A Migration Signature and Plasma Biomarker Panel for Pancreatic Adenocarcinoma

Published OnlineFirst November 11, 2010; DOI: 10.1158/1940-6207.CAPR-10-0025

Cancer Prevention Research Article Research

Seetharaman Balasenthil1, Nanyue Chen1, Steven T. Lott1, Jinyun Chen2, Jennifer Carter3, William E. Grizzle4, Marsha L. Frazier2, Subrata Sen3, and Ann McNeill Killary1

Abstract Pancreatic ductal adenocarcinoma is a disease of extremely poor prognosis for which there are no reliable markers of asymptomatic disease. To identify pancreatic cancer biomarkers, we focused on a genomic interval proximal to the most common fragile site in the human genome, chromosome 3p12, which undergoes smoking-related breakage, loss of heterozygosity, and homozygous deletion as an early event in many epithelial tumors, including pancreatic cancers. Using a functional genomic approach, we identified a seven-gene panel (TNC, TFPI, TGFBI, SEL-1L, L1CAM, WWTR1, and CDC42BPA) that was differentially expressed across three different expression platforms, including pancreatic tumor/normal samples. In addition, Ingenuity Pathways Analysis (IPA) and literature searches indicated that this seven-gene panel functions in one network associated with cellular movement/morphology/development, indicative of a "migration signature" of the 3p pathway. We tested whether two secreted proteins from this panel, tenascin C (TNC) and tissue factor pathway inhibitor (TFPI), could serve as plasma biomarkers. Plasma ELISA assays for TFPI/TNC resulted in a combined area under the curve (AUC) of 0.88 and, with addition of CA19-9, a combined AUC for the three-gene panel (TNC/TFPI/CA19-9), of 0.99 with 100% specificity at 90% sensitivity and 97.22% sensitivity at 90% specificity. Validation studies using TFPI only in a blinded sample set increased the performance of CA19-9 from an AUC of 0.84 to 0.94 with the two-gene panel. Results identify a novel 3p pathway–associated migration signature and plasma biomarker panel that has utility for discrimination of pancreatic cancer from normal controls and promise for clinical application. Cancer Prev Res; 4(1); 137–49. 2010 AACR.

Introduction population-based screening have not been discovered. CA19-9 has been studied extensively and yet has failed Pancreatic cancer is the fourth leading cause of cancer- to show the desired predictive value necessary for early related mortality in both men and women in the United detection and diagnosis (2). Although many high- States. Estimates suggest that virtually 83% of the more throughput discovery platforms including proteomic, than 42,470 cases in the United States diagnosed with the genomic, and transcriptomic approaches have been uti- disease will also die from it, making pancreatic cancer the lized and biomarker candidates identified, no one plat- most deadly of cancers when grouped by organ site (1). form or molecule has been successfully validated in large Biomarkers for the early detection of pancreatic cancer are population screens. As a part of the National Cancer urgently needed. However, individual molecular bio- Institute (NCI) Early Detection Research Network markers with high sensitivity and specificity needed for (EDRN), our goal is to assemble a panel of blood-based biomarker candidates, given that no one biomarker has yet shown promise for early detection. We hypothesized Authors' Affiliations: Departments of 1Genetics, 2Epidemiology, and 3Molecular Pathology, The University of Texas M. D. Anderson Cancer that a panel of early detection biomarkers for pancreatic Center, Houston, Texas; and 4The Department of Pathology and The cancer could be discovered by the identification of the Comprehensive Cancer Center, The University of Alabama, Birmingham, earliest genetic pathways aberrant in smoking-related Alabama cancers such as lung, renal, and pancreatic cancers. Evi- Note: Supplementary data for this article are available at Cancer Preven- tion Research Online (http://cancerprevres.aacrjournals.org/). dence that the driver events in smoking-related cancers remain to be discovered comes from the sequencing of S. Balasenthil, N. Chen, and S.T. Lott contributed equally to this work. the cancer genomes of 24 cases of pancreatic adenocarci- Corresponding Author: Ann McNeill Killary, Department of Genetics, The University of Texas M. D. Anderson Cancer Center, Houston, TX 77030. noma (3). Results indicated that although smokers have a Phone: 713-834-6395; Fax: 713-792-1474; E-mail: akillary@mdanderson. significantly higher number of genetic alterations than do org. nonsmokers, including mutations, homozygous dele- doi: 10.1158/1940-6207.CAPR-10-0025 tions, and amplifications, smoking-related genetic altera- 2010 American Association for Cancer Research. tions did not seem to correlate with known driver genes

