Beatrix Jahnke

Carl Gustav Carus-Institut, Pforzheim, Baden-Württemberg, Germany

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Publications (18)

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    Dataset: suppl
    Full-text Dataset · Jan 2016
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    [Show abstract] [Hide abstract] ABSTRACT: Pancreatic cancer is one of the most lethal tumor types worldwide and an effective therapy is still elusive. Targeted therapy focused against a specific alteration is by definition unable to attack broad pathway signaling modification. Tumor heterogeneity will render targeted therapies ineffective based on the regrowth of cancer cell sub-clones. Therefore multimodal therapy strategies, targeting signaling pathways simultaneously should improve treatment. SiRNAs against KRAS and the apoptosis associated genes BCLXL, FLIP, MCL1L, SURVIVIN and XIAP were transfected into human and murine pancreatic cancer cell lines. Induction of apoptosis was measured by Caspase 3/7 activation, subG1 FACS analysis and PARP cleavage. The therapeutic approach was tested in a subcutaneous allograft model with a murine cancer cell line. By using siRNAs as a systematic approach to remodel signal transduction in pancreatic cancer the results showed increasing inhibition of proliferation and apoptosis induction in vitro and in vivo. Thus, siRNAs are suitable to model multimodal therapy against signaling pathways in pancreatic cancer. Improvements in in vivo delivery of siRNAs against a multitude of targets might therefore be a potential therapeutic approach.
    Full-text Article · Dec 2015 · Oncotarget
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    Full-text Dataset · Dec 2015
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    Dataset: Table S3
    [Show abstract] [Hide abstract] ABSTRACT: KEGG pathways most affected by signature genes and their interaction partners. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S3
    [Show abstract] [Hide abstract] ABSTRACT: Survival by adjuvant therapy. Out of 412 patients in the validation dataset, 172 patients who received adjuvant therapy had a lower 5-year-survival than the 240 patients who did not receive adjuvant therapy, although the difference is not significant (, logrank test). (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S4
    [Show abstract] [Hide abstract] ABSTRACT: Receiver operating characteristic curves of signatures to predict risk. (A) Signature to predict risk in patients with adjuvant therapy. The signature was developed with patients receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. The signature consisted of the marker proteins STAT3, FOS, JUN, CDX2, CEBPA, and BRCA1. The receiver operating characteristic (ROC) curve of a classifier trained with this signature shows an area under the curve of 68% using leave-one-out cross-validation. (B) Signature to predict risk in patients without adjuvant therapy. The signature was developed with patients not receiving adjuvant therapy separated by their median survival into two groups, a high risk group with shorter survival and a low risk group with longer survival. The signature consisted of the marker proteins STAT3, JUN, SP1, CDX2, and BRCA1. The ROC curve of a classifier trained with this signature shows an area under the curve of 59% using leave-one-out cross-validation. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Table S1
    [Show abstract] [Hide abstract] ABSTRACT: Microarray-based cancer classification studies on finding predictive signatures published in high-impact journals. The studies were published in Science, Nature, Nature Medicine, PNAS, PLoS Medicine, Cancer Cell, Lancet, or New England Journal of Medicine. Some studies exhibit considerable flaws in methodology, as pointed out in the notes at the table bottom. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S5
    [Show abstract] [Hide abstract] ABSTRACT: Comparison of NetRank with a direct neighbor algorithm. The plot shows the accuracy of a direct neighbor approach that only takes direct neighbors into account (as opposed to NetRank, which considers all nodes in the network) on the TRANSFAC network with different training set sizes. The direct neighbor approach performs almost identically to the Pearson correlation method (shown here for comparison). See below for a description of the direct neighbor method. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S6
    [Show abstract] [Hide abstract] ABSTRACT: Distribution of expression levels and correlation with survival in four distinct subsets of the full screening dataset. (A) Histogram (density) of gene expression levels. Our filtering keeps only the high expression, high variance genes (red curve). Sizes of the four subsets are shown in the upper right. (B) Histogram (density) of absolute Pearson correlation coefficients of gene expression levels with patient survival. Since the red and the blue curve have very similar distribution, ranking by correlation (which is the starting point for our NetRank algorithm) will allow selection of uninformative, low variance genes (blue curve) that will impair prediction accuracy when included in a classifier. Hence, it is important to filter such genes out. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S2
    [Show abstract] [Hide abstract] ABSTRACT: Examples of immunohistochemical staining of the marker candidates. Antibody staining intensities were scored semi-quantitatively by a pathologist using four grades of negative (), faint (), moderate (), and strong () staining. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Table S4
    [Show abstract] [Hide abstract] ABSTRACT: Accuracies and standard errors for Monte Carlo cross-validations. These numbers are the basis for the plots shown in Figure 2 and Figure S5. (XLS)
    Full-text Dataset · May 2012
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    Dataset: Table S2
    [Show abstract] [Hide abstract] ABSTRACT: Fifty-one immunohistochemistry markers prognostic for survival in pancreatic cancer, found with a literature search. (PDF)
    Full-text Dataset · May 2012
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    Dataset: Figure S1
