Integration of genomic data enables selective discovery of breast cancer drivers.

Identifying driver genes in cancer remains a crucial bottleneck in therapeutic development and basic understanding of the disease. We developed Helios, an algorithm that integrates genomic data from primary tumors with data from functional RNAi screens to pinpoint driver genes within large recurrently amplified regions of DNA. Applying Helios to breast cancer data identified a set of candidate drivers highly enriched with known drivers (p < 10(-14)). Nine of ten top-scoring Helios genes are known drivers of breast cancer, and in vitro validation of 12 candidates predicted by Helios found ten conferred enhanced anchorage-independent growth, demonstrating Helios's exquisite sensitivity and specificity. We extensively characterized RSF-1, a driver identified by Helios whose amplification correlates with poor prognosis, and found increased tumorigenesis and metastasis in mouse models. We have demonstrated a powerful approach for identifying driver genes and how it can yield important insights into cancer.

An integrated approach to uncover drivers of cancer.

Systematic characterization of cancer genomes has revealed a staggering number of diverse aberrations that differ among individuals, such that the functional importance and physiological impact of most tumor genetic alterations remain poorly defined. We developed a computational framework that integrates chromosomal copy number and gene expression data for detecting aberrations that promote cancer progression. We demonstrate the utility of this framework using a melanoma data set. Our analysis correctly identified known drivers of melanoma and predicted multiple tumor dependencies. Two dependencies, TBC1D16 and RAB27A, confirmed empirically, suggest that abnormal regulation of protein trafficking contributes to proliferation in melanoma. Together, these results demonstrate the ability of integrative Bayesian approaches to identify candidate drivers with biological, and possibly therapeutic, importance in cancer.

Carcinoembryonic Antigen Cell Adhesion Molecule 1 long isoform modulates malignancy of poorly differentiated colon cancer cells.

OBJECTIVE: Nearly 20%-29% of patients with colorectal cancer (CRC) succumb to liver or lung metastasis and there is a dire need for novel targets to improve the survival of patients with metastasis. The long isoform of the Carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1-L or CC1-L) is a key regulator of immune surveillance in primary CRC, but its role in metastasis remains largely unexplored. We have examined how CC1-L expression impacts on colon cancer liver metastasis. DESIGN: Murine MC38 transfected with CC1-L were evaluated in vitro for proliferation, migration and invasion, and for in vivo experimental liver metastasis. Using shRNA silencing or pharmacological inhibition, we delineated the role in liver metastasis of Chemokine (C-C motif) Ligand 2 (CCL2) and Signal Transducer and Activator of Transcription 3 (STAT3) downstream of CC1-L. We further assessed the clinical relevance of these findings in a cohort of patients with CRC. RESULTS: MC38-CC1-L-expressing cells exhibited significantly reduced in vivo liver metastasis and displayed decreased CCL2 chemokine secretion and reduced STAT3 activity. Down-modulation of CCL2 expression and pharmacological inhibition of STAT3 activity in MC38 cells led to reduced cell invasion capacity and decreased liver metastasis. The clinical relevance of our findings is illustrated by the fact that high CC1 expression in patients with CRC combined with some inflammation-regulated and STAT3-regulated genes correlate with improved 10-year survival. CONCLUSIONS: CC1-L regulates inflammation and STAT3 signalling and contributes to the maintenance of a less-invasive CRC metastatic phenotype of poorly differentiated carcinomas.

Modulatory profiling identifies mechanisms of small molecule-induced cell death.

Cell death is a complex process that plays a vital role in development, homeostasis, and disease. Our understanding of and ability to control cell death is impeded by an incomplete characterization of the full range of cell death processes that occur in mammalian systems, especially in response to exogenous perturbations. We present here a general approach to address this problem, which we call modulatory profiling. Modulatory profiles are composed of the changes in potency and efficacy of lethal compounds produced by a second cell death-modulating agent in human cell lines. We show that compounds with the same characterized mechanism of action have similar modulatory profiles. Furthermore, clustering of modulatory profiles revealed relationships not evident when clustering lethal compounds based on gene expression profiles alone. Finally, modulatory profiling of compounds correctly predicted three previously uncharacterized compounds to be microtubule-destabilizing agents, classified numerous compounds that act nonspecifically, and identified compounds that cause cell death through a mechanism that is morphologically and biochemically distinct from previously established ones.

JISTIC: identification of significant targets in cancer.