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mutated in pancreatic cancer, including KRAS, p53, were free of malignancy or any benign condition. For CDKN2A/p16, etc., suggesting that the genetic determi- validation purposes, a blinded set of EDTA plasma samples nants of smoking-related tumors are not driven by these from controls and pancreatic adenocarcinoma patients major genes (3). Thus, we furthermore hypothesized that, were also obtained from The University of Alabama to identify the earliest genetic alterations associated with (UAB; W.E.G.). pancreatic cancer, we must target the most common intervals of cytogenetic deletion associated with tobacco RNA extraction, microarray, and quantitative real- exposure, given that these alterations might be shared by time PCR analyses both smoking-related and non–smoking-related pancrea- Frozen pancreatic tumors and adjacent macroscopically tic cancer. Loss of chromosome 3p has been documented and microscopically normal appearing pancreas from the as an initiating event in a cytogenetic pathway involved in same patients (matched) were obtained from untreated, smoking-related cancers; thus, we chose a functional retrospective pancreatic adenocarcinoma samples available genomic approach to target the genetic pathway deregu- from the M. D. Anderson Cancer Center tumor bank and lated by the deletion of the chromosome 3p12 region. our collaborator (MLF). Total RNA was extracted from This genomic interval is known to undergo loss of het- these samples using a miRNeasy Mini Kit (Qiagen). Micro- erozygosity (LOH) and homozygous deletion in smok- array hybridization and scanning was done according to ing-related tumors and is proximal to the most common Affymetrix protocols. Quantitative real-time PCR (RT-PCR) fragile site in the human genome, FRA3B, which has been analysis was carried out as per manufacturer’s protocol shown to be expressed in active smokers and thought to (Applied Biosystems), using specific primers. Data were facilitate chromosome breakage following carcinogenic analyzed according to the comparative Ct method and were exposure from tobacco (4). High-frequency LOH has normalized by glyceraldehyde-3-phosphate dehydrogenase been observed in the FRA3B region encompassing expression. 3p13–3p21 in a variety of epithelial cancers with homozygous deletions found proximal to the fragile site in Suppression subtractive hybridization library lung, breast, and pancreatic tumors, suggestive that dele- The suppression subtractive hybridization (SSH) library tion of this genetic interval could be an early event in the was previously constructed using as starting materials for genesis of smoking-related cancers (5). Our previous library construction microcell hybrids formed by the intro- physical and functional mapping experiments showed duction of defined fragments of a normal chromosome 3p that the introduction of a normal copy of chromosome into a renal cell carcinoma cell line and subsequent assay of 3p into renal cell carcinoma cell lines via microcell fusion those microcell hybrids for tumor formation in athymic suppressed tumorigenicity in nude mice in both ortho- nude mice (10). We hypothesized that the resultant differ- tropic and subcutaneous injections (6–8). Fine mapping entially expressed cDNAs obtained from the SSH library of suppressed and unsuppressed hybrids localized the should represent genes up- or downregulated by the intro- NRC-1 tumor suppressor locus to a 4.75-Mb interval duction of the NRC-1 tumor suppressor locus and could within chromosome 3p12 (9). In addition, we previously represent genes in a functional chromosome 3p12 tumor demonstrated high-frequency LOH in distinct intervals suppression pathway. Conversely, identification of this along 3p in malignant pancreatic islet cell tumors but not pathway could elucidate how loss of this genomic region in precursor cystic lesions in a kindred with von Hippel– and deregulation of this pathway could be involved in the Lindau disease, an autosomal dominant cancer syndrome early stages of pancreatic cancer. We furthermore hypothe- characterized by the development of multiple tumor sized that characterization of this library could define types, two of which progress to malignancy (renal and genetic networks in pancreatic cancer that could serve as pancreas), suggestive that loss of 3p proximal to VHL a source for biomarkers for early detection. is requisite for malignant conversion (7). Experiments reported herein document the utility of a functional Bioinformatic analyses genomic approach not only to identify 3p12 pathway To generate the highest quality expression data, the genes differentially expressed in pancreatic tumor/normal PDNN (positional-dependent-nearest-neighbor) model samples, but also to determine their relevance as blood- was chosen to account for existing probe variation in based pancreatic cancer biomarkers. specific binding with the labeled target material (11). Existing algorithms, such as MAS 5.0, do not take into Materials and Methods account probe-specific variation in binding efficiency and can result in variation in probe signal that vary more than Patients and clinical samples 2 orders of magnitude within a single probe set. Probe EDTA plasma samples from pancreatic adenocarcinoma normalization and summarization was done using the cases were obtained from the TEXGEN repository, a Texas PerfectMatch software suite utilizing the PDNN algorithm Medical Center consortium that houses sera and plasma (http://bioinformatics.mdanderson.org/software.html). from MD Anderson Cancer Center, Baylor, and Texas For each probe set, the software outputs both the natural Children’s Hospital. Control plasma was obtained from logarithm transformed expression level and correlation individuals who were screened for different cancers and coefficient between the observed and modeled data. Data

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Path designer network 1m

Extracellular space

Legend Complex Cytokinel/Growth Factor Enzyme Group/Complex/Other Growth factor Kinase Peptidase Phosphatase Transcription Regulator Transporter Unknown Relationship Relationship

Figure 1. A, schematic flow diagram of functional genomic approach to identify chromosome 3p12 pathway genes. Three expression platforms are denoted that were screened to identify candidates for validation in plasma as potential pancreatic cancer biomarkers. B, putative gene network derived from IPA software. Edges are displayed with labels that describe the nature of the relationship between the nodes. The lines between genes represent known interactions, with solid lines representing direct interactions and dashed lines representing indirect or hypothetical interactions. Color highlighting (red, upregulation; green, downregulation) indicates pathway-associated genes discovered by this study and nonhighlighted genes are those identified by IPA.

