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Original Article

Editor's Choice - Free Access

Metastasis-Related Processes Show Various Degrees of Activation in Different Stages of Pancreatic Cancer Rat Liver Metastasis

Al-Taee K.K.a · Ansari S.a · Hielscher T.b · Berger M.R.a · Adwan H.a

Author affiliations

a Toxicology and Chemotherapy Unit, G401, German Cancer Research Center, Heidelberg, Germany b Division of Biostatistics, German Cancer Research Center, Heidelberg, Germany

Corresponding Author

Dr. sc. hum. Hassan Adwan

Toxicology and Chemotherapy Unit

Deutsches Krebsforschungszentrum Heidelberg

Im Neuenheimer Feld 581, 69120 Heidelberg, Germany

h.adwan@gmx.de

Related Articles for ""

Oncol Res Treat 2014;37:464-470

Abstract

Background: Most pancreatic ductal adenocarcinoma (PDAC) patients who undergo tumor resection will develop postoperative liver metastasis within the first 2 years. Our hypothesis was that, during liver colonization, the temporal modulation of processes related to metastasis will change in a specific manner and that information on these changes might be used for new therapeutic approaches. Material and Methods: PDAC rat ASML cells were inoculated into the liver of BDX rats and re-isolated after different time periods of liver colonization (early, intermediate, advanced, and terminal). The total RNA of these samples was used to evaluate the expression profiles of more than 23,000 genes by chip array analysis. Results: Depending on the time span following re-isolation, 7-15% of all known genes were deregulated. These genes were assigned to metastasis-related processes during the 4 stages of colonization. Except for apoptosis, all other processes were not activated in the early and middle colonization stages. In the terminal phase of liver colonization, cell proliferation, cell homing, cell movement, and vasculogenesis were significantly activated. Conclusion: We hypothesize that targeting the relatively few deregulated genes in the early stage of liver colonization could ultimately improve the survival of PDAC patients.

© 2014 S. Karger GmbH, Freiburg


Introduction

Pancreatic ductal adenocarcinoma (PDAC), being the 4th most prevalent type of cancer in the USA, continues to be one of the most lethal forms of cancer in the western countries, with 73% of patients dying within the first year of diagnosis [1,2]. Currently, surgical intervention offers the best treatment option, although only about 20% of PDAC cases are diagnosed early enough for surgery [3,4]. Furthermore, the disease recurs in approximately 80% of patients who undergo surgery, and these will succumb to the disease within 5 years of recurrence [5,6,7].

The poor survival rate of PDAC patients is due to the lack of early diagnosis, its propensity to metastasize, and its resistance to radiation and chemotherapy. Albeit there are different therapeutic strategies for patients with advanced PDAC, including chemotherapy, radiation and combinatory therapy, no effective treatment is available for this aggressive form of cancer [1,5,7]. Thus, a thorough understanding of the molecular progression of the disease is a necessary precondition to possibly find effective therapeutic options.

PDAC progression results from the dissemination of tumor cells [8]. Without surgical intervention, close to 100% of patients with PDAC will develop metastases and die [9]. Metastasis is a multi-step, nonrandom process. During metastatic dissemination, cancer cells from a primary tumor execute various events such as intravasation, in which the cancer cells enter into the microvasculature of the blood vessels and lymph nodes, and then extravasation, i.e. the exit from the blood vessels once they have reached a distant ‘foreign' tissue. This is followed by the process of homing and colonization, where the cells get acclimatized to the new microenvironment and establish their early metastatic stage. This marks the beginning of micrometastasis, which includes cell proliferation and induction of angiogenesis and which is finally followed by macrometastasis [7,10,11,12].

The most common site for PDAC metastasis is the liver [13], and liver metastasis is usually present at the time of diagnosis [14]. Even if resection with curative intention is done, nearly all patients develop local recurrence and/or distant metastases. In most cases, liver metastases are not resectable. Chemo- and radiation therapy have been used for locally advanced pancreatic cancer [15,16], but these treatments showed only limited effectiveness [17]. Therefore, inhibiting metastasis is the key step toward a successful treatment of patients with this devastating disease. Such progress could be expected from the identification of molecular and genetic factors that facilitate the establishment of early metastatic cells in liver niches and the subsequent development into a full-blown secondary tumor.

