A Four-Factor Immunoscore System That Predicts Clinical Outcome for Stage II/III Gastric Cancer

Published OnlineFirst June 15, 2017; DOI: 10.1158/2326-6066.CIR-16-0381

Title: A Four-Factor Immunoscore System that Predicts Clinical Outcome for Stage II/III Gastric Cancer

Running Title: Immunoscore as Prognostic Model for Gastric Cancer

Ti Wen1†PhD., Zhenning Wang2†MD., Yi Li3 MD., Zhi Li1 MD., Xiaofang Che1 PhD., Yibo Fan1 MD., Shuo Wang1 MD., Jinglei Qu1 MD., Xianghong Yang5 MD., Kezuo Hou1BS., Wenyang Zhou5 MS., Ling Xu1 MD., Ce Li1 MS., Jin Wang1 MD., Jing Liu1 MD., Liqun Chen3BS., Jingdong Zhang4 MD., Xiujuan Qu1* MD., Yunpeng Liu1* MD. 1 Department of Medical Oncology, Key Laboratory of Anticancer Drugs and Biotherapy of Liaoning Province 2 Department of Surgical Oncology and General Surgery, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang, 110001, China 3 Department of Pathology, 4 Department of Medical Oncology, Cancer Hospital of Liaoning Province, No.44, Xiaoheyan Road, Dadong District, Shenyang, 110042, China 5 Department of Pathology, Shengjing Hospital of China Medical University, No.36, Sanhao Street, Heping District, Shenyang, 110004, China

*Corresponding Author: Yunpeng Liu, MD. PhD., Tel/Fax: +86-(0)24-83282312; E-mail: [email protected] and Xiujuan Qu, MD. PhD. Tel/Fax: +86-(0)24-83282312; E-mail:[email protected]; Department of Medical Oncology, The First Hospital of China Medical University, No.155, Nanjing North Street, Heping District, Shenyang 110001, China; †These two authors contributed equally to this work.

Keywords: gastric cancer, immunoscore system, PD-L1, PD-1, CD8, prognostic model

Grant Support This work was supported by Key National Science and Technology Major Project of China (No. 2013ZX09303002, to YP Liu), National Natural Science Foundation of China (NSFC) (No. 31300743 to T Wen, 81673025 to L Xu, 81602098 to YB Fan), Science and Technology Plan Project of Liaoning Province (No. 2015020734 to T Wen, 2014225013, 2014226033, to YP Liu), the Key Laboratory Project of the Education Department of Liaoning Province (No.LZ2014037, to YP Liu) and Health and Family Planning Commission of Liaoning Province (No. LNCCC-D07-2015, to T Wen).

Author Contributions: Ti Wen, Xiujuan Qu, and Yunpeng Liu were responsible for study design, data collection, data analysis and manuscript writing. Yi Li, Zhi Li, Xiaofang Che, Yibo Fan, Shuo Wang, Jinglei Qu, Xianghong Yang, Kezuo Hou, and Wenyang Zhou were responsible for data collection, data analysis. Zhenning Wang, Ling Xu, Ce Li, Jin Wang, Jing Liu, Liqun Chen, and Jingdong Zhang were responsible for data collection.

Abstract

The American Joint Committee on Cancer (AJCC) staging system is insufficiently prognostic for

operable gastric cancer (GC) patients, therefore complementary factors are under intense

investigation. Although the focus is on immune markers, the prognostic impact of a single immune

factor is minimal, due to complex antitumor immune responses. A more comprehensive evaluation

may engender more accurate predictions. We analyzed immune factors by immunohistochemical

staining in two independent cohorts. The association with patients’ survival was analyzed by the

Kaplan-Meier method. Our immunoscore system was constructed using Cox proportional hazard

analysis. PD-L1+ immune cells (ICs), PD-L1+ tumor cells (TCs), PD-1hi, and CD8More were found

among 33.33%, 31.37%, 33.33%, and 49%, respectively, of patients from the discovery cohort, and