was filtered to identify genes exhibiting 2-fold or greater lihood of the Focus Genes in a network from the Ingenuity changes in gene expression. knowledge base being found together due to random chance. A score of 3, as the cutoff for identifying gene networks, Network and gene ontology analysis indicates that there is only a 1/1,000 chance that the focus Selected genes were investigated for network and gene genes shown in a network are due to random chance. There- functional interrelation by Ingenuity Pathways Analysis fore, a score of 3 or higher indicates a 99.9% confidence level (IPA) software (Ingenuity Systems, www.ingenuity.com; to exclude random chance. ref. 12). IPA scans the set of input genes to identify networks by using Ingenuity Pathways Knowledge Base for interactions TNC-L ELISA between identified "Focus Genes," in our case, the common Plasma levels of the predominant isoform of tenascin C genes identified from our pathways approach and known (TNC), TNC-large variant (TNC-L), were determined using and hypothetical interacting genes stored in the knowledge a Human Tenascin-C Large (HMV; FNIII-B) ELISA kit (IBL- base in IPA software, to generate a set of networks with a America), which detects the human TNC high-molecular- maximum network size of 35 genes/proteins. Networks are weight variant by sandwich ELISA. Samples were diluted displayed graphically as genes/gene products ("nodes") with 100-fold and then incubated in 96-well ELISA plates pre- the biological relationships between the nodes ("edges") coated with anti-TNC (4C8MS) antibody (Ab) for 1 hour at identified. All edges are from canonical information stored 37C. After washing the wells 7 times with wash buffer, a in the Ingenuity Pathways Knowledge Base. In addition, IPA horseradish peroxidase–conjugated anti-TNC (4F10TT) Ab computes a score for each network according to the fit of the was added and incubated for 30 minutes at 4C. After user’s set of significant genes. The score indicates the like- washing the wells 9 times with wash buffer, chromogen

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was added and incubated at room temperature in dark for (ROC) curves and calculated the area under the curve 30 minutes. The reaction was stopped by the addition of (AUC) to evaluate the specificity and sensitivity of predict- stop solution and the absorbance at 450 nm was determined ing cases and controls by each protein and by the combi- using an ELISA plate reader (Spectramax Plus384 Microplate nation of these proteins. All statistical analyses were done Reader; Molecular Devices) within 30 minutes of addition using the Stata 10.1 (Stata Corporation). of stop solution, with the correction wavelength set at 540 nm. Results were mean absorbance of duplicate wells. Results

TFPI ELISA SSH library identifies candidate chromosome 3p12 Plasma tissue factor pathway inhibitor (TFPI) levels were pathway genes determined using a commercially available Quantikine Three different expression platforms were utilized to Human TFPI ELISA kit (DTFP10; R&D Systems, Inc.), which identify genes in the chromosome 3p12 pathway to tumor- detects predominantly free TFPI and a very small percentage igenesis in pancreatic cancer (schematically illustrated in of LDL and HDL (low- and high-density lipoprotein)– Fig. 1A). We previously constructed an SSH library using as bound TFPI by sandwich ELISA. The samples were diluted starting materials for library construction, microcell hybrid 200-fold and then incubated along with an assay diluent in clones containing defined fragments of chromosome 3p12 96-well ELISA plates precoated with anti-human TFPI that were either suppressed or unsuppressed for tumori- monoclonal Ab for 2 hours at room temperature. After gencity following injection of microcell hybrid clones into washing the wells 4 times with wash buffer, a horseradish athymic nude mice (10). cDNAs differentially expressed peroxidase–conjugated anti-human TFPI was added and from this SSH library should represent genes up- or down- incubated for 1 hour at room temperature. After washing regulated by the 3p12 tumor suppressor locus and, there- the wells 4 times with wash buffer, chromogen with sub- fore, represent a 3p12 downstream pathway for biomarker strate was added and incubated at room temperature in dark discovery. From the SSH library, 880 partial cDNAs were for 30 minutes. The reaction was stopped by the addition of obtained that were differentially expressed between sup- stop solution and the absorbance at 450 nm was determined pressed and unsuppressed hybrids. PCR products were using an ELISA plate reader (Spectramax Plus384 Microplate obtained from 763 clones (87% of the library) and used Reader; Molecular Devices) within 30 minutes of addition as templates for sequencing. A total of 569 of 763 clones of stop solution, with the correction wavelength set at (75%) were identified as subtracted because of the presence 540 nm. Results were mean absorbance of duplicate wells. of appropriate adaptors and BLAST searches against the RefSeq database indicated 117 clones had no matches, CA19-9 ELISA short or poor quality sequence, or chimerism. The remain- CA19-9 levels were measured in plasma samples (10 mL) der of the 452 of 763 clones produced 2,297 sequences; using a commercially available ELISA kit (DRG Interna- however, a number of duplications were present in the tional Inc.) according to manufacturer’s instructions. Ten library. After filtering for redundancy and for only those microliters of each sample was incubated along with an sequences with Entrez Gene matches, 507 Entrez Gene assay buffer in 96-well ELISA plates precoated with murine matches were obtained (Supplementary Table S1, Supple- monoclonal anti-CA19-9 Ab for 90 minutes at 37C. After mentary Appendix). washing the wells 5 times with wash buffer, a horseradish peroxidase–conjugated anti-CA19-9 was added and incu- Expression profiling validates SSH library bated for 90 minutes at 37C. After washing the wells 5 times To validate differential expression observed in the with wash buffer, chromogen with substrate was added and sequenced cDNA clones obtained from the SSH library, incubated at room temperature in dark for 20 minutes. The we utilized a second expression platform. Following reaction was stopped by the addition of stop solution and analysis using the PerfectMatch software, probe sets over- the absorbance at 450 nm was determined using an ELISA lapping the 507 Entrez Gene sequences from the SSH plate reader (Spectramax Plus384 Microplate Reader; Mole- library were identified on a GeneChip U133 plus 2.0 array cular Devices) within 15 minutes of addition of stop solu- (Affymetix, Inc.) and examined for differential expression tion. Results were mean absorbance of duplicate wells. by interrogating the array withthestartingmaterialsfor For these experiments, we did not assign predetermined construction of the SSH library, i.e., microcell hybrids cutoff values to assess the specificity and sensitivity. containing fragments of 3p used to construct the SSH library. By screening the same cDNAs identified from Statistics the SSH library on a commercial array, we were able to Differences in plasma levels between normal and pan- directly compare expression profiles across platforms creatic adenocarcinoma were analyzed using the Student’s to identify differentially expressed sequences, which we t test. To provide additional statistical rigor, the Mann– subsequently filtered by bioinformatics analyses to iden- Whitney U test was also used to analyze the difference tify those genes with expression differences of 2-fold or between normal and pancreatic adenocarcinoma samples. greater. Supplementary Table S2 illustrates the 82-gene Two-sided P < 0.05 values were considered statistically list of chromosome 3p12 pathways genes identified by significant. We constructed receiver operating characteristic this approach.

