Alexander Herr

Gene signatures of pulmonary metastases of renal cell carcinoma reflect the disease‐free interval and the number of metastases per patient

Our understanding of metastatic spread is limited and molecular mechanisms causing particular characteristics of metastasis are largely unknown. Herein, transcriptome‐wide expression profiles of a unique cohort of 20 laser‐resected pulmonary metastases (Mets) of 18 patients with clear‐cell renal cell carcinoma (RCC) were analyzed to identify expression patterns associated with two important prognostic factors in RCC: the disease‐free interval (DFI) after nephrectomy and the number of Mets per patient. Differentially expressed genes were identified by comparing early (DFI ≤ 9 months) and late (DFI ≥ 5 years) Mets, and Mets derived from patients with few (≤8) and multiple (≥16) Mets. Early and late Mets could be separated by the expression of genes involved in metastasis‐associated processes, such as angiogenesis, cell migration and adhesion (e.g., PECAM1, KDR). Samples from patients with multiple Mets showed an elevated expression of genes associated with cell division and cell cycle (e.g., PBK, BIRC5, PTTG1) which indicates that a high number of Mets might result from an increased growth potential. Minimal sets of genes for the prediction of the DFI and the number of Mets per patient were identified. Microarray results were confirmed by quantitative PCR by including nine further pulmonary Mets of RCC. In summary, we showed that subgroups of Mets are distinguishable based on their expression profiles, which reflect the DFI and the number of Mets of a patient. To what extent the identified molecular factors contribute to the development of these characteristics of metastatic spread needs to be analyzed in further studies.

Nuclear karyopherin α2 expression predicts poor survival in patients with advanced breast cancer irrespective of treatment intensity

Intensive lymph node involvement indicates poor prognosis in breast cancer patients. The significance of other molecular prognostic factors in this subgroup is unclear. Karyopherin α2 (KPNA2) has been reported as an important factor of tumorgenesis and progression of breast cancer. The aim of present study was to evaluate the impact of KPNA2 expression on prognosis of patients with high risk breast cancer (HRBC) and response intensive chemotherapy within the randomized WSG‐AM‐01 trial. KPNA2 nuclear expression (>10% vs. <10% of nuclei) was measured by immunohistochemistry on tissue arrays of 191 patients randomized to tandem high dose vs. conventional dose‐dense chemotherapy in HRBC with >9 positive lymph nodes and correlated with clinical outcome (median follow‐up of 63.3 months) by Kaplan–Meier and multivariate Cox hazard model analysis, including, molecular subtypes determined by k‐clustering (k = 5). KPNA2 overexpression (n = 74, 39%) significantly correlated with shorter event‐free and overall survival (OS) in both therapy arms by univariate analysis. Multivariate analysis showed that the overexpression of KPNA2 was an independent prognostic factor of decreased OS HR = 1.86 [95% CI: 1.07–3.23, p = 0.03]. This predictive value was independent of basal‐like/Her‐2/neu subtypes, significantly associated with KPNA2 and was addressed particularly to G2 tumors. Our data suggest the use of KPNA2 nuclear expression as novel prognostic marker in node‐positive patients, especially in determination of G2 tumors in 2 subgroups of different prognosis. KPNA2 expression may be also considered as a marker for global chemoresistance, which can not be overcome by conventional dose‐modification of chemotherapy in advanced breast cancer.

Transcription profiling of adult and fetal human neuroprogenitors identifies divergent paths to maintain the neuroprogenitor cell state.

Global gene expression profiling was performed using RNA from adult human hippocampus‐derived neuroprogenitor cells (NPCs) and multipotent frontal cortical fetal NPCs compared with adult human mesenchymal stem cells (hMSCs) as a multipotent adult stem cell control, and adult human hippocampal tissue, to define a gene expression pattern that is specific for human NPCs. The results were compared with data from various databases. Hierarchical cluster analysis of all neuroectodermal cell/tissue types revealed a strong relationship of adult hippocampal NPCs with various white matter tissues, whereas fetal NPCs strongly correlate with fetal brain tissue. However, adult and fetal NPCs share the expression of a variety of genes known to be related to signal transduction, cell metabolism and neuroectodermal tissue. In contrast, adult NPCs and hMSCs overlap in the expression of genes mainly involved in extracellular matrix biology. We present for the first time a detailed transcriptome analysis of human adult NPCs suggesting a relationship between hippocampal NPCs and white matter‐derived precursor cells. We further provide a framework for standardized comparative gene expression analysis of human brain‐derived NPCs with other stem cell populations or differentiated tissues.

