PROMO: an interactive tool for analyzing clinically-labeled multi-omic cancer datasets.

BACKGROUND: Analysis of large genomic datasets along with their accompanying clinical information has shown great promise in cancer research over the last decade. Such datasets typically include thousands of samples, each measured by one or several high-throughput technologies ('omics') and annotated with extensive clinical information. While instrumental for fulfilling the promise of personalized medicine, the analysis and visualization of such large datasets is challenging and necessitates programming skills and familiarity with a large array of software tools to be used for the various steps of the analysis. RESULTS: We developed PROMO (Profiler of Multi-Omic data), a friendly, fully interactive stand-alone software for analyzing large genomic cancer datasets together with their associated clinical information. The tool provides an array of built-in methods and algorithms for importing, preprocessing, visualizing, clustering, clinical label enrichment testing, and survival analysis that can be performed on a single or multi-omic dataset. The tool can be used for quick exploration and stratification of tumor samples taken from patients into clinically significant molecular subtypes. Identification of prognostic biomarkers and generation of simple subtype classifiers are additional important features. We review PROMO's main features and demonstrate its analysis capabilities on a breast cancer cohort from TCGA. CONCLUSIONS: PROMO provides a single integrated solution for swiftly performing a complete analysis of cancer genomic data for subtype discovery and biomarker identification without writing a single line of code, and can, therefore, make the analysis of these data much easier for cancer biologists and biomedical researchers. PROMO is freely available for download at http://acgt.cs.tau.ac.il/promo/.

MicroRNAs are essential for differentiation of the retinal pigmented epithelium and maturation of adjacent photoreceptors.

Dysfunction of the retinal pigmented epithelium (RPE) results in degeneration of photoreceptors and vision loss and is correlated with common blinding disorders in humans. Although many protein-coding genes are known to be expressed in RPE and are important for its development and maintenance, virtually nothing is known about the in vivo roles of non-coding transcripts. The expression patterns of microRNAs (miRNAs) have been analyzed in a variety of ocular tissues, and a few were implicated to play role in RPE based on studies in cell lines. Here, through RPE-specific conditional mutagenesis of Dicer1 or Dgcr8 in mice, the importance of miRNAs for RPE differentiation was uncovered. miRNAs were found to be dispensable for maintaining RPE fate and survival, and yet they are essential for the acquisition of important RPE properties such as the expression of genes involved in the visual cycle pathway, pigmentation and cell adhesion. Importantly, miRNAs of the RPE are required for maturation of adjacent photoreceptors, specifically for the morphogenesis of the outer segments. The alterations in the miRNA and mRNA profiles in the Dicer1-deficient RPE point to a key role of miR-204 in regulation of the RPE differentiation program in vivo and uncover the importance of additional novel RPE miRNAs. This study reveals the combined regulatory activity of miRNAs that is required for RPE differentiation and for the development of the adjacent neuroretina.

Expression and methylation patterns partition luminal-A breast tumors into distinct prognostic subgroups.

BACKGROUND: Breast cancer is a heterogeneous disease comprising several biologically different types, exhibiting diverse responses to treatment. In the past years, gene expression profiling has led to definition of several "intrinsic subtypes" of breast cancer (basal-like, HER2-enriched, luminal-A, luminal-B and normal-like), and microarray based predictors such as PAM50 have been developed. Despite their advantage over traditional histopathological classification, precise identification of breast cancer subtypes, especially within the largest and highly variable luminal-A class, remains a challenge. In this study, we revisited the molecular classification of breast tumors using both expression and methylation data obtained from The Cancer Genome Atlas (TCGA). METHODS: Unsupervised clustering was applied on 1148 and 679 breast cancer samples using RNA-Seq and DNA methylation data, respectively. Clusters were evaluated using clinical information and by comparison to PAM50 subtypes. Differentially expressed genes and differentially methylated CpGs were tested for enrichment using various annotation sets. Survival analysis was conducted on the identified clusters using the log-rank test and Cox proportional hazards model. RESULTS: The clusters in both expression and methylation datasets had only moderate agreement with PAM50 calls, while our partitioning of the luminal samples had better five-year prognostic value than the luminal-A/luminal-B assignment as called by PAM50. Our analysis partitioned the expression profiles of the luminal-A samples into two biologically distinct subgroups exhibiting differential expression of immune-related genes, with one subgroup carrying significantly higher risk for five-year recurrence. Analysis of the luminal-A samples using methylation data identified a cluster of patients with poorer survival, characterized by distinct hyper-methylation of developmental genes. Cox multivariate survival analysis confirmed the prognostic significance of the two partitions after adjustment for commonly used factors such as age and pathological stage. CONCLUSIONS: Modern genomic datasets reveal large heterogeneity among luminal breast tumors. Our analysis of these data provides two prognostic gene sets that dissect and explain tumor variability within the luminal-A subgroup, thus, contributing to the advancement of subtype-specific diagnosis and treatment.

