Skip to content

Advertisement

  • Research article
  • Open Access
  • Open Peer Review

Novel biomarker-based model for the prediction of sorafenib response and overall survival in advanced hepatocellular carcinoma: a prospective cohort study

Contributed equally
BMC Cancer201818:307

https://doi.org/10.1186/s12885-018-4211-2

  • Received: 21 November 2017
  • Accepted: 9 March 2018
  • Published:
Open Peer Review reports

Abstract

Background

Prediction of the outcome of sorafenib therapy using biomarkers is an unmet clinical need in patients with advanced hepatocellular carcinoma (HCC). The aim was to develop and validate a biomarker-based model for predicting sorafenib response and overall survival (OS).

Methods

This prospective cohort study included 124 consecutive HCC patients (44 with disease control, 80 with progression) with Child-Pugh class A liver function, who received sorafenib. Potential serum biomarkers (namely, hepatocyte growth factor [HGF], fibroblast growth factor [FGF], vascular endothelial growth factor receptor-1, CD117, and angiopoietin-2) were tested. After identifying independent predictors of tumor response, a risk scoring system for predicting OS was developed and 3-fold internal validation was conducted.

Results

A risk scoring system was developed with six covariates: etiology, platelet count, Barcelona Clinic Liver Cancer stage, protein induced by vitamin K absence-II, HGF, and FGF. When patients were stratified into low-risk (score ≤ 5), intermediate-risk (score 6), and high-risk (score ≥ 7) groups, the model provided good discriminant functions on tumor response (concordance [c]-index, 0.884) and 12-month survival (area under the curve [AUC], 0.825). The median OS was 19.0, 11.2, and 6.1 months in the low-, intermediate-, and high-risk group, respectively (P < 0.001). In internal validation, the model maintained good discriminant functions on tumor response (c-index, 0.825) and 12-month survival (AUC, 0.803), and good calibration functions (all P > 0.05 between expected and observed values).

Conclusions

This new model including serum FGF and HGF showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC.

Keywords

  • Hepatocellular carcinoma
  • Sorafenib
  • Response
  • Biomarker
  • Prediction

Background

Sorafenib is an oral multikinase inhibitor targeting the receptor tyrosine kinase activity of VEGF receptors (VEGFR 1–3), PDGFRb, serine-threonine kinases Raf-1 and B-Raf, c-KIT, and RET [13]. Sorafenib therapy prolonged overall survival (OS) with acceptable safety and tolerability in patients with advanced HCC in the SHARP (Sorafenib Hepatocellular Carcinoma Assessment Randomized Protocol) trial and the Asia-Pacific study [4, 5]. Based on these two phase III trials, sorafenib therapy is currently the standard of care for patients with advanced-stage or Barcelona Clinic Liver Cancer (BCLC) stage C HCC and for patients in the control arm of ongoing clinical trials [6, 7]. However, considering the various adverse effects, incomplete response, modest survival benefit and cost of sorafenib therapy, the necessity of predictive biomarkers has been constantly raised, to select patients who could benefit most from this treatment [8].

Cancer biomarkers have been widely used for the prediction of the natural course, prognosis, and treatment response in certain malignancies [9]. Predictive biomarkers can be best explored in the setting of properly designed clinical trials [10]. Baseline plasma angiopoietin 2 and vascular endothelial growth factor (VEGF) were identified as independent predictors of survival in the SHARP study; however, no biomarker was predictive of sorafenibresponse [11]. Because the diagnosis of HCC is usually made without obtaining tumor tissue, serum biomarkers for the prediction of sorafenib response would be of significant benefit for proper selection of patients [12]. Furthermore, there has been no attempt to date to integrate potential serum markers and other relevant clinical characteristics for a response prediction model as an approach toward precision medicine in patients with advanced HCC.

This exploratory study aimed (i) to develop a novel relevant predictive model using a serum biomarker for the prediction of sorafenib response, (ii) to validate this model internally, and (iii) to determine its role in predicting outcome in a prospectively collected database from a large-scale in-hospital cohort.Specifically, we chose analytes for serum biomarker analysis on the basis of the molecular targets (or ligands of those targeted receptors) of sorafenib or those related to the outcome and/or pathogenesis of HCC, including VEGF [11], platelet-derived growth factor receptor β (PDGFRb) [2], CD117 [1], hepatocyte growth factor (HGF) [13], angiopoietin 2 [11], lysyl oxidase-like 2 (LOXL2) [14], and basic fibroblast growth factor (bFGF) [15].

Methods

Patients

Between May 2013 and June 2015, a total of 460 consecutive patients who had a diagnosis of HCC and received sorafenib were identified in a prospective HCC cohort registry of Seoul National University Hospital. Written informed consent was obtained from each patient before registration in the cohort. The diagnosis of HCC was based on histological examination or clinico-radiological criteria, with reference to the practice guidelines from the American Association for the Study of Liver Diseases or the European Association for the Study of the Liver [6, 7]. All patients were not indicated for surgical resection, liver transplantation, local ablation, or transarterial chemoembolization at the time of initiation of sorafenib therapy because of their advanced stages at the time of diagnosis or tumor progression despite prior (repeated) locoregional treatments. Among these 460 patients, adequate blood samples were obtained from 161 patients for serum biomarker analyses. Patients with Child-Pugh score ≥ 7 (n = 37) were excluded because the outcome assessment of sorafenib therapy in these patients could be misleading, owing to increased risk of mortality from their impaired liver function [16]. Finally, 124 patients were included in the analysis.