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Expression analyses of pancreatic tumor/normal data sets from Oncomine (http://www.oncomine.org) to samples provide a third platform to stratify data determine whether the 7 genes identified by our functional To identify those genes of the 82-gene list that were genomic screen were also found as differentially expressed in relevant to pancreatic cancer, we identified probe sets on published pancreatic tumor /normal expression screens. a U133A plus 2.0 array corresponding to the 82-gene set Representative results of an individual data set (27), in which and then interrogated the array with pancreatic tumor/ all our gene biomarkers of the 7-gene panel are expressed, are normal samples. Frozen tumor and adjacent macroscopi- present in Supplementary Figure S1. Analysis of the biomar- cally and microscopically normal appearing pancreatic ker panel showed that the expression of 6 genes (TNC, TFPI, tissues from the same patient (matched) were obtained TGFBI, LICAM, CDC42BPA,andWWTR1) was found to be from untreated, retrospective pancreatic adenocarcinoma significantly upregulated in pancreatic cancer when com- samples available from the M. D. Anderson Cancer Center pared with normal pancreatic tissue (Supplementary Fig. S1). tumor bank and our collaborator (MLF). The characteristics In contrast, SEL-1L was found to be significantly downregu- of the patients used for this study are presented in Supple- lated in pancreatic adenocarcinomas compared with the mentary Table S3. Total RNA from matched tumor/adja- normal pancreas (Supplementary Fig. S1). Furthermore, cent normal samples (8 paired samples) was utilized to we also analyzed the expression of our panel in 3 different interrogate the array and resultant bioinformatics analysis expression data sets obtained from microdissected pancreatic done to identify differentially expressed sequences across tumor/normal samples (28–30). Importantly, although all 3 platforms. Bioinformatics analysis involved the stra- none of the 3 databases listed all 7 genes as differentially tification of the data set by selecting only those genes from expressed, subsets of each gene biomarker of the 7-gene panel the 82 gene list that were significantly differentially were represented in these data sets as well (Supplementary expressed across all 3 expression platforms resulting in a Figs. S2 and S3). Thus, in silico analyses confirmed our studies 7 gene set (P < 0.05; Table 1). The 7-gene panel [WWTR1 using cross-platform functional approaches, although none (13, 14), TGFBI (15, 16), TFPI (17, 18), CDC42BPA (19, of the previously published compendiums of expression 20), L1CAM (21, 22), TNC (23, 24), and SEL-1L (25, 26)] profiles identified these genes as a panel or studied their showed at least 2-fold or greater difference in gene expres- potential as blood-based pancreatic cancer biomarkers. Thus, sion and consistently remained as top candidates that were our functional approach identified a novel panel differen- differentially expressed across 3 platforms. One gene, SEL- tially expressed in multiple data sets that hitherto had not 1L, was significantly downregulated in 8 of 8 tumors as been studied for blood-based biomarker development. compared with normal adjacent tissue, although the other 6 genes were upregulated. Importantly, 5 of 7 genes had IPA identifies a single network and migration also been previously published as being differentially signature for 3p pathway genes expressed by immunohistochemistry (IHC) in pancreatic To determine the functional relationships among the tumor samples. In addition, SEL-1L and TNC were differ- 7 genes confirmed by our functional genomic pathway entially expressed by quantitative RT-PCR in 26 matched studies, IPA was queried for known or hypothetical inter- pancreatic tumor/normal samples (P ¼ 0.002 and actions among the 7 genes in the panel and also all other P ¼ 0.038, respectively; Supplementary Table S4). genes in the Ingenuity database. With the exception of As another validation of our 7-gene biomarker panel, we WWTR1/TAZ, which was not present in the IPA database, conducted in silico analyses of publicly available microarray all the other 6 genes were used as focus genes for IPA.

Table 1. Seven-gene panel of candidate biomarkers identified from screening across 3 expression platforms

Symbol Title Major biological functions References

WWTR1/TAZ WW domain containing Cofactor of transcription, cell migration, EMT 13, 14 transcription regulator 1 TGFBI Transforming growth factor, Cell adhesion, migration, cell–matrix 15, 16 beta-induced, 68 kDa interaction TFPI Tissue factor pathway inhibitor Cell adhesion, migration, and proliferation 17, 18 CDC42BPA CDC42 binding protein Cell morphogenesis, cell signaling 19, 20 kinase alpha (DMPK-like) L1CAM L1 cell adhesion molecule Cell morphogenesis, migration, and cell 21, 22 survival TNC Tenascin C (hexabrachion) Cell adhesion, migration, proliferation, 23, 24 and angiogenesis SEL-1L Sel-1 suppressor of lin-12-like Negative regulation of colony formation, 25, 26 growth, and invasion; cell–matrix interaction

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Table 2. Networks identified from IPA

Network Genes in Ingenuity networka Functions Scoreb

1 ACAN, ALCAM, CAT, CD47, CDC42, CDC42BPA, CDC42BPB, Cellular movement, 16 CHI3L1, CR1, DKK1, FDXR, GCH1, L1CAM, LIMK2, MMP19, Cell morphology, NCAN, NFkB (complex), PRRX1, PTHLH, PTK2, PTPRZ1, Cellular development SAA@, SDC4, SEL-1L, SNAP91, SQLE, SYVN1, TFPI, TGFB3, TGFBI, TNC, TNF, TP53, TPM1, VCAN

aGenes in boldface were identified from our functional genomic pathway approach as differentially expressed across 3 platforms as focus genes; additional genes listed were identified by IPA. b A score >3 is considered significant.

