Moyez Dharsee

EMT transcription factors snail and slug directly contribute to cisplatin resistance in ovarian cancer

BackgroundThe epithelial to mesenchymal transition (EMT) is a molecular process through which an epithelial cell undergoes transdifferentiation into a mesenchymal phenotype. The role of EMT in embryogenesis is well-characterized and increasing evidence suggests that elements of the transition may be important in other processes, including metastasis and drug resistance in various different cancers.MethodsAgilent 4 × 44 K whole human genome arrays and selected reaction monitoring mass spectrometry were used to investigate mRNA and protein expression in A2780 cisplatin sensitive and resistant cell lines. Invasion and migration were assessed using Boyden chamber assays. Gene knockdown of snail and slug was done using targeted siRNA. Clinical relevance of the EMT pathway was assessed in a cohort of primary ovarian tumours using data from Affymetrix GeneChip Human Genome U133 plus 2.0 arrays.ResultsMorphological and phenotypic hallmarks of EMT were identified in the chemoresistant cells. Subsequent gene expression profiling revealed upregulation of EMT-related transcription factors including snail, slug, twist2 and zeb2. Proteomic analysis demonstrated up regulation of Snail and Slug as well as the mesenchymal marker Vimentin, and down regulation of E-cadherin, an epithelial marker. By reducing expression of snail and slug, the mesenchymal phenotype was largely reversed and cells were resensitized to cisplatin. Finally, gene expression data from primary tumours mirrored the finding that an EMT-like pathway is activated in resistant tumours relative to sensitive tumours, suggesting that the involvement of this transition may not be limited to in vitro drug effects.ConclusionsThis work strongly suggests that genes associated with EMT may play a significant role in cisplatin resistance in ovarian cancer, therefore potentially leading to the development of predictive biomarkers of drug response or novel therapeutic strategies for overcoming drug resistance.

Proteomic Analyses Reveal High Expression of Decorin and Endoplasmin (HSP90B1) Are Associated with Breast Cancer Metastasis and Decreased Survival

Background Breast cancer is the most common malignancy among women worldwide in terms of incidence and mortality. About 10% of North American women will be diagnosed with breast cancer during their lifetime and 20% of those will die of the disease. Breast cancer is a heterogeneous disease and biomarkers able to correctly classify patients into prognostic groups are needed to better tailor treatment options and improve outcomes. One powerful method used for biomarker discovery is sample screening with mass spectrometry, as it allows direct comparison of protein expression between normal and pathological states. The purpose of this study was to use a systematic and objective method to identify biomarkers with possible prognostic value in breast cancer patients, particularly in identifying cases most likely to have lymph node metastasis and to validate their prognostic ability using breast cancer tissue microarrays. Methods and Findings Differential proteomic analyses were employed to identify candidate biomarkers in primary breast cancer patients. These analyses identified decorin (DCN) and endoplasmin (HSP90B1) which play important roles regulating the tumour microenvironment and in pathways related to tumorigenesis. This study indicates that high expression of Decorin is associated with lymph node metastasis (p<0.001), higher number of positive lymph nodes (p<0.0001) and worse overall survival (p = 0.01). High expression of HSP90B1 is associated with distant metastasis (p<0.0001) and decreased overall survival (p<0.0001) these patients also appear to benefit significantly from hormonal treatment. Conclusions Using quantitative proteomic profiling of primary breast cancers, two new promising prognostic and predictive markers were found to identify patients with worse survival. In addition HSP90B1 appears to identify a group of patients with distant metastasis with otherwise good prognostic features.

