Project description:To correlate the variable clinical features of oestrogen-receptor-positive breast cancer with somatic alterations, we studied pretreatment tumour biopsies accrued from patients in two studies of neoadjuvant aromatase inhibitor therapy by massively parallel sequencing and analysis. Eighteen significantly mutated genes were identified, including five genes (RUNX1, CBFB, MYH9, MLL3 and SF3B1) previously linked to haematopoietic disorders. Mutant MAP3K1 was associated with luminal A status, low-grade histology and low proliferation rates, whereas mutant TP53 was associated with the opposite pattern. Moreover, mutant GATA3 correlated with suppression of proliferation upon aromatase inhibitor treatment. Pathway analysis demonstrated that mutations in MAP2K4, a MAP3K1 substrate, produced similar perturbations as MAP3K1 loss. Distinct phenotypes in oestrogen-receptor-positive breast cancer are associated with specific patterns of somatic mutations that map into cellular pathways linked to tumour biology, but most recurrent mutations are relatively infrequent. Prospective clinical trials based on these findings will require comprehensive genome sequencing.
Project description:Various studies have shown that selective molecular recognition of RNA targets by small molecules in cells, although challenging, is indeed possible. One facile strategy to enhance selectivity and potency is binding two or more sites within an RNA simultaneously with a single molecule. To simplify the identification of targets amenable to such a strategy, we informatically mined all human microRNA (miRNA) precursors to identify those with two proximal noncanonically paired sites. We selected oncogenic microRNA-27a (miR-27a) for further study as a lead molecule binds its Drosha site and a nearby internal loop, affording a homodimer that potently and specifically inhibits miR-27a processing in both breast cancer and prostate cancer cells. This reduction of mature miR-27a ameliorates an oncogenic cellular phenotype with nanomolar activity. Collectively, these studies demonstrate that synergistic bioinformatic and experimental approaches can define targets that may be more amenable to small molecule targeting than others.
Project description:Cells mount a transcriptional anti-oxidative stress (AOS) response program to scavenge reactive oxygen species (ROS) that arise from chemical, physical, and metabolic challenges. This protective program has been shown to reduce carcinogenesis triggered by chemical and physical insults. However, it is also hijacked by established cancers to thrive and proliferate within the hostile tumor microenvironment and to gain resistance against chemo- and radiotherapies. Therefore, targeting the AOS response proteins that are exploited by cancer cells is an attractive therapeutic strategy. In order to identify the AOS genes that are suspected to support cancer progression and resistance, we analyzed the expression patterns of 285 genes annotated for being involved in oxidative stress in 994 tumors and 353 normal tissues. Thereby we identified a signature of 116 genes that are highly overexpressed in multiple carcinomas while being only minimally expressed in normal tissues. To establish which of these genes are more likely to functionally drive cancer resistance and progression, we further identified those whose overexpression correlates with negative patient outcome in breast and lung carcinoma. Gene-set enrichment, GO, network, and pathway analyses revealed that members of the thioredoxin and glutathione pathways are prominent components of this oncogenic signature and that activation of these pathways is common feature of many cancer entities. Interestingly, a large fraction of these AOS genes are downstream targets of the transcription factors NRF2, NF-kappaB and FOXM1, and relay on NADPH for their enzymatic activities highlighting promising drug targets. We discuss these findings and propose therapeutic strategies that may be applied to overcome cancer resistance.
Project description:Lineage or cell of origin of cancers is often unknown and thus is not a consideration in therapeutic approaches. Alveolar rhabdomyosarcoma (aRMS) is an aggressive childhood cancer for which the cell of origin remains debated. We used conditional genetic mouse models of aRMS to activate the pathognomonic Pax3:Foxo1 fusion oncogene and inactivate p53 in several stages of prenatal and postnatal muscle development. We reveal that lineage of origin significantly influences tumor histomorphology and sensitivity to targeted therapeutics. Furthermore, we uncovered differential transcriptional regulation of the Pax3:Foxo1 locus by tumor lineage of origin, which led us to identify the histone deacetylase inhibitor entinostat as a pharmacological agent for the potential conversion of Pax3:Foxo1-positive aRMS to a state akin to fusion-negative RMS through direct transcriptional suppression of Pax3:Foxo1.