Unsupervised IPA network analysis identified a network of Student’s t test, P ¼ 0.0006; Mann–Whitney’s U test, P ¼ 35 genes that included all 6 focus genes and 29 additional 0.0004). Figure 2B illustrates the ROC curve for TNC. The genes (score ¼ 16; Table 2). The interactive relationship AUC was 0.79, with a specificity of 47% at 90% sensitivity between the genes in the network is shown in Figure 1B. and sensitivity of 25% at 90% specificity. Importantly, all 6 genes were classified into a single net- We next extended our study to further validate the signi- work related to cellular movement, cell morphology, and ficance of TFPI as a potential plasma biomarker by ELISA. cellular development (Table 2). Of the 6 genes, 5 were also Figure 2C depicts the individual plasma TFPI levels of normal a part of a network involving cell signaling and cell inter- and pancreatic cancer patients. Plasma TFPI levels of patients action (P ¼ 7.07E-06 to 3.07E-02) and cell movement (P ¼ with pancreatic adenocarcinoma were significantly higher 1.38E-04 to 3.51E-02). The extremely low probability of than normal subjects (Student’s t test, P ¼ 0.0004; Mann– obtaining this number of differentially expressed mole- Whitney’s U test, P< 0.0001), with the median plasma TFPI cules in one network by chance alone is reflected by the level of 27.0 ng/mL in pancreatic adeonocarcinoma patients P value for the network (P ¼ 1.0E-16), indicating that this (n ¼ 36) compared with normal subjects (15.3; n ¼ 19). network is deregulated in a highly significant, nonrandom Figure 2D illustrates the ROC curve which compares the manner in pancreatic cancer cells. In addition, WWTR1/ ability of plasma TFPI to distinguish between patients with TAZ has also been reported to function in the regulation of pancreatic cancer and normal subjects. The AUC for TFPI was cell migration in breast cancer (13). Therefore, we conclude 0.87, with a specificity of 63% given 90% sensitivity and that our functional genomic pathway approach has iden- a sensitivity of 64% given 90% specificity. tified a gene signature related to cell movement, morphol- We next tested whether the combination of the 2 markers ogy, and organization, suggestive that the loss of the 3p12 TNC and TFPI could increase sensitivity and specificity for locus in pancreatic cancer could be related to change in cell discrimination between cancer and normal plasma sam- morphology and aberrant migration associated with early ples. The combined AUC for both markers was 0.88 events in malignant transformation of pancreatic ductal (Fig. 2E). The combination of markers resulted in a speci- epithelial cells (Table 2). ficity of 63% given 90% sensitivity and a sensitivity of 67% given 90% specificity. These combined results, then, suggest TNC and TFPI are candidate plasma biomarkers that that the 2-gene panel identified through our functional distinguish pancreatic cancer from normal screening genomic studies has high sensitivity and specificity to dis- controls criminate tumor and normal samples in the plasma and Four of the genes of the 7-gene panel (TGFBI, TFPI, indicate that these genes are candidate blood-based bio- LICAM, and TNC) were also secreted proteins. Sandwich markers for pancreatic cancer. The diagnostic potential of ELISA assays were then done on 2 of the 4 secreted proteins, candidate biomarkers TNC and TFPI, relative to and in TFPI and TNC, for which commercial ELISAs were available combination with CA19-9, the standard serum biomarker to determine their ability to function as plasma biomarkers. for pancreatic cancer, was determined. Results indicated The patient population characteristics used in the present that individual plasma CA19-9 levels, presented in the form study with respect to age, sex, alcohol intake history, smok- of a scatter plot (Fig. 2F), were significantly different ing history, diabetic history, site of the disease, staging, and between pancreatic cancer patients and normal screen- survival data are presented in Table 3. Results indicated that ing controls in that the median plasma CA19-9 levels were individual plasma TNC-L levels, presented in the form of a 173 U/mL in patients with pancreatic adenocarcinoma (n ¼ scatter plot (Fig. 2A), were significantly different between 36) as compared with levels in normal subjects (11.9; n ¼ pancreatic cancer patients and normal screening controls in 19; Student’s t test, P < 0.00001; Mann–Whitney’s U test, P < that the median plasma TNC-L levels was 342.6 pg/mL in 0.00001). Figure 2G illustrates the ROC curve for CA19-9, patients with pancreatic adenocarcinoma (n ¼ 36) as com- and the AUC was 0.93, with a specificity of 94.74% at 90% pared with levels in normal subjects (243.3; n ¼ 19; sensitivity and sensitivity of 91.67% at 90% specificity.

