Targeted Proteomics Predicts a Sustained Complete-Response after

Subscriber access provided by University of Newcastle, Australia

Article

Targeted proteomics predict a sustained complete-response after transarterial chemoembolization and clinical outcomes in patients with hepatocellular carcinoma: a prospective cohort study Su Jong Yu, Hyunsoo Kim, Hophil Min, A-Reum Sohn, Young Youn Cho, JeongJu Yoo, Dong Hyeon Lee, Eun Ju Cho, Jeong-Hoon Lee, Jungsoo Gim, Taesung Park, Yoon Jun Kim, Chung Yong Kim, Jung-Hwan Yoon, and Youngsoo Kim J. Proteome Res., Just Accepted Manuscript • DOI: 10.1021/acs.jproteome.6b00833 • Publication Date (Web): 23 Jan 2017 Downloaded from http://pubs.acs.org on January 24, 2017

Just Accepted “Just Accepted” manuscripts have been peer-reviewed and accepted for publication. They are posted online prior to technical editing, formatting for publication and author proofing. The American Chemical Society provides “Just Accepted” as a free service to the research community to expedite the dissemination of scientific material as soon as possible after acceptance. “Just Accepted” manuscripts appear in full in PDF format accompanied by an HTML abstract. “Just Accepted” manuscripts have been fully peer reviewed, but should not be considered the official version of record. They are accessible to all readers and citable by the Digital Object Identifier (DOI®). “Just Accepted” is an optional service offered to authors. Therefore, the “Just Accepted” Web site may not include all articles that will be published in the journal. After a manuscript is technically edited and formatted, it will be removed from the “Just Accepted” Web site and published as an ASAP article. Note that technical editing may introduce minor changes to the manuscript text and/or graphics which could affect content, and all legal disclaimers and ethical guidelines that apply to the journal pertain. ACS cannot be held responsible for errors or consequences arising from the use of information contained in these “Just Accepted” manuscripts.

Journal of Proteome Research is published by the American Chemical Society. 1155 Sixteenth Street N.W., Washington, DC 20036 Published by American Chemical Society. Copyright © American Chemical Society. However, no copyright claim is made to original U.S. Government works, or works produced by employees of any Commonwealth realm Crown government in the course of their duties.

Page 1 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Journal of Proteome Research

Targeted proteomics predict a sustained complete-response after transarterial chemoembolization and clinical outcomes in patients with hepatocellular carcinoma: a prospective cohort study

Su Jong Yu1,#, Hyunsoo Kim2,3,#, Hophil Min2, Areum Sohn2, Young Youn Cho1, Jeong-Ju Yoo1, Dong Hyeon Lee1, Eun Ju Cho1, Jeong-Hoon Lee1, Jungsoo Gim4, Taesung Park4,5,Yoon Jun Kim1, Chung Yong Kim1, Jung-Hwan Yoon1,*, and Youngsoo Kim2,3,*

1

Department of Internal Medicine and Liver Research Institute, 2Department of Biomedical

Engineering, and 3Institute of Medical and Biological Engineering, Medical Research Center, Yongon-Dong, Seoul 110-799 Korea; 5

4

Interdisciplinary Program in Bioinformatics and

Department of Statistics, Seoul National University, Daehak-dong, Seoul 151-742 Korea

# The first 2 authors contributed equally to this work.

* Corresponding author Dr. Youngsoo Kim, PhD Department of Biomedical Engineering, Seoul National University College of Medicine, 28 Yongon-Dong, Chongno-Ku, Seoul 110-799, Korea; (Email) [email protected]; (Tel) +82-2-740-8073; (Fax) +82-2-741-0253

ACS Paragon Plus Environment

1


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 2 of 43

or Jung-Hwan Yoon, MD, PhD Department of Internal Medicine, Seoul National University College of Medicine 101 Daehak-ro, Jongno-gu, Seoul, 110-744, Republic of Korea Tel: 82-2-2072-2228, Fax:82-2-743-6701, E-mail: [email protected]

Author contact details: Su Jong Yu – email: [email protected] Hyunsoo Kim – email: [email protected] Hophil Min – email: [email protected] Areum Sohn – email: [email protected] Young Youn Cho – email: [email protected] Jeong-Ju Yoo – email: [email protected] Dong Hyeon Lee – email: [email protected] Eun Ju Cho – email: [email protected] Jeong-Hoon Lee – email: [email protected] Jungsoo Gim – email: [email protected] Taesung Park – email: [email protected] Yoon Jun Kim – email: [email protected] Chung Yong Kim – email: [email protected] Jung-Hwan Yoon – email: [email protected] Youngsoo Kim – email: [email protected]


2

Page 3 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Keywords: TACE, HCC, proteomics, prognostic biomarker, multiple reaction monitoring-mass spectrometry (MRM-MS)


3


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 4 of 43

ABSTRACT This study was aimed to identify blood-based biomarkers to predict a sustained complete response (CR) after transarterial chemoembolization (TACE) using targeted proteomics. Consecutive patients with HCC who had undergone TACE were prospectively enrolled [training (n = 100) and validation set (n = 80)]. Serum samples were obtained before and 6 months after TACE. Treatment responses were evaluated using the modified Response Evaluation Criteria in Solid Tumors (mRECIST). In the training set, the MRM-MS assay identified 5 marker candidate proteins (MCPs) (LRG1, APCS, BCHE, C7, and FCN3). When this 5-marker panel was combined with the best-performing clinical variables (tumor number, baseline PIVKA, and baseline AFP), the resulting ensemble model had the highest area under the receiver operating curve (AUROC) value in predicting a sustained CR after TACE in the training and validation sets (0.881 and 0.813, respectively). Further, the ensemble model was an independent predictor of rapid progression (hazard ratio [HR], 2.889; 95% confidence interval [CI], 1.612 – 5.178; Pvalue < 0.001) and overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024 – 3.848; Pvalue = 0.042) in the entire population by multivariate analysis. Targeted proteomics-based ensemble model can predict clinical outcomes after TACE. Therefore, this model can aid in determining the best candidates for TACE and the need for adjuvant therapy.


4

Page 5 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


INTRODUCTION Hepatocellular carcinoma (HCC) is the fifth most common cancer and the third most frequent cause of cancer-related deaths worldwide.1 The treatment of HCC has advanced recently after the implementation of curative therapeutic practices, such as surgical resection, liver transplantation, and local ablation.2 However, most HCC patients are diagnosed in the intermediate to advanced stage, when curative treatment is not applicable. For these patients, transarterial chemoembolization (TACE) might be an effective option for reducing systemic toxicity, increasing local antitumor effects, and improving chances of survival.3 The International Bridge study reported that TACE is the most widely used approach for HCC worldwide, ahead of surgical removal and systemic treatments.4 However, there are often unforeseeable outcomes after TACE with regard to treatment response and survival. In actual clinical practice, TACE is associated with a high rate of recurrence and unsatisfactory treatment outcomes, often necessitating repeated TACE procedures, because an ideal response is not always achieved after 1 session of TACE, especially with large tumors.5 Georgiades et al. recommend that at least 2 TACE sessions be performed before abandoning the procedure, based on their observations that approximately half of the patients who do not respond to the initial TACE ultimately achieve a response with improved clinical outcomes after a second course.6 Moreover, Kim et al. reported that a complete response (CR) after the initial TACE strongly predicts survival in patients with intermediate-stage HCC.7 Nevertheless, the clinical parameters and serum markers that yield more accurate predictions in patients with HCC who undergo TACE remain unknown.


