Specialized DNA arrays for the differentiation of pancreatic tumors

Clinical Cancer Research

Volumn 11, Issue 22, 2005, Pages 8048-8054

  Buchholz, Malte ^a   Kestler, Hans A ^a   Bauer, Andrea ^b   Böck, Wolfgang ^a   Rau, Bettina ^c   Leder, Gerhard ^a   Kratzer, Wolfgang ^a   Bommer, Martin ^a   Scarpa, Aldo ^d   Schilling, Martin K ^c   Adler, Guido ^a   Hoheisel, Jörg D ^b   Gress, Thomas M ^a  

^a UNIVERSITY OF ULM   (Germany)

^b GERMAN CANCER RESEARCH CENTER DKFZ   (Germany)

^c SAARLAND UNIVERSITY   (Germany)

^d UNIVERSITY OF VERONA   (Italy)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; ASPIRATION BIOPSY; CHRONIC PANCREATITIS; COMPUTER ASSISTED TOMOGRAPHY; CONTROLLED STUDY; CYTOLOGY; DIAGNOSTIC ACCURACY; DIAGNOSTIC VALUE; DIFFERENTIAL DIAGNOSIS; ECHOGRAPHY; EVALUATION; GENE EXPRESSION PROFILING; HUMAN; HUMAN TISSUE; NEEDLE BIOPSY; PANCREAS ADENOCARCINOMA; PANCREAS DUCTAL ADENOCARCINOMA; PANCREAS TUMOR; PRIORITY JOURNAL; RELIABILITY; STATISTICAL ANALYSIS;

ADENOCARCINOMA; ALGORITHMS; BIOPSY, FINE-NEEDLE; DIAGNOSIS, DIFFERENTIAL; HUMANS; OLIGONUCLEOTIDE ARRAY SEQUENCE ANALYSIS; PANCREAS; PANCREATIC NEOPLASMS; REPRODUCIBILITY OF RESULTS; RNA, NEOPLASM; SENSITIVITY AND SPECIFICITY;

EID: 28144452515     PISSN: 10780432     EISSN: None     Source Type: Journal    
DOI: 10.1158/1078-0432.CCR-05-1274     Document Type: Article

References (30)

