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Molecular alterations in exocrine neoplasms of the pancreas.

Pancreatic cancer is one of the leading causes of cancerrelated deaths in the Western world. Pancreatic neoplasms are broadly classified as exocrine neoplasms and endocrine neoplasms. Most pancreatic neoplasms are of exocrine type, which includes invasive ductal adenocarcinoma, intraductal neoplasms, pancreatic intraepithelial neoplasms, serous neoplasms, mucinous cystic neoplasms, and acinar cell neoplasms. (1) Infiltrating ductal adenocarcinoma is the most common exocrine neoplasm associated with most pancreatic cancer-related deaths in the United States. Pancreatic cancer largely remains asymptomatic and is often diagnosed at an advanced stage when surgical intervention is not possible. This has prompted efforts to identify molecular markers that could potentially identify pancreatic cancers at an early stage. Currently available markers such as carbohydrate antigen (CA) 19-9 lack the desired sensitivity and specificity because they are also overexpressed in inflammatory conditions such as chronic pancreatitis.

Advances in molecular techniques have greatly facilitated our ability to study pancreatic cancers at the molecular level. High-throughput molecular studies to characterize various molecular alterations at genomic, epigenetic, tran scriptomic and proteomic levels have provided insights into mechanisms of cancer initiation and progression. This review provides an overview of various molecular alterations documented in precursor lesions and invasive cancer of the pancreas.


Histologically, distinct precursor lesions to pancreatic cancers have been well described and the associated genetic alterations have also been characterized. Although these preneoplastic lesions were initially observed as morphologically distinct features most commonly associated with pancreatic cancers, developments in molecular genetic techniques established them as precursors to infiltrating ductal adenocarcinomas on the basis of some common mutations observed in these lesions and associated infiltrating cancer. (2) This led to the development of stepwise progression model of pancreatic neoplasia based on pathologic, clinical, and genetic observations. (3) This model describes progression of normal pancreatic epithelium to infiltrating cancer through a series of histologically defined lesions called pancreatic intraepithelial neoplasms (PanINs). The model is further supported by molecular evidence of increasing prevalence of genetic alterations as the PanINs progress from minimal cytologic and architectural atypia to invasive cancer. Most of the genetic alterations observed in invasive cancers are also observed in these precursor lesions, thus establishing them as early events leading to cancer.

As predicted by the progression model, some of the earliest lesions (PanIN-1 and PanIN-2) show genetic alterations such as telomere shortening, (4) activating mutation in codon 12 of KRAS2 oncogene (5) and inactivation of CDKN2A/p16 (cyclin-dependent kinase inhibitor 2A) tumor suppressor gene. (6) Late-stage precursor lesions (PanIN-3) harbor a variety of additional molecular alterations including inactivation of tumor suppressor genes SMAD4/DPC4 (SMAD family member 4/deleted in pancreatic carcinoma locus 4) and TP53 (tumor protein p53), along with DNA maintenance gene BRCA2 (7). KRAS2 mutations have been reported in 36% to 44% of early precursor lesions while about 87% of high-grade PanINs are known to harbor these mutations. (8) Advances in gene expression analysis during the past decade have led to an explosion in gene expression studies to determine differentially regulated genes/proteins in precursor lesions and invasive cancers. Several expression abnormalities have been characterized in these precursor lesions both at the mRNA and protein level. (9-11) However, the prevalence of overexpressing candidate genes has largely come from immuno-labeling experiments on panels of tissue sections derived from multiple patients. Immunohistochemical labeling of several genes including CDKN1A, (12) PSCA, (7,13) MMP7, (14) MUC4, (15,16) CLDN18, (17) ANXA2, (18) BIRC5, (19,20) MUC5, (7) and S100P (21) in multiple PanIN lesions has shown a progressive increase in their expression from low- to high-grade PanINs and leading into invasive cancer. Their gene products should be good candidates to validate in serum and urine an early diagnosis of pancreatic cancer.