5


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 6 of 43

Dynamic changes in protein markers, including alpha-fetoprotein (AFP), Lens culinaris agglutinin A-reactive fraction of AFP (AFP-L3), and prothrombin induced by the absence of vitamin K or antagonist-II (PIVKA-II), predict outcomes after TACE.8-9 However, they cannot help predict treatment outcomes before TACE, necessitating the development of pre-TACE biomarkers to select the ideal candidates for TACE. Traditionally, the enzyme-linked immunosorbent assay (ELISA) has been used to develop diagnostic markers with high specificity and sensitivity.10 However, it has major constraints, such as its cost, and the time-consuming development of specific antibodies, and the technical limitations of multiplex quantitation. In contrast, targeted proteomics approaches through multiple reaction monitoring-mass spectrometry (MRM-MS) are cost-effective and suitable for multiplex quantitation of hundreds of proteins with high accuracy and a lower limit of quantitation (LLOQ).11 In addition, the MRM-MS assay generates consistent and reproducible datasets between laboratories in highly complex samples.12 Recently, Silvia et al. established an automated MRM-MS data analysis workflow to validate markers in large-scale clinical cohorts.13 In a previous study, HCC diagnostic markers by MRM-MS and immunoassay through global data-mining were identified. Also, the levels of these markers differed between the HCC and recovery state between treatments.14 This study was aimed to develop MRM-MS-based prognostic markers that predict outcomes after TACE. Further, we combined these markers and clinical predictors in order to propose a novel strategy for HCC patients who undergo TACE. To this end, we established an ensemble model to predict the sustained CR after TACE using pre-TACE serum samples, based on a quantitative proteomics approach.


6

Page 7 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


EXPERIMENTAL SECTION

Materials Trypsin was obtained from Promega (Madison, WI). HPLC-grade water and acetonitrile were purchased from Thermo Fisher Scientific (Bremen, Germany). Serum depletion was performed for the 6 most abundant proteins using a multiple affinity removal system (MARS), consisting of an LC column (Agilent, 5185–5984); buffer A for sample loading, washes, and equilibration (Agilent, 5185–5987); and buffer B for elution (Agilent, 5185–5988). Unpurified synthetic peptides [isotopically labeled (13C and

15

N) amino acids] were obtained from JPT

(Berlin, Germany) (30% to 70% purity, according to the manufacturer). Purified synthetic peptides (unlabeled) were obtained from 21st Century Biochemicals, Inc. (Marlborough, England). After synthesis, the peptides were purified by HPLC, with subsequent quantification by amino acid analysis (purity > 95%).

Study Population This study comprised 180 HCC patients who were enrolled in a prospective cohort at Seoul National University Hospital (Seoul, Republic of Korea) as part of an ongoing study to identify biomarkers that were associated with treatment responses and the prognosis in HCC between January 2006 and May 2014. The training set consisted of 100 HCC patients, from whom we collected paired serum samples before and 6 months after TACE. The validation set comprised 80 patients, from whom pre-TACE serum samples were collected. Serum samples


7


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 8 of 43

were collected from a single center (Seoul National University Hospital) according to standard operating procedures (SOPs).15 The inclusion criteria were treatment-naive patients with HCC who had undergone TACE as a firstline treatment. Liver transplantation recipients were excluded. HCC was diagnosed by histological or radiological evaluation with reference to American Association for the Study of Liver (AASLD) or European Association for the Study of the Liver (EASL) guidelines.16-17 Treatment modalities were discussed and decided at a multidisciplinary team meeting. The study protocol met the ethical guidelines of the Declaration of Helsinki and was approved by the institutional review board of Seoul National University Hospital (IRB Number 0506-150-005). All participants provided written informed consent. The Reporting Recommendations for Tumor Marker Prognostic Studies (REMARK) criteria were followed throughout the study.18

Serum Protein Preparation for Multiple Reaction Monitoring-Mass Spectrometry (MRMMS) To discover candidate markers, we adopted the recently established LiverAtlas,19 which includes 19,801 genes and 50,265 proteins that are related to the liver and various hepatic diseases from 53 databases, such as the Hepatocellular Carcinoma Network Database (HCC.net), Oncomine, Human Protein Atlas (HPA), and BiomarkerDigger. Of these databases, we searched through MCPs as prognostic markers through a prescreening study. Serum samples were randomized statistically and subjected to immunodepletion, denaturation, trypsin digestion, and desalting, followed by reversed-phase liquid chromatography


8

Page 9 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


(LC). All MRM-MS analyses were performed on an Agilent 6490 triple quadrupole (QqQ) mass spectrometer with a Jetstream electrospray source, coupled to a 1260 Capillary LC system (Agilent Technologies, Santa Clara, CA). Skyline (McCoss Lab, University of Washington, USA) was used to import and align all MRM-MS raw data files and quantitate features (see details in Supporting Information).

TACE procedures TACE was conducted according to the Seoul National University Hospital protocol, as described.20 Chemoembolization was performed as selectively as possible through the lobar, segmental, or subsegmental arteries, depending on the tumor distribution and liver function reserve using a microcatheter (Microferret [Cook, Bloomington, Ind] or Progreat [Terumo, Tokyo, Japan]). The procedure was initially performed by infusing 2 to 12 mL of iodized oil (Lipiodol; Andre Gurbet, Aulnay-sous-Bois, France) and 10 to 60 mg of a doxorubicin hydrochloride emulsion (Adriamycin RDF; Ildong Pharmaceutical, Seoul, Korea) until arterial flow stasis was achieved and/or iodized oil appeared in the portal branches. If the initial hepatic arterial blockade was insufficient due to arterioportal shunting or a large mass, then embolization was performed with absorbable gelatin sponge particles (1 – 2 mm in diameter; Gelfoam; Upjohn, Kalamazoo, MI) that were soaked in a mixture of 4 to 6 mg crystalline mitomycin (Mitomycin-C; Kyowa Hakko Kogyo, Tokyo, Japan) and 10 mL nonionic contrast medium. The extent of chemoembolization was adjusted individually by adopting a superselective catheterization technique, depending on the liver function reserve, similar to that used with surgical hepatic resection.20


9


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 10 of 43

Assessment of Tumor Response and Outcome Tumor response was assessed every 4 – 8 weeks according to the modified Response Evaluation Criteria in Solid Tumors (mRECIST) for HCC21 by contrast-enhanced CT or MRI by 2 independent radiologists who were blinded to the clinical information. CR was defined as the disappearance of any intratumoral arterial enhancement in all target lesions at 4 weeks after TACE.21 Good responders were defined as patients who maintained the CR for 6 months after TACE, and poor responders were considered to be patients who did not. Time to progression (TTP) was measured from the date of enrollment to the day of documented tumor progression in imaging studies according to mRECIST by independent radiological assessment.