Project description:Osteoporosis is a severe chronic skeletal disorder that increases the risks of disability and mortality; however, the mechanism of this disease and the protein markers for prognosis of osteoporosis have not been well characterized. This study aims to characterize the imbalanced serum proteostasis, the disturbed pathways, and potential serum markers in osteoporosis by using a set of bioinformatic analyses. In the present study, the large-scale proteomics datasets (PXD006464) were adopted from the Proteome Xchange database and processed with MaxQuant. The differentially expressed serum proteins were identified. The biological process and molecular function were analyzed. The protein-protein interactions and subnetwork modules were constructed. The signaling pathways were enriched. We identified 209 upregulated and 230 downregulated serum proteins. The bioinformatic analyses revealed a highly overlapped functional protein classification and the gene ontology terms between the upregulated and downregulated protein groups. Protein-protein interactions and pathway analyses showed a high enrichment in protein synthesis, inflammation, and immune response in the upregulated proteins, and cell adhesion and cytoskeleton regulation in the downregulated proteins. Our findings greatly expand the current view of the roles of serum proteins in osteoporosis and shed light on the understanding of its underlying mechanisms and the discovery of serum proteins as potential markers for the prognosis of osteoporosis.
Project description:The twin epidemic of diabetes and obesity pose daunting challenges worldwide. The dramatic rise in obesity-associated diabetes resulted in an alarming increase in the incidence and prevalence of obesity an important complication of diabetes. Differences among individuals in their susceptibility to both these conditions probably reflect their genetic constitutions. The dramatic improvements in genomic and bioinformatic resources are accelerating the pace of gene discovery. It is tempting to speculate the key susceptible genes/proteins that bridges diabetes mellitus and obesity. In this regard, we evaluated the role of several genes/proteins that are believed to be involved in the evolution of obesity associated diabetes by employing multiple sequence alignment using ClustalW tool and constructed a phylogram tree using functional protein sequences extracted from NCBI. Phylogram was constructed using Neighbor-Joining Algorithm a bioinformatic tool. Our bioinformatic analysis reports resistin gene as ominous link with obesity associated diabetes. This bioinformatic study will be useful for future studies towards therapeutic inventions of obesity associated type 2 diabetes.
Project description:BackgroundThe programmed cell death 1 (PD-1)/PD-1 ligand 1 (PD-L1) signaling pathway is significantly upregulated in influenza virus infection, which impairs the antiviral response. Blocking this signaling pathway may reduce the damage, lower the virus titer in lung tissue, and alleviate the symptoms of infection to promote recovery. In addition to the enhanced viral immune response, using of immune checkpoint inhibitors in influenza virus infection is controversial, the aim of this study was to identify the key factors and regulatory mechanisms in the PD-1 checkpoint blockade response microenvironment in influenza infection.MethodsA BALB/c mouse model of influenza A/PR8(H1N1) infection was established then constructed, and whole-transcriptome sequencing including mRNAs, miRNAs (microRNAs), lncRNAs (long noncoding RNAs), and circRNAs (circular RNAs) of mice treated with PD-1 checkpoint blockade by antibody treatment and IgG2a isotype control before infection with A/PR8(H1N1) were performed. Subsequently, the differential expression of transcripts between these two groups was analyzed, followed by functional interaction prediction analysis to investigate gene-regulatory circuits.ResultsIn total, 84 differentially expressed dif-mRNAs, 36 dif-miRNAs, 90 dif-lncRNAs and 22 dif-circRNAs were found in PD-1 antagonist treated A/PR8(H1N1) influenza-infected lungs compared with the controls (IgG2a isotype control treated before infection). In spleens between the above two groups, 45 dif-mRNAs, 36 dif-miRNAs, 57 dif-lncRNAs, and 24 dif-circRNAs were identified. Direct function enrichment analysis of dif-mRNAs and dif-miRNAs showed that these genes were mainly involved in myocardial damage related to viral infection, mitogen activated protein kinase (MAPK) signaling pathways, RAP1 (Ras-related protein 1) signaling pathway, and Axon guidance. Finally, 595 interaction pairs were obtained for the lungs and 462 interaction pairs for the spleens were obtained in the competing endogenous RNA (ceRNA) complex network, in which the downregulated mmu-miR-7043-3p and Vps39-204 were enriched significantly in PD-1 checkpoint blockade treated A/PR8(H1N1) infection group.ConclusionsThe present study provided a basis for the identification of potential pathways and hub genes that might be involved in the PD-1 checkpoint blockade response microenvironment in influenza infection.
