Improved performance of Hangzhou criteria for liver transplantation of hepatocellular carcinoma: the role of liver resident FoxP3⁺ regulatory T cells

Abstract

Tumor-infiltrating lymphocytes (TILs) represent the host immune response to a tumor. In this study, we investigated the prognostic value of tumor-infiltrating lymphocytes (TILs) in liver transplant candidates with hepatocellular carcinoma (HCC) and established an improved prognostic model for predicting clinical outcome. CD3⁺, CD4⁺, CD8⁺, and FoxP3⁺ TILs were assessed by immunohistochemistry in tumor tissue from 153 patients who had undergone liver transplantation for HCC. Prognostic effects of these TIL subsets and other clinicopathologic factors were evaluated by Kaplan-Meier and Cox regression analysis. The area under the curve (AUC) and net reclassification improvement (NRI) were calculated to determine if the new model improved risk prediction. We found that the prevalence of intra-tumoral FoxP3⁺ Tregs among CD4⁺ TILs, but not the density of intra-tumoral FoxP3⁺ Tregs, was an independent predictor for disease-free (DFS) and overall survival (OS) (P<0.05). A Cox model combining the prevalence of intra-tumoral FoxP3⁺ Tregs with Hangzhou criteria was highly predictive of tumor recurrence and death. The AUCs of the Cox model for recurrence (0.733; 95% CI, 0.656-0.802) and survival (0.765; 95% CI, 0.690-0.830) were significantly increased when compared with those of Hangzhou criteria (P<0.001). Net reclassification improvement showed that predictability of the Cox models for both recurrence and survival was superior to Hangzhou criteria (P<0.05). Our results collectively showed that the prevalence of intra-tumoral FoxP3⁺ Tregs is a promising prognostic predictor for HCC patients after OLT. Inclusion of FoxP3⁺ Tregs into Hangzhou criteria could improveme risk prediction.

Introduction

Hepatocellular carcinoma (HCC) is the sixth most common cancer worldwide with over 740,000 new cases per year and the third leading cause of cancer-related deaths [1]. Despite improved diagnostic and treatment strategies, especially orthotopic liver transplantation (OLT) as the most radical surgical treatment, high post-operative recurrence rate is still a major problem [2,3]. Many biomarkers, mainly from tumor tissue and tumor cells, have been extensively studied [4-6] and several selection criteria for HCC patients, such as the Milan criteria [7] and the UCSF criteria [8], have been established. In China, nearly 40% donor livers are offered to HCC patients, who have more HBV-related backgrounds and more advanced or aggressive tumor characteristics than those in Western countries [9]. Therefore, in 2008, we established our own criteria (Hangzhou criteria) [10]. In the selected patients based on these criteria, the 5-year overall and tumor-free survival rates are both more than 50%. However, patients exceeding these criteria had an unacceptable clinical outcome [11].

Recently, biologic behaviors of HCC have been found to be associated with a unique immune response signature of the liver microenvironment [12], and the clinical outcome is governed predominantly by immune responses within the tumor site [13-15], which suggests that precise evaluation of local immune responses could be useful for predicting prognosis.

Tumor-infiltrating lymphocytes (TILs) are considered as one of the manifestations of host immune reaction against cancers. Patients with a prominent lymphocyte infiltration have improved prognoses [16,17]. TILs are heterogeneous and contain various immune cell subsets, such as cytotoxic and regulatory T cells, which can suppress and promote the progression of tumors, respectively.

Regulatory T cells (Tregs) have a crucial role in impeding immune surveillance against cancer and hampering the development of effective antitumor immunity. It has been reported that Tregs are increased in both peripheral blood and the tumor microenvironment in many human carcinoma types [18-20] and can suppress proliferation and production of granzyme and perforin of autologous CD8⁺ cytotoxic T cells (CTLs) through direct cell-to-cell contact or via the release of cytokines [21]. The most specific Treg cells marker identified to date is the nuclear transcription factor known as FoxP3, which can define Tregs in human tumors [22]. Accumulating studies have documented a link between the infiltration of FoxP3⁺ Tregs and prognosis in several human carcinoma types after curative resection [23,24]. However, there is little data on clinical outcomes of lymphocytic infiltration in HCC patients following liver transplantation.

In this study, we assessed the prognostic value of various subtypes of TILs to establish a new predictive model of HCC incorporating immune factors and to compare the accuracy of this model against the Hangzhou criteria.

