Marianna Grinberg

Altered stress-induced regulation of genes in monocytes in adults with a history of childhood adversity

Exposure to serious or traumatic events early in life can lead to persistent alterations in physiological stress response systems, including enhanced cross talk between the neuroendocrine and immune system. These programming effects may be mechanistically involved in mediating the effects of adverse childhood experience on disease risk in adulthood. We investigated hormonal and genome-wide mRNA expression responses in monocytes to acute stress exposure, in a sample of healthy adults (n=30) with a history of early childhood adversity, and a control group (n=30) without trauma experience. The early adversity group showed altered hypothalamus-pituitary-adrenal axis responses to stress, evidenced by lower ACTH and cortisol responses. Analyses of gene expression patterns showed that stress-responsive transcripts were enriched for genes involved in cytokine activity, cytokine–cytokine receptor interaction, chemokine activity, and G-protein coupled receptor binding. Differences between groups in stress-induced regulation of gene transcription were observed for genes involved in steroid binding, hormone activity, and G-protein coupled receptor binding. Transcription factor binding motif analysis showed an increased activity of pro-inflammatory upstream signaling in the early adversity group. We also identified transcripts that were differentially correlated with stress-induced cortisol increases between the groups, enriched for genes involved in cytokine–cytokine receptor interaction and glutamate receptor signaling. We suggest that childhood adversity leads to persistent alterations in transcriptional control of stress-responsive pathways, which—when chronically or repeatedly activated—might predispose individuals to stress-related psychopathology.

Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration

The in vitro test battery of the European research consortium ESNATS (‘novel stem cell-based test systems’) has been used to screen for potential human developmental toxicants. As part of this effort, the migration of neural crest (MINC) assay has been used to evaluate chemical effects on neural crest function. It identified some drug-like compounds in addition to known environmental toxicants. The hits included the HSP90 inhibitor geldanamycin, the chemotherapeutic arsenic trioxide, the flame-retardant PBDE-99, the pesticide triadimefon and the histone deacetylase inhibitors valproic acid and trichostatin A. Transcriptome changes triggered by these substances in human neural crest cells were recorded and analysed here to answer three questions: (1) can toxicants be individually identified based on their transcript profile; (2) how can the toxicity pattern reflected by transcript changes be compacted/dimensionality-reduced for practical regulatory use; (3) how can a reduced set of biomarkers be selected for large-scale follow-up? Transcript profiling allowed clear separation of different toxicants and the identification of toxicant types in a blinded test study. We also developed a diagrammatic system to visualize and compare toxicity patterns of a group of chemicals by giving a quantitative overview of altered superordinate biological processes (e.g. activation of KEGG pathways or overrepresentation of gene ontology terms). The transcript data were mined for potential markers of toxicity, and 39 transcripts were selected to either indicate general developmental toxicity or distinguish compounds with different modes-of-action in read-across. In summary, we found inclusion of transcriptome data to largely increase the information from the MINC phenotypic test.

Prognostic Influence of Pre-Operative C-Reactive Protein in Node-Negative Breast Cancer Patients

The importance of inflammation is increasingly noticed in cancer. The aim of this study was to analyze the prognostic influence of pre-operative serum C-reactive protein (CRP) in a cohort of 148 lymph node-negative breast cancer patients. The prognostic significance of CRP level for disease-free survival (DFS), metastasis-free survival (MFS) and overall survival (OS) was evaluated using univariate and multivariate Cox regression, also including information on age at diagnosis, tumor size, tumor grade, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status, proliferation index (Ki67) and molecular subtype, as well as an assessment of the presence of necrosis and inflammation in the tumor tissue. Univariate analysis showed that CRP, as a continuous variable, was significantly associated with DFS (P = 0.002, hazard ratio [HR]  = 1.04, 95% confidence interval [CI]  = 1.02–1.07) and OS (P = 0.036, HR  = 1.03, 95% CI  = 1.00–1.06), whereas a trend was observed for MFS (P = 0.111). In the multivariate analysis, CRP retained its significance for DFS (P = 0.033, HR  = 1.01, 95% CI  = 1.00–1.07) as well as OS (P = 0.023, HR  = 1.03, 95% CI  = 1.00–1.06), independent of established prognostic factors. Furthermore, large-scale gene expression analysis by Affymetrix HG-U133A arrays was performed for 72 (48.6%) patients. The correlations between serum CRP and gene expression levels in the corresponding carcinoma of the breast were assessed using Spearmans rank correlation, controlled for false-discovery rate. No significant correlation was observed between CRP level and gene expression indicative of an ongoing local inflammatory process. In summary, pre-operatively elevated CRP levels at the time of diagnosis were associated with shorter DFS and OS independent of established prognostic factors in node-negative breast cancer, supporting a possible link between inflammation and prognosis in breast cancer.

