applicability of spectral models have been evaluated for specific cancer types. In this study, we have evaluated the utility of spectroscopic models representing normal and malignant tissues of the breast, cervix, colon, larynx, and oral cavity in a broader perspective, using different multivariate tests. The limit test, which was used in our earlier study, gave high sensitivity but suffered from poor specificity. The performance of other methods such as factorial discriminant analysis and partial least square discriminant analysis are at par with more complex nonlinear methods such as decision trees, but they provide very little information about the classification model. This comparative study thus demonstrates not just the efficacy of Raman spectroscopic models but also the applicability and limitations of different multivariate tools for discrimination under complex conditions such as the multicancer scenario.

Multiple, dissimilar genetic defects in cancers of the same origin contribute to heterogeneity in tumor phenotypes and therapeutic responses of patients, yet the associated molecular mechanisms remain elusive. Here, we show at the systems level that serous ovarian carcinoma is marked by the activation of interconnected modules associated with a specific gene set that was derived from three independent tumor-specific gene expression data sets. Network prediction algorithms combined with preestablished protein interaction networks and known functionalities affirmed the importance of genes associated with ovarian cancer as predictive biomarkers, besides "discovering" novel ones purely on the basis of interconnectivity, whose precise involvement remains to be investigated. Copy number alterations and aberrant epigenetic regulation were identified and validated as significant influences on gene expression. More importantly, three functional modules centering on c-Myc activation, altered retinoblastoma signaling, and p53/cell cycle/DNA damage repair pathways have been identified for their involvement in transformation-associated events. Further studies will assign significance to and aid the design of a panel of specific markers predictive of individual-and tumorspecific pathways. In the parlance of this emerging field, such networks of gene-hub interactions may define personalized therapeutic decisions.