The protocol of the present study conformed to the ethical guidelines of the World Medical Association Declaration of Helsinki, and was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. 0506–150-005). All study participants provided written informed consent. REMARK (Reporting Recommendations for Tumor Marker Prognostic Studies) criteria were followed throughout this study [17].

Treatment scheme and response evaluation

The standard daily oral dose of sorafenib (Nexavar; Bayer HealthCare AG, Leverkusen, Germany) was 800 mg (i.e., 400 mg twice daily) on a continuous dosing schedule. Follow-up evaluation schedules included (i) clinical examination (toxicity assessment and blood tests) 2 weeks after the first administration of sorafenib and every 4 weeks thereafter, and (ii) imaging tests (contrast-enhanced dynamic computed tomography or magnetic resonance imaging) every 6–8 weeks until death or the last follow-up. Dose reduction was allowed in cases of drug-related grade 3 or 4 toxicities. The toxicity grade was assessed before each treatment cycle using the National Cancer Institute Common Toxicity Criteria for Adverse Events (version 4.0). We used three-step dose reduction as follows: 800 mg daily to 600 mg daily, 600 mg daily to 400 mg daily, and 400 mg daily to 400 mg every other day. Treatment response was evaluated every 6–8 weeks, according to the modified Response Evaluation Criteria in Solid Tumors criteria using contrast-enhanced dynamic computed tomography or magnetic resonance imaging [18]. Sorafenib therapy was discontinued if one or more of the following occurred: disease progression, development of intolerable toxicity, or patient refusal.

Sample collection and biomarker assays

A 5-mL blood sample was collected from each study participant at baseline (before the initiation of sorafenib therapy), and was centrifuged at 1500 rpm for 15 min to separate the serum. The serum samples were then stored at ≤ − 70 °C in 1.5-mL aliquots until further assays.

The serum concentrations of biomarkers were measured with commercially available ELISA kits for angiopoietin 2 (R&D Systems, Minneapolis, MN, USA), VEGF (eBioscience, San Diego, CA, USA), PDGFRb (Raybiotech, Norcross, GA, USA), HGF (R&D Systems), CD117 (R&D Systems), LOXL2 (USCN Life Science, Wuhan, China), and bFGF (R&D Systems).

Statistical analysis

For baseline characteristics, continuous variables are expressed as medians and ranges, and categorical variables as frequencies with percentages. Survival analysis was performed using the Kaplan-Meier method from the date of initial diagnosis of HCC to the date of death or last follow-up, with the log-rank test to compare subgroups. Single binary logistic regression analysis was used to identify relevant features associated with response to sorafenib, in which variables with P < 0.1 were subsequently included in the multivariate analysis. The selected variables for logistic regression analysis included clinical characteristics, laboratory parameters, serum biomarkers, and hepatic fibrosis indices [1922]. Forward and backward stepwise selection procedures were sequentially used to select the best-fitted model on the basis of the Akaike information criterion [23]. In the final model, scores (0, 1, 2) were assigned to the corresponding levels of categorical covariates. For continuous covariates, scores were assigned to the corresponding subranges within cutoff values, to maximize the concordance index (c-index). Hence, the risk score for the prediction of sorafenib response was calculated in each patient through the summation of the scores of the covariates in the final prediction model. Patients were further categorized into subgroups according to their predictive scores. For an internal validation of the predictive model, a 3-fold cross-validation was performed. Calibration function was examined by comparing the observed response with the expected response estimated with the risk score, using the Hosmer-Lemeshow test. In addition, survival prediction was performed among subgroups according to the risk scores, with internal validation and examination of calibration function, in the same manner as that for the response prediction.

All tests were based on a two-sided probability, and P < 0.05 was considered statistically significant. All statistical analyses were performed with R language ver. 3.1.1 (R Foundation for Statistical Computing, Vienna, Austria).

Results

Patient characteristics and treatment outcomes

Table 1 summarizes the baseline characteristics of the enrolled patients according to their best objective responses to sorafenib, i.e., progressive disease (PD) vs. non-PD. During the treatment period (median, 2.6 months; interquartile range, 1.4–3.8 months), 44 patients had no disease progression (non-PD group: complete response, 2 patients; partial response, 10 patients; and stable disease, 32 patients) and 80 patients had disease progression (PD group). The median time to progression was 2.7 months (95% confidence interval [CI], 2.4–3.1). The main underlying cause of liver disease was hepatitis B infection (59.1% in the non-PD group, 81.2% in the PD group). There was no significant difference in the frequencies of macrovascular invasion or extrahepatic spread and baseline Child-Pugh score between the two groups. Platelet count was significantly lower in the PD group than in the non-PD group (P = 0.006). Of the baseline tumor markers, PIVKA-II (protein induced by vitamin K absence-II) was significantly higher in the PD group than in the non-PD group (P = 0.003), unlike alpha-fetoprotein (P = 0.187). Among the serum biomarkers, only bFGF was significantly higher in the PD group than in the non-PD group (P < 0.001).
Table 1

Baseline characteristics according to the sorafenib response

Variable

All (n = 124)

Non-PD (n = 44)

PD (n = 80)

P

Age (year)

61 ± 11

62 ± 10

61 ± 12

0.707

Sex, N (%)

107 (86.3)

36 (81.8)

71 (88.8)

0.423

ECOG performance status 0, N (%)

112 (90.3)

42 (95.5)

70 (87.5)

0.210

Etiology, N (%)

   

0.02

 HBV

91 (73.4)

26 (59.1)

65 (81.2)

 

 HCV

21 (16.9)

10 (22.7)

11 (13.8)

 

 Alcohol

12 (9.7)

8 (18.2)

4 (5)

 

Body mass index (kg/m2)