Enzyme-linked immunosorbent assay The serum levels of 5 proteins were measured using a commercial enzyme-linked immunosorbent assay (ELISA) kit. All ELISAs for leucine-rich alpha-2-glycoprotein (LRG1), serum amyloid P-component (APCS), cholinesterase (BCHE), complement component C7 (C7), and ficolin-3 (FCN3) (USCN Life Science, Houston, USA) were performed per the manufacturers’ instructions.

Statistical analysis To analyze the MRM-MS results, all raw files (.d format) were inputted into Skyline. All transition signals were integrated manually with the Savitzky-Golay smoothing algorithm and


10

Page 11 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


subjected to MSstats as described.22 Protein significance and relative abundance were analyzed with the MSstats package in R language, ver. 3.0.1 (R Foundation for Statistical Computing, Vienna, Austria). Briefly, for the data preprocessing, all transition intensities were logtransformed using base 2. Then, we equalized the median peak intensities of reference transitions between runs. Finally, significant differences and relative abundance of the proteins were calculated using the linear mixed-effects model in MSstats. Receiver operator characteristic (ROC) curves and the logistic regression model were analyzed using the PanelComposer webbased statistical tool23 and MedCalc (Mariakerke, Belgium, ver 12.2.1) with the relative abundance of each protein. In addition, ROC curves were generated by 10-fold cross validation. Areas under the ROC curves (AUROCs) were calculated to determine the prediction of good responders to TACE. The best cutoff value was defined as the value with the maximal sum of sensitivity and specificity.24 Fisher's exact test, chi-square test, and student’s t-test were used to determine the statistical significance of the demographic and clinical parameters. The Kaplan-Meier method was used to estimate the cumulative probability of radiological progression of HCC after TACE. Log-rank test was used to determine the significance of variables with respect to differences in the cumulative probability of radiological progression. Multivariate analysis was performed using a Cox proportional hazards model to identify predictors of radiological progression. Variables (P-value < 0.10) in the univariate analysis were included in the Cox proportional hazards model. A stepwise variable selection method was used in the multivariate analysis to select variables in the final model: the conditional probabilities for stepwise entry and removal of a variable were 0.05 and 0.10, respectively. Statistical analyses were performed in


11


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 12 of 43

SPSS 19.0 (SPSS Inc., Chicago, IL), and a P-value of < 0.05 was considered to be statistically significant.


12

Page 13 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


RESULTS AND DISCUSSION

Baseline patient characteristics and treatment outcomes The baseline characteristics of the study population are summarized in Table 1. Patients in both sets had similar baseline characteristics. The median follow-up time was 23.0 months (range, 8.0 – 101.0 months) in the training set and 18.0 months (range, 2.0 – 43.0 months) in the validation set. In the training set, 50 patients (50.0%) were good responders, and the median time to progression (TTP) was 11.0 months [95% confidence interval (CI), 8.7 – 13.3]. In the validation set, 40 patients (50.0%) were good responders, and the median TTP was 10.0 months (95% CI, 9.6 – 10.4).

Clinical parameters predicting sustained complete response after TACE Prior to the MRM-MS assay, we analyzed the correlations between outcomes after TACE and the clinicopathologic characteristics of good (n = 50) and poor responders (n = 50) in the training set (Table 2). In the univariate analysis, number of tumors [odds ratio (OR) = 6.83, 95% CI = 2.73 to 17.09] and concentration of PIVKA-II (OR = 2.47, 95% CI = 1.10 to 5.55) were significantly associated with a sustained CR after TACE (Table 2). In contrast, there was no significant association between sustained CR and clinical parameters with regard to albumin, prothrombin time, creatinine, platelet, ALT, bilirubin, or tumor size.

Development of MRM marker panel predicting sustained complete response after TACE


13


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 14 of 43

We then considered the discriminatory power of AFP and PIVKA-II, which are early detection and prognostic markers, in the training set to select for significant MCPs. In the classification using ROC curves, AFP had an AUROC value of 0.60 (sensitivity 76.0%, specificity 52.0%), and PIVKA-II had an AUROC value of 0.59 (sensitivity 56.3%, specificity 68.8%). Thus, we selected MCPs with AUROC values that were greater than 0.60 in the MRMMS assay. The development of the MRM marker panel is described in Supporting Information. Briefly, of the 572 liver-related MCPs for predicting the prognosis and outcome, 104 were filtered using a theoretical or experimental library from a common dataset, and we assessed their detectability in pooled serum by mProphet analysis (Table S-1). Next, quantitative levels of the 104 MCPs were validated against their labeled reference peptides, of which 89 could be quantitated. Of the 89 quantitative proteins, 47 were significantly differentially expressed in 20 patients (10 good responders and 10 poor responders from the training set) by linear mixed model analysis (Table S-2). To determine the prognostic value of the 47 MCPs, we quantified them in the good and poor responders to TACE in the training set by MRM-MS assay with labeled reference peptides. The relative protein abundance from the MRM-MS assay were calculated using the MSstats linear mixed model with their multiple peptides and transitions (2 technical replicates).25 To identify the best-performing individual marker, we performed ROC analysis using the relative abundance of the 47 MCPs, which turned out to be leucine-rich-alpha-2-glycoprotein1 (LRG1) (AUROC of 0.708) and C2 (AUROC of 0.688). Also, 17 proteins with AUROC values over 0.60 discriminated poor responders from all patients who underwent TACE (Table S-3).


14

Page 15 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In the logistic regression-based multivariable analysis, the combination of 5 proteins— Leucine-rich alpha-2-glycoprotein (LRG1), serum amyloid P-component (APCS), cholinesterase (BCHE), complement component 7 (C7), and ficolin-3 (FCN3)—differentiated patients better (AUROC of 0.825) than LRG1. Further, to maintain the redundancy of markers that had similar abundance trends, we determine their correlation coefficients. LRG1 correlated well (r > 0.5) with SERPINA3, C4BPA, C2, C5, CRP, ITIH4, and LBP. However, the 5 proteins in our combination panel had low correlation coefficients.