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Table 3. Characteristics of samples used in the study

Characteristics Test set (TEXGEN) Validation set (UAB)

No. of patients No. of controls No. of patients No. of controls (n ¼ 36) (n ¼ 19) (n ¼ 37) (n ¼ 15)

Sex Male 20 11 22 4 Female 16 8 15 11 Age, y <50 2324 50–60 12 12 6 3 61–70 15 3 12 3 71–80 7 1 13 5 81–90 0040 Race White 36 19 33 13 Black 0 0 4 2 Histology Adenocarcinoma 36 0 37 0 Alcohol history Current 10 N/A N/A N/A Former 14 N/A N/A N/A Never 12 N/A N/A N/A Smoking history Current 10 N/A N/A N/A Former 13 N/A N/A N/A Never 13 N/A N/A N/A Diabetes history Yes 7 N/A N/A N/A No 29 N/A N/A N/A Stage I0––– II 6 – 1 – III 5 – 2 – IV 8 – 9 – N/A 17 – 25 – Site Body 4 – N/A – Head 21 – N/A – Pancreas overlapping lesion 7 – N/A – Tail 4 – N/A – Stage Direct extension 9 – N/A – Direct extension þ lymph node 3 – N/A – Distant 17 – N/A – Localized 2 – N/A – Regional lymph node involvement 1 – N/A – N/A 4 – N/A –

Abbreviation: N/A, not available.

Figure 2H illustrates the ROC curve for CA19-9 and TFPI tivity and specificity for discrimination between cancer and combined. The AUC was 0.99, with a specificity of 100% at normal plasma samples. The combined AUC for all the 90% sensitivity and sensitivity of 97.22% at 90% specificity. markers was 0.99 (Supplementary Fig. S4). The combina- We next tested whether the combination of the 2 markers, tion of markers resulted in a specificity of 100% given 90% TNC and TFPI, with CA19-9 could further increase sensi- sensitivity and a sensitivity of 97.22% given 90% specificity.

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A BCD

EFGH

Figure 2. Candidate biomarkers from functional genomic approach validated in plasma of pancreatic cancer patients versus controls. A, plasma TNC-L concentrations of pancreatic carcinoma patients and normal subjects. Line, median plasma TNC level. The difference between normal and pancreatic adenocarcinoma samples is statistically significant (Mann–Whitney's U test, P ¼ 0.0004). B, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of the plasma TNC ELISA assay. The AUC was 0.79. The specificity was 47% given 90% sensitivity and the sensitivity was 25% given 90% specificity; C, plasma TFPI concentrations in pancreatic carcinoma patients and normal subjects. Line, median plasma TFPI level. The difference between normal and pancreatic adenocarcinoma samples is statistically significant (Mann–Whitney's U test, P < 0.0001). D, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of the plasma TFPI ELISA assay. The AUC was 0.87. The specificity was 63% given 90% sensitivity and the sensitivity was 64% given 90% specificity; E, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of the combinations of 2 markers, plasma TNC and TFPI ELISA. The combined AUC is 0.88. F, plasma CA19-9 concentrations of pancreatic carcinoma patients and normal subjects. Line, median plasma CA19-9 level. The difference between normal and pancreatic adenocarcinoma samples is statistically significant (Mann–Whitney's U test, P < 0.00001). G, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of plasma CA19-9 ELISA assay. The AUC was 0.93, with a specificity of 94.74% at 90% sensitivity and sensitivity of 91.67% at 90% specificity; H, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of combinations of 2 markers, plasma CA19-9 and TFPI ELISA. The combined AUC was 0.99.

To further strengthen our findings and to validate our Next, we analyzed CA19-9 by ELISA in these samples. results in a different sample set, we tested TFPI levels by Results indicated that individual plasma CA19-9 levels, ELISA in plasma samples from normal and pancreatic presented in the form of a scatter plot (Fig. 3C), were adenocarcinoma patients collected at the UAB. A descrip- significantly different between pancreatic cancer patients tion of the UAB sample set is given in Table 3. Because and normal screening controls in that the median the addition of TNC did not add significantly to the plasma CA19-9 levels was 171.2 U/mL in patients with sensitivity and specificity (SupplementaryFig.S4),we pancreatic adenocarcinoma (n ¼ 37) as compared with analyzed only TFPI levels in these samples. Figure 3A levels in normal subjects (15.7; n ¼ 15; Student’s t test, P ¼ depicts the individual plasma TFPI levels of normal and 0.0000000400227; Mann–Whitney’s U test, P ¼ 0.0001). pancreatic cancer patients. Plasma TFPI levels of patients Figure 3D illustrates the ROC curve for CA19-9, in which with pancreatic adenocarcinoma were significantly the AUC was 0.84, with a specificity of 13.33% at 90% higher than normal subjects (Student’s t test, P ¼ sensitivity and 75.68% sensitivity at 90% specificity. The 0.000014; Mann–Whitney’s U test, P ¼ 0.0001), with combined AUC for TFPI and CA19-9 was 0.94 (Fig. 3E). the median plasma TFPI level of 45.7 ng/mL in pancreatic The combination of markers resulted in a specificity of adeonocarcinoma patients (n ¼ 37) compared with nor- 86.67% at 90% sensitivity and 83.78% sensitivity at 90% mal subjects (25.6; n ¼ 15). Figure 3B illustrates the ROC specificity. curve to compare the ability of plasma TFPI in distin- guishing between patients with pancreatic cancer and Discussion normal subjects. The AUC for TFPI was 0.87 with a specificity of 46.67% at 90% sensitivity and 70.27% Attempts to identify pancreatic cancer biomarkers have sensitivity at 90% specificity. failed to produce a single marker with the sensitivity and

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Pathways Approach Identifies Pancreatic Cancer Biomarkers