    [Show abstract] [Hide abstract] ABSTRACT: Survival time distribution in ten published cancer outcome studies. (A)–(J) Histograms of survival times from ten published studies using microarray data from cancer patients for outcome prediction. For studies which define two prognosis groups, these groups are indicated by color (red, poor prognosis and blue, good prognosis). The dashed vertical line indicates the median. (PDF)
    Full-text Dataset · May 2012
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    [Show abstract] [Hide abstract] ABSTRACT: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.
    Full-text Article · May 2012 · PLoS Computational Biology
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    [Show abstract] [Hide abstract] ABSTRACT: Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice.
    Full-text Article · May 2012 · PLoS Computational Biology
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    [Show abstract] [Hide abstract] ABSTRACT: To investigate biological differences and prognostic indicators of different ampullary cancer (AC) subtypes. AC is associated with a favorable prognosis compared with other periampullary carcinomas. Aside from other prognostic factors, the histological origin of AC may determine survival. Specifically, the pancreatobiliary subtype of AC displays worse prognosis compared with the intestinal subtype. However, knowledge of inherent molecular characteristics of different periampullary tumors and their effects on prognosis has been limited. Gene expression profiling was used to screen for differential gene expression between 6 PDAC cases and 12 AC cases. Among others, hepatocyte nuclear factor 4α (HNF4α) mRNA overexpression was observed in AC cases. Nuclear HNF4α protein expression was assessed using tissue microarrays consisting of 99 individual AC samples. The correlation of HNF4α expression with clinicopathological data (n = 99) and survival (n = 84) was assessed. HNF4α mRNA is 7.61-fold up-regulated in AC compared with that in PDAC. Bioinformatics analyses indicated its key role in dysregulated signaling pathways. Nuclear HNF4α expression correlates with histological subtype, grading, CDX2 positivity, MUC1 negativity and presence of adenomatous components in the carcinoma. The presence of HNF4α is a univariate predictor of survival in AC mean survival (50 months versus 119 months, P = 0.002). Multivariate analysis revealed that HNF4α negativity (HR = 17.95, 95% CI: 2.35-136.93, P = 0.005) and lymph node positivity (HR = 3.33, 95% CI: 1.36-8.18, P = 0.009) are independent negative predictors of survival. Immunohistochemical determination of HNF4α expression is an effective tool for distinguishing different AC subtypes. Similarly, HNF4α protein expression is an independent predictor of favorable prognosis in carcinoma of the papilla of Vater and may serve for risk stratification after curative resection.
    Full-text Article · Apr 2011 · Annals of surgery
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    [Show abstract] [Hide abstract] ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) is characterized by an abundant desmoplastic stroma. Interactions between cancer and stromal cells play a critical role in tumour invasion, metastasis and chemoresistance. Therefore, we hypothesized that gene expression profile of the stromal components of pancreatic carcinoma is different from chronic pancreatitis and reflects the interaction with the tumour. We investigated the gene expression of eleven stromal tissues from PDAC, nine from chronic pancreatitis and cell lines of stromal origin using the Affymetrix U133 GeneChip set. The tissue samples were microdissected, the RNA was extracted, amplified and labelled using a repetitive in vitro transcription protocol. Differentially expressed genes were identified and validated using quantitative RT-PCR and immuno-histochemistry. We found 255 genes to be overexpressed and 61 genes to be underexpressed within the stroma of pancreatic carcinoma compared to the stroma of chronic pancreatitis. Analysis of the involved signal transduction pathways revealed a number of genes associated with the Wnt pathway of which the differential expression of SFRP1 and WNT5a was confirmed using immunohistochemistry. Moreover, we could demonstrate that WNT5a expression was induced in fibroblasts during cocultivation with a pancreatic carcinoma cell line. The identified differences in the expression profile of stroma cells derived from tumour compared to cells of inflammatory origin suggest a specific response of the tissue surrounding malignant cells. The overexpression of WNT5a, a gene involved in the non canonical Wnt signalling and chondrocyte development might contribute to the strong desmoplastic reaction seen in pancreatic cancer.
    Full-text Article · Mar 2008 · Journal of Cellular and Molecular Medicine
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    [Show abstract] [Hide abstract] ABSTRACT: Pancreatic ductal adenocarcinoma (PDAC) remains an important cause of malignancy-related death and is the eighth most common cancer with the lowest overall 5-year relative survival rate. To identify new molecular markers and candidates for new therapeutic regimens, we investigated the gene expression profile of microdissected cells from 11 normal pancreatic ducts, 14 samples of PDAC, and 4 well-characterized pancreatic cancer cell lines using the Affymetrix U133 GeneChip set. RNA was extracted from microdissected samples and cell lines, amplified, and labeled using a repetitive in vitro transcription protocol. Differentially expressed genes were identified using the significance analysis of microarrays program. We found 616 differentially expressed genes. Within these, 140 were also identified in PDAC by others, such as Galectin-1, Galectin-3, and MT-SP2. We validated the differential expression of several genes (e.g., CENPF, MCM2, MCM7, RAMP, IRAK1, and PTTG1) in PDAC by immunohistochemistry and reverse transcription polymerase chain reaction. We present a whole genome expression study of microdissected tissues from PDAC, from microdissected normal ductal pancreatic cells and pancreatic cancer cell lines using high-density microarrays. Within the panel of genes, we identified novel differentially expressed genes, which have not been associated with the pathogenesis of PDAC before.
    Full-text Article · Sep 2004 · Neoplasia