Establishment of ensemble model combining protein markers and clinical predictors The protein marker panel (LRG1, APCS, BCHE, C7, and FCN3) was combined with the set of best-performing clinical variables (number of tumors, PIVKA-II level, and AFP level) by logistic regression modeling. Although AFP level showed low significance in the univariate analysis (Table 2), we added it into the panel, because it had the appropriate discriminatory power. Prior to the combination, the clinical variable panel was encoded as follows: number of tumors = 0 if ≤ 2 or = 1 if > 2; PIVKA-II level = 0 if ≤ 40 mAU/mL or = 1 if > 40 mAU/mL; and AFP level = 0 if ≤ 20 ng/mL or = 1 if > 20 ng/mL. The protein marker panels were encoded as protein abundance from MSstats. The ensemble model, comprising the protein marker and clinical variable panels, had the highest AUROC value (0.881) compared with 0.825 in the protein marker panel (P-value = 0.0347) and 0.737 in the clinical variable panel (P-value = 0.0018). The ROC curves of the ensemble model and the other panels are shown in Figure 1A. We stratified the patients into 2 groups using a cutoff of 0.5 in the ensemble model, which provided the maximum sum of sensitivity and specificity. In the training set, the ensemble


15


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 16 of 43

panel (5 proteins and 3 clinical variables) correctly classified 82.0% (41 of 50) of good responders and 80.0% (40 of 50) of poor responders, whereas the clinical model panel in the training set correctly classified 82.0% (41 of 50) of good responders and 60.0% (30 of 50) of poor responders (Figure 1B). To further evaluate the potential of the ensemble model that was identified in the training set, we performed an MRM-MS assay (2 technical replicates) in the validation set, consisting of 40 good responders and 40 poor responders. From the logistic regression modeling, the ensemble panel (5 proteins and 3 clinical variables) correctly classified 77.5% (31 of 40) of good responders and 72.5% (29 of 40) of poor responders, whereas the clinical model panel in the validation set correctly classified 67.5% (27 of 40) of good responders and 62.5% (25 of 40) of poor responders (Figure 2A). In addition, the ensemble model panel had the highest AUROC value (0.813), similar to that of the training set. The ROC curves of the ensemble model in the validation set are shown in Figure 2B.

Ensemble model as an independent prognostic factor after TACE Next, the prognostic value of the ensemble model was analyzed by the survival analyses. The ensemble model was an independent predictor of progression after TACE. By univariate analysis of the entire population, male gender, poor hepatic functional reserve, and higher values of the ensemble model were significantly associated with progression (Table 3). The hazard ratio (HR) for time to progression in the high-ensemble model score (≥ 0.5) group was 2.889 [95% CI, 1.612 – 5.178; P-value < 0.001] in the multivariate analysis (Figure 3A). The levels of the ensemble model also showed a significant association with the overall survival rate (Figure 3B).


16

Page 17 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


In the multivariate analysis, a high-ensemble model score (≥ 0.5) was the only independent risk factor for overall an unfavorable survival rate (HR, 1.985; 95% CI, 1.024 – 3.848; P-value = 0.042) (Table 4).

Longitudinal changes in protein markers To examine longitudinal changes in the newly identified 5 protein markers, paired samples (pre-TACE as baseline and 6 months after TACE) were analyzed by MRM-MS assay in the training set. From the linear mixed model analysis, longitudinal fold-changes in the proteins were estimated, and we found that 5 proteins experienced significant longitudinal changes in each group (Figure 4). Baseline LRG1 levels were significantly higher in poor versus good responders. In poor responders, LRG1 level rose significantly after TACE, whereas that in good responders was similar before and after TACE. APCS was significantly higher in poor compared with good responders. In poor responders, APCS level declined significantly after TACE but was similar before and after TACE in good responders. Baseline BCHE level was significantly lower in poor than in good responders. In poor responders, BCHE level fell significantly after TACE, whereas it was similar before and after TACE in good responders. C7 was significantly higher in poor versus good responders. In poor responders, C7 level increased significantly after TACE but was similar in good responders before and after TACE. Baseline FCN3 level was significantly higher in good responders than in poor responders, and FCN3 levels decreased significantly after TACE in both groups.


17


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 18 of 43

Discussion The principal findings of this study relate to the prediction of a sustained complete response after TACE, based on a quantitative proteomics approach. Specifically, our ensemble model [an MRM-MS protein panel (LRG1, APCS, BCHE, C7, and FCN3) combined with the best-performing clinical variable panel (number of tumors, PIVKA-II level, and AFP level)] was an independent predictor of rapid progression and unfavorable overall survival after TACE. This is the first study to report the prognostic value of quantitative proteomics in HCC patients who have been treated by TACE. To patients who are not suitable for curative treatments, such as surgical resection, local ablation, and liver transplantation, TACE might be an effective option for improving survival. However, TACE usually needs repeated treatments due to the frequent recurrence. Thus, the prediction of outcomes before deciding on treatment with TACE is an important challenge. The recent development of proteomic techniques has allowed clinicians to generate a full-scale profile of alterations in protein expression in a particular disease and thus identify significant disease-related protein biomarkers and therapeutic targets.26-27 Specifically, HCC-related serum proteins can be used as prognostic markers for HCC after treatment. The vast number of candidates has a limitation, in that they require high cost and significant effort to be validated one by one. To this end, we performed this study to identify new marker candidate proteins (MCPs) from 572 liver-related proteins to predict the prognosis and outcomes. Of the 572 MCPs, we detected 89 proteins in serum by multistep MRM-MS assay with and without labeled reference peptides. Initially, 104 proteins were filtered by a theoretical or experimental library from a common dataset, and we checked their detectability in pooled


18

Page 19 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


serum using mProphet. Next, the levels of the 104 MCPs were validated against their reference synthetic isotope-labeled peptides, of which 89 could be measured quantitatively. Of the 89 quantitative proteins, 47 were significantly differentially expressed in a small cohort by linear mixed model analysis. Finally, we established a 5-protein panel (LRG1, APCS, BCHE, C7, and FCN3) from the training and validation sets that discriminated individuals who were good and poor responders after TACE. Of the proteins in the MRM-MS panel, baseline LRG1 level was significantly higher in poor versus good responders. In poor responders, LRG1 level was significantly increased after TACE but similar before and after TACE in good responders. Our data are consistent with other studies that have indicated that a rise in LRG1 expression correlates significantly with tumor size, tumor differentiation, TNM stage, and vascular invasion, suggesting that LRG1 mediates the progression of HCC by promoting HCC cell mobility.28 Serum amyloid P-component (APCS) was significantly higher in poor compared with good responders. In poor responders, APCS level was significantly lower after TACE, whereas it was similar before and after TACE in good responders. APCS has been reported to be a marker of liver disease and correlates closely with the degree of disease activity and hepatic impairment.29 Cholinesterase (BCHE) appears to originate in the liver and is linked to the synthesis of serum albumin and coagulation factors.30 BCHE also reflects liver function and is associated with increased mortality31-32 in patients with chronic liver diseases. In our results, HCC patients who had low BCHE levels tended to respond poorly after TACE. Moreover, in our longitudinal study, baseline BCHE level was significantly lower in poor than in good responders. In poor responders, BCHE level was significantly decreased after TACE but similar before and after TACE in good responders. With poor hepatic functional reserve, reflected by lower serum


