RESEARCH ARTICLE Discovery and Validation of Biomarkers that … · 2015/6/27 · 81 of pancreatic...
Transcript of RESEARCH ARTICLE Discovery and Validation of Biomarkers that … · 2015/6/27 · 81 of pancreatic...
1
RESEARCH ARTICLE 1
2
Discovery and Validation of Biomarkers that distinguish mucinous 3
and nonmucinous pancreatic cysts 4
5
Jisook Park1*, Hwan Sic Yun2*, Kwang Hyuck Lee2, Kyu Taek Lee2, Jong Kyun Lee2, and 6
Soo-Youn Lee3,4 7
8
1Samsung Biomedical Research Institute, Samsung Medical Center, Sungkyunkwan 9
University School of Medicine, Seoul, Korea.2Departments of Medicine, Samsung Medical 10
Center, Sungkyunkwan University School of Medicine, Seoul, Korea. 3Department of 11
Clinical Pharmacology and Therapeutics, Samsung Medical Center, Seoul, Korea. 12
4Department of Laboratory & Genetics, Samsung Medical Center, Sungkyunkwan University 13
School of Medicine, Seoul, Korea. 14
15
*These authors contributed equally to this work. 16
17
Running Title: Biomarker for Differential Diagnosis of Pancreatic Cysts 18
19
Keywords: Biomarker, MRM, Pancreatic cyst, Mass spectrometry, Validation 20
21
Corresponding Authors: Jong Kyun Lee, Department of Medicine, Samsung Medical 22
Center, Sungkyunkwan University School of Medicine, 81 Irwon-ro, Gangnam-gu, Seoul 23
135-710, Korea. Phone: +82-2-3410-3407; Fax: +82-2-3410-6983; E-mail: 24
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
2
[email protected]; and Soo-Youn Lee, Department of Laboratory Medicine & 25
Genetics, Samsung Medical Center, Sungkyunkwan University School of Medicine, 81 26
Irwon-ro, Gangnam-gu, Seoul 135-710, Korea. Phone: +82-2-3410-1834; Fax: +82-2-3410-27
2719; E-mail: [email protected] 28
29
Disclosure of Potential Conflicts of Interest 30
The authors declare no conflicts of interest. 31
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
3
ABBREVIATIONS 32
GeLC-SID-MRM-MS, GeLC-Stable Isotope Dilution-Multiple Reaction Monitoring-mass 33
spectrometry; PIGR, polymeric immunoglobulin receptor; LCN2, lipocalin 2; FCGBP, Fc 34
fragment of IgG binding protein; REG1A, lithostathine-1-alpha; AFM, afamin; CTRC, 35
chymotrypsin C (caldecrin); AMY2B, amylase, alpha 2B (pancreatic); LGALS3BP, lectin, 36
galactoside-binding, soluble, 3 binding protein; CELA3A, chymotrypsin-like elastase family, 37
member 3A; EUS-FNA, endoscopic ultrasound-guided fine needle aspiration; CEA, 38
carcinoembryogenic antigen; DEPs, differentially expressed proteins; IHC, 39
immunohistochemistry; ELISA, enzyme-linked immunosorbent assays; EPI, Enhanced 40
product ion; DP, declustering potential; ROC, receiver operating characteristic; AUC, area 41
under the ROC curve; IPMN, intraductal papillary mucinous neoplasm 42
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
4
Abstract 43
The use of advanced imaging technologies for the identification of pancreatic cysts has 44
become widespread. However, accurate differential diagnosis between mucinous cysts (MC) 45
and nonmucinous cysts (NMC) consisting of pseudocysts (NMC1) and nonmucinous 46
neoplastic cysts (NMC2) remains a challenge. Thus, it is necessary to develop novel 47
biomarkers for the differential diagnosis of pancreatic cysts. An integrated proteomics 48
approach yielded differentially expressed proteins in MC that were verified subsequently in 49
99 pancreatic cysts (21 NMC1, 41 NMC2 and 37 MC), using a method termed GeLC-stable 50
isotope dilution-multiple reaction monitoring-mass spectrometry (GeLC-SID-MRM-MS) 51
along with established immunoassay techniques. We identified 223 proteins by nanoscale 52
liquid chromatography coupled to tandem mass spectrometry (nano LC-MS/MS). Nine 53
candidate biomarkers were identified, including polymeric immunoglobulin receptor (PIGR), 54
lipocalin 2 (LCN2), Fc fragment of IgG binding protein (FCGBP), lithostathine-1-alpha 55
(REG1A), afamin (AFM), chymotrypsin C (caldecrin) (CTRC), amylase, alpha 2B 56
(pancreatic) (AMY2B), lectin, galactoside-binding, soluble, 3 binding protein (LGALS3BP) 57
and chymotrypsin-like elastase family, member 3A (CELA3A), which were established as 58
biomarker candidates for MC. In particular, we showed a biomarker subset including AFM, 59
REG1A, PIGR and LCN2 could differentiate MC not only from NMC (including NMC1) but 60
also from NMC2. Overall, the MS-based comprehensive proteomics approach employed in 61
this study established a novel set of candidate biomarkers that address a gap in efforts to 62
distinguish early pancreatic lesions at a time when more successful therapeutic interventions 63
may be possible. 64
65
66
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
5
Introduction 67
Recently, the use of ultrasound (US), multidetector computed tomography (CT), and 68
magnetic resonance imaging (MRI) techniques have been most commonly used for the 69
detection of pancreatic cysts. The prevalence of pancreatic cysts within the population ranges 70
from 2.6% to as high as 44.7% worldwide (1-4), and a study from Japan reported that 24.3% 71
of the population has pancreatic cysts based on autopsy cases. However, an accurate 72
differential diagnosis of cystic pancreatic lesions remains a challenge. 73
Accurate differential diagnosis of mucinous cysts (MC) from nonmucinous cysts 74
(NMC) including pseudocysts (NMC1) and nonmucinous neoplastic cysts (NMC2) is 75
extremely important, because MC has malignant potential and requires surgical resection. 76
Usually NMC1 are easily differentiated from MC because of their typical history and imaging 77
characteristics, but sometimes these cysts are difficult to differentiate without histology. 78
Although endoscopic ultrasound (EUS), EUS-guided fine needle aspiration (EUS-79
FNA), and cyst fluid analysis all provide additional information for the differential diagnosis 80
of pancreatic cysts in clinical practice, the diagnostic accuracy of these techniques is not 81
satisfactory. The diagnostic accuracy of CT or MRI ranges from 55-75% depending on the 82
reviewers. The accuracy of EUS morphology, cytology, and cystic carcinoembryogenic 83
antigen (CEA) ranges from 50-79% (5). 84
