Combining VPS34 inhibitors with STING agonists enhances type I interferon signaling and anti-tumor efficacy.

An immunosuppressive tumor microenvironment promotes tumor growth and is one of the main factors limiting the response to cancer immunotherapy. We have previously reported that inhibition of vacuolar protein sorting 34 (VPS34), a crucial lipid kinase in the autophagy/endosomal trafficking pathway, decreases tumor growth in several cancer models, increases infiltration of immune cells and sensitizes tumors to anti-programmed cell death protein 1/programmed cell death 1 ligand 1 therapy by upregulation of C-C motif chemokine 5 (CCL5) and C-X-C motif chemokine 10 (CXCL10) chemokines. The purpose of this study was to investigate the signaling mechanism leading to the VPS34-dependent chemokine increase. NanoString gene expression analysis was applied to tumors from mice treated with the VPS34 inhibitor SB02024 to identify key pathways involved in the anti-tumor response. We showed that VPS34 inhibitors increased the secretion of T-cell-recruitment chemokines in a cyclic GMP-AMP synthase (cGAS)/stimulator of interferon genes protein (STING)-dependent manner in cancer cells. Both pharmacological and small interfering RNA (siRNA)-mediated VPS34 inhibition increased cGAS/STING-mediated expression and secretion of CCL5 and CXCL10. The combination of VPS34 inhibitor and STING agonist further induced cytokine release in both human and murine cancer cells as well as monocytic or dendritic innate immune cells. Finally, the VPS34 inhibitor SB02024 sensitized B16-F10 tumor-bearing mice to STING agonist treatment and significantly improved mice survival. These results show that VPS34 inhibition augments the cGAS/STING pathway, leading to greater tumor control through immune-mediated mechanisms. We propose that pharmacological VPS34 inhibition may synergize with emerging therapies targeting the cGAS/STING pathway.

Protein Profiling in Presymptomatic Individuals Separates Myeloperoxidase-Antineutrophil Cytoplasmic Antibody and Proteinase 3-Antineutrophil Cytoplasmic Antibody Vasculitides.

OBJECTIVE: Antineutrophil cytoplasmic antibody (ANCA)-associated vasculitis (AAV) is a chronic relapsing condition with unknown etiology. To gain insight into the molecular processes underlying the disease, we examined biomarkers in blood samples collected prior to symptom onset. METHODS: The National Patient Register and Cause of Death register were searched for AAV-related International Classification of Diseases, Ninth Revision and Tenth Revision codes and linked to the registers from 5 biobanks. Eighty-five AAV patients with samples predating symptom onset of AAV were identified. For each case of AAV, 2 matched controls were included. Proteinase 3 (PR3)-ANCA and myeloperoxidase (MPO)-ANCA expression levels were analyzed using enzyme-linked immunosorbent assays. Using an Olink Inflammation panel, 73 of 92 proteins were included after quality control. Data were replicated in a second cohort of 48 presymptomatic individuals and 96 controls. RESULTS: Of the 20 proteins with the lowest P values in the original cohort, 7 were replicated in the second cohort and 5 proteins were found to be significant between the groups in a meta-analysis. Eleven different pathways were identified in network enrichment analyses and were found to be significant in both cohorts. Stratification of samples obtained ≤5 years before symptom onset showed significant levels of CCL23, vascular endothelial growth factor A, and hepatocyte growth factor, which were also increased at borderline significant levels in the replication cohort (interleukin-6 was found to be significantly increased in the replication cohort). In presymptomatic AAV patients, 6 proteins were associated with MPO-ANCA positivity, and 7 proteins were associated with PR3-ANCA positivity. CONCLUSION: To our knowledge, this is the first study to identify protein markers preceding symptom onset in AAV patients. These findings set the stage for further research into the underlying cellular and molecular mechanisms in the pathogenesis of AAV and the diversification of patients into PR3-ANCA+ and MPO-ANCA+ subphenotypes.

Plasma RNA profiling unveils transcriptional signatures associated with resistance to osimertinib in EGFR T790M positive non-small cell lung cancer patients.

