Visual barcodes for clonal-multiplexing of live microscopy-based assays.

While multiplexing samples using DNA barcoding revolutionized the pace of biomedical discovery, multiplexing of live imaging-based applications has been limited by the number of fluorescent proteins that can be deconvoluted using common microscopy equipment. To address this limitation, we develop visual barcodes that discriminate the clonal identity of single cells by different fluorescent proteins that are targeted to specific subcellular locations. We demonstrate that deconvolution of these barcodes is highly accurate and robust to many cellular perturbations. We then use visual barcodes to generate 'Signalome' cell-lines by mixing 12 clones of different live reporters into a single population, allowing simultaneous monitoring of the activity in 12 branches of signaling, at clonal resolution, over time. Using the 'Signalome' we identify two distinct clusters of signaling pathways that balance growth and proliferation, emphasizing the importance of growth homeostasis as a central organizing principle in cancer signaling. The ability to multiplex samples in live imaging applications, both in vitro and in vivo may allow better high-content characterization of complex biological systems.

Predicting and affecting response to cancer therapy based on pathway-level biomarkers.

Identifying robust, patient-specific, and predictive biomarkers presents a major obstacle in precision oncology. To optimize patient-specific therapeutic strategies, here we couple pathway knowledge with large-scale drug sensitivity, RNAi, and CRISPR-Cas9 screening data from 460 cell lines. Pathway activity levels are found to be strong predictive biomarkers for the essentiality of 15 proteins, including the essentiality of MAD2L1 in breast cancer patients with high BRCA-pathway activity. We also find strong predictive biomarkers for the sensitivity to 31 compounds, including BCL2 and microtubule inhibitors (MTIs). Lastly, we show that Bcl-xL inhibition can modulate the activity of a predictive biomarker pathway and re-sensitize lung cancer cells and tumors to MTI therapy. Overall, our results support the use of pathways in helping to achieve the goal of precision medicine by uncovering dozens of predictive biomarkers.

Resistance to paclitaxel is associated with a variant of the gene BCL2 in multiple tumor types.

Paclitaxel, the most commonly used form of chemotherapy, is utilized in curative protocols in different types of cancer. The response to treatment differs among patients. Biological interpretation of a mechanism to explain this personalized response is still unavailable. Since paclitaxel is known to target BCL2 and TUBB1, we used pan-cancer genomic data from hundreds of patients to show that a single-nucleotide variant in the BCL2 sequence can predict a patient's response to paclitaxel. Here, we show a connection between this BCL2 genomic variant, its transcript structure, and protein abundance. We demonstrate these findings in silico, in vitro, in formalin-fixed paraffin-embedded (FFPE) tissue, and in patient lymphocytes. We show that tumors with the specific variant are more resistant to paclitaxel. We also show that tumor and normal cells with the variant express higher levels of BCL2 protein, a phenomenon that we validated in an independent cohort of patients. Our results indicate BCL2 sequence variations as determinants of chemotherapy resistance. The knowledge of individual BCL2 genomic sequences prior to the choice of chemotherapy may improve patient survival. The current work also demonstrates the benefit of community-wide, integrative omics data sources combined with in-lab experimentation and validation sets.

The linker histone H1.0 generates epigenetic and functional intratumor heterogeneity.

Tumors comprise functionally diverse subpopulations of cells with distinct proliferative potential. Here, we show that dynamic epigenetic states defined by the linker histone H1.0 determine which cells within a tumor can sustain the long-term cancer growth. Numerous cancer types exhibit high inter- and intratumor heterogeneity of H1.0, with H1.0 levels correlating with tumor differentiation status, patient survival, and, at the single-cell level, cancer stem cell markers. Silencing of H1.0 promotes maintenance of self-renewing cells by inducing derepression of megabase-sized gene domains harboring downstream effectors of oncogenic pathways. Self-renewing epigenetic states are not stable, and reexpression of H1.0 in subsets of tumor cells establishes transcriptional programs that restrict cancer cells' long-term proliferative potential and drive their differentiation. Our results uncover epigenetic determinants of tumor-maintaining cells.

hsa-miR-9 controls the mobility behavior of glioblastoma cells via regulation of MAPK14 signaling elements.