The successful completion of human genome projects has led to the development of 85
various proteomics-based approaches used to identify novel potential biomarkers that are 86
capable of predicting diseases at early stages with minimal invasiveness (6-10). In fact, recent 87
technical advances in mass spectrometry (MS) have been able to detect a large number of 88
low-abundance proteins in biological specimens, which results in the ability to identify many 89
protein surrogates that may indicate malignant properties. As such, this is an attractive 90
method to easily monitor global proteome alterations to identify meaningful protein 91
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
6
differences among disease states. Identifying proteomic differences in pancreatic cysts may 92
provide differences between malignant tumors and other controls due to its proximity to the 93
disease site (11). 94
Currently, the analysis of cyst fluid CEA is considered the best discriminating tool 95
for mucinous and nonmucionous lesions of the pancreas; however, obtaining sufficient 96
amounts of cyst fluid is not easy for accurate CEA analysis. Furthermore, the utility of cyst 97
fluid CEA has been limited due to its insufficient sensitivity or specificity. On the other hand, 98
a proteomic analysis would require only small amounts of cyst fluid for testing, which would 99
be a distinct advantage. Several proteins have been suggested as potential biomarkers to 100
differentiate potentially malignant MC from NMC (11-15). Previous work on the global 101
proteome of pancreatic cysts using mass spectrometry has discovered novel biomarkers and 102
several biomarker candidates (e.g. olfactomedin-4, mucin-18 and solubilized CEA-related 103
cell adhesion molecules) that were differentially expressed in malignant lesions. To verify 104
these biomarker candidates, immunohistochemistry (IHC) or enzyme-linked immunosorbent 105
assays (ELISA) were performed. However, most of the biomarker candidates identified could 106
not be sufficiently validated as different between patients and healthy donors. Antibody-based 107
approaches have generally been used to verify an identified novel biomarker, but several 108
limitations of these platforms should be carefully considered, including i) the large amount of 109
sample needed in cases where of a large number of required proteins screens, ii) antibody 110
problems in developing a suitable experimental design, and iii) the difficulty of obtaining 111
high quality antibodies. 112
Therefore, in this study, we performed a MS-based approach to overcome these 113
obstacles. We identified potential proteomic marker candidates discriminating MC from other 114
pancreatic cysts by differential proteomics analysis. For biomarker validation, a GeLC-Stable 115
Isotope Dilution-Multiple Reaction Monitoring-mass spectrometry (GeLC-SID-MRM-MS), 116
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
7
which is a very sensitive and specific proteomics-based quantitative method, allowed for the 117
detection of protein at low attomole concentrations with a minimal amount of specimen 118
consumed without the need for the use of antibodies. 119
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
8
Methods 120
Study subjects 121
The Institutional Review Board at Samsung Medical Center (Seoul, Korea) approved 122
this study. The patients were recruited prospectively from February 2008 to January 2015. 123
Surgical candidates were directly consulted for surgery. Other patients, who were assumed to 124
have benign cysts with thin walls of small size (less than 2.0 cm) or typical serous cyst 125
adenomas on CT or EUS, underwent follow-up on a regular basis without EUS-FNA. 126
Enrolled patients with pancreatic cysts underwent EUS-FNA. A Hitachi-Aloka® with 127
sector scan transducer (5 MHz) was used for the linear endoscopic ultrasounds. Two 128
specialized endoscopic ultrasonographers performed each EUS-FNA. Aspirated pancreatic 129
cysts were described in terms of location, size, viscosity and fluid volume. Each sample was 130
subjected to cytology, tumor marker identification and chemical analysis. Pancreatic cyst 131
fluids were harvested, frozen immediately and stored at – 70 ℃ for further cytological 132
examination and proteomic analysis. 133
134
Sample preparation for proteomics experiments 135
A MARS column (Agilent Technologies, Santa Clara, CA, USA) was used to 136
remove six abundant proteins (albumin, transferring, IgF, IgA, anti-trypsin and haptoglobin) 137
from pancreatic cyst fluid while all other proteins were enriched. Seventy micrograms of 138
depleted cyst fluid was resolved on 4 - 12% NuPAGE gels to reduce the complexity. Briefly, 139
Coomassie blue-stained protein lanes on the SDS-PAGE gel were manually cut into 18 bands 140
and subjected to in-gel tryptic digestion prior to LC-MS/MS. 141
For GeLC-SID-MRM-MS experiments, cyst fluid was fractionated on SDS-PAGE 142
and stained with Coomassie blue and then cut into 3 bands followed by in-gel trypsin 143
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
9
digestion at a 50:1 ratio for 16 h at 37℃. The tryptic digests were recovered by extraction 144
with 50% acetonitrile (ACN) / 0.1% formic acid and were dried. Synthetic peptides 145
containing stable-isotope labels were spiked in tryptic digests of cyst fluid as standards (100 146
Fmole/uL of each peptide). These mixtures were purified using an OMIX C18 tip (Agilent, 147
Technologies, Santa Clara, CA, USA). Samples were dried and reconstituted with 10 uL 0.1% 148
TFA in water. 149
150
Mass spectrometry-based experiments 151
For the proteomic profiling of pancreatic cyst fluids, MS analysis of cyst fluid tryptic 152
digests were performed using a 6520 quadrupole time-of-flight (Q-TOF) mass spectrometer 153
(Agilent Technologies, Santa Clara, CA, United States). The Q-TOF LC/MS was operated at 154
high resolution (4 GHz) on positive ion mode for all experiments. All data was acquired in 155
the data-dependent MS/MS mode, where a full MS scan was followed by three MS/MS scans 156
for the three most abundant precursor ions in the MS survey scan. 157