Patients and methods

Patients and samples

Archival specimens were obtained from 153 patients at the Division of Hepatobiliary and Pancreatic Surgery, Department of Surgery, the First Affiliated Hospital, Zhejiang University School of Medicine (Hangzhou, China). The inclusion criteria included the following: (1) diagnosis of HCC confirmed by pathology; (2) underwent OLT for HCC between 2010 and 2015; (3) with complete clinicopathologic and follow-up data. The clinical classification of tumors was determined according to the TNM classification system of International Union Against Cancer (edition 6). The histologic grade of tumor differentiation was assigned by the Edmondson grading system. Liver function was assessed by Child-Pugh score system. Written informed consents were obtained from all donors and recipients before transplantation. Each organ donation or transplant in our center was strictly under the guideline of the Ethical Committee of our hospital, the regulation of Organ Transplant Committee of Zhejiang province and the Declaration of Helsinki. The study protocol was approved by the Institutional Review Board of Key Lab of Combined Multi-organ Transplantation, Ministry of Public Health.

Peri-transplantation management and follow-up

According to the pre-OLT management protocol for HCC patients since 2004, 30 patients received radiofrequency ablation or transarterial chemoembolization to prevent tumor progression. The post-OLT immunosuppressive protocols were tacrolimus or cyclosporin, mycophenolate mofetil, and steroids. The post-operative anti-HBV protocol was lamivudine combined with low-dose intramuscular hepatitis B immunoglobulin therapy [25]. The last follow-up was completed on September 1st, 2017. The mean follow-up time reached 24.4 months (range, 1.0-93.7). Follow-up procedures were described in our previous study [10]. Patients with liver or lung recurrence were referred for corresponding resection or interventional treatment, whereas bone metastases were referred for ⁸⁹Sr internal radiation. Overall survival (OS) was defined as the interval between transplantation and death or between transplantation and the last observation for surviving patients. Disease-free survival (DFS) was defined as the interval between transplantation and recurrence; if recurrence was not diagnosed, patients were censored on the date of death or the last follow-up.

Immunohistochemistry analysis

Immunohistochemistry was done on formalin-fixed, paraffin-embedded tissue sections as previously described [21,26]. The monoclonal antibodies used were CD3, CD4, CD8 (Novocastra, Newcastle, UK) and FoxP3 (Abcam, Cambridge, UK). Briefly, the sections were deparaffinized and rehydrated. Before blocking of endogenous peroxidase with methanol containing 3% H₂O₂, the sections were autoclaved at 121°C for 10 min in citrate buffer (pH 6.0) for antigen retrieval. After blocking with goat serum, the sections were reacted overnight with appropriately diluted primary antibodies. Slides were then incubated with labeled polymer horseradish peroxidase rabbit/mouse antibody for 15 minutes (Envision⁺ Detection System; Dako, Carpinteria, CA). Diaminobenzidine was used as the chromogen, and the nuclei were counterstained with hematoxylin.

Evaluation of immunohistochemical variables

The number of TILs was counted using a computerized image analysis system composed of a Leica DFC420 CCD camera, installed on a Leica DMIR2 light microscope (Leica, Wetzlar, Germany) and attached to a personal computer. Permanent section hematoxylin and eosin (H&E)-stained slides were reviewed without knowledge of clinical characteristics or outcome. Each section on an H&E-stained slide was evaluated for the presence of the tumor cells (intra-tumoral region) and the invasive margin (peri-tumoral region). For each tumor specimen, H&E stained slides containing CT and IM regions were selected. For each region, five cores were taken from the areas containing the highest density of immune cells [24]. Variations in counts exceeding 5% were re-counted and a consensus decision was made. The prevalence of FoxP3⁺ Tregs among CD4⁺ TILs and that of CD8⁺ CTLs among CD3⁺ TILs were calculated using the mean number of total fields and the averages were compared.

Statistic analysis

Statistical analyses were performed with SPSS 15.0 software (SPSS, Chicago, IL). Cumulative survival time was calculated by the Kaplan-Meier method and analyzed by the log-rank test. Univariate and multivariate analyses were based on the Cox proportional hazards regression model. For all immune markers, the cutoff for definition of subgroups was derived from the area under the receiver operating characteristic (AUROC) curve of the score based on the highest Youden index [5]. The correlation of lymphocytic variables with clinicopathologic characteristics was assessed using χ² tests. Independent risk factors were determined using Binary Logistic Regression. Receiver operating characteristic (ROC) curve analysis was used to determine the predictive value of the parameters. Statistical significance was set at P<0.05. To compare the predictability of the new model with the Hangzhou criteria, net reclassification improvement (NRI) [27] were estimated as previous reported [5].