Several genomic anomalies have been characterized in pancreatic cancers, which include chromosomal aberrations and mutations. The most frequent chromosomal abnormalities observed in pancreatic adenocarcinomas include loss of chromosomes 18, 10, 4, 15p, 14p, 5, 13p, 21p, and 17p and gain of chromosomes 2, 16, and 1q. The most common chromosomal losses observed in most pancreatic adenocarcinomas include chromosome 18 (78%), 17 (56%), 6 (44%), 21 (42%), 22 (42%), Y (36%), and 4 (33%), while gain of chromosome 20 has been observed in about 28% of cases.22 Studies on allelic loss in preneoplastic lesions of pancreatic adenocarcinomas have identified frequent losses in 9p, 17p, and 18q. (23-25) This is in agreement with inactivation or loss of expression of CDKN2A/p16, TP53, and SMAD4/DPC4 genes which are located on chromosome 9, 17, and 18, respectively. Frequent loss of expression of these tumor suppressor genes has been observed as an early event by immunohistochemistry-based protein expression analyses. (7,12,26)

Copy number variations in pancreatic cancer have been studied by several groups using various techniques such as nonradioisotopic slot-blot technique and array comparative genomic hybridization. Amplification of KRAS2 and CMYC and loss of TP53 genes have been identified by nonradioisotopic slot-blot method. These changes are correlated with tumor grade and survival and hence are of prognostic significance. Since such changes also give an insight into the tumor behavior, they could be useful in deciding the therapeutic strategy. (27) Array-based comparative genomic hybridization is a powerful technique to identify changes in the chromosomal copy number. A genomewide copy number variation analysis on pancreatic cancer has shown that SKAP2/SCAP2 is amplified frequently in pancreatic ductal adenocarcinoma and this amplification also results in an increased expression of its gene product. This amplification and the resulting increase in mRNA expression are likely to be associated with the development of pancreatic cancer. (28) A similar approach has shown that the SEC11L3 gene is deleted in most pancreatic cancers and raises the possibility of this gene being a tumor suppressor gene. (29) A study using xenografts of pancreatic cancer and cell lines has identified several chromosomal copy number changes in pancreatic cancer and also differences between cell lines and xenograft models. With this approach, it is possible to derive genomic signatures of the cancer types and illustrate the utility of array comparative genomic hybridization technology for diagnostics. Although large-scale studies are lacking, this approach, based on the genomic signature, has the potential to differentiate a primary neoplasm from a recurrent secondary tumor. (30) Large-scale studies to further identify and characterize chromosomal changes specific to pancreatic cancer are required before this technique can be pursued for diagnosis.

Mutations in oncogenes and tumor suppressor genes have long been characterized as one of the important events in carcinogenesis. One of the earliest genetic anomalies to be characterized in pancreatic adenocarcinomas was the occurrence of an activating point mutation in codon 12 of KRAS2. Point mutations in KRAS2 have been observed in greater than 90% of pancreatic adenocarcinomas. (31) Although an activating mutation in codon 12 is the most frequently observed KRAS2 mutation in pancreatic adenocarcinomas, mutations in codons 13 and 61 have also been observed.31 Because the codon 12 mutation is also prevalent among precursor lesions of pancreatic cancer, this is thought to be one of the earliest events in the multistep progression of pancreatic cancers. This is also evident from several mouse models of pancreatic cancer where KRAS2 mutation seems to be a prerequisite to cancer. (32-36) The other frequently observed genetic anomalies in pancreatic cancers include inactivation or loss of expression of various tumor suppressor genes such as CDKN2A/p16, (6,26,37) TP53, (38) and SMAD4/DPC4. (39,40) These genes are inactivated through various mechanisms including mutations, homozygous deletions, and promoter methylation. For example, loss of CDKN2A/p16 function occurs in approximately 95% of pancreatic adenocarcinomas. Inactivation of CDKN2A/p16 by point mutation in one allele with a concomitant loss of the other allele (loss of heterozygosity) has been observed in 40% of these cases, while homozygous deletion and hypermethylation of the promoter have been observed in 40% and 15% of cases, respectively.26,37 These anomalies in tumor suppressor genes coexist and cooperate in tumor development. (41)