22.3 ± 3.1

23.1 ± 3.2

21.9 ± 3.0

0.052

Macrovascular invasion, N (%)

39 (31.5)

11 (25.0)

28 (35.0)

0.344

Extrahepatic spread, N (%)

53 (42.7%)

16 (36.4)

37 (46.2)

0.382

BCLC stage C, N (%)

83 (66.9%)

25 (56.8)

58 (72.5)

0.185

Child-Pugh score, N (%)

   

0.492

 5

114 (91.9)

42 (95.5)

72 (90)

 

 6

10 (8.1)

2 (4.5)

8 (10)

 

MELD

8.8 ± 2.2

8.36 ± 1.54

8.96 ± 2.47

0.1

Albumin (g/dL)

3.79 ± 0.42

3.78 ± 0.46

3.81 ± 0.41

0.739

Total bilirubin (mg/dL)

1.06 ± 0.50

0.99 ± 0.47

1.09 ± 0.51

0.273

Prothrombin time (INR)

1.14 ± 0.10

1.13 ± 0.09

1.15 ± 0.1

0.299

Creatinine (mg/dL)

0.89 ± 0.56

0.87 ± 0.13

0.9 ± 0.69

0.705

Platelet (×103/μL)

122 (17–787)

113 (17–291)

128 (52–787)

0.006

Alpha-fetoprotein (ng/mL)

128.8 (1.3–730,000)

114.7 (2.6–226,100)

152.2 (1.3–730,000)

0.187

PIVKA-II (mAU/mL)

109.5 (12–339,858)

25.5 (12–25,884)

351 (15–339,858)

0.003

Angiopoietin 2 (ng/mL)

2962.7 (1099.4–12,907.8)

2762.2 (1289.6–8360.4)

3026.9 (1099.4–12,907.8)

0.109

CD117 (ng/mL)

3.76 (1.01–226.85)

3.36 (1.01–10.23)

3.87 (1.10–226.85)

0.234

bFGF (pg/mL)

2.38 (0–61.57)

1.65 (0–14.62)

3.07 (0.43–61.57)

< 0.001

HGF (pg/mL)

1781.9 (913.9–6116.0)

1605.7 (913.9–4152.4)

1965.2 (1020.3–6117.0)

0.056

LOXL2 (ng/mL)

0.816 (0.034–4.007)

0.718 (0.034–4.007)

0.850 (0.039–3.765)

0.792

PDGFRb (pg/mL)

2045.6 (647.5–22,904.5)

2087.9 (777.7–22,904.5)

1965.3 (647.5–8214.9)

0.419

sVEGFR1 (ng/mL)

0.16 (0.04–13.65)

0.16 (0.06–13.65)

0.14 (0.04–0.53)

0.314

AAR

1.29 (0.58–11.6)

1.31 (0.58–2.67)

1.29 (0.61–11.6)

0.105

APRI

1.01 (0.17–6.54)

1.17 (0.17–4.54)

0.95 (0.17–6.54)

0.847

FIB-4

4.0 (0.6–21.0)

4.4 (0.9–21.0)

3.6 (0.6–12.3)

0.161

Lok index

0.66 ± 0.24

0.67 ± 0.24

0.65 ± 0.24

0.68

P2/MS

29.6 (0.46–1060.7)

27.3 (0.5–202.4)

36.7 (6.2–1060.7)

0.102

Values are expressed as mean ± standard deviation, medians (ranges), or frequencies (percentages)

Abbreviations: PD, progressive disease, ECOG Eastern Cooperative Oncology Group, HBV hepatitis B virus, HCV hepatitis C virus, BCLC Barcelona Clinic Liver Cancer, MELD model for end-stage liver disease, INR international normalized ratio, PIVKA-II protein induced by vitamin K absence-II, Ang2 angiopoietin-2, bFGF basic fibroblast growth factor, HGF hepatocyte growth factor, LOXL2 lysyl oxidase-like 2, PDGFRb platelet-derived growth factor receptor β, sVEGFR1 soluble vascular endothelial growth factor receptor-1, AAR aspartate aminotransferase/alanine aminotransferase ratio, APRI aspartate aminotransferase to platelet ratio index, FIB-4 fibrosis 4

The median follow-up duration was 6.6 months (interquartile range, 4.3–12.1 months) and OS was 11.2 months (95% CI, 9.4–13.7).

Development of a scoring system for the prediction of sorafenib response

From the single binary logistic regression analysis, variables with P < 0.1 were first selected for multivariate analysis, including etiology, platelet count, PIVKA-II, bFGF, macrovascular invasion, extrahepatic spread, BCLC stage, and log HGF (Table 2). Among these variables, multivariate logistic regression analysis with forward stepwise selection process identified six variables for the final model (Table 2), which included four tumor-related factors (BCLC stage, bFGF, log PIVKA-II, and log HGF) and two liver disease-related factors (etiology and platelets). The risk scoring system for the prediction of sorafenib response was generated using these six covariates, in which a score of 0, 1, or 2 was given to each cutoff value of the six covariates (Table 3). The risk score for each patient was calculated through the summation of the scores of the six covariates, and the scores ranged from 0 to 12. Sorafenib response was expected to be PD if the risk score was > 6, showing maximal sensitivity and specificity with this cutoff level (Fig. 1). When all patients were stratified into the low-risk group (risk score ≤ 5; n = 43), intermediate-risk group (risk score 6, n = 24), and high-risk group (risk score ≥ 7, n = 57), the model provided good discriminant functions on sorafenib response (c-index, 0.884; 95% CI, 0.827–0.941). In the internal validation with 3-fold cross-validation, the model maintained good discriminant functions on tumor response (c-index, 0.825; 95% CI, 0.745–0.906), and the Hosmer-Lemeshow P-value for the calibration function of the risk score was 0.945.
Table 2