19


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 20 of 43

BCHE levels, hepatocytes can be damaged easily by TACE, leading to active hepatitis.33 In patients with active hepatitis, the upregulation of adhesion molecules on cells that line the sinusoids can accelerate intrahepatic metastasis and progression after TACE.20 Complement component 7 (C7) was significantly higher in poor responders than in good responders. In poor responders, C7 levels rose significantly after TACE but were similar before and after TACE in good responders. C7 is significantly lower in other malignancies, including esophageal cancer, colon cancer, and kidney cancer.34 However, the clinical significance of C7 is unknown in patients with HCC. A previous study reported increased expression of FCN3 in HBV-related HCC cases compared with HBV-related cirrhotic patients.35 Elevated levels of this protein have also been noted in other cancers, including lung cancer, ovarian cancer, and melanoma.36-38 In our study, baseline FCN3 level was significantly higher in good responders than in poor responders and significantly decreased after TACE in both groups. Regarded as a serum lectin protein, FCN3 might have a significant function in innate immunity.39 Moreover, FCN3 binds specifically to apoptotic cells and participates in the clearance of apoptotic cells.40 Several clinicopathologic characteristic variables, including AFP level, PIVKA-II level, and number of tumor lesions, were significantly associated with the prognosis in the multivariate analysis. However, the levels of AFP and PIVKA-II had AUROC values of 0.603 and 0.593, respectively. Thus, we generated an ensemble model that combined with protein marker panel and the clinicopathologic variables. Our ensemble panel predicted good responders after TACE significantly better (AUROC of 0.881 in the training cohorts and AUROC of 0.813 in the validation cohorts) than protein marker panel or clinical predictors. Moreover, the ensemble


20

Page 21 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


panel was an independent predictor of rapid progression after TACE. Collectively, high ensemble model might be related to the rapid HCC progression after TACE through increased tumor burden (number of tumors, PIVKA II, AFP, and LRG1), poor hepatic reserve and active hepatitis after TACE (APCS and BCHE) and decreased immune surveillance (C7 and FCN3) (Figure S-10). There are several limitations that should be acknowledged. First, we could not determine overall survival due to the relatively short follow-up period. Second, the instrument platform, sample preparation methods, and purity of reference peptides could have affected our results. Third, the increase in AUROC was relatively small, and the absolute AUROC value was less than 0.9. Thus, for the application of the ensemble model to actual clinical settings, further studies that are based on ELISA or stable isotope dilution MRM-MS (SID-MRM-MS) assay are needed to improve the sensitivity and specificity of the ensemble model.


21


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 22 of 43

CONCLUSIONS In conclusion, this report is the first study to establish a new protein marker panel that is significantly associated with the prediction of patients who undergo TACE. Moreover, our ensemble model (AFP level, PIVKA-II level, number of lesions, LRG1, APCS, BCHE, C7, and FCN3), applied before TACE, can predict the maintenance of a CR after TACE and is an independent predictor of rapid progression after TACE. Further large-scale prospective studies are needed to validate the results of this study.


22

Page 23 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


SUPPORTING INFORMATION Supplementary Methods, Tables S1-S3 and Figures S1-S10. Table S-1. List of 104 MCPs selected for labeled MRM-MS assays. Table S-2. Differentially expressed proteins in 20 patients (10 good responders and 10 poor responders from training set). Table S-3. Proteins with high AUROC values in predicting poor responders after TACE. Figure S-1. List of detectable candidate proteins from the LiverAtlas Database. Figure S-2. Product ion scan (PIC) and MRM-MS analysis of two types of synthetic peptide. Figure S-3. Selection of quantifiable proteins/peptides by MRM-MS assay. Figure S-4. Calibration curves for the peptide used to construct the ensemble model in the study. Figure S-5. Quantification of MCPs by MSstats. Figure S-6. Validation by western blot. Figure S-7. Scatter plot of the correlation between technical replicates of all peptides. Figure S-8. Scatter plot of the correlation between technical replicates in entire sample set. Figure S-9. Comparison of LRG1 (A-C), APCS (D, E), BCHE (F, G), C7 (H, I), and FCN3 (J) levels between MRM-MS and ELISA analyses. Figure S-10. Suggested mechanism of high ensemble model for rapid HCC progression after TACE.


23


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 24 of 43

AUTHOR CONTRIBUTIONS Su Jong Yu, Hyunsoo Kim, Jung-Hwan Yoon, and Youngsoo Kim contributed to study concept and design; Su Jong Yu, Hyunsoo Kim, Hophil Min, Areum Sohn, Young Youn Cho, Jeong-Ju Yoo, Dong Hyeon Lee Eun Ju Cho, and Jeong-Hoon Lee contributed to acquisition of data; Su Jong Yu, Hyunsoo Kim, Hophil Min, Jungsoo Gim, and Taesung Park contributed to statistical analysis; Chung Yong Kim contributed to obtained funding; Hyunsoo Kim and Yoon Jun Kim contributed to administrative, technical, or material support; Su Jong Yu, Hyunsoo Kim, Hophil Min, Youngsoo Kim, and Jung-Hwan Yoon contributed to drafting of the manuscript. #These authors contributed equally.

CONFLICT OF INTEREST DISCLOSURE The authors who have taken part in this study declared that they do not have anything to disclose regarding funding or conflict of interest with respect to this manuscript.

ACKNOWLEDGEMENTS This work was supported by the Multi-omics Research Program, a National Research Foundation grant (No. 2011-0030740) and the Industrial Strategic Technology Development Program (#10045352). It was also supported by grants from the SNUH Research Fund (No. 04-20130830), the Liver Research Foundation of Korea and the grant No. 34-2013-005 from the SK Telecom Research Fund (Seoul National University Hospital). Authors thank to a grant from the Korea Health Technology R&D Project (No. HI14C2640).


24

Page 25 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


ABBREVIATIONS CR, complete response; TACE, transcatheter arterial chemoembolization; HCC, hepatocellular carcinoma; mRECIST, Modified Response Evaluation Criteria in Solid Tumors; MRM-MS, multiple reaction monitoring-mass spectrometry; MCP, marker candidate protein; AFP, alphafetoprotein; PIVKA-II, prothrombin induced by vitamin K absence II; ELISA, enzyme-linked immunosorbent assay; AASLD, American Association for the Study of Liver Diseases; EASL, European Association for the Study of the Liver; REMARK, Reporting Recommendations for Tumor Marker Prognostic Studies; BCA, bicinchoninic acid; ACN, acetonitrile; FA, formic acid; TTP, time to progression; ROC, receiver operator characteristic; AUROCs, area under the ROC curves; CI, confidence interval; OR, odds ratio; LRG1, leucine-rich-alpha-2-glycoprotein1; APCS, serum amyloid P-component; BCHE, cholinesterase; C7, complement component 7; FCN3, ficolin-3; HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; CTP, ChildTurcotte-Pugh; ALT, alanine transaminase; BCLC, Barcelona Clinic Liver Cancer; HR, hazard ratio.


25


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 26 of 43

REFERENCES 1.