Results

Patient and immunohistochemical characteristics

Supplementary Table 1 shows the clinicopathologic characteristics of this cohort. There were 141 men and 12 women, ranging in age from 20 to 71 years (mean 50.0 years). Most of the patients, 79.7% (122/153), had a history of hepatitis B virus (HBV) infection. Forty-one patients (26.8%) had post-LT histologically proved microvascular invasion. Most of them had tumor thrombi in small branches of portal vein or hepatic vein as shown by pathologic examination of explants but which could not be detected by pre-LT imaging examinations. There were 104 patients meeting the Hangzhou criteria and the remaining 49 patients exceeding it.

The densities of CD3⁺, CD4⁺ and CD8⁺ TILs were higher in peri-tumoral area than in the intra-tumoral area (P<0.001; Figure 1A and 1B). However, the density of FoxP3⁺ cells in intra-tumoral area was close to that in peri-tumoral area (mean, 3.7 vs. 5.5, P=0.262; Figure 1A and 1B), thus resulting in significantly higher prevalence of FoxP3⁺ cells among CD4⁺ TILs in intra-tumoral area (mean, 42% vs. 12% in peri-tumoral area, P<0.001; Figure 1C). Different from FoxP3⁺ cells, the prevalence of CD8⁺ CTLs among CD3⁺ TILs was similar in the two areas (Figure 1C).

Figure 1.

A. Representative features of CD3⁺, CD4⁺, CD8⁺ and FoxP3⁺ T cells infiltrating the intra-tumoral (T) and peri-tumoral (P) area of HCCs. bar, 50 μm; magnification, ×400. B. Statistical analysis of the density of CD3⁺, CD4⁺, CD8⁺ and FoxP3⁺ T cells in the intra-tumoral (black column) and peri-tumoral (white column) area. ***, P<0.001, Mann-Whitney test. C. Statistical analysis of the prevalence of CD8⁺ cells among CD3⁺ T cells and FoxP3⁺ cells among CD4⁺ T cells in the intra-tumoral (black column) and peri-tumoral (white column) area. ***, P<0.001, Mann-Whitney test.

Univariate analysis

The overall 1-, 3-, and 5-year survival rates were 79.1%, 58.2%, and 52.8%, respectively. The 1-, 3-, and 5-year tumor-free survival rates were 66.1%, 54.4%, and 51.4%, respectively. Among clinical parameters of the patients, 7 variables, including preoperative AFP level, largest tumor diameter, tumor number, microvascular invasion, tumor distribution, underlying liver cirrhosis and tumor differentiation, were predictors for DFS (P=0.000-0.046) and OS (P=0.000-0.021) (Table 1).

Table 1.

Univariate analysis of variables related to post-LT recurrence and survival (log-rank test)