For the determination of the cystic proteome of interest, we achieved MRM using a 158
QTRAP 5500 (ABSciex, Framingham, MA, USA) equipped with a nano-electrospray ion 159
source. A MRM scan was performed on the positive mode with ion spray voltages in a 160
2,200~2,500 V range. 161
162
Proteomic data acquisition 163
All MS/MS spectra were searched against the human Swiss-Prot database using the 164
Spectrum Mill search engine (rev A.03.03.084 SR4, Agilent Technologies, Santa Clara, CA, 165
USA). Search results were automatically validated using Spectrum Mill software (Agilent 166
Technologies, Santa Clara, CA, USA) to meet a false discovery rate (FDR) < 1%. Peptides 167
with a 1% FDR cutoff were used to identify proteins via automatic validation using the 168
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
10
following parameters: scored percent intensity (SPI) > 70% for matches with scores > 13 for 169
+1, > 11 for +2, and > 13 for +3. Then, proteins were filtered based on the following criteria: 170
SPI > 70% for matches with scores > 6 for 1+, 2+ and 3+, rank1-2 score threshold = 2, and 171
protein score > 20. Label free and relative semi-quantification by spectral counts, which are 172
useful for quantifying protein abundance changes in shotgun proteomics, were performed for 173
biomarker discovery. The ratio of spectral counts (Rsc-value) (16), which is the log2 (protein 174
ratio) measured from spectral counts, was used for statistical validation of the proteomic data 175
acquired. The Rsc > 1 indicates a 2-fold change in group comparison was allowed for the first 176
screen of the protein list. 177
MRM transitions generated by MRMPilot v. 2.0 (AB SCIEX, Framingham, MA, 178
USA) used were monitored for MRM experiments. At least three transitions from one 179
proteotrypic peptide were generated; 2 or 3 peptide charge states containing 8-25 amino acids, 180
no post-translational modification (PTM), and one missed cleavage were allowed. 181
182
Statistical analysis 183
Data acquired from targeted quantitative proteomics were analyzed using the SPSS 184
statistical package (IBM Corporation, Somers, NY, USA), ver 21.0 and MedCalc software ver 185
12.7.0.0 (Mariakerke, Belgium). When the distribution of data was not normal (i.e., 186
proteomic markers), non-parametric tests were used and data with a normal distribution (i.e., 187
gender and age) were analyzed with parametric tests. Differences in the concentration of 188
proteomic markers between groups were assessed with the non-parametric Mann–Whitney 189
test. The Chi-square (χ2) test was used to assess differences in gender between groups. A T-190
test was conducted to assess age between groups. A P-value ≤ 0.05 was considered 191
statistically significant. ROC analysis was used to access the diagnostic performance of 192
proteomic markers and the area under the curve (AUC) was estimated. 193
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
11
Results 194
Subject characteristics 195
Ninety-two pancreatic cysts were collected prospectively between 2008 and 2012 at 196
Samsung Medical Center. A shortage of cystic fluid (n = 15), not belonging to one of the 197
subgroups (n = 2) or indeterminate diagnosis (n = 21) were the reasons for exclusion from the 198
study. Finally, a total of 99 pancreatic cysts were subjected to analysis and classified into 199
three subgroups (21 NMC1, 41 NMC2 and 37 MC; 38 NMC and 18 MC were included in the 200
validation cohort 1 and 24 NMC and 19 MC in the validation cohort 2) (Table 1). The mean 201
age of the patients was 52 years (range, 20-76 years). No statistically significant differences 202
were observed in age or sex among the subgroups. The amount of aspiration fluid ranged 203
from 1 ㎖ to 120 ㎖, with an average fluid volume of 19.9 ㎖. The most common location 204
of the cysts among the patients was in the pancreatic tail and head (64.3%). Fourteen (25.0%) 205
and six (10.7%) cysts were located in the pancreatic body and peri-pancreas, respectively. 206
Only one patient experienced pancreatitis after EUS-FNA. 207
The final diagnosis was made based on histological and cytological findings. For 208
patients in whom tissue could not be obtained, CT, MRI, EUS findings, cystic fluid CEA, 209
clinical history and cystic change during the follow-up period were used to make a final 210
clinical diagnosis (Supplementary Fig. S1) (17). MC were diagnosed on histology or based 211
on any 2 of the following characteristics: 1) cystic fluid CEA > 192 ng/mL (5), 2) EUS 212
consistent with intraductal papillary mucinous neoplasm (IPMN) or MCN, 3) cytology 213
consistent with MC. NMC2 were diagnosed on histology or based on any 2 of the following 214
characteristics: 1) cystic fluid CEA <5 ng/mL, 2) EUS consistent with NMC, 3) cytology 215
consistent with NMC. Pseudocysts were diagnosed on histology based on EUS consistent 216
with pseudocyst and a typical clinical history including cyst resolution on follow-up. 217
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
12
CEAs were measured using a radioimmunoassay kit (RIAKEY®, Shin Jin Medics 218
Inc. Seoul, Korea). Histological or cytological diagnoses were made in 44 patients, and 219
clinical diagnoses were made in 56 patients. 220
221
Biomarker identification 222
The analytical strategies of biomarker development in pancreatic cysts are shown in 223
Fig. 1. We identified 223 proteins (Supplementary Table S1) and performed semi-224
quantification by spectral counts, which represent the abundance of each protein, with the 225
aim to identify differentially expressed proteins (DEPs) (Supplementary Table S2). In the 226
current study, we paid attention to a set of proteins that changed in MC with high malignant 227
potential. Thus, we chose proteins with Rsc > 1 value that indicates a 2-fold change in each 228
group compared to the malignant group. Finally, we identified 62 DEPs among the disease 229
groups (NMC1, NMC2, MC and malignancy). 230
231
Biomarker prioritization 232
We used MRM assay to prioritize a set of proteins of interest derived from the 233