Classification of node-positive melanomas into prognostic subgroups using keratin, immune, and melanogenesis expression patterns.

Cutaneous melanoma tumors are heterogeneous and show diverse responses to treatment. Identification of robust molecular biomarkers for classifying melanoma tumors into clinically distinct and homogenous subtypes is crucial for improving the diagnosis and treatment of the disease. In this study, we present a classification of melanoma tumors into four subtypes with different survival profiles based on three distinct gene expression signatures: keratin, immune, and melanogenesis. The melanogenesis expression pattern includes several genes that are characteristic of the melanosome organelle and correlates with worse survival, suggesting the involvement of melanosomes in melanoma aggression. We experimentally validated the secretion of melanosomes into surrounding tissues by melanoma tumors, which potentially affects the lethality of metastasis. We propose a simple molecular decision tree classifier for predicting a tumor's subtype based on representative genes from the three identified signatures. Key predictor genes were experimentally validated on melanoma samples taken from patients with varying survival outcomes. Our three-pattern approach for classifying melanoma tumors can contribute to advancing the understanding of melanoma variability and promote accurate diagnosis, prognostication, and treatment.

Melanoma-Secreted Lysosomes Trigger Monocyte-Derived Dendritic Cell Apoptosis and Limit Cancer Immunotherapy.

The recent success of checkpoint blockade therapies has established immunotherapy as one of the most promising treatments for melanoma. Nonetheless, a complete curative response following immunotherapy is observed only in a fraction of patients. To identify what factors limit the efficacy of immunotherapies, we established mouse models that cease to respond to immunotherapies once their tumors exceed a certain stage. Analysis of the immune systems of the organisms revealed that the numbers of tumor-infiltrating dendritic cells (TIDC) drastically decreased with time. Further, in contrast to the current paradigm, once melanoma was established, TIDC did not migrate into sentinel lymph nodes. Instead, they underwent local cell death due to excessive phagocytosis of lysosomes. Importantly, TIDC were required to license the cytotoxic activity of tumor CD8+ T cells, and in their absence, T cells did not lyse melanoma cells. Our results offer a paradigm shift regarding the role of TIDC and a framework to increase the efficacy of immunotherapies. SIGNIFICANCE: This work redefines the role of monocyte-derived dendritic cells in melanoma and provides a novel strategy to increase the efficacy of T-cell-based immunotherapies in nonresponding individuals. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/10/1942/F1.large.jpg.

Network analysis of protein structures identifies functional residues.

Identifying active site residues strictly from protein three-dimensional structure is a difficult task, especially for proteins that have few or no homologues. We transformed protein structures into residue interaction graphs (RIGs), where amino acid residues are graph nodes and their interactions with each other are the graph edges. We found that active site, ligand-binding and evolutionary conserved residues, typically have high closeness values. Residues with high closeness values interact directly or by a few intermediates with all other residues of the protein. Combining closeness and surface accessibility identified active site residues in 70% of 178 representative structures. Detailed structural analysis of specific enzymes also located other types of functional residues. These include the substrate binding sites of acetylcholinesterases and subtilisin, and the regions whose structural changes activate MAP kinase and glycogen phosphorylase. Our approach uses single protein structures, and does not rely on sequence conservation, comparison to other similar structures or any prior knowledge. Residue closeness is distinct from various sequence and structure measures and can thus complement them in identifying key protein residues. Closeness integrates the effect of the entire protein on single residues. Such natural structural design may be evolutionary maintained to preserve interaction redundancy and contribute to optimal setting of functional sites.

Transcription factor/microRNA axis blocks melanoma invasion program by miR-211 targeting NUAK1.

Melanoma is one of the deadliest human cancers, responsible for approximately 80% of skin cancer mortalities. The aggressiveness of melanoma is due to its capacity to proliferate and rapidly invade surrounding tissues, leading to metastases. A recent model suggests melanoma progresses by reversibly switching between proliferation and invasion transcriptional signatures. Recent studies show that cancer cells are more sensitive to microRNA (miRNA) perturbation than are non-cancer cells; however, the roles of miRNAs in melanoma plasticity remain unexplored. Here, we use the gene expression profiles of melanoma and normal melanocytes to characterize the transcription factor-miRNA relationship that modulates the proliferative and invasive programs of melanoma. We identified two sets of miRNAs that likely regulate these programs. Interestingly, one of the miRNAs involved in melanoma invasion is miR-211, a known target of the master regulator microphthalmia-associated transcription factor (MITF). We demonstrate that miR-211 contributes to melanoma adhesion by directly targeting a gene, NUAK1. Inhibition of miR-211 increases NUAK1 expression and decreases melanoma adhesion, whereas upregulation of miR-211 restores adhesion through NUAK1 repression. This study defines the MITF/miR-211 axis that inhibits the invasive program by blocking adhesion. Furthermore, we have identified NUAK1 as a potential target for the treatment of metastatic melanoma.