In the present study, we therefore explored the modulation of gene expression during various phases of liver colonization by pancreatic cancer cells. Gene expression was analyzed in PDAC cells that had been re-isolated from the rat liver after the early, middle, and late colonization phases, to assess the roles of the diverse genes in metastasis-related processes such as proliferation, apoptosis, migration, homing, angiogenesis, and neovascularization.

We anticipate that the gene expression profiles of these cells will help to better understand the establishment of liver metastasis and to detect biomarkers for early diagnosis as well as targets for therapeutic intervention.

Materials and Methods

Cell Culture

The rat ASML PDAC cell line was maintained under standard culture conditions (37 °C, humidified atmosphere with 5% CO2) in RPMI-1640 medium (Invitrogen, Karlsruhe, Germany) supplemented with 10% fetal calf serum (FCS), L-glutamine (2 mM), penicillin (100 IU/ml), and streptomycin (100 μg/ml; Invitrogen). For isolation and propagation, the cells were washed with phosphate-buffered saline (PBS), trypsinized (0.25% trypsin/EDTA), pelleted at 1,500 rpm for 5 min and suspended at the desired concentration in RPMI-1640 medium.

Animal Experiments

All animal experiments were performed as described before [18]. BDX rats of both sexes were obtained at an age of 5-7 weeks and a corresponding body weight of 120-160 g.

Tumor Cell Transplantation

Logarithmically growing ASML GFP-Luc cells (2 × 106 cells) were implanted as described before [18]. The animals were kept thereafter for various periods until the re-isolation of tumor cells (see below).

Tumor Cell Re-Isolation

Before tumor cell re-isolation, groups of 3 rats each were kept for 1, 3, 6, 15, and 21 days after tumor cell implantation. Then, the abdominal cavity was opened and a 22-G cannula was inserted into the portal vein, through which the liver was perfused with Hank's balanced salt solution (HBSS) (20 ml/min, 37 °C for 10 min). This medium was replaced with pre-warmed perfusion medium (125 ml HBSS containing 1 M CaCl2, 0.1% pronase, 100 mg collagenase type IV (Serva); 37 °C for the following 10 min) to digest the connective tissue. After preparation of a cell suspension, the cells were filtered through a sterile filter (cell strainer, 70 μm, nylon; BD, Heidelberg, Germany) and centrifuged (300 × g for 10 min). The resulting cell suspension of liver and tumor cells was transferred into 50-ml tubes and layered carefully onto a Ficoll gradient (Amersham Pharmacia Biotech AB, Uppsala, Sweden). After centrifugation (15 min at 500 × g), the tumor cells were obtained from the top of the interface and resuspended in RPMI medium. To obtain isolated tumor cells of high purity, the ASML cells were subsequently isolated by fluorescence-activated cell sorting (FACS) using green fluorescent protein (GFP) as a marker. Afterwards, the pure cells were pelleted at 3,000 rpm for 5 min and snap-frozen at -80 °C. An aliquot of the cells that were isolated on day 21 was used for reculturing ASML cells in vitro. These cells were propagated every 3 days, and 2 time points (14 and 22 days after tumor cell explantation) were chosen for subsequent microarray analysis and reverse transcription-polymerase chain reaction (RT-PCR; see below).

RNA Isolation

The RNeasy mini kit (Qiagen) was used for RNA isolation from ASML cells. The amount and purity of the isolated RNA was measured in a spectrophotometer using the 260 nm/280 nm absorbance ratio.

mRNA Microarray

Messenger RNA (mRNA) expression analysis was done by Illumina Human Sentrix-12 BeadChip arrays (Illumina, Inc.) as described before [18,19].

Statistics

Quantile-normalized mRNA and microRNA (miRNA) data were log2 transformed. Differentially expressed transcripts between groups were identified with the empirical Bayes approach based on moderated t statistics as implemented in the Bioconductor package limma [20]. Pairing of samples was accounted for as appropriate. Polynomials of first (linear) and second (quadratic) degree were used to identify transcripts with specific expression pattern over time. All p values were adjusted for multiple testing using Benjamini-Hochberg correction in order to control the false discovery rate. All p values are 2-sided; p values below 0.05 were considered statistically significant. All analyses were carried out using R 3.0 [21].