Proteomics Analyses of Human Optic Nerve Head Astrocytes Following Biomechanical Strain

We investigate the role of glial cell activation in the human optic nerve caused by raised intraocular pressure, and their potential role in the development of glaucomatous optic neuropathy. To do this we present a proteomics study of the response of cultured, optic nerve head astrocytes to biomechanical strain, the magnitude and mode of strain based on previously published quantitative models. In this case, astrocytes were subjected to 3 and 12% stretches for either 2 h or 24 h. Proteomic methods included nano-liquid chromatography, tandem mass spectrometry, and iTRAQ labeling. Using controls for both stretch and time, a six-plex iTRAQ liquid chromatography- tandem MS (LC/MS/MS) experiment yielded 573 proteins discovered at a 95% confidence limit. The pathways included transforming growth factor β1, tumor necrosis factor, caspase 3, and tumor protein p53, which have all been implicated in the activation of astrocytes and are believed to play a role in the development of glaucomatous optic neuropathy. Confirmation of the iTRAQ analysis was performed by Western blotting of various proteins of interest including ANXA 4, GOLGA2, and αB-Crystallin.

Identification of the IGF1/PI3K/NF κB/ERK gene signalling networks associated with chemotherapy resistance and treatment response in high-grade serous epithelial ovarian cancer

BackgroundResistance to platinum-based chemotherapy remains a major impediment in the treatment of serous epithelial ovarian cancer. The objective of this study was to use gene expression profiling to delineate major deregulated pathways and biomarkers associated with the development of intrinsic chemotherapy resistance upon exposure to standard first-line therapy for ovarian cancer.MethodsThe study cohort comprised 28 patients divided into two groups based on their varying sensitivity to first-line chemotherapy using progression free survival (PFS) as a surrogate of response. All 28 patients had advanced stage, high-grade serous ovarian cancer, and were treated with standard platinum-based chemotherapy. Twelve patient tumours demonstrating relative resistance to platinum chemotherapy corresponding to shorter PFS (< eight months) were compared to sixteen tumours from platinum-sensitive patients (PFS > eighteen months). Whole transcriptome profiling was performed using an Affymetrix high-resolution microarray platform to permit global comparisons of gene expression profiles between tumours from the resistant group and the sensitive group.ResultsMicroarray data analysis revealed a set of 204 discriminating genes possessing expression levels which could influence differential chemotherapy response between the two groups. Robust statistical testing was then performed which eliminated a dependence on the normalization algorithm employed, producing a restricted list of differentially regulated genes, and which found IGF1 to be the most strongly differentially expressed gene. Pathway analysis, based on the list of 204 genes, revealed enrichment in genes primarily involved in the IGF1/PI3K/NF κB/ERK gene signalling networks.ConclusionsThis study has identified pathway specific prognostic biomarkers possibly underlying a differential chemotherapy response in patients undergoing standard platinum-based treatment of serous epithelial ovarian cancer. In addition, our results provide a pathway context for further experimental validations, and the findings are a significant step towards future therapeutic interventions.

MicroRNA Signature Helps Distinguish Early from Late Biochemical Failure in Prostate Cancer

PURPOSE Prostate-specific antigen testing has led to overtreatment of prostate cancer (PCa). Only a small subset of PCa patients will have an aggressive disease that requires intensive therapy, and there is currently no biomarker to predict disease aggressiveness at the time of surgery. MicroRNAs (miRNAs) are reported to be involved in PCa pathogenesis. METHODS This study involved 105 participants. For the discovery phase, prostatectomy samples were dichotomized to high-risk (n = 27, biochemical failure <36 months after prostatectomy) and low-risk groups (n = 14, ≥ 36 months without biochemical failure). Expression of 754 mature miRNAs was compared between the 2 groups. Linear regression models were built to accurately predict biochemical failure risk. miRNA mimics were transfected into PCa model cell lines to test effects on proliferation and to deduce responding signaling pathways. RESULTS We identified 25 differentially expressed miRNAs between the biochemical failure risk groups. Based on the expression of 2-3 miRNAs, 3 logistic regression models were developed, each with a high positive predictive value. Candidate miRNAs and the best-performing model were also verified on an independent PCa set. miRNA-152, featured in the models, was further investigated by using cell line models and was shown to affect cell proliferation. Predicted interaction between miR-152 and (mRNA)ERBB3 (erythroblastic leukemia viral oncogene homolog 3) was experimentally validated in vitro. CONCLUSIONS miRNAs can help to predict biochemical failure risk at the time of prostatectomy.