41.74%, 17.4%, 38.26%, and 30.43% from the validation cohort. PD-L1+ ICs and PD-1hi ICs

correlated with poorer overall survival (OS), but PD-L1+ TCs correlated with better OS and clinical

outcomes and infiltration of more CD8+ T cells. These four factors were independently prognostic

after tumor/lymph nodes/metastasis (TNM) stage adjustment. An immunoscore system based on

hazard ratios of the four factors further separated GC patients with similar TNM staging into low-,

medium-, or high-risk groups, with significantly different survival. Our prognostic model yielded an

area under the receiver operating characteristic curve (AUC) of 0.856 for prediction of mortality at

5 years, superior to that of TNM staging (AUC of 0.676). Thus, this more comprehensive

immunoscore system can provide more accurate prognoses and is an essential complement to the

AJCC staging system for operable GC patients.

Introduction

Gastric cancer (GC) is one of the most common malignancies worldwide [1]. Conventionally,

tumor/lymphnode/metastasis (TNM) staging has been used to assess the prognosis of GC patients,

however, the clinical prognosis may vary significantly among patients with the same TNM stage

due to its highly heterogeneous nature [2]. Thus, researchers are intensely searching for

complementary factors such as molecular signatures, or genetic features [3]. In addition to

tumor-intrinsic factors, ongoing work is revealing extrinsic immune factors that affect tumor

prognosis [4-7]. However, due to the complicated characteristics of tumor immune response, the

prognostic value of single immune factors are unreliable and have limitations when applied to

large-scale populations. Thus, a comprehensive immunoscore system is needed to make more

accurate prognoses for GC patients. A designated TNM-Immune (TNM-I) system has been defined

in colorectal cancer by assessment of intratumor T cells, which is a prognostic factor superior to the

AJCC/UICC TNM classification [8]. However, an immunoscore system with prognostic

significance for GC patients in clinical settings has not been identified so far.

The TNM-I system for colorectal cancer is based on density and location of CD3/CD8-positive

cells, rather than the function of T cells. However, the latter has an important impact on the

prognosis of GC. A hallmark of tumor progression is evasion of immune destruction , and

PD-L1/PD-1 is one of the most important signaling pathways mediating tumor immune escape [9,

10]. Pembrolizumab and nivolumab, monoclonal antibodies that block PD-L1/PD-1 interactions,

are now used therapeutically for advanced melanoma and non-small-cell lung cancer (NSCLC) [11,

12]. A completed phase Ib trial (Keynote-012) also showed an overall response rate (ORR) to

pembrolizumab treatment was 33.3% among patients with GC [13]. Because blockade of

PD-L1/PD-1 can be effective in GC therapy, expression of PD-L1 and PD-1 were thought to have

prognostic significance. However, the prognostic value of PD-L1/PD-1 remains controversial

[14-23]. One reason is that PD-L1 can be expressed by many cell types, such as tumor cells or

immune cells; whether PD-L1 expressed by different cells has different prognostic values is unclear

[14, 15, 18]. Additionally, PD-L1 is regulated by intrinsic and extrinsic mechanisms; whether this

has a different impact on the prognostic value of PD-L1 is not known [24]. GC is closely associated

with inflammation, large numbers of pre-existing CD8+ T cells are found at the tumor sites of GC

patients. However, the prognostic effect of increased CD8+ T cells infiltration in GC is still under

debate [6, 7, 14, 25]. These controversial viewpoints may be attributed to the complicated

characteristics of tumor immune response. Therefore, a more reliable model incorporating multiple

immune effectors is necessary and may provide better prognosis compared with single-factor

predictors.

In the current study, expression of PD-L1 in tumor cells and immune cells, expression of PD-1 in

immune cells, and infiltration of CD8+ T cells were evaluated in two independent patient cohorts

with stage II-III GC and analyzed for clinical characteristics. We developed an immunoscore system

based on the hazard ratios (HRs) derived from the multivariable analyses. This approach can further

divide patients with the same TNM staging into subgroups who are at low, moderate, or high risk,

thereby providing more accurate prognoses and clues for the selection of immunotherapy options

for GC patients.

