A Six-Gene Signature Predicts Survival of Patients with Localized Pancreatic Ductal Adenocarcinoma

Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America.
PLoS Medicine (Impact Factor: 14.43). 07/2010; 7(7):e1000307. DOI: 10.1371/journal.pmed.1000307
Source: PubMed


Pancreatic ductal adenocarcinoma (PDAC) remains a lethal disease. For patients with localized PDAC, surgery is the best option, but with a median survival of less than 2 years and a difficult and prolonged postoperative course for most, there is an urgent need to better identify patients who have the most aggressive disease.
We analyzed the gene expression profiles of primary tumors from patients with localized compared to metastatic disease and identified a six-gene signature associated with metastatic disease. We evaluated the prognostic potential of this signature in a training set of 34 patients with localized and resected PDAC and selected a cut-point associated with outcome using X-tile. We then applied this cut-point to an independent test set of 67 patients with localized and resected PDAC and found that our signature was independently predictive of survival and superior to established clinical prognostic factors such as grade, tumor size, and nodal status, with a hazard ratio of 4.1 (95% confidence interval [CI] 1.7-10.0). Patients defined to be high-risk patients by the six-gene signature had a 1-year survival rate of 55% compared to 91% in the low-risk group.
Our six-gene signature may be used to better stage PDAC patients and assist in the difficult treatment decisions of surgery and to select patients whose tumor biology may benefit most from neoadjuvant therapy. The use of this six-gene signature should be investigated in prospective patient cohorts, and if confirmed, in future PDAC clinical trials, its potential as a biomarker should be investigated. Genes in this signature, or the pathways that they fall into, may represent new therapeutic targets. Please see later in the article for the Editors' Summary.

