pathDIP: an annotated resource for known and predicted human gene-pathway associations and pathway enrichment analysis

Epithelial tumor suppressor ELF3 is a lineage-specific amplified oncogene in lung adenocarcinoma

Gene function in cancer is often cell type-specific. The epithelial cell-specific transcription factor ELF3 is a documented tumor suppressor in many epithelial tumors yet displays oncogenic properties in others. Here, we show that ELF3 is an oncogene in the adenocarcinoma subtype of lung cancer (LUAD), providing genetic, functional, and clinical evidence of subtype specificity. We discover a region of focal amplification at chromosome 1q32.1 encompassing the ELF3 locus in LUAD which is absent in the squamous subtype. Gene dosage and promoter hypomethylation affect the locus in up to 80% of LUAD analyzed. ELF3 expression was required for tumor growth and a pan-cancer expression network analysis supports its subtype and tissue specificity. We further show that ELF3 displays strong prognostic value in LUAD but not LUSC. We conclude that, contrary to many other tumors of epithelial origin, ELF3 is an oncogene and putative therapeutic target in LUAD.

Tissue context can dictate why a gene can have seemingly opposing functions in different settings. ELF3 is tumor suppressive in many cancers of epithelial origin but in lung cancer, the authors describe an oncogenic role in the adenocarcinoma histology of non-small cell lung cancer.

Expanding the Transcriptome of Head and Neck Squamous Cell Carcinoma Through Novel MicroRNA Discovery

Head and neck squamous cell carcinoma (HNSCC) has a poor survival rate mainly due to late stage diagnosis and recurrence. Despite genomic efforts to identify driver mutations and changes in protein-coding gene expression, developing effective diagnostic and prognostic biomarkers remains a priority to guide disease management and improve patient outcome. Recent reports of previously-unannotated microRNAs (miRNAs) from multiple somatic tissues have raised the possibility of HNSCC-specific miRNAs. In this study, we applied a customized in-silico analysis pipeline to identify novel miRNAs from raw small-RNA sequencing datasets from public repositories. We discovered 146 previously-unannotated sequences expressed in head and neck samples that share structural properties highly characteristic of miRNAs. The combined expression of the novel miRNAs revealed tissue and context-specific patterns. Furthermore, comparison of tumor with non-malignant tissue samples (n = 43 pairs) revealed 135 of these miRNAs as differentially expressed, most of which were overexpressed or exclusively found in tumor samples. Additionally, a subset of novel miRNAs was significantly associated with HPV infection status and patient outcome. A prognostic-model combining novel and known miRNA was developed (multivariate Cox regression analysis) leading to an improved death and relapse risk stratification (log rank p < 1e-7). The presence of these miRNAs was corroborated both in an independent dataset and by RT-qPCR analysis, supporting their potential involvement in HNSCC. In this study, we report the discovery of 146 novel miRNAs in head and neck tissues and demonstrate their potential biological significance and clinical relevance to head and neck cancer, providing a new resource for the study of HNSCC.

Upgrading the Repertoire of miRNAs in Gastric Adenocarcinoma to Provide a New Resource for Biomarker Discovery

Recent studies have uncovered microRNAs (miRNAs) that have been overlooked in early genomic explorations, which show remarkable tissue- and context-specific expression. Here, we aim to identify and characterize previously unannotated miRNAs expressed in gastric adenocarcinoma (GA). Raw small RNA-sequencing data were analyzed using the miRMaster platform to predict and quantify previously unannotated miRNAs. A discovery cohort of 475 gastric samples (434 GA and 41 adjacent nonmalignant samples), collected by The Cancer Genome Atlas (TCGA), were evaluated. Candidate miRNAs were similarly assessed in an independent cohort of 25 gastric samples. We discovered 170 previously unannotated miRNA candidates expressed in gastric tissues. The expression of these novel miRNAs was highly specific to the gastric samples, 143 of which were significantly deregulated between tumor and nonmalignant contexts (p-adjusted < 0.05; fold change > 1.5). Multivariate survival analyses showed that the combined expression of one previously annotated miRNA and two novel miRNA candidates was significantly predictive of patient outcome. Further, the expression of these three miRNAs was able to stratify patients into three distinct prognostic groups (p = 0.00003). These novel miRNAs were also present in the independent cohort (43 sequences detected in both cohorts). Our findings uncover novel miRNA transcripts in gastric tissues that may have implications in the biology and management of gastric adenocarcinoma.