Materials and Methods

Patients

This study was conducted on two independent cohorts of GC patients with stage II-III, who were

followed up at the First Hospital of China Medical University. Patients were enrolled into two

cohorts if they met the following criteria: Gastric cancer with stage 2-3; D2 lymphadenectomy; no

neoadjuvant therapy before surgery; received 5-flurouracil-based standardized combination

chemotherapy if relapsed. Newly collected specimen with more than 2 years of follow-up (from

February 2012 to September 2012) were assigned to the discovery cohort (n = 51) and patients with

5 years of follow-up data (between April 2006 and July 2011) were selected according to the

random number generated by the R/sample function to form validation cohort (n = 115). The

median follow-up period was 31 months (range 3 ~ 100 months), and 73 patients (43.98%) died

during this period, with a 5-year overall survival (OS) probability of 50.1%. The study was

approved by the Ethics Committee of China Medical University, and all procedures were conducted

in accordance with ethical principles. Clinical information of all patients was retrieved from the

Hospital Information System (characteristics are listed in Supplementary Table S1).

Immunohistochemistry (IHC)

Standard indirect immunoperoxidase protocols were used for the immunohistochemistry assay.

Briefly, embedded tumor tissues were sectioned to 4-μm thickness, dewaxed and rehydrated.

Heat-induced antigen retrieval in citrate buffer was conducted followed by 3% hydrogen peroxide.

After blocked with bovine serum albumin (BSA), sections were incubated with indicated primary

antibodies PD-L1 (#13684, clone E1L3N, CST), PD-1 (ZM-0381, clone MRQ-22, OriGene), CD8

(MAB-0021, clone c8/144B, Maixin Biotech, China) at 4℃ overnight. Sections were further

incubated with UltraSensitiveTM SP (Mouse/Rabbit) IHC Kit (9710, Maixin Biotech, China) and

visualized by staining with 3,30-diamino-benzidine tetrahydrochloride (Maixin Biotech, China) and

Hematoxylin. For double-color IHC, sections were stained with DouMaxVisionTM (BCIP/AEC)

IHC Kit (Kit-9998, Maixin Biotech, China). Briefly, specimens were incubated with primary mouse

antibodies PD-1 (ZM-0381, clone MRQ-22, OriGene) at 25℃ for 60 mins, and visualized by AP

(alkaline phosphatase)-BCIP/NBT substrate system. Subsequently, specimens were blocked with

double-stain enhancer and incubated with primary rabbit antibodies CD8 (RMA-0514, clone sp16,

Maixin Biotech, China) at 25℃ for 60 mins, and visualized by HRP (horseradish peroxidase)-AEC

(3-amino-9-ethylcarbazole) detection system.

Evaluation of immunohistochemical staining

All specimens were examined by two independent pathologists in a blinded manner based on

staining percentage and intensity of positive cells. Staining pattern of PD-L1 was defined as

negative or positive if ＜1% or ≥1% in TCs [13], respectively, or if ＜5% or ≥5% in ICs [26],

respectively, according to reference. Staining of CD8 cells was defined as less or more if infiltration

＜20% or ≥20% in tumor site; PD-1 was quantified by staining intensity in a 2-grading system as

followed: PD-1low if intensity = 0 or 1 (Fig. 1D and E), PD-1high if intensity = 2 (Fig.1 F); The

staining intensity of PD-1 was defined as 0 = negative, 1 = weak (with canary yellow staining), and

2 = intermediate-strong (with brown staining being similar to germinal center T cells in reactive

tonsil tissues) according to reference [27, 28]. For analysis of heterogeneity of these immune

antigens, a series of optimal experimental processes have been carried out to reduce the deviation.

First of all, HE stains of a number of wax blocks from the same patient`s sample were examined

carefully by pathologist. The most representative block, which covered multiple heterogeneous

regions, was selected to make a whole tissue section for IHC staining. Secondly, expression of

immune antigens was reviewed in total tumor area, and results were estimated as relative

percentage staining and intensity staining. To minimize the impact of this spatial heterogeneity,

tumor samples were collected from gastric cancer patients with stage 2-3 who underwent resection

without receiving radiochemotherapy. Thus, the interference from late stage of disease and

treatment of radiochemotherapy were excluded.