BACKGROUND: Glioblastoma Multiforme (GBM) is the most common and lethal primary tumor of the brain. GBM is associated with one of the worst 5-year survival rates among all human cancers, despite much effort in different modes of treatment. RESULTS: Here, we demonstrate specific GBM cancer phenotypes that are governed by modifications to the MAPAKAP network. We then demonstrate a novel regulation mode by which a set of five key factors of the MAPKAP pathway are regulated by the same microRNA, hsa-miR-9.We demonstrate that hsa-miR-9 overexpression leads to MAPKAP signaling inhibition, partially by interfering with the MAPK14/MAPKAP3 complex. Further, hsa-miR-9 overexpression initiates re-arrangement of actin filaments, which leads us to hypothesize a mechanism for the observed phenotypic shift. CONCLUSION: The work presented here exposes novel microRNA features and situates hsa-miR-9 as a therapeutic target, which governs metastasis and thus determines prognosis in GBM through MAPKAP signaling.

Experimental Support for the Ecoimmunity Theory: Distinct Phenotypes of Nonlymphocytic Cells in SCID and Wild-Type Mice.

Immune tolerance toward "self" is critical in multiple immune disorders. While there are several mechanisms to describe the involvement of immune cells in the process, the role of peripheral tissue cells in that context is not yet clear. The theory of ecoimmunity postulates that interactions between immune and tissue cells represent a predator-prey relationship. A lifelong interaction, shaped mainly during early ontogeny, leads to selection of nonimmune cell phenotypes. Normally, therefore, nonimmune cells that evolve alongside an intact immune system would be phenotypically capable of evading immune responses, and cells whose phenotype falls short of satisfying this steady state would expire under hostile immune responses. This view was supported until recently by experimental evidence showing an inferior endurance of severe combined immunodeficiency (SCID)-derived pancreatic islets when engrafted into syngeneic immune-intact wild-type (WT) mice, relative to islets from WT. Here we extend the experimental exploration of ecoimmunity by searching for the presence of the phenotypic changes suggested by the theory. Immune-related phenotypes of islets, spleen, and bone marrow immune cells were determined, as well as SCID and WT nonlymphocytic cells. Islet submass grafting was performed to depict syngeneic graft functionality. Islet cultures were examined under both resting and inflamed conditions for expression of CD40 and major histocompatibility complex (MHC) class I/II and release of interleukin-1α (IL-1α), IL-1ß, IL-6, tumor necrosis factor-α (TNF-α), IL-10, and insulin. Results depict multiple pathways that appear to be related to the sculpting of nonimmune cells by immune cells; 59 SCID islet genes displayed relative expression changes compared with WT islets. SCID cells expressed lower tolerability to inflammation and higher levels of immune-related molecules, including MHC class I. Accordingly, islets exhibited a marked increase in insulin release upon immunocyte depletion, in effect resuming endocrine function that was otherwise suppressed by resident immunocytes. This work provides further support of the ecoimmunity theory and encourages subsequent studies to identify its role in the emergence and treatment of autoimmune pathologies, transplant rejection, and cancer.

Reciprocal Regulation between SIRT6 and miR-122 Controls Liver Metabolism and Predicts Hepatocarcinoma Prognosis.

Mice overexpressing the longevity protein SIRT6 or deficient for the liver's most prevalent microRNA miR-122 display a similar set of phenotypes, including improved lipid profile and protection against damage linked to obesity. Here, we show that miR-122 and SIRT6 negatively regulate each other's expression. SIRT6 downregulates miR-122 by deacetylating H3K56 in the promoter region. MiR-122 binds to three sites on the SIRT6 3' UTR and reduces its levels. The interplay between SIRT6 and miR-122 is manifested in two physiologically relevant ways in the liver. First, they oppositely regulate a similar set of metabolic genes and fatty acid ß-oxidation. Second, in hepatocellular carcinoma patients, loss of a negative correlation between SIRT6 and miR-122 expression is significantly associated with better prognosis. These findings show that SIRT6 and miR-122 negatively regulate each other to control various aspects of liver physiology and SIRT6-miR-122 correlation may serve as a biomarker for hepatocarcinoma prognosis.

MicroRNA regulation of molecular pathways as a generic mechanism and as a core disease phenotype.

The role of microRNAs as key regulators of a wide variety of fundamental cellular processes, such as apoptosis, differentiation, proliferation and cell cycle is increasingly recognized in most aspects of biology and biomedicine. Accretion of results from multiple microRNA studies over multiple pathway networks, led us to hypothesize that microRNAs target molecular pathways. As we show here, this is a network-wide phenomenon. The work presented, uses statistical tools that show how single microRNAs target molecular pathways. We demonstrate that this targeting could not be the result of random associations and cannot be the result of the sheer numeracy of microRNA targets. Furthermore, the strongest evidence for the association microRNA-pathway, is in a demonstration of the way by which these associations are disease-relevant. In our analyses we study ten different types of cancer involving thousands of samples, and show that the identified microRNA-pathway associations demonstrate a clinical affiliation and an ability to stratify patients. The work presented here shows the first evidence for a mechanism of microRNAs-pathway generic regulation. This regulation is tightly associated with clinical phenotype. The presented approach may catalyze targeted treatment through exposure of hidden regulatory mechanisms and a systems-medicine view of clinical observation.

