Abstract

Background

   Intraductal papillary mucinous neoplasms (IPMNs) are pancreatic ductal
   adenocarcinoma (PDAC) precursors. Differentiating between high-risk
   IPMNs that warrant surgical resection and low-risk IPMNs that can be
   monitored is a significant clinical problem, and we sought to discover
   a panel of mi(cro)RNAs that accurately classify IPMN risk status.

Methodology/Principal Findings

   In a discovery phase, genome-wide miRNA expression profiling was
   performed on 28 surgically-resected, pathologically-confirmed IPMNs (19
   high-risk, 9 low-risk) using Taqman MicroRNA Arrays. A validation phase
   was performed in 21 independent IPMNs (13 high-risk, 8 low-risk). We
   also explored associations between miRNA expression level and various
   clinical and pathological factors and examined genes and pathways
   regulated by the identified miRNAs by integrating data from
   bioinformatic analyses and microarray analysis of miRNA gene targets.
   Six miRNAs (miR-100, miR-99b, miR-99a, miR-342-3p, miR-126, miR-130a)
   were down-regulated in high-risk versus low-risk IPMNs and
   distinguished between groups (P<10^−3, area underneath the curve (AUC)
   = 87%). The same trend was observed in the validation phase (AUC =
   74%). Low miR-99b expression was associated with main pancreatic duct
   involvement (P = 0.021), and serum albumin levels were positively
   correlated with miR-99a (r = 0.52, P = 0.004) and miR-100 expression (r
   = 0.49, P = 0.008). Literature, validated miRNA:target gene
   interactions, and pathway enrichment analysis supported the candidate
   miRNAs as tumor suppressors and regulators of PDAC development.
   Microarray analysis revealed that oncogenic targets of miR-130a (ATG2B,
   MEOX2), miR-342-3p (DNMT1), and miR-126 (IRS-1) were up-regulated in
   high- versus low-risk IPMNs (P<0.10).

Conclusions

   This pilot study highlights miRNAs that may aid in preoperative risk
   stratification of IPMNs and provides novel insights into miRNA-mediated
   progression to pancreatic malignancy. The miRNAs identified here and in
   other recent investigations warrant evaluation in biofluids in a
   well-powered prospective cohort of individuals newly-diagnosed with
   IPMNs and other pancreatic cysts and those at increased genetic risk
   for these lesions.

Introduction

   Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of
   cancer mortality in the United States, claiming the lives of nearly
   40,000 individuals each year [[65]1]. Surgical resection offers the
   best chance for improved survival, but 80–85% of cases are unresectable
   at diagnosis [[66]1]. These statistics underscore the urgent need to
   develop strategies to detect PDAC at an early, operable stage. It is
   established that PDAC does not arise de novo, but instead marks the end
   of progression from one of three types of non-invasive precursor
   lesions arising within pancreatic ducts: pancreatic intraepithelial
   neoplasia (PanIN), mucinous cystic neoplasms (MCNs), and intraductal
   papillary mucinous neoplasms (IPMNs) [[67]2]. While PanINs are
   microscopic lesions in ducts < 5 mm in diameter, MCNs and IPMNs are
   macroscopic mucinous cysts accounting for over half of the estimated
   150,000 asymptomatic pancreatic cysts detected incidentally in the
   general population each year due to increased computed tomography and
   magnetic resonance imaging [[68]3,[69]4]. IPMNs have also been detected
   among those at high genetic risk for PDAC [[70]5,[71]6]. Although
   improvements in imaging, cytology, and molecular studies have enabled
   proper classification and management of some benign non-neoplastic
   pancreatic cysts, mucinous cysts such as IPMNs are challenging to
   manage due to the inability to accurately predict which lesions can be
   monitored, which are likely to progress to invasion, and which may have
   an associated invasive component [[72]7]. Since data highlight a
   two-decade window of opportunity for early detection efforts in PDAC
   [[73]8], IPMNs represent prime targets for early detection and
   prevention of progression to invasive, fatal disease.

   IPMNs present within the main pancreatic duct (MD-IPMN), side branch
   ducts (BD-IPMN), or both (mixed-IPMN), and are further classified based
   on the degree of dysplasia which ranges from adenoma (low-grade
   dysplasia, LG) and borderline (moderate-grade dysplasia, MG) to
   carcinoma in situ (high-grade dysplasia, HG) and invasive carcinoma
   [[74]2] ([75]S1 Fig.). MD-IPMNs are associated with a higher grade and
   faster growth compared to BD-IPMNs, with the 5-year risk of developing
   HG or invasive disease from an adenoma to be ~ 63% for MD-IPMNs and 15%
   for BD-IPMNs [[76]9]. Other predictors of malignant potential include
   main duct dilation (≥5mm), mural nodules, cyst size (≥3 cm), and
   symptoms such as jaundice and abdominal pain [[77]3,[78]10]. Consensus
   guidelines [[79]10] recommend resection for surgically-fit patients
   with MD-IPMNs and careful observation for asymptomatic BD-IPMNs
   measuring <3 cm in the absence of mural nodules, main-duct dilation, or
   positive cytology. However, these guidelines do not reliably predict
   the degree of dysplasia pre-operatively [[80]11–[81]14]. To date, the
   only way to treat IPMNs and accurately identify the grade of dysplasia
   is through surgical resection and pathological evaluation, but risks of
   morbidity (ie. long-term diabetes) and mortality associated with a
   Whipple procedure or a distal or total pancreatectomy may outweigh the
   benefits, especially for patients with low-grade disease [[82]15].
   Alternatively, taking a ‘watch and wait’ approach could lead to a
   missed opportunity to cure a patient with occult invasive disease
   [[83]15].