Parkin, D. M.; Bray, F.; Ferlay, J.; Pisani, P., Global cancer statistics, 2002. CA Cancer J

Clin 2005, 55 (2), 74-108. 2.

Bruix, J.; Sherman, M.; Practice Guidelines Committee, A. A. f. t. S. o. L. D.,

Management of hepatocellular carcinoma. Hepatology 2005, 42 (5), 1208-36. 3.

Llovet, J. M.; Real, M. I.; Montana, X.; Planas, R.; Coll, S.; Aponte, J.; Ayuso, C.; Sala,

M.; Muchart, J.; Sola, R.; Rodes, J.; Bruix, J.; Barcelona Liver Cancer, G., Arterial embolisation or chemoembolisation versus symptomatic treatment in patients with unresectable hepatocellular carcinoma: a randomised controlled trial. Lancet 2002, 359 (9319), 1734-9. 4.

Park, J. W.; Chen, M.; Colombo, M.; Roberts, L. R.; Schwartz, M.; Chen, P. J.; Kudo, M.;

Johnson, P.; Wagner, S.; Orsini, L. S.; Sherman, M., Global patterns of hepatocellular carcinoma management from diagnosis to death: the BRIDGE Study. Liver Int 2015, 35 (9), 2155-66. 5.

Kim, D. Y.; Ryu, H. J.; Choi, J. Y.; Park, J. Y.; Lee, D. Y.; Kim, B. K.; Kim, S. U.; Ahn, S.

H.; Chon, C. Y.; Han, K. H., Radiological response predicts survival following transarterial chemoembolisation in patients with unresectable hepatocellular carcinoma. Aliment Pharmacol Ther 2012, 35 (11), 1343-50. 6.

Georgiades, C.; Geschwind, J. F.; Harrison, N.; Hines-Peralta, A.; Liapi, E.; Hong, K.;

Wu, Z.; Kamel, I.; Frangakis, C., Lack of response after initial chemoembolization for hepatocellular carcinoma: does it predict failure of subsequent treatment? Radiology 2012, 265 (1), 115-23. 7.

Kim, B. K.; Kim, S. U.; Kim, K. A.; Chung, Y. E.; Kim, M. J.; Park, M. S.; Park, J. Y.;

Kim do, Y.; Ahn, S. H.; Kim, M. D.; Park, S. I.; Won, J. Y.; Lee do, Y.; Han, K. H., Complete


26

Page 27 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


response at first chemoembolization is still the most robust predictor for favorable outcome in hepatocellular carcinoma. Journal of hepatology 2015, 62 (6), 1304-10. 8.

Wang, Y.; Chen, Y.; Ge, N.; Zhang, L.; Xie, X.; Zhang, J.; Chen, R.; Wang, Y.; Zhang, B.;

Xia, J.; Gan, Y.; Ren, Z.; Ye, S., Prognostic significance of alpha-fetoprotein status in the outcome of hepatocellular carcinoma after treatment of transarterial chemoembolization. Ann Surg Oncol 2012, 19 (11), 3540-6. 9.

Park, H.; Park, J. Y., Clinical significance of AFP and PIVKA-II responses for monitoring

treatment outcomes and predicting prognosis in patients with hepatocellular carcinoma. Biomed Res Int 2013, 2013, 310427. 10.

Kitteringham, N. R.; Jenkins, R. E.; Lane, C. S.; Elliott, V. L.; Park, B. K., Multiple

reaction monitoring for quantitative biomarker analysis in proteomics and metabolomics. J Chromatogr B Analyt Technol Biomed Life Sci 2009, 877 (13), 1229-39. 11.

Domanski, D.; Percy, A. J.; Yang, J.; Chambers, A. G.; Hill, J. S.; Freue, G. V.; Borchers,

C. H., MRM-based multiplexed quantitation of 67 putative cardiovascular disease biomarkers in human plasma. Proteomics 2012, 12 (8), 1222-43. 12.

Kennedy, J. J.; Abbatiello, S. E.; Kim, K.; Yan, P.; Whiteaker, J. R.; Lin, C.; Kim, J. S.;

Zhang, Y.; Wang, X.; Ivey, R. G.; Zhao, L.; Min, H.; Lee, Y.; Yu, M. H.; Yang, E. G.; Lee, C.; Wang, P.; Rodriguez, H.; Kim, Y.; Carr, S. A.; Paulovich, A. G., Demonstrating the feasibility of large-scale development of standardized assays to quantify human proteins. Nat Methods 2014, 11 (2), 149-55. 13.

Surinova, S.; Huttenhain, R.; Chang, C. Y.; Espona, L.; Vitek, O.; Aebersold, R.,

Automated selected reaction monitoring data analysis workflow for large-scale targeted proteomic studies. Nat Protoc 2013, 8 (8), 1602-19.


27


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

14.

Page 28 of 43

Kim, H.; Kim, K.; Yu, S. J.; Jang, E. S.; Yu, J.; Cho, G.; Yoon, J. H.; Kim, Y.,

Development of biomarkers for screening hepatocellular carcinoma using global data mining and multiple reaction monitoring. PLoS One 2013, 8 (5), e63468. 15.

Tuck, M. K.; Chan, D. W.; Chia, D.; Godwin, A. K.; Grizzle, W. E.; Krueger, K. E.; Rom,

W.; Sanda, M.; Sorbara, L.; Stass, S.; Wang, W.; Brenner, D. E., Standard operating procedures for serum and plasma collection: early detection research network consensus statement standard operating procedure integration working group. J Proteome Res 2009, 8 (1), 113-7. 16.

Bruix, J.; Sherman, M.; American Association for the Study of Liver, D., Management of

hepatocellular carcinoma: an update. Hepatology 2011, 53 (3), 1020-2. 17.

European Association For The Study Of The, L.; European Organisation For, R.;

Treatment Of, C., EASL-EORTC clinical practice guidelines: management of hepatocellular carcinoma. J Hepatol 2012, 56 (4), 908-43. 18.

McShane, L. M.; Altman, D. G.; Sauerbrei, W.; Taube, S. E.; Gion, M.; Clark, G. M.;

Statistics Subcommittee of the, N. C. I. E. W. G. o. C. D., Reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst 2005, 97 (16), 1180-4. 19.

Zhang, Y.; Yang, C.; Wang, S.; Chen, T.; Li, M.; Wang, X.; Li, D.; Wang, K.; Ma, J.; Wu,

S.; Zhang, X.; Zhu, Y.; Wu, J.; He, F., LiverAtlas: a unique integrated knowledge database for systems-level research of liver and hepatic disease. Liver Int 2013, 33 (8), 1239-48. 20.

Yu, S. J.; Lee, J. H.; Jang, E. S.; Cho, E. J.; Kwak, M. S.; Yoon, J. H.; Lee, H. S.; Kim, C.

Y.; Kim, Y. J., Hepatocellular carcinoma: high hepatitis B viral load and mortality in patients treated with transarterial chemoembolization. Radiology 2013, 267 (2), 638-47. 21.