Germline mutations in BRCA2 are a predisposing factor to pancreatic cancer, as seen in the Ashkenazi Jewish population. (42) A major drawback to using these gene alterations for diagnostic purposes is their mode of detection. Although many of these mutations represent some of the early events that may have a causal effect in pancreatic cancers, they can only be confirmed by DNA sequencing. Apart from familial cases with a family history of predisposition to pancreatic cancers, a DNA sequencing-based approach is thus of limited utility for diagnosis. Table 1 lists a subset of genetic defects reported in pancreatic cancers.


Epigenetic changes such as DNA methylation, posttranslational modifications of histones, and changes in microRNA profiles, all of which can potentially lead to changes in gene expression, have gained considerable importance in the context of cancers. Below, we will discuss some of the studies, which have catalogued epigenetic variations in pancreatic cancers.

Aberrant Methylation in Pancreatic Cancers

Aberrant hypermethylation of promoters is commonly observed in cancers and is one of the contributing factors for inactivation of tumor suppressor genes. (43) Hypermethylation of p16/CDKN1A in a subset of pancreatic cancers was one of the early reports of aberrant methylation in pancreatic cancers. (6) A study carried out on a large panel of pancreatic cancers identified specific genes showing hypermethylation in pancreatic cancer. (44) Several genes important in the pathogenesis of pancreatic cancer such as APC, (45) TSLC/IGSF4, (46) SOCS-1, (47) cyclin D2, (48) RASSF1A, (49) WWOX, (50) RUNX3, (51) CDH13, (52) DUSP6, (53) HHIP, (54) and SLC5A8 (55) undergo aberrant methylation, leading to reduced expression or loss of expression. Most of these were identified by methylation-specific polymerase chain reaction of candidate genes. The aberrant methylation of preproenkephalin and p16 genes has also been observed in precursor lesions of the pancreas, PanINs, and intraductal papillary mucinous neoplasms. The aberrant methylation was found to increase progressively with the advancing stage of pancreatic cancer. (56,57)

A high-throughput approach to identify novel methylation sites, by Sato and colleagues,58 exploits the reversal of methylation by DNA methyl transferase inhibitor, 5-aza-2'-deoxycytidine, which results in increased mRNA expression of these genes. This approach identified 475 genes that were induced by 5-aza-2'-deoxycytidine in 4 pancreatic cancer cell lines but not in a normal pancreatic ductal epithelial cell line (HPDE). Subsequently, it was found that 11 genes showed aberrant hypermethylation in primary pancreatic cancers. (58) UCHL1/PGP9.5, which codes for a carboxyl terminal ubiquitin ligase, was one of the genes identified by this approach and is reported to be methylated in several other cancers, such as head and neck and esophageal cancer. (59) Table 2 lists some of the genes reported to be aberrantly methylated in pancreatic cancers.


MicroRNAs (miRNAs) are RNA molecules 18 to 24 bases long that regulate the stability and translational efficiency of the complementary target mRNAs. The role of miRNAs in development and disease has become evident during the past few years. (60) Several groups have carried out profiling of miRNAs in a variety of cancers including pancreatic cancer. Studies have shown that a microRNA profile consisting of 217 molecules could identify the cancer type better than the mRNA profile. (61) The microRNA profile for different cancers is unique and therefore could serve as a useful biomarker. Lee and colleagues (62) have carried out profiling of miRNAs from normal, adjacent benign, and pancreatic cancer tissues and have identified unique profiles of micro RNA expression. Differential expression pattern of a group of 112 miRNAs could correctly distinguish pancreatic cancer tissue from adjacent normal and benign tissues including pancreatitis. Notably, pancreatic cancer cell lines also had a distinct miRNA expression profile as compared to pancreatic cancer tissue. Bloomston and coworkers (63) have also carried out similar miRNA profiling and identified a global expression pattern of miRNAs that can differentiate normal pancreas, chronic pancreatitis, and pancreatic ductal adenocarcinoma. This study also identified miR-196a-2 as a significant predictor of survival. Another study has demonstrated potential utility of miR-217 and miR-196a expression profile in discriminating pancreatic cancer tissue from normal pancreas and chronic pancreatitis. (64) Since the miRNA profiles in the various cancer types are expected to be unique, these studies are promising for identification of unique signatures of pancreatic cancer and its subtypes. (65) Table 3 lists some of the aberrantly expressed miRNAs in pancreatic cancer.