   Although many DNA-, RNA- and protein-based markers are under
   investigation as markers of early pancreatic neoplasia, most require
   further validation [[84]16]. MicroRNAs (miRNAs) are small non-coding
   RNAs that regulate thousands of protein-coding genes by binding to the
   3’ untranslated region of the targeted messenger RNA (mRNA) [[85]17].
   Their ability to regulate (and serve as) tumor suppressors and
   oncogenes [[86]17], their remarkable stability in formalin-fixed
   paraffin-embedded (FFPE) tissue [[87]18] and biofluids [[88]19], and
   their altered expression in PDACs compared to normal pancreas tissue
   [[89]20] makes miRNAs excellent candidate biomarkers of early
   progression to pancreatic malignancy. Indeed, early studies of small
   numbers of miRNAs in tumor versus normal tissue supported a role for
   altered miRNA expression in PanINs [[90]21] and IPMNs [[91]22]. Since
   more than 1,000 miRNAs exist [[92]23], several recent studies
   [[93]24,[94]25] have conducted focused investigations of genome-wide
   miRNA expression in IPMN tissue. Matthaei et al [[95]24] profiled
   archived tissue and cyst fluid from patients with IPMNs and other
   pancreatic lesions and identified 9 miRNAs (miR-24, miR-18a,
   miR-30a-3p, miR-92a, miR-106b, miR-342-3p, miR-99b, miR-142-3p,
   miR-532-3p) that predicted cyst pathology with a sensitivity of 89% and
   a specificity of 100%, whereas Lubezky et al [[96]25] highlighted four
   different miRNAs (miR-217, miR-21, miR-708, miR-155). While these
   studies [[97]24,[98]25] were underway, we also conducted a pilot
   investigation of genome-wide miRNA expression in IPMN tissue, with the
   goal of discovering miRNAs that may differentiate ‘high-risk’ IPMNs
   (defined by pathologically-confirmed invasive disease or high-grade
   dysplasia without associated invasion) that require resection from
   ‘low-risk’ IPMNs (defined by pathologically-confirmed low- or
   moderate-grade dysplasia) that can be monitored. Our study is novel in
   that we also aimed to explore a) associations between candidate miRNA
   expression and selected clinical and pathologic factors and b) genes
   and pathways regulated by the candidate miRNAs by integrating data from
   bioinformatic analyses and existing microarray analysis of miRNA gene
   targets.

Materials and Methods

Study population and biospecimens

   A prospectively maintained clinical database was retrospectively
   reviewed to identify individuals who underwent pancreatic resection for
   an IPMN between 1999 and 2011 at Moffitt Cancer Center and Research
   Institute (Moffitt) and had provided written consent for tissue to be
   donated for research through several protocols approved by the
   Institutional Review Board (IRB) of the University of South Florida,
   including Total Cancer Care
   ([99]http://moffitt.org/patient-services/total-cancer-care/research)
   [[100]26]. IRB approval was also specifically granted for the research
   described herein (IRB# Pro4971). A pathologist with expertise in PDAC
   and IPMN pathology (DC) used hematoxylin and eosin (H&E) stained slides
   from selected blocks to histologically confirm the diagnosis and degree
   of dysplasia using World Health Organization guidelines [[101]27], and
   consulted with another pancreatic pathologist (BC) as needed. The final
   diagnosis represented the most severe grade of dysplasia observed in
   the neoplastic epithelium of each resected lesion, and multiple
   representative areas of the corresponding grade were electronically
   marked on the H&Es. Examples of grades of IPMN dysplasia are shown in
   [102]S1 Fig.

   Laser capture microdissection (LCM) and RNA isolation

   Under RNAse-free conditions, four 8-micron sections were cut from the
   FFPE block corresponding to each respective H&E. Sections were placed
   in a water bath, mounted on uncharged and uncoated glass slides,
   air-dried overnight, and transferred to Moffitt’s Analytic Microscopy
   Core for LCM. Sections were deparaffinized, hydrated, and stained using
   nuclease-free Histogene solution (Applied Biosystems (ABI), Austin,
   TX), dehydrated, and then air-dried before placement in an Autopix LCM
   instrument (Arcturus, Molecular Devices, Sunnyvale, CA). Locations of
   dysplasia were verified using the marked electronic images. Cells of
   interest were captured from each section using Macro LCM Caps (Arcturus
   CapSure, #09F10A). Caps of cells from each case were pooled and 50 uL
   of lysis buffer was added to stop RNA degradation. Qiagen’s miRNeasy
   FFPE Isolation Kit was used for total RNA isolation, which included
   isolation of small non-coding RNAs, according to the manufacturer’s
   procedures. RNA quantity and quality was assessed by Optical Density
   (OD) at 260 and 280 nm using a Nanodrop spectrophotometer. When RNA
   quantity was insufficient, additional tissue sections were processed.
   If RNA quality was poor, an ethanol precipitation was performed.

High-throughput miRNA expression analysis

   In our discovery phase we conducted genome-wide miRNA profiling using
   Taqman MicroRNA Arrays, also known as Taqman Low Density Array (TLDA)
   ‘Pool A’ Card version 3.0, due to their established use for miRNA
   expression analysis in FFPE tissue [[103]28,[104]29]. This
   384-microfluidic array was designed to perform quantitative reverse
   transcriptase (qRT-PCR) reactions simultaneously (Applied Biosystems,
   Austin, TX, USA) on 378 mature miRNAs (and 6 endogenous controls) that
   tend to be functionally defined. Using 20 nanograms (ng) of total RNA
   as input, cDNA was synthesized with highly multiplexed Megaplex RT
   primers, pre-amplified with Megaplex PreAmp Primers, mixed with TaqMan
   Universal PCR Master Mix (Applied Biosystems), and loaded onto TLDA
   cards for expression analysis.