Synaptojanin 2 is a druggable mediator of metastasis and the gene is overexpressed and amplified in breast cancer.

Amplified HER2, which encodes a member of the epidermal growth factor receptor (EGFR) family, is a target of effective therapies against breast cancer. In search for similarly targetable genomic aberrations, we identified copy number gains in SYNJ2, which encodes the 5'-inositol lipid phosphatase synaptojanin 2, as well as overexpression in a small fraction of human breast tumors. Copy gain and overexpression correlated with shorter patient survival and a low abundance of the tumor suppressor microRNA miR-31. SYNJ2 promoted cell migration and invasion in culture and lung metastasis of breast tumor xenografts in mice. Knocking down SYNJ2 impaired the endocytic recycling of EGFR and the formation of cellular lamellipodia and invadopodia. Screening compound libraries identified SYNJ2-specific inhibitors that prevented cell migration but did not affect the related neural protein SYNJ1, suggesting that SYNJ2 is a potentially druggable target to block cancer cell migration.

Shift in GATA3 functions, and GATA3 mutations, control progression and clinical presentation in breast cancer.

INTRODUCTION: GATA binding protein 3 (GATA3) is a regulator of mammary luminal cell differentiation, and an estrogen receptor (ER) associated marker in breast cancer. Tumor suppressor functions of GATA3 have been demonstrated primarily in basal-like breast cancers. Here, we focused on its function in luminal breast cancer, where GATA3 is frequently mutated, and its levels are significantly elevated. METHODS: GATA3 target genes were identified in normal- and luminal cancer- mammary cells by ChIP-seq, followed by examination of the effects of GATA3 expressions and mutations on tumorigenesis-associated genes and processes. Additionally, mutations and expression data of luminal breast cancer patients from The Cancer Genome Atlas were analyzed to characterize genetic signatures associated with GATA3 mutations. RESULTS: We show that some GATA3 effects shift from tumor suppressing to tumor promoting during tumorigenesis, with deregulation of three genes, BCL2, DACH1, THSD4, representing major GATA3-controlled processes in cancer progression. In addition, we identify an altered activity of mutant GATA3, and distinct associated genetic signatures. These signatures depend on the functional domain mutated; and, for a specific subgroup, are shared with basal-like breast cancer patients, who are a clinical group with regard to considerations of mode of treatment. CONCLUSIONS: The GATA3 dependent mechanisms may call for special considerations for proper prognosis and treatment of patients.

PhenoNet: identification of key networks associated with disease phenotype.

MOTIVATION: At the core of transcriptome analyses of cancer is a challenge to detect molecular differences affiliated with disease phenotypes. This approach has led to remarkable progress in identifying molecular signatures and in stratifying patients into clinical groups. Yet, despite this progress, many of the identified signatures are not robust enough to be clinically used and not consistent enough to provide a follow-up on molecular mechanisms. RESULTS: To address these issues, we introduce PhenoNet, a novel algorithm for the identification of pathways and networks associated with different phenotypes. PhenoNet uses two types of input data: gene expression data (RMA, RPKM, FPKM, etc.) and phenotypic information, and integrates these data with curated pathways and protein-protein interaction information. Comprehensive iterations across all possible pathways and subnetworks result in the identification of key pathways or subnetworks that distinguish between the two phenotypes. AVAILABILITY AND IMPLEMENTATION: Matlab code is available upon request. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

SENP5 mediates breast cancer invasion via a TGFßRI SUMOylation cascade.

Identifying novel mechanisms, which are at the core of breast cancer biology, is of critical importance. Such mechanisms may explain response to treatment, reveal novel targets or drive detection assays. To uncover such novel mechanisms, we used survival analysis on gene expression datasets encompassing 1363 patients. By iterating over the compendia of genes, we screened for their significance as prognosis biomarkers and identified SUMO-specific protease 5 (SENP5) to significantly stratify patients into two survival groups across five unrelated tested datasets. According to these findings, low expression of SENP5 is associated with good prognosis among breast cancer patients. Following these findings, we analyzed SENP5 silencing and show it is followed by inhibition of anchorage-independence growth, proliferation, migration and invasion in breast cancer cell lines. We further show that these changes are conducted via regulation of TGFßRI levels. These data relate to recent reports about the SUMOylation of TGFßRI. Following TGFßRI changes in expression, we show that one of its target genes, MMP9, which plays a key role in degrading the extracellular matrix and contributes to TGFß-induced invasion, is dramatically down regulated upon SENP5 silencing. This is the first report represents SENP5-TGFß-MMP9 cascade and its mechanistic involvement in breast cancer.