biomarker identification using shotgun proteomics and select specific peptides from these 234
markers suitable for the MRM assay. A total of 1,021 MRM transitions against 62 DEPs were 235
created and then MRM experiments were achieved on target peptides screened through the 236
first discovery process using QTRAP 5500 MS. Based on this analysis, 33 proteins 237
consequently satisfied the filtering criteria on pooled pancreatic cyst samples. Briefly, a 238
reproducible chromatographic retention time property and strong MS signal intensity were 239
considered as follows: high MS response (signal to noise ratio > 8) and high reproducibility 240
(± 20 s retention time tolerance of three transitions). A total of 33 protein levels were 241
relatively quantified on 56 pancreatic cysts (17 NMC1, 21 NMC2 and 18 MC) using MRM-242
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
13
MS without IS for initial screening. A disease specificity and diagnostic performance of 243
candidates were referred for the second triage, and specific biomarkers were selected using 244
Kruskal-Wallis tests and ROC analysis. After this assessment, 17 proteins were found to be 245
significantly altered in MC compared to NMC1 or NMC2 (P < 0.05 and AUC > 0.7). 246
Several proteins found in the discovery stage of our study overlapped with a set of 247
biomarker surrogates previously reported in pancreatic cysts (Supplementary Table S3). 248
However, these proteins were not specific for mucinous cysts and were not measureable 249
using MRM assay; thus, they were eliminated in a further validation phase. 250
251
Biomarker validation 252
Of the 17 proteins isolated, nine proteins (LGALS3BP, PIGR, LCN2, FCGBP, 253
REG1A, AFM, CTRC, AMY2B and CELA3A) were finally selected for the validation phase 254
based on the preliminary results by MRM experiments with isotope labeled peptides. Herein, 255
we conducted SDS-PAGE prior to MRM experiments in order to reduce the complexity of 256
cyst fluids and to increase the sensitivity for the target proteins of interest (named GeLC-SID-257
MRM-MS). Their concentrations were then measured (Table 2, Fig. 2) in cyst fluids using 258
GeLC-SID-MRM-MS. In the validation cohort 1, the concentration of the 7 proteins (PIGR, 259
LCN2, FCGBP, REG1A, CTRC, AMY2B and CELA3A) was higher in MC than NMC2 by 260
approximately 7-fold. AFM levels were significantly decreased by 6-8-fold in MC (41 261
amol/㎕) compared to NMC1 (260.7 amol/㎕) or NMC2 (340 amol/㎕), respectively. 262
Specifically, four proteins, AFM, FCGBP, REG1A and AMY2B, were found to be 263
significantly altered in MC compared to NMC (including NMC1 and NMC2; P < 0.05) 264
(Table 3, Fig. 2). Thus, we additionally validated 4 proteins in a separate validation cohort 2. 265
These markers also demonstrated surprisingly good diagnostic performance for 266
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
14
discriminating MC and NMC (P < 0.001) as follows: AUCAFM=0.839 (95% CI= 0.687 to 267
0.937), AUCLCN2=0.895 (95% CI= 0.754 to 0.970), AUCREG1A=0.945 (95% CI= 0.821 to 268
0.992) and AUCPIGR=0.887 (95% CI= 0.744 to 0.966). Here, a biomarker subset including 269
AFM, FCGBP, REG1A and AMY2B showed good discriminatory performance in not only 270
the validation cohort 1, but also the validation cohort 2. 271
In addition to the results of SID-MRM-MS, we performed immunoassays to detect 272
LCN2, which had previously been shown to be present at higher levels in MC. Fig. 3 shows 273
that LCN2 levels are increased in MC by both methods. The Spearman correlation coefficient 274
was used to determine a correlation between SID-MRM-MS and immunoassays (r = 0.499, P 275
= 0.0014). 276
277
Evaluation of multiple biomarkers 278
A total of 9 proteins were finally selected that showed differential expression in 279
cystic fluids relative to NMC1, NMC2 and MC. To enhance the diagnostic power, we 280
assessed the diagnostic performance, such as sensitivity, specificity, accuracy and AUC 281
among the three groups. These proteomic markers had a wide range of approximately 44~100% 282
and 47~95% for sensitivity and specificity, respectively (Supplementary Table S4, Table 3). 283
Of these, four markers (AFM, REG1A, LCN2 and PIGR) selected through the validation 284
cohort 1 also significantly differentiated MC from NMC (P < 0.001, AUC > 0.8) in the 285
validation cohort 2. Interestingly, PIGR and LCN2 were specific to discrimination of MC 286
from NMC and NMC2, respectively. Thus, we generated biomarker panel 1(AUC, 0.919) by 287
combining AFM, REG1A and LCN2 for NMC2 vs. MC. AFM, REG1A and PIGR were 288
chosen for differentiating MC from NMC as panel 2 (AUC, 0.933) (Fig. 4). 289
To evaluate the diagnostic performance of four markers, we calculated the diagnostic 290
probability score (PS) based on the sum of the hit numbers of 5 proteins, including cystic 291
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
15
CEA, over cutoff values ranging from 0~5. We described PS with (PS+) and without (PS-) a 292
CEA cutoff in both validation cohorts and the cutoff PS value of 3 was adopted for 293
differential diagnosis of MC. Using a PS- cutoff, 25 of 37 patients with MC were diagnosed 294
with 80% accuracy (Supplementary Table S5). Moreover, when a PS+ cutoff was applied to 295
both validation cohorts, the performance was enhanced, with 30 of 37 MC patients accurately 296
diagnosed, representing 81% sensitivity, 90% specificity and 86% accuracy. We strongly 297
suggest that a 3 ≤ PS is indicative of high MC diagnostic potential. 298
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
16
Discussion 299
Pancreatic cysts have a wide spectrum of disease states ranging from benign to 300
malignant. Differential diagnosis between NMC and MC is crucial to determining the most 301
appropriate treatment because MC has malignant potential. Cystic fluid CEA, EUS-FNA, CT 302
and MRI have been used to aid in determining the final diagnosis in clinical practice, but 303
their diagnostic accuracies are not satisfactory. Cystic fluid cytology has low sensitivity due 304