A B

Figure 3. A, plasma TFPI concentrations of pancreatic carcinoma patients and normal subjects. Line, median plasma TFPI level. The difference between normal and pancreatic adenocarcinoma samples is statistically significant (Mann– Whitney's U test, P¼ 0.0001). B, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of plasma TFPI ELISA assay. The AUC was CD 0.87 with a specificity of 46.67% at 90% sensitivity and 70.27% sensitivity at 90% specificity. C, plasma CA19-9 concentrations of pancreatic carcinoma patients and normal subjects. Line, median plasma CA19-9 level. The difference between normal and pancreatic adenocarcinoma samples is statistically significant (Mann–Whitney's U test, P¼ 0.0001). D, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of plasma CA19-9 ELISA assay. The AUC was 0.84, with a E specificity of 13.33% at 90% sensitivity and 75.68% sensitivity at 90% specificity. E, ROC curve for differentiating normal and pancreatic carcinoma patients on the basis of combinations of 2 markers, plasma CA19-9 and TFPI ELISA. The combined AUC was 0.94 and resulted in a specificity of 86.67% at 90% sensitivity and 83.78% sensitivity at 90% specificity.

specificity necessary for population screening. We reasoned pancreatic cancer. Several published reports document the that a targeted strategy to identify differentially expressed potential of using immunohistochemical staining of TNC, genes related to the earliest cytogenetic aberrations might an extracellular matrix protein, as a potential marker of be more successful in developing such biomarkers because early disease as well as a predictor of poor prognosis in we would be able to focus on those aberrantly expressed several tumor types, including colon, bladder, and pan- genes that may be involved in initiating the pathways that creas (31–34). TNC has been shown to be overexpressed in ultimately lead to tumorigenesis, invasion, and metastasis. the stroma by IHC in a variety of different cancers, includ- Using 3 different expression platforms, we identified a 7- ing pancreatic cancer (31–33, 35). In addition to stromal gene set as being differentially expressed between pancrea- expression, TNC expression increases from low-grade tic cancer and normal samples and from which we have PanIN-1A and -1B intraductal precursor lesions to high- validated a subset of markers for differential expression by grade PanIN-2 and -3 lesions to invasive lesions, suggestive ELISA assays. Our results indicate that we have been able to that TNC could be an early immunohistochemical marker identify 2 relevant blood-based biomarker candidates for of disease (31). However, reports of the utility of TNC as a

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blood-based biomarker have been limited to colorectal line carrying a DEAR1 mutation resulted in restoration of cancer (36), in which TNC spliced variant overexpression acinar morphogenesis whereas knockdown resulted in loss in plasma was observed and is considered a potential of polarity and tissue architecture in 3-dimensional (3D) biomarker for colorectal cancer. Our data implicate TNC culture (10). Thus, DEAR1 falls within this 3p pathway and as a potential plasma marker for pancreatic cancer, which functions in the regulation of cellular morphology and based on its expression in precursor lesions and upregula- differentiation, as it relates to changes in 3D acinar mor- tion with increasing stage, suggests that it might be a phogenesis. In addition, other members of the 7-gene marker of early disease. panel have also been documented to play a role in cell Our results also identify TFPI as a novel pancreatic plasma migration and early stages of pancreatic cancer, suggestive biomarker candidate. TFPI is a major inhibitor of the tissue that this network may be critically deregulated in early factor pathway of blood coagulation in vivo. Plasma TFPI pancreatic cancer. TNC has been shown to mediate pro- levels have been shown to increase significantly at the time of liferation and migration of astrocytes in a wound assay diagnosis compared with controls and reach near normal (41). Tumor-associated isoforms of TNC has been shown levels following surgical removal of pancreatic tumor (37). to promote breast cancer cell invasion and growth (42). Plasma TFPI has also been reported to be increased in acute TNC signaling has been reported to play an important role pancreatitis compared with normal subjects (38). Plasma in mammary tumor growth and metastasis, and knock- levels of total TFPI has been found to be upregulated in a down of TNC exhibited significant impairment in cell number of solid tumors involving colon, pancreas, and migration and anchorage-dependent cell proliferation in stomach (39). Recently, increased TFPI expression levels breast cancer cell line (43). In addition, TNC has also been have also been reported in colon and breast tumor tissues shown to stimulate glioma cell migration (44). TFPI has (40). Our study suggests that plasma TFPI levels may be a been shown to control migration of endothelial cells (45). potential biomarker for pancreatic cancer and can also serve Extracellular matrix bound TFPI through an interaction as a prognostic marker owing to its modulation following with tissue factor/VIIa complex localized on cancer cells surgery. has also been shown to facilitate cancer cell migration and Our results indicate that the combined analysis of TFPI adhesion (18). and CA19-9 in plasma can discriminate pancreatic adeno- SEL-1L is the only member of the 7-gene panel shown to carcinoma patients from normal screening controls with be downregulated in pancreatic tumors versus normal better sensitivity and specificity than CA19-9 alone, at least samples in this study. It is expressed abundantly only in in our pilot studies. These results could have significance the normal pancreas and is downregulated in pancreatic for the early screening of pancreatic cancer given that CA19- cancers. SEL-1L was first reported as a pancreas-specific 9 alone fails to have adequate predictive value. Owing to transcript but later found to be highly expressed in normal CA19-9 being typically a serum marker, these results would pancreas and present at very low levels in several other furthermore suggest that future analyses of the value of adult tissues (25, 46, 47). Its loss of expression has been CA19-9 screening in plasma as well as its use in combina- observed in 17% of pancreatic adenocarcinomas. Thus, tion with TFPI are warranted to determine whether the SEL-1L represents a gene that shows a tissue-restricted combined panel could result in a viable test for general pattern of expression, with the only tissue showing abun- population screening. dant expression being the pancreas. In addition, SEL-1L Furthermore, using a functional genomic approach, we maps into a genomic interval for the insulin-dependent have identified a cancer-associated network associated with diabetes mellitus locus at chromosome 14q24.3-q31 but the differential expression of the chromosome 3p12 locus was later excluded as a candidate gene for diabetes (48). implicated in smoking-related malignancies. IPA identified Induced expression of SEL-1L in pancreatic cancer cells has cell movement/cell morphology/cell differentiation as the been shown to decrease the clonogenity and anchorage- single critical network associated with the 3p12 pathway, independent growth, and it also delayed tumor growth in with 7 of 7 genes in the panel functionally implicated immunodeficient mice (49). In addition, SEL-1L has been previously in the regulation of cell movement and migra- reported to affect pancreatic cancer cell cycle and inva- tion. Thus, although the importance of migration has been siveness by modulating the expression of PTEN and genes thoroughly characterized in relationship to metastasis, the involved in cell–matrix interactions (26). SEL-1L has been role of cell movement and migration in early stages of shown to be a negative regulator of Notch signaling. The cancer initiation is not well understood. Because the chro- Notch pathway has been extensively studied and regulates mosome 3p12 region has been shown to undergo LOH cell fate decisions in a large number of adult and embryo- and homozygous deletion as an early event in smoking- nic tissues. Components of the Notch signaling pathway related cancers, potentially, loss of this region could play a have been shown to be overexpressed in pancreatic role in the loss of polarity, cell migration, and movement in adenocarcinomas (50), with activation of Notch signal- the earliest stages of malignant transformation and epithe- ing observed in PanIN lesions. Thus, SEL-1L loss of lial-mesenchymal transition (EMT) related to invasive dis- expression could represent a very early marker of pan- ease. We have earlier characterized one novel gene DEAR1 creatic cancer. (annotated as TRIM62) in detail from our SSH library. L1CAM has been reported to be overexpressed in a Genetic complementation of DEAR1 in a breast cancer cell number of different tumors types including colon, breast,

