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Keywords:

  • hepatocellular carcinoma;
  • staging;
  • CLIP;
  • VEGF;
  • prognosis

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

BACKGROUND:

Several staging systems have been proposed for hepatocellular carcinoma (HCC); however, none has incorporated circulating angiogenic biomarkers. The purpose of this study was to determine whether vascular endothelial growth factor (VEGF) could independently predict overall survival in patients with HCC, and whether adding VEGF level into the Cancer of the Liver Italian Program (CLIP) score could improve patient stratification and prediction of overall survival.

METHODS:

Between 2001 and 2008, baseline plasma VEGF levels were available from 288 patients, and multivariate Cox regression models and median survival (95% confidence intervals) were calculated. Recursive partitioning was used to determine the optimal cutpoint for VEGF, using 10 repeated training/validation samples, each using two-thirds of the data to determine the best cutpoint and the remaining one-third to validate it. Prognostic ability of CLIP and V-CLIP was compared using the concordence index.

RESULTS:

Plasma VEGF was a significant independent predictor of overall survival, with an optimal VEGF cutpoint of 450 pg/mL. After CLIP validation in our patients, we added VEGF to the CLIP score and found that the new V-CLIP score better separates patients into homogenous prognostic groups (P = .005).

CONCLUSIONS:

The assessment of baseline plasma VEGF levels increases the precision of the CLIP scoring system for predicting HCC prognosis, which may assist in equally randomizing patients with HCC in clinical trials. Prospective validation of the V-CLIP scoring system is warranted. Cancer 2011. © 2010 American Cancer Society.

In the United States, the incidence of hepatocellular carcinoma (HCC), the most common form of primary liver cancer, has been steadily rising over the last 3 decades.1 A recent study indicated that the incidence rate of HCC in the United States tripled from 1975 to 2005.2

The management of patients with advanced, unresectable HCC presents several challenges, including the need for prognostic staging systems to predict prognosis and stratify patients on clinical trial. Therefore, increasingly specific parameters have been used to evaluate survival and prognosis of HCC patients, starting with the presence of cirrhosis, because the prognosis of HCC depends not only on tumor size but also on underlying liver function. A limitation of the Child-Pugh score, which reflects the degree of hepatic reserve in patients with cirrhosis, is the lack of any parameter that directly pertains to the tumor itself.3 Therefore, the concept of adding more parameters to assess the tumor status was established, and subsequently several clinical staging systems for HCC have been proposed,4-8 including the Cancer of the Liver Italian Program (CLIP),6 and the Barcelona Clinic Liver Cancer (BCLC) staging system.8 Derived from European patients with predominantly hepatitis C- and alcohol-related HCC, the CLIP score has gained wide acceptance among scientists in the Western world. The CLIP score has been compared with another scoring system known as the Chinese University Prognostic Index,7 which was derived from Asian patients with predominantly hepatitis B-related HCC. The investigators' attempt to apply the CLIP scoring system to their population led to false predictions of outcome, suggesting that different scoring systems may apply to different patient populations, most likely related to the different risk factors, disease stage (early vs advanced), and demographics.7

Therefore, among the many and varied systems for HCC staging, the CLIP scoring system is among the most commonly used systems in Europe and the United States to predict prognosis and stratify patients on clinical trials. Furthermore, several groups have validated the CLIP score,9-13 and most recently, a United States study evaluated 6 HCC staging systems for their ability to predict survival by using the concordance index (C-index). The study concluded that CLIP score was among the top 3 most informative systems in predicting survival in advanced HCC patients.14 However, because nearly 80% of the patient population is classified as having a CLIP score of 0 to 3, questions regarding poor stratification ability have been raised. Furthermore, α-fetoprotein (AFP), one of the CLIP parameters, is detectable in only ≈70% of HCC cases, and hence both false-negative and false-positive rates are high with the use of AFP as the serological marker for the detection of HCC.15

HCC is highly vascular and frequently associated with vascular invasion. In fact, angiogenesis is involved in the development of HCC from the initial stage of carcinogenesis to the end stage of metastatic disease.16 Vascular endothelial growth factor (VEGF) is the major mediator of angiogenesis in HCC, and was found to be correlated with prognosis in several studies.17-20 In addition, the VEGF pathway has been studied extensively as a target for therapy, and recent clinical trial results have validated anti-VEGF- or anti-VEGF receptor-directed therapy in HCC.21-26 After an extensive review of the literature, we concluded that there was sufficient evidence to warrant investigating the use of plasma VEGF measured via enzyme-linked immunoassay (ELISA) as a marker of VEGF levels in tumor tissue and as a prognostic indicator.27

Given the fundamental importance of angiogenesis for HCC tumor growth and progression, as well as the key role of VEGF in these processes, we chose to study the value of adding the plasma level of VEGF to the CLIP score, after validating it in our patient population, as a prognostic indicator in HCC patients. We sought to determine whether VEGF plasma levels measured at diagnosis could better stratify patients with HCC and independently predict their overall survival.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Study Population