Binary logistic regression analysis for sorafenib response

Variable

 

Univariate analysis

Multivariate analysis

 

OR (95% CI)

P

OR (95% CI)

P

Age (year)

 

0.994 (0.963–1.027)

0.719

  

Sex

(Female)

0.570 (0.203–1.603)

0.287

  

Body mass index (kg/m2)

 

0.885 (0.783–1.000)

0.050

  

ECOG PS

(1 vs. 0)

3.000 (0.627–14.358)

0.169

  

Etiology

HCV vs. nonviral

0.440 (0.167–1.16)

0.097

0.612 (0.179–2.088)

0.433

 

HBV vs. nonviral

0.200 (0.055–0.722)

0.014

0.133 (0.027–0.666)

0.014

Ascites

2 vs. 1

2.333 (0.473–11.506)

0.298

  

Platelet (×103/μL)

 

1.006 (1.001–1.011)

0.017

1.005 (0.999–1.011)

0.103

Creatinine (mg/dL)

 

1.115 (0.529–2.348)

0.775

  

Albumin (g/dL)

 

1.168 (0.488–2.794)

0.727

  

Total bilirubin (mg/dL)

 

1.541 (0.698–3.405)

0.285

  

Prothrombin time (INR)

 

6.922 (0.164–291.871)

0.311

  

MELD

 

1.208 (0.937–1.558)

0.145

  

Macrovascular invasion

 

1.615 (0.71–3.677)

0.253

  

Extrahepatic spread

 

1.506 (0.708–3.205)

0.288

  

BCLC stage

B vs. A

1.000 (0.315–3.174)

1.000

0.808 (0.203–3.224)

0.763

 

C vs. A

2.895 (1.002–8.364)

0.050

3.476 (0.987–12.243)

0.052

Log AFP (ng/dL)

 

1.013 (0.779–1.317)

0.925

  

Log PIVKA-II (mAU/mL)

 

1.989 (1.359–2.911)

< 0.001

1.724 (1.124–2.644)

0.013

Log Angiopoietin 2 (ng/mL)

 

3.754 (0.534–26.374)

0.183

  

Log CD117 (ng/mL)

 

2.895 (0.612–13.687)

0.18

  

bFGF (pg/mL)

 

1.321 (1.092–1.598)

0.004

1.326 (1.077–1.634)

0.008

Log HGF (pg/mL)

 

16.534 (1.275–214.349)

0.032

23.438 (0.750–732.109)

0.072

LOXL2 (ng/mL)

 

0.769 (0.533–1.11)

0.160

  

Log sVEGFR1 (ng/mL)

 

0.272 (0.047–1.592)

0.149

  

AAR

 

1.373 (0.84–2.246)

0.206

  

APRI

 

0.971 (0.718–1.314)

0.85

  

FIB4

 

0.926 (0.842–1.02)

0.12

  

Lok index

 

0.722 (0.154–3.386)

0.68

  

P2/MS

 

1.004 (0.998–1.01)

0.179

  

Abbreviations: OR odds ratio, CI confidence interval, ECOG PS Eastern Cooperative Oncology Group performance status, HBV hepatitis B virus, HCV hepatitis C virus, BCLC Barcelona Clinic Liver Cancer, MELD model for end-stage liver disease, INR international normalized ratio, AFP alpha-fetoprotein, PIVKA-II protein induced by vitamin K absence-II, Ang2 angiopoietin-2, bFGF basic fibroblast growth factor, HGF hepatocyte growth factor, LOXL2 lysyl oxidase-like 2, PDGFRb platelet-derived growth factor receptor β, sVEGFR1 soluble vascular endothelial growth factor receptor-1, AAR aspartate aminotransferase/alanine aminotransferase ratio, APRI aspartate aminotransferase to platelet ratio index, FIB-4 fibrosis 4

Table 3

Scoring system for the prediction of sorafenib response by using six covariates selected in the multivariable binary logistic regression analysis

Variable

Cutoff value

Score

bFGF (pg/mL)

< 2.0

0

2–5.5

1

≥5.5

2

Log PIVKA-II (mAU/mL)

< 30

0

30–1780

1

≥1780

2

BCLC stage

A, B

0

C

1

Etiology

Nonviral

0

HCV

1

HBV

2

Log HGF (pg/mL)

< 1380

0

1380–1860

1

≥1860

2

Platelet (×103/μL)

< 70

0

70–184

1

≥184

2

Abbreviations: bFGF basic fibroblast growth factor, PIVKA-II protein induced by vitamin K absence-II, BCLC Barcelona Clinic Liver Cancer, HGF hepatocyte growth factor

Fig. 1
Fig. 1

Area under the receiver-operating curve (AUC) analysis for exploring a threshold score for predicting sorafenib response. The risk score was calculated by using six covariates from the multivariable binary logistic regression analysis (Table 3). When the score was 6 or higher, the sorafenib response was expected to be progressive disease with maximal sensitivity and specificity

Effect of the sorafenib response score on the OS

Median OS duration was 19.0 months in the low-risk group (95% CI, 16.1–not available [N/A]), 11.2 months in the intermediate-risk group (95% CI, 8.5–N/A), and 6.1 months in the high-risk group (95% CI, 5.0–8.1) (P < 0.001, Fig. 2). Univariate Cox proportional hazards analysis with the above-mentioned risk scoring system showed a significant difference between group A and group C (P < 0.001, Table 4). The area under the receiver-operating curve for OS prediction was 0.825 (95% CI, 0.734–0.915) at 12 months. In the internal validation with 3-fold cross-validation, the area under the receiver-operating curve for OS prediction at 12 months was 0.803 (95% CI, 0.699–0.907). The Hosmer-Lemeshow P-value for the calibration function of the risk score was 0.207.
Fig. 2
Fig. 2

