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To generate new mechanistic hypotheses on the pathogenesis and disease progression of neuroHIV and identify novel therapeutic targets to improve neuropsychological function in people with HIV, we investigated host genes and pathway dysregulations associated with brain HIV RNA load in gene expression profiles of the frontal cortex, basal ganglia, and white matter of HIV+ patients. Pathway analyses showed that host genes correlated with HIV expression in all three brain regions were predominantly related to inflammation, neurodegeneration, and bioenergetics. HIV RNA load directly correlated particularly with inflammation genesets representative of cytokine signaling, and this was more prominent in white matter and the basal ganglia. Increases in interferon signaling were correlated with high brain HIV RNA load in the basal ganglia and the white matter although not in the frontal cortex. Brain HIV RNA load was inversely correlated with genesets that are indicative of neuronal and synaptic genes, particularly in the cortex, indicative of synaptic injury and neurodegeneration. Brain HIV RNA load was inversely correlated with genesets that are representative of oxidative phosphorylation, electron transfer, and the tricarboxylic acid cycle in all three brain regions. Mitochondrial dysfunction has been implicated in the toxicity of some antiretrovirals, and these results indicate that mitochondrial dysfunction is also associated with productive HIV infection. Genes and pathways correlated with brain HIV RNA load suggest potential therapeutic targets to ameliorate neuropsychological functioning in people living with HIV.

Summary: We present rCGH, a comprehensive array-based comparative genomic hybridization analysis workflow, integrating computational improvements and functionalities specifically designed for precision medicine. rCGH supports the major microarray platforms, ensures a full traceability and facilitates profiles interpretation and decision-making through sharable interactive visualizations. Availability and implementation: The rCGH R package is available on bioconductor (under Artistic-2.0). The aCGH-viewer is available at https://fredcommo.shinyapps.io/aCGH_viewer, and the application implementation is freely available for installation at https://github.com/fredcommo/aCGH_viewer. Contact: frederic.commo@gustaveroussy.fr Supplementary information: Supplementary data are available at Bioinformatics online.

DNA extracted from cancer patients' whole blood may contain somatic mutations from circulating tumor DNA (ctDNA) fragments. In this study, we introduce cmDetect, a computational method for the systematic identification of ctDNA mutations using whole-exome sequencing of a cohort of tumor and corresponding peripheral whole-blood samples. Through the analysis of simulated data, we demonstrated an increase in sensitivity in calling somatic mutations by combining cmDetect to two widely used mutation callers. In a cohort of 93 breast cancer metastatic patients, cmDetect identified ctDNA mutations in 54% of the patients and recovered somatic mutations in cancer genes EGFR, PIK3CA, and TP53. We further showed that cmDetect detected ctDNA in 89% of patients with confirmed mutated cell–free tumor DNA by plasma analyses (n = 9) within 46 pan-cancer patients. Our results prompt immediate consideration of the use of this method as an additional step in somatic mutation calling using whole-exome sequencing data with blood samples as controls. Cancer Res; 76(20); 5954–61. ©2016 AACR.

Cisplatin (cis-diaminedichloroplatin (II), CDDP) is part of the standard therapy for a number of solid tumors including Non-Small-Cell Lung Cancer (NSCLC). The initial response observed is in most cases only transient and tumors quickly become refractory to the drug. Tumor cell resistance to CDDP relies on multiple mechanisms, some of which still remain unknown. In search for such mechanisms, we examined the impact of CDDP on mRNA translation in a sensitive and in a matched resistant NSCLC cell line. We identified a set of genes whose mRNAs are differentially translated in CDDP resistant vs. sensitive cells. The translation of the mRNA encoding the Ubiquitin-Specific Peptidase 1 (USP1), a Ubiquitin peptidase with important function in multiple DNA repair pathways, is inhibited by CDDP exposure in the sensitive cells, but not in the resistant cells. This lack of down-regulation of USP1 expression at the translational level plays a primary role in CDDP resistance since inhibition of USP1 expression or activity by siRNA or the small molecule inhibitor ML323, respectively is sufficient to re-sensitize resistant cells to CDDP. We involved the USP1 mRNA translation as a major mechanism of CDDP resistance in NSCLC cells and suggest that USP1 could be evaluated as a candidate predictive marker and as a therapeutic target to overcome CDDP resistance. More generally, our results indicate that analysis of gene expression at the level of mRNA translation is a useful approach to identify new determinants of CDDP resistance.