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Pathways Approach Identifies Pancreatic Cancer Biomarkers

and ovarian tumors, melanoma, gliomas, neuroblastomas, finding that our study identified 3 of 7 genes as being and pancreatic neuroendocrine tumors (21). Immunohis- mutated in hereditary diseases indicates the functional tochemical staining of L1CAM was observed in chronic significance of this migration pathway in early develop- pancreatitis tissues and was absent in normal pancreatic ment, the deregulation of which could be of critical impor- tissues (51). Importantly, L1CAM has been shown to play a tance in pancreatic cancer initiation and progression. role both in migration and in the malignant transforma- In conclusion, we have taken a pathways approach to tion of pancreatic adenocarcinoma (51). Importantly, biomarker discovery by utilizing 3 different expression- upregulation of L1CAM expression by IHC has been based platforms to identify chromosome 3p12 pathway observed in later stage, high-grade PanIN lesions as com- genes differentially expressed between pancreatic tumor/ pared with PanIN-1A/1B lesions that are not thought to normal samples, which could serve as candidate biomar- have a high risk for progression to pancreatic cancer, kers for the early detection of pancreatic cancer. Biomarker suggestive of the role of L1CAM early in transition to panels described herein will be further validated in larger pancreatic adenocarcinoma (52). Interestingly, WWTR1/ case–control studies with the EDRN of the NCI. Additional TAZ, a transcription cofactor, was also found to regulate cell candidates from the 7-gene list and associated IPA network migration and invasion (13, 14). WWTR1 has also been members should also be investigated for their ability to reported to be amplified in pancreatic cancer cell lines and improve performance of current panels. Future studies are in pancreatic cancer (53). It was found to play a role in the also warranted to investigate the role of cell polarity and migration, invasion, and tumorigenesis of breast cancer migration in the initiation of pancreatic cancer and the cells (13). TGFBI has been reported to be overexpressed in potential for biomarker discovery by a targeted pathway colon and pancreatic cancer (16, 54). TGFBI is an excreted approach. extracellular matrix protein reported to play a role in cell– matrix regulation as well as cell migration in bone (55). It Disclosure of Potential Conflicts of Interest has recently been found as one of a gene panel upregulated during hematopoietic stem cell lineages as they dif- No potential conflicts of interest were disclosed. ferentiated and became migratory, suggesting a role for TGFBI in stem cell migration between niches (56). Acknowledgments CDC42BPA, a protein kinase, has also been implicated in tumor cell invasion (20) and forms a complex with a We gratefully acknowledge the NCI Early Detection Research Network for support and guidance in our ongoing biomarker discovery projects. We are leucine-rich adaptor protein, LRAP35a, and MYO18A, also grateful to Drs. David Gold and David Stivers, who provided bioinfor- shown to play a crucial role in cell protrusion and migra- matics support for this project. tion (57). Therefore, all 7 genes in our panel have been closely linked functionally to the control of cell migration Grant Support in cancer and potentially in the early stages of pancreatic tumorigenesis. This research was supported by grant UO1CA111302 to A.M. Killary from the In addition, 3 of the 7 genes, L1CAM, TGFBI, and National Cancer Institute Early Detection Research Network. The costs of publication of this article were defrayed in part by the CDC42BPA, identified as most differentially expressed, payment of page charges. This article must therefore be hereby marked are mutated in the germline in genetic disorders including advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate CRASH syndrome, Thiel–Behnke corneal dystrophy, and this fact. Crohn’s disease, respectively (58, 59). Given that germline Received February 17, 2010; revised October 27, 2010; accepted October mutations underlying genetic disorders are very rare, the 29, 2010; published OnlineFirst November 11, 2010.

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A Migration Signature and Plasma Biomarker Panel for Pancreatic Adenocarcinoma

Seetharaman Balasenthil, Nanyue Chen, Steven T. Lott, et al.

Cancer Prev Res 2011;4:137-149. Published OnlineFirst November 11, 2010.

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