Kaplan-Meier survival curves for subgroups according to the risk scores. The median overall survivals were 19.0 months in group A (score ≤ 5, solid black line), 11.2 months in group B (score 6, dashed line), and 6.1 months in group C (score ≥ 7, dotted line) (P < 0.001, log-rank test)

Table 4

Cox proportional hazards analysis for predicting survival in the subgroups according to risk scores

Risk group

HR (95% CI)

P

Intermediate risk vs. low risk

2.529 (1.120–5.707)

0.025

High risk vs. low risk

6.577 (3.419–12.655)

< 0.001

Risk groups according to risk scores: low-risk group, score ≤ 5; intermediate-risk group, score 6; high-risk group C, score ≥ 7

Abbreviations: HR hazard ratio, CI confidence interval

Discussion

We developed a new prediction model for sorafenib response that combines relevant serum markers, tumor-related factors, and cirrhosis-related factors in a scoring system. The risk score showed good performance in predicting the response to sorafenib and survival in patients with advanced HCC in our cohort. The robustness of the prediction model was also verified with internal validation.

Since the approval of sorafenib in 2007, no clinical trial with newer agents has shown superior outcomes as a first-line treatment, until recently [8]. More importantly, there is no predictive biomarker for the selection of patients who could benefit most from sorafenib, unlike other malignancies including breast cancer, lung cancer, and melanoma [2426]. Most of the prospective clinical trials of molecular targeted therapies in patients with HCC to date were not designed for a prespecified patient population based on molecular classification and biomarkers. In addition, because noninvasive diagnosis is feasible in most cases with the characteristic imaging features, this omission of obtaining tumor tissue is another obstacle for biomarker exploration in HCC [12]. Thus, we explored candidate biomarkers for the prediction of sorafenib response in a prospectively collected clinical database and serum samples from an in-hospital cohort.

Comparing the present study with the Asia-Pacific sorafenib study [5], the median time to progression (2.7 months) was similar; however, the median OS (11.2 months) was longer in the present study than in the Asia-Pacific study (6.5 months). Although most of the enrolled patients had chronic hepatitis B in both studies, patients in the Asia-Pacific study had more advanced tumors in that macrovascular invasion or extrahepatic spread was more frequently observed, which might have been responsible for the difference in OS. Indeed, a subanalysis of the GIDEON study from Japan reported that the median OS duration of patients with sorafenib therapy was 17.4 months in those with Child-Pugh class A disease and 4.9 months in those with Child-Pugh class B [27]. The longer OS in Japanese patients with Child-Pugh class A might have resulted from the inclusion of fewer patients in BCLC stage C, compared with our study population (54.7 vs. 66.9%).

The selected covariates for the prediction of sorafenib response from the logistic regression analysis included etiology (B-viral), platelet count, BCLC stage, PIVKA-II, serum bFGF, and serum HGF. Concerning the etiology, hepatitis B-associated HCC was suggested as one of the possible reasons for the shorter OS in the Asia-Pacific study than that in the SHARP study [5]. High serum PIVKA-II level has been reported as a significant predisposing factor for aggressive HCC biology including vascular invasion, intrahepatic spread, and extrahepatic spread [2830]. Among serum angiogenic factors, bFGF and HGF were selected as covariates in the final risk scoring model. FGF2 or bFGF is known to stimulate HCC proliferation through an autocrine mechanism, to activate HCC invasion, and to induce angiogenesis [15]. In addition, previous studies reported that bFGF level increased as the tumor stage became more advanced, and was predictive of worse postoperative survival in patients with HCC [31, 32]. HGF/MET (mesenchymal-epithelial transition) factor is frequently dysregulated, playing a pivotal role in malignancies, including HCC [33]. Activation of the c-MET pathway promotes tumor cell growth, angiogenesis, and metastasis, leading to more aggressive forms of HCC and poor outcomes [34]. A scoring system was developed by combining all the above-mentioned covariates, which showed good predictive performance for sorafenib response (area under the receiver-operating curve of 0.884 at the cutoff score of 6). More desirably, when patients were stratified into three subgroups according to risk scores, OS of the high-risk group was significantly shorter than that of the low-risk group. Recent clinical trials have investigated the efficacy of newer molecular targeted agents in advanced HCC, such as tivantinib (NCT01755767) and lenvatinib (NCT01761266). Tivantinib is an HGF/c-MET inhibitor, and lenvatinib is a multitargeted receptor kinase inhibitor against VEGFR 1, 2, and 3; fibroblast growth factor receptor 1, 2, 3, and 4; platelet-derived growth factor receptor; RET; and c-KIT [33, 35]. Given that the signaling pathways of these new agents do not entirely overlap with the targets of sorafenib, the relevance of HGF and bFGF in the prediction of the response to and the outcome of sorafenib therapy suggests the possibility of an individualized approach in the selection of systemic agents in this difficult-to-treat population, based on the prediction model in the present study. Lastly, platelets are the source of multiple growth factors and cytokines, and are known to promote tumor growth, angiogenesis, and metastatic potential [3638]. Several reports have provided evidence supporting the role of platelets in HCC, e.g., reduction of hepatitis B virus–associated experimental HCC by platelet inhibitors [39], antagonism of sorafenib action by platelet factors in HCC cell lines [40], and complete remission of advanced HCC with sorafenib in combination with clopidogrel [41]. Taken together, platelet count was also included in the final risk prediction model in the present study; however, its precise role in molecular targeted therapy needs more investigation.