Stem Cell-Derived Immature Human Dorsal Root Ganglia Neurons to Identify Peripheral Neurotoxicants

Safety sciences and the identification of chemical hazards have been seen as one of the most immediate practical applications of human pluripotent stem cell technology. Protocols for the generation of many desirable human cell types have been developed, but optimization of neuronal models for toxicological use has been astonishingly slow, and the wide, clinically important field of peripheral neurotoxicity is still largely unexplored. A two‐step protocol to generate large lots of identical peripheral human neuronal precursors was characterized and adapted to the measurement of peripheral neurotoxicity. High content imaging allowed an unbiased assessment of cell morphology and viability. The computational quantification of neurite growth as a functional parameter highly sensitive to disturbances by toxicants was used as an endpoint reflecting specific neurotoxicity. The differentiation of cells toward dorsal root ganglia neurons was tracked in relation to a large background data set based on gene expression microarrays. On this basis, a peripheral neurotoxicity (PeriTox) test was developed as a first toxicological assay that harnesses the potential of human pluripotent stem cells to generate cell types/tissues that are not otherwise available for the prediction of human systemic organ toxicity. Testing of more than 30 chemicals showed that human neurotoxicants and neurite growth enhancers were correctly identified. Various classes of chemotherapeutic agents causing human peripheral neuropathies were identified, and they were missed when tested on human central neurons. The PeriTox test we established shows the potential of human stem cells for clinically relevant safety testing of drugs in use and of new emerging candidates.

Prognostic impact of COX-2 in non-small cell lung cancer : a comprehensive compartment-specific evaluation of tumor and stromal cell expression

Cyclooxygenase-2 (COX-2) is an enzyme that has been extensively investigated as a prognostic marker in cancer. In non-small cell lung cancer (NSCLC) previous results regarding the prognostic impact of COX-2 expression are inconsistent. Therefore we evaluated the association between transcript levels and overall survival in nine publicly available gene expression data sets (total n = 1337) and determined in situ compartment-specific tumor and stromal cell protein expression in two independent cohorts (n = 616). Gene expression did not show any correlation with clinical parameters or with overall survival. Protein expression in tumor and stromal cells did not correlate with any clinical parameter or with overall survival in one of the analyzed cohorts, while a significant association of high stromal expression with longer survival was observed in both univariate and multivariate analysis in the other cohort. Stromal expression of COX-2 has not been separately evaluated in NSCLC previously and may be a subject of further investigation, whereas the presented findings from this comprehensive compartment specific evaluation clearly reject the hypothesis of COX-2 tumor cell expression having a prognostic value in NSCLC.

Reaching the limits of prognostication in non-small cell lung cancer: an optimized biomarker panel fails to outperform clinical parameters

Numerous protein biomarkers have been analyzed to improve prognostication in non-small cell lung cancer, but have not yet demonstrated sufficient value to be introduced into clinical practice. Here, we aimed to develop and validate a prognostic model for surgically resected non-small cell lung cancer. A biomarker panel was selected based on (1) prognostic association in published literature, (2) prognostic association in gene expression data sets, (3) availability of reliable antibodies, and (4) representation of diverse biological processes. The five selected proteins (MKI67, EZH2, SLC2A1, CADM1, and NKX2-1 alias TTF1) were analyzed by immunohistochemistry on tissue microarrays including tissue from 326 non-small cell lung cancer patients. One score was obtained for each tumor and each protein. The scores were combined, with or without the inclusion of clinical parameters, and the best prognostic model was defined according to the corresponding concordance index (C-index). The best-performing model was subsequently validated in an independent cohort consisting of tissue from 345 non-small cell lung cancer patients. The model based only on protein expression did not perform better compared to clinicopathological parameters, whereas combining protein expression with clinicopathological data resulted in a slightly better prognostic performance (C-index: all non-small cell lung cancer 0.63 vs 0.64; adenocarcinoma: 0.66 vs 0.70, squamous cell carcinoma: 0.57 vs 0.56). However, this modest effect did not translate into a significantly improved accuracy of survival prediction. The combination of a prognostic biomarker panel with clinicopathological parameters did not improve survival prediction in non-small cell lung cancer, questioning the potential of immunohistochemistry-based assessment of protein biomarkers for prognostication in clinical practice.

