A multi-scale map of cell structure fusing protein images and interactions.

The cell is a multi-scale structure with modular organization across at least four orders of magnitude1. Two central approaches for mapping this structure-protein fluorescent imaging and protein biophysical association-each generate extensive datasets, but of distinct qualities and resolutions that are typically treated separately2,3. Here we integrate immunofluorescence images in the Human Protein Atlas4 with affinity purifications in BioPlex5 to create a unified hierarchical map of human cell architecture. Integration is achieved by configuring each approach as a general measure of protein distance, then calibrating the two measures using machine learning. The map, known as the multi-scale integrated cell (MuSIC 1.0), resolves 69 subcellular systems, of which approximately half are to our knowledge undocumented. Accordingly, we perform 134 additional affinity purifications and validate subunit associations for the majority of systems. The map reveals a pre-ribosomal RNA processing assembly and accessory factors, which we show govern rRNA maturation, and functional roles for SRRM1 and FAM120C in chromatin and RPS3A in splicing. By integration across scales, MuSIC increases the resolution of imaging while giving protein interactions a spatial dimension, paving the way to incorporate diverse types of data in proteome-wide cell maps.

Copy number aberrations from Affymetrix SNP 6.0 genotyping data-how accurate are commonly used prediction approaches?

Copy number aberrations (CNAs) are known to strongly affect oncogenes and tumour suppressor genes. Given the critical role CNAs play in cancer research, it is essential to accurately identify CNAs from tumour genomes. One particular challenge in finding CNAs is the effect of confounding variables. To address this issue, we assessed how commonly used CNA identification algorithms perform on SNP 6.0 genotyping data in the presence of confounding variables. We simulated realistic synthetic data with varying levels of three confounding variables-the tumour purity, the length of a copy number region and the CNA burden (the percentage of CNAs present in a profiled genome)-and evaluated the performance of OncoSNP, ASCAT, GenoCNA, GISTIC and CGHcall. Furthermore, we implemented and assessed CGHcall*, an adjusted version of CGHcall accounting for high CNA burden. Our analysis on synthetic data indicates that tumour purity and the CNA burden strongly influence the performance of all the algorithms. No algorithm can correctly find lost and gained genomic regions across all tumour purities. The length of CNA regions influenced the performance of ASCAT, CGHcall and GISTIC. OncoSNP, GenoCNA and CGHcall* showed little sensitivity. Overall, CGHcall* and OncoSNP showed reasonable performance, particularly in samples with high tumour purity. Our analysis on the HapMap data revealed a good overlap between CGHcall, CGHcall* and GenoCNA results and experimentally validated data. Our exploratory analysis on the TCGA HNSCC data revealed plausible results of CGHcall, CGHcall* and GISTIC in consensus HNSCC CNA regions. Code is available at https://github.com/adspit/PASCAL.

A prognostic mRNA expression signature of four 16q24.3 genes in radio(chemo)therapy-treated head and neck squamous cell carcinoma (HNSCC).

Previously, we have shown that copy number gain of the chromosomal band 16q24.3 is associated with impaired clinical outcome of radiotherapy-treated head and neck squamous cell carcinoma (HNSCC) patients. We set out to identify a prognostic mRNA signature from genes located on 16q24.3 in radio(chemo)therapy-treated HNSCC patients of the TCGA (The Cancer Genome Atlas, n = 99) cohort. We applied stepwise forward selection using expression data of 41 16q24.3 genes. The resulting optimal Cox-proportional hazards regression model included the genes APRT, CENPBD1, CHMP1A, and GALNS. Afterward, the prognostic value of the classifier was confirmed in an independent cohort of HNSCC patients treated by adjuvant radio(chemo)therapy (LMU-KKG cohort). The signature significantly differentiated high- and low-risk patients with regard to overall survival (HR = 2.01, 95% CI 1.10-3.70; P = 0.02125), recurrence-free survival (HR = 1.84, 95% CI 1.01-3.34; P = 0.04206), and locoregional recurrence-free survival (HR = 1.87, 95% CI 1.03-3.40; P = 0.03641). The functional impact of the four signature genes was investigated after reconstruction of a gene association network from transcriptome data of the TCGA HNSCC cohort using a partial correlation approach. Subsequent pathway enrichment analysis of the network neighborhood (first and second) of the signature genes suggests involvement of HNSCC-associated signaling pathways such as apoptosis, cell cycle, cell adhesion, EGFR, JAK-STAT, and mTOR. Furthermore, a detailed analysis of the first neighborhood revealed a cluster of co-expressed genes located on chromosome 16q, substantiating the impact of 16q24.3 alterations in poor clinical outcome of HNSCC. The reported gene expression signature represents a prognostic marker in HNSCC patients following postoperative radio(chemo)therapy.