Background: Targeted therapy with tyrosine kinases inhibitors (TKIs) against epidermal growth factor receptor (EGFR) is part of routine clinical practice for EGFR mutant advanced non-small cell lung cancer (NSCLC) patients. These patients eventually develop resistance, frequently accompanied by a gatekeeper mutation, T790M. Osimertinib is a third-generation EGFR TKI displaying potency to the T790M resistance mutation. Here we aimed to analyze if exosomal RNAs, isolated from longitudinally sampled plasma of osimertinib-treated EGFR T790M NSCLC patients, could provide biomarkers of acquired resistance to osimertinib. Methods: Plasma was collected at baseline and progression of disease from 20 patients treated with osimertinib in the multicenter phase II study TKI in Relapsed EGFR-mutated non-small cell lung cancer patients (TREM). Plasma was centrifuged at 16,000 g followed by exosomal RNA extraction using Qiagen exoRNeasy kit. RNA was subjected to transcriptomics analysis with Clariom D. Results: Transcriptome profiling revealed differential expression [log2(fold-change) >0.25, false discovery rate (FDR) P<0.15, and P(interaction) >0.05] of 128 transcripts. We applied network enrichment analysis (NEA) at the pathway level in a large collection of functional gene sets. This overall enrichment analysis revealed alterations in pathways related to EGFR and PI3K as well as to syndecan and glypican pathways (NEA FDR <3×10-10). When applied to the 40 individual, sample-specific gene sets, the NEA detected 16 immune-related gene sets (FDR <0.25, P(interaction) >0.05 and NEA z-score exceeding 3 in at least one sample). Conclusions: Our study demonstrates a potential usability of plasma-derived exosomal RNAs to characterize molecular phenotypes of emerging osimertinib resistance. Furthermore, it highlights the involvement of multiple RNA species in shaping the transcriptome landscape of osimertinib-refractory NSCLC patients.

EviCor: Interactive Web Platform for Exploration of Molecular Features and Response to Anti-cancer Drugs.

Experimental biologists are often left alone with the task to download, process, and analyze big datasets in order to perform correlation or other simpler analyses. To address these issues, we introduce EviCor, a handy toolbox for exploration of data from large public resources such as The Cancer Genome Atlas and The Cancer Cell Line Encyclopedia, complemented with follow-up information on same samples, which couples omics datasets with drug response profiles (https://www.evicor.org/). The data was processed for easy retrieval from the server-side database and includes pre-computed drug-feature correlation tables. Using information from multiple independent sources, the task-oriented web interface presents relations between phenotype, single-molecule, and pathway variables with graphical, statistical, and network analysis tools. Building custom multivariate models is enabled via user-friendly web interface and programmatic access via RESTinterface. Project code is available at https://github.com/aveviort/HyperSet.

Robust derivation of transplantable dopamine neurons from human pluripotent stem cells by timed retinoic acid delivery.

Stem cell therapies for Parkinson's disease (PD) have entered first-in-human clinical trials using a set of technically related methods to produce mesencephalic dopamine (mDA) neurons from human pluripotent stem cells (hPSCs). Here, we outline an approach for high-yield derivation of mDA neurons that principally differs from alternative technologies by utilizing retinoic acid (RA) signaling, instead of WNT and FGF8 signaling, to specify mesencephalic fate. Unlike most morphogen signals, where precise concentration determines cell fate, it is the duration of RA exposure that is the key-parameter for mesencephalic specification. This concentration-insensitive patterning approach provides robustness and reduces the need for protocol-adjustments between hPSC-lines. RA-specified progenitors promptly differentiate into functional mDA neurons in vitro, and successfully engraft and relieve motor deficits after transplantation in a rat PD model. Our study provides a potential alternative route for cell therapy and disease modelling that due to its robustness could be particularly expedient when use of autologous- or immunologically matched cells is considered.

Individualized discovery of rare cancer drivers in global network context.