One of the most widely used methods to identify genes that are differentially regulated is to carry out mRNA and protein expression profiling. The number of studies is too large to cover in this section. We will illustrate these types of alterations from some of the representative studies. A more comprehensive compendium of molecules that are overexpressed in pancreatic cancer is being prepared by our group. Several transcriptome profiling studies have been carried out with a variety of DNA microarray platforms and serial analysis of gene expression to identify differentially expressed genes in pancreatic cancers. (10,66-74) A subset of molecules found to be overexpressed in pancreatic cancer by multiple expression profiling studies is listed in Table 4.

One of the inherent problems with mRNA-based gene expression data is that mRNA levels do not necessarily correlate with protein expression. Mass spectrometry is an emerging technology, which is increasingly being used for global proteomic profiling studies. Data from such highthroughput proteomic studies complement the available mRNA expression data and facilitate identification of potential candidates for further validation. Proteomic approaches also provide an advantage over mRNA-based studies by making it possible to analyze body fluids like serum and urine. Several mass spectrometry-based proteomic profiling studies have been carried out to identify differentially regulated proteins in pancreatic cancers. (11,75-80) Two-dimensional gel electrophoresis followed by mass spectrometry have identified several differentially expressed proteins such as cyclin 1, GDP dissociation inhibitor 2, (81) haptoglobin, [[alpha].sub.1]-antitrypsin (82) and T-box 4 (83) in pancreatic cancer from serum and pancreatic juice. (84) In another study in which a quantitative proteomics approach was used, IGFBP2, kallikrein 1 precursor, and several other proteins were identified from pancreatic juice, a finding that can potentially differentiate chronic pancreatitis and pancreatic cancer. (75,77) An extensive analysis of the pancreatic cancer secretome, by stable isotope labeling with amino acids in cell culture, identified 145 proteins differentially secreted by pancreatic cancer cells as compared to normal pancreatic ductal cells. Some of these molecules were subsequently validated in tumor tissue. (85) Proteomic profiling of pancreatic juice from a patient with pancreatic cancer has led to identification of about 170 proteins, including several known biomarkers. (86) This could also prove valuable for identifying candidate markers that have the potential of being detectable in body fluids like serum and urine. Although proteomic studies on body fluids like serum and pancreatic juice have identified several proteins that are differentially expressed in pancreatic cancers, experiments to determine if these changes are tumor derived are generally not carried out. Future proteomic profiling studies on body fluids should also analyze the corresponding cancer tissue to clarify specific and nonspecific observations in the context of cancers. Other proteomic approaches do not involve mass spectrometry. For example, Lemoine and colleagues used power blot analysis with several well-characterized antibodies in normal pancreas, chronic pancreatitis, and pancreatic cancer. (71) Single-chain antibody microarrays have been used by Borrebaeck and colleagues (87) to identify protein signature in pancreatic cancer serum.