Using a protocol approved by M. D. Anderson's Institutional Review Board, we enrolled new patients with histologically confirmed HCC who lived in the United States and were evaluated and treated at the Gastrointestinal Center of The University of Texas M. D. Anderson Cancer Center in Houston. All patients gave written informed consent prior to participation. The inclusion criteria were as follows: pathologically confirmed diagnosis of HCC, United States residency, and the ability to communicate in English. The exclusion criteria included the presence of other types of primary liver cancer (such as cholangiocarcinoma or fibrolamellar HCC), or concurrent or past history of other types of cancers. The primary endpoint of the study was evaluation of the correlation between baseline plasma biomarkers and overall survival. From January 2000 through March 2008, from all patients referred to our center, we enrolled 394 eligible patients with HCC. Baseline plasma samples were available for 288 (73%) of the recruited population and were missed or insufficient for 106 (27%) HCC patients. Although all subjects agreed to participate in the study, the main reason for missing these blood samples was related to insufficient time to obtain blood samples during the initial clinical assessment of HCC patients.

Patient characteristics are shown in Table 1. Notably, statistical analyses indicated no difference between recruited subjects with and without blood samples in terms of demographic characteristics (age, sex, race, education level); HCC risk factors (hepatitis C virus, hepatitis B virus, diabetes history, alcohol consumption, and cigarette smoking); cirrhosis; Child-Pugh classification; pathological tumor differentiation; baseline value of alanine aminotransferase and albumin; or CLIP scoring. However, patients without plasma samples tended to have multinodular tumors but had tumor size <50% of the liver, radiological evidence of portal vein thrombosis, and high baseline AFP.

Table 1. Patient Characteristics
VariableNo. (%)
  1. AFP indicates α-fetoprotein.

Sex 
 Men199 (69.1)
 Women89 (30.9)
AFP, ng/mL 
 <400199 (69.1)
 ≥40086 (29.8)
 Missing3 (1)
Differentiation 
 Well112 (38.9)
 Moderate95 (33)
 Poor50 (17.4)
 Unknown31 (10.8)
Tumor size 
 ≤50% liver191 (66.3)
 >50% liver97 (33.7)
Vascular invasion 
 Yes53 (18.4)
 No235 (81.6)
Metastases 
 Yes60 (20.8)
 No228 (79.2)
Nodularity 
 Uninodular105 (36.5)
 Multinodular183 (63.5)
Lymph nodes 
 Yes122 (42.4)
 No166 (57.6)
Bilirubin, mg/dL 
 ≤1.6260 (90.3)
 >1.628 (9.7)
Cirrhosis 
 Yes173 (60.1)
 No115 (39.9)
Child-Pugh score 
 A206 (71.5)
 B76 (26.4)
 C6 (2.1)

Baseline Plasma VEGF Assay

Plasma was prepared from 3-5 mL of peripheral blood collected in ethylenediaminetetraacetic acid-containing tubes through 21-gauge needles. Samples were then centrifuged at 4°C for 15 minutes (3000 rpm), and removed, aliquoted, and snap frozen at −20°C. We measured plasma VEGF-A (the VEGF165 isoform) via ELISA (Quantikine Human VEGF Immunoassay ELISA Kit; R&D Systems, Minneapolis, MN). Each measurement was made in duplicate, and the VEGF level was determined from a standard curve generated for each set of samples assayed.

Statistical Analysis

We used Wilcoxon rank sum test to correlate baseline VEGF levels with various clinical characteristics and staging systems and Cox regression to assess factors associated with overall survival.

To find an optimal VEGF cutpoint, we randomly split the data into training (two-thirds) and validation (test) (one-third) sets, and applied recursive partitioning28 to the training set to find the optimal cutpoint maximizing the survival difference between the low and high VEGF groups, and then validated that cutpoint by fitting a Cox regression model to the dichotomized VEGF factor on the test data. We repeated this process for 10 different random splits of the data into training/test sets.

To assess whether VEGF was an independent prognostic factor after adjusting for other known factors, we fit multivariable Cox regression models including VEGF dichotomized at the optimal cutpoint and the variables in the CLIP scoring system.

To assess the performance of the scoring systems, we computed the median survival for the patients in each V-CLIP group (0, 1, 2, 3, 4, and 5+) and compared the groups using log-rank tests, and did likewise for the CLIP score. The sign test was used to assess whether the VEGF-high groups tended to have shorter median survival within the CLIP groups than VEGF-low groups. The prognostic ability of the CLIP, V-CLIP, and BCLC were compared using a C-index test.