Epigenetically Regulated Chromosome 14q32 miRNA Cluster Induces Metastasis and Predicts Poor Prognosis in Lung Adenocarcinoma Patients.

Most lung cancer deaths are related to metastases, which indicates the necessity of detecting and inhibiting tumor cell dissemination. Here, we aimed to identify miRNAs involved in metastasis of lung adenocarcinoma as prognostic biomarkers and therapeutic targets. To that end, lymph node metastasis-associated miRNAs were identified in The Cancer Genome Atlas lung adenocarcinoma patient cohort (sequencing data; n = 449) and subsequently validated by qRT-PCR in an independent clinical cohort (n = 108). Overexpression of miRNAs located on chromosome 14q32 was associated with metastasis in lung adenocarcinoma patients. Importantly, Kaplan-Meier analysis and log-rank test revealed that higher expression levels of individual 14q32 miRNAs (mir-539, mir-323b, and mir-487a) associated with worse disease-free survival of never-smoker patients. Epigenetic analysis including DNA methylation microarray data and bisulfite sequencing validation demonstrated that the induction of 14q32 cluster correlated with genomic hypomethylation of the 14q32 locus. CRISPR activation technology, applied for the first time to functionally study the increase of clustered miRNA levels in a coordinated manner, showed that simultaneous overexpression of 14q32 miRNAs promoted tumor cell migratory and invasive properties. Analysis of individual miRNAs by mimic transfection further illustrated that miR-323b-3p, miR-487a-3p, and miR-539-5p significantly contributed to the invasive phenotype through the indirect regulation of different target genes. In conclusion, overexpression of 14q32 miRNAs, associated with the respective genomic hypomethylation, promotes metastasis and correlates with poor patient prognosis in lung adenocarcinoma.Implications: This study points to chromosome 14q32 miRNAs as promising targets to inhibit tumor cell dissemination and to predict patient prognosis in lung adenocarcinoma. Mol Cancer Res; 16(3); 390-402. ©2018 AACR.

Expression of miRNA-26b-5p and its target TRPS1 is associated with radiation exposure in post-Chernobyl breast cancer.

Ionizing radiation is a well-recognized risk factor for the development of breast cancer. However, it is unknown whether radiation-specific molecular oncogenic mechanisms exist. We investigated post-Chernobyl breast cancers from radiation-exposed female clean-up workers and nonexposed controls for molecular changes. Radiation-associated alterations identified in the discovery cohort (n = 38) were subsequently validated in a second cohort (n = 39). Increased expression of hsa-miR-26b-5p was associated with radiation exposure in both of the cohorts. Moreover, downregulation of the TRPS1 protein, which is a transcriptional target of hsa-miR-26b-5p, was associated with radiation exposure. As TRPS1 overexpression is common in sporadic breast cancer, its observed downregulation in radiation-associated breast cancer warrants clarification of the specific functional role of TRPS1 in the radiation context. For this purpose, the impact of TRPS1 on the transcriptome was characterized in two radiation-transformed breast cell culture models after siRNA-knockdown. Deregulated genes upon TRPS1 knockdown were associated with DNA-repair, cell cycle, mitosis, cell migration, angiogenesis and EMT pathways. Furthermore, we identified the interaction partners of TRPS1 from the transcriptomic correlation networks derived from gene expression data on radiation-transformed breast cell culture models and sporadic breast cancer tissues provided by the TCGA database. The genes correlating with TRPS1 in the radiation-transformed breast cell lines were primarily linked to DNA damage response and chromosome segregation, while the transcriptional interaction partners in the sporadic breast cancers were mostly associated with apoptosis. Thus, upregulation of hsa-miR-26b-5p and downregulation of TRPS1 in radiation-associated breast cancer tissue samples suggests these molecules representing radiation markers in breast cancer.