In many pathologic conditions, mRNA or protein expression might not be altered; instead, proteins may be differently localized or posttranslationally modified. One of the important posttranslational modifications implicated in human diseases is glycosylation. (88) For example, serum levels of the carbohydrate antigen, CA 19-9, are routinely used as a prognostic indicator in monitoring pancreatic cancers. However, the major drawback of this test is the lack of specificity because CA 19-9 is elevated not only in pancreatic cancers but also in inflammatory conditions such as chronic pancreatitis.89 However, a combination of different markers such as TSGF, CA 242, and CA 19-9 was reported to be more specific than was the use of any single marker. (90) An alteration of the glycan patterns of MUC1 and carcinoembryonic antigen has been reported in pancreatic cancer. (91) Comparison of the glycoprotein profiles from normal, chronic pancreatitis, and pancreatic cancer sera showed differential sialylation and fucosylation of proteins such as hemopexin, kininogen 1, and antithrombin III that were observed only in cancer. (92-94) Table 5 lists some of the proteins that show altered glycosylation patterns in pancreatic cancer.

Differential humoral immune response in diseases is being studied for diagnostic purposes as well as for monitoring therapy. (95-97) It is known that tumor cells express certain proteins that can elicit an immune response. Antibodies to these proteins therefore can be detected in patients and could be used as biomarkers for cancer. (98) Several autoantibodies to tumor-associated antigens can be detected at very early stages of malignancy, long before the standard clinical tests can detect cancer and therefore are of immense value as diagnostic markers for early detection. (95) Tumor-associated antigen arrays for profiling autoantibodies could be sensitive enough to identify and differentiate cancers. Some of the antibodies against tumor-associated antigens in pancreatic cancer sera are listed in Table 6. In 2007, Tomaino et al138 carried out an extensive proteomic study with several normal, chronic pancreatitis, and pancreatic cancer samples to profile the antibodies against pancreatic cancer-associated antigens. This study revealed that several metabolic enzymes (glucose-6-phosphate 1-dehydrogenase, isocitrate dehydrogenase) and cytoskeletal proteins (keratin 10, cofilin 1) are responsible for the humoral response in pancreatic cancer.


The exocrine neoplasms that are less common include acinar cell neoplasms, serous neoplasms, mucinous cystic neoplasms, and intraductal neoplasms. Because of their rarity, relatively few studies have addressed molecular alterations in these neoplasms. Loss of heterozygosity in acinar cell carcinoma has been reported on chromosomes 1p, 4q, 17p, 11q, 13q, 15q, 3q, 6q, 8q, 18q, and 21q. (99) Allelic loss of chromosome 11p and molecular alterations in APC/[beta]-catenin pathway have also been reported in acinar cell neoplasms. (100) However, genetic alterations more common in ductal adenocarcinomas, like mutations in KRAS2, TP53, CDKN2A/p16, and SMAD4/DPC4, have not been observed in these neoplasms. (101) Immunolabeling-based studies have reported overexpression of CTNNB1, (100) INHA, MUC6, ENO2, MUC1, (102) ANXA1, (103) CA9, (104) and SPARCL1 (105) in some of these less common exocrine neoplasms.


Systematic identification and characterization of molecular alterations associated with pancreatic cancers should improve our understanding of this disease. A large number of genomic, epigenetic, transcriptomic, and proteomic changes associated with pancreatic cancers have already been documented in the literature. Efforts to integrate this data and carry out systematic validation to determine prevalence and clinical utility of these molecular alterations are major tasks that lie ahead and could lead to development of novel diagnostic markers and therapeutic targets. Toward this end, one of us (H.C.H.), together with colleagues, has compiled a list of all the genes reported to be overexpressed in pancreatic cancers both at the mRNA and protein level (unpublished data, 2008).

This study was supported by a grant from The Sol Goldman Pancreatic Cancer Research Center at The Johns Hopkins University, Baltimore, Maryland.

Accepted for publication October 6, 2008.


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Prathibha Ranganathan, PhD; H. C. Harsha, MSc; Akhilesh Pandey, MD, PhD

From the Institute of Bioinformatics, International Technology Park, Bangalore, India (Dr Ranganathan and Mr Harsha); Manipal University, Manipal, Karnataka, India (Mr Harsha); the McKusick-Nathans Institute of Genetic Medicine and the Departments of Biological Chemistry (Mr Harsha and Dr Pandey) and Pathology and Oncology (Dr Pandey), The Johns Hopkins University, Baltimore, Maryland.