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Patient Characteristics

The estimated overall median survival duration and 95% confidence interval (CI) of 288 patients was 13.8 months (95% CI, 11.7-17.3) (Figure 1). A total of 87 patients had hepatitis C virus infection (30.2%). As shown in Table 2, the hazard ratio (HR) estimated from Cox regression models indicated that the strongest associations were with tumor parameters (tumor size, nodularity, differentiation, vascular invasion, and AFP) in addition to liver function parameters (bilirubin, alanine aminotransferase, and aspartate aminotransferase).

thumbnail image

Figure 1. Kaplan-Meier estimates of overall survival are shown.

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Table 2. Survival Predictors: Univariate Cox Regression Analysis
PredictorHR (95% CI)P
  1. HR indicates hazard ratio; CI, confidence interval; HBV, hepatitis B virus; HCV, hepatitis C virus; AFP, α-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; VEGF, vascular endothelial growth factor.

Age, y (>60 vs ≤60)0.88 (0.66-1.16).351
Gender (male vs female)1.44 (1.06-1.96).018
Race (white vs nonwhite)0.75 (0.56-1.00).051
Hepatitis virus infection  
 No infection vs HBV + HCV0.51 (0.32-0.80).004
 HBV alone vs HBV + HCV0.76 (0.44-1.32).334
 HCV alone vs HBV + HCV0.72 (0.43-1.18).192
AFP (≥400 vs <400)2.26 (1.69-3.02)<.0001
Tumor differential (poor vs other)1.63 (1.15-2.31).006
Tumor nodularity (multinodular vs uninodular)2.28 (1.68-3.11)<.0001
Tumor size (>50% liver vs ≤50% liver)2.92 (2.19-3.90)<.0001
Vascular invasive (yes vs no)2.65 (1.90-3.70)<.0001
Lymph node involvement (yes vs no)1.82 (1.38-2.40)<.0001
Metastasis (yes vs no)1.76 (1.27-2.45).001
Bilirubin, mg/dL (>1.6 vs ≤1.6)2.74 (1.78-4.22)<.0001
Serum ALT, U/L (>40 vs ≤40)1.77 (1.34-2.34)<.0001
Serum AST, U/L (>45 vs ≤45)2.17 (1.57-3.00)<.0001
Cirrhosis (yes vs no)1.35 (1.02-1.79).036
Treatment  
 Chemotherapy vs none0.56 (0.38-0.84).0047
 Surgery vs none0.19 (0.12-0.31)<.0001
 Chemoembolization vs none0.38 (0.22-0.67).0008
VEGF (100-unit increase)1.04 (1.01-1.07).007

Validation of CLIP Scoring System

First, we validated the CLIP scoring system by fitting a multivariable Cox regression model to our data including the factors contained in the CLIP score (Table 3). Because AFP values were missing from 3 patients, only 285 patients were included.

Table 3. Multivariable Cox Proportional Hazards Model for CLIP Score Variables
CLIP Score VariablesPHR (95% CI)HR from CLIP Study6
  1. CLIP indicates Cancer of the Liver Italian Program; HR, hazard ratio; CI, confidence interval; AFP, α-fetoprotein.

Child-Pugh score   
 B vs A.00081.72 (1.26-2.37)1.72
 C vs A.033.10 (1.12-8.58)3.92
Tumor morphology   
 1 vs 0.0031.80 (1.23-2.65)1.74
 2 vs 0<.00014.28 (2.87-6.37)3.18
AFP, ng/mL (≥400 vs <400).00021.81 (1.33-2.46)1.79
Portal vein thrombosis (yes vs no).141.42 (0.90-2.25)1.58

We found that Child-Pugh score (HR, 1.72 for B vs A, P = .0008; HR, 3.10 for C vs A, P = .030), tumor morphology (HR, 1.80 for 1 vs 0, P = .0027; HR, 4.28 for 2 vs 0, P < .0001), and AFP (HR, 1.81 for >400 vs ≤400; P = .0002) were all highly significant, with HRs very close to those reported in the original CLIP paper.6 Although the presence or absence of portal vein thrombosis (HR, 1.42; P = .14) was not statistically significant, it nonetheless had an estimated effect size close to that observed in the original description of CLIP.

As expected, we found that the CLIP score separated the patients very effectively into different prognostic groups (P < .0001), with median survival durations of 37.0, 23.1, 11.7, 7.6, and 2.5 months for CLIP scores of 0, 1, 2, 3, and 4+, respectively. Note that the HRs for the factors in the CLIP model are all very similar in magnitude, and the HRs for the 3-level factors increase in a roughly linear fashion. These results strongly justify the use of a simple count-based scoring system such as the CLIP model. Finally, we compared C-index between CLIP score and BCLC staging in our patients. In the C-index analysis, the concordance probabilities for CLIP and BCLC were 0.70 and 0.65, respectively. Using U-statistics, the difference was significant (P = .007). Our results confirm that the CLIP scoring system better predicted patients' survival compared with BCLC staging.