		Disease-Free Survival*				Overall Survival*
Variables	N	1-yr, % (SD)	3-yr, % (SD)	5-yr, % (SD)	P	1-yr, % (SD)	3-yr, % (SD)	5-yr, % (SD)	P
Preoperative AFP level
≤400 ng/mL	99	76 (4.4)	63 (5.0)	60 (5.1)	0.002	84 (3.7)	66 (3.4)	57 (3.9)	0.011
>400 ng/mL	54	47 (7.0)	38 (6.9)	38 (6.9)		70 (6.2)	44 (6.8)	41 (6.1)
Largest tumor diameter
≤8 cm	102	77 (4.2)	67 (4.1)	64 (4.0)	0.000	84 (3.6)	73 (3.4)	65 (3.1)	0.000
>8 cm	51	43 (7.9)	27 (7.6)	27 (7.7)		69 (6.4)	30 (6.2)	26 (6.0)
Tumor number
≤3	103	71 (4.5)	59 (4.7)	57 (4.3)	0.046	83 (4.5)	65 (4.6)	57 (4.3)	0.021
>3	50	54 (7.3)	44 (7.0)	41 (7.1)		70 (6.8)	44 (6.5)	40 (6.8)
Microvascular invasion
No	112	74 (4.2)	61 (4.5)	59 (4.0)	0.000	82 (2.9)	64 (2.4)	53 (2.9)	0.010
Yes	41	44 (7.9)	35 (7.2)	35 (7.7)		71 (7.7)	41 (7.3)	34 (7.2)
Tumor encapsulation
No	41	79 (6.1)	60 (6.5)	60 (5.9)	0.047	85 (5.5)	68 (5.4)	61 (5.6)	0.041
Yes	112	60 (4.8)	52 (4.3)	50 (4.0)		78 (3.8)	56 (3.9)	50 (3.6)
Tumor differentiation
I+II	76	71 (5.2)	65 (5.0)	64 (5.0)	0.016	80 (6.3)	66 (6.4)	63 (6.0)	0.021
III	77	61 (5.8)	42 (5.4)	40 (5.0)		78 (4.6)	51 (4.0)	40 (4.3)
Underlying liver cirrhosis
No	15	62 (13.5)	31 (13.0)	21 (13.2)	0.007	73 (11.4)	27 (11.4)	27 (11.0)	0.012
Yes	138	66 (4.1)	57 (4.9)	55 (4.5)		80 (3.3)	63 (3.7)	56 (3.5)
Ratio of intra-tumoral FoxP3+ to CD4+ cells
≤0.223	107	74 (4.3)	63 (4.8)	62 (4.8)	0.000	83 (3.6)	69 (4.5)	61 (4.7)	0.000
>0.223	46	47 (7.6)	34 (7.4)	31 (7.3)		70 (6.8)	33 (6.6)	28 (6.6)

^*Kaplan-Meier estimates.

The density of each immune marker in the intra-tumoral and peri-tumoral areas did not show any prognostic significance for both DFS and OS (Supplementary Figure 1A-H). Further, we assessed the prognostic value of the prevalence of FoxP3⁺ and CD8⁺ TILs in the two areas. The prevalence of intra-tumoral FoxP3⁺ cells among CD4⁺ TILs was a predictor for DFS and OS (both P<0.001, Figure 2A-D; Table 1), but not for that of peri-tumoral FoxP3⁺ cells (Supplementary Figure 2A and 2B). The prevalence of CD8⁺ cells among CD3⁺ TILs in either intra-tumoral or peri-tumoral area had no prognostic value (Supplementary Figure 3A-D).

Figure 2.

(A and B) Representative example of high (A) and low (B) prevalence of intra-tumoral FoxP3⁺ cells among CD4⁺ T showed using consecutive sections. (C and D) Kaplan-Meier curves illustrate the duration of disease-free (C) and overall survival (D) according to the prevalence of intra-tumoral FoxP3⁺ cells. Solid line for low-Treg prevalence group, dashed line for high-Treg prevalence group.

Multivariate analysis and model establishment

Cox regression analysis identified four and three independent predictors of recurrence and survival, respectively. Table 2 shows the value of the β regression coeffeicients and the corresponding hazard ratios for each independent predictor. The prevalence of intra-tumoral FoxP3⁺ TILs was independently associated with DFS (P=0.002) and OS (P=0.018). HCC patients with higher prevalence of intra-tumoral FoxP3⁺ TILs had worse clinical outcome.

Table 2.

Multivariate analysis of predictors associated with tumor recurrence and overall survival post-transplant (n=153)

Variables*	MST (months)	β coefficient	HR	95% CI	P
Disease-Free Survival
Preoperative AFP level, ≤400 ng/mL vs. >400 ng/mL	60 vs. 36	0.488	1.629	1.011-2.622	0.045
Largest tumor diameter, ≤8 cm vs. >8 cm	63 vs. 27	0.896	2.449	1.493-4.019	0.000
Tumor differentiation, I+II vs. III	60 vs. 44	0.515	1.673	1.031-2.713	0.037
Intra-tumoral FoxP3+/CD4+ cells, ≤0.223 vs. >0.223	61 vs. 30	0.759	2.136	1.311-3.481	0.002
Overall Survival
Largest tumor diameter, ≤8 cm vs. >8 cm	66 vs. 33	0.992	2.697	1.689-4.308	0.000
Tumor number, ≤3 vs. >3	60 vs. 44	0.456	1.578	0.999-2.491	0.049
Intra-tumoral FoxP3+/CD4+ cells, ≤0.223 vs. >0.223	63 vs. 35	0.572	1.772	1.102-2.852	0.018

Abbreviations: MST, mean survival time, HR, hazard ratio; CI, confidence interval; AFP, α-fetoprotein.

