Structural similarity assessment for drug sensitivity prediction in cancer

<p>Abstract</p> <p>Background</p> <p>The ability to predict drug sensitivity in cancer is one of the exciting promises of pharmacogenomic research. Several groups have demonstrated the ability to predict drug sensitivity by integrating chemo-sensitivity data and associated gene expression measurements from large anti-cancer drug screens such as NCI-60. The general approach is based on comparing gene expression measurements from sensitive and resistant cancer cell lines and deriving drug sensitivity profiles consisting of lists of genes whose expression is predictive of response to a drug. Importantly, it has been shown that such profiles are generic and can be applied to cancer cell lines that are not part of the anti-cancer screen. However, one limitation is that the profiles can not be generated for untested drugs (i.e., drugs that are not part of an anti-cancer drug screen). In this work, we propose using an existing drug sensitivity profile for drug A as a substitute for an untested drug B given high structural similarities between drugs A and B.</p> <p>Results</p> <p>We first show that structural similarity between pairs of compounds in the NCI-60 dataset highly correlates with the similarity between their activities across the cancer cell lines. This result shows that structurally similar drugs can be expected to have a similar effect on cancer cell lines. We next set out to test our hypothesis that we can use existing drug sensitivity profiles as substitute profiles for untested drugs. In a cross-validation experiment, we found that the use of substitute profiles is possible without a significant loss of prediction accuracy if the substitute profile was generated from a compound with high structural similarity to the untested compound.</p> <p>Conclusion</p> <p>Anti-cancer drug screens are a valuable resource for generating omics-based drug sensitivity profiles. We show that it is possible to extend the usefulness of existing screens to untested drugs by deriving substitute sensitivity profiles from structurally similar drugs part of the screen.</p

Human antimicrobial protein hCAP18/LL-37 promotes a metastatic phenotype in breast cancer

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Microarray analysis of genes associated with cell surface NIS protein levels in breast cancer

<p>Abstract</p> <p>Background</p> <p>Na⁺/I^-symporter (NIS)-mediated iodide uptake allows radioiodine therapy for thyroid cancer. NIS is also expressed in breast tumors, raising potential for radionuclide therapy of breast cancer. However, NIS expression in most breast cancers is low and may not be sufficient for radionuclide therapy. We aimed to identify biomarkers associated with NIS expression such that mechanisms underlying NIS modulation in human breast tumors may be elucidated.</p> <p>Methods</p> <p>Published oligonucleotide microarray data within the National Center for Biotechnology Information Gene Expression Omnibus database were analyzed to identify gene expression tightly correlated with NIS mRNA level among human breast tumors. NIS immunostaining was performed in a tissue microarray composed of 28 human breast tumors which had corresponding oligonucleotide microarray data available for each tumor such that gene expression associated <it>w</it>ith cell surface NIS protein level could be identified.</p> <p>Results and Discussion</p> <p>NIS mRNA levels do not vary among breast tumors or when compared to normal breast tissues when detected by Affymetrix oligonucleotide microarray platforms. Cell surface NIS protein levels are much more variable than their corresponding NIS mRNA levels. Despite a limited number of breast tumors examined, our analysis identified cysteinyl-tRNA synthetase as a biomarker that is highly associated with cell surface NIS protein levels in the ER-positive breast cancer subtype.</p> <p>Conclusions</p> <p>Further investigation on genes associated with cell surface NIS protein levels within each breast cancer molecular subtype may lead to novel targets for selectively increasing NIS expression/function in a subset of breast cancers patients.</p

Nuclear β-catenin and CD44 upregulation characterize invasive cell populations in non-aggressive MCF-7 breast cancer cells

<p>Abstract</p> <p>Background</p> <p>In breast cancer cells, the metastatic cell state is strongly correlated to epithelial-to-mesenchymal transition (EMT) and the CD44⁺/CD24^-stem cell phenotype. However, the MCF-7 cell line, which has a luminal epithelial-like phenotype and lacks a CD44⁺/CD24^-subpopulation, has rare cell populations with higher Matrigel invasive ability. Thus, what are the potentially important differences between invasive and non-invasive breast cancer cells, and are the differences related to EMT or CD44/CD24 expression?</p> <p>Methods</p> <p>Throughout the sequential selection process using Matrigel, we obtained MCF-7-14 cells of opposite migratory and invasive capabilities from MCF-7 cells. Comparative analysis of epithelial and mesenchymal marker expression was performed between parental MCF-7, selected MCF-7-14, and aggressive mesenchymal MDA-MB-231 cells. Furthermore, using microarray expression profiles of these cells, we selected differentially expressed genes for their invasive potential, and performed pathway and network analysis to identify a set of interesting genes, which were evaluated by RT-PCR, flow cytometry or function-blocking antibody treatment.</p> <p>Results</p> <p>MCF-7-14 cells had enhanced migratory and invasive ability compared with MCF-7 cells. Although MCF-7-14 cells, similar to MCF-7 cells, expressed E-cadherin but neither vimentin nor fibronectin, β-catenin was expressed not only on the cell membrane but also in the nucleus. Furthermore, using gene expression profiles of MCF-7, MCF-7-14 and MDA-MB-231 cells, we demonstrated that MCF-7-14 cells have alterations in signaling pathways regulating cell migration and identified a set of genes (<it>PIK3R1</it>, <it>SOCS2</it>, <it>BMP7</it>, <it>CD44 </it>and <it>CD24</it>). Interestingly, MCF-7-14 and its invasive clone CL6 cells displayed increased CD44 expression and downregulated CD24 expression compared with MCF-7 cells. Anti-CD44 antibody treatment significantly decreased cell migration and invasion in both MCF-7-14 and MCF-7-14 CL6 cells as well as MDA-MB-231 cells.</p> <p>Conclusions</p> <p>MCF-7-14 cells are a novel model for breast cancer metastasis without requiring constitutive EMT and are categorized as a "metastable phenotype", which can be distinguished from both epithelial and mesenchymal cells. The alterations and characteristics of MCF-7-14 cells, especially nuclear β-catenin and CD44 upregulation, may characterize invasive cell populations in breast cancer.</p

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice

Epithelial-mesenchymal transition and cancer stem cells: a dangerously dynamic duo in breast cancer progression

Aberrant activation of a latent embryonic program - known as the epithelial-mesenchymal transition (EMT) - can endow cancer cells with the migratory and invasive capabilities associated with metastatic competence. The induction of EMT entails the loss of epithelial characteristics and the de novo acquisition of a mesenchymal phenotype. In breast cancer, the EMT state has been associated with cancer stem cell properties including expression of the stem cell-associated CD44+/CD24-/low antigenic profile, self-renewal capabilities and resistance to conventional therapies. Intriguingly, EMT features are also associated with stem cells isolated from the normal mouse mammary gland and human breast reduction tissues as well as the highly aggressive metaplastic and claudin-low breast tumor subtypes. This has implications for the origin of these breast tumors as it remains unclear whether they derive from cells that have undergone EMT or whether they represent an expansion of a pre-existing stem cell population that expresses EMT-associated markers to begin with. In the present review, we consider the current evidence connecting EMT and stem cell attributes and discuss the ramifications of these newly recognized links for our understanding of the emergence of distinct breast cancer subtypes and breast cancer progression