HMNPPID-human malignant neoplasm protein-protein interaction database

BACKGROUND

Protein-protein interaction (PPI) information extraction from biomedical literature helps unveil the molecular mechanisms of biological processes. Especially, the PPIs associated with human malignant neoplasms can unveil the biology behind these neoplasms. However, such PPI database is not currently available.

RESULTS

In this work, a database of protein-protein interactions associated with 171 kinds of human malignant neoplasms named HMNPPID is constructed. In addition, a visualization program, named VisualPPI, is provided to facilitate the analysis of the PPI network for a specific neoplasm.

CONCLUSIONS

HMNPPID can hopefully become an important resource for the research on PPIs of human malignant neoplasms since it provides readily available data for healthcare professionals. Thus, they do not need to dig into a large amount of biomedical literatures any more, which may accelerate the researches on the PPIs of malignant neoplasms.

Failed immune responses across multiple pathologies share pan-tumor and circulating lymphocytic targets

Rationale Tumor infiltrating lymphocytes are widely associated with positive outcomes, yet carry key indicators of a systemic failed immune response against unresolved cancer. Cancer immunotherapies can reverse their tolerance phenotypes, while preserving tumor-reactivity and neoantigen-specificity shared with circulating immune cells. Objectives We performed comprehensive transcriptomic analyses to identify gene signatures common to circulating and tumor infiltrating lymphocytes in the context of clear cell renal cell carcinoma. Modulated genes also associated with disease outcome were validated in other cancer types. Findings Using bioinformatics, we identified practical diagnostic markers and actionable targets of the failed immune response. On circulating lymphocytes, three genes, LEF1, FASLG, and MMP9, could efficiently stratify patients from healthy control donors. From their associations with resistance to cancer immunotherapies and microbial infections, we uncovered not only pan-cancer, but pan-pathology failed immune response profiles. A prominent lymphocytic matrix metallopeptidase cell migration pathway, is central to a panoply of diseases and tumor immunogenicity, correlates with multi-cancer recurrence, and identifies a feasible, non-invasive approach to pan-pathology diagnoses. Conclusions The non-invasive differently expressed genes we have identified warrant future investigation towards the development of their potential in precision diagnostics and precision pan-disease immunotherapeutics.

Urine Angiotensin II Signature Proteins as Markers of Fibrosis in Kidney Transplant Recipients

BACKGROUND

Interstitial fibrosis/tubular atrophy (IFTA) is an important cause of kidney allograft loss; however, noninvasive markers to identify IFTA or guide antifibrotic therapy are lacking. Using angiotensin II (AngII) as the prototypical inducer of IFTA, we previously identified 83 AngII-regulated proteins in vitro. We developed mass spectrometry-based assays for quantification of 6 AngII signature proteins (bone marrow stromal cell antigen 1, glutamine synthetase [GLNA], laminin subunit beta-2, lysophospholipase I, ras homolog family member B, and thrombospondin-I [TSP1]) and hypothesized that their urine excretion will correlate with IFTA in kidney transplant patients.

METHODS

Urine excretion of 6 AngII-regulated proteins was quantified using selected reaction monitoring and normalized by urine creatinine. Immunohistochemistry was used to assess protein expression of TSP1 and GLNA in kidney biopsies.

RESULTS

The urine excretion rates of AngII-regulated proteins were found to be increased in 15 kidney transplant recipients with IFTA compared with 20 matched controls with no IFTA (mean log2[fmol/µmol of creatinine], bone marrow stromal cell antigen 1: 3.8 versus 3.0, P = 0.03; GLNA: 1.2 versus -0.4, P = 0.03; laminin subunit beta-2: 6.1 versus 5.4, P = 0.06; lysophospholipase I: 2.1 versus 0.6, P = 0.002; ras homolog family member B: 1.2 versus -0.1, P = 0.006; TSP1_GGV: 2.5 versus 1.9; P = 0.15; and TSP1_TIV: 2.0 versus 0.6, P = 0.0006). Receiver operating characteristic curve analysis demonstrated an area under the curve = 0.86 for the ability of urine AngII signature proteins to discriminate IFTA from controls. Urine excretion of AngII signature proteins correlated strongly with chronic IFTA and total inflammation. In a separate cohort of 19 kidney transplant recipients, the urine excretion of these 6 proteins was significantly lower following therapy with AngII inhibitors (P < 0.05).