Individual qRT-PCR validation of miRNA candidates

   The most deregulated miRNAs were evaluated in an independent set of 21
   IPMNs (13 high-risk and 8 low-risk) as part of a validation phase.
   Total RNA was isolated from microdissected cells, and singleplex
   qRT-PCR assays were performed using 10 ng total RNA per reaction using
   pre-designed Taqman MicroRNA Assays (Applied Biosystems, Foster City,
   CA). The expression level of the most stable and abundantly expressed
   endogenous control from the discovery phase (RNU44) was used for
   normalization. All assays were carried out in triplicate to ensure
   reproducibility. Positive and no-template control (nuclease free water)
   samples were used to evaluate reagent performance and contamination.
   PCR was run on the 7900HT instrument according to the manufacturer’s
   instructions. For each sample, the threshold cycle (Ct) was calculated
   by the ABI Sequence Detection software v2.3.

Statistical Analyses

   Descriptive statistics were determined using frequencies and percents
   for categorical variables and means and standard deviations (SD) for
   continuous variables. The distributions of covariates were compared
   across the low- versus high-risk IPMN groups using t-tests for
   continuous variables and Chi-squared or Fisher’s exact tests for
   categorical variables, as appropriate. Relative miRNA expression levels
   were calculated using a method similar to the comparative Ct (2^−∆∆CT)
   method [[105]30]. Briefly, ∆CT was calculated for each miRNA so that
   each miRNA was first normalized to the most stably expressed endogenous
   control, RNU44 (∆CT = CT -RNU44). Normalized CT values were further
   calculated as log[2] [Max (∆CT)—∆CT]. The miRNA expression data for the
   discovery phase is available at:
   [106]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63105.

   Nonparametric tests (Wilcoxon rank sum tests) were performed to compare
   the normalized expression levels between groups for each miRNA. False
   discovery rates (FDR) adjusting for multiple comparisons were estimated
   using q-values [[107]31]. Correlations between expression of the most
   deregulated miRNAs and selected clinical and pathological factors were
   explored using Pearson correlations for continuous variables and ANOVA
   and logistic regression for categorical variables. Multivariable
   regression analysis was conducted to identify miRNAs associated with
   high-risk IPMN status independent of selected variables. To assess the
   accuracy and clinical utility of candidate miRNAs in differentiating
   between high-risk and low-risk IPMNs, receiver operating characteristic
   (ROC) curves were constructed using ∆CT values, with pathological
   diagnosis as the gold standard. Logistic regression models predicting
   risk status were fit using values from the discovery dataset, and ROC
   curves were used to predict high- versus low-risk IPMN status in the
   validation dataset. A P-value <0.05 was used as the threshold for
   statistically significance in most analyses. All statistical analyses
   were performed using Matlab version 2009b and R version 2.13.1. To
   visualize miRNA expression patterns, we generated heatmaps and
   performed unsupervised, hierarchical clustering using Matlab.

Bioinformatic Analyses

   To gain insight into mechanisms responsible for miRNA-mediated
   progression to pancreatic malignancy, publicly-available tools were
   used to identify genes and pathways controlled by the candidate miRNAs.
   We determined experimentally-verified mRNA targets of the most
   deregulated miRNAs using the miRecords [[108]32] and TarBase [[109]33]
   databases and published literature. Using identified mRNAs as
   candidates, a pathway enrichment analysis was conducted using Gene
   Ontology’s MetaCore database ([110]www.genego.com). Pathways related to
   PDAC and interaction hubs (genes with more than 5 interactions) were
   identified and overlapped with gene targets to narrow down the number
   of biologically important genes.

   We also conducted additional analyses to explore novel predicted
   miRNA:mRNA pairs that may be involved in the in the histological
   progression of IPMNs. We used the miRWalk tool [[111]34] because it
   hosts predicted miRNA-target interaction information for more than
   2,000 miRNAs on the complete sequence of all known genes produced by
   both miRWalk and 8 established miRNA-target prediction programs (Diana
   microT v 3, miRanda August 2010, miRDB April 2009, PICTAR March 2007,
   PITA Aug 2008, RNA22 May 2008, RNAhybrid v 2.1, and Targetscan 5.1).

Microarray gene expression analysis of IPMNs

   Prior to the current miRNA-based investigation, under an IRB-approved
   protocol [[112]26] frozen tumor tissue from patients treated at Moffitt
   was previously arrayed on Affymetrix HuRSTA-2a520709 GeneChips
   (Affymetrix, Santa Clara, CA) which contained ~60,000 probe sets
   representing ~25,037 unique genes (Affymetrix HuRSTA-2a520709, GEO:
   [113]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GPL10379). Of
   distinct solid tumors that were arrayed, 23 represented
   surgically-resected, pathologically-confirmed IPMNs (17 invasive and 6
   non-invasive (1 low-grade, 1 moderate-grade, 4 high-grade)). For the 23
   IPMNs that were arrayed, expression data for experimentally-validated
   and predicted gene targets of interest highlighted by bioinformatics
   analysis were normalized using Robust Multi-array Average and then
   extracted. Due to small cell counts, the high-risk group represented
   all invasive IPMNs and was compared to a low-risk group that included
   all non-invasive IPMNs. Gene expression data for the 23 IPMNs is
   available at:
   [114]http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE63105.
   Nonparametric tests (rank sum tests) were used to compare expression
   between groups for each target gene. FDRs were estimated using q-values
   [[115]31]. Of the 23 IPMN cases with microarray data, 8 had available
   tissue that was evaluated as part of the current miRNA discovery (n =
   2; 1 low-grade, 1 high-grade) or validation phase (n = 6; 1
   moderate-grade, 3 high-grade, 2 invasive), which enabled preliminarily
   exploration of miRNA:target mRNA expression relationships for paired
   samples using Pearson correlations.

