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/.

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.

The Norway spruce genome sequence and conifer genome evolution.

Conifers have dominated forests for more than 200 million years and are of huge ecological and economic importance. Here we present the draft assembly of the 20-gigabase genome of Norway spruce (Picea abies), the first available for any gymnosperm. The number of well-supported genes (28,354) is similar to the >100 times smaller genome of Arabidopsis thaliana, and there is no evidence of a recent whole-genome duplication in the gymnosperm lineage. Instead, the large genome size seems to result from the slow and steady accumulation of a diverse set of long-terminal repeat transposable elements, possibly owing to the lack of an efficient elimination mechanism. Comparative sequencing of Pinus sylvestris, Abies sibirica, Juniperus communis, Taxus baccata and Gnetum gnemon reveals that the transposable element diversity is shared among extant conifers. Expression of 24-nucleotide small RNAs, previously implicated in transposable element silencing, is tissue-specific and much lower than in other plants. We further identify numerous long (>10,000 base pairs) introns, gene-like fragments, uncharacterized long non-coding RNAs and short RNAs. This opens up new genomic avenues for conifer forestry and breeding.

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.

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/.

Inhibition of tumor cell proliferation and motility by fibroblasts is both contact and soluble factor dependent.

Normal human and murine fibroblasts can inhibit proliferation of tumor cells when cocultured in vitro. The inhibitory capacity varies depending on the donor and the site of origin of the fibroblast. We showed previously that effective inhibition requires formation of a morphologically intact fibroblast monolayer before seeding of the tumor cells. Here we show that inhibition is extended to motility of tumor cells and we dissect the factors responsible for these inhibitory functions. We find that inhibition is due to two different sets of molecules: (i) the extracellular matrix (ECM) and other surface proteins of the fibroblasts, which are responsible for contact-dependent inhibition of tumor cell proliferation; and (ii) soluble factors secreted by fibroblasts when confronted with tumor cells (confronted conditioned media, CCM) contribute to inhibition of tumor cell proliferation and motility. However, conditioned media (CM) obtained from fibroblasts alone (nonconfronted conditioned media, NCM) did not inhibit tumor cell proliferation and motility. In addition, quantitative PCR (Q-PCR) data show up-regulation of proinflammatory genes. Moreover, comparison of CCM and NCM with an antibody array for 507 different soluble human proteins revealed differential expression of growth differentiation factor 15, dickkopf-related protein 1, endothelial-monocyte-activating polypeptide II, ectodysplasin A2, Galectin-3, chemokine (C-X-C motif) ligand 2, Nidogen1, urokinase, and matrix metalloproteinase 3.

Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis.

BACKGROUND: In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis. RESULTS: We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership. CONCLUSIONS: The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

Efficient de novo assembly of large and complex genomes by massively parallel sequencing of Fosmid pools.

BACKGROUND: Sampling genomes with Fosmid vectors and sequencing of pooled Fosmid libraries on the Illumina platform for massive parallel sequencing is a novel and promising approach to optimizing the trade-off between sequencing costs and assembly quality. RESULTS: In order to sequence the genome of Norway spruce, which is of great size and complexity, we developed and applied a new technology based on the massive production, sequencing, and assembly of Fosmid pools (FP). The spruce chromosomes were sampled with ~40,000 bp Fosmid inserts to obtain around two-fold genome coverage, in parallel with traditional whole genome shotgun sequencing (WGS) of haploid and diploid genomes. Compared to the WGS results, the contiguity and quality of the FP assemblies were high, and they allowed us to fill WGS gaps resulting from repeats, low coverage, and allelic differences. The FP contig sets were further merged with WGS data using a novel software package GAM-NGS. CONCLUSIONS: By exploiting FP technology, the first published assembly of a conifer genome was sequenced entirely with massively parallel sequencing. Here we provide a comprehensive report on the different features of the approach and the optimization of the process.We have made public the input data (FASTQ format) for the set of pools used in this study:ftp://congenie.org/congenie/Nystedt_2013/Assembly/ProcessedData/FosmidPools/.(alternatively accessible via http://congenie.org/downloads).The software used for running the assembly process is available at http://research.scilifelab.se/andrej_alexeyenko/downloads/fpools/.

Comparative interactomics with Funcoup 2.0.

FunCoup (http://FunCoup.sbc.su.se) is a database that maintains and visualizes global gene/protein networks of functional coupling that have been constructed by Bayesian integration of diverse high-throughput data. FunCoup achieves high coverage by orthology-based integration of data sources from different model organisms and from different platforms. We here present release 2.0 in which the data sources have been updated and the methodology has been refined. It contains a new data type Genetic Interaction, and three new species: chicken, dog and zebra fish. As FunCoup extensively transfers functional coupling information between species, the new input datasets have considerably improved both coverage and quality of the networks. The number of high-confidence network links has increased dramatically. For instance, the human network has more than eight times as many links above confidence 0.5 as the previous release. FunCoup provides facilities for analysing the conservation of subnetworks in multiple species. We here explain how to do comparative interactomics on the FunCoup website.