miR-221/222 Are Involved in Response to Sunitinib Treatment in Metastatic Renal Cell Carcinoma

Sunitinib is a multitargeting tyrosine kinase inhibitor used for metastatic renal cancer. There are no biomarkers that can predict sunitinib response. Such markers are needed to avoid administration of costly medication with side effects to patients who would not benefit from it. We compared global miRNA expression between patients with a short (≤12 months) versus prolonged (>12 months) progression-free survival (PFS) under sunitinib as first-line therapy for metastatic renal cell carcinoma. We identified a number of differentially expressed miRNAs and developed miRNA statistical models that can accurately distinguish between the two groups. We validated our models in the discovery set and an independent set of 57 patients. Target prediction and pathway analysis showed that these miRNAs are involved in vascular endothelial growth factor (VEGF), TGFβ, and mammalian target of rapamycin (mTOR)-mediated signaling and cell-cell communication. We tested the effect of these miRNAs on cellular proliferation and angiogenesis. We validated the negative correlation between miR-221 and its target, VEGFR2.miR-221 overexpression was associated with a poor PFS while its target, VEGFR2 was associated with longer survival. Gain of function experiments showed that miR-221 and miR-222 decreased angiogenesis and cellular proliferation in human umbilical vein endothelial cells (HUVEC) while increasing cellular proliferation in ACHN cells. miRNAs represent potential predictive markers for sunitinib response.

Proteomics analyses of activated human optic nerve head lamina cribrosa cells following biomechanical strain.

PURPOSE To determine protein regulation following activation of human, optic nerve head (ONH), lamina cribrosa (LC) cells in response to mechanical strain. METHODS LC cells were isolated and grown from donor tissue in specific media at 37°C and 5% CO(2) humidified incubator. Cells were grown to confluence on collagen I-coated flexible-bottom culture plates, rinsed with Dulbeccos phosphate-buffered saline, and left for 24 hours in serum-free media. They were subjected to 3% or 12% cyclic equiaxial stretch for 2 or 24 hours using a commercial strain-unit system. Control cells were serum-deprived and incubated without stretch for 24 hours. Nano liquid chromatography-mass spectrometry analysis with isobaric tags for relative and absolute quantitation labeling was used to determine protein regulation. RESULTS In all, 526 proteins were discovered at a 95% confidence limit. Analysis of associated pathways and functional annotation indicated that the LC cells reacted in vitro to mechanical strain by activating pathways involved in protein synthesis, cellular movement, cell-to-cell signaling, and inflammation. These pathways indicated consistent major protein hubs across all stretch/time conditions involving transforming growth factor-β1 (TGFβ1), tumor necrosis factor (TNF), caspase-3 (CASP3), and tumor protein-p53 (p53). Among proteins of particular interest, also found in multiple stretch/time conditions, were bcl-2-associated athanogene 5 (BAG5), nucleolar protein 66 (NO66), and eukaryotic translation initiation factor 5A (eIF-5A). CONCLUSIONS Pathway analysis identified major protein hubs (TGFβ1, TNF, CASP3, p53) and pathways all previously implicated in cellular activation and in the pathogenesis of glaucomatous optic neuropathy. Several specific proteins of interest (BAG5, NO66, eIF-5A) were identified for future investigation as to their role in ONH glial activation.

Applying mass spectrometry based proteomic technology to advance the understanding of multiple myeloma.