Tumor infiltrating cell isolation and flow cytometry

Fresh gastrectomy tissues were kept in precooled Hank`s solution immediately, minced, and

digested with medium containing collagenase IV (Gibco, 17104-019, 1 mg/ml) and DNase I (sigma,

D4527, 200 IU/ml) at room temperature for 1 h. After digestion, single-cell suspension was

obtained by filtered and centrifuged. For flow cytometric analysis, cells were stained with following

Abs: anti-human CD3 (BD, 555332), anti-human CD4 (BD, 555349), anti-human CD8 (BD,

555369 or 300908), anti-human PD-1 (biolegend, 329906 or 329910), anti-human PD-L1 (BD,

557924) in BD Accuri C6. Isotype control Ab staining was used as negative control.

In situ immunofluorescence staining of TILs

Frozen specimens were sectioned to 4-μm thickness, fixed for 20 min with 4% PFA

(Paraformaldehyde). After blocked with 5% BSA (bovine serum albumin), sections were incubated

with indicated primary antibodies: rabbit anti-human CD8 (ab4055, abcam), rat anti-human CD8

(ab60076, abcam), mouse anti-human PD-1 (ab52587, abcam), rabbit anti-human PD-L1 (#13684,

CST) at 4℃ overnight, followed by sections-paired secondary antibody: Alexa Fluor Plus 488 goat

anti-rabbit (A-11034, Thermo fisher), Alexa Fluor Plus 555 goat anti-mouse (A-32727, Thermo

fisher), Alexa Fluor Plus 647 goat anti-rat (A-21247, Thermo fisher) at 25℃ for 2 h. Isotype abs

were used as negative control. Slides were mounted with DAPI mounting medium.

Statistical analysis.

Characteristics of the patients were compared using a t test for continuous variables or a Spearman

test for categorical variables. OS was calculated from the time of surgery till death or the last

follow-up visit (2014-8-11). Survival analysis was performed using a Kaplan-Meier method, and

the differences were assessed by a two-tailed log-rank test. To evaluate the effect of the expression

of PD-1, PD-L1 and CD8 on the OS, univariate and multivariate analyses using a Cox proportional

hazard regression classification were carried out, and HRs were estimated with 95% confidence

interval (95% CI) limits. The prognostic accuracy of our predict model compared with TNM stage

model was conducted by receiver operating characteristic (ROC) analysis.

Development of the immunoscore system

Factors found to be associated with the OS by multivariate analysis were enrolled for the

development of the prognostic system: TC PD-L1, IC PD-L1, IC PD-1, CD8, Lauren Classification

and AJCC stage (as listed in Table 1). Hazard ratios (HRs) of each factor from multivariate analyses

were used to derive coefficient scores calculated as follows: HRs of each prognostic factor were

divided by the smallest one (1.72) and rounded to the nearest integer value [5]. Coefficient scores of

four immune effectors were then added up to assign each patient with an immunoscore index (from

0 to 5), on which the final system was based.

Results

Patient characteristics

The clinical characteristics of two independent cohorts of GC patients with stage II-III are

summarized in Supplemnetary Table S1. For the discovery cohort, 41 males and 10 females were

enrolled with the median age at the diagnosis being 58 years (range: 41–76 years). The validation 8

cohort has included 90 males and 25 females, with the median age at the diagnosis being 60 years

(range: 25–79 years). The majority of tumors were located in lower or middle part of the stomach,

only 10.24% were located in upper 1/3 part of the stomach. Together, 77.7% patients had lymphatic

invasion, and 80.7% patients belonged to stage III according to the criteria released by the 7th

AJCC. The characteristics of patients in current study were consistent with those of our previous

study [5] and those of Chinese GC patients [29].

PD-L1+ or PD-1hi tumor-infiltrating immune cells predicted poor progress of GC

We detected the expression of PD-L1 and PD-1 in ICs infiltrating the tumor site. As shown by IHC

staining, most PD-L1+ ICs or PD-1hi ICs were detected in IC aggregates infiltrating into the tumor

tissues (Fig. 1B,F), whereas the remainder were scattered within the tumor region (Fig. 1C,G).