The results and the risk scoring system of our study need to be interpreted and applied with caution owing to the following limitations. Firstly and most importantly, although the samples for various biomarker assays were archived in a prospective cohort, multiple use of statistical algorithms raises concerns about false-positive results or overfitting [42, 43]. The most desirable setting to avoid these concerns would be a prospective trial that focuses on a specific (group of) biomarker(s) [42]. However, exploration of predictive biomarkers for sorafenib response in a new trial is not logistically feasible, except for large-scale comparative studies with newer agents [44]. Thus, we chose a retrospective analysis as an alternative approach using prospectively collected samples. Secondly, a split-sample method for the validation of the results could not be applied in our cohort owing to the limited number of study subjects [45]. Instead, we performed internal validation using 3-fold cross-validation. An external validation with consistent results is a prerequisite for the application of this scoring system in other patient populations with a larger sample size [42]. Finally, differences in the baseline clinical characteristics of the study subjects also hinder the imprudent generalization of our results in patient populations with a different etiology or tumor status.

Conclusions

In conclusion, we developed a biomarker-based prediction model of sorafenib response and survival from a prospective cohort. By using baseline serum bFGF and HGF levels as covariates, a total score of < 6 could be a relevant cutoff value for selecting patients who are most likely to benefit from sorafenib therapy. Furthermore, the cutoff value may also be used as guide to avoid unnecessary toxicity and inconvenience in patients with a score above this cutoff.

Notes

Abbreviations

AUR: 

Area under the curve

BCLC: 

Barcelona Clinic Liver Cancer

bFGF: 

Basic fibroblast growth factor

C-index: 

Concordance-index

FGF: 

Fibroblast growth factor

HCC: 

Hepatocellular carcinoma

HGF: 

Hepatocyte growth factor

LOXL2: 

Lysyl oxidase-like 2

N/A: 

Not available

OS: 

Overall survival

PD: 

Progressive disease

PDGFRb: 

Platelet-derived growth factor receptor β

PIVKA: 

Protein induced by vitamin K absence-II

REMARK: 

Reporting Recommendations for Tumor Marker Prognostic Studies

SHARP: 

Sorafenib Hepatocelluar Carcinoma Assessment Randomized Protocol

VEGF: 

Vascular endothelial growth factor

VEGFR: 

Vascular endothelial growth factor receptor

Declarations

Acknowledgements

We thank Dr. Jinho Yoo (YooJin BioSoft, Goyang, Republic of Korea) for his excellent help with statistical analyses.

Funding

This work was funded by Doosan Yonkang Foundation (Grant No. 30–2016-0240), Liver Research Foundation of Korea as part of Bio Future Strategies Research Project, and Ewha Womans University research grant (2016). The funding bodies had no role in the design of the study, collection, analysis, and interpretation of data and in writing of the manuscript.

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

Study conception and design, data collection, analysis and interpretation, and manuscript writing by HYK, DHL and J-HL; data collection, analysis and interpretation and manuscript writing by HYK, DHL, J-HL, YYC, EJC, SJY, YJK and J-HY. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The protocol of the present study conformed to the ethical guidelines of the World Medical Association Declaration of Helsinki, and was approved by the Institutional Review Board of Seoul National University Hospital (IRB No. 0506–150-005). All study participants provided written informed consent.

Consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
Department of Internal Medicine, College of Medicine, Ewha Womans University, Seoul, Republic of Korea
(2)
Department of Internal Medicine and Liver Research Institute, Seoul National University College of Medicine, Seoul, Republic of Korea
(3)
Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea
(4)
Department of Internal Medicine, Seoul National University Hospital, 101, Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea

References

  1. Wilhelm SM, Carter C, Tang L, Wilkie D, McNabola A, Rong H, Chen C, Zhang X, Vincent P, McHugh M, et al. BAY 43-9006 exhibits broad spectrum oral antitumor activity and targets the RAF/MEK/ERK pathway and receptor tyrosine kinases involved in tumor progression and angiogenesis. Cancer Res. 2004;64(19):7099–109.View ArticlePubMedGoogle Scholar
  2. Wilhelm SM, Adnane L, Newell P, Villanueva A, Llovet JM, Lynch M. Preclinical overview of sorafenib, a multikinase inhibitor that targets both Raf and VEGF and PDGF receptor tyrosine kinase signaling. Mol Cancer Ther. 2008;7(10):3129–40.View ArticlePubMedGoogle Scholar
  3. Wilhelm S, Carter C, Lynch M, Lowinger T, Dumas J, Smith RA, Schwartz B, Simantov R, Kelley S. Discovery and development of sorafenib: a multikinase inhibitor for treating cancer. Nat Rev Drug Discov. 2006;5(10):835–44.View ArticlePubMedGoogle Scholar
  4. Llovet JM, Ricci S, Mazzaferro V, Hilgard P, Gane E, Blanc JF, de Oliveira AC, Santoro A, Raoul JL, Forner A, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008;359(4):378–90.View ArticlePubMedGoogle Scholar
  5. Cheng AL, Kang YK, Chen Z, Tsao CJ, Qin S, Kim JS, Luo R, Feng J, Ye S, Yang TS, et al. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet Oncol. 2009;10(1):25–34.View ArticlePubMedGoogle Scholar
  6. Bruix J, Sherman M. American Association for the Study of liver D: management of hepatocellular carcinoma: an update. Hepatology. 2011;53(3):1020–2.View ArticlePubMedPubMed CentralGoogle Scholar
  7. 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.View ArticleGoogle Scholar
  8. Bruix J, Han KH, Gores G, Llovet JM, Mazzaferro V. Liver cancer: approaching a personalized care. J Hepatol. 2015;62(1 Suppl):S144–56.View ArticlePubMedPubMed CentralGoogle Scholar
  9. Sawyers CL. The cancer biomarker problem. Nature. 2008;452(7187):548–52.View ArticlePubMedGoogle Scholar
  10. Kelloff GJ, Sigman CC. Cancer biomarkers: selecting the right drug for the right patient. Nat Rev Drug Discov. 2012;11(3):201–14.View ArticlePubMedGoogle Scholar
  11. Llovet JM, Pena CE, Lathia CD, Shan M, Meinhardt G, Bruix J, Group SIS. Plasma biomarkers as predictors of outcome in patients with advanced hepatocellular carcinoma. Clin Cancer Res. 2012;18(8):2290–300.View ArticlePubMedGoogle Scholar
  12. Llovet JM, Hernandez-Gea V. Hepatocellular carcinoma: reasons for phase III failure and novel perspectives on trial design. Clin Cancer Res. 2014;20(8):2072–9.View ArticlePubMedGoogle Scholar
  13. Horiguchi N, Takayama H, Toyoda M, Otsuka T, Fukusato T, Merlino G, Takagi H, Mori M. Hepatocyte growth factor promotes hepatocarcinogenesis through c-met autocrine activation and enhanced angiogenesis in transgenic mice treated with diethylnitrosamine. Oncogene. 2002;21(12):1791–9.View ArticlePubMedGoogle Scholar
  14. Wong CC, Tse AP, Huang YP, Zhu YT, Chiu DK, Lai RK, Au SL, Kai AK, Lee JM, Wei LL, et al. Lysyl oxidase-like 2 is critical to tumor microenvironment and metastatic niche formation in hepatocellular carcinoma. Hepatology. 2014;60(5):1645–58.View ArticlePubMedGoogle Scholar
  15. Kin M, Sata M, Ueno T, Torimura T, Inuzuka S, Tsuji R, Sujaku K, Sakamoto M, Sugawara H, Tamaki S, et al. Basic fibroblast growth factor regulates proliferation and motility of human hepatoma cells by an autocrine mechanism. J Hepatol. 1997;27(4):677–87.View ArticlePubMedGoogle Scholar
  16. Llovet JM, Di Bisceglie AM, Bruix J, Kramer BS, Lencioni R, Zhu AX, Sherman M, Schwartz M, Lotze M, Talwalkar J, et al. Design and endpoints of clinical trials in hepatocellular carcinoma. J Natl Cancer Inst. 2008;100(10):698–711.View ArticlePubMedGoogle Scholar
  17. McShane LM, Altman DG, Sauerbrei W, Taube SE, Gion M, Clark GM. Statistics subcommittee of the NCIEWGoCD: reporting recommendations for tumor marker prognostic studies (REMARK). J Natl Cancer Inst. 2005;97(16):1180–4.View ArticlePubMedGoogle Scholar
  18. Lencioni R, Llovet JM. Modified RECIST (mRECIST) assessment for hepatocellular carcinoma. Semin Liver Dis. 2010;30(1):52–60.View ArticlePubMedGoogle Scholar
  19. Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, Lok AS. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C. Hepatology. 2003;38(2):518–26.View ArticlePubMedGoogle Scholar
  20. Shah AG, Lydecker A, Murray K, Tetri BN, Contos MJ, Sanyal AJ. Nash clinical research N: comparison of noninvasive markers of fibrosis in patients with nonalcoholic fatty liver disease. Clin Gastroenterol Hepatol. 2009;7(10):1104–12.View ArticlePubMedPubMed CentralGoogle Scholar
  21. Lee JH, Yoon JH, Lee CH, Myung SJ, Keam B, Kim BH, Chung GE, Kim W, Kim YJ, Jang JJ, et al. Complete blood count reflects the degree of oesophageal varices and liver fibrosis in virus-related chronic liver disease patients. J Viral Hepat. 2009;16(6):444–52.View ArticlePubMedGoogle Scholar
  22. Lok AS, Ghany MG, Goodman ZD, Wright EC, Everson GT, Sterling RK, Everhart JE, Lindsay KL, Bonkovsky HL, Di Bisceglie AM, et al. Predicting cirrhosis in patients with hepatitis C based on standard laboratory tests: results of the HALT-C cohort. Hepatology. 2005;42(2):282–92.View ArticlePubMedGoogle Scholar