MicroRNA-gene association as a prognostic biomarker in cancer exposes disease mechanisms.

The transcriptional networks that regulate gene expression and modifications to this network are at the core of the cancer phenotype. MicroRNAs, a well-studied species of small non-coding RNA molecules, have been shown to have a central role in regulating gene expression as part of this transcriptional network. Further, microRNA deregulation is associated with cancer development and with tumor progression. Glioblastoma Multiform (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. To study the transcriptional network and its modifications in GBM, we utilized gene expression, microRNA sequencing, whole genome sequencing and clinical data from hundreds of patients from different datasets. Using these data and a novel microRNA-gene association approach we introduce, we have identified unique microRNAs and their associated genes. This unique behavior is composed of the ability of the quantifiable association of the microRNA and the gene expression levels, which we show stratify patients into clinical subgroups of high statistical significance. Importantly, this stratification goes unobserved by other methods and is not affiliated by other subsets or phenotypes within the data. To investigate the robustness of the introduced approach, we demonstrate, in unrelated datasets, robustness of findings. Among the set of identified microRNA-gene associations, we closely study the example of MAF and hsa-miR-330-3p, and show how their co-behavior stratifies patients into prognosis clinical groups and how whole genome sequences tells us more about a specific genomic variation as a possible basis for patient variances. We argue that these identified associations may indicate previously unexplored specific disease control mechanisms and may be used as basis for further study and for possible therapeutic intervention.

IL-27 acts on DCs to suppress the T cell response and autoimmunity by inducing expression of the immunoregulatory molecule CD39.

Dendritic cells (DCs) control the balance between effector T cells and regulatory T cells in vivo. Hence, the study of DCs might identify mechanisms of disease pathogenesis and guide new therapeutic approaches for disorders mediated by the immune system. We found that interleukin 27 (IL-27) signaling in mouse DCs limited the generation of effector cells of the TH1 and TH17 subsets of helper T cells and the development of experimental autoimmune encephalomyelitis (EAE). The effects of IL-27 were mediated at least in part through induction of the immunoregulatory molecule CD39 in DCs. IL-27-induced CD39 decreased the extracellular concentration of ATP and downregulated nucleotide-dependent activation of the NLRP3 inflammasome. Finally, therapeutic vaccination with IL-27-conditioned DCs suppressed established relapsing-remitting EAE. Thus, IL-27 signaling in DCs limited pathogenic T cell responses and the development of autoimmunity.

Altered immune pathway activity under exercise challenge in Gulf War Illness: an exploratory analysis.

Though potentially linked to the basic physiology of stress response we still have no clear understanding of Gulf War Illness (GWI), a debilitating illness presenting with a complex constellation of immune, endocrine and neurological symptoms. Here we compared male GWI (n=20) with healthy veterans (n=22) and subjects with chronic fatigue syndrome/myalgic encephalomyelitis (CFS/ME) (n=7). Blood was drawn during a Graded eXercise Test (GXT) prior to exercise, at peak effort (VO2 max) and 4-h post exercise. Affymetrix HG U133 plus 2.0 microarray gene expression profiling in peripheral blood mononuclear cells (PBMCs) was used to estimate activation of over 500 documented pathways. This was cast against ELISA-based measurement of 16 cytokines in plasma and flow cytometric assessment of lymphocyte populations and cytotoxicity. A 2-way ANOVA corrected for multiple comparisons (q statistic <0.05) indicated significant increases in neuroendocrine-immune signaling and inflammatory activity in GWI, with decreased apoptotic signaling. Conversely, cell cycle progression and immune signaling were broadly subdued in CFS. Partial correlation networks linking pathways with symptom severity via changes in immune cell abundance, function and signaling were constructed. Central to these were changes in IL-10 and CD2+ cell abundance and their link to two pathway clusters. The first consisted of pathways supporting neuronal development and migration whereas the second was related to androgen-mediated activation of NF-κB. These exploratory results suggest an over-expression of known exercise response mechanisms as well as illness-specific changes that may involve an overlapping stress-potentiated neuro-inflammatory response.

RBM38 is a direct transcriptional target of E2F1 that limits E2F1-induced proliferation.