CONCLUSIONS

AngII-regulated proteins may represent markers of IFTA and guide antifibrotic therapies.

FOXO3-Engineered Human ESC-Derived Vascular Cells Promote Vascular Protection and Regeneration

FOXO3 is an evolutionarily conserved transcription factor that has been linked to longevity. Here we wanted to find out whether human vascular cells could be functionally enhanced by engineering them to express an activated form of FOXO3. This was accomplished via genome editing at two nucleotides in human embryonic stem cells, followed by differentiation into a range of vascular cell types. FOXO3-activated vascular cells exhibited delayed aging and increased resistance to oxidative injury compared with wild-type cells. When tested in a therapeutic context, FOXO3-enhanced vascular cells promoted vascular regeneration in a mouse model of ischemic injury and were resistant to tumorigenic transformation both in vitro and in vivo. Mechanistically, constitutively active FOXO3 conferred cytoprotection by transcriptionally downregulating CSRP1. Taken together, our findings provide mechanistic insights into FOXO3-mediated vascular protection and indicate that FOXO3 activation may provide a means for generating more effective and safe biomaterials for cell replacement therapies.

Capturing variation impact on molecular interactions in the IMEx Consortium mutations data set

The current wealth of genomic variation data identified at nucleotide level presents the challenge of understanding by which mechanisms amino acid variation affects cellular processes. These effects may manifest as distinct phenotypic differences between individuals or result in the development of disease. Physical interactions between molecules are the linking steps underlying most, if not all, cellular processes. Understanding the effects that sequence variation has on a molecule’s interactions is a key step towards connecting mechanistic characterization of nonsynonymous variation to phenotype. We present an open access resource created over 14 years by IMEx database curators, featuring 28,000 annotations describing the effect of small sequence changes on physical protein interactions. We describe how this resource was built, the formats in which the data is provided and offer a descriptive analysis of the data set. The data set is publicly available through the IntAct website and is enhanced with every monthly release.

Genetic variants might exert their functional effects via influencing molecular interaction. Here, the authors present a resource featuring almost 28,000 annotations describing the effect of small sequence changes on physical protein interactions, curated by IMEx Consortium curators.

The complex landscape of microRNAs in articular cartilage: biology, pathology, and therapeutic targets

The disabling degenerative disease osteoarthritis (OA) is prevalent among the global population. Articular cartilage degeneration is a central feature of OA; therefore, a better understanding of the mechanisms that maintain cartilage homeostasis is vital for developing effective therapeutic interventions. MicroRNAs (miRs) modulate cell signaling pathways and various processes in articular cartilage via posttranscriptional repression of target genes. As dysregulated miRs frequently alter the homeostasis of articular cartilage, modulating select miRs presents a potential therapeutic opportunity for OA. Here, we review key miRs that have been shown to modulate cartilage-protective or -destructive mechanisms and signaling pathways. Additionally, we use an integrative computational biology approach to provide insight into predicted miR gene targets that may contribute to OA pathogenesis, and highlight the complexity of miR signaling in OA by generating both unique and overlapping gene targets of miRs that mediate protective or destructive effects. Early OA detection would enable effective prevention; thus, miRs are being explored as diagnostic biomarkers. We discuss these ongoing efforts and the applicability of miR mimics and antisense inhibitors as potential OA therapeutics.