Results

Study Population

   Of 58 IPMN cases that were pathologically evaluated, 49 contributed
   tissue for the discovery (n = 28) or validation phase (n = 21). In the
   discovery phase, 19 cases were high-risk (all high-grade without
   invasion) and 9 were low-risk (all low-grade). In the validation phase,
   13 cases were high-risk (11 high-grade without invasion, 2 invasive)
   and 8 were low-risk (4 low-grade and 4 moderate-grade). Clinical and
   pathologic characteristics of the 32 high-risk and 17 low-risk IPMN
   participants are shown in [116]Table 1. Overall, characteristics were
   not significantly different between the high- and low-risk groups
   ([117]Table 1). Age at diagnosis was slightly older in individuals with
   high-risk IPMNs (69.1 years) compared to those with low-risk IPMNs
   (65.1 years). The predominant tumor location for 59% of the high-risk
   IPMNs was the pancreatic head, whereas most (65%) low-risk IPMNs
   occurred in the pancreatic body or tail. On endoscopic ultrasound
   (EUS), signs of malignant potential were observed more frequently among
   high-risk (72%) compared to low-risk IPMNs (47%) (P = 0.09). Only 40%
   of high-risk IPMNs were observed to be > 3 cm on EUS. High-risk IPMNs
   were significantly more likely to involve the main pancreatic duct upon
   pathological review compared to low-risk IPMNs (P = 7× 10^−4). The
   distribution of characteristics was similar among cases in each phase.

Table 1. Clinical and Pathologic Characteristics of Patients with IPMNs (N =
49).

   Variable Low-risk^[118]1 IPMNs (n = 17) High-risk^[119]2 IPMNs (n = 32)
   P-value^[120]3
   Age at diagnosis, mean (SD)(yrs) 65.1 (9.6) 69.1 (9.7) 0.18
   Gender
     Male 11 (65) 18 (56) 0.57
     Female 6 (35) 14 (44)
   Race
     White, Non-Hispanic 15 (88) 29 (91) 0.79
     Other 2 (12) 3 (9)
   Year of Surgery
     1999–2005 1 (6) 5 (16) 0.32
     2006–2011 16 (94) 27 (84)
   Predominant tumor location
     Pancreatic Head 6 (35) 19 (59) 0.11
     Pancreatic Body or Tail 11 (65) 13 (41)
   Signs of malignant potential ^[121]4on EUS 8 (47) 23 (72) 0.09
   Size of largest cyst on EUS ^[122]4
     <3 cm. 14 (82) 18 (60) 0.11
     ≥3 cm. 3 (18) 12 (40)
   Size of largest cyst^[123]4, mean (SD) (cm) 2.0 (1.2) 2.5 (1.3) 0.21
   Pancreatic duct involvement ^[124]5
     Main duct or mixed 5 (29) 25 (78) 7 × 10^−4
     Side branch duct 9 (53) 4 (13)
   Asymptomatic
     Yes 3 (18) 4 (13) 0.62
     No 14 (82) 28 (88)
   Personal history of chronic pancreatitis
     Yes 6 (35) 16 (50) 0.32
     No 11 (65) 16 (50)
   Family history of pancreatic cancer
     Yes 1 (6) 2 (6) 0.97
     No 15 (88) 29 (91)
   Ever Smoker
     Yes 11 (65) 20 (63) 0.88
     No 6 (35) 12 (38)
   [125]Open in a new tab

   Data represent counts (percentages) unless otherwise indicated. Counts
   may not add up to the total due to missing values, and percentages may
   not equal 100 due to rounding.

   ^1Low-risk IPMNs are represented by 12 low-grade and 5 moderate-grade
   IPMNs.

   ^2High-risk IPMNs are represented by 30 high-grade and 2 invasive
   IPMNs.

   ^3P-value for differences between low- and high-risk groups using
   chi-squared or Fisher’s exact tests and t-tests for categorical and
   continuous variables, respectively. Values in bold are statistically
   significant (P<0.05).

   ^4Signs of malignant potential on endoscopic ultrasound (EUS) include
   main duct (MD) involvement, MD dilation (≥5 mm), mural nodules,
   septation, wall thickness, or cyst size ≥3 cm.

   ^5Based on pathological review post-resection.

Biospecimen quality

   Surgically-resected tissue was pathologically evaluated for 58 unique
   IPMNs. Tissue was not profiled for 6 cases due to an inconclusive grade
   of dysplasia (n = 1), sparse regions of dysplasia (n = 2), or technical
   issues during LCM (n = 3). Representative examples of pre- and post-
   microdissection images of low- and high-grade IPMNs are shown in
   [126]Fig. 1A and 1B, respectively. The average total number of cells
   captured per case was 8,498 (range: 780–65,956), and the average total
   RNA recovery was 139 ng (range: 48–591 ng). The quality of RNA was
   appropriate for most cases as evidenced by optical density 260/280
   readings in the range of 1.8–2.0. Sub-optimal RNA quantity or quality
   was observed for 3 additional cases, leaving 49 cases with adequate
   tissue for miRNA expression analyses.

Figure 1. Laser capture microdissection (LCM) of epithelium from A) low-
grade and B) high-grade IPMN tissue.

   [127]Figure 1
   [128]Open in a new tab

   Left Panel: Hematoxylin (H&E) stained slide (× 4). Middle Panel: H&E
   stained slide before LCM (× 4), with the red area representing cells of
   interest marked for capture. Right Panel: Cap showing adherent cells.

miRNA expression analysis in the discovery and validation phase

   In the discovery phase, 236 of 378 miRNAs evaluated (62.4%) were
   detectable in at least half of the 28 samples evaluated and were
   included in subsequent analyses. This percentage is comparable to other
   studies [[129]24]. Thirty-five miRNA probes were significantly
   deregulated in high-risk versus low-risk IPMNs (rank sum P<0.05,
   [130]S1 Table). The top deregulated miRNAs separated most low-risk from
   high-risk IPMNs as shown in the heatmap ([131]Fig. 2A), though outliers
   existed, consistent with other studies [[132]25]. Several outliers may
   be explained by focal areas of dysplasia available for sampling and/or
   inter-sample heterogeneity.