Late advances in genome sequencing expanded the space of known cancer driver genes several-fold. However, most of this surge was based on computational analysis of somatic mutation frequencies and/or their impact on the protein function. On the contrary, experimental research necessarily accounted for functional context of mutations interacting with other genes and conferring cancer phenotypes. Eventually, just such results become 'hard currency' of cancer biology. The new method, NEAdriver employs knowledge accumulated thus far in the form of global interaction network and functionally annotated pathways in order to recover known and predict novel driver genes. The driver discovery was individualized by accounting for mutations' co-occurrence in each tumour genome - as an alternative to summarizing information over the whole cancer patient cohorts. For each somatic genome change, probabilistic estimates from two lanes of network analysis were combined into joint likelihoods of being a driver. Thus, ability to detect previously unnoticed candidate driver events emerged from combining individual genomic context with network perspective. The procedure was applied to 10 largest cancer cohorts followed by evaluating error rates against previous cancer gene sets. The discovered driver combinations were shown to be informative on cancer outcome. This revealed driver genes with individually sparse mutation patterns that would not be detectable by other computational methods and related to cancer biology domains poorly covered by previous analyses. In particular, recurrent mutations of collagen, laminin, and integrin genes were observed in the adenocarcinoma and glioblastoma cancers. Considering constellation patterns of candidate drivers in individual cancer genomes opens a novel avenue for personalized cancer medicine.

A Shh/Gli-driven three-node timer motif controls temporal identity and fate of neural stem cells.

How time is measured by neural stem cells during temporal neurogenesis has remained unresolved. By combining experiments and computational modeling, we define a Shh/Gli-driven three-node timer underlying the sequential generation of motor neurons (MNs) and serotonergic neurons in the brainstem. The timer is founded on temporal decline of Gli-activator and Gli-repressor activities established through down-regulation of Gli transcription. The circuitry conforms an incoherent feed-forward loop, whereby Gli proteins not only promote expression of Phox2b and thereby MN-fate but also account for a delayed activation of a self-promoting transforming growth factor-ß (Tgfß) node triggering a fate switch by repressing Phox2b. Hysteresis and spatial averaging by diffusion of Tgfß counteract noise and increase temporal accuracy at the population level, providing a functional rationale for the intrinsically programmed activation of extrinsic switch signals in temporal patterning. Our study defines how time is reliably encoded during the sequential specification of neurons.

Protein profiling and network enrichment analysis in individuals before and after the onset of rheumatoid arthritis.

BACKGROUND: Antibodies and upregulated cytokines and chemokines predate the onset of rheumatoid arthritis (RA) symptoms. We aimed to identify the pathways related to the early processes leading to RA development, as well as potential novel biomarkers, using multiple protein analyses. METHODS: A case-control study was conducted within the Biobank of northern Sweden. The plasma samples from 118 pre-symptomatic individuals (207 samples; median predating time 4.1 years), 79 early RA patients, and 74 matched controls were analyzed. The levels of 122 unique proteins with an acknowledged relationship to autoimmunity were analyzed using 153 antibodies and a bead-based multiplex system (FlexMap3D; Luminex Corp.). The data were analyzed using multifactorial linear regression model, random forest, and network enrichment analysis (NEA) based on the 10 most significantly differentially expressed proteins for each two-by-two group comparison, using the MSigDB collection of hallmarks. RESULTS: There was a high agreement between the different statistical methods to identify the most significant proteins. The adipogenesis and interferon alpha response hallmarks differentiated pre-symptomatic individuals from controls. These two hallmarks included proteins involved in innate immunity. Between pre-symptomatic individuals and RA patients, three hallmarks were identified as follows: apical junction, epithelial mesenchymal transition, and TGF-ß signaling, including proteins suggestive of cell interaction, remodulation, and fibrosis. The adipogenesis and heme metabolism hallmarks differentiated RA patients from controls. CONCLUSIONS: We confirm the importance of interferon alpha signaling and lipids in the early phases of RA development. Network enrichment analysis provides a tool for a deeper understanding of molecules involved at different phases of the disease progression.

Prediction of response to anti-cancer drugs becomes robust via network integration of molecular data.