High Levels of VEGF as an Independent Prognostic Factor

The recursive partitioning was applied to the 10 randomly selected training/test sets to find the optimal single cutpoint for baseline VEGF in terms of predicting survival (Table 4). We observed that 5 of 10 training sets found an optimal cutpoint of roughly 450 ng/mL, and that for 4 of these 5 sets, this split was found to significantly separate low-risk from high-risk groups for overall survival in the corresponding test sets. This finding suggests that patients with high VEGF levels (>450 ng/mL) had a worse prognosis. When this factor was considered in a univariate Cox regression model fit to the entire data set, the effect was highly significant (P = .0002; HR, 1.89; 95% CI, 1.36-2.65).

As shown in Table 5, tumor size, lymph node involvement, extrahepatic metastases, Child-Pugh score, CLIP score, BCLC staging, and ECOG performance status score were all significantly associated with the baseline level of VEGF in plasma. The strongest association was with tumor size (the mean plasma VEGF level for tumors involving <50% of the liver was 218, and the mean level for tumors involving >50% of the liver was 425 [P < .0001]).

Table 4. Summary of Search for Optimal VEGF Cutpoint
ResamplingCutpointStrataTraining SetTesting Set
NEPNEP
  1. N indicates number of patients; E, event (death).

1450.7480150105.000248059.2720
  14032 1612 
2450.2740150111.012368053.0065
  14031 1613 
3382.71350144102.013957454.0734
  14635 2217 
4317.8525012991.003877253.4686
  16146 2418 
5496.01950157117.001068152.1357
  13329 1510 
6450.2740155108.035227556.0008
  13526 2118 
7450.7480151106.003617958.0184
  13931 1713 
8509.590161103.000018068.9052
  12926 1611 
9450.7480160114.007847050.0078
  13022 2622 
1060.88905239.017682920.7040
  1138105 6744 
Table 5. Correlations Between Plasma VEGF Level and Patient Characteristics via Wilcoxon Rank-Sum Test
Patient CharacteristicsNo. (%)Plasma VEGF, pg/mL, Mean ± SEP
  1. VEGF indicates vascular endothelial growth factor; HCV, hepatitis C virus; HBV, hepatitis B virus; AFP, α-fetoprotein; ALT, alanine aminotransferase; AST, aspartate aminotransferase; CLIP, Cancer of the Liver Italian Program; BCLC, Barcelona Clinic Liver Cancer; ECOG, Eastern Cooperative Oncology Group.

Age, y   
 <60111 (38.5%)284.77 ± 390.29.69
 ≥60177 (61.5%)290.44 ± 399.55 
Gender   
 Male199 (69.1%)285.53 ± 412.83.82
 Female89 (30.9%)294.34 ± 355.23 
Race   
 Nonwhite89 (30.9%)303.54 ± 426.43.31
 White199 (69.1%)281.42 ± 381.53 
Hepatitis infection   
 HCV60 (20.8%)284.21 ± 341.33.27
 HBV38 (13.2%)375.54 ± 478.99 
 HBV + HCV27 (9.4%)220.94 ± 318.27 
 None163 (56.6%)280.54 ± 403.96 
AFP, ng/mL   
 <400199 (69.1%)270.31 ± 411.94.15
 ≥40086 (29.9%)333.39 ± 358.85 
 Unknown3 (1%)184.19 ± 138.13 
Tumor differentiation   
 Well112 (38.9%)290.83 ± 477.27.19
 Moderate95 (33%)280.92 ± 336.95 
 Poor50 (17.4%)268.85 ± 355.72 
 Unknown31 (10.8%)332.67 ± 295.05 
Tumor nodularity   
 Uninodular105 (36.5%)261.32 ± 433.68.25
 Multinodular183 (63.5%)303.71 ± 371.91 
Tumor size   
 ≤50% liver191 (66.3%)218.60 ± 288.27<.0001
 >50% liver97 (33.7%)425.41 ± 523.55 
Vascular invasion   
 No235 (81.6%)287.43 ± 397.77.94
 Yes53 (18.4%)291.88 ± 388.02 
Lymph node involvement   
 No166 (57.6%)277.76 ± 422.76.04
 Yes122 (42.4%)302.53 ± 355.84 
Metastasis   
 No228 (79.2%)273.06 ± 399.39.01
 Yes60 (20.8%)345.96 ± 377.16 
Bilirubin, mg/dL   
 ≤1.6260 (90.3%)290.02 ± 408.00.54
 >1.628 (9.7%)271.87 ± 253.22 
ALT, U/L   
 ≤40134 (46.5%)284.62 ± 359.07.27
 >40153 (53.1%)286.13 ± 421.82 
 Unknown1 (0.3%)1099.61 
AST, U/L   
 ≤4588 (30.6%)288.88 ± 479.80.16
 >45179 (62.2%)276.28 ± 346.12 
 Unknown21 (7.3%)387.63 ± 404.26 
Cirrhosis   
 No115 (39.9%)299.46 ± 437.41.88
 Yes173 (60.1%)280.80 ± 365.83 
Child-Pugh score   
 A206 (71.5%)288.35 ± 399.99.05
 B76 (26.4%)269.45 ± 388.11 
 C6 (2.1%)523.16 ± 282.89 
CLIP score   
 055 (19.2%)239.43 ± 284.11.0076
 175 (26.3%)213.34 ± 307.17 
 277 (27%)304.86 ± 494.83 
 353 (18.5%)380.46 ± 462.37 
 419 (6.6%)346.42 ± 310.68 
 56 (2.1%)529.93 ± 288.68 
BCLC stage   
 021 (7.3%)131.91 ± 106.28.0003
 A28 (9.7%)216.35 ± 374.48 
 B29 (10.1%)273.49 ± 372.76 
 C189 (65.6%)288.30 ± 398.10 
 D21 (7.3%)560.38 ± 496.84 
ECOG performance status   
 0127 (44.1%)210.87 ± 282.83.0002
 1104 (36.1%)266.44 ± 327.80 
 238 (13.2%)454.75 ± 645.24 
 315 (5.2%)486.88 ± 364.26 
 44 (1.4%)985.52 ± 825.74 