Figure 2. Heatmap and unsupervised hierarchical clustering of low-risk
(adenoma) and high-risk (carcinoma-in-situ) IPMN samples according to the
expression of the most differentially expressed miRNAs.

   [133]Figure 2
   [134]Open in a new tab

   A) The heatmap is supervised, and is ordered by the type of IPMN, and
   shows the expression for the 25 most deregulated miRNAs. B)
   Unsupervised hierarchical clustering for the 6 most differentially
   expressed miRNAs. Expression values for the miRNAs are represented in a
   matrix format, with columns representing samples and rows representing
   miRNAs. Low expression values are colored green, and high expression
   values are colored red. Colored bars indicate the range of normalized
   log2-based signals.

   Using a FDR of 10%, 13 of the 35 miRNAs (miR-100, miR-99b, miR-99a,
   miR-342-3p, miR-126, miR-888, miR-130a, let-7c, miR-150, miR-296,
   miR-199a, miR-199a-3p, and miR-302a) were significantly deregulated
   between the groups ([135]S1 Table). Of these 13 miRNAs, we selected the
   top 6 (miR-100, miR-99b, miR-99a, miR-342-3p, miR-126, miR-130a) for
   further evaluation based on their statistical significance, evidence to
   support their biological role in pancreatic carcinogenesis, and the
   fact that they were detectable in all evaluated samples. Our analysis
   showed that expression levels of each of these 6 miRNAs were
   down-regulated in most high- versus low-risk IPMNs ([136]Table 2).
   Unsupervised hierarchical clustering analysis also illustrates reduced
   expression for these 6 miRNAs in the high-risk compared to the low-risk
   group ([137]Fig. 2B). Experimentally-validated gene targets of the 6
   miRNAs are listed in [138]Table 2, and include well-known oncogenes.

Table 2. Select candidate miRNAs differentially expressed in high- (N = 19)
vs. low-risk (N = 9) IPMN tissue.

   miRNA P-value^[139]1 Median Fold change^[140]2 Mean Fold change^[141]2
   Experimentally validated gene target(s)^[142]3
   miR-100 1.6 × 10^−3 5.9 4.9 ATM, FGFR3, IGF1R, MMP13, mTOR, PLK1, RPTOR
   miR-99b 2.7 × 10^−3 4.7 3.7 RAVER2
   miR-99a 2.7 × 10^−3 4.8 4.7 AGO2, COX2, FGFR3, IGF1R, MEF2D, mTOR,
   RAVER2, RPTOR, SERPINE1, SKI, TRIB1
   miR-342–3p 3.7 × 10^−3 4.8 3.3 BMP7, DNMT1, GEMIN4
   miR-126 3.7 × 10^−3 3.1 6.7 ADAM9, CCNE2, CRH, CRK, CRKL, DNMT1, EGFL7,
   KRAS, HOXA9, IRS1, PGF, PIK3R2, PLK2, PTPN7, RGS3, SLC45A3, SOX2,
   SPRED1, TOM1, TWF2, VCAM1, VEGFA
   miR-130a 5.9 × 10^−3 4.7 5.0 APP, ATG2B, ATXN1, CSF1, DICER1, ESR1,
   HOXA10, HOXA5, KLF4, MAFB, MEOX2, PPARG, RUNX3, TAC1, TP53INP1
   [143]Open in a new tab

   ^1Wilcoxon rank-sum test.

   ^2All fold-changes represent decreased expression in the high-risk
   group (all high-grade IPMNs) versus the low-risk group (all low-grade
   IPMNs).

   ^3According to data in Tarbase
   ([144]http://diana.cslab.ece.ntua.gr/tarbase/), miRecords
   ([145]http://mirecords.biolead.org/), or other sources.

   In the validation phase, the top 6 miRNAs were evaluated in 21
   independent IPMNs (13 high-risk, 8 low-risk) ([146]S2 Fig.). We
   observed the same trends in expression as in the discovery phase, with
   the expression of each miRNA down-regulated in high-risk versus
   low-risk IPMNs ([147]S3A and S3B Fig.). Using a threshold of P<0.05,
   results reached marginal statistical significance, possibly due to the
   small sample size. Among the 6 miRNAs evaluated, miR-130a was most
   strongly associated with high-risk IPMN status (2.9 median fold-change
   between the high- and low-risk group, P = 0.065), followed by miR-99b
   (2.7 median fold-change, P = 0.103) and miR-100 and miR-342-3p (2.2
   median fold-change, P = 0.119). The miRNA data analyzed in the
   validation phase can be found in the [148]S1 Supporting Information.

   Most clinical and pathological factors were not correlated with miRNA
   expression in the discovery phase ([149]S2 Table). However, an
   association was observed between low miR-99b expression and main duct
   involvement (P = 0.021), a variable independently associated with
   high-risk IPMN status (P = 0.044). After including both miR-99b
   expression level and main duct involvement in multivariate regression
   models, low miR-99b expression was marginally associated with high-risk
   IPMN status (P = 0.051). Serum albumin levels were positively
   correlated with miR-99a (r = 0.52, P =0.004) and miR-100 expression (r
   = 0.49, P = 0.008). The expression level of several miRNAs (miR-99a,
   miR-99b, miR-100) was highly correlated (0.79<r<0.97), which may be
   expected since they are from the same miRNA family [[150]23]([151]S2
   Table). No factors were associated with miRNA expression (P<0.05) in
   the validation phase.