BACKGROUND: Cancer is caused through a multistep process, in which a succession of genetic changes, each conferring a competitive advantage for growth and proliferation, leads to the progressive conversion of normal human cells into malignant cancer cells. Interrogation of cancer genomes holds the promise of understanding this process, thus revolutionizing cancer research and treatment. As datasets measuring copy number aberrations in tumors accumulate, a major challenge has become to distinguish between those mutations that drive the cancer versus those passenger mutations that have no effect. RESULTS: We present JISTIC, a tool for analyzing datasets of genome-wide copy number variation to identify driver aberrations in cancer. JISTIC is an improvement over the widely used GISTIC algorithm. We compared the performance of JISTIC versus GISTIC on a dataset of glioblastoma copy number variation, JISTIC finds 173 significant regions, whereas GISTIC only finds 103 significant regions. Importantly, the additional regions detected by JISTIC are enriched for oncogenes and genes involved in cell-cycle and proliferation. CONCLUSIONS: JISTIC is an easy-to-install platform independent implementation of GISTIC that outperforms the original algorithm detecting more relevant candidate genes and regions. The software and documentation are freely available and can be found at: http://www.c2b2.columbia.edu/danapeerlab/html/software.html.

Meta-analysis and profiling of cardiac expression modules.

Heart failure is a complex, complicated disease that is not yet fully understood. We used the Module Map algorithm to uncover groups of genes that have a similar pattern of expression under various conditions of heart stress. These groups of genes are called modules and may serve as computational predictions of biological pathways for the various clinical situations. The Module Map algorithm allows a large-scale analysis of genes expressed. We applied this algorithm to 700 different mouse experiments downloaded from the Gene Expression Omnibus database, which identified 884 modules. The analysis reconstructed partially known principles that play a role in governing the response of heart to stress, thus demonstrating the strength of the method. We have shown a role of genes related to the immune system in conditions of heart remodeling and failure. We have also shown changes in the expression of genes involved with energy metabolism and changes in the expression of contractile proteins of the heart following myocardial infarction. When focusing on another module we noted a new correlation between genes related to osteogenesis and heart failure, including Runx2 and Ahsg, whose role in heart failure was unknown so far. Despite a lack of prior biological knowledge, the Module Map algorithm has reconstructed known pathways, which demonstrates the strength of this new method for analyzing gene profiles related to clinical phenomenon. The method and the analysis presented are a new avenue to uncover the correlation of clinical conditions to the molecular level.

Comparing the transcriptional profile of mesenchymal cells to cardiac and skeletal muscle cells.

Marrow-derived stroma cells (MSCs) can differentiate into multiple lineages including myogenic cells. However, the molecular mechanisms that direct MSCs to each differentiation pathway are poorly understood. Our study was designed to gain insights into the potential regulatory pathways that may assist in defining MSC commitment and differentiation properties. This will delineate the similarities or differences in the expression of genes between several cell types of mesenchymal origin. In this study, we established in vitro models, which allow following the discrete stages of differentiation of cardio- and myogenic-cells compared with MSC. Gene expression of each cell type at several stages of their differentiation path was evaluated by means of Affymetrix Gene Chips. Bioinformatic clustering of genes confirmed that with time in culture the myogenic cells ceased proliferating and commenced with differentiation. The expression profile analysis revealed the similarity and differences between myogenic cells and MSCs. This research compared at the molecular levels snapshots of gene expression patterns and elaborated on the overlap or differences between the analyzed cellular systems. Our results shed light on gene profiles of cells throughout their differentiation pathways. Establishing the gene signature of the differentiation process of cells that belong to several mesenchymal lineages may contribute to the understanding of molecular pathways that underlay mesenchymal tissue remodeling.

Double-Stranded Biotinylated Donor Enhances Homology-Directed Repair in Combination with Cas9 Monoavidin in Mammalian Cells.

Homology-directed repair (HDR) induced by site specific DNA double-strand breaks with CRISPR-Cas9 is a precision gene editing approach that occurs at low frequency in comparison to indel forming non-homologous end joining (NHEJ). In order to obtain high HDR percentages in mammalian cells, we engineered a Cas9 protein fused to a monoavidin domain to bind biotinylated donor DNA. In addition, we used the cationic polymer, polyethylenimine, to deliver Cas9-donor DNA complexes into cells. Improved HDR percentages of up to 90% in three loci tested (CXCR4, EMX1, and TLR) in standard HEK293T cells were observed. Our results suggest that donor DNA biotinylation and Cas9-donor conjugation in addition to delivery influence HDR efficiency.

EphA2 signaling is impacted by carcinoembryonic antigen cell adhesion molecule 1-L expression in colorectal cancer liver metastasis in a cell context-dependent manner.