The authors have no relevant financial interest in the products or companies described in this article.

Reprints: Akhilesh Pandey, MD, PhD, The Johns Hopkins University, McKusick-Nathans Institute of Genetic Medicine, 733 N Broadway, BRB 527, Baltimore, MD 21205 (e-mail:
Table 1. Partial List of Genetic Alterations Found in Pancreatic Cancer

Gene Symbol   Alteration            Method

FHIT          Deletion              aCGH
FBXW7         Deletion              aCGH
CDKN2A/B      Deletion              aCGH
SMAD4         Deletion              aCGH
EGFR          Amplification         aCGH
              Activating mutation   DNA sequencing
LUNX          Amplification         aCGH
DNMT3b        Amplification         aCGH
ERBB2         Amplification         aCGH
KRAS          Point mutation        RNase A mismatch
                                    cleavage; PCR-RFLP
                                    and allele-specific
                                    DNA sequencing
TP53          Point mutation        PCR-SSCP

Gene Symbol   Source, y
FHIT          Nowak et al, (30) 2005
FBXW7         Nowak et al, (30) 2005
CDKN2A/B      Nowak et al, (30) 2005
SMAD4         Nowak et al, (30) 2005
EGFR          Nowak et al, (30) 2005
              Kwak et al, (106) 2006
LUNX          Loukopoulos et al, (107) 2007
DNMT3b        Loukopoulos et al, (107) 2007
ERBB2         Nowak et al, (30) 2005
KRAS          Almoguera et al, (5) 1988
              Hruban et al, (108) 1993
              Smit et al, (109) 1988

              Mariyama et al, (110) 1989

TP53          Lu et al, (111) 2002

Abbreviations: aCGH, array comparative genomic hybridization; PCR-RFLP,
polymerase chain reaction-restriction fragment length polymorphism;
PCR-SSCP, polymerase chain reaction-single-strand conformation

Table 2. Partial List of Molec-les With Aberrant Methylation in
Pancreatic Cancer

Gene Symbol   Methylation Stat-s    Techniq-e for Detecting
                                    Methylation Stat-s

14-3-3        Hypomethylation       Microarray
SPARC         Hypermethylation      Ind-ction by methylation
-CHL1         Hypermethylation      Reversal of methylation
                                    by dr-gs
Reprimo       Hypermethylation      Reversal of methylation
                                    by dr-gs
TFPI-2        Hypermethylation      Reversal of methylation
                                    by dr-gs
CDKN2A/p16    Hypermethylation      Methylation-specific PCR

ppENK         Hypermethylation      Methylation-specific PCR
BNIP3         Hypermethylation      Methylation-specific PCR
                                    and reversal of methylation
                                    by 5-aza-2-dC
NPTX2         Hypermethylation      Microarray after reversal of
                                    methylation by 5-aza-2-dC
FOXE1         Hypermethylation      Microarray after reversal of
                                    methylation by 5-aza-2-dC
MICP 27       Hypermethylation      MCA co-pled with
                                    representational difference
RARB          Hypermethylation      Methylation-specific PCR and
                                    seq-encing of bis-lfite-
                                    modified DNA

Gene Symbol   So-rce, y

14-3-3        Sato et al, (112) 2003
SPARC         Sato et al, (113) 2003

-CHL1         Sato et al, (58) 2003

Reprimo       Sato et al, (58) 2003

TFPI-2        Sato et al, (114) 2005

CDKN2A/p16    F-k-shima et al, (56) 2002
              -eki et al, (44) 2000
ppENK         F-k-shima et al, (56) 2002
BNIP3         Okami et al, (115) 2004

NPTX2         Sato et al, (58) 2003

FOXE1         Sato et al, (58) 2003

MICP 27       -eki et al, (116) 2001

RARB          -eki et al, (44) 2000

Abbreviations: MCA, methylated CpG island amplification; PCR,
polymerase chain reaction; 5-aza-2'-dC, 5-aza-2'-deoxycytidine.