The E2F family of transcription factors plays a pivotal role in the regulation of cell proliferation in higher eukaryotes and is a critical downstream target of the tumor suppressor pRB. The pRB/E2F pathway is defective in most human tumors, resulting in deregulated E2F activity that induces uncontrolled cell proliferation, a hallmark of tumor cells. The RNA-binding protein RBM38, also named RNPC1, induces cell-cycle arrest in G(1), at least in part, via binding to and stabilizing the mRNA of the cyclin-dependent kinase inhibitor p21. RBM38 levels are altered in human cancer. Generally, RBM38 is overexpressed in various tumors; however, RBM38 mRNA levels are reduced in some breast tumors due to increased methylation of its promoter region. We show here that expression of RBM38 is regulated by E2F1. Specifically, RBM38 mRNA and protein levels are elevated upon activation of either exogenous E2F1 or endogenous E2Fs. Moreover, endogenous E2F1 binds the human RBM38 promoter and E2F1 knockdown reduces RBM38 levels. Our data raise the possibility that E2F1 together with E2F1-regulated RBM38 constitute a negative feedback loop that modulates E2F1 activity. In support of this, inhibition of RBM38 expression increases E2F1-mediated cell-cycle progression. Moreover, in human ovarian cancer, high correlation between expression of E2F1 and RBM38 is associated with increased survival. Overall, our data identify RBM38 as novel transcriptional target of E2F1 that restricts E2F1-induced proliferation. Furthermore, this negative feedback loop seems to restrict tumor aggressiveness, thereby promoting survival of patients with cancer.

Biomarker robustness reveals the PDGF network as driving disease outcome in ovarian cancer patients in multiple studies.

BACKGROUND: Ovarian cancer causes more deaths than any other gynecological cancer. Identifying the molecular mechanisms that drive disease progress in ovarian cancer is a critical step in providing therapeutics, improving diagnostics, and affiliating clinical behavior with disease etiology. Identification of molecular interactions that stratify prognosis is key in facilitating a clinical-molecular perspective. RESULTS: The Cancer Genome Atlas has recently made available the molecular characteristics of more than 500 patients. We used the TCGA multi-analysis study, and two additional datasets and a set of computational algorithms that we developed. The computational algorithms are based on methods that identify network alterations and quantify network behavior through gene expression.We identify a network biomarker that significantly stratifies survival rates in ovarian cancer patients. Interestingly, expression levels of single or sets of genes do not explain the prognostic stratification. The discovered biomarker is composed of the network around the PDGF pathway. The biomarker enables prognosis stratification. CONCLUSION: The work presented here demonstrates, through the power of gene-expression networks, the criticality of the PDGF network in driving disease course. In uncovering the specific interactions within the network, that drive the phenotype, we catalyze targeted treatment, facilitate prognosis and offer a novel perspective into hidden disease heterogeneity.

Gene expression and network-based analysis reveals a novel role for hsa-miR-9 and drug control over the p38 network in glioblastoma multiforme progression.

BACKGROUND: Glioblastoma multiforme (GBM) is the most common, aggressive and malignant primary tumor of the brain and is associated with one of the worst 5-year survival rates among all human cancers. Identification of molecular interactions that associate with disease progression may be key in finding novel treatments. METHODS: Using five independent molecular and clinical datasets with a set of computational algorithms we were able to identify a gene-gene and gene-microRNA network that significantly stratifies patient prognosis. By combining gene expression microarray data with microRNA expression levels, copy number alterations, drug response and clinical data, combined with network knowledge, we were able to identify a single pathway at the core of glioblastoma. RESULTS: This network, the p38 network, and an associated microRNA, hsa-miR-9, facilitate prognostic stratification. The microRNA hsa-miR-9 correlated with network behavior and presents binding affinities with network members in a manner that suggests control over network behavior. A similar control over network behavior is possible through a set of drugs. These drugs are part of the treatment regimen for a subpopulation of the patients that participated in the TCGA study and for which the study provides clinical information. Interestingly, the patients that were treated with these specific sets of drugs, all of which targeted against p38 network members, demonstrate highly significant stratification of prognosis. CONCLUSIONS: Combined, these results call for attention to p38 network targeted treatment and present the p38 network-hsa-miR-9 control mechanism as critical in GBM progression.

Detecting cancer gene networks characterized by recurrent genomic alterations in a population.

High resolution, system-wide characterizations have demonstrated the capacity to identify genomic regions that undergo genomic aberrations. Such research efforts often aim at associating these regions with disease etiology and outcome. Identifying the corresponding biologic processes that are responsible for disease and its outcome remains challenging. Using novel analytic methods that utilize the structure of biologic networks, we are able to identify the specific networks that are highly significantly, nonrandomly altered by regions of copy number amplification observed in a systems-wide analysis. We demonstrate this method in breast cancer, where the state of a subset of the pathways identified through these regions is shown to be highly associated with disease survival and recurrence.