Protein–protein interactions, essential for understanding how a cell functions, are predicted using a new method that combines protein structure with other computationally and experimentally derived clues. The analysis of protein-interaction networks is essential to an understanding of the regulatory processes in a living cell. Many methods have been developed with a view to predicting protein–protein interactions (PPIs) at a genome-wide level, although the differences obtained using these approaches suggest that there are still factors unaccounted for. Barry Honig and colleagues have developed a new way of predicting PPIs that is based on the proteins' three-dimensional structures and functional data. Tests of several predictions of the new algorithm, known as PREPPI, confirm the accuracy of the results. The genome-wide identification of pairs of interacting proteins is an important step in the elucidation of cell regulatory mechanisms1,2. Much of our present knowledge derives from high-throughput techniques such as the yeast two-hybrid assay and affinity purification3, as well as from manual curation of experiments on individual systems4. A variety of computational approaches based, for example, on sequence homology, gene co-expression and phylogenetic profiles, have also been developed for the genome-wide inference of protein–protein interactions (PPIs)5,6. Yet comparative studies suggest that the development of accurate and complete repertoires of PPIs is still in its early stages7,8,9. Here we show that three-dimensional structural information can be used to predict PPIs with an accuracy and coverage that are superior to predictions based on non-structural evidence. Moreover, an algorithm, termed PrePPI, which combines structural information with other functional clues, is comparable in accuracy to high-throughput experiments, yielding over 30,000 high-confidence interactions for yeast and over 300,000 for human. Experimental tests of a number of predictions demonstrate the ability of the PrePPI algorithm to identify unexpected PPIs of considerable biological interest. The surprising effectiveness of three-dimensional structural information can be attributed to the use of homology models combined with the exploitation of both close and remote geometric relationships between proteins.

Mapping the chromosomal locations of transcription factors, nucleosomes, histone modifications, chromatin remodeling enzymes, chaperones, and polymerases is one of the key tasks of modern biology, as evidenced by the Encyclopedia of DNA Elements (ENCODE) Project. To this end, chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) is the standard methodology. Mapping such protein-DNA interactions in vivo using ChIP-seq presents multiple challenges not only in sample preparation and sequencing but also for computational analysis. Here, we present step-by-step guidelines for the computational analysis of ChIP-seq data. We address all the major steps in the analysis of ChIP-seq data: sequencing depth selection, quality checking, mapping, data normalization, assessment of reproducibility, peak calling, differential binding analysis, controlling the false discovery rate, peak annotation, visualization, and motif analysis. At each step in our guidelines we discuss some of the software tools most frequently used. We also highlight the challenges and problems associated with each step in ChIP-seq data analysis. We present a concise workflow for the analysis of ChIP-seq data in Figure 1 that complements and expands on the recommendations of the ENCODE and modENCODE projects. Each step in the workflow is described in detail in the following sections.

Combinatorial activation of PI3-kinase and RAS signaling occurs frequently in advanced prostate cancer and is associated with adverse patient outcome. We now report that the oncogenic Ets variant 4 (Etv4) promotes prostate cancer metastasis in response to coactivation of PI3-kinase and Ras signaling pathways in a genetically engineered mouse model of highly penetrant, metastatic prostate cancer. Using an inducible Cre driver to simultaneously inactivate Pten while activating oncogenic Kras and a fluorescent reporter allele in the prostate epithelium, we performed lineage tracing in vivo to define the temporal and spatial occurrence of prostate tumors, disseminated tumor cells, and metastases. These analyses revealed that though disseminated tumors cells arise early following the initial occurrence of prostate tumors, there is a significant temporal lag in metastasis, which is temporally coincident with the up-regulation of Etv4 expression in primary tumors. Functional studies showed that knockdown of Etv4 in a metastatic cell line derived from the mouse model abrogates the metastatic phenotype but does not affect tumor growth. Notably, expression and activation of ETV4, but not other oncogenic ETS genes, is correlated with activation of both PI3-kinase and Ras signaling in human prostate tumors and metastases. Our findings indicate that ETV4 promotes metastasis in prostate tumors that have activation of PI3-kinase and Ras signaling, and therefore, ETV4 represents a potential target of therapeutic intervention for metastatic prostate cancer.