Genome-wide expression profiling and functional network analysis upon neuroectodermal conversion of human mesenchymal stem cells suggest HIF-1 and miR-124a as important regulators

Tissue-specific stem cells, such as bone-marrow-derived human mesenchymal stem cells (hMSCs), are thought to be lineage restricted and therefore, could only be differentiated into cell types of the tissue of origin. Several recent studies however have suggested that these types of stem cells might be able to break barriers of germ layer commitment and differentiate in vitro into cells with neuroectodermal properties. We reported earlier about efficient conversion of adult hMSCs into a neural stem cell (NSC)-like population (hmNSCs, for human marrow-derived NSC-like cells) with all major properties of NSCs including functional neuronal differentiation capacity. Here we compared the transcriptomes from hMSCs and hmNSCs using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses to shed light on regulatory protein networks involved in this neuroectodermal conversion process. We found differential regulation of extracellular matrix protein transcripts, up-regulation of distinct neuroectodermal and NSCs marker genes and local chromosomal transcriptional up-regulation at chromosome 4q13.3. In comparison to hMSCs and primary adult hippocampal NSCs, the transcriptome of hmNSCs displayed minor overlap with both other cell populations. Advanced bioinformatics of regulated genes upon neuroectodermal conversion identified transcription factor networks with HIF-1 and microRNA miR-124a as potential major regulators. Together, transgerminal neuroectodermal conversion of hMSCs into NSC-like cells is accompanied by extensive changes of their global gene expression profile, which might be controlled in part by transcription factor networks related to HIF-1 and miR-124a.

Microarray analyses in bladder cancer cells: Inhibition of hTERT expression down‐regulates EGFR

The human telomerase reverse transcriptase (hTERT) contributes to the immortal phenotype of the majority of cancers. Targeting hTERT by transfection with antisense oligonucleotides (AS‐ODNs) induced immediate growth inhibition in human bladder cancer (BCa) cells. The molecular basis of the antiproliferative capacity of hTERT AS‐ODNs was investigated by oligonucleotide microarray analyses and was compared to effects caused by siRNA‐mediated knock‐down of hTERT in EJ28 BCa cells. Two different AS‐ODNs—both down‐regulated the expression of hTERT—changed the expression of different genes mainly involved in stress response (including EGR1, ATF3 and GDF15), but without an association to telomerase function. This indicates that the immediate growth inhibition was caused, at least in part, by off‐target effects. In comparison to that the blockade of the expression of hTERT using 2 different siRNAs was accompanied by the down‐regulation of the oncogenes FOS‐like antigen 1 (FOSL1) and epidermal growth factor receptor (EGFR), known to be overexpressed in BCa. We show here for the first time that repression of the hTERT transcript number decreased the expression of EGFR both at the mRNA and protein levels, suggesting a potential new function of hTERT in the regulation of EGFR‐stimulated proliferation. Furthermore, the suppression of hTERT by siRNAs caused an enhancement of the antiproliferative capacity of the chemotherapeutics mitomycin C and cisplatin. The results presented herein may support the hypothesis that hTERT promotes the growth of tumor cells by mechanisms independent from telomere lengthening. The detailed clarification of these processes will shed light on the question, whether telomerase inhibitors might constitute suitable anticancer tools.

PARK2 and PACRG are commonly downregulated in clear‐cell renal cell carcinoma and are associated with aggressive disease and poor clinical outcome

PARK2 is an E3 ligase, known to be involved in ubiquitination of several proteins and to play a role in neuronal protection. The gene PARK2 and its potentially co‐regulated gene PACRG have been previously found to be deleted in clear‐cell renal cell carcinomas (ccRCCs). The aim of our study was to evaluate the mRNA and protein expression of PARK2 and PACRG in a large cohort of ccRCC, and to investigate their association with outcome. The expression of both genes was measured by quantitative PCR in 94 primary ccRCCs and autologous nonmalignant kidney tissues. PACRG and PARK2 protein expression was determined immunohistochemically using tissue microarrays comprising 133 ccRCCs. The mRNA and protein expression of PARK2 and PACRG was significantly downregulated in ccRCCs compared with nonmalignant tissues. Low levels of PARK2 mRNA were associated with high‐grade ccRCC and lymph node metastasis. Patients with low PARK2 mRNA levels showed a higher tumor‐specific mortality rate and a shorter overall survival (OS) than those with high PARK2 expression. Patients without PACRG mRNA expression in the tumor had a shorter disease‐free survival and OS than those with tumors expressing PACRG. In multivariate analyses, neither PARK2 nor PACRG expression were independent prognostic factors. The protein expression of PARK2 and PACRG was significantly downregulated in ccRCCs (82.8, and 96.9%, respectively), but no association with clinical outcome was noticed.

Whole blood genome-wide expression profiling and network analysis suggest MELAS master regulators

Abstract Background: The heteroplasmic mitochondrial DNA (mtDNA) mutation A3243G causes the mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) syndrome as one of the most frequent mitochondrial diseases. The process of reconfiguration of nuclear gene expression profile to accommodate cellular processes to the functional status of mitochondria might be a key to MELAS disease manifestation and could contribute to its diverse phenotypic presentation. Objective: To determine master regulatory protein networks and disease-modifying genes in MELAS syndrome. Methods: Analyses of whole blood transcriptomes from 10 MELAS patients using a novel strategy by combining classic Affymetrix oligonucleotide microarray profiling with regulatory and protein interaction network analyses. Results: Hierarchical cluster analysis elucidated that the relative abundance of mutant mtDNA molecules is decisive for the nuclear gene expression response. Further analyses confirmed not only transcription factors already known to be involved in mitochondrial diseases (such as TFAM), but also detected the hypoxia-inducible factor 1 complex, nuclear factor Y and cAMP responsive element-binding protein-related transcription factors as novel master regulators for reconfiguration of nuclear gene expression in response to the MELAS mutation. Correlation analyses of gene alterations and clinico-genetic data detected significant correlations between A3243G-induced nuclear gene expression changes and mutant mtDNA load as well as disease characteristics. These potential disease-modifying genes influencing the expression of the MELAS phenotype are mainly related to clusters primarily unrelated to cellular energy metabolism, but important for nucleic acid and protein metabolism, and signal transduction. Discussion: Our data thus provide a framework to search for new pathogenetic concepts and potential therapeutic approaches to treat the MELAS syndrome.