Ingenuity

The data were analyzed by Ingenuity Pathways Analysis (IPA; Ingenuity® Systems, www.ingenuity.com). A data set containing gene identifiers and corresponding expression values was uploaded into in the IPA application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity Pathways Knowledge Base. A fold change cutoff of 3 was set to identify genes whose expression was significantly differentially regulated. These genes, called focus genes, were overlaid onto a global molecular network developed from information contained in the Ingenuity Pathways Knowledge Base. Networks of these focus genes were then algorithmically generated based on their connectivity. The functional analysis identified the biological functions and/or diseases that were most significant to the data set. Genes from the data set that met the fold change cutoff of 3 and that were associated with biological functions and/or diseases in the Ingenuity Pathways Knowledge Base were considered for the analysis. Fisher's exact test was used to calculate a p value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone. The IPA regulation z-score algorithm was used to predict the direction of change for a given function (increased or decreased expression). A z-score ≥ 2 means that a function is significantly increased whereas a z-score ≤ 2 indicates a significantly decreased function.

Results

Re-Isolation of Tumor Cells from Liver Tissue

ASML cells that had been implanted into BDX rats were re-isolated after various periods. The purity of these cells was ensured by a procedure that involved gradient centrifugation and FACS. Depending on the number of tumor cells, the final grade of purity varied from 90% (day 1) to 99% (day 15) based on 1.6-52% of tumor cells present in the cell suspension after gradient centrifugation (fig. 1).

Fig. 1

FACS analyses of ASML rat pancreatic cancer cells following re-isolation from rat liver used for cell sorting. Rows 1-3 of the upper part of the diagram show the distribution of the respective cells after 1, 6, and 15 days following tumor cell implantation and re-isolation. Columns show the distribution of cells by forward (FSC-A) and side (SSC-A) scatter as well as the window selected for population P1 (column 1), by enhanced GFP (eGFP; EGFP-A) fluorescence intensity versus forward scatter with population P2 (column 2), and by FSC-A versus FCS-H for the discrimination of doublets, yielding population P3 (column 3). Column 4 shows the intensity of eGFP fluorescence versus cell count, with the eGFP-positive cells positioned at the far-right side. The table at the bottom part of the figure shows the relative distributions of populations P1, P2, and P3 relative to the respective preceding populations and relative to the total cell counts.

http://www.karger.com/WebMaterial/ShowPic/150118

mRNA Modulation during Liver Colonization

In order to comprehend the changes that take place during colonization in pancreatic cancer liver metastasis, we compared the gene expression profiles of ASML cells re-isolated from rat liver after days 1, 3, 6, 15, and 21 post injection into the portal vein. 23,400 genes were analyzed at different stages of colonization, and the modulations in the expression levels of these genes are summarized in table 1.

Table 1

Overview on the modulated mRNA expression in re-isolated ASML rat pancreatic cancer cells

http://www.karger.com/WebMaterial/ShowPic/150121

The gene expression profiles of the cells isolated after days 1 and 3 were classified as expression data reflecting early colonization, as no tumor burden was visible with the naked eye. The mRNA data of the cells isolated on days 1 and 3 revealed that 882 genes were ≥ 3 times up-regulated over the control sample. Out of these genes, 12% were 20-fold up-regulated. On the other hand, 1,189 genes were down-regulated on days 1 and 3 post isolation, with 3% of the genes showing 20-fold down-regulation.

On day 6, the ASML cells showed signs of infiltrative growth into the rat liver, visible as white spots of 1-2 mm in diameter which were macroscopically discernible. This stage of colonization was classified as intermediate colonization, and the expression profile from this stage showed that 710 genes were up-regulated and 1,032 genes were down-regulated, with 9.4% of the genes being up-regulated 20 times and 5.1% of the genes being down-regulated 20 times compared to the control.

In the advanced colonization stage 15 days post injection, 2,356 genes were found to be deregulated. In this stage, the ASML cells colonized about 40% of the rat liver and the tumor spot size had increased to ∼5 mm in diameter. Out of the 252 ≥ 3-fold up-regulated genes, 7.5% showed over 20-fold up-regulation. Similarly, 2,112 genes were ≥ 3-fold down-regulated, 5.7% of which were down-regulated over 20-fold.

