Discovery and Qualification of Serum Protein Biomarker Candidates

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Discovery and Qualification of Serum Protein Biomarker Candidates for Cholangiocarcinoma Diagnosis Kassaporn Duangkumpha, Thomas Stoll, Jutarop Phetcharaburanin, Puangrat Yongvanit, Raynoo Thanan, Anchalee Techasen, Nisana Namwat, Narong Khuntikeo, Nittaya Chamadol, Sittiruk Roytrakul, Jason Mulvenna, Ahmed Mohamed, Alok K. Shah, Michelle M. Hill, and Watcharin Loilome J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.9b00242 • Publication Date (Web): 16 Jul 2019 Downloaded from pubs.acs.org on July 17, 2019

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Journal of Proteome Research

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Discovery and Qualification of Serum Protein

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Biomarker Candidates for Cholangiocarcinoma

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Diagnosis

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Kassaporn Duangkumpha, †,‡ Thomas Stoll, ¥ Jutarop Phetcharaburanin, †,‡Puangrat

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Yongvanit, ‡ Raynoo Thanan, † Anchalee Techasen, ‡,ǁ Nisana Namwat, †,‡ Narong Khuntikeo,

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‡,€

Nittaya Chamadol, ‡,Ꜫ Sittiruk Roytrakul, Ω Jason Mulvenna, ¥ Ahmed Mohamed, ¥ Alok K. Shah, ¥ Michelle M. Hill, ¥,* and Watcharin Loilome †,‡,*

8 9 †

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Department of Biochemistry, Faculty of Medicine, Khon Kaen University, Khon Kaen,

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Thailand ‡

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Cholangiocarcinoma Research Institute, Khon Kaen University, Khon Kaen, Thailand; ¥

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14 15 16

€

QIMR Berghofer Medical Research Institute, Queensland, Australia

Faculty of Associated Medical Sciences, Khon Kaen University, Khon Kaen, Thailand

Department of Surgery, Faculty of Medicine, Khon Kaen University, Khon Kaen, Thailand Ꜫ

Department of Radiology, Faculty of Medicine, Khon Kaen University, Khon Kaen,

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Thailand Ω

Proteomics Research Laboratory, Genome Institute, National Center for Genetic

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Engineering and Biotechnology, National Science and Technology Development Agency,

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Pathum Thani, Thailand

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Corresponding authors:

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Associate Professor Watcharin Loilome, Email: [email protected]

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Associate Professor Michelle Hill, Email: [email protected] 1 ACS Paragon Plus Environment

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ABSTRACT

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Cholangiocarcinoma ( CCA) is a major health problem in northeastern Thailand. The

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majority of CCA cases are clinically silent and difficult to detect at an early stage. Although

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abdominal ultrasonography ( US) can detect pre- malignant periductal fibrosis ( PDF) , this

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method is not suitable for screening populations in remote areas. With the goal of developing

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a blood test for detecting CCA in the at-risk population, we carried out serum protein biomarker

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discovery and qualification. Label-free shotgun proteomics was performed on depleted serum

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samples from 30 participants (n=10 for US-normal, US-PDF and CCA groups). Of 40 protein

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candidates selected using multiple reactions monitoring on 90 additional serum samples (n=30

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per group) , 11 discriminatory proteins were obtained using supervised multivariate statistical

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analysis. We further evaluated 3 candidates using ELISA and immunohistochemistry (IHC) .

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S100A9, thioredoxin ( TRX) and cadherin- related family member 2 ( CDHR2) were

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significantly different between CCA and normal, and CCA and PDF groups when measured in

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an additional 247 serum samples (p0.9 Pearson’s correlation. Then, normalized peptide intensities were averaged to protein

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intensity, and a log2 transformation was performed to obtain a near-normal distribution needed

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for statistical tests using in the computing environment R (Table S6). For statistical analysis,

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missing values were replaced by the minimum detected intensity for each peptide 27.

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Bioinformatics and statistical analysis

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Protein interaction network analysis was generated using STRING software based on

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the STRING database and Gene Oncology ( GO) term. All of analyses were constructed and

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visualized in SPSS 19. 0 ( IBM, USA) , GraphPad Prism 5 and R statistical software. The

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ANOVA test was conducted with Shiny MixOmics online software ( http: / / mixomics-

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projects. di. uq. edu. au/ Shiny) . Principal component analysis ( PCA) and orthogonal signal

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correction projection to latent structures discriminant analysis (O-PLS-DA) was conducted in

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SIMCA 15.0 (Umetrics, Sweden). Described and detailed of statistical analysis are available

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in the Supplementary Methods.

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Antibody-based methods

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The following primary antibodies were used for indirect enzyme- linked

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immunosorbent assay ( ELISA) and immunohistochemical staining ( IHC) : S100A9 ( Cat.

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#ab24111) , TRX ( Cat. #ab185329) purchased from Abcam ( Cambridge, MA) and

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CDHR2/PCLKC (Cat. #orb158119) purchased from Biorbyt (San Fransisco, CA). Detailed

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methods for ELISA and IHC are available in the Supplementary Methods.

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RESULTS

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Overview of biomarker workflow and baseline characteristics of samples

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We performed a multi- phased biomarker discovery and development study as

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illustrated in Figure 1. Participant recruitment, ultrasound, blood sample collection and

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biobanking were completed before biomarker discovery.