to scant cell numbers in samples. CEA, a known clinical tumor marker, is also often 305
suggested as a differential diagnostic marker for MC (5, 18-20). However, the optimal cut-off 306
value of cystic fluid CEA ranges widely, between 109.9 ng/㎖ and 480 ng/㎖ depending on 307
the study. Its diagnostic accuracy for differentiating MC and NMC ranges from 72% to 86% 308
in the literature. Although the primary aim of EUS-FNA is to determine malignancy, the 309
diagnostic yield of EUS-FNA in this study was very disappointing (52.6%). Additionally, CT 310
or MRI images were included in the characterization of cystic pancreatic masses (55-75%). 311
Thus, high quality diagnostic and prognostic tools are required allowing for minimally 312
invasive testing with better accuracy. 313
For decades, various MS techniques have increased for diagnostic use in clinical 314
laboratories for clinical application. Despite these efforts, MS-based approaches are being 315
used more often to identify small molecules rather than protein markers. In fact, only a few 316
proteomic investigations of pancreatic cyst fluids have been applied to overcome the current 317
diagnostic limitations (11, 13, 15) for the differential diagnosis of cysts and early prediction 318
of pancreatic cancer. Nevertheless, no reliable biomarkers have been discovered that can 319
discriminate MC with malignant potential from NMC. Thus, our group focused on 320
identifying proteomic markers that are differentially expressed in MC with high malignant 321
potential compared with NMC, which would be helpful for the differential diagnosis of 322
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
17
pancreatic cysts. 323
We analyzed pancreatic cyst fluids using integrated proteomics and described the 324
differential expression profiles in different cystic lesions including NMC1, NMC2 and MC. 325
Specially, 9 proteins of interest (PIGR, LCN2, FCGBP, REG1A, AFM, CTRC, AMY2B, 326
LGALS3BP and CELA3A) were verified using MS-based targeted proteomics technologies 327
and immunoassays. These markers have been previously described as potential biomarker 328
candidates associated with multiple cancer types in other studies. For instance, AFM was 329
reported as a potential biomarker for ovarian cancer (21) and breast cancer (22), where AFM 330
may play a role in the progression of cancer. Plasma AFM concentrations were validated by 331
western blotting and immunoassays in patients with ovarian cancer. Serum REG1A 332
concentrations were shown to be significantly over-expressed in patients with various 333
pancreatic diseases, cirrhosis and various cancers of the digestive system (23). Abnormal 334
expression of PIGR was reported in hepatocellular carcinoma (24) and lung cancer (25). 335
LCN2 has been proposed as an early screening marker for human primary breast cancer (26, 336
27), ovarian cancer (28), colorectal cancer and pancreatic cancer (29-31). The exact 337
mechanism for how these proteins correlate with pathogenesis has yet to be clearly 338
understood, despite that these proteins have been suggested as potential biomarkers in various 339
cancers. 340
Our data also showed that 3 proteins (REG1A, PIGR and LCN2) were up-regulated 341
and AFM was down-regulated in MC compared with NMC2. This was in agreement with 342
previous reports that showed alteration of these protein levels depending on malignant 343
potential. In particular, PIGR protein was consistently up-regulated in MC in comparison 344
with not only NMC1 but also NMC2. NMC1 are inflammatory cysts and are not difficult to 345
differentiate from NMC2 because the clinical history and course are different. Some 346
inflammatory markers could be also up-regulated in MC. Therefore, useful markers of MC 347
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
18
need to be altered discriminately in both NMC1 and NMC2. 348
As mentioned above, cross-sectional imaging (e.g. CT, MRI) or conventional tumor 349
markers (e.g. CEA, CA 19-9, CA 72-4) for the characterization of pancreatic cyst lesions 350
have not provided effective diagnoses in previous studies. In contrast, our data show that the 351
4 marker candidates (AFM, REG1A, PIGR, and LCN2) and the combination of these proteins 352
(panel 1, panel 2) can differentiate MC from NMC groups. Furthermore, the multi-marker 353
panel has greater discriminatory power for NMC versus MC (AUC, 0.933) than that of each 354
single marker (AUC, < 0.829) in the validation phase. 355
Our study demonstrates the usefulness of MS-based comprehensive proteomics 356
methodologies for the discovery and validation of candidate biomarkers for differential 357
diagnosis of pancreatic cysts. Multiple biomarkers (AFM, REG1A, PIGR, LCN2) have been 358
identified, but their reliability and usefulness need to be confirmed in a clinical setting using 359
independent patient cohorts. 360
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
19
361
Acknowledgments 362
This study was supported by a grant of Korea Health Technology R&D Project 363
through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of 364
Health & Welfare, Republic of Korea (grant number : HI13C2098) and Samsung Medical 365
Center Clinical Research Development Program grant, #CRS110-56-2.366
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
20
References
1. Lee KS, Sekhar A, Rofsky NM, Pedrosa I. Prevalence of incidental pancreatic cysts
in the adult population on MR imaging. Am J Gastroenterol 2010;105:2079-84.
2. de Jong K, Nio CY, Hermans JJ, Dijkgraaf MG, Gouma DJ, van Eijck CH, et al.
High prevalence of pancreatic cysts detected by screening magnetic resonance
imaging examinations. Clin Gastroenterol Hepatol 2010;8:806-11.
3. Laffan TA, Horton KM, Klein AP, Berlanstein B, Siegelman SS, Kawamoto S, et al.
Prevalence of unsuspected pancreatic cysts on MDCT. AJR Am J Roentgenol
2008;191:802-7.
4. Girometti R, Intini S, Brondani G, Como G, Londero F, Bresadola F, et al. Incidental
pancreatic cysts on 3D turbo spin echo magnetic resonance
cholangiopancreatography: prevalence and relation with clinical and imaging
features. Abdom Imaging 2011;36:196-205.