Expanding the miRNA Transcriptome of Human Kidney and Renal Cell Carcinoma

Despite advancements in therapeutic strategies, diagnostic and prognostic molecular markers of kidney cancer remain scarce, particularly in patients who do not harbour well-defined driver mutations. Recent evidence suggests that a large proportion of the human noncoding transcriptome has escaped detection in early genomic explorations. Here, we undertake a large-scale analysis of small RNA-sequencing data from both clear cell renal cell carcinoma (ccRCC) and nonmalignant samples to generate a robust set of miRNAs that remain unannotated in kidney tissues. We find that these novel kidney miRNAs are also expressed in renal cancer cell lines. Moreover, these sequences are differentially expressed between ccRCC and matched nonmalignant tissues, implicating their involvement in ccRCC biology and potential utility as tumour-specific markers of disease. Indeed, we find some of these miRNAs to be significantly associated with patient survival. Finally, target prediction and subsequent pathway analysis reveals that miRNAs previously unannotated in kidney tissues may target genes involved in ccRCC tumourigenesis and disease biology. Taken together, our results represent a new resource for the study of kidney cancer and underscore the need to characterize the unexplored areas of the transcriptome.

Citrobacter rodentium alters the mouse colonic miRNome

Citrobacter rodentium is a murine pathogen causing transmissible colonic hyperplasia and colitis with a pathogenic mechanism similar to foodborne enterohaemorrhagic Escherichia coli in humans. Mechanisms underlying intestinal responses to C. rodentium infection are incompletely understood. We identified 24 colonic microRNAs (miRNAs) as significantly deregulated in response to C. rodentium, including miR-7a, -17, -19a, -20a, -20b, -92a, -106a, -132, -200a, and -2137; most of these miRNAs belong to the oncogenic miR-17-92 clusters. Pathways involved in cell cycle, cancers, and immune responses were enriched among the predicted targets of these miRNAs. We further demonstrated that an apoptosis facilitator, Bim, is a candidate gene target of miRNA-mediated host response to the infection. These findings suggest that host miRNAs participate in C. rodentium pathogenesis and may represent novel treatment targets.

In silico cancer research towards 3R

BACKGROUND

Improving our understanding of cancer and other complex diseases requires integrating diverse data sets and algorithms. Intertwining in vivo and in vitro data and in silico models are paramount to overcome intrinsic difficulties given by data complexity. Importantly, this approach also helps to uncover underlying molecular mechanisms. Over the years, research has introduced multiple biochemical and computational methods to study the disease, many of which require animal experiments. However, modeling systems and the comparison of cellular processes in both eukaryotes and prokaryotes help to understand specific aspects of uncontrolled cell growth, eventually leading to improved planning of future experiments. According to the principles for humane techniques milestones in alternative animal testing involve in vitro methods such as cell-based models and microfluidic chips, as well as clinical tests of microdosing and imaging. Up-to-date, the range of alternative methods has expanded towards computational approaches, based on the use of information from past in vitro and in vivo experiments. In fact, in silico techniques are often underrated but can be vital to understanding fundamental processes in cancer. They can rival accuracy of biological assays, and they can provide essential focus and direction to reduce experimental cost.

MAIN BODY

We give an overview on in vivo, in vitro and in silico methods used in cancer research. Common models as cell-lines, xenografts, or genetically modified rodents reflect relevant pathological processes to a different degree, but can not replicate the full spectrum of human disease. There is an increasing importance of computational biology, advancing from the task of assisting biological analysis with network biology approaches as the basis for understanding a cell's functional organization up to model building for predictive systems.

CONCLUSION

Underlining and extending the in silico approach with respect to the 3Rs for replacement, reduction and refinement will lead cancer research towards efficient and effective precision medicine. Therefore, we suggest refined translational models and testing methods based on integrative analyses and the incorporation of computational biology within cancer research.

GWAS-based machine learning approach to predict duloxetine response in major depressive disorder

Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders and is commonly treated with antidepressant drugs. However, large variability is observed in terms of response to antidepressants. Machine learning (ML) models may be useful to predict treatment outcomes. A sample of 186 MDD patients received treatment with duloxetine for up to 8 weeks were categorized as "responders" based on a MADRS change >50% from baseline; or "remitters" based on a MADRS score ≤10 at end point. The initial dataset (N = 186) was randomly divided into training and test sets in a nested 5-fold cross-validation, where 80% was used as a training set and 20% made up five independent test sets. We performed genome-wide logistic regression to identify potentially significant variants related to duloxetine response/remission and extracted the most promising predictors using LASSO regression. Subsequently, classification-regression trees (CRT) and support vector machines (SVM) were applied to construct models, using ten-fold cross-validation. With regards to response, none of the pairs performed significantly better than chance (accuracy p > .1). For remission, SVM achieved moderate performance with an accuracy = 0.52, a sensitivity = 0.58, and a specificity = 0.46, and 0.51 for all coefficients for CRT. The best performing SVM fold was characterized by an accuracy = 0.66 (p = .071), sensitivity = 0.70 and a sensitivity = 0.61. In this study, the potential of using GWAS data to predict duloxetine outcomes was examined using ML models. The models were characterized by a promising sensitivity, but specificity remained moderate at best. The inclusion of additional non-genetic variables to create integrated models may improve prediction.

Metazoan Parasite Vaccines: Present Status and Future Prospects

Eukaryotic parasites and pathogens continue to cause some of the most detrimental and difficult to treat diseases (or disease states) in both humans and animals, while also continuously expanding into non-endemic countries. Combined with the ever growing number of reports on drug-resistance and the lack of effective treatment programs for many metazoan diseases, the impact that these organisms will have on quality of life remain a global challenge. Vaccination as an effective prophylactic treatment has been demonstrated for well over 200 years for bacterial and viral diseases. From the earliest variolation procedures to the cutting edge technologies employed today, many protective preparations have been successfully developed for use in both medical and veterinary applications. In spite of the successes of these applications in the discovery of subunit vaccines against prokaryotic pathogens, not many targets have been successfully developed into vaccines directed against metazoan parasites. With the current increase in -omics technologies and metadata for eukaryotic parasites, target discovery for vaccine development can be expedited. However, a good understanding of the host/vector/pathogen interface is needed to understand the underlying biological, biochemical and immunological components that will confer a protective response in the host animal. Therefore, systems biology is rapidly coming of age in the pursuit of effective parasite vaccines. Despite the difficulties, a number of approaches have been developed and applied to parasitic helminths and arthropods. This review will focus on key aspects of vaccine development that require attention in the battle against these metazoan parasites, as well as successes in the field of vaccine development for helminthiases and ectoparasites. Lastly, we propose future direction of applying successes in pursuit of next generation vaccines.

Differentially expressed microRNAs in lung adenocarcinoma invert effects of copy number aberrations of prognostic genes

In many cancers, significantly down- or upregulated genes are found within chromosomal regions with DNA copy number alteration opposite to the expression changes. Generally, this paradox has been overlooked as noise, but can potentially be a consequence of interference of epigenetic regulatory mechanisms, including microRNA-mediated control of mRNA levels.

To explore potential associations between microRNAs and paradoxes in non-small-cell lung cancer (NSCLC) we curated and analyzed lung adenocarcinoma (LUAD) data, comprising gene expressions, copy number aberrations (CNAs) and microRNA expressions. We integrated data from 1,062 tumor samples and 241 normal lung samples, including newly-generated array comparative genomic hybridization (aCGH) data from 63 LUAD samples.

We identified 85 “paradoxical” genes whose differential expression consistently contrasted with aberrations of their copy numbers. Paradoxical status of 70 out of 85 genes was validated on sample-wise basis using The Cancer Genome Atlas (TCGA) LUAD data. Of these, 41 genes are prognostic and form a clinically relevant signature, which we validated on three independent datasets. By meta-analysis of results from 9 LUAD microRNA expression studies we identified 24 consistently-deregulated microRNAs. Using TCGA-LUAD data we showed that deregulation of 19 of these microRNAs explains differential expression of the paradoxical genes.

Our results show that deregulation of paradoxical genes is crucial in LUAD and their expression pattern is maintained epigenetically, defying gene copy number status.