The effect of simulated microgravity on human mesenchymal stem cells cultured in an osteogenic differentiation system: a bioinformatics study.

One proposed strategy for bone regeneration involves ex vivo tissue engineering, accomplished using bone-forming cells, biodegradable scaffolds, and dynamic culture systems, with the goal of three-dimensional tissue formation. Rotating wall vessel bioreactors generate simulated microgravity conditions ex vivo, which lead to cell aggregation. Human mesenchymal stem cells (hMSCs) have been extensively investigated and shown to possess the potential to differentiate into several cell lineages. The goal of the present study was to evaluate the effect of simulated microgravity on all genes expressed in hMSCs, with the underlying hypothesis that many important pathways are affected during culture within a rotating wall vessel system. Gene expression was analyzed using a whole genome microarray and clustering with the aid of the National Institutes of Health's Database for Annotation, Visualization and Integrated Discovery database and gene ontology analysis. Our analysis showed 882 genes that were downregulated and 505 genes that were upregulated after exposure to simulated microgravity. Gene ontology clustering revealed a wide variety of affected genes with respect to cell compartment, biological process, and signaling pathway clusters. The data sets showed significant decreases in osteogenic and chondrogenic gene expression and an increase in adipogenic gene expression, indicating that ex vivo adipose tissue engineering may benefit from simulated microgravity. This finding was supported by an adipogenic differentiation assay. These data are essential for further understanding of ex vivo tissue engineering using hMSCs.

Human cancers overexpress genes that are specific to a variety of normal human tissues.

We have analyzed gene expression data from three different kinds of samples: normal human tissues, human cancer cell lines, and leukemic cells from lymphoid and myeloid leukemia pediatric patients. We have searched for genes that are overexpressed in human cancer and also show specific patterns of tissue-dependent expression in normal tissues. Using the expression data of the normal tissues, we identified 4,346 genes with a high variability of expression and clustered these genes according to their relative expression level. Of 91 stable clusters obtained, 24 clusters included genes preferentially expressed either only in hematopoietic tissues or in hematopoietic and one to two other tissues; 28 clusters included genes preferentially expressed in various nonhematopoietic tissues such as neuronal, testis, liver, kidney, muscle, lung, pancreas, and placenta. Analysis of the expression levels of these two groups of genes in the human cancer cell lines and leukemias identified genes that were highly expressed in cancer cells but not in their normal counterparts and, thus, were overexpressed in the cancers. The different cancer cell lines and leukemias varied in the number and identity of these overexpressed genes. The results indicate that many genes that are overexpressed in human cancer cells are specific to a variety of normal tissues, including normal tissues other than those from which the cancer originated. It is suggested that this general property of cancer cells plays a major role in determining the behavior of the cancers, including their metastatic potential.

Induction in myeloid leukemic cells of genes that are expressed in different normal tissues.

Using DNA microarray and cluster analysis of expressed genes in a cloned line (M1-t-p53) of myeloid leukemic cells, we have analyzed the expression of genes that are preferentially expressed in different normal tissues. Clustering of 547 highly expressed genes in these leukemic cells showed 38 genes preferentially expressed in normal hematopoietic tissues and 122 other genes preferentially expressed in different normal nonhematopoietic tissues, including neuronal tissues, muscle, liver, and testis. We have also analyzed the genes whose expression in the leukemic cells changed after activation of WT p53 and treatment with the cytokine IL-6 or the calcium mobilizer thapsigargin. Of 620 such genes in the leukemic cells that were differentially expressed in normal tissues, clustering showed 80 genes that were preferentially expressed in hematopoietic tissues and 132 genes in different normal nonhematopoietic tissues that also included neuronal tissues, muscle, liver, and testis. Activation of p53 and treatment with IL-6 or thapsigargin induced different changes in the genes preferentially expressed in these normal tissues. These myeloid leukemic cells thus express genes that are expressed in normal nonhematopoietic tissues, and various treatments can reprogram these cells to induce other such nonhematopoietic genes. The results indicate that these leukemic cells share with normal hematopoietic stem cells the plasticity of differentiation to different cell types. It is suggested that this reprogramming to induce in malignant cells genes that are expressed in different normal tissues may be of clinical value in therapy.