^*Variables were adopted for their prognostic significance by univariate analysis.

However, the predictive performance of each parameter was far from satisfactory. The AUC of each parameter was lower than 0.7 under ROC analysis (Table 3). Therefore, the combined influence of all independent predictors was also evaluated. A Cox model was built to predict the probability of recurrence or death. On the basis of the ROC analysis, the best cut-off value of the Cox score to stratify between low and high risk of recurrence and survival was 3.6 (AUROC, 0.733; 95% confidence interval [CI], 0.65-0.81; sensitivity, 54.9%; specificity, 80.3%) and 2.5 (AUROC, 0.765; 95% confidence interval [CI], 0.69-0.84; sensitivity, 67.1%; specificity, 73.0%), respectively. Five-year recurrence and overall survival rates statistically differed between patients at low and high risk of recurrence and survival, as shown in Figure 3A and 3B.

Table 3.

Receiver operating curve analyses of independent predictors for DFS and OS

	DFS			OS
Variable	AUC	95% CI	P	AUC	95% CI	P
Preoperative AFP level	0.578	0.496-0.657	0.044*	NA	NA	NA
Tumor differentiation	0.582	0.500-0.661	0.040*	NA	NA	NA
Tumor number	NA	NA	NA	0.581	0.499-0.660	0.031*
Largest tumor diameter	0.635	0.553-0.711	0.000*	0.653	0.572-0.728	0.000*
Intra-tumoral FoxP3+/CD4+ cells	0.627	0.545-0.704	0.001*	0.634	0.553-0.710	0.000*
Hangzhou Criteria	0.636	0.554-0.712	0.000*	0.679	0.599-0.752	0.000*
The Cox model	0.733	0.656-0.802	0.007†	0.765	0.690-0.830	0.004†

Abbreviations: AUC, area under curve; CI, confidence interval; NA, not adopted.

^* P value compared with reference.

^† P value compared with Hangzhou Criteria.

Figure 3.

Kaplan-Meier curves illustrate the duration of disease-free (A) and overall survival (B) according to Cox score. Solid line for low Cox score group, dashed line for high Cox score group.

Impact of the Cox model in the selection of liver transplant candidates as compared with the Hangzhou criteria

Among patients within the Hangzhou criteria, DFS and OS statistically differed based on the cut-off value of 3.6 (MST, 70.9 vs. 39.2 months, P=0.002; Figure 4A) and 2.5 (MST, 71.9 vs. 43.2 months, P=0.002; Figure 4C), respectively. Similarly, among patients exceeding the Hangzhou criteria, DFS and OS statistically differed according to the cut-off value of 3.6 (MST, 69.7 vs. 17.7 months, P=0.001; Figure 4B) and 2.5 (MST, 63.0 vs. 29.9 months, P=0.045; Figure 4D), respectively. The AUCs of Cox model for recurrence and survival were significantly larger than that of Hangzhou Criteria (P<0.01, Table 3). Net reclassification improvements, estimated between the Hangzhou criteria and the Cox model, were 0.166 (P=0.039, Table 4) and 0.186 (P=0.022, Table 5). This indicates that prediction of recurrence or survival was improved significantly by the Cox model, compared with Hangzhou criteria.

Figure 4.

Kaplan-Meier curves illustrate the duration of disease-free (A and B) and overall survival (C and D) in patients within Hangzhou criteria (A and C) and exceeding Hangzhou criteria (B and D).

Table 4.

Comparison of Cox model vs. Hangzhou Criteria for prediction of recurrence

	Cox Score
	Low risk	High risk	Total
Recurrence
Within criteria	17	19	36
Exceeding criteria	1	32	33
Total	18	51	69
No recurrence
Within criteria	56	12	68
Exceeding criteria	4	12	16
Total	60	24	84

Note: NRI value is as follows: (19/71-1/71) - (12/82-4/82) =0.166, Z=0.166/√((20/69)/69 + (16/84)/84) =2.060, P=0.039.

Table 5.

Comparison of Cox model vs. Hangzhou Criteria for prediction of survival

	Cox Score
	Low risk	High risk	Total
Death
Within criteria	19	23	42
Exceeding criteria	4	33	37
Total	23	56	79
No Death
Within criteria	54	8	62
Exceeding criteria	4	8	12
Total	58	16	74

Note: NRI value is as follows: (23/79-4/79) - (8/74-4/74) =0.186, Z=0.186/√((27/79)/79 + (12/74)/74) =2.295, P=0.022.