Table 3. Partial List of MicroRNAs Overexpressed in
Pancreatic Cancer

MicroRNA     Expression     So-rce, y

miR-221      -preg-lated    Lee et al, (62) 2007
             -preg-lated    Bloomston et al, (63) 2007
miR-424      -preg-lated    Lee et al, (62) 2007
             Not reported   Bloomston et al, (63) 2007
miR-301      -preg-lated    Lee et al, (62) 2007
             Not reported   Bloomston et al, (63) 2007
miR-100      -preg-lated    Lee et al, (62) 2007
                            Bloomston et al, (63) 2007
let-7d       -preg-lated    Lee et al, (62) 2007
             Not reported   Bloomston et al, (63) 2007
miR-107      -preg-lated    Lee et al, (62) 2007
             -preg-lated    Bloomston et al, (63) 2007
miR-181a-c   -preg-lated    Lee et al, (62) 2007
             -preg-lated    Bloomston et al, (63) 2007
miR-155      -preg-lated    Lee et al,  (62) 2007
             -preg-lated    Bloomston et al, (63) 2007

Table 4. Partial List of Overexpressed Molec-les in Pancreatic Cancer

Gene Symbol   Method            So-rce, y

MSLN          DNA microarray    Lowe et al, (117) 2007; Logsdon et
                                al, (118) 2003; Iacob-zio-Donah-e et
                                al, (66) 2003

              SAGE              Argani et al, (119) 2001; H-stinx et
                                al, (120) 2004; Ry- et al, (121) 2002
SPARC         DNA microarray    Nakam-ra et al, (122) 2004; Lowe et al,
                                (117) 2007; Tan et al, (123) 2003;
                                Friess et al, (73) 2003; Binkley
                                et al, (124) 2004; G-weidhi et al,
                                (125) 2005
S100P         DNA microarray    Lowe et al, (117) 2007; Iacob-zio-
                                Donah-e et al, (126) 2002;
                                Crnogorac-J-rcevic et al, (72) 2003;
                                Nakam-ra et al, (122) 2004;
                                Iacob-zio-Donah-e et al, (66) 2003;
                                Iacob-zio-Donah-e et al, (67) 2003;
                                Logsdon et al, (118) 2003; Segara et
                                al, (127) 2005; Johnson et al, (128)
                                2006; Friess et al, (73) 2003;
                                B-chholz et al, (10) 2005; Pfeffer et
                                al, (129) 2004
MMP11         DNA microarray    Nakam-ra et al, (122) 2004;
                                Crnogorac-J-rcevic et al, (72) 2003;
                                Lowe et al, (117) 2007; Iacob-zio-
                                Donah-e et al, (66) 2003; Gr-tzmann et
                                al, (74) 2004 ; La-rell et al, (130)
                                2006; Pfeffer et al, (129)
                                2004; Iacob-zio-Donah-e et al, (131)
                                2002 SAGE H-stinx et al, (120) 2004
S100A11       DNA microarray    Logsdon et al, (118) 2003; Nakam-ra et
                                al, (122) 2004; Lowe et al, (117)
                                2007; Iacob-zio-Donah-e et al, (66)
                                2003; Iacob-zio-Donah-e et al, (67)
                                2003; Johnson et al, (128) 2006
SFN           DNA microarray    Nakam-ra et al, (122) 2004; Lowe et
                                al, (117) 2007; Friess et al, (73)
                                2003; Iacob-zio-Donah-e et al, (67)
                                2003; Logsdon et al, (118) 2003;
                                Johnson et al, (128) 2006; G-weidhi
                                et al, (132) 2004; Pfeffer et al,
                                (129) 2004
M-C5AC        DNA microarray    Iacob-zio-Donah-e et al, (66) 2003;
                                Lowe et al, (117) 2007; Gr-tzmann et
                                al, (133) 2003; Friess et al, (73)
ITGA2         DNA microarray    Lowe et al, (117) 2007; Iacob-zio-
                                Donah-e et al, (67) 2003; Logsdon et
                                al, (118) 2003; Cao et al, (134)
TFF2          DNA microarray    Iacob-zio-Donah-e et al, (66) 2003;
                                Johnson et al, (128) 2006
              SAGE              H-stinx et al, (120) 2004; Terris et
                                al, (135) 2002
CEACAM5       DNA microarray    Lowe et al, (117) 2007; Iacob-zio-
                                Donah-e et al, (66) 2003;
                                Iacob-zio-Donah-e et al, (67)
                                2003; Logsdon et al, (118) 2003;
                                Johnson et al, (128) 2006;
                                Kristiansen et al, (136) 2006; Friess
                                et al, (73) 2003;
                                Pfeffer et al, (129) 2004