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.

Network enrichment analysis: extension of gene-set enrichment analysis to gene networks.

BACKGROUND: Gene-set enrichment analyses (GEA or GSEA) are commonly used for biological characterization of an experimental gene-set. This is done by finding known functional categories, such as pathways or Gene Ontology terms, that are over-represented in the experimental set; the assessment is based on an overlap statistic. Rich biological information in terms of gene interaction network is now widely available, but this topological information is not used by GEA, so there is a need for methods that exploit this type of information in high-throughput data analysis. RESULTS: We developed a method of network enrichment analysis (NEA) that extends the overlap statistic in GEA to network links between genes in the experimental set and those in the functional categories. For the crucial step in statistical inference, we developed a fast network randomization algorithm in order to obtain the distribution of any network statistic under the null hypothesis of no association between an experimental gene-set and a functional category. We illustrate the NEA method using gene and protein expression data from a lung cancer study. CONCLUSIONS: The results indicate that the NEA method is more powerful than the traditional GEA, primarily because the relationships between gene sets were more strongly captured by network connectivity rather than by simple overlaps.

Analysis of lipid pathway genes indicates association of sequence variation near SREBF1/TOM1L2/ATPAF2 with dementia risk.

We conducted dense linkage disequilibrium (LD) mapping of a series of 25 genes putatively involved in lipid metabolism in 1567 dementia cases [including 1270 with Alzheimer disease (AD)] and 2203 Swedish controls. Across a total of 448 tested genetic markers, the strongest evidence of association was as anticipated for APOE (rs429358 at P approximately 10(-72)) followed by a previously reported association of ABCA1 (rs2230805 at P approximately 10(-8)). In the present study, we report two additional markers near the SREBF1 locus on chromosome 17p that were also significant after multiple testing correction (best P = 3.1 x 10(-6) for marker rs3183702). There was no convincing evidence of association for remaining genes, including candidates highlighted from recent genome-wide association studies of plasma lipids (CELSR2/PSRC1/SORT1, MLXIPL, PCSK9, GALNT2 and GCKR). The associated markers near SREBF1 reside in a large LD block, extending more than 400 kb across seven candidate genes. Secondary analyses of gene expression levels of candidates spanning the LD region together with an investigation of gene network context highlighted two possible susceptibility genes including ATPAF2 and TOM1L2. Several markers in strong LD (r(2) > 0.7) with rs3183702 were found to be significantly associated with AD risk in recent genome-wide association studies with similar effect sizes, providing independent support of the current findings.

Network analysis of functional genomics data: application to avian sex-biased gene expression.

Gene expression analysis is often used to investigate the molecular and functional underpinnings of a phenotype. However, differential expression of individual genes is limited in that it does not consider how the genes interact with each other in networks. To address this shortcoming we propose a number of network-based analyses that give additional functional insights into the studied process. These were applied to a dataset of sex-specific gene expression in the chicken gonad and brain at different developmental stages. We first constructed a global chicken interaction network. Combining the network with the expression data showed that most sex-biased genes tend to have lower network connectivity, that is, act within local network environments, although some interesting exceptions were found. Genes of the same sex bias were generally more strongly connected with each other than expected. We further studied the fates of duplicated sex-biased genes and found that there is a significant trend to keep the same pattern of sex bias after duplication. We also identified sex-biased modules in the network, which reveal pathways or complexes involved in sex-specific processes. Altogether, this work integrates evolutionary genomics with systems biology in a novel way, offering new insights into the modular nature of sex-biased genes.

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.

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.

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.

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.

Genome-wide pathway analysis implicates intracellular transmembrane protein transport in Alzheimer disease.

We developed and implemented software for the analysis of genome-wide association studies in the context of biological pathway enrichment and have here applied our algorithm to the study of Alzheimer disease (AD). Using genome-wide association data in a large French population, we observed a highly significant enrichment of genes involved in intracellular protein transmembrane transport, including several mitochondrial proteins and nucleoporins. An intriguing aspect of these findings is the implication that TOMM40, the channel-forming subunit of the translocase of the mitochondrial outer membrane complex, and a gene generally considered to be indiscernible from APOE because of linkage disequilibrium, may itself contribute to Alzheimer pathology. Results provide an indication that protein trafficking, in particular across the nuclear and mitochondrial membranes, may contribute to risk for AD.