Lencioni, R.; Llovet, J. M., Modified RECIST (mRECIST) assessment for hepatocellular

carcinoma. Semin Liver Dis 2010, 30 (1), 52-60.


28

Page 29 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


22.

Choi, M.; Chang, C. Y.; Clough, T.; Broudy, D.; Killeen, T.; MacLean, B.; Vitek, O.,

MSstats: an R package for statistical analysis of quantitative mass spectrometry-based proteomic experiments. Bioinformatics 2014, 30 (17), 2524-6. 23.

Jeong, S. K.; Na, K.; Kim, K. Y.; Kim, H.; Paik, Y. K., PanelComposer: a web-based

panel construction tool for multivariate analysis of disease biomarker candidates. J Proteome Res 2012, 11 (12), 6277-81. 24.

Fluss, R.; Faraggi, D.; Reiser, B., Estimation of the Youden Index and its associated

cutoff point. Biom J 2005, 47 (4), 458-72. 25.

Cerciello, F.; Choi, M.; Nicastri, A.; Bausch-Fluck, D.; Ziegler, A.; Vitek, O.; Felley-

Bosco, E.; Stahel, R.; Aebersold, R.; Wollscheid, B., Identification of a seven glycopeptide signature for malignant pleural mesothelioma in human serum by selected reaction monitoring. Clin Proteomics 2013, 10 (1), 16. 26.

Feng, H.; Wang, M.; Chen, W. N., iTRAQ-coupled 2D LC-MS/MS analysis of secreted

proteome of HBV-replicating HepG2 cells: potential in biomarkers for prognosis of HCC. Curr Microbiol 2010, 61 (4), 280-4. 27.

Yu, Y.; Pan, X.; Ding, Y.; Liu, X.; Tang, H.; Shen, C.; Shen, H.; Yang, P., An iTRAQ

based quantitative proteomic strategy to explore novel secreted proteins in metastatic hepatocellular carcinoma cell lines. Analyst 2013, 138 (16), 4505-11. 28.

Wang, C. H.; Li, M.; Liu, L. L.; Zhou, R. Y.; Fu, J.; Zhang, C. Z.; Yun, J. P., LRG1

expression indicates unfavorable clinical outcome in hepatocellular carcinoma. Oncotarget 2015. 29.

Levo, Y.; Shalit, M.; Tur-Kaspa, R., Serum amyloid P-component as a marker of liver

disease. Am J Gastroenterol 1982, 77 (6), 427-30.


29


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

30.

Page 30 of 43

McQueen, M. J., Clinical and analytical considerations in the utilization of cholinesterase

measurements. Clin Chim Acta 1995, 237 (1-2), 91-105. 31.

Donadon, M.; Cimino, M.; Procopio, F.; Morenghi, E.; Montorsi, M.; Torzilli, G.,

Potential role of cholinesterases to predict short-term outcome after hepatic resection for hepatocellular carcinoma. Updates Surg 2013, 65 (1), 11-8. 32.

Ohashi, N.; Tsuji, N.; Naito, Y.; Iwakura, T.; Isobe, S.; Ono, M.; Fujikura, T.; Tsuji, T.;

Sakao, Y.; Yasuda, H.; Kawamura, K.; Sakaguchi, T.; Kato, A.; Fujigaki, Y., Relationship between urinary fractional excretion of sodium and life prognosis in liver cirrhosis patients. Hepatol Res 2013, 43 (11), 1156-62. 33.

Takeda, H.; Nishikawa, H.; Iguchi, E.; Ohara, Y.; Sakamoto, A.; Hatamaru, K.; Henmi,

S.; Saito, S.; Nasu, A.; Komekado, H.; Kita, R.; Kimura, T.; Osaki, Y., Impact of pretreatment serum cholinesterase level in unresectable advanced hepatocellular carcinoma patients treated with sorafenib. Mol Clin Oncol 2013, 1 (2), 241-248. 34.

Oka, R.; Sasagawa, T.; Ninomiya, I.; Miwa, K.; Tanii, H.; Saijoh, K., Reduction in the

local expression of complement component 6 (C6) and 7 (C7) mRNAs in oesophageal carcinoma. Eur J Cancer 2001, 37 (9), 1158-65. 35.

Sarvari, J.; Mojtahedi, Z.; Kuramitsu, Y.; Malek-Hosseini, S. A.; Shamsi Shahrabadi, M.;

Ghaderi, A.; Nakamura, K., Differential expression of haptoglobin isoforms in chronic active hepatitis, cirrhosis and HCC related to HBV infection. Oncol Lett 2011, 2 (5), 871-877. 36.

Andersen, J. D.; Boylan, K. L.; Xue, F. S.; Anderson, L. B.; Witthuhn, B. A.; Markowski,

T. W.; Higgins, L.; Skubitz, A. P., Identification of candidate biomarkers in ovarian cancer serum by depletion of highly abundant proteins and differential in-gel electrophoresis. Electrophoresis 2010, 31 (4), 599-610.


30

Page 31 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


37.

Okano, T.; Kondo, T.; Kakisaka, T.; Fujii, K.; Yamada, M.; Kato, H.; Nishimura, T.;

Gemma, A.; Kudoh, S.; Hirohashi, S., Plasma proteomics of lung cancer by a linkage of multidimensional liquid chromatography and two-dimensional difference gel electrophoresis. Proteomics 2006, 6 (13), 3938-48. 38.

Greco, M.; Mitri, M. D.; Chiriaco, F.; Leo, G.; Brienza, E.; Maffia, M., Serum proteomic

profile of cutaneous malignant melanoma and relation to cancer progression: association to tumor derived alpha-N-acetylgalactosaminidase activity. Cancer Lett 2009, 283 (2), 222-9. 39.

Sarvari, J.; Mojtahedi, Z.; Kuramitsu, Y.; Fattahi, M. R.; Ghaderi, A.; Nakamura, K.;

Erfani, N., Comparative Proteomics of Sera From HCC Patients With Different Origins. Hepat Mon 2014, 14 (1), e13103. 40.

Kuraya, M.; Ming, Z.; Liu, X.; Matsushita, M.; Fujita, T., Specific binding of L-ficolin

and H-ficolin to apoptotic cells leads to complement activation. Immunobiology 2005, 209 (9), 689-97.