Despite the widening range of high-throughput platforms and exponential growth of generated data volume, the validation of biomarkers discovered from large-scale data remains a challenging field. In order to tackle cancer heterogeneity and comply with the data dimensionality, a number of network and pathway approaches were invented but rarely systematically applied to this task. We propose a new method, called NEAmarker, for finding sensitive and robust biomarkers at the pathway level. scores from network enrichment analysis transform the original space of altered genes into a lower-dimensional space of pathways. These dimensions are then correlated with phenotype variables. The method was first tested using in vitro data from three anti-cancer drug screens and then on clinical data of The Cancer Genome Atlas. It proved superior to the single-gene and alternative enrichment analyses in terms of (1) universal applicability to different data types with a possibility of cross-platform integration, (2) consistency of the discovered correlates between independent drug screens, and (3) ability to explain differential survival of treated patients. Our new screen of anti-cancer compounds validated the performance of multivariate models of drug sensitivity. The previously proposed methods of enrichment analysis could achieve comparable levels of performance in certain tests. However, only our method could discover predictors of both in vitro response and patient survival given administration of the same drug.

Protein profiling of fine-needle aspirates reveals subtype-associated immune signatures and involvement of chemokines in breast cancer.

There are increasing demands for informative cancer biomarkers, accessible via minimally invasive procedures, both for initial diagnostics and for follow-up of personalized cancer therapy, including immunotherapy. Fine-needle aspiration (FNA) biopsy provides ready access to relevant tissue samples; however, the minute amounts of sample require sensitive multiplex molecular analysis to be of clinical biomarker utility. We have applied proximity extension assays (PEA) to analyze 167 proteins in FNA samples from patients with breast cancer (BC; n = 25) and benign lesions (n = 32). We demonstrate that the FNA BC samples could be divided into two main clusters, characterized by differences in expression levels of the estrogen receptor (ER) and the proliferation marker Ki67. This clustering corresponded to some extent to established BC subtypes. Our analysis also revealed several proteins whose expression levels differed between BC and benign lesions (e.g., CA9, GZMB, IL-6, VEGFA, CXCL11, PDL1, and PCD1), as well as several chemokines correlating with ER and Ki67 status (e.g., CCL4, CCL8, CCL20, CXCL8, CXCL9, and CXCL17). Finally, we also identified three signatures that could predict Ki67 status, ER status, and tumor grade, respectively, based on a small subset of proteins, which was dominated by chemokines. To our knowledge, expression profiles of CCL13 in benign lesions and BC have not previously been described but were shown herein to correlate with proliferation (P = 0.00095), suggesting a role in advanced BC. Given the broad functional range of the proteins analyzed, immune-related proteins were overrepresented among the observed alterations. Our pilot study supports the emerging role of chemokines in BC progression. Due to the minimally traumatic sampling and clinically important molecular information for therapeutic decisions, this methodology is promising for future immunoscoring and monitoring of treatment efficacy in BC.

A fine-needle aspiration-based protein signature discriminates benign from malignant breast lesions.

There are increasing demands for informative cancer biomarkers, accessible via minimally invasive procedures, both for initial diagnostics and to follow-up personalized cancer therapy. Fine-needle aspiration (FNA) biopsy provides ready access to relevant tissues; however, the minute sample amounts require sensitive multiplex molecular analysis to achieve clinical utility. We have applied proximity extension assays (PEA) and NanoString (NS) technology for analyses of proteins and of RNA, respectively, in FNA samples. Using samples from patients with breast cancer (BC, n = 25) or benign lesions (n = 33), we demonstrate that these FNA-based molecular analyses (a) can offer high sensitivity and reproducibility, (b) may provide correct diagnosis in shorter time and at a lower cost than current practice, (c) correlate with results from routine analysis (i.e., benchmarking against immunohistochemistry tests for ER, PR, HER2, and Ki67), and (d) may also help identify new markers related to immunotherapy. A specific 11-protein signature, including FGF binding protein 1, decorin, and furin, distinguished all cancer patient samples from all benign lesions in our main cohort and in smaller replication cohort. Due to the minimally traumatic sampling and rich molecular information, this combined proteomics and transcriptomic methodology is promising for diagnostics and evaluation of treatment efficacy in BC.

EviNet: a web platform for network enrichment analysis with flexible definition of gene sets.