DNA methylation signatures at endoplasmic reticulum stress genes are associated with adiposity and insulin resistance

A sustained activation of the unfolded protein response and the subsequent endoplasmic reticulum (ER) stress has been involved in the onset and severity of several metabolic diseases. The aim of this study was to analyze the association of DNA methylation signatures at ER stress genes with adiposity traits and related metabolic disorders. An epigenomic analysis within the Methyl Epigenome Network Association (MENA) project was conducted in an adult population (n=474). DNA methylation status in peripheral white blood cells was analyzed by a microarray approach. KEGG database was used to the characterization and discrimination of genes involved in the "protein processing in endoplasmic reticulum pathway". Anthropometric measurements and plasma metabolic profiles were analyzed. A total of 15 CpG sites at genes participating in ER pathway were strongly correlated with BMI after adjusted linear regression analyses (p<0.0001). These included cg08188400 (MAP2K7), cg20541779 (CASP12), cg24776411 (EIF2AK1), cg14190817 (HSPA5), cg21376454 (ERN1), cg06666486 (EIF2AK1), cg03211481 (DNAJC1), cg18357645 (OS9), cg05801879 (MBTPS1), cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg03384128 (EIF2AK4), cg02712587 (EIF2AK4), cg04972384 (SELS), cg02240686 (EIF2AK2). Noteworthy, most of them were implicated in ER stress (p=2.9E-09). However, only methylation levels at cg20964082 (ERO1LB), cg17300868 (NFE2L2), cg05801879 (MBTPS1), and cg03384128 (EIF2AK4) also correlated with total fat mass. Interestingly, significant associations between methylation patterns at cg20964082 (ERO1LB) and cg17300868 (NFE2L2) and insulin and HOMA-IR index were found, whereas cg05801879 (MBTPS1) and cg03384128 (EIF2AK4) were correlated with triglyceride levels. This study suggests associations of methylation signatures at ER stress genes with adiposity and insulin resistance, as revealed by discriminative pathway analyses.

Functional network community detection can disaggregate and filter multiple underlying pathways in enrichment analyses

Differential expression experiments or other analyses often end in a list of genes. Pathway enrichment analysis is one method to discern important biological signals and patterns from noisy expression data. However, pathway enrichment analysis may perform suboptimally in situations where there are multiple implicated pathways - such as in the case of genes that define subtypes of complex diseases. Our simulation study shows that in this setting, standard overrepresentation analysis identifies many false positive pathways along with the true positives. These false positives hamper investigators' attempts to glean biological insights from enrichment analysis. We develop and evaluate an approach that combines community detection over functional networks with pathway enrichment to reduce false positives. Our simulation study demonstrates that a large reduction in false positives can be obtained with a small decrease in power. Though we hypothesized that multiple communities might underlie previously described subtypes of high-grade serous ovarian cancer and applied this approach, our results do not support this hypothesis. In summary, applying community detection before enrichment analysis may ease interpretation for complex gene sets that represent multiple distinct pathways.

miR-194 as predictive biomarker of responsiveness to neoadjuvant chemoradiotherapy in patients with locally advanced rectal adenocarcinoma

AIMS

Curative surgery remains the primary form of treatment for locally advanced rectal cancer (LARC). Recent data support the use of preoperative chemoradiotherapy (pCRT) to improve the prognosis of LARC with a significant reduction of local relapse and an increase of overall survival. Unfortunately, only 20% of the patients with LARC present complete pathological response after pCRT, whereas in 20%-40%, the response is poor or absent.

METHODS

We investigated the expression level of miR-194 in n=38 patients with LARC using our public microRNA (miRNA) expression dataset. miR-194 expression was further validated by real-time quantitative PCR (qRT-PCR) and in situ hybridisation (ISH). Protein-protein interaction network and pathway enrichment analysis were performed on miR-194 targets.

RESULTS AND DISCUSSION

Using biopsy samples collected at diagnosis, mir-194 was significantly upregulated in patients responding to treatment (p value=0.016). The data was confirmed with qRT-PCR (p value=0.0587) and ISH (p value=0.026). Protein-protein interaction network and pathway enrichment analysis reveal a possible mechanism of susceptibility to pCRT involving Wnt pathway via its downstream mediator TRAF6. Finally, we interrogated the Comparative Toxicogenomics Database database in order to identify those chemical compounds able to mimic the biological effects of miR-194 as new possible therapeutic option in LARC treatment. The present study combining miRNA expression profiling with integrative computational biology identified miR-194 as predictive biomarker of response to pCRT. Using known and predicted drug mechanism of action, we then identified possible chemical compounds for further in vitro validation.