Glycerol-3-phosphate acyltransferase 1 promotes tumor cell migration and poor survival in ovarian carcinoma

Glycerophosphodiesterase EDI3 (GPCPD1; GDE5; GDPD6) has been suggested to promote cell migration, adhesion, and spreading, but its mechanisms of action remain uncertain. In this study, we targeted the glycerol-3-phosphate acyltransferase GPAM along with choline kinase-α (CHKA), the enzymes that catabolize the products of EDI3 to determine which downstream pathway is relevant for migration. Our results clearly showed that GPAM influenced cell migration via the signaling lipid lysophosphatidic acid (LPA), linking it with GPAM to cell migration. Analysis of GPAM expression in different cancer types revealed a significant association between high GPAM expression and reduced overall survival in ovarian cancer. Silencing GPAM in ovarian cancer cells decreased cell migration and reduced the growth of tumor xenografts. In contrast to these observations, manipulating CHKA did not influence cell migration in the same set of cell lines. Overall, our findings show how GPAM influences intracellular LPA levels to promote cell migration and tumor growth. Cancer Res; 77(17); 4589-601. ©2017 AACR.

Integrative analysis of genome-wide gene copy number changes and gene expression in non-small cell lung cancer

Non-small cell lung cancer (NSCLC) represents a genomically unstable cancer type with extensive copy number aberrations. The relationship of gene copy number alterations and subsequent mRNA levels has only fragmentarily been described. The aim of this study was to conduct a genome-wide analysis of gene copy number gains and corresponding gene expression levels in a clinically well annotated NSCLC patient cohort (n = 190) and their association with survival. While more than half of all analyzed gene copy number-gene expression pairs showed statistically significant correlations (10,296 of 18,756 genes), high correlations, with a correlation coefficient >0.7, were obtained only in a subset of 301 genes (1.6%), including KRAS, EGFR and MDM2. Higher correlation coefficients were associated with higher copy number and expression levels. Strong correlations were frequently based on few tumors with high copy number gains and correspondingly increased mRNA expression. Among the highly correlating genes, GO groups associated with posttranslational protein modifications were particularly frequent, including ubiquitination and neddylation. In a meta-analysis including 1,779 patients we found that survival associated genes were overrepresented among highly correlating genes (61 of the 301 highly correlating genes, FDR adjusted p<0.05). Among them are the chaperone CCT2, the core complex protein NUP107 and the ubiquitination and neddylation associated protein CAND1. In conclusion, in a comprehensive analysis we described a distinct set of highly correlating genes. These genes were found to be overrepresented among survival-associated genes based on gene expression in a large collection of publicly available datasets.

Highlight report: quality control for genome-wide expression data: how to identify sample mix-up

Publicly available datasets have been particularly often used in clinical cancer research to identify prognostic genes (Schmidt et al. 2008, 2012; Cadenas et al. 2010, 2014; Stewart et al. 2012; Godoy et al. 2014; Stock et al. 2015). However, genome-wide expression data are also increasingly used in toxicological research (Song et al. 2013; Faust et al. 2013; Shao et al. 2014; Shinde et al. 2015; Reif 2014; Stober 2014; Marchan 2014; Hammad and Ahmed 2014; Hengstler 2011; Stewart 2010). Cutting-edge topics are the establishment of classifiers and signatures in developmental toxicity (Krug et al. 2013; Rempel et al. 2015; Balmer et al. 2014; Zimmer et al. 2014; Waldmann et al. 2014) and hepatotoxicity (Campos et al. 2014; Yafune et al. 2013; Doktorova et al. 2012; Zellmer et al. 2010; Godoy et al. 2015). For these purposes, large public databases are available (Grinberg et al. 2014). Using the novel methods for the identification of sample annotation errors described by Lohr et al. (2015) will improve the reliability of genome-wide biostatistical analyses in future.