Proteoglycans like syndecan-3 have complex signaling roles in addition to their function as structural components of the extracellular matrix. Here, we show that syndecan-3 in the lateral hypothalamus has an unexpected new role in limiting compulsive cocaine intake. In particular, we observe that syndecan-3 null mice self-administer greater amounts of cocaine than wild-type mice. This effect can be rescued by re-expression of syndecan-3 in the lateral hypothalamus with an adeno-associated viral vector. Adeno-associated viral vector delivery of syndecan-3 to the lateral hypothalamus also reduces motivation for cocaine in normal mice. Syndecan-3 limits cocaine intake by modulating the effects of glial-cell-line-derived neurotrophic factor, which uses syndecan-3 as an alternative receptor. Our findings indicate syndecan-3-dependent signaling as a novel therapeutic target for the treatment of cocaine addiction., The lateral hypothalamus is implicated in drug reward and addiction. Chen and colleagues find that in the lateral hypothalamus of mice, the proteoglycan syndecan-3 negatively regulates cocaine-seeking behaviour by modulating the effects of glial cell line-derived neurotrophic factor.

Although the prognosis for clinically localized prostate cancer is now favorable, there are still no curative treatments for castration-resistant prostate cancer (CRPC) and, therefore, it remains fatal. In this study, we investigate a new therapeutic approach for treatment of CRPC, which involves dual targeting of a major signaling pathway that is frequently deregulated in the disease. We found that dual targeting of the Akt and mTOR signaling pathways with their respective inhibitors, MK-2206 and ridaforolimus (MK-8669), is highly effective for inhibiting CRPC in preclinical studies in vivo using a refined genetically engineered mouse model of the disease. The efficacy of the combination treatment contrasts with their limited efficacy as single agents, since delivery of MK-2206 or MK-8669 individually had a modest impact in vivo on the overall tumor phenotype. In human prostate cancer cell lines, although not in the mouse model, the synergistic actions of MK-2206 and ridaforolimus (MK-8669) are due in part to limiting the mTORC2 feedback activation of Akt. Moreover, the effects of these drugs are mediated by inhibition of cellular proliferation via the retinoblastoma (Rb) pathway. Our findings suggest that dual targeting of the Akt and mTOR signaling pathways using MK-2206 and ridaforolimus (MK-8669) may be effective for treatment of CRPC, particularly for patients with deregulated Rb pathway activity. Cancer Res; 72(17); 4483–93. ©2012 AACR.