An Integrated Approach Identifies Mediators of Local Recurrence in Head and Neck Squamous Carcinoma

Purpose: Head and neck squamous cell carcinomas (HNSCCs) cause more than 300,000 deaths worldwide each year. Locoregional and distant recurrences represent worse prognostic events and accepted surrogate markers of patients' overall survival. No valid biomarker and salvage therapy exist to identify and treat patients at high-risk of recurrence. We aimed to verify if selected miRNAs could be used as biomarkers of recurrence in HNSCC.Experimental Design: A NanoString array was used to identify miRNAs associated with locoregional recurrence in 44 patients with HNSCC. Bioinformatic approaches validated the signature and identified potential miRNA targets. Validation experiments were performed using an independent cohort of primary HNSCC samples and a panel of HNSCC cell lines. In vivo experiments validated the in vitro results.Results: Our data identified a four-miRNA signature that classified HNSCC patients at high- or low-risk of recurrence. These miRNAs collectively impinge on the epithelial-mesenchymal transition process. In silico and wet lab approaches showed that miR-9, expressed at high levels in recurrent HNSCC, targets SASH1 and KRT13, whereas miR-1, miR-133, and miR-150, expressed at low levels in recurrent HNSCC, collectively target SP1 and TGFβ pathways. A six-gene signature comprising these targets identified patients at high risk of recurrences, as well. Combined pharmacological inhibition of SP1 and TGFβ pathways induced HNSCC cell death and, when timely administered, prevented recurrence formation in a preclinical model of HNSCC recurrence.Conclusions: By integrating different experimental approaches and competences, we identified critical mediators of recurrence formation in HNSCC that may merit to be considered for future clinical development. Clin Cancer Res; 23(14); 3769-80. ©2017 AACR.

HABP2 p.G534E variant in patients with family history of thyroid and breast cancer

Familial Papillary Thyroid Carcinoma (PTC) has been described as a hereditary predisposition cancer syndrome associated with mutations in candidate genes including HABP2. Two of 20 probands from families with history of PTC and breast carcinoma (BC) were evaluated by whole exome sequencing (WES) revealing HABP2 p.G534E. Sanger sequencing was used to confirm the involvement of this variant in three families (F1: 7 relatives; F2: 3 and F3: 3). The proband and his sister (with no malignant tumor so far) from F1 were homozygous for the variant whereas one relative with PTC from F2 was negative for the variant. Although the proband of the F3 with PTC was HABP2 wild type, three relatives presented the variant. Five of 170 healthy Brazilian individuals with no family history of BC or PTC and three of 50 sporadic PTC presented the p.G534E. These findings suggested no association of this variant with our familial PTC cases. Genes potentially associated with deregulation of the extracellular matrix organization pathway (CTSB, TNXB, COL4A3, COL16A1, COL24A1, COL5A2, NID1, LOXL2, MMP11, TRIM24 and MUSK) and DNA repair function (NBN and MSH2) were detected by WES, suggesting that other cancer-associated genes have pathogenic effects in the risk of familial PTC development.

Systematic protein-protein interaction mapping for clinically relevant human GPCRs

G‐protein‐coupled receptors (GPCRs) are the largest family of integral membrane receptors with key roles in regulating signaling pathways targeted by therapeutics, but are difficult to study using existing proteomics technologies due to their complex biochemical features. To obtain a global view of GPCR‐mediated signaling and to identify novel components of their pathways, we used a modified membrane yeast two‐hybrid (MYTH) approach and identified interacting partners for 48 selected full‐length human ligand‐unoccupied GPCRs in their native membrane environment. The resulting GPCR interactome connects 686 proteins by 987 unique interactions, including 299 membrane proteins involved in a diverse range of cellular functions. To demonstrate the biological relevance of the GPCR interactome, we validated novel interactions of the GPR37, serotonin 5‐HT4d, and adenosine ADORA2A receptors. Our data represent the first large‐scale interactome mapping for human GPCRs and provide a valuable resource for the analysis of signaling pathways involving this druggable family of integral membrane proteins.