Because baseline VEGF was correlated with other clinical prognostic factors, we tested whether baseline VEGF was an independent prognostic factor (Table 6). We observed that even after adjusting for Child-Pugh score, tumor morphology, AFP, and portal vein thrombosis, the baseline level of VEGF was a significant independent prognostic factor for overall survival (P = .0013; HR, 1.78; 95% CI, 1.25-2.52). Note that even with VEGF incorporation in the model, the HRs for the other CLIP factors did not change much, and all retained the same degree of statistical significance.

Table 6. Multivariable Cox Proportional Hazards Model for V-CLIP Score Variables
VariablesPHR (95% CI)
  1. V-CLIP indicates Cancer of the Liver Italian Program + vascular endothelial growth factor; HR, hazard ratio; CI, confidence interval; AFP, α-fetoprotein; VEGF, vascular endothelial growth factor.

Child-Pugh score  
 B vs A.00031.82 (1.32-2.51)
 C vs A.07672.54 (0.91-7.13)
Tumor morphology  
 1 vs 0.00241.82 (1.23-2.67)
 2 vs 0<.00014.11 (2.75-6.14)
AFP, ng/mL (≥400 vs <400).00021.80 (1.32-2.44)
Portal vein thrombosis (yes vs no).1181.44 (0.91-2.28)
VEGF, pg/mL (>450 vs ≤450).00131.78 (1.25-2.52)

VEGF Separates High- and Low-Risk Groups Within Each CLIP Score

We divided the patients within each CLIP score group according to whether they had low or high VEGF (Table 7). At each of the 5 CLIP levels, the estimated median survival for VEGF-high patients was less than the median survival for VEGF-low patients, suggesting that overall, VEGF-high patients had worse prognosis than VEGF-low patients (P = .031, Sign test). Looking at comparisons of VEGF-high versus VEGF-low within each specific CLIP group, we found that the VEGF-high/low comparison was statistically significant for V-CLIP 3 and 4+ (P = .05), whereas the other groups (V-CLIP 0, 1, 2) demonstrated strong trends that were not statistically significant. Although our overall test assessing the prognostic information of VEGF was significant (P = .031), it was not too surprising that the specific comparisons were not statistically significant within some of the CLIP groups, given the low power (<0.15) for these subgroup analyses, each of which had relatively small numbers of VEGF-high patients (≤15 subjects). Note that, in most cases, the VEGF-high patients in a particular CLIP group tended to have median survivals more similar to the next highest CLIP score. Given this observation, and the fact that the magnitude of the effect of high VEGF in our multivariate Cox regression model (HR, 1.78; P = .0013) is as strong as the effect of the other factors in the CLIP score, we devised a new scoring system. Our system, termed the V-CLIP score, adds a high VEGF level (>450 ng/mL) to the factors already included in the CLIP score, resulting in an integer score between 0 and 7 for each patient (Table 8).

Table 7. Median Survival Divided by CLIP Score and Further Divided by VEGF Level
CLIP ScoreAllVEGF <450 pg/mLVEGF >450 pg/mL 
No.Median Survival, mo (95% CI)No.Median Survival, mo (95% CI)No.Median Survival, mo (95% CI)PaPowerb
  • CLIP indicates Cancer of the Liver Italian Program; VEGF, vascular endothelial growth factor; CI, confidence interval; NA, not available.

  • Note how the VEGF-high patients in a given CLIP group have median survivals more similar to VEGF-low patients in the next CLIP group than VEGF-low patients in their own CLIP group.

  • a

    Corresponds to a test comparing median survival in VEGF-high (>450 pg/mL) and VEGF-low (<450 pg/mL) patients within the specified CLIP score.

  • b

    Indicates power to detect a significant difference given the observed sample sizes, assuming the true difference in median survival was the same as the observed difference in these data.