The terminal stage of liver colonization was classified as the stage when ASML cells had almost completely infiltrated the rat liver (21 days post injection). 20-fold up-regulation was seen in 18.1% from a total of 1,065 up-regulated genes. In addition, 45 (1.7%) of 2,576 genes were down-regulated by a factor of 20.

The Ingenuity software was used to shortlist cancer-associated genes among the deregulated genes at each colonization stage. Figure 2 represents the cancer-associated genes from each of the colonization processes and the number of overlapping genes that play a role in the transition from one stage of colonization to another. A set of 146 cancer genes was found to be shared between the early and intermediate stages of colonization, and an overlap of 115 genes was seen between the advanced and terminal stages.

Fig. 2

Number and distribution of modulated genes related to metastasis. The circles indicate the 4 phases of liver colonization (early, intermediate, advanced, and terminal). The total number of the respective modulated genes is given in the centers of the circles; the number of overlapping genes (modulated in both phases of liver colonization) is given in the respective overlapping part of 2 circles.

http://www.karger.com/WebMaterial/ShowPic/150117

Processes Altered during Colonization of the Liver

We next analyzed the processes that were altered during the colonization of the rat liver based on those genes that were altered during the various phases of colonization. The processes of cell homing, cell proliferation, cell movement, apoptosis, angiogenesis, and vasculogenesis, which are associated with metastasis, were analyzed using the Ingenuity Pathway analysis software. The significance of these processes was scored based on the cumulative p value and activation z-score (table 2). The p value indicates the significance of alteration for the genes analyzed, and the z-score indicates the extent of activation of the process. Each stage of colonization was compared to the control sample and also with respect to the previous stage.

Table 2

Overview of the function annotations and their activation z-scores in the different liver colonization stages

http://www.karger.com/WebMaterial/ShowPic/150120

Based on the cumulative p value, in the early stage, the process of cell proliferation was highly deregulated (p = 10-17) and cell homing was the least deregulated (p = 10-5). Except for the process of apoptosis, all other processes were negatively activated and did not result in a significant z-score when compared to the control.

In the intermediate stage, again all the genes associated with the processes under investigation were found to be significantly deregulated based on the p value. However, the activation z-scores were not significantly altered, with the possible exception of the cell movement process, for which a down-regulation trend was perceived. When compared to the early stage of colonization, the intermediate stage showed significantly negative activation z-scores of the cell movement, proliferation, and homing processes.

In the advanced stage, the processes of cell proliferation and cell movement were significantly down-regulated, with activation z-scores of -2.8, when compared to the in vitro control sample. Also, the genes involved in all the processes, except for cell homing, were significantly deregulated with p values between 10-14 and 10-5.

In the terminal stage, all the processes, except for apoptosis, showed a tendency of positive activation when compared to the control. The genes involved in these processes were significantly deregulated, with p values ranging from 10-24 to 10-8. When the terminal stage was compared to the advanced stage, all the processes were positively activated based on the z-score values and the process of cell movement was the most highly activated with a z-score of 3.2.

Genes with Distinctly Modulated mRNA Expression

Table 3 gives an overview of the 34 genes that were more than 10-fold modulated during the early stage of liver colonization. It is obvious that the majority (n = 20) of these genes was up-regulated and only a smaller fraction (n = 10) was down-regulated.

Table 3

Overview of the most relevant metastasis-relateda genes in the early stage of liver colonization

http://www.karger.com/WebMaterial/ShowPic/150119

Discussion

Liver metastasis caused by PDAC is still the main reason for its mortality. Most patients who undergo tumor resection will develop postoperative liver metastases within the first 2 years.

The ability of tumor cells to detach from the primary and to form distant metastases depends on their genetic flexibility. These cells need to adapt to surrounding tissues in a continuous way. For example, they have to escape the immune response, overcome hypoxia, adapt to new environments and form new blood vessels.

In this study, we analyzed the stages and processes that play a role in the colonization of rat liver by isogenic PDAC cells, mimicking liver metastasis in PDAC. To that purpose, we have re-isolated, after various periods, PDAC cells from rat liver at sufficiently high purity to study the modulation of PDAC gene expression in detail. The experimental design for the re-isolation of tumor cells was based on the established rat ASML PDAC model [18]. As shown before, after intraportal injection, the ASML cells grew aggressively and consistently in the rat liver. To a certain extent, the resulting colonization simulates liver PDAC metastasis with regard to growth behavior and resistance towards chemotherapy [1,5,15,22].