Multiple myeloma (MM) is the second most common hematological malignancy in adults. It is characterized by clonal proliferation of terminally differentiated B lymphocytes and over-production of monoclonal immunoglobulins. Recurrent genomic aberrations have been identified to contribute to the aggressiveness of this cancer. Despite a wealth of knowledge describing the molecular biology of MM as well as significant advances in therapeutics, this disease remains fatal. The identification of biomarkers, especially through the use of mass spectrometry, however, holds great promise to increasing our understanding of this disease. In particular, novel biomarkers will help in the diagnosis, prognosis and therapeutic stratification of MM. To date, results from mass spectrometry studies of MM have provided valuable information with regards to MM diagnosis and response to therapy. In addition, mass spectrometry was employed to study relevant signaling pathways activated in MM. This review will focus on how mass spectrometry has been applied to increase our understanding of MM.

Exploring high dimensional data with Butterfly: a novel classification algorithm based on discrete dynamical systems

MOTIVATION We introduce a novel method for visualizing high dimensional data via a discrete dynamical system. This method provides a 2D representation of the relationship between subjects according to a set of variables without geometric projections, transformed axes or principal components. The algorithm exploits a memory-type mechanism inherent in a certain class of discrete dynamical systems collectively referred to as the chaos game that are closely related to iterative function systems. The goal of the algorithm was to create a human readable representation of high dimensional patient data that was capable of detecting unrevealed subclusters of patients from within anticipated classifications. This provides a mechanism to further pursue a more personalized exploration of pathology when used with medical data. For clustering and classification protocols, the dynamical system portion of the algorithm is designed to come after some feature selection filter and before some model evaluation (e.g. clustering accuracy) protocol. In the version given here, a univariate features selection step is performed (in practice more complex feature selection methods are used), a discrete dynamical system is driven by this reduced set of variables (which results in a set of 2D cluster models), these models are evaluated for their accuracy (according to a user-defined binary classification) and finally a visual representation of the top classification models are returned. Thus, in addition to the visualization component, this methodology can be used for both supervised and unsupervised machine learning as the top performing models are returned in the protocol we describe here. RESULTS Butterfly, the algorithm we introduce and provide working code for, uses a discrete dynamical system to classify high dimensional data and provide a 2D representation of the relationship between subjects. We report results on three datasets (two in the article; one in the appendix) including a public lung cancer dataset that comes along with the included Butterfly R package. In the included R script, a univariate feature selection method is used for the dimension reduction step, but in the future we wish to use a more powerful multivariate feature reduction method based on neural networks (Kriesel, 2007). AVAILABILITY AND IMPLEMENTATION A script written in R (designed to run on R studio) accompanies this article that implements this algorithm and is available at http://butterflygeraci.codeplex.com/. For details on the R package or for help installing the software refer to the accompanying document, Supporting Material and Appendix.

Standardization of electroencephalography for multi-site, multi-platform and multi-investigator studies: insights from the canadian biomarker integration network in depression

Subsequent to global initiatives in mapping the human brain and investigations of neurobiological markers for brain disorders, the number of multi-site studies involving the collection and sharing of large volumes of brain data, including electroencephalography (EEG), has been increasing. Among the complexities of conducting multi-site studies and increasing the shelf life of biological data beyond the original study are timely standardization and documentation of relevant study parameters. We present the insights gained and guidelines established within the EEG working group of the Canadian Biomarker Integration Network in Depression (CAN-BIND). CAN-BIND is a multi-site, multi-investigator, and multi-project network supported by the Ontario Brain Institute with access to Brain-CODE, an informatics platform that hosts a multitude of biological data across a growing list of brain pathologies. We describe our approaches and insights on documenting and standardizing parameters across the study design, data collection, monitoring, analysis, integration, knowledge-translation, and data archiving phases of CAN-BIND projects. We introduce a custom-built EEG toolbox to track data preprocessing with open-access for the scientific community. We also evaluate the impact of variation in equipment setup on the accuracy of acquired data. Collectively, this work is intended to inspire establishing comprehensive and standardized guidelines for multi-site studies.