Assembly of a mixed interaction network specific to human B cells. Identification and validation of master regulators of germinal center reaction. MYB and FOXM1 are synergistic master regulators of proliferation in germinal center B cells and control a new protein complex involving replication and mitotic-related genes., We have assembled an interaction network specific to human B cells (the human B-cell interactome or HBCI), containing protein–DNA and protein–protein interactions using an evidence integration approach. The integration of different interaction layers in one network allowed us to elucidate master regulator (MR) genes controlling specific cellular processes as well as transcriptional regulation of proteins in complexes whose availability must be regulated in context-dependent manner. The latter is a poorly understood process, as transcriptional networks and protein–protein interaction networks are usually studied in isolation. We have developed a new algorithm called master regulator inference analysis (MARINa) for discovering MRs of specific phenotypes and applied it to the HBCI to infer MRs of germinal center (GC) formation. GCs are structures where antigen-stimulated B cells highly proliferate, undergo somatic hypermutation of immunoglobulin genes, and are selected based on the production of high-affinity antibodies. GC B cells (centroblasts) derive from naive B cells, from which they differ for the activation of genetic programs controlling cell proliferation, DNA metabolism, and pro-apoptotic programs and for the repression of anti-apoptotic, cell-cycle arrest, DNA repair, and signal transduction programs from cytokines and chemokines. MARINa recovered known MRs of GC B cells and also revealed a new transcription factor module controlling their proliferation. In particular, we identified MYB and FOXM1 as being key MRs of GC B cells. Indeed, 80% of the genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a predicted complex regulating DNA pre-replication, replication, and mitosis. We first tested whether MYB and FOXM1 may regulate each other as predicted in the HBCI, and show that MYB is a transcriptional activator of FOXM1, suggesting that they form a feed-forward loop, involved in the synergistic activation of a large subset of GC-specific genes. We then validated that common MYB/FOXM1 targets and other predicted MRs were affected by the silencing of either TF, using gene expression profiling. Furthermore, we showed that downregulated targets (AURKA, BUBR1, CCNB2, FANCI, MCM3, and PTTG1) and MRs (NFYB, E2F1, and E2F5) after MYB or FOXM1 silencing are indeed directly bound by them in their promoter region. Silencing of FOXM1 and MYB showed a decrease in proliferating cells and an increase in apoptotic cells, indicating that MYB and FOXM1 are necessary for viability and rapid proliferation of GC B cells. To gain more insight into the control of GC-proliferation phenotype by MYB and FOXM1, we further examined specific targets involved in the formation of a predicted protein complex. Approximately half of MYB/FOXM1 targets cluster within a complex, including new interactions between pre-replication and mitotic proteins. We experimentally validate that two mitotic kinases in the inferred complex, BUBR1 and AURKA, physically interact with MCM3, all of them being confirmed to be direct targets of FOXM1 and MYB. In summary, these results document that coordinated analysis of both transcriptional and post-translational interactions in the HBCI can identify synergistic MRs of human phenotypes, as well as provide insight on the functional regulatory role of these proteins. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues., Assembly of a transcriptional and post-translational molecular interaction network in B cells, the human B-cell interactome (HBCI), reveals a hierarchical, transcriptional control module, where MYB and FOXM1 act as synergistic master regulators of proliferation in the germinal center (GC). Eighty percent of genes jointly regulated by these transcription factors are activated in the GC, including those encoding proteins in a complex regulating DNA pre-replication, replication, and mitosis. These results indicate that the HBCI analysis can be used for the identification of determinants of major human cell phenotypes and provides a paradigm of general applicability to normal and pathologic tissues.

Celine Lefebvre is a computational biologist with research goals directed towards the identification of biomarkers and therapeutic targets for cancer by developing innovative statistical and computational methods. Specialist of the inference and interrogation of gene regulatory networks, she has developed a method for the identification of master regulators of physiological and pathological cellular phenotypes [1, 2]. The application of the method successfully retrieved key regulators of proliferation of germinal center B cells [2] and synergistic regulators of aggressive prostate cancers [3]. At Gustave Roussy Cancer Institute, she is leading the bioinformatics team of the translational research unit where she is focusing her research on deciphering the relations between tumor molecular alterations and associated response to treatments. To this aim, the research group is integrating different data types including molecular and pharmacological profiles for cell lines and molecular profiles and clinical data for patients from public databases and from Gustave Roussy personalized medicine clinical trials.

SummaryUnsustained enzyme inhibition is a barrier to targeted therapy for cancer. Here, resistance to a class I PI3K inhibitor in a model of metastatic breast cancer driven by PI3K and MYC was associated with feedback activation of tyrosine kinase receptors (RTKs), AKT, mTOR, and MYC. Inhibitors of bromodomain and extra terminal domain (BET) proteins also failed to affect tumor growth. Interestingly, BET inhibitors lowered PI3K signaling and dissociated BRD4 from chromatin at regulatory regions of insulin receptor and EGFR family RTKs to reduce their expression. Combined PI3K and BET inhibition induced cell death, tumor regression, and clamped inhibition of PI3K signaling in a broad range of tumor cell lines to provide a strategy to overcome resistance to kinase inhibitor therapy.

SummaryTo identify regulatory drivers of prostate cancer malignancy, we have assembled genome-wide regulatory networks (interactomes) for human and mouse prostate cancer from expression profiles of human tumors and of genetically engineered mouse models, respectively. Cross-species computational analysis of these interactomes has identified FOXM1 and CENPF as synergistic master regulators of prostate cancer malignancy. Experimental validation shows that FOXM1 and CENPF function synergistically to promote tumor growth by coordinated regulation of target gene expression and activation of key signaling pathways associated with prostate cancer malignancy. Furthermore, co-expression of FOXM1 and CENPF is a robust prognostic indicator of poor survival and metastasis. Thus, genome-wide cross-species interrogation of regulatory networks represents a valuable strategy to identify causal mechanisms of human cancer.