We have shown that carcinoembryonic antigen cell adhesion molecule 1 long isoform (CEACAM1-L) expression in MC38 metastatic colorectal cancer (CRC) cells results in liver metastasis inhibition via CCL2 and STAT3 signaling. But other molecular mechanisms orchestrating CEACAM1-L-mediated metastasis inhibition remain to be defined. We screened a panel of mouse and human CRC cells and evaluated their metastatic outcome after CEACAM1 overexpression or downregulation. An unbiased transcript profiling and a phospho-receptor tyrosine kinase screen comparing MC38 CEACAM1-L-expressing and non-expressing (CT) CRC cells revealed reduced ephrin type-A receptor 2 (EPHA2) expression and activity. An EPHA2-specific inhibitor reduced EPHA2 downstream signaling in CT cells similar to that in CEACAM1-L cells with decreased proliferation and migration. Human CRC patients exhibiting high CEACAM1 in combination with low EPHA2 expression benefited from longer time to first recurrence/metastasis compared to those with high EPHA2 expression. With the added interaction of CEACAM6, we denoted that CEACAM1 high- and EPHA2 low-expressing patient samples with lower CEACAM6 expression also exhibited a longer time to first recurrence/metastasis. In HT29 human CRC cells, down-regulation of CEACAM1 along with CEA and CEACAM6 up-regulation led to higher metastatic burden. Overall, CEACAM1-L expression in poorly differentiated CRC can inhibit liver metastasis through cell context-dependent EPHA2-mediated signaling. However, CEACAM1's role should be considered in the presence of other CEACAM family members.

Transcriptional profiling of mesenchymal stromal cells from young and old rats in response to Dexamethasone.

BACKGROUND: Marrow-derived stromal cells (MSCs) maintain the capability of self-renewal and differentiation into multiple lineages in adult life. Age-related changes are recognized by a decline in the stemness potential that result in reduced regeneration potential of the skeleton. To explore the molecular events that underline skeletal physiology during aging we catalogued the profile of gene expression in ex vivo cultured MSCs derived from 3 and 15 month old rats. The ex vivo cultured cells were analyzed following challenge with or without Dexamethasone (Dex). RNA retrieved from these cells was analyzed using Affymetrix Gene Chips to compare the effect of Dex on gene expression in both age groups. RESULTS: The molecular mechanisms that underline skeletal senescence were studied by gene expression analysis of RNA harvested from MSCs. The analysis resulted in complex profiles of gene expression of various differentiation pathways. We revealed changes of lineage-specific gene expression; in general the pattern of expression included repression of proliferation and induction of differentiation. The functional analysis of genes clustered were related to major pathways; an increase in bone remodeling, osteogenesis and muscle formation, coupled with a decrease in adipogenesis. We demonstrated a Dex-related decrease in immune response and in genes that regulate bone resorption and an increase in osteoblastic differentiation. Myogenic-related genes and genes that regulate cell cycle were induced by Dex. While Dex repressed genes related to adipogenesis and catabolism, this decrease was complementary to an increase in expression of genes related to osteogenesis. CONCLUSION: This study summarizes the genes expressed in the ex vivo cultured mesenchymal cells and their response to Dex. Functional clustering highlights the complexity of gene expression in MSCs and will advance the understanding of major pathways that trigger the natural changes underlining physiological aging. The high throughput analysis shed light on the anabolic effect of Dex and the relationship between osteogenesis, myogenesis and adipogenesis in the bone marrow cells.

RHPN2 drives mesenchymal transformation in malignant glioma by triggering RhoA activation.

Mesenchymal transformation is a hallmark of aggressive glioblastoma (GBM). Here, we report the development of an unbiased method for computational integration of copy number variation, expression, and mutation data from large datasets. Using this method, we identified rhophilin 2 (RHPN2) as a central genetic determinant of the mesenchymal phenotype of human GBM. Notably, amplification of the human RHPN2 gene on chromosome 19 correlates with a dramatic decrease in the survival of patients with glioma. Ectopic expression of RHPN2 in neural stem cells and astrocytes triggered the expression of mesenchymal genes and promoted an invasive phenotype without impacting cell proliferation. Mechanistically, these effects were implemented through RHPN2-mediated activation of RhoA, a master regulator of cell migration and invasion. Our results define RHPN2 amplification as a central genetic determinant of a highly aggressive phenotype that directs the worst clinical outcomes in patients with GBM.

NFkB disrupts tissue polarity in 3D by preventing integration of microenvironmental signals.