   Receiver operating characteristic (ROC) curves were constructed based
   on the expression of miR-100, miR-99b, miR-99a, miR-342-3p, miR-126,
   and miR-130a. Most areas underneath the curve (AUC) values were
   comparable for the six individual miRNAs, with expression of miR-99a
   yielding the highest AUC value of 0.87 in classifying between high- and
   low-risk IPMNs in the discovery phase ([152]S4 Fig.). When using a
   signature consisting of three miRNAs (miR-99b, miR-130a, miR-342-3p)
   from the discovery model to predict high-risk IPMN status in the
   validation set, the AUC was 0.74 (95% CI:0.51–0.97) ([153]Fig. 3). A
   model combining expression of miR-99b, miR-130a, and miR-342-3p with
   presence of main duct involvement enabled slightly higher utility in
   discriminating between groups (AUC = 0.81).

Figure 3. Receiver operating characteristic (ROC) curve analysis using miRNA
expression to discriminate high-risk from low-risk IPMN samples.

   [154]Figure 3
   [155]Open in a new tab

   Using a logistic regression model built on data from the discovery
   dataset, a miRNA signature consisting of miR-99b, miR-130a, and
   mir-342-3p yielded an area underneath the curve (AUC) value of 0.74
   (95% CI: 0.51–0.97) in differentiating between 13 high-risk and 8
   low-risk IPMNs in the validation phase.

Follow-up with bioinformatic analyses and gene expression profiling

   Gene ontology pathway enrichment network analysis of
   experimentally-validated miRNA gene targets listed in [156]Table 2
   revealed several important interaction hubs, including ESR1, PPARG,
   VEGF-A, mTOR, IRS-1, and SOX2 ([157]S5 Fig.). The top four most marked
   gene ontology maps that were identified contribute to tumor progression
   through interactions with histone deacetylase and
   calcium/calmodulin-dependent kinases, hypoxia inducible factor
   regulation, growth factor signaling, and cytoskeleton remodeling
   ([158]Table 3). This analysis also confirmed that identified target
   genes were associated with pancreatic diseases (P = 2.2 × 10^−30) and
   pancreatic neoplasms (P = 7.6 × 10^−29).

Table 3. Gene ontology categories of biological pathways overrepresented by
miRNA-mediated changes in target gene expression that may differentiate
between high- and low-risk IPMNs.

   Pathway P-value False Discovery Rate (FDR)
   Developmental role of histone deacetylase (HDAC) and
   calcium/calmodulin-depdendent kinase (CaMK) 9.5 × 10^−8 2.4 × 10^−5
   Transcription receptor-mediated hypoxia inducible factor (HIF)
   regulation 5.9 × 10^−7 7.5 × 10^−5
   Developmental membrane-bound ESR1: interaction with growth factors
   signaling 1.3 × 10^−6 1.0 × 10^−4
   Cytoskeleton remodeling with TGF and WNT 6.9 × 10^−6 3.8 × 10^−4
   DNA damage with BRCA1 as a transcription regulator 7.5 × 10^−7 3.8 ×
   10^−4
   Signal transduction-AKT signaling 3.3 × 10^−5 8.9 × 10^−4
   Development: VEGF signaling and activation 3.3 × 10^−5 8.9 × 10^−4
   Development: ligand-independent activation of ESR1 and ESR2 3.9 × 10^−5
   8.9 × 10^−4
   Role of alpha-6/beta-4 integrins in carcinoma progression 3.9 × 10^−5
   8.9 × 10^−4
   [159]Open in a new tab

   Gene expression was significantly up-regulated in 17 high-risk versus 6
   low-risk IPMNs for DNMT1, ATG2B and MEOX2, and IRS1 (P<0.10), which are
   experimentally-validated targets for miR-342-3p, miR-130a, and miR-126,
   respectively. A total of 297 unique genes represented by 458 probes
   were identified as predicted targets of the 6 miRNAs highlighted in our
   investigation by seven or more target prediction programs. When
   reviewing the gene expression data, there are 40 genes differentially
   expressed between high- and low-risk IPMNs (P<0.10), with some being
   down-regulated and some being up-regulated by the candidate miRNAs, and
   miR-130a in particular ([160]S3 Table). In the small subset of IPMN
   cases with miRNA and mRNA expression data, positive correlations
   between miRNA and mRNA expression were evident in 2 miRNA-mRNA pairs,
   miR-342-3p: DNMT1 (r = 0.81, P = 0.05) and miR-126: IRS1 (r = 0.78, P
   =0.07). No statistically significant inverse correlations between
   miRNAs and their target genes were observed; this is contrary to the
   anti-correlated expression that is expected and may be attributed to
   the small number of IPMN cases with both data types.

Discussion

   We conducted a genome-wide miRNA expression analysis exclusively in
   ‘high-risk’ and ‘low-risk’ IPMNs that was followed by both a validation
   and a follow-up phase, and discovered biologically-meaningful miRNAs
   that may help distinguish between these groups. Six miRNAs (miR-100,
   miR-99b, miR-99a, miR-342-3p, miR-126, and miR-130a) were
   under-expressed in high-risk compared to low-risk IPMNs in our
   discovery and validation phase, suggesting that low or reduced levels
   of these miRNAs (and possibly increased levels of target genes they
   regulate) may be associated with progression to invasion. Moreover, ROC
   analysis suggested a combination of miRNAs may help to classify IPMNs
   based on histologic severity ([161]Fig. 3).