31


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 32 of 43

TABLES

Table 1. Baseline characteristics of the study population. Total

Training set

Validation set

(n = 180)

(n = 100)

(n = 80)

Age ≥ 60 years

117 (65.0%)

65 (65.0%)

52 (65.0%)

1.000

Male

154 (85.6%)

85 (85.0%)

69 (86.3%)

0.835

Characteristics

Etiology

0.574

HBsAg-positive

132 (73.3%)

75 (75.0%)

57 (71.3%)

Anti-HCV-positive

21 (11.7%)

11 (11.0%)

10 (12.5%)

9 (5.0%)

5 (5.0%)

4 (5.0%)

18 (10.0%)

9 (9.0%)

9 (11.3%)

Alcohol Others

P-value

CTP classification

0.380

A

137 (76.1%)

79 (79.0%)

58 (72.5%)

B

43 (23.9%)

21 (21.0%)

22 (27.5%)

Total bilirubin, mg/dL

1.10 ± 0.92

1.12 ± 1.04

1.08 ± 0.74

0.773

Albumin, g/dL

3.75 ± 0.55

3.74 ± 0.57

3.76 ± 0.53

0.864

Prothrombin time, INR

1.15 ± 0.15

1.13 ± 0.14

1.17 ± 0.17

0.112

ALT, IU/L

36.2 ± 23.4

38.3 ± 24.7

33.6 ± 21.5

0.177

Creatinine, mg/dL

0.97 ± 0.68

0.90 ± 0.30

1.06 ± 0.96

0.153

Alpha-fetoprotein

17 (9.4%)

11 (11.0%)

6 (7.5%)

0.456

2,559 ± 11,057

1,840 ± 8,299

3,424 ± 13,661

0.345

Mean

baseline biochemistry

≥ 400 ng/mL PIVKA, mAU/mL


32

Page 33 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Maximal tumor size,

2.33 ± 1.99

2.50 ± 2.12

2.11 ± 1.79

0.191

2.79 ± 2.46

2.72 ± 2.31

2.89 ± 2.66

0.651

cm Number of tumors BCLC stage

0.254

0

47 (26.1%)

23 (23.0%)

24 (30.0%)

A

61 (33.9%)

34 (34.0%)

27 (33.8%)

B

72 (40.0%)

43 (43.0%)

29 (36.3%)

Treatment response Good responders

1.000 90 (50.0%)

50 (50.0%)

40 (50.0%)

HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; CTP, Child-Turcotte-Pugh; ALT, alanine transaminase; BCLC, Barcelona Clinic Liver Cancer.


33


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 34 of 43

Table 2. Univariate analysis of clinical variables

Clinical variable

OR

95% CI

P-value

Albumin

0.73

0.3635 to 1.4727

0.38

Prothrombin time

0.18

0.0102 to 3.0339

0.22

Creatinine

1.71

0.4220 to 6.9491

0.44

Platelet (103/uL)

1.00

0.9907 to 1.0052

0.58

ALT, IU/L

1.01

0.9950 to 1.0303

0.14

Bilirubin, mg/dL

1.11

0.7381 to 1.6766

0.60

No. of lesions

6.83

2.7317 to 17.0935

< 0.0001

Tumor size, cm

1.67

0.6818 to 4.0828

0.26

Pre-TACE AFP, ng/mL

1.91

0.8618 to 4.2198

0.11

Pre-TACE PIVKA-II mAU/mL

2.47

1.1003 to 5.5472

0.03

OR, odds ratio; CI, confidence interval; ALT, alanine transaminase; AFP, alpha-fetoprotein; PIVKA-II, prothrombin induced by vitamin K absence II.


34

Page 35 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Table 3. Factors identified in univariate and multivariate analyses that affect time to progression in HCC patients undergoing TACE in all populations (n = 180). Univariate Analysis

Multivariate Analysis

Variable Age (≥ 60 years vs < 60 years) Gender

HR

P-value*

1.076 (0.655 – 1.768)

0.772

2.544 (1.022 – 6.334)

0.045

Adjusted HR

P-value*

2.255 (0.905 – 5.620)

0.081

1.645 (0.977 – 2.771)

0.061

2.889 (1.612 – 5.178)

< 0.001

(male vs female) Etiology

0.474

HBsAg-positive vs others

0.748 (0.367 – 1.525)

anti-HCV-positive vs others

0.468 (0.166 – 1.320)

Alcohol vs others

0.505 (0.109 – 2.343)

CTP classification (B vs. A)

2.034 (1.216 – 3.404)

0.007

Maximum tumor size, cm

0.965 (0.856 – 1.088)

0.562

3.242 (1.824 – 5.762)

< 0.001

Ensemble score (≥ 0.5 vs. < 0.5)

HR, hazard ratio; HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; CTP, Child-Turcotte-Pugh; BCLC, Barcelona Clinic Liver Cancer.


35


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 36 of 43

Table 4. Factors identified in univariate and multivariate analyses that affect overall survival in HCC patients undergoing TACE in all populations (n = 180) Univariate Analysis

Multivariate Analysis

Variable Age (≥ 60 years vs < 60 years) Gender

HR

P-value*

1.127 (0.568 – 2.237)

0.732

0.330 (0.079 – 1.373)

0.128

Adjusted HR

P-value*

1.985 (1.024 – 3.848)

0.042

(male vs female) Etiology

0.219

HBsAg-positive vs others

0.428 (0.184 – 0.999)

anti-HCV-positive vs others

0.405 (0.118 – 1.387)

Alcohol vs others

0.276 (0.034 – 2.243)

CTP classification (B vs. A)

1.377 (0.663 – 2.859)

0.390

Maximum tumor size, cm

1.072 (0.943 – 1.219)

0.286

1.985 (1.024 – 3.848)

0.042

Ensemble score (≥ 0.5 vs. < 0.5)

HR, hazard ratio; HBsAg, hepatitis B surface antigen; HCV, hepatitis C virus; CTP, Child-Turcotte-Pugh; BCLC, Barcelona Clinic Liver Cancer.


36

Page 37 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURE LEGENDS

Figure 1. Performance of the best protein panel, clinical panel, and ensemble model in predicting the prognosis after TACE. Discrimination between good and poor responders in the training sets. (A) AUROC values and 95% confidence intervals (CIs) were calculated by logistic regression. (B) For comparisons of the best protein panel, clinical panel, and ensemble model, the results are presented as confusion matrices.

Figure 2. Comparison of the discriminatory power of the best single protein marker and the ensemble panel in the validation set. (A) For comparisons between the clinical model and ensemble panels, the results are presented as confusion matrices. (B) AUROC values and 95% confidence intervals (CIs), calculated by logistic regression, are represented by ROC curves.

Figure 3. Kaplan-Meier estimates of clinical outcomes in patients with HCC after TACE based on ensemble model panel in all populations. (A) Time to progression. (B) Overall survival. Patients are grouped into low-ensemble (≤ 0.5) and high-ensemble value (> 0.5) groups. HR; hazard ratio, CI; confidence interval.

Figure 4. Evaluation of longitudinal changes in protein marker panels in the training set. Relative fold-change in selected proteins at baseline and after 6 months in each group of responders. Red and blue dots indicate relative average abundance of good and poor


37


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Page 38 of 43

responders. Linear mixed models in MSstats were used to calculate significant fold-changes (* < adjusted P-value 0.001 and ** < adjusted P-value 0.005).


38

Page 39 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


FIGURES


39


Page 40 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


40

Page 41 of 43


1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


41


Page 42 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


42

Page 43 of 43

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60


Photo courtesy of Youngsoo Kim, Copyright 2016


43

Targeted Proteomics Predicts a Sustained Complete-Response after

Recommend Documents