The new web resource EviNet provides an easily run interface to network enrichment analysis for exploration of novel, experimentally defined gene sets. The major advantages of this analysis are (i) applicability to any genes found in the global network rather than only to those with pathway/ontology term annotations, (ii) ability to connect genes via different molecular mechanisms rather than within one high-throughput platform, and (iii) statistical power sufficient to detect enrichment of very small sets, down to individual genes. The users' gene sets are either defined prior to upload or derived interactively from an uploaded file by differential expression criteria. The pathways and networks used in the analysis can be chosen from the collection menu. The calculation is typically done within seconds or minutes and the stable URL is provided immediately. The results are presented in both visual (network graphs) and tabular formats using jQuery libraries. Uploaded data and analysis results are kept in separated project directories not accessible by other users. EviNet is available at https://www.evinet.org/.

Expression of scavenger receptor MARCO defines a targetable tumor-associated macrophage subset in non-small cell lung cancer.

Tumor-associated macrophages (TAMs) are attractive targets for immunotherapy. Recently, studies in animal models showed that treatment with an anti-TAM antibody directed against the scavenger receptor MARCO resulted in suppression of tumor growth and metastatic dissemination. Here we investigated the expression of MARCO in relation to other macrophage markers and immune pathways in a non-small cell lung cancer (NSCLC) cohort (n = 352). MARCO, CD68, CD163, MSR1 and programmed death ligand-1 (PD-L1) were analyzed by immunohistochemistry and immunofluorescence, and associations to other immune cells and regulatory pathways were studied in a subset of cases (n = 199) with available RNA-seq data. We observed a large variation in macrophage density between cases and a strong correlation between CD68 and CD163, suggesting that the majority of TAMs present in NSCLC exhibit a protumor phenotype. Correlation to clinical data only showed a weak trend toward worse survival for patients with high macrophage infiltration. Interestingly, MARCO was expressed on a distinct subpopulation of TAMs, which tended to aggregate in close proximity to tumor cell nests. On the transcriptomic level, we found a positive association between MARCO gene expression and general immune response pathways including strong links to immunosuppressive TAMs, T-cell infiltration and immune checkpoint molecules. Indeed, a higher macrophage infiltration was seen in tumors expressing PD-L1, and macrophages residing within tumor cell nests co-expressed MARCO and PD-L1. Thus, MARCO is a potential new immune target for anti-TAM treatment in a subset of NSCLC patients, possibly in combination with available immune checkpoint inhibitors.

Protein Expression in Tonsillar and Base of Tongue Cancer and in Relation to Human Papillomavirus (HPV) and Clinical Outcome.

Human papillomavirus (HPV) is a major etiological factor for tonsillar and the base of tongue cancer (TSCC/BOTSCC). HPV-positive and HPV-negative TSCC/BOTSCC present major differences in mutations, mRNA expression and clinical outcome. Earlier protein studies on TSCC/BOTSCC have mainly analyzed individual proteins. Here, the aim was to compare a larger set of cancer and immune related proteins in HPV-positive and HPV-negative TSCC/BOTSCC in relation to normal tissue, presence of HPV, and clinical outcome. Fresh frozen tissue from 42 HPV-positive and 17 HPV-negative TSCC/BOTSCC, and corresponding normal samples, were analyzed for expression of 167 proteins using two Olink multiplex immunoassays. Major differences in protein expression between TSCC/BOTSCC and normal tissue were identified, especially in chemo- and cytokines. Moreover, 34 proteins, mainly immunoregulatory proteins and chemokines, were differently expressed in HPV-positive vs HPV-negative TSCC/BOTSCC. Several proteins were potentially related to clinical outcome for HPV-positive or HPV-negative tumors. For HPV-positive tumors, these were mostly related to angiogenesis and hypoxia. Correlation with clinical outcome of one of these, VEGFA, was validated by immunohistochemistry. Differences in immune related proteins between HPV-positive and HPV-negative TSCC/BOTSCC reflect the stronger activity of the immune defense in the former. Angiogenesis related proteins might serve as potential targets for therapy in HPV-positive TSCC/BOTSCC.

The regulation of hydroxysteroid 17ß-dehydrogenase type 1 and 2 gene expression in breast cancer cell lines by estradiol, dihydrotestosterone, microRNAs, and genes related to breast cancer.