Abbreviation: SAGE, serial analysis of gene expression.

Table 5. Partial List of Molec-les With Differential Glycosylation in
Pancreatic Cancer

Protein Name          Nat-re of Modification

Hemopexin             Increased sialylation and f-cosylation
Kininogen-1           Increased sialylation and f-cosylation
Antithrombin III      Increased sialylation and f-cosylation
Haptoglobin-related   Increased sialylation and f-cosylation
Plasma protease       Decreased sialylation
C1 inhibitor
[[alpha].s-b.1]-      Decreased N83 glycosylation
CEA                   Increased glycosylation
M-C1                  Increased glycosylation

Protein Name          Method

Hemopexin             Glycoprotein microarrays, lectin blotting
Kininogen-1           Glycoprotein microarrays, lectin blotting
Antithrombin III      Glycoprotein microarrays, lectin blotting
Haptoglobin-related   Glycoprotein microarrays, lectin blotting
Plasma protease       Glycoprotein microarrays, lectin blotting
C1 inhibitor
[[alpha].s-b.1]-      Lectin affinity followed by mass spectrometry
CEA                   Antibody arrays
M-C1                  Antibody arrays

Protein Name          So-rce, y

Hemopexin             Zhao et al, (93) 2007
Kininogen-1           Zhao et al, (93) 2007
Antithrombin III      Zhao et al, (93) 2007
Haptoglobin-related   Zhao et al, (93) 2007
Plasma protease       Zhao et al, (93) 2007
C1 inhibitor
[[alpha].s-b.1]-      Zhao et al, (137) 2006
CEA                   Chen et al, (91) 2007
M-C1                  Chen et al, (91) 2007

Table 6. Partial List of T-mor-Associated Antigens in
Pancreatic Cancer That Elicit a H-moral Response

Protein Name                       So-rce, y

Triosephosphate isomerase (TPI)    Tomaino et al, (138) 2007
Retinal dehydrogenase (AL1A1)      Tomaino et al, (138) 2007
Gl-cose-6-phosphate 1-dehydroge-
nase (G6PD)                        Tomaino et al, (138) 2007
Elongation factor T- (EF-T-)       Tomaino et al, (138) 2007
Isocitrate dehydrogenase (IDH)     Tomaino et al, (138) 2007
Keratin 10 (K1C10)                 Tomaino et al, (138) 2007
Cofilin-1 (COF1)                   Tomaino et al, (138) 2007
Transgelin (TAGL)                  Tomaino et al, (138) 2007
Calretic-lin                       Hong et al, (139) 2004
Rad51                              Maacke et al, (140) 2002
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Author:Ranganathan, Prathibha; Harsha, H.C.; Pandey, Akhilesh
Publication:Archives of Pathology & Laboratory Medicine
Article Type:Report
Geographic Code:1USA
Date:Mar 1, 2009
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