A 4-miRNA signature predicts the therapeutic outcome of glioblastoma.

Multimodal therapy of glioblastoma (GBM) reveals inter-individual variability in terms of treatment outcome. Here, we examined whether a miRNA signature can be defined for the a priori identification of patients with particularly poor prognosis.FFPE sections from 36 GBM patients along with overall survival follow-up were collected retrospectively and subjected to miRNA signature identification from microarray data. A risk score based on the expression of the signature miRNAs and cox-proportional hazard coefficients was calculated for each patient followed by validation in a matched GBM subset of TCGA. Genes potentially regulated by the signature miRNAs were identified by a correlation approach followed by pathway analysis.A prognostic 4-miRNA signature, independent of MGMT promoter methylation, age, and sex, was identified and a risk score was assigned to each patient that allowed defining two groups significantly differing in prognosis (p-value: 0.0001, median survival: 10.6 months and 15.1 months, hazard ratio = 3.8). The signature was technically validated by qRT-PCR and independently validated in an age- and sex-matched subset of standard-of-care treated patients of the TCGA GBM cohort (n=58). Pathway analysis suggested tumorigenesis-associated processes such as immune response, extracellular matrix organization, axon guidance, signalling by NGF, GPCR and Wnt. Here, we describe the identification and independent validation of a 4-miRNA signature that allows stratification of GBM patients into different prognostic groups in combination with one defined threshold and set of coefficients that could be utilized as diagnostic tool to identify GBM patients for improved and/or alternative treatment approaches.

MicroRNA-Target Network Inference and Local Network Enrichment Analysis Identify Two microRNA Clusters with Distinct Functions in Head and Neck Squamous Cell Carcinoma.

MicroRNAs represent ~22 nt long endogenous small RNA molecules that have been experimentally shown to regulate gene expression post-transcriptionally. One main interest in miRNA research is the investigation of their functional roles, which can typically be accomplished by identification of mi-/mRNA interactions and functional annotation of target gene sets. We here present a novel method "miRlastic", which infers miRNA-target interactions using transcriptomic data as well as prior knowledge and performs functional annotation of target genes by exploiting the local structure of the inferred network. For the network inference, we applied linear regression modeling with elastic net regularization on matched microRNA and messenger RNA expression profiling data to perform feature selection on prior knowledge from sequence-based target prediction resources. The novelty of miRlastic inference originates in predicting data-driven intra-transcriptome regulatory relationships through feature selection. With synthetic data, we showed that miRlastic outperformed commonly used methods and was suitable even for low sample sizes. To gain insight into the functional role of miRNAs and to determine joint functional properties of miRNA clusters, we introduced a local enrichment analysis procedure. The principle of this procedure lies in identifying regions of high functional similarity by evaluating the shortest paths between genes in the network. We can finally assign functional roles to the miRNAs by taking their regulatory relationships into account. We thoroughly evaluated miRlastic on a cohort of head and neck cancer (HNSCC) patients provided by The Cancer Genome Atlas. We inferred an mi-/mRNA regulatory network for human papilloma virus (HPV)-associated miRNAs in HNSCC. The resulting network best enriched for experimentally validated miRNA-target interaction, when compared to common methods. Finally, the local enrichment step identified two functional clusters of miRNAs that were predicted to mediate HPV-associated dysregulation in HNSCC. Our novel approach was able to characterize distinct pathway regulations from matched miRNA and mRNA data. An R package of miRlastic was made available through: http://icb.helmholtz-muenchen.de/mirlastic.

Integrative analysis of the microRNA-mRNA response to radiochemotherapy in primary head and neck squamous cell carcinoma cells.