AIM: To investigate the influence of estrogen, androgen, microRNAs, and genes implicated in breast cancer on the expression of HSD17B1 and HSD17B2. MATERIALS: Breast cancer cell lines ZR-75-1, MCF7, T47D, SK-BR-3, and the immortalized epithelial cell line MCF10A were used. Cells were treated either with estradiol or dihydrotestosterone for 6, 24, 48 hours, or 7 days or treated with miRNAs or siRNAs predicted to influence HSD17B expression Results and discussion. Estradiol treatment decreased HSD17B1 expression and had a time-dependent effect on HSD17B2 expression. This effect was lost in estrogen receptor-α down-regulated or negative cell lines. Dihydrotestosterone treatment increased HSD17B2 expression, with limited effect on HSD17B1 expression. No effect was seen in cells without AR or in combination with the AR inhibitor hydroxyflutamide. The miRNA-17 up-regulated HSD17B1, while miRNA-210 and miRNA-7-5p had up- and down-regulatory effect and miRNA-1304-3p reduced HSD17B1 expression. The miRNA-204-5p, 498, 205-3p and 579-3p reduced HSD17B2 expression. Downregulation of CX3CL1, EPHB6, and TP63 increased HSD17B1 and HSD17B2 expression, while GREB1 downregulation suppressed HSD17B1 and promoted HSD17B2 expression. CONCLUSION: We show that HSD17B1 and HSD17B2 are controlled by estradiol, dihydrotestosterone, and miRNAs, as well as modulated by several breast cancer-related genes, which could have future clinical applications.

Spatially resolved transcriptome profiling in model plant species.

Understanding complex biological systems requires functional characterization of specialized tissue domains. However, existing strategies for generating and analysing high-throughput spatial expression profiles were developed for a limited range of organisms, primarily mammals. Here we present the first available approach to generate and study high-resolution, spatially resolved functional profiles in a broad range of model plant systems. Our process includes high-throughput spatial transcriptome profiling followed by spatial gene and pathway analyses. We first demonstrate the feasibility of the technique by generating spatial transcriptome profiles from model angiosperms and gymnosperms microsections. In Arabidopsis thaliana we use the spatial data to identify differences in expression levels of 141 genes and 189 pathways in eight inflorescence tissue domains. Our combined approach of spatial transcriptomics and functional profiling offers a powerful new strategy that can be applied to a broad range of plant species, and is an approach that will be pivotal to answering fundamental questions in developmental and evolutionary biology.

NEArender: an R package for functional interpretation of 'omics' data via network enrichment analysis.

BACKGROUND: The statistical evaluation of pathway enrichment, i.e. of gene profiles' confluence to the pathway level, allows exploring molecular landscapes using functionally annotated gene sets. However, pathway scores can also be used as predictive features in machine learning. That requires, firstly, increasing statistical power and biological relevance via a network enrichment analysis (NEA) and, secondly, a fast and convenient procedure for rendering the original data into a space of pathway scores. However, previous implementations of NEA involved multiple runs of network randomization and were therefore slow. RESULTS: Here, we present a new R package NEArender which can transform raw 'omics' features of experimental or clinical samples into matrices describing the same samples with many fewer NEA-based pathway scores. This is done via a parametric estimation of the null binomial distribution and is thus much faster and less biased than randomization procedures. Further, we compare estimates from these two alternative procedures and demonstrate that the summarization of individual genes to pathways increases the statistical power compared to both the default differential expression analysis on individual genes and the state-of-the-art gene set enrichment analysis. The package also contains functions for preparing input, modeling null distributions, and evaluating alternative versions of the global network. CONCLUSIONS: Beyond the state-of-the-art exploration of molecular data through pathway enrichment, score matrices produced by NEArender can be used in larger bioinformatics pipelines as input for phenotype modeling, predicting disease outcomes etc. This approach is often more sensitive and robust than using the original data. The package NEArender is complementary to the online NEA tool EviNet ( https://www.evinet.org ) and, unlike of the latter, enables high performance of computations off-line. The R package NEArender version 1.4 is available at CRAN repository https://cran.r-project.org/web/packages/NEArender/.

RhoA knockout fibroblasts lose tumor-inhibitory capacity in vitro and promote tumor growth in vivo.