Project description:The outbreak of monkeypox may be considered a novel and urgent threat after the coronavirus disease (COVID-19). No wide-ranging studies have been conducted on this disease since it was first reported. We systematically assessed the functional role of gene expression in cells infected with the monkeypox virus using transcriptome profiling and compared the functional relation with that of COVID-19. Based on the Gene Expression Omnibus database, we obtained 212 differentially expressed genes (DEGs) of GSE36854 and GSE21001 of monkeypox datasets. Enrichment analyses, including KEGG and gene ontology (GO) analyses, were performed to identify the common function of 212 DEGs of GSE36854 and GSE21001. CytoHubba and Molecular Complex Detection were performed to determine the core genes after a protein-protein interaction (PPI). Metascape/COVID-19 was used to compare DEGs of monkeypox and COVID-19. GO analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed cellular response to cytokine stimulus, cell activation, and cell differentiation regulation. KEGG analysis of 212 DEGs of GSE36854 and GSE21001 for monkeypox infection showed involvement of monkeypox in COVID-19, cytokine-cytokine receptor interaction, inflammatory bowel disease, atherosclerosis, TNF signaling, and T cell receptor signaling. By comparing our data with published transcriptome of severe acute respiratory syndrome coronavirus 2 infections in other cell lines, the common function of monkeypox and COVID-19 includes cytokine signaling in the immune system, TNF signaling, and MAPK cascade regulation. Thus, our data suggest that the molecular connections identified between COVID-19 and monkeypox elucidate the causes of monkeypox.
Project description:BackgroundThe mechanism of action for most cancer drugs is not clear. Large-scale pharmacogenomic cancer cell line datasets offer a rich resource to obtain this knowledge. Here, we present an analysis strategy for revealing biological pathways that contribute to drug response using publicly available pharmacogenomic cancer cell line datasets.MethodsWe present a custom machine-learning based approach for identifying biological pathways involved in cancer drug response. We test the utility of our approach with a pan-cancer analysis of ML210, an inhibitor of GPX4, and a melanoma-focused analysis of inhibitors of BRAFV600. We apply our approach to reveal determinants of drug resistance to microtubule inhibitors.ResultsOur method implicated lipid metabolism and Rac1/cytoskeleton signaling in the context of ML210 and BRAF inhibitor response, respectively. These findings are consistent with current knowledge of how these drugs work. For microtubule inhibitors, our approach implicated Notch and Akt signaling as pathways that associated with response.ConclusionsOur results demonstrate the utility of combining informed feature selection and machine learning algorithms in understanding cancer drug response.
Project description:Several therapeutic agents have been approved for treating multiple myeloma, a cancer of bone marrow-resident plasma cells. Predictive biomarkers for drug response could help guide clinical strategies to optimize outcomes. In this study, we present an integrated functional genomic analysis of tumor samples from patients multiple myeloma that were assessed for their ex vivo drug sensitivity to 37 drugs, clinical variables, cytogenetics, mutational profiles, and transcriptomes. This analysis revealed a multiple myeloma transcriptomic topology that generates "footprints" in association with ex vivo drug sensitivity that have both predictive and mechanistic applications. Validation of the transcriptomic footprints for the anti-CD38 mAb daratumumab (DARA) and the nuclear export inhibitor selinexor (SELI) demonstrated that these footprints can accurately classify clinical responses. The analysis further revealed that DARA and SELI have anticorrelated mechanisms of resistance, and treatment with a SELI-based regimen immediately after a DARA-containing regimen was associated with improved survival in three independent clinical trials, supporting an evolutionary-based strategy involving sequential therapy. These findings suggest that this unique repository and computational framework can be leveraged to inform underlying biology and to identify therapeutic strategies to improve treatment of multiple myeloma. Significance: Functional genomic analysis of primary multiple myeloma samples elucidated predictive biomarkers for drugs and molecular pathways mediating therapeutic response, which revealed a rationale for sequential therapy to maximize patient outcomes.