In re-isolated ASML cells, about 7-15% of all known genes were found to be modulated, depending on the prior growth period within the liver (colonization stage). To identify genes that play a role in metastasis, our chip array data were assigned by the Ingenuity program to specific categories related to the metastatic process. These included cell proliferation, cell movement, apoptosis, angiogenesis, and vasculogenesis. Remarkably, there were important differences in the modulation of gene expression when comparing the early, intermediate, advanced, and terminal stages of liver colonization.

The early stage of colonization was characterized by the activation of only apoptosis genes. Thus, this stage is a bottleneck that could be exploited therapeutically. Possible targets are given in table 3. With the progression of colonization, more processes were significantly involved, as can be seen from the up-regulation of genes involved in cell proliferation, cell homing, cell movement, and vasculogenesis, which were all activated in the terminal phase of liver colonization. The observed progression is probably indicative of and possibly due to the failure of the host to function as a biosystem that maintains physiological tasks.

In summary, in this study we showed that the genetic properties of the tumor cells clearly vary during the different stages of colonization. Targeting the deregulated genes in the early colonization stage of the tumor cells could reduce their ability to develop macroscopic metastasis, thus improving the survival rate of PDAC patients.

Disclosure Statement

The authors declare that there is no conflict of interest and that they did not receive any support from pharmaceutical companies or other interest groups.


References

  1. Burris HA, Moore MJ, Andersen J, Green MR, Rothenberg ML, Madiano MR, Cripps MC, Portenoy RK, Storniolo AM, Tarassoff P, Nelson R, Dorr FA, Stephens CD, VanHoff DD:Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 1997;15:2403-2413.
    External Resources
  2. Jemal A, Siegel R, Xu J, Ward E: Cancer statistics, 2010. CA Cancer J Clin 2010;60:277-300.
  3. Sant M, Allemani C, Santaquilani M, Knijn A, Marchesi F, Capocaccia R: Eurocare-4. Survival of cancer patients diagnosed in 1995-1999. Results and commentary. Eur J Cancer 2009;45:931-991.
  4. Yeo CJ, Abrams RA, Grochow LB, Sohn TA, Ord SE, Hruban RH, Zahurak ML, Dooley WC, Coleman J, Sauter PK, Pitt HA, Lillemoe KD, Cameron JL: Pancreaticoduodenectomy for pancreatic adenocarcinoma: postoperative adjuvant chemoradiation improves survival. A prospective, single-institution experience. Ann Surg 1997;225:621-633.
  5. Conroy T, Desseigne F, Ychou M, Bouche O, Guimbaud R, Becouarn Y, Adenis A, Raoul JL, Gourgou-Bourgade S, de la Fouchardiere C, Bennouna J, Bachet JB, Khemissa-Akouz F, Pere-Verge D, Delbaldo C, Assenat E, Chauffert B, Michel P, Montoto-Grillot C, Ducreux M: Folfirinox versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 2011;364:1817-1825.
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  7. Schneider G, Siveke JT, Eckel F, Schmid RM: Pancreatic cancer: basic and clinical aspects. Gastroenterology 2005;128:1606-1625.
  8. Fidler IJ: Critical determinants of metastasis. Semin Cancer Biol 2002;12:89-96.
  9. Li D, Xie K, Wolff R, Abbruzzese JL: Pancreatic cancer. Lancet 2004;363:1049-1057.
  10. Chaffer CL, Weinberg RA: A perspective on cancer cell metastasis. Science 2011;331:1559-1564.
  11. Chambers AF, Groom AC, MacDonald IC: Dissemination and growth of cancer cells in metastatic sites. Nat Rev Cancer 2002;2:563-572.
  12. Valastyan S, Weinberg RA: Tumor metastasis: molecular insights and evolving paradigms. Cell 2011;147:275-292.
  13. Yachida S, Iacobuzio-Donahue CA: The pathology and genetics of metastatic pancreatic cancer. Arch Pathol Lab Med 2009;133:413-422.
    External Resources
  14. Nakao A, Fujii T, Sugimoto H, Kanazumi N, Nomoto S, Kodera Y, Inoue S, Takeda S: Oncological problems in pancreatic cancer surgery. World J Gastroenterol 2006;12:4466-4472.
    External Resources
  15. Colucci G, Giuliani F, Gebbia V, Biglietto M, Rabitti P, Uomo G, Cigolari S, Testa A, Maiello E, Lopez M: Gemcitabine alone or with cisplatin for the treatment of patients with locally advanced and/or metastatic pancreatic carcinoma: a prospective, randomized phase III study of the Gruppo Oncologia dell'Italia Meridionale. Cancer 2002;94:902-910.
  16. Shore S, Raraty MG, Ghaneh P, Neoptolemos JP: Review article: chemotherapy for pancreatic cancer. Aliment Pharmacol Ther 2003;18:1049-1069.
  17. Glimelius B, Hoffman K, Sjoden PO, Jacobsson G, Sellstrom H, Enander LK, Linne T, Svensson C: Chemotherapy improves survival and quality of life in advanced pancreatic and biliary cancer. Ann Oncol 1996;7:593-600.
  18. Eyol E, Murtaga A, Zhivkova-Galunska M, Georges R, Zepp M, Djandji D, Kleeff J, Berger MR, Adwan H: Few genes are associated with the capability of pancreatic ductal adenocarcinoma cells to grow in the liver of nude rats. Oncol Rep 2012;28:2177-2187.
  19. Georges R, Bergmann F, Hamdi H, Zepp M, Eyol E, Hielscher T, Berger MR, Adwan H: Sequential biphasic changes in claudin1 and claudin4 expression are correlated to colorectal cancer progression and liver metastasis. J Cell Mol Med 2012;16:260-272.
  20. Smyth GK: limma: Linear models for microarray data; in Gentleman RC, Carey VJ, Huber W, Irizarry R, Dudoit S (eds): Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York, Springer, 2005, pp 397-420.
    External Resources
  21. R Development Core Team: R: a language and environment for statistical computing. Vienna, R Foundation for Statistical Computing, 2011.
  22. Adwan H: Bayer H, Perivaiz A, Sagini M, Berger MR: Riproximin is a recently discovered type II ribosome inactivating protein with potential for treating cancer. Biotechnol Adv 2014; DOI: 10.1016/j.biotecadr. 2014.03.008.