Fibroblasts are a main player in the tumor-inhibitory microenvironment. Upon tumor initiation and progression, fibroblasts can lose their tumor-inhibitory capacity and promote tumor growth. The molecular mechanisms that underlie this switch have not been defined completely. Previously, we identified four proteins overexpressed in cancer-associated fibroblasts and linked to Rho GTPase signaling. Here, we show that knocking out the Ras homolog family member A (RhoA) gene in normal fibroblasts decreased their tumor-inhibitory capacity, as judged by neighbor suppression in vitro and accompanied by promotion of tumor growth in vivo. This also induced PC3 cancer cell motility and increased colony size in 2D cultures. RhoA knockout in fibroblasts induced vimentin intermediate filament reorganization, accompanied by reduced contractile force and increased stiffness of cells. There was also loss of wide F-actin stress fibers and large focal adhesions. In addition, we observed a significant loss of α-smooth muscle actin, which indicates a difference between RhoA knockout fibroblasts and classic cancer-associated fibroblasts. In 3D collagen matrix, RhoA knockout reduced fibroblast branching and meshwork formation and resulted in more compactly clustered tumor-cell colonies in coculture with PC3 cells, which might boost tumor stem-like properties. Coculturing RhoA knockout fibroblasts and PC3 cells induced expression of proinflammatory genes in both. Inflammatory mediators may induce tumor cell stemness. Network enrichment analysis of transcriptomic changes, however, revealed that the Rho signaling pathway per se was significantly triggered only after coculturing with tumor cells. Taken together, our findings in vivo and in vitro indicate that Rho signaling governs the inhibitory effects by fibroblasts on tumor-cell growth.

Genome-wide identification of Wig-1 mRNA targets by RIP-Seq analysis.

RNA-binding proteins (RBPs) play important roles in the regulation of gene expression through a variety of post-transcriptional mechanisms. The p53-induced RBP Wig-1 (Zmat3) binds RNA through its zinc finger domains and enhances stability of p53 and N-Myc mRNAs and decreases stability of FAS mRNA. To identify novel Wig-1-bound RNAs, we performed RNA-immunoprecipitation followed by high-throughput sequencing (RIP-Seq) in HCT116 and Saos-2 cells. We identified 286 Wig-1-bound mRNAs common between the two cell lines. Sequence analysis revealed that AU-rich elements (AREs) are highly enriched in the 3'UTR of these Wig-1-bound mRNAs. Network enrichment analysis showed that Wig-1 preferentially binds mRNAs involved in cell cycle regulation. Moreover, we identified a 2D Wig-1 binding motif in HIF1A mRNA. Our findings confirm that Wig-1 is an ARE-BP that regulates cell cycle-related processes and provide a novel view of how Wig-1 may bind mRNA through a putative structural motif. We also significantly extend the repertoire of Wig-1 target mRNAs. Since Wig-1 is a transcriptional target of the tumor suppressor p53, these results have implications for our understanding of p53-dependent stress responses and tumor suppression.

Identification of Novel Epigenetic Markers of Prostate Cancer by NotI-Microarray Analysis.

A significant need for reliable and accurate cancer diagnostics and prognosis compels the search for novel biomarkers that would be able to discriminate between indolent and aggressive tumors at the early stages of disease. The aim of this work was identification of potential diagnostic biomarkers for characterization of different types of prostate tumors. NotI-microarrays with 180 clones associated with chromosome 3 genes/loci were applied to determine genetic and epigenetic alterations in 33 prostate tumors. For 88 clones, aberrations were detected in more than 10% of tumors. The major types of alterations were DNA methylation and/or deletions. Frequent methylation of the discovered loci was confirmed by bisulfite sequencing on selective sampling of genes: FGF12, GATA2, and LMCD1. Three genes (BHLHE40, BCL6, and ITGA9) were tested for expression level alterations using qPCR, and downregulation associated with hypermethylation was shown in the majority of tumors. Based on these data, we proposed the set of potential biomarkers for detection of prostate cancer and discrimination between prostate tumors with different malignancy and aggressiveness: BHLHE40, FOXP1, LOC285205, ITGA9, CTDSPL, FGF12, LOC440944/SETD5, VHL, CLCN2, OSBPL10/ZNF860, LMCD1, FAM19A4, CAND2, MAP4, KY, and LRRC58. Moreover, we probabilistically estimated putative functional relations between the genes within each set using the network enrichment analysis.