Disruption of the gene C12orf35 leads to increased productivities in recombinant CHO cell lines.

Recently, we reported that the loss of a telomeric region of chromosome 8 in Chinese Hamster Ovary (CHO) cells correlates with higher recombinant productivities. New cell lines lacking this region, called CHO-C8DEL, showed several advantages during cell line generation and for the production of recombinant proteins (Ritter et al., 2016, Biotechnol Bioeng). Here, we performed knock-down and knock-out experiments of genes located within this telomeric region of chromosome 8 to identify the genes causing the observed phenotypes of CHO-C8DEL cell lines. We present evidence that loss or reduced expression of the gene C12orf35 is responsible for higher productivities and shorter recovery times during selection pressure. These effects are mediated by increased levels of mRNA of the exogenes heavy chain (HC) and light chain (LC) as well as dihydrofolate reductase (DHFR) and neomycin phosphotransferase (Neo) during the stable expression of antibodies. Biotechnol. Bioeng. 2016;113: 2433-2442. © 2016 Wiley Periodicals, Inc.

Systematic in vitro analysis of therapy resistance in glioblastoma cell lines by integration of clonogenic survival data with multi-level molecular data.

Despite intensive basic scientific, translational, and clinical efforts in the last decades, glioblastoma remains a devastating disease with a highly dismal prognosis. Apart from the implementation of temozolomide into the clinical routine, novel treatment approaches have largely failed, emphasizing the need for systematic examination of glioblastoma therapy resistance in order to identify major drivers and thus, potential vulnerabilities for therapeutic intervention. Recently, we provided proof-of-concept for the systematic identification of combined modality radiochemotherapy treatment vulnerabilities via integration of clonogenic survival data upon radio(chemo)therapy with low-density transcriptomic profiling data in a panel of established human glioblastoma cell lines. Here, we expand this approach to multiple molecular levels, including genomic copy number, spectral karyotyping, DNA methylation, and transcriptome data. Correlation of transcriptome data with inherent therapy resistance on the single gene level yielded several candidates that were so far underappreciated in this context and for which clinically approved drugs are readily available, such as the androgen receptor (AR). Gene set enrichment analyses confirmed these results, and identified additional gene sets, including reactive oxygen species detoxification, mammalian target of rapamycin complex 1 (MTORC1) signaling, and ferroptosis/autophagy-related regulatory circuits to be associated with inherent therapy resistance in glioblastoma cells. To identify pharmacologically accessible genes within those gene sets, leading edge analyses were performed yielding candidates with functions in thioredoxin/peroxiredoxin metabolism, glutathione synthesis, chaperoning of proteins, prolyl hydroxylation, proteasome function, and DNA synthesis/repair. Our study thus confirms previously nominated targets for mechanism-based multi-modal glioblastoma therapy, provides proof-of-concept for this workflow of multi-level data integration, and identifies novel candidates for which pharmacological inhibitors are readily available and whose targeting in combination with radio(chemo)therapy deserves further examination. In addition, our study also reveals that the presented workflow requires mRNA expression data, rather than genomic copy number or DNA methylation data, since no stringent correlation between these data levels could be observed. Finally, the data sets generated in the present study, including functional and multi-level molecular data of commonly used glioblastoma cell lines, represent a valuable toolbox for other researchers in the field of glioblastoma therapy resistance.

Improved risk stratification in younger IDH wild-type glioblastoma patients by combining a 4-miRNA signature with MGMT promoter methylation status.

BACKGROUND: The potential benefit of risk stratification using a 4-miRNA signature in combination with MGMT promoter methylation in IDH1/2 wild-type glioblastoma patients was assessed. METHODS: Primary tumors from 102 patients with comparable treatment from the LMU Munich (n = 37), the University Hospital Düsseldorf (n = 33), and The Cancer Genome Atlas (n = 32) were included. Risk groups were built using expressions of hsa-let-7a-5p, hsa-let-7b-5p, hsa-miR-615-5p, and hsa-miR-125a-5p to assess prognostic performance in overall survival (OS). MGMT promoter methylation and age were considered as cofactors. Integrated miRNA, DNA methylome, and transcriptome analysis were used to explore the functional impact of signature miRNAs. RESULTS: The 4-miRNA signature defined high-risk (n = 46, median OS: 15.8 months) and low-risk patients (n = 56, median OS: 20.7 months; univariable Cox proportional hazard analysis: hazard ratio [HR]: 1.8, 95% confidence interval [CI]: 1.14-2.83, P = .01). The multivariable Cox proportional hazard model including the 4-miRNA signature (P = .161), MGMT promoter methylation (P < .001), and age (P = .034) significantly predicted OS (Log-rank P < .0001). Likewise to clinical routine, analysis was performed for younger (≤60 years, n = 50, median OS: 20.2 months) and older patients (>60 years, n = 52, median OS: 15.8) separately. In younger patients, the 4-miRNA signature had prognostic value (HR: 1.92, 95% CI: 0.93-3.93, P = .076). Particularly, younger, MGMT methylated, 4-miRNA signature low-risk patients (n = 18, median OS: 37.4 months) showed significantly improved survival, compared to other younger patients (n = 32, OS 18.5 months; HR: 0.33, 95% CI: 0.15-0.71, P = .003). Integrated data analysis revealed 4-miRNA signature-associated genes and pathways. CONCLUSION: The prognostic 4-miRNA signature in combination with MGMT promoter methylation improved risk stratification with the potential for therapeutic substratification, especially of younger patients.

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.