Typical staining of PD-L1 in ICs appeared to be membranous with variable cytoplasmic staining

(Fig. 1B,C), whereas staining of PD-1 in ICs was membranous-like (Fig. 1F,G). PD-L1+ ICs were

observed in 33.33% (17 out of 51) and 17.4% (20 out of 115) of samples from the discovery and

validation cohorts, respectively. Immune cells categorized as PD-1hi were detected in 33.33% (17

out of 51) and 30.43% (35 out of 115) patients in the discovery and validation cohorts, respectively.

Frequencies of immune-factor expression in tumor-infiltrating cells (Fig. 2F–H) were confirmed by

flow cytometry (Fig. 2A–E) and in situ multicolor immunofluorescence staining by confocal

analysis (Fig. 2I–M).

Patients with PD-L1+ ICs had shorter overall survival (OS) compared with those with PD-L1– ICs

(P = 0.0798 in the discovery cohort, P = 0.0191 in the validation cohort) (Fig. 3A and D). In the

validation cohort, 5-year survival rate was 30% in PD-L1+ IC group vs. 51.18% in PD-L1– IC group,

and median survival was 22 months in PD-L1+ IC group vs. 80 months in the PD-L1– IC group after

5 years of observation. Patients with PD-1hi ICs had poorer OS compared with those with PD-1low

IC (P = 0.0012 in discovery cohort, P = 0.0246 in validation cohort), and the 5-year survival rate

was 35.96% in the PD-1hi IC group vs. 52.52% in the PD-1low IC group in validation cohort (Fig. 3B

and E). In the validation cohort, median survival was not reached in the PD-1low group, but was 33

months in the PD-1hi group after 5 years of observation. No correlation was found between PD-L1

or PD-1 expressed by ICs and the clinical characteristics of the patients.

Prognostic value of CD8+ T cell infiltration combined with PD-1 expression

CD8+ T cells play a pivotal role in antitumor immunity, however their prognostic value is

controversial [6, 7, 14, 25]. We detected infiltrations of CD8+ T cells in tumor tissues by IHC

staining. The density of CD8+ T cells in patients varied widely (Fig. 1H and I). Infiltration of >20%

of CD8+ T cells (referred to as CD8More) was observed in 41.57% patients (25 out of 51) in the

discovery cohort and 38.26% patients (44 out of 115) in the validation cohort. Patients with

CD8More tended to have better OS compared to those with CD8Less, but the differences were not

significant (P = 0.377 in the discovery cohort, P = 0.187 in the validation cohort) (Fig. 3C and F),

and 5-year survival rate was 58.45% in CD8More group vs. 40.71% in CD8Less group in the

validation cohort. In the validation cohort, median survival was not reached in the CD8More group,

whereas the CD8Less group had a median survival of 49 months after 5 years of observation. We

found that patients with CD8More/PD-1low had significantly better survival than those with

CD8Less/PD-1hi (Fig. 4A, B), which might explain the previously contradictory value of intratumor

CD8+ T cells in the prognosis of GC. In addition, the positive staining areas of CD8 and PD-1

overlapped, which was confirmed by flow cytometry analysis on isolated tumor-infiltrating cells

(Fig. 4C and D), IHC staining in tumor serial sections (Fig. 4E and F), double IHC staining (Fig.

4G and H), and in situ immunofluorescence staining of co-expression of CD8 and PD-1 (Fig. 4I–L).

Together, these clinical data demonstrated that PD-1 expression could distinguish subsets of

intratumor CD8+ T cells with possibly different function in GC, and the infiltration of CD8+ T cells

with PD-1hi was associated with worse prognoses. No correlation was observed between expression

of CD8 protein and other clinical characteristics of patients.

Tumor cell expression of PD-L1+ correlated with CD8+ T cell infiltration and better OS

Expression of PD-L1 in TCs of GC patients was on cell membranes, with variable cytoplasmic

staining (Fig. 5A). Tumor cells were 31.37% and 41.74% PD-L1+ in the two cohorts, using the 1%

standard threshold of PD-L1 in TCs, as was used to detect 40% positive PD-L1 expression in the

Keynote-012 clinical trial. We found the typical distribution of PD-L1+ TCs (Fig. 5A, panel 2) to be

restricted primarily to the lymphocyte infiltration region in GC (Fig. 5A Panel 3). Tumor cell

expression of PD-L1+ was correlated with CD8More, as confirmed by Spearman test (Supplementary

Table S2).