  23. Harrell FE Jr, Lee KL, Mark DB. Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors. Stat Med. 1996;15(4):361–87.View ArticlePubMedGoogle Scholar
  24. Verma S, Miles D, Gianni L, Krop IE, Welslau M, Baselga J, Pegram M, Oh DY, Dieras V, Guardino E, et al. Trastuzumab emtansine for HER2-positive advanced breast cancer. N Engl J Med. 2012;367(19):1783–91.View ArticlePubMedPubMed CentralGoogle Scholar
  25. Shaw AT, Kim DW, Nakagawa K, Seto T, Crino L, Ahn MJ, De Pas T, Besse B, Solomon BJ, Blackhall F, et al. Crizotinib versus chemotherapy in advanced ALK-positive lung cancer. N Engl J Med. 2013;368(25):2385–94.View ArticlePubMedGoogle Scholar
  26. Chapman PB, Hauschild A, Robert C, Haanen JB, Ascierto P, Larkin J, Dummer R, Garbe C, Testori A, Maio M, et al. Improved survival with vemurafenib in melanoma with BRAF V600E mutation. N Engl J Med. 2011;364(26):2507–16.View ArticlePubMedPubMed CentralGoogle Scholar
  27. Kudo M, Ikeda M, Takayama T, Numata K, Izumi N, Furuse J, Okusaka T, Kadoya M, Yamashita S, Ito Y, et al. Safety and efficacy of sorafenib in Japanese patients with hepatocellular carcinoma in clinical practice: a subgroup analysis of GIDEON. J Gastroenterol. 2016;51(12):1150–60.View ArticlePubMedPubMed CentralGoogle Scholar
  28. Pote N, Cauchy F, Albuquerque M, Voitot H, Belghiti J, Castera L, Puy H, Bedossa P, Paradis V. Performance of PIVKA-II for early hepatocellular carcinoma diagnosis and prediction of microvascular invasion. J Hepatol. 2015;62(4):848–54.View ArticlePubMedGoogle Scholar
  29. Bae HM, Lee JH, Yoon JH, Kim YJ, Heo DS, Lee HS. Protein induced by vitamin K absence or antagonist-II production is a strong predictive marker for extrahepatic metastases in early hepatocellular carcinoma: a prospective evaluation. BMC Cancer. 2011;11:435.View ArticlePubMedPubMed CentralGoogle Scholar
  30. Xu JF, Liu XY. PIVKA-II is an independent prognostic factor for overall survival of HCC patients and maybe associated with epithelial-mesenchymal transition. J Hepatol. 2015;63(4):1040–1.View ArticlePubMedGoogle Scholar
  31. Hsu PI, Chow NH, Lai KH, Yang HB, Chan SH, Lin XZ, Cheng JS, Huang JS, Ger LP, Huang SM, et al. Implications of serum basic fibroblast growth factor levels in chronic liver diseases and hepatocellular carcinoma. Anticancer Res. 1997;17(4A):2803–9.PubMedGoogle Scholar
  32. Poon RT, Ng IO, Lau C, Yu WC, Fan ST, Wong J. Correlation of serum basic fibroblast growth factor levels with clinicopathologic features and postoperative recurrence in hepatocellular carcinoma. Am J Surg. 2001;182(3):298–304.View ArticlePubMedGoogle Scholar
  33. Santoro A, Rimassa L, Borbath I, Daniele B, Salvagni S, Van Laethem JL, Van Vlierberghe H, Trojan J, Kolligs FT, Weiss A, et al. Tivantinib for second-line treatment of advanced hepatocellular carcinoma: a randomised, placebo-controlled phase 2 study. Lancet Oncol. 2013;14(1):55–63.View ArticlePubMedGoogle Scholar
  34. Trojan J, Zeuzem S. Tivantinib in hepatocellular carcinoma. Expert Opin Investig Drugs. 2013;22(1):141–7.View ArticlePubMedGoogle Scholar
  35. Ikeda M, Okusaka T, Mitsunaga S, Ueno H, Tamai T, Suzuki T, Hayato S, Kadowaki T, Okita K, Kumada H. Safety and pharmacokinetics of Lenvatinib in patients with advanced hepatocellular carcinoma. Clin Cancer Res. 2016;22(6):1385–94.View ArticlePubMedGoogle Scholar
  36. Jain S, Harris J, Ware J. Platelets: linking hemostasis and cancer. Arterioscler Thromb Vasc Biol. 2010;30(12):2362–7.View ArticlePubMedPubMed CentralGoogle Scholar
  37. Bambace NM, Holmes CE. The platelet contribution to cancer progression. J Thromb Haemost. 2011;9(2):237–49.View ArticlePubMedGoogle Scholar
  38. Buergy D, Wenz F, Groden C, Brockmann MA. Tumor-platelet interaction in solid tumors. Int J Cancer. 2012;130(12):2747–60.View ArticlePubMedGoogle Scholar
  39. Sitia G, Iannacone M, Guidotti LG. Anti-platelet therapy in the prevention of hepatitis B virus-associated hepatocellular carcinoma. J Hepatol. 2013;59(5):1135–8.View ArticlePubMedGoogle Scholar
  40. D’Alessandro R, Refolo MG, Lippolis C, Giannuzzi G, Carella N, Messa C, Cavallini A, Carr BI. Antagonism of sorafenib and regorafenib actions by platelet factors in hepatocellular carcinoma cell lines. BMC Cancer. 2014;14:351.View ArticlePubMedPubMed CentralGoogle Scholar
  41. Tan-Shalaby JL. Reproducible complete remission of advanced hepatocellular carcinoma with sorafenib in combination with clopidogrel. BMJ Care Rep. 2014;2014. https://doi.org/10.1136/bcr-2014-203962.
  42. Simon RM, Paik S, Hayes DF. Use of archived specimens in evaluation of prognostic and predictive biomarkers. J Natl Cancer Inst. 2009;101(21):1446–52.View ArticlePubMedPubMed CentralGoogle Scholar
  43. Ludwig JA, Weinstein JN. Biomarkers in cancer staging, prognosis and treatment selection. Nat Rev Cancer. 2005;5(11):845–56.View ArticlePubMedGoogle Scholar
  44. Mandrekar SJ, Sargent DJ. Predictive biomarker validation in practice: lessons from real trials. Clin Trials. 2010;7(5):567–73.View ArticlePubMedPubMed CentralGoogle Scholar
  45. Simon R. Validation of pharmacogenomic biomarker classifiers for treatment selection. Cancer Biomark. 2006;2(3–4):89–96.View ArticlePubMedGoogle Scholar

Copyright

Advertisement