Author Contacts

Dr. sc. hum. Hassan Adwan

Toxicology and Chemotherapy Unit

Deutsches Krebsforschungszentrum Heidelberg

Im Neuenheimer Feld 581, 69120 Heidelberg, Germany

h.adwan@gmx.de


Article / Publication Details

First-Page Preview
Abstract of Original Article

Received: June 10, 2014
Accepted: June 26, 2014
Published online: July 11, 2014
Issue release date: August 2014

ISSN: 2296-5270 (Print)
eISSN: 2296-5262 (Online)

For additional information: https://www.karger.com/ORT


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References

  1. Burris HA, Moore MJ, Andersen J, Green MR, Rothenberg ML, Madiano MR, Cripps MC, Portenoy RK, Storniolo AM, Tarassoff P, Nelson R, Dorr FA, Stephens CD, VanHoff DD:Improvements in survival and clinical benefit with gemcitabine as first-line therapy for patients with advanced pancreas cancer: a randomized trial. J Clin Oncol 1997;15:2403-2413.
    External Resources
  2. Jemal A, Siegel R, Xu J, Ward E: Cancer statistics, 2010. CA Cancer J Clin 2010;60:277-300.
  3. Sant M, Allemani C, Santaquilani M, Knijn A, Marchesi F, Capocaccia R: Eurocare-4. Survival of cancer patients diagnosed in 1995-1999. Results and commentary. Eur J Cancer 2009;45:931-991.
  4. Yeo CJ, Abrams RA, Grochow LB, Sohn TA, Ord SE, Hruban RH, Zahurak ML, Dooley WC, Coleman J, Sauter PK, Pitt HA, Lillemoe KD, Cameron JL: Pancreaticoduodenectomy for pancreatic adenocarcinoma: postoperative adjuvant chemoradiation improves survival. A prospective, single-institution experience. Ann Surg 1997;225:621-633.
  5. Conroy T, Desseigne F, Ychou M, Bouche O, Guimbaud R, Becouarn Y, Adenis A, Raoul JL, Gourgou-Bourgade S, de la Fouchardiere C, Bennouna J, Bachet JB, Khemissa-Akouz F, Pere-Verge D, Delbaldo C, Assenat E, Chauffert B, Michel P, Montoto-Grillot C, Ducreux M: Folfirinox versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 2011;364:1817-1825.
  6. Oettle H, Post S, Neuhaus P, Gellert K, Langrehr J, Ridwelski K, Schramm H, Fahlke J, Zuelke C, Burkart C, Gutberlet K, Kettner E, Schmalenberg H, Weigang-Koehler K, Bechstein WO, Niedergethmann M, Schmidt-Wolf I, Roll L, Doerken B, Riess H: Adjuvant chemotherapy with gemcitabine vs observation in patients undergoing curative-intent resection of pancreatic cancer: a randomized controlled trial. JAMA 2007;297:267-277.
  7. Schneider G, Siveke JT, Eckel F, Schmid RM: Pancreatic cancer: basic and clinical aspects. Gastroenterology 2005;128:1606-1625.
  8. Fidler IJ: Critical determinants of metastasis. Semin Cancer Biol 2002;12:89-96.
  9. Li D, Xie K, Wolff R, Abbruzzese JL: Pancreatic cancer. Lancet 2004;363:1049-1057.