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Available from: Jeran Stratford
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    • "However, given that there are only limited treatment options for PDAC and our limited understanding of the predisposing risk factors at the molecular level [35], low-and high-risk grouping is, perhaps, most tractable from a clinical point of view. We also demonstrate a lack of overlap between existing PDAC gene signatures that show prognostic and/or predictive potential [3,6,131415161725,26]. This could be due to the small discovery cohort size, the inherent noise in different microarray experiments leading to confounded results [36], and/or, the impact of the clinicopathological characteristics of samples selected for a particular study on candidate gene selection. "
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    ABSTRACT: Improved usage of the repertoires of pancreatic ductal adenocarcinoma (PDAC) profiles is crucially needed to guide the development of predictive and prognostic tools that could inform the selection of treatment options. Using publicly available mRNA abundance datasets, we performed a large retrospective meta-analysis on 466 PDAC patients to discover prognostic gene signatures. These signatures were trained on two clinical cohorts (n = 70), and validated on four independent clinical cohorts (n = 246). Further validation of the identified gene signature was performed using quantitative real-time RT-PCR. We identified 225 candidate prognostic genes. Using these, a 36-gene signature was discovered and validated on fully independent clinical cohorts (hazard ratio (HR) = 2.06, 95% confidence interval (CI) = 1.51 to 2.81, P = 3.62 × 10(-6), n = 246). This signature serves as a good alternative prognostic stratification marker compared to tumour grade (HR = 2.05, 95% CI = 1.45 to 2.88, P = 3.18 × 10(-5)) and tumour node metastasis (TNM) stage (HR = 1.13, 95% CI = 0.66 to 1.94, P = 0.67). Upon multivariate analysis with adjustment for TNM stage and tumour grade, the 36-gene signature remained an independent prognostic predictor of clinical outcome (HR = 2.21, 95% CI = 1.17 to 4.16, P = 0.01). Univariate assessment revealed higher expression of ITGA5, SEMA3A, KIF4A, IL20RB, SLC20A1, CDC45, PXN, SSX3 and TMEM26 was correlated with shorter survival while B3GNT1, NOSTRIN and CADPS down-regulation was associated with poor outcome. Our 36-gene classifier is able to prognosticate PDAC independent of patient cohort and microarray platforms. Further work on the functional roles, downstream events and interactions of the signature genes is likely to reveal true molecular candidates for PDAC therapeutics.
    Full-text · Article · Dec 2014 · Genome Medicine
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    • "Many of the altered biological pathways in PDXs are typically deregulated in metastatic tumors compared with primary tumors. In fact, it has been proposed that PDX models mimic aggressive and/or metastatic tumors derived from the original primary tumors [42,46,54]. The tumor engraftment could be seen as a 'forced' metastatic situation, because the tumor needs to colonize a new environment to survive. "
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    ABSTRACT: Engraftment of primary pancreas ductal adenocarcinomas (PDAC) in mice to generate patient-derived xenograft (PDX) models is a promising platform for biological and therapeutic studies in this disease. However, these models are still incompletely characterized. Here, we measured the impact of the murine tumor environment on the gene expression of the engrafted human tumoral cells. We have analyzed gene expression profiles from 35 new PDX models and compared them with previously published microarray data of 18 PDX models, 53 primary tumors and 41 cell lines from PDAC. The results obtained in the PDAC system were further compared with public available microarray data from 42 PDX models, 108 primary tumors and 32 cell lines from hepatocellular carcinoma (HCC). We developed a robust analysis protocol to explore the gene expression space. In addition, we completed the analysis with a functional characterization of PDX models, including if changes were caused by murine environment or by serial passing. Our results showed that PDX models derived from PDAC, or HCC, were clearly different to the cell lines derived from the same cancer tissues. Indeed, PDAC- and HCC-derived cell lines are indistinguishable one from the other based on their gene expression profiles. In contrast, the transcriptomes of PDAC and HCC PDX models can be separated into two different groups that share some partial similarity with their corresponding original primary tumors. Our results point to the lack of human stromal involvement in PDXs as a major factor contributing to their differences with the original primary tumors. The main functional differences between pancreatic PDX models and human PDAC are the lower expression of genes involved in pathways related to extracellular matrix and hemostasis and the up regulation of cell cycle genes. Importantly, most of these differences are detected in the first passages after the tumor engraftment. Our results suggest that PDX models of PDAC and HCC retain, to some extent, a gene expression memory of the original primary tumors, while this pattern is not detected in conventional cancer cell lines. Expression changes in PDXs are mainly related to pathways reflecting the lack of human infiltrating cells and the adaptation to a new environment. We also provide evidence of the stability of gene expression patterns over subsequent passages, indicating early phases of the adaptation process.
    Full-text · Article · Apr 2014 · Genome Medicine
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    • "K-ras, TP53, SMAD4, p16, Plectin-1, Caveolin-1, hENT1, miR-216, miR-217, miR-196, miR-196a, miR-186, miR-222, miR-200b, miR-15b, miR-95, FosB, KLF6, ATP4A, NFKBIZ, GSG1, SIGLEC11 [22] [43] [84] [85] [100] [125] [131] [213] [214] [215] [216] [217] [218] [219] [220] Pancreatic juice Quantitative proteomics AGR2, S100A6, MMP-9, DJ-1, A1BG, MMP-7, miRNA-21, miRNA- 155, Twist, PRSS2, PLRP1, hTERT [72] [166] [221] [222] [223] [224] [225] [226] Serum Quantitative proteomics, ELISA, CA 19-9, CA 125, CEACAM1, MMP-7, REG4, HSP-27, IGF, IGFR, cyclin I, GDI2, PF4, SAA, fibrinogen [37] [50] [227] [228] [229] [230] [231] [232] [233] [234] [235] [236] Plasma Functional genomics TNC, TFP1, TGFBI, SEL-1L, LICAM, WWTR1, CDC4BPA, CK 18 [101] [237] CTC RT-PCR C-MET, h-TERT, CK20, ELC, PlGF [238] [239] Saliva Human genome array K-ras, MBD3L2, ACRV1, DPM1 [80] Stool PCR K-ras, TP53 [81] [240] "
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    ABSTRACT: Pancreatic cancer (PC) is a leading cause of cancer related deaths in United States. The lack of early symptoms results in latestage detection and a high mortality rate. Currently, the only potentially curative approach for PC is surgical resection, which is often unsuccessful because the invasive and metastatic nature of the tumor masses makes their complete removal difficult. Consequently, patients suffer relapses from remaining cancer stem cells or drug resistance that eventually lead to death. To improve the survival rate, the early detection of PC is critical. Current biomarker research in PC indicates that a serum carbohydrate antigen, CA 19-9, is the only available biomarker with approximately 90% specificity to PC. However, the efficacy of CA 19-9 for assessing prognosis and monitoring patients with PC remains contentious. Thus, advances in technology and the detection of new biomarkers with high specificity to PC are needed to reduce the mortality rate of pancreatic cancer.
    Full-text · Article · Feb 2012 · Current pharmaceutical design
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