05537.0 (24.8-51.3)4837.5 (29.4-68.1)719.5 (9.23-NA).100.14
17523.1 (17.5-34.2)6423.1 (17.5-34.2)1118.1 (13.4-38.4).500.08
27711.7 (8.3-15.3)6212.4 (8.3-17.3)159.6 (4.7-21.7).230.10
3537.6 (4.6-9.3)407.8 (6.1-11.7)133.6 (2.3-10.6).050.48
4+252.5 (2.2-3.9)154.4 (2.1-12.6)102.5 (0.9-2.7).050.17
Table 8. V-CLIP Scoring System (0-7)
VariableScore
012
  1. V-CLIP indicates Cancer of the Liver Italian Program + vascular endothelial growth factor; AFP, α-fetoprotein; VEGF, vascular endothelial growth factor.

Child-Pugh scoreABC
Tumor morphologyUninodular and ≤50% liverMultinodular and ≤50% liverMassive or >50% liver
AFP, ng/mL<400≥400 
Portal vein thrombosisNoYes 
VEGF, pg/mL≤450>450 

V-CLIP Scoring Provides More Accurate Stratification than CLIP Scoring Alone

The V-CLIP score divided patients very well (P<.0001), with median survivals of 37.5, 23.1, 14.5, 8.7, 3.6, and 2.5 months for V-CLIP scores of 0, 1, 2, 3, and 4+, respectively (Table 9). Based on a C-index analysis,28 we compared the predictive ability of CLIP versus V-CLIP and found that the V-CLIP index was more able to predict patients' prognosis than the CLIP index (P = .005).

Table 9. Survival by V-CLIP Scoring System
V-CLIP ScoreNo. of PatientsMedian Survival, mo (95% CI)
  1. V-CLIP indicates Cancer of the Liver Italian Program + vascular endothelial growth factor; CI, confidence interval.

04837.5 (29.4-68.1)
17123.1 (17.5-31.3)
27314.5 (10.1-18.0)
3558.7 (6.3-11.7)
4+382.7 (2.3-4.1)

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

We validated the CLIP scoring in our patient population with HRs very close to those reported in the original CLIP study,6 and observed that baseline plasma VEGF, a key mediator of angiogenesis in HCC, was a significant independent predictor of overall survival, with an optimal VEGF cutpoint of 450 pg/mL. Therefore, we chose to incorporate it into CLIP, one of the most widely accepted prognostic scoring systems for HCC in the Western world. Our newly developed V-CLIP score provides an ordinal score from 0 to 7 for each patient based on their CLIP factors, and their optimal cutpoint dichotomized VEGF levels. V-CLIP showed better discriminative ability than CLIP for stratifying patients with HCC into different risk groups. This could be because the tumor parameters are not well represented in CLIP, whereas VEGF correlated significantly with all of them based on our univariate analysis.

We internally validated our results by randomly splitting the data into training (two-thirds) and validation (test) (one-third) sets to find the optimal VEGF cutpoint, then validated that cutpoint on the test data. We had a successful rate (73%) of accrual of HCC patients to our biomarker study, given the challenge of accruing patients with such a poor prognosis disease. The major aim of our large, single-institution biomarker study was to create a novel and simple prognostic scoring system that would provide a more precise prediction of overall survival in patients with HCC. Notably, we compared the prognostic ability of the CLIP scoring system with the BCLC staging system,8 another widely used HCC staging system, using C-index analysis. Remarkably, we observed that the concordance probabilities for CLIP and BCLC were 0.70 and 0.65, respectively, with a significant P value of .007. Notably, the first version of BCLC8 was designed to be a treatment allocation system, not a prognostic system to predict survival and stratify patients for clinical trials. However, after validation, BCLC has been accepted for use as a prognostic system to stratify patients for clinical trials. However, advanced HCC patients who are candidates for systemic therapy clinical trials are grouped in a single category: BCLC stage C. Furthermore, the BCLC system links patients' survival not only to the liver and tumor parameters, but also to the type of treatment received. Both systems are conceptually different; therefore, it is challenging to directly compare their respective prognostic abilities. Nevertheless, the CLIP score validation in our patient population was very successful, as was integrating VEGF into the new V-CLIP system. Therefore, we believe that our approach, after independent prospective validation, may prove very promising in stratifying patients on clinical trials. However, integration of baseline plasma VEGF into other commonly used HCC staging systems is warranted to compare their predictive abilities with that of the V-CLIP system.