  10. Chaffer CL, Weinberg RA: A perspective on cancer cell metastasis. Science 2011;331:1559-1564.
  11. Chambers AF, Groom AC, MacDonald IC: Dissemination and growth of cancer cells in metastatic sites. Nat Rev Cancer 2002;2:563-572.
  12. Valastyan S, Weinberg RA: Tumor metastasis: molecular insights and evolving paradigms. Cell 2011;147:275-292.
  13. Yachida S, Iacobuzio-Donahue CA: The pathology and genetics of metastatic pancreatic cancer. Arch Pathol Lab Med 2009;133:413-422.
    External Resources
  14. Nakao A, Fujii T, Sugimoto H, Kanazumi N, Nomoto S, Kodera Y, Inoue S, Takeda S: Oncological problems in pancreatic cancer surgery. World J Gastroenterol 2006;12:4466-4472.
    External Resources
  15. Colucci G, Giuliani F, Gebbia V, Biglietto M, Rabitti P, Uomo G, Cigolari S, Testa A, Maiello E, Lopez M: Gemcitabine alone or with cisplatin for the treatment of patients with locally advanced and/or metastatic pancreatic carcinoma: a prospective, randomized phase III study of the Gruppo Oncologia dell'Italia Meridionale. Cancer 2002;94:902-910.
  16. Shore S, Raraty MG, Ghaneh P, Neoptolemos JP: Review article: chemotherapy for pancreatic cancer. Aliment Pharmacol Ther 2003;18:1049-1069.
  17. Glimelius B, Hoffman K, Sjoden PO, Jacobsson G, Sellstrom H, Enander LK, Linne T, Svensson C: Chemotherapy improves survival and quality of life in advanced pancreatic and biliary cancer. Ann Oncol 1996;7:593-600.
  18. Eyol E, Murtaga A, Zhivkova-Galunska M, Georges R, Zepp M, Djandji D, Kleeff J, Berger MR, Adwan H: Few genes are associated with the capability of pancreatic ductal adenocarcinoma cells to grow in the liver of nude rats. Oncol Rep 2012;28:2177-2187.
  19. Georges R, Bergmann F, Hamdi H, Zepp M, Eyol E, Hielscher T, Berger MR, Adwan H: Sequential biphasic changes in claudin1 and claudin4 expression are correlated to colorectal cancer progression and liver metastasis. J Cell Mol Med 2012;16:260-272.
  20. Smyth GK: limma: Linear models for microarray data; in Gentleman RC, Carey VJ, Huber W, Irizarry R, Dudoit S (eds): Bioinformatics and Computational Biology Solutions Using R and Bioconductor. New York, Springer, 2005, pp 397-420.
    External Resources
  21. R Development Core Team: R: a language and environment for statistical computing. Vienna, R Foundation for Statistical Computing, 2011.
  22. Adwan H: Bayer H, Perivaiz A, Sagini M, Berger MR: Riproximin is a recently discovered type II ribosome inactivating protein with potential for treating cancer. Biotechnol Adv 2014; DOI: 10.1016/j.biotecadr. 2014.03.008.
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