Beatrix Jahnke

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

Are you Beatrix Jahnke?

Claim your profile

Publications (5)27.86 Total impact

  • Source
    [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.
    PLoS Computational Biology 05/2012; 8(5):e1002511. · 4.87 Impact Factor
  • Source
    [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.
    PLoS Computational Biology 05/2012; 8(5):e1002511. · 4.87 Impact Factor
  • Source
    [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.
    Annals of surgery 04/2011; 254(2):302-10. · 7.90 Impact Factor
  • Source
    [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.
    Journal of Cellular and Molecular Medicine 03/2008; 12(6B):2823-35. · 4.75 Impact Factor
  • Source
    [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.
    Neoplasia 01/2004; 6(5):611-22. · 5.47 Impact Factor