5. Brugge WR, Lewandrowski K, Lee-Lewandrowski E, Centeno BA, Szydlo T, Regan
S, et al. Diagnosis of pancreatic cystic neoplasms: a report of the cooperative
pancreatic cyst study. Gastroenterology 2004;126:1330-6.
6. Krieg RC, Paweletz CP, Liotta LA, Petricoin EF, 3rd. Clinical proteomics for cancer
biomarker discovery and therapeutic targeting. Technol Cancer Res Treat
2002;1:263-72.
7. Liang S, Xu Z, Xu X, Zhao X, Huang C, Wei Y. Quantitative proteomics for cancer
biomarker discovery. Comb Chem High Throughput Screen 2012;15:221-31.
8. Liu NQ, Braakman RB, Stingl C, Luider TM, Martens JW, Foekens JA, et al.
Proteomics pipeline for biomarker discovery of laser capture microdissected breast
cancer tissue. J Mammary Gland Biol Neoplasia 2012;17:155-64.
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
21
9. Lu M, Faull KF, Whitelegge JP, He J, Shen D, Saxton RE, et al. Proteomics and mass
spectrometry for cancer biomarker discovery. Biomark Insights 2007;2:347-60.
10. Haab BB, Porter A, Yue T, Li L, Scheiman J, Anderson MA, et al. Glycosylation
variants of mucins and CEACAMs as candidate biomarkers for the diagnosis of
pancreatic cystic neoplasms. Ann Surg 2010;251:937-45.
11. Ke E, Patel BB, Liu T, Li XM, Haluszka O, Hoffman JP, et al. Proteomic analyses of
pancreatic cyst fluids. Pancreas 2009;38:e33-42.
12. Scarlett CJ, Samra JS, Xue A, Baxter RC, Smith RC. Classification of pancreatic
cystic lesions using SELDI-TOF mass spectrometry. ANZ J Surg 2007;77:648-53.
13. Cuoghi A, Farina A, Z'Graggen K, Dumonceau JM, Tomasi A, Hochstrasser DF, et al.
Role of proteomics to differentiate between benign and potentially malignant
pancreatic cysts. J Proteome Res 2011;10:2664-70.
14. Jabbar KS, Verbeke C, Hyltander AG, Sjovall H, Hansson GC, Sadik R. Proteomic
mucin profiling for the identification of cystic precursors of pancreatic cancer. J Natl
Cancer Inst 2014;106:djt439.
15. Mann BF, Goetz JA, House MG, Schmidt CM, Novotny MV. Glycomic and
proteomic profiling of pancreatic cyst fluids identifies hyperfucosylated lactosamines
on the N-linked glycans of overexpressed glycoproteins. Mol Cell Proteomics
2012;11:M111.015792.
16. Old WM, Meyer-Arendt K, Aveline-Wolf L, Pierce KG, Mendoza A, Sevinsky JR, et
al. Comparison of label-free methods for quantifying human proteins by shotgun
proteomics. Mol Cell Proteomics 2005;4:1487-502.
17. Shen J, Brugge WR, Dimaio CJ, Pitman MB. Molecular analysis of pancreatic cyst
fluid: a comparative analysis with current practice of diagnosis. Cancer
2009;117:217-27.
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
22
18. Cizginer S, Turner BG, Bilge AR, Karaca C, Pitman MB, Brugge WR. Cyst fluid
carcinoembryonic antigen is an accurate diagnostic marker of pancreatic mucinous
cysts. Pancreas 2011;40:1024-8.
19. Linder JD, Geenen JE, Catalano MF. Cyst fluid analysis obtained by EUS-guided
FNA in the evaluation of discrete cystic neoplasms of the pancreas: a prospective
single-center experience. Gastrointest Endosc 2006;64:697-702.
20. Park WG, Mascarenhas R, Palaez-Luna M, Smyrk TC, O'Kane D, Clain JE, et al.
Diagnostic performance of cyst fluid carcinoembryonic antigen and amylase in
histologically confirmed pancreatic cysts. Pancreas 2011;40:42-5.
21. Jackson D, Craven RA, Hutson RC, Graze I, Lueth P, Tonge RP, et al. Proteomic
profiling identifies afamin as a potential biomarker for ovarian cancer. Clin Cancer
Res 2007;13:7370-9.
22. Opstal-van Winden AW, Krop EJ, Karedal MH, Gast MC, Lindh CH, Jeppsson MC,
et al. Searching for early breast cancer biomarkers by serum protein profiling of pre-
diagnostic serum; a nested case-control study. BMC Cancer 2011;11:381.
23. Shen J, Person MD, Zhu J, Abbruzzese JL, Li D. Protein expression profiles in
pancreatic adenocarcinoma compared with normal pancreatic tissue and tissue
affected by pancreatitis as detected by two-dimensional gel electrophoresis and mass
spectrometry. Cancer Res 2004;64:9018-26.
24. Ai J, Tang Q, Wu Y, Xu Y, Feng T, Zhou R, et al. The role of polymeric
immunoglobulin receptor in inflammation-induced tumor metastasis of human
hepatocellular carcinoma. J Natl Cancer Inst 2011;103:1696-712.
25. Ocak S, Pedchenko TV, Chen H, Harris FT, Qian J, Polosukhin V, et al. Loss of
polymeric immunoglobulin receptor expression is associated with lung
tumourigenesis. Eur Respir J 2012;39:1171-80.
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
23
26. Bauer M, Eickhoff JC, Gould MN, Mundhenke C, Maass N, Friedl A. Neutrophil
gelatinase-associated lipocalin (NGAL) is a predictor of poor prognosis in human
primary breast cancer. Breast Cancer Res Treat 2008;108:389-97.
27. Fernandez CA, Yan L, Louis G, Yang J, Kutok JL, Moses MA. The matrix
metalloproteinase-9/neutrophil gelatinase-associated lipocalin complex plays a role in
breast tumor growth and is present in the urine of breast cancer patients. Clin Cancer
Res 2005;11:5390-5.