1. Kehagias D, Smyrniotis V, Kalovidouris A, et al. Cystic tumors of the pancreas: preoperative imaging, diagnosis, and treatment. Int Surg 2002;87:171-4.
2. Brandwein SL, Farrell JJ, Centeno BA, Brugge WR. Detection and tumor staging of malignancy in cystic, intraductal, and solid tumors of the pancreas by EUS. Gastrointest Endosc 2001;53:722-7.
3. Chhieng DC, Jhala D, Jhala N, et al. Endoscopic ultrasound-guided fine-needle aspiration biopsy: a study of 103 cases. Cancer 2002;96:232-9.
4. Voss M, Hammel P, Molas G, et al. Value of endoscopic ultrasound guided fine needle aspiration biopsy in the diagnosis of solid pancreatic masses. Gut 2000;46: 244-9.
5. Afify AM, al Khafaji BM, Kim B, Scheiman JM. Endoscopic ultrasound-guided fine needle aspiration of the pancreas. Diagnostic utility and accuracy. Acta Cytol 2003;47:341-8.
6. Li D, Xie K, Wolff R, Abbruzzese JL. Pancreatic cancer. Lancet 2004;363:1049-57.
7. Pugliese V, Pujic N, Saccomanno S, et al. Pancreatic intraductal sampling during ERCP in patients with chronic pancreatitis and pancreatic cancer: cytologic studies and k-ras-2 codon 12 molecular analysis in 47 cases. Gastrointest Endosc 2001;54: 595-9.
8. Wang Y, Yamaguchi Y, Watanabe H, et al. Detection of p53 gene mutations in the supernatant of pancreatic juice and plasma from patients with pancreatic carcinomas. Pancreas 2004;28:13-9.
9. Van Laethem JL, Bourgeois V, Parma J, et al. Relative contribution of Ki-ras gene analysis and brush cytology during ERCP for the diagnosis of biliary and pancreatic diseases. Gastrointest Endosc 1998; 47:479-85.
10. Urgell E, Puig P, Boadas J, et al. Prospective evaluation of the contribution of K-ras mutational analysis and CA 19.9 measurement to cytological diagnosis in patients with clinical suspicion of pancreatic cancer. Eur J Cancer 2000;36:2069-75.
11. Chhieng DC, Benson E, Eltoum I, et al. MUC1 and MUC2 expression in pancreatic ductal carcinoma obtained by fine-needle aspiration. Cancer 2003; 99:365-71.
12. Pinto MM, Emanuel JR, Chaturvedi V, Costa J. Ki-ras mutations and the carcinoembryonic antigen level in fine needle aspirates of the pancreas. Acta Cytol 1997;41:427-34.
13. Lohr M, Muller P, Mora J, et al. p53 and K-ras mutations in pancreatic juice samples from patients with chronic pancreatitis. Gastrointest Endosc 2001;53: 734-43.
14. Gress TM, Muller-Pillasch F, Geng M, et al. A pancreatic cancer-specific expression profile. Oncogene 1996;13:1819-30.
15. Crnogorac-Jurcevic T, Efthimiou E, Nielsen T, et al. Expression profiling of microdissected pancreatic adenocarcinomas. Oncogene 2002;21:4587-94.
16. Han H, Bearss DJ, Browne LW, et al. Identification of differentially expressed genes in pancreatic cancer cells using cDNA microarray. Cancer Res 2002;62: 2890-6.
17. Nakamura T, Furukawa Y, Nakagawa H, et al. Genome-wide cDNA microarray analysis of gene expression profiles in pancreatic cancers using populations of tumor cells and normal ductal epithelial cells selected for purity by laser microdissection. Oncogene 2004;23:2385-400.
18. Iacobuzio-Donahue CA, Ashfaq R, Maitra A, et al. Highly expressed genes in pancreatic ductal adenocarcinomas: a comprehensive characterization and comparison of the transcription profiles obtained from three major technologies. Cancer Res 2003;63: 8614-22.
19. Maitra A, Hansel DE, Argani P, et al. Global expression analysis of well-differentiated pancreatic endocrine neoplasms using oligonucleotide microarrays. Clin Cancer Res 2003;9:5988-95.
20. Carriaga MT, Henson DE. Liver, gallbladder, extrahepatic bile ducts, and pancreas. Cancer 1995;75: 171-90.
21. Jolliffe IT. Principal component analysis. 2nd ed. New York: Springer: 2002.
22. Kestler HA. ROC with confidence-a Perl program for receiver operator characteristic curves. Comput Methods Programs Biomed 2001;64:133-6.
23. Lang TA, Secic M. How to report statistics in medicine. Philadelphia (PA): American College of Physicians; 1997.
24. Swets JA, Pickett RM. Evaluation of diagnostic systems. New York: Academic Press; 1982.
25. Gress TM, Wallrapp C, Frohme M, et al. Identification of genes with specific expression in pancreatic cancer by cDNA representational difference analysis. Genes Chromosomes Cancer 1997;19:97-103.
26. Geng M, Wallrapp C, Muller-Pillasch F, et al. Isolation of differentially expressed genes by combining representational difference analysis (RDA) and cDNA library arrays. Biotechniques 1998;25:434-8.
27. Cover TM. Geometrical and statistical properties of systems of linear inequalities with applications in pattern recognition. IEEE Trans Electronic Comput 1965; 14:326-34.
28. Khan J, Wei JS, Ringner M, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nat Med 2001;7:673-9.
29. Golub TR, Slonim DK, Tamayo P, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.
30. 'tVeer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-6.