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‘triangular’ biomarker study design for development of early cancer biomarkers

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small cohort for discovery using shotgun proteomics to measure a broad range of proteins, then

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increased the sample size while reducing the number of biomarker candidates. For the

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discovery and qualification phases, we selected age-sex matched participants with ultrasound-

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confirmed normal liver and PDF pathology. CCA cases were confirmed by pathology

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diagnosis. In the qualification cohort, smoking status and alcohol consumption were

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significantly different (Table 1 and Table S9). Orthogonal investigation using antibody-based

According to the proposed 28

, we used a


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methods were conducted in independent samples using ELISA and IHC techniques on serum

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samples and tumor micro tissue arrays, respectively.

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Figure 1 Generalized workflow diagram for serum protein biomarker discovery. A; Serum

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samples from respective patient groups were stored at - 80ºc until analysis. B; Discovery

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samples (n=30) were depleted for the top 12 serum proteins and spiked with internal standard

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protein. Tryptic peptides were analysed by label- free proteomics and MaxQuant software.

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Biomarker candidates were selected after analysis with Shiny MixOmics. C; A custom multiple

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reaction monitoring-mass spectrometry (MRM-MS) was developed for biomarker verification

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in an independent cohort of 90 participants. Data processing and analysis used Skyline, R,

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SIMCA and SPSS. D; Antibody-based assays were used to validate peptide level MS data for

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selected candidates at the protein level in additional independent cohorts, n= 247 for ELISA,

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n=208 for IHC.

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Table 1 Baseline characteristics of serum samples in the discovery and qualification phases Discovery phase (N=30) Normal US

PDF US

CCA

Sample size

10

10

10

Gender Male/Female

5/5

5/5

5/5

Age in year (Median ± SD)

60 ± 11

60 ± 12

61 ± 11

Diagnosed with Diabetes Yes No Unknown*

1 (10%) 5 (50%) 4 (40%)

2 (20%) 8 (80%) -

Smoking status Yes No Unknown*

1 (10%) 4 (40%) 5 (50%)

Alcohol consumption Yes No Unknown*

2 (20%) 3 (30%) 5 (50%)

Qualification phase (N=90) p-value

Normal US

PDF US

CCA

30

30

30

1.000

15/15

15/15

15/15

1.000

1.000

63 ± 5

65 ± 6

64 ± 5

0.497

9 (90%) 1 (10%)

0.376 6 (20%) 4 (13%) 6 (20%) 24(80%) 26 (87%) 18 (60%) 6 (20%)

0.738

3 (30%) 7 (70%) -

4 (40%) 5 (50%) 1 (10%)

0.649 13 (43%) 6 (20%) 14 (47%) 13 (43%) 22 (73%) 10 (33%) 4 (14%) 2 (7%) 6 (20%)

0.042

4 (40%) 6 (60%) -

6 (60%) 3 (30%) 1 (10%)

0.449 18(60%) 8 (27%) 21 (70%) 8 (27%) 20 (67%) 3 (10%) 4 (13%) 2 (6%) 6 (20%)

P0. 9 ( Pearson’ s correlation test) were selected and 12 ACS Paragon Plus Environment

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converted to protein intensities and log2 transformed. The qualification data set was then

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subjected to multivariate statistical analysis (Figure 3). PCA with Pareto scaling showed a

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clear separation of the CCA group from the normal and PDF groups (Figure 3A). The first two

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principal components expressed 35.9% for PC1 and 21.9% for PC2 in the PCA model (Figure

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3B). Orthogonal signal correction projection to latent structures discriminant analysis (O-PLS-

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DA) was applied to conduct pair-wise comparisons of normal versus PDF, normal versus CCA,

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and PDF versus CCA ( Figure S2) . Although normal and PDF groups were unable to be

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separated, the CCA group showed clear separation from the normal as well as PDF groups. The

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O- PLS- DA regression model confirmed the above visual observations, with significant CV-

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ANOVA for the normal versus CCA comparison, PDF versus CCA comparison, but not for

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the normal versus PDF comparison (Table 2).

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The loadings of the pairwise O- PLS- DA model identified significantly higher

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normalized protein intensities for 11 proteins: Haptoglobin ( HP) , Alpha- 1- antichymotrypsin

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( A1AC / SERPIN3) , Complement component C9 ( C9) , Intercellular adhesion molecule 1

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(ICAM1), Protein S100A9 (S100A9), Thioredoxin (TRX/TXN), Aminopeptidase N (ANPEP),

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Fumarylacetoacetase (FAH), Lipopolysaccharide-binding protein (LBP), Inter-alpha-trypsin

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inhibitor heavy chain H3 (ITIH3), Cadherin-related family member 2 (CHDR2/PCLKC) in the

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CCA group when compared with the normal and PDF groups (Table 2). Data for all measured

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proteins are provided in Table S7.

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Figure 3 Biomarker qualification and multivariate analysis. A and B; PCA score plot and

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loading plot of MRM results of candidates that shows sample differentiation. The three

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significant candidate proteins are shown in red circles.

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Table 2 Summary of O-PLS-DA qualified serum biomarker candidate proteins O-PLS-DA Model CCA (-) vs Normal (+) R2X = 56.7%; Q2Y = 0.645; p

Discovery and Qualification of Serum Protein Biomarker Candidates

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