28. Lim R, Ahmed N, Borregaard N, Riley C, Wafai R, Thompson EW, et al. Neutrophil
gelatinase-associated lipocalin (NGAL) an early-screening biomarker for ovarian
cancer: NGAL is associated with epidermal growth factor-induced epithelio-
mesenchymal transition. Int J Cancer 2007;120:2426-34.
29. Brand RE, Nolen BM, Zeh HJ, Allen PJ, Eloubeidi MA, Goldberg M, et al. Serum
biomarker panels for the detection of pancreatic cancer. Clin Cancer Res
2011;17:805-16.
30. Moniaux N, Chakraborty S, Yalniz M, Gonzalez J, Shostrom VK, Standop J, et al.
Early diagnosis of pancreatic cancer: neutrophil gelatinase-associated lipocalin as a
marker of pancreatic intraepithelial neoplasia. Br J Cancer 2008;98:1540-7.
31. Tong Z, Kunnumakkara AB, Wang H, Matsuo Y, Diagaradjane P, Harikumar KB, et al.
Neutrophil gelatinase-associated lipocalin: a novel suppressor of invasion and
angiogenesis in pancreatic cancer. Cancer Res 2008;68:6100-8.
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
24
Figure legends
Figure 1. Brief strategies for the discovery and validation of candidate biomarkers; In the
discovery phase, label free and relative semi-quantification by spectral counts, which are
useful for quantifying protein abundance changes in shotgun proteomics, were performed for
biomarker discovery (Rsc > 1 was allowed). The MRM-MS without IS was achieved for
initial candidate marker selection. Logistic regression analysis with Bonferroni's correction
and receiver operating characteristic (ROC) curves were used to prioritize identified marker
candidates (P < 0.05, AUC >0.7). The selected candidates were then subjected to the
validation phase (cohort 1 and cohort 2) using GeLC-SID-MRM-MS including IS responding
to 9 proteomic makers. AFM, REG1A, PIGR and LCN2 was finally selected for the
diagnostic panel development.
Figure 2. Concentrations of 9 biomarkers in cystic fluids from patients diagnosed with
NMC1, NMC2 and MC.
Figure 3. Measurement of lipocalin 2 (LCN2) in cystic fluids using (A) immunoassay and (B)
GeLC-Stable Isotope Dilution-Multiple Reaction Monitoring (GeLC-SID-MRM) techniques.
Figure 4. ROC curves for multi-marker panel 1 (A) in NMC2 and MC and panel 2 (B) in
NMC and MC.
Supplementary Fig. S1. Flow chart of study subjects for the validation cohort 1 (A) and cohort
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
25
2 (B).
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
26
Table 1. Baseline clinical characteristics 1
Characteristic
Validation cohort 1 Validation cohort 2
NMCa (n = 38) MCd (n = 18)
NMC (n = 24) MC (n = 19)
NMC1b (n = 17) NMC2c (n = 21) NMC1 (n = 4) NMC2 (n = 20)
Age, years Median 57 49 56 44 50 60
Range 20-74 23-67 38-76 25-52 32-65 33-85
Sex, N
Male / Female 11/6 6/15 7/11 3/1 4/16 5/14
EUS findings
Size, mm 5.5 (3-15) 3.2 (2-10) 3.5 (2-12) 6.5 (3-9) 3.5(2-4) 3.7(2-7)
Unilocular/Multilocular 15/2 6/15 4/14 4/0 9/11 10/9
Aspiration, cc 6 (scanty-150) 8.5 (scanty-120) 3 (scanty-40) 12(8-20) 7(1-15) 11 (1-70)
Location of cyst, N (%)
head 2 (11.8) 8 (38.1) 6 (33.3) 0 (0) 10 (50) 6 (31.6)
body and neck 3 (17.6) 8 (38.1) 3 (16.7) 1 (25) 5 (25) 6 (31.6)
tail 8 (47.1) 3 (14.3) 9 (50.0) 3 (75) 5 (25) 7 (36.5)
peri-pancreas 4 (23.5) 2 (9.5) 0 (0) 0 (0) 0 (0) 0 (0)Diagnostic methods
surgery 1 2 16 1 2 9 cytology 1 3 2 0 3 4
clinical 15 16 0 3 15 6 Abbreviations: a, nonmucinous cysts; b, pseudocysts; c, nonmucious neoplastic cysts; d, mucinous cysts; EUS, endoscopic ultrasound. 2
on March 6, 2021. ©
2015 Am
erican Association for C
ancer Research.
cancerres.aacrjournals.org D
ownloaded from
Author m
anuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Author M
anuscript Published O
nlineFirst on June 29, 2015; D
OI: 10.1158/0008-5472.C
AN
-14-2896
27
Table 2. MRM transitions of the representative peptides corresponding to 9 proteins
Description Peptide sequence Mass Information
Target ion Light (Q1/Q3) Heavy (Q1/Q3) CE(eV)
AFM AIPVTQYL*K 2/y5 516.8 / 652.4 520.3 / 659.4 28 2/y6 516.8 / 751.4 520.3 / 758.5 28 2/y7 516.8 / 848.5 520.3 / 855.5 28
CTRC VSAYIDWI*NEK 2/y5 669.3 / 689.4 672.8 / 696.4 34 2/y7 669.3 / 917.5 672.8 / 924.5 34 2/y8 669.3 / 1080.5 672.8 / 1087.6 34
CELA3A LANPVSL*TDK 2/y6 529.3 / 662.4 532.8 / 669.4 28 2/y7 529.3 / 759.4 532.8 / 766.4 28 2/y8 529.3 / 873.5 532.8 / 880.5 28
LGALS3BP IDITLSSV*K 2/y6 488.3 / 634.4 491.3 / 640.4 26 2/y7 488.3 / 747.5 491.3 / 753.5 26 2/y8 488.3 / 862.5 491.3 / 868.5 26
FCGBP GNPAVSYV*R 2/y5 481.8 / 623.3 484.8 / 629.4 26 2/y6 481.8 / 694.4 484.8 / 700.4 26 2/y7 481.8 / 791.4 484.8 / 797.5 26
REG1A WHWSSGSLV*SYK 2/y10 718.8 / 1113.6 721.9 / 1119.6 37 2/y8 718.8 / 840.5 721.9 / 846.5 37 2/y9 718.8 / 927.5 721.9 / 933.5 37
LCN2 WYVVGLAGNAIL*R 2/y10 716.4 / 983.6 719.9 / 990.6 37 2/y11 716.4 / 1082.7 719.9 / 1089.7 37 2/y9 716.4 / 884.5 719.9 / 891.5 37
AMY2B LTGLLDLAL*EK 2/y7 593.4 / 801.5 596.9 / 808.5 27 2/y8 593.4 / 914.6 596.9 / 921.6 27 2/y9 593.4 / 971.6 596.9 / 978.6 27
PIGR IIEGEPNL*K 2/y6 506.8 / 657.4 510.3 / 664.4 27 2/y7 506.8 / 786.4 510.3 / 793.4 27 2/y8 506.8 / 899.5 510.3 / 906.5 27
NOTE: Endogenous peptides (light) and isotope-labeled peptides (heavy) were chosen to measure biomarkers of interest. * represents an amino acid labeled with a heavy isotope, 13C15N. The transitions (Q1/Q3) were optimized using MRMPilot software. Abbreviations: AFM, afamin; CTRC, chymotrypsin C (caldecrin); CELA3A, chymotrypsin-like elastase family, member 3A ; LGALS3BP, lectin, galactoside-binding, soluble, 3 binding protein; FCGBP, Fc fragment of IgG binding protein; REG1A, lithostathine-1-alpha; LCN2, lipocalin 2; AMY2B, amylase, alpha 2B (pancreatic); PIGR, polymeric immunoglobulin receptor.