Artificial Intelligence Technique for Gene Expression Profiling of Urinary Bladder Cancer

The purpose of this study is to develop a method of classifying cancers to specific diagnostic categories based on their gene expression signatures using artificial intelligence (AI) techniques which provide better predictions than standard traditional statistical methods. The predictive accuracies of neuro-fuzzy modelling (NFM), artificial neural networks (ANN) and traditional logistic regression (LR) methods are compared for the behaviour of bladder cancer. Gene expression profiles of non-invasive and invasive bladder cancer were used to identify potential therapeutic or screening targets in bladder cancer, and to define genetic changes relevant for tumour progression of recurrent papillary bladder cancer (pTa). For all three methods, models were produced to predict the presence and timing of a tumour progression, stage and grade. AI methodology predicted progression with an accuracy ranging up to 100%. This was superior to logistic regression

MP15-15 URINE SAMPLE DERIVED CK20- AND IGF2-EXPRESSION AS BIOMARKER FOR THE DETECTION OF BLADDER CANCER

INTRODUCTION AND OBJECTIVES: Non muscle invasive bladder cancer is a recurrent and progressive disease; currently we are unable to forecast recurrence in the individual patient. Recently we developed a mathematical model that found NLR as a good prognostic tool. The model was tested retrospectively in an additional study and found accurate too. The aim of the current study is to assess its accuracy to forecast recurrence prospectively in patients with NMIBC METHODS: All patients admitted to bladder tumor resection (TURBT) and agreed to participate in the study had blood drawn for blood count 24 hours prior to surgery. Patients with non-muscle invasive tumor were recruited and prospectively followed. Patients had urine cytology and cystoscopy every 3 months for 2 years following resection. Time to recurrence and recurrence free of tumor were recorded. Statistical analysis was done with X2 test for categorical parameters and T test for serial parameters. Logistic regression was performed to forecast prognosis. RESULTS: 123 patients were recruited, mean age was 71 years, all patients had at least 1 year follow up. Twenty nine patients (23.6%) experienced biopsy proven tumor recurrence. The mean time for recurrence was 7.38 months.Neutrophil to Lymphocyte rate > 2 showed direct statistically significant correlation with tumor recurrence (p1⁄40.038), tumor stage showed the same correlation (p1⁄40.048). The specificity of our recurrence forecasting model was 96.8%. EORTC score did not demonstrate significance between the recurrent and nonrecurrent groups. CONCLUSIONS: Our mathematical model that found NLR as a prognostic tool in patients with NMIBC was tested for the first time prospectively. The model demonstrated its ability to forecast recurrence more accurately then tumor stage grade and EORT score in the individual patient with NMIBC.The main limitation of this work is the relatively low number of patients.

MICROARRAY ANALYSES IN BLADDER CANCER CELLS: INHIBITION OF HTERT EXPRESSION DOWN-REGULATES EGFR

The human telomerase reverse transcriptase (hTERT) contributes to the immortal phenotype of the majority of cancers. Targeting hTERT by transfection with antisense oligonucleotides (AS-ODNs) induced immediate growth inhibition in human bladder cancer (BCa) cells. The molecular basis of the antiproliferative capacity of hTERT AS-ODNs was investigated by oligonucleotide microarray analyses and was compared to effects caused by siRNA-mediated knock-down of hTERT in EJ28 BCa cells. Two different AS-ODNs -- both down-regulated the expression of hTERT -- changed the expression of different genes mainly involved in stress response (including EGR1, ATF3 and GDF15), but without an association to telomerase function. This indicates that the immediate growth inhibition was caused, at least in part, by off-target effects. In comparison to that the blockade of the expression of hTERT using 2 different siRNAs was accompanied by the down-regulation of the oncogenes FOS-like antigen 1 (FOSL1) and epidermal growth factor receptor (EGFR), known to be overexpressed in BCa. We show here for the first time that repression of the hTERT transcript number decreased the expression of EGFR both at the mRNA and protein levels, suggesting a potential new function of hTERT in the regulation of EGFR-stimulated proliferation. Furthermore, the suppression of hTERT by siRNAs caused an enhancement of the antiproliferative capacity of the chemotherapeutics mitomycin C and cisplatin. The results presented herein may support the hypothesis that hTERT promotes the growth of tumor cells by mechanisms independent from telomere lengthening. The detailed clarification of these processes will shed light on the question, whether telomerase inhibitors might constitute suitable anticancer tools.