The microenvironment of cells controls their phenotype, and thereby the architecture of the emerging multicellular structure or tissue. We have reported more than a dozen microenvironmental factors whose signaling must be integrated in order to effect an organized, functional tissue morphology. However, the factors that prevent integration of signaling pathways that merge form and function are still largely unknown. We have identified nuclear factor kappa B (NFkB) as a transcriptional regulator that disrupts important microenvironmental cues necessary for tissue organization. We compared the gene expression of organized and disorganized epithelial cells of the HMT-3522 breast cancer progression series: the non-malignant S1 cells that form polarized spheres ('acini'), the malignant T4-2 cells that form large tumor-like clusters, and the 'phenotypically reverted' T4-2 cells that polarize as a result of correction of the microenvironmental signaling. We identified 180 genes that display an increased expression in disorganized compared to polarized structures. Network, GSEA and transcription factor binding site analyses suggested that NFkB is a common activator for the 180 genes. NFkB was found to be activated in disorganized breast cancer cells, and inhibition of microenvironmental signaling via EGFR, beta1 integrin, MMPs, or their downstream signals suppressed its activation. The postulated role of NFkB was experimentally verified: Blocking the NFkB pathway with a specific chemical inhibitor or shRNA induced polarization and inhibited invasion of breast cancer cells in 3D cultures. These results may explain why NFkB holds promise as a target for therapeutic intervention: Its inhibition can reverse the oncogenic signaling involved in breast cancer progression and integrate the essential microenvironmental control of tissue architecture.

PGC-1α supports glutamine metabolism in breast cancer.

BACKGROUND: Glutamine metabolism is a central metabolic pathway in cancer. Recently, reductive carboxylation of glutamine for lipogenesis has been shown to constitute a key anabolic route in cancer cells. However, little is known regarding central regulators of the various glutamine metabolic pathways in cancer cells. METHODS: The impact of PGC-1α and ERRα on glutamine enzyme expression was assessed in ERBB2+ breast cancer cell lines with quantitative RT-PCR, chromatin immunoprecipitation, and immunoblotting experiments. Glutamine flux was quantified using 13C-labeled glutamine and GC/MS analyses. Functional assays for lipogenesis were performed using 14C-labeled glutamine. The expression of glutamine metabolism genes in breast cancer patients was determined by bioinformatics analyses using The Cancer Genome Atlas. RESULTS: We show that the transcriptional coactivator PGC-1α, along with the transcription factor ERRα, is a positive regulator of the expression of glutamine metabolism genes in ERBB2+ breast cancer. Indeed, ERBB2+ breast cancer cells with increased expression of PGC-1α display elevated expression of glutamine metabolism genes. Furthermore, ERBB2+ breast cancer cells with reduced expression of PGC-1α or when treated with C29, a pharmacological inhibitor of ERRα, exhibit diminished expression of glutamine metabolism genes. The biological relevance of the control of glutamine metabolism genes by the PGC-1α/ERRα axis is demonstrated by consequent regulation of glutamine flux through the citric acid cycle. PGC-1α and ERRα regulate both the canonical citric acid cycle (forward) and the reductive carboxylation (reverse) fluxes; the latter can be used to support de novo lipogenesis reactions, most notably in hypoxic conditions. Importantly, murine and human ERBB2+ cells lines display a significant dependence on glutamine availability for their growth. Finally, we show that PGC-1α expression is positively correlated with that of the glutamine pathway in ERBB2+ breast cancer patients, and high expression of this pathway is associated with reduced patient survival. CONCLUSIONS: These data reveal that the PGC-1α/ERRα axis is a central regulator of glutamine metabolism in ERBB2+ breast cancer. This novel regulatory link, as well as the marked reduction in patient survival time associated with increased glutamine pathway gene expression, suggests that targeting glutamine metabolism may have therapeutic potential in the treatment of ERBB2+ breast cancer.

Tracking the molecular signature of developing skeletal tissues.

We isolated cells from their native in vivo microenvironment using the Laser Capture Micro dissection (LCM). Bone and cartilage tissues were studied from mouse embryonic (18dpc) processed by cry sections enabled the cell isolation from anatomical complexity of skeletal tissues using the LCM technique. RNA was purified from the isolated cells and followed with amplification stage to hybridize on gene array for high through (HT) put analysis to profile the tissues gene expression. Bioinformatics profiling of the differential expression performed according to the tissue origin highlighted the common and divergent genes in the regulation of these tissues. Specifically, we identified that genes related to cell replication and cell metabolism were more prominent in bone, while organic acid metabolism was more prominent in cartilage. This study has demonstrated the utility of applying HT microarray analysis using RNA from small number of cells isolated by LCM from skeletal tissues. The bioinformatics provides insight which has not yet been explored for the developing skeletal tissues. The power of LCM application provides a platform to make a broad molecular analysis using transcriptom analysis to reveal the molecular signature of tissues in their nature environment.