BACKGROUND: Head and neck squamous cell carcinoma (HNSCC) is a very heterogeneous disease resulting in huge differences in the treatment response. New individualized therapy strategies including molecular targeting might help to improve treatment success. In order to identify potential targets, we developed a HNSCC radiochemotherapy cell culture model of primary HNSCC cells derived from two different patients (HN1957 and HN2092) and applied an integrative microRNA (miRNA) and mRNA analysis in order to gain information on the biological networks and processes of the cellular therapy response. We further identified potential target genes of four therapy-responsive miRNAs detected previously in the circulation of HNSCC patients by pathway enrichment analysis. RESULTS: The two primary cell cultures differ in global copy number alterations and P53 mutational status, thus reflecting heterogeneity of HNSCC. However, they also share many copy number alterations and chromosomal rearrangements as well as deregulated therapy-responsive miRNAs and mRNAs. Accordingly, six common therapy-responsive pathways (direct P53 effectors, apoptotic execution phase, DNA damage/telomere stress induced senescence, cholesterol biosynthesis, unfolded protein response, dissolution of fibrin clot) were identified in both cell cultures based on deregulated mRNAs. However, inflammatory pathways represented an important part of the treatment response only in HN1957, pointing to differences in the treatment responses of the two primary cultures. Focused analysis of target genes of four therapy-responsive circulating miRNAs, identified in a previous study on HNSCC patients, revealed a major impact on the pathways direct P53 effectors, the E2F transcription factor network and pathways in cancer (mainly represented by the PTEN/AKT signaling pathway). CONCLUSIONS: The integrative analysis combining miRNA expression, mRNA expression and the related cellular pathways revealed that the majority of radiochemotherapy-responsive pathways in primary HNSCC cells are related to cell cycle, proliferation, cell death and stress response (including inflammation). Despite the heterogeneity of HNSCC, the two primary cell cultures exhibited strong similarities in the treatment response. The findings of our study suggest potential therapeutic targets in the E2F transcription factor network and the PTEN/AKT signaling pathway.

Changes in circulating microRNAs after radiochemotherapy in head and neck cancer patients.

INTRODUCTION: Circulating microRNAs (miRNAs) are easily accessible and have already proven to be useful as prognostic markers in cancer patients. However, their origin and function in the circulation is still under discussion. In the present study we analyzed changes in the miRNAs in blood plasma of head and neck squamous cell carcinoma (HNSCC) patients in response to radiochemotherapy and compared them to the changes in a cell culture model of primary HNSCC cells undergoing simulated anti-cancer therapy. MATERIALS AND METHODS: MiRNA-profiles were analyzed by qRT-PCR arrays in paired blood plasma samples of HNSCC patients before therapy and after two days of treatment. Candidate miRNAs were validated by single qRT-PCR assays. An in vitro radiochemotherapy model using primary HNSCC cell cultures was established to test the possible tumor origin of the circulating miRNAs. Microarray analysis was performed on primary HNSCC cell cultures followed by validation of deregulated miRNAs via qRT-PCR. RESULTS: Unsupervised clustering of the expression profiles using the six most regulated miRNAs (miR-425-5p, miR-21-5p, miR-106b-5p, miR-590-5p, miR-574-3p, miR-885-3p) significantly (p = 0.012) separated plasma samples collected prior to treatment from plasma samples collected after two days of radiochemotherapy. MiRNA profiling of primary HNSCC cell cultures treated in vitro with radiochemotherapy revealed differentially expressed miRNAs that were also observed to be therapy-responsive in blood plasma of the patients (miR-425-5p, miR-21-5p, miR-106b-5p, miR-93-5p) and are therefore likely to stem from the tumor. Of these candidate marker miRNAs we were able to validate by qRT-PCR a deregulation of eight plasma miRNAs as well as miR-425-5p and miR-93-5p in primary HNSCC cultures after radiochemotherapy. CONCLUSION: Changes in the abundance of circulating miRNAs during radiochemotherapy reflect the therapy response of primary HNSCC cells after an in vitro treatment. Therefore, the responsive miRNAs (miR-425-5p, miR-93-5p) may represent novel biomarkers for therapy monitoring. The prognostic value of this exciting observation requires confirmation using an independent patient cohort that includes clinical follow-up data.