   Consistent with our findings, data support a role for the identified
   miRNAs as tumor suppressors and regulators of oncogenes that contribute
   to cell proliferation and invasion in pancreatic and other malignancies
   ([162]Table 2)[[163]35–[164]42]. For example, an
   experimentally-validated target of miR-100 is polo-like kinase 1 (PLK1)
   [[165]38,[166]39], a regulator of proliferative activity over-expressed
   in early PDAC [[167]41] that represents a novel target for
   chemoprevention and therapeutic strategies [[168]41,[169]43]. Also
   noteworthy, miR-342-3p can inhibit cancer cell proliferation and
   invasion by targeting DNA methyltransferase 1 (DNMT1), a gene that
   maintains DNA methylation [[170]40]. DNMT1 mRNA expression has been
   correlated with PDAC progression; those with higher DNMT1 tissue
   expression had poorer survival than those with lower expression
   [[171]42]. Loss of another candidate, miR-126, has been associated with
   PDAC progression by targeting oncogenes such as KRAS [[172]36,[173]37]
   and insulin receptor substrate-1 (IRS-1), a mediator of
   phosphoinositide 3-kinase (PI3K) activation in quiescent PDAC cells
   [[174]44]. Pathway enrichment network analysis underscored that these
   and other miRNA-mediated mechanisms may explain how IPMNs progress to
   invasive disease ([175]Table 3).

   Even though miRNAs are generally believed to regulate expression at the
   protein level [[176]17], we postulated that measuring mRNA expression
   may be useful for determining differences in transcriptional machinery
   between a pre-malignant and a malignant state. Consistent with our
   predictions, mRNA targets of miR-130a (ATG2B and MEOX2), miR-342-3p
   (DNMT1), and miR-126 (IRS-1) were up-regulated in high-risk versus
   low-risk IPMNs. Other miRNA targets may not have demonstrated
   noticeable mRNA level changes since mRNA and protein abundance may not
   be strongly correlated [[177]45]. We also explored correlations between
   miRNA and mRNA expression in the reduced set of IPMNs with both data
   types. Although positive correlations were observed in 2 miRNA-mRNA
   pairs (miR-342-3p: DNMT1 (r = 0.81, P= 0.05) and miR-126: IRS1 (r =
   0.78, P = 0.07)), no statistically significant inverse miRNA:target
   gene correlations were observed. Indeed, previous studies observed more
   positively-correlated than negatively-correlated interactions
   [[178]46], supporting a positive regulatory role of miRNAs [[179]47].
   Due to the small sample of IPMNs evaluated and the fact that paired
   miRNA:mRNA expression data was not available for all evaluated samples,
   caution should be taken when interpreting these mRNA-based findings.
   Tissue microarrays are being constructed on a larger series of IPMNs so
   that protein expression can be evaluated.

   When the current investigation was underway, studies from the United
   States [[180]24], Korea [[181]48], Israel [[182]25], and Europe
   [[183]49] also focused on examining miRNA expression in IPMN tissue,
   but only Matthaei et al [[184]24] and Lubezky et al [[185]25] conducted
   genome-wide miRNA profiling ([186]S4 Table). Matthaei and colleagues
   [[187]24] evaluated miRNA expression in 22 IPMN tissue and 7 pancreatic
   cyst fluid (CF) samples, and performed validation using 23 additional
   IPMN FFPE samples and CF samples. miR-342-3p and miR-99b were among the
   miRNAs that differentiated between high-and low-risk groups using FFPE
   tissue (and CF) [[188]24], demonstrating consistency of findings across
   our two studies and providing a form of external validation.
   Additionally, in a recent study by Lee et al [[189]50] that aimed to
   identify miRNAs that distinguish between mucinous (IPMNs and MCNs) and
   non-mucinous pancreatic cysts (serous cystadenomas), several of the
   miRNA families identified by our study and/or Matthaei et al. [[190]24]
   helped differentiate between mucinous and non-mucinous cysts (miR-99)
   and within mucinous cyst types (miR-130). No overlap existed between
   the main findings observed by Lubezky et al [[191]25] and the current
   study. Since study population characteristics, sample preparation
   procedures, platforms (qRT-PCR versus microarray), normalization
   approaches, and other factors differed between studies, it is not
   surprising that different miRNAs were characteristic of high-risk IPMN
   status ([192]S3 Table).

   Although the sample sizes of our discovery and validation phases were
   relatively modest, they were comparable or even larger than the
   aforementioned studies with regard to the number of high-grade cases
   without associated invasion that were evaluated. This is important
   clinically because it would be opportune to reliably detect high-grade
   dysplasia so intervention could occur prior to invasion. Also of
   clinical importance, low miR-99b expression was associated with main
   duct involvement, a marker of histologic progression that was not
   observed pre-operatively by imaging for six high-grade cases in our
   study. We also observed positive correlations between serum albumin
   levels and expression of miR-99a and miR-100, and showed higher serum
   albumin levels in cases with low-risk IPMNs. This is in line with
   evidence that high serum albumin is correlated with better survival in
   PDAC patients [[193]51], inferring it may be helpful to monitor serum
   albumin in patients with IPMNs since lower levels may be suggestive of
   a possible obstruction caused by a high-risk IPMN involving the main
   pancreatic duct. Larger prospective studies are needed to validate
   these observations.

   Thus far, we are aware of only 5 study patients (10.2%) that went on to
   develop metastatic PDAC within a four year follow-up period; 1 was
   initially diagnosed with moderate-grade dysplasia; two had high-grade
   dysplasia at diagnosis; and 2 were initially diagnosed with localized
   invasive disease. All had main duct involvement at the initial
   diagnosis. Given that nearly 90% of study patients did not develop
   invasion or metastasis in the years post resection, our single
   institutional experience reiterates that the detection and management
   of high-risk IPMNs can offer a unique opportunity to prevent malignancy
   and improve outcomes.