Importantly, when comparing the CLIP and V-CLIP scores, we noted that the key differences were in the moderate risk patients (CLIP 2-3 and V-CLIP 2-4), because the median survival for the lowest-risk patients (CLIP 0-1 and V-CLIP 0-1) and highest-risk patients (CLIP 4+, V-CLIP 5+) were similar to each other. For the moderate-risk patients, the CLIP system only separated patients into 2 groups (CLIP 2 and CLIP 3), with median survivals of 11.7 and 7.6 months, respectively, whereas the V-CLIP system separated these patients into 3 prognostic groups (V-CLIP 2, 3, and 4) with disparate median survivals (14.5, 8.7, and 3.6 months, respectively). This more precise stratification of the moderate-risk patients is of particular importance in stratifying patients for therapeutic clinical trials and in predicting the likelihood of patients' survival at certain time points. Several clinical trials have used and validated the CLIP scoring system based on the difference between categories of CLIP ≤3 versus CLIP >3. Table 7 shows that the CLIP 3 group is heterogeneous, with VEGF-high patients having worse median survival (3.6 months) than VEGF-low patients (7.8 months) (P = .05). Using V-CLIP, the VEGF-high CLIP 3 patients are stratified with the high-risk group, which appears to be more accurate in terms of predicting survival.

One of the limitations of our study is that our patient population had mainly unresectable disease. However, predicting prognosis of patients with unresectable HCC is critically important for clinical trial stratification and interpretation purposes. Therefore, our single-institution study will benefit from prospective validation in other patient populations with different demographics, risk factors, and stages of disease. To that end, our system to estimate prognosis in patients with HCC is advantageous because it is simple, is based on variables that are easily testable, and therefore can be independently validated. Another limitation of our study was that our patient population tended to be selective of subjects who were able to return to our center to have their blood withdrawn. Therefore, patients who were missed (27%) tended to have more advanced disease. Nevertheless, our results indicated that even in patients with possibly better prognosis, the VEGF level was significantly associated with overall survival and correlated with other features of advanced HCC. However, this further reinforces the need to validate our results prospectively.

Notably, considerable efforts have been made by several groups to obtain a molecular classification of HCC that would reflect the tumor parameters more accurately, but the overwhelming genomic complexity of this disease has rendered this goal challenging. Therefore, a molecular classification of this highly complex disease has remained elusive. Moreover, HCC is a heterogeneous disease, in terms of the risk factors, natural history, and even response to different modalities of therapy. This has become more evident recently, based on the difference in systemic therapy outcome between Western patients on SHARP trial,21 and Eastern patients on the Asia-Pacific trial using the same drug, sorafenib.26

Finally, this research and other work have demonstrated the prognostic importance of proangiogenic molecules that are expected to play a role in HCC initiation and progression. Our study also showed that the BCLC staging system, one of the most commonly used systems for stratifying patients with HCC, was significantly associated with the baseline level of VEGF in plasma (P = .0003) (Tables 4 and 5). Therefore, future prospective studies in different patient populations will be necessary not only to validate the V-CLIP scoring system, but also to investigate the integration of VEGF into other staging systems to predict prognosis and refine stratification of patients with HCC. In addition, other biomarkers involved in hepatocarcinogenesis should be examined for their effect on prognosis. These emerging molecular approaches to designing newer prognostic systems may prove to be more accurate in predicting prognosis and in stratifying patients with HCC during therapeutic clinical trials, and may also be helpful when used to guide treatment decisions.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

We thank Lee M. Ellis at The University of Texas M. D. Anderson Cancer Center for guidance in research.