Correlation of immunohistochemical variables with clinicopathologic features

None of the clinicopathologic features were found to be associated with the prevalence of intra-tumoral Tregs directly (Supplementary Table 2). However, the density of intra-tumoral Tregs was found to be associated with age (P=0.043), child stage (P=0.031), tumor encapsulation (P=0.024) and, especially, tumor differentiation (P=0.006) in univariate analysis (Supplementary Table 2). Multivariate analysis showed that child stage (P=0.018), tumor encapsulation (P=0.027) and tumor differentiation (P=0.028) were three independent risk factors. In addition, no correlation was found between immune markers and clinical features (Supplementary Tables 2, 3 and 4), except that between the prevalence of intra-tumoral CD8⁺ TILs and age (P=0.041, Supplementary Table 2).

Discussion

Tumor-infiltrating lymphocytes represent the host immune response to a tumor with CTLs as critical positive responders and Tregs as main immunosuppressors. In this study, we investigated the relationship between host immune response and clinical outcome in HCC patients after OLT. We analyzed TIL infiltration in both intra-tumoral and peri-tumoral areas as their localization may potentially affect the prognosis of cancers [17,20,28]. Our results showed the prevalence of intra-tumoral Tregs was an independent predictor of recurrence after transplantation for HCC and also predicted post-transplantation survival. Our results, along with a previous large-scale study, showed that the prognostic impact of Tregs was limited to intra-tumoral but not peri-tumoral infiltration, suggesting that Tregs may have distinct roles depending on their localization. However, the explanation for this difference remains unknown. Possible factors include the following. First, intra-tumoral FoxP3⁺ cells are in direct contact with tumor cells and differ phenotypically and functionally from peri-tumoral compartments [13,14,29]. Second, FoxP3⁺ Tregs have been shown to suppress the proliferation of other T cells by cell-to-cell contact [21,30,31], in addition to cytokine-mediated regulatory effects. Third, the high density of peri-tumoral T cells may sculpt tumors for increased aggressiveness, which may lead to the lack of prognostic significance of peri-tumoral FoxP3⁺ cells [32].

In the past decades, several criteria have been established as selection guidelines for OLT of HCC patients [7,8,33]. The Milan criteria have been universally recognized as the guidelines for selecting patients with hepatocellular carcinoma for orthotopic liver transplantation. However, the Milan criteria are quite strict because their rules can cause patients to be excluded from wait lists who could in fact benefit from transplant. Therefore, many expanded criteria are now incorporating different biologic markers, such as alpha-fetoprotein [5] and 18F-fluorodeoxyglucose positron emission tomography-computed tomography [34], etc. We also established our own criteria (Hangzhou criteria) [10], and have been trying to improve our criteria [35]. The liver was also regarded as an lymphoid organ with a distinctive local immune environment [36]. However, none of these criteria has taken the immune factors into account. Immune factors undoubtedly play extremely important role in transplanted patients. Moreover, the infiltration of Tregs has been found to be an independent prognostic factor for HCC patients following curative resection [37]. Therefore, we designed a new predictive model, that combined Treg value with the factors of Hangzhou criteria. The model was shown to significantly improve prediction of HCC recurrence compared with the Hangzhou criteria, as assessed by net reclassification improvement. Among patients exceeding the Hangzhou criteria, the Cox model identified a subset of patients with lower prevalence of intra-tumoral Tregs as low risk of recurrence (MST, 69.7 vs. 17.7 months, P=0.001) and better survival (MST, 63.0 vs. 29.9 months, P=0.045). On the other hand, among patients within Hangzhou criteria, the model also identified a subgroup of patients with higher prevalence of intra-tumoral Tregs at high risk of recurrence (MST, 70.9 vs. 39.2 months, P=0.002) and significantly reduced survival (MST, 71.9 vs. 43.2 months, P=0.002).

In conclusion, our data suggest that the prevalence of intra-tumoral Tregs is a reliable predictor of HCC patients after OLT. The combination of Tregs with Hangzhou criteria can predict clinical outcome more effectively. Therefore, we propose to add Treg to the Hangzhou criteria in the decision-making algorithm for selection of HCC patients for liver transplantation.

Disclosure of conflict of interest

None.