   Strengths of our pilot study include the well-annotated tissue and
   sound methodologic approach which capitalized upon confirmatory
   pathological review, standardized procedures for microdissection and
   RNA isolation of multiple representative regions per case, an
   established platform for miRNA profiling, and our follow-up using
   bioinformatic tools and available microarray data. Internal validity is
   evidenced by high correlations between expression levels of miRNAs from
   the same family. Among the limitations, we only analyzed 378 of the
   estimated 1,800 human miRNAs identified to date. Although we focused on
   this panel of well-defined miRNAs because of their higher detection
   rate and lower background signals, novel miRNAs not evaluated here may
   help distinguish between high- and low-risk IPMNs. It is noteworthy,
   however, that all but one of the candidate miRNAs highlighted in recent
   studies of IPMNs [[194]24,[195]25] were evaluated in the current study.
   Another possible limitation of the current study is that samples from
   the discovery phase that had been profiled using the Taqman MicroRNA
   Arrays were not evaluated in the validation phase using single qRT-PCR
   assays. Despite this limitation, it is meaningful that the same trends
   in expression were observed in the validation phase using an
   independent series of IPMN tissues and a different platform.

   Taken together, the new miRNAs identified here (miR-99a, miR-100,
   miR-126, miR-130a) and those also highlighted by others (miR-99b,
   −342-3p) warrant evaluation in a well-powered multicenter prospective
   cohort of individuals presenting with pancreatic cysts. Moreover, given
   that miRNAs are released from tissues into circulation in a stable form
   protected from endogenous RNAse activity [[196]52] and preliminary data
   support the clinical utility of miRNAs circulating in pancreatic juice
   or aspirate [[197]22,[198]53], cyst fluid [[199]24], and serum
   [[200]54], there is great potential for a minimally-invasive
   miRNA-based assay to be used to classify newly-diagnosed pancreatic
   cysts based on their malignant potential. Such an assay should be
   evaluated in conjunction with clinical and pathologic factors and
   emerging markers [[201]55,[202]56] to increase sensitivity and
   specificity. Furthermore, functional evaluation of the emerging miRNAs
   and their target genes may aid in understanding the molecular
   underpinnings of progression to pancreatic malignancy so that novel
   prevention and early detection strategies can be developed. In
   conclusion, a miRNA signature has the potential to serve as a promising
   diagnostic adjunct for directing management of IPMNs toward watchful
   waiting or resection.

SUPPORTING INFORMATION

   S1 Table. The top 35 most differentially expressed miRNAs between
   high-risk (N = 19) and low-risk IPMNs (N = 9).

   (PDF)
   [203]Click here for additional data file.^ (21.5KB, pdf)
   S2 Table. Correlations between candidate miRNA expression level and
   selected continuous clinical and pathologic characteristics.

   (PDF)
   [204]Click here for additional data file.^ (29.6KB, pdf)
   S3 Table. Candidate MiRNAs and their Predicted Target Genes and their
   Expression in Invasive (n = 17) versus Non-invasive IPMNs (n = 6),
   sorted by statistical significance.

   (XLS)
   [205]Click here for additional data file.^ (213KB, xls)
   S4 Table. Studies of tissue-based miRNA expression in
   surgically-resected IPMNs.

   (PDF)
   [206]Click here for additional data file.^ (17.5KB, pdf)
   S1 Fig. Representative histologic images of IPMNs with A) low-grade, B)
   moderate-grade, and C) high-grade dysplasia and D) invasive carcinoma.

   The region enclosed by the black line represents the area isolated by
   LCM. Reference bar = 50 micrometers (μm).

   (PDF)
   [207]Click here for additional data file.^ (129KB, pdf)
   S2 Fig. Schema illustrating the three study phases.

   In the discovery phase, genome-wide miRNA expression profiling of
   formalin-fixed paraffin-embedded (FFPE) tissue from 28 IPMNs was
   conducted. This was followed by a validation phase in which the six
   most degregulated miRNAs from the discovery phase (miR-100, miR −99b,
   miR-99a, miR-342-3p, miR-126, and miR-130a) were evaluated in an
   independent set of 21 IPMNs, while accounting for pertinent clinical
   and pathologic variables. In the final phase, a two-pronged approach
   was used to follow up findings: a) bioinformatic analyses were
   conducted to identify genes and pathways regulated by the candidate
   miRNAs and b) analysis was performed for candidate genes believed to be
   regulated by the identified miRNAs using existing microarray data for
   23 IPMNs.

   (PDF)
   [208]Click here for additional data file.^ (73.1KB, pdf)
   S3 Fig. Box plots of candidate miRNA expression in IPMN tissue by
   real-time PCR.

   A) Discovery phase B) Validation phase. On each boxplot, the central
   mark is the median, and the edges of the box are the 25th and 75th
   percentiles. The whiskers extend to the most extreme data points within
   1.5 of the interquartile range above the 75^th or below the 25^th
   percentiles. Data points beyond the whiskers, displayed using “o”, are
   potential outliers.

   (PDF)
   [209]Click here for additional data file.^ (49.9KB, pdf)
   S4 Fig. Receiver operating characteristic (ROC) curve analysis using
   miRNA expression to discriminate high-risk from low-risk IPMNs in the
   A) discovery and B) validation phase.

   (PDF)
   [210]Click here for additional data file.^ (166.3KB, pdf)
   S5 Fig. Network of genes regulated by candidate miRNAs (miR-100,
   miR-99a, miR-99b, miR-342-3p, miR-126, and miR-130a) that were found to
   be differentially expressed between high- and low-risk IPMNs.

   (PDF)
   [211]Click here for additional data file.^ (50.7KB, pdf)
   S1 Supporting Information. Raw Ct Counts from the Validation Phase.

   (XLS)
   [212]Click here for additional data file.^ (135.5KB, xls)

Acknowledgments