CONFLICT OF INTEREST DISCLOSURES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Supported by National Institutes of Health RO3 grant ES11481, CA106458-01 (to M. H.) and by philanthropic funds to the Department of Gastrointestinal Medical Oncology. All principal investigators had full access to all the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES
  • 1
    El-Serag HB, Mason AC. Rising incidence of hepatocellular carcinoma in the United States. N Engl J Med. 1999; 340: 745-750.
  • 2
    Altekruse SF, McGlynn KA, Reichman ME. Hepatocellular carcinoma incidence, mortality, and survival trends in the United States from 1975 to 2005. J Clin Oncol. 2009; 27: 1485-1491.
  • 3
    Pugh RN, Murray-Lyon IM, Dawson JL, Pietroni MC, Williams R. Transection of the oesophagus for bleeding oesophageal varices. Br J Surg. 1973; 60: 646-649.
  • 4
    Kudo M, Chung H, Osaki Y. Prognostic staging system for hepatocellular carcinoma (CLIP score): its value and limitations, and a proposal for a new staging system, the Japan Integrated Staging Score (JIS score). J Gastroenterol. 2003; 38: 207-215.
  • 5
    Okuda K, Ohtsuki T, Obata H, et al. Natural history of hepatocellular carcinoma and prognosis in relation to treatment. Study of 850 patients. Cancer. 1985; 56: 918-928.
  • 6
    A new prognostic system for hepatocellular carcinoma: a retrospective study of 435 patients: the Cancer of the Liver Italian Program (CLIP) investigators. Hepatology. 1998; 28: 751-755.
  • 7
    Leung TW, Tang AM, Zee B, et al. Construction of the Chinese University Prognostic Index for hepatocellular carcinoma and comparison with the TNM staging system, the Okuda staging system, and the Cancer of the Liver Italian Program staging system: a study based on 926 patients. Cancer. 2002; 94: 1760-1769.
  • 8
    Llovet JM, Bru C, Bruix J. Prognosis of hepatocellular carcinoma: the BCLC staging classification. Semin Liver Dis. 1999; 19: 329-338.
  • 9
    Prospective validation of the CLIP score: a new prognostic system for patients with cirrhosis and hepatocellular carcinoma. The Cancer of the Liver Italian Program (CLIP) Investigators. Hepatology. 2000; 31: 840-845.
  • 10
    Farinati F, Rinaldi M, Gianni S, Naccarato R. How should patients with hepatocellular carcinoma be staged? Validation of a new prognostic system. Cancer. 2000; 89: 2266-2273.
  • 11
    Levy I, Sherman M. Staging of hepatocellular carcinoma: assessment of the CLIP, Okuda, and Child-Pugh staging systems in a cohort of 257 patients in Toronto. Gut. 2002; 50: 881-885.
  • 12
    Ueno S, Tanabe G, Sako K, et al. Discrimination value of the new western prognostic system (CLIP score) for hepatocellular carcinoma in 662 Japanese patients. Cancer of the Liver Italian Program. Hepatology. 2001; 34: 529-534.
  • 13
    Llovet JM, Bruix J. Prospective validation of the Cancer of the Liver Italian Program (CLIP) score: a new prognostic system for patients with cirrhosis and hepatocellular carcinoma. Hepatology. 2000; 32: 679-680.
  • 14
    Huitzil-Melendez FD, Capanu M, O'Reilly EM, et al. Advanced hepatocellular carcinoma: which staging systems best predict prognosis? J Clin Oncol. 2010; 28: 2889-2895.
  • 15
    Zhou L, Liu J, Luo F. Serum tumor markers for detection of hepatocellular carcinoma. World J Gastroenterol. 2006; 12: 1175-1181.
  • 16
    Poon RT, Fan ST, Wong J. Clinical significance of angiogenesis in gastrointestinal cancers: a target for novel prognostic and therapeutic approaches. Ann Surg. 2003; 238: 9-28.
  • 17
    Yao DF, Wu XH, Zhu Y, et al. Quantitative analysis of vascular endothelial growth factor, microvascular density and their clinicopathologic features in human hepatocellular carcinoma. Hepatobiliary Pancreat Dis Int. 2005; 4: 220-226.
  • 18
    Tseng CS, Lo HW, Chen PH, Chuang WL, Juan CC, Ker CG. Clinical significance of plasma D-dimer levels and serum VEGF levels in patients with hepatocellular carcinoma. Hepatogastroenterology. 2004; 51: 1454-1458.
  • 19
    Poon RT, Ho JW, Tong CS, Lau C, Ng IO, Fan ST. Prognostic significance of serum vascular endothelial growth factor and endostatin in patients with hepatocellular carcinoma. Br J Surg. 2004; 91: 1354-1360.
  • 20
    Kamel L, Nessim I, Abd-el-Hady A, Ghali A, Ismail A. Assessment of the clinical significance of serum vascular endothelial growth factor and matrix metalloproteinase-9 in patients with hepatocellular carcinoma. J Egypt Soc Parasitol. 2005; 35: 875-890.
  • 21
    Llovet JM, Ricci S, Mazzaferro V, et al. Sorafenib in advanced hepatocellular carcinoma. N Engl J Med. 2008; 359: 378-390.
  • 22
    Abou-Alfa GK, Schwartz L, Ricci S, et al. Phase II study of sorafenib in patients with advanced hepatocellular carcinoma. J Clin Oncol. 2006; 24: 4293-4300.
  • 23
    Thomas MB, Morris JS, Chadha R, et al. Phase II trial of the combination of bevacizumab and erlotinib in patients who have advanced hepatocellular carcinoma. J Clin Oncol. 2009; 27: 843-850.
  • 24
    Siegel AB, Cohen EI, Ocean A, et al. Phase II trial evaluating the clinical and biologic effects of bevacizumab in unresectable hepatocellular carcinoma. J Clin Oncol. 2008; 26: 2992-2998.
  • 25
    Zhu AX, Sahani DV, Duda DG, di Tomaso E, Ancukiewicz M, Catalano OA, et al. Efficacy, safety, and potential biomarkers of sunitinib monotherapy in advanced hepatocellular carcinoma: a phase II study. J Clin Oncol. 2009; 27: 3027-3035.
  • 26
    Cheng AL, Kang YK, Chen Z, 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: 25-34.
  • 27
    Kaseb AO, Hanbali A, Cotant M, Hassan MM, Wollner I, Philip PA. Vascular endothelial growth factor in the management of hepatocellular carcinoma: a review of literature. Cancer. 2009; 115: 4895-4906.
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  • 28
    Harrell FE Jr, Califf RM, Pryor DB, Lee KL, Rosati RA. Evaluating the yield of medical tests. JAMA. 1982; 247: 2543-2546.