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
28
Table 3. A comparison of potential proteomic markers 1
Marker (amol/uL) Median (95% CI) P-value AUC
Validation cohort 1 NMC (n = 38)
MC (n = 18) NMC vs. MC
NMC2 vs. MC
NMC vs. MC
NMC2 vs. MC NMC1 (n = 17) NMC2 (n = 21)
AFM 261 (139 - 724) 241 (171 - 577) 41.0 (20 - 77) 0.000 0.000 0.825 0.837
CTRC 374 (207 - 1717) 92 (44 - 115) 354 (95 - 904) 0.188 0.011 0.608 0.73
CELA3A 253 (76 - 996) 31 (22 – 186) 287 (139 - 827) 0.162 0.035 0.617 0.69
LGALS3BP 94 (45 - 157) 44 (22 - 87) 41 (14 - 58) 0.149 0.681 0.635 0.559
FCGBP 1149 (617 - 3273) 223 (119 - 418) 1273 (701 - 4431) 0.021 0.001 0.696 0.715
REG1A 321 (51 - 688) 59 (39 - 153) 490 (148 - 1107) 0.017 0.001 0.727 0.815
LCN2 1414 (470 - 4755) 218 (95 - 608) 1717 (649 - 5910) 0.067 0.004 0.719 0.771
AMY2B 1680 (251 - 4928) 88 (39 - 380) 1304 (668 - 5446) 0.074 0.003 0.652 0.731
PIGR 13 (9 - 45) 32.5 (11 - 93) 89 (29 - 609) 0.004 0.065 0.728 0.664
Validation cohort 2 NMC (n = 24)
MC (n = 19) NMC vs. MC
NMC2 vs. MC
NMC vs. MC
NMC2 vs. MC NMC1 (n = 4) NMC2 (n = 20)
AFM 304 (187 - 510) 48 (21 - 77) 0.000 0.000 0.839 0.827
REG1A 178 (109 - 194) 9709 (7631 - 28600) 0.000 0.000 0.945 0.906
on March 6, 2021. ©
2015 Am
erican Association for C
ancer Research.
cancerres.aacrjournals.org D
ownloaded from
Author m
anuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Author M
anuscript Published O
nlineFirst on June 29, 2015; D
OI: 10.1158/0008-5472.C
AN
-14-2896
29
LCN2 51 (41-89) 1130 (441 - 422) 0.000 0.000 0.895 0.882
PIGR 29 (22-55) 920 (346 - 3032) 0.000 0.000 0.887 0.897
NOTE: Statistical comparisons were made among the three groups of pancreatic cysts for the top 9 markers (NMC [controls] vs. MC [cases] or NMC2 1
[controls] vs. MC [cases]). A P-value ≤ 0.05 was considered statistically significant. 2
Abbreviations: CI, confidence interval; AUC, the area under the curve. 3
4
5
6
on March 6, 2021. ©
2015 Am
erican Association for C
ancer Research.
cancerres.aacrjournals.org D
ownloaded from
Author m
anuscripts have been peer reviewed and accepted for publication but have not yet been edited.
Author M
anuscript Published O
nlineFirst on June 29, 2015; D
OI: 10.1158/0008-5472.C
AN
-14-2896
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896
Published OnlineFirst June 29, 2015.Cancer Res Jisook Park, Hwan Sic Yun, Kwang Hyuck Lee, et al. mucinous and nonmucinous pancreatic cystsDiscovery and Validation of Biomarkers that distinguish
Updated version
10.1158/0008-5472.CAN-14-2896doi:
Access the most recent version of this article at:
Material
Supplementary
http://cancerres.aacrjournals.org/content/suppl/2021/03/03/0008-5472.CAN-14-2896.DC1
Access the most recent supplemental material at:
Manuscript
Authoredited. Author manuscripts have been peer reviewed and accepted for publication but have not yet been
E-mail alerts related to this article or journal.Sign up to receive free email-alerts
Subscriptions
Reprints and
To order reprints of this article or to subscribe to the journal, contact the AACR Publications
Permissions
Rightslink site. Click on "Request Permissions" which will take you to the Copyright Clearance Center's (CCC)
.http://cancerres.aacrjournals.org/content/early/2015/06/27/0008-5472.CAN-14-2896To request permission to re-use all or part of this article, use this link
on March 6, 2021. © 2015 American Association for Cancer Research. cancerres.aacrjournals.org Downloaded from
Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on June 29, 2015; DOI: 10.1158/0008-5472.CAN-14-2896