Patients with PD-L1+ TCs had significantly better OS compared with those with PD-L1– TC (P =

0.0375 in discovery cohort, P = 0.0029 in validation cohort) (Fig. 5B and C). In the validation

cohort, 5-year survival rate was 66.11% in the PD-L1+ TC group vs. 33.88% in the PD-L1– TC

group. Median survival had not been reached after 5 years in the PD-L1+ TC group, whereas the

PD-L1– TC group had a median survival of 37 months. Also, patients with PD-L1+TC and CD8More

had the best OS (Fig. 5D), which represented an adaptive immune status in GC. In addition, PD-L1+

TC was correlated to better Borrmann classification and AJCC stage as shown by Supplementary

Table S2.

Univariable and Multivariable analyses of independent prognostic factors

Clinical characteristics and immune effectors of total 166 patients were included and subjected to

univariable analysis. Lauren classification (diffuse type: HR = 2.099, P = 0.009; mix: HR = 2.838,

P=0.004), differentiation (moderate: HR= 1.199, P = 0.751; poorly: HR = 2.215, P = 0.125), AJCC

(stage III HR = 4.778, P = 0.001), TC PD-L1 (negative HR = 2.474, P = 0.001), IC PD-L1 (positive

HR = 1.944, P = 0.011), IC PD-1 (positive HR = 2.236, P = 0.001), and CD8 (CD8Less HR =1.513,

P = 0.097) were selected for the multivariable analyses. Two tumor characteristics (Lauren

classification, AJCC stage) and four immune variables (TC PD-L1, IC PD-L1, IC PD-1, and CD8)

had independent prognostic value for the OS of GC patients, according to the results of the

multivariable analyses (Table 1). However, differentiation was not found to be significant.

Same-stage patients were separated into different risk subgroups by immunoscore

Although four factors-expression of PD-L1 on TCs or ICs, expression of PD-1 on ICs, and numbers

of CD8+ ICs—had independent prognostic significance, their complicated interaction during

antitumor immune responses prevented any one of these factors from providing accurate predictions

of patients’ survival. Therefore, we develop a comprehensive immunoscore system for the

prediction of patients’ survival. The coefficient of each immune factor was first calculated

according to their HR values gained from the multivariable Cox hazard analysis (Materials and

Methods; Table 1). Then each patient was assigned an immunoscore index by summing the HR

values of four immune variables. Finally, all the patients were included in a Kaplan-Meier survival

analysis, according to their immunoscores index. Patients could be separated into different risk

groups (Fig. 6A). Among them, 87% with score 0 for the observation period were still alive.. In

contrast, 75% patients with a high score (3-5) died during the period. The immunoscore system

could split patients into different risk groups who were in the same stage II or stage III (Fig. 6B and

C), or even substage (Supplementary Fig. S1), which may improve the prognostic accuracy of TNM

staging. To verify the accuracy of our four-factors prognostic model compared to the TNM staging

prognostic model, receiver operating characteristic (ROC) analysis was performed. Our four-factor

model yielded an area under the ROC curve (AUC) of 0.856 for prediction of mortality at 5 years,

which was superior to TNM staging with an AUC of 0.676 (Fig. 6D).

Discussion

Gastric cancer is a highly heterogeneous cancer. Although patients can receive

gastrectomy, the clinical outcome may vary significantly among patients with same

stage. Thus, AJCC stage alone does not provide accurate outcome prediction for

operable GC patients. Accumulating evidence has suggested that tumor progression is

affected not only by its intrinsic characteristics, but also by extrinsic immune effectors

[30]. In addition to the infiltration of T cells, immune checkpoint molecules such as

PD-L1 and PD-1 play a pivotal role in cancer [31]. However, the impact of immune

checkpoint molecules on prognoses for GC patients remains to be elucidated [14-17],

due to not only the lack of uniform criteria, but also the complex characteristics of the

antitumor immune response. In the current study, we analyzed the expression of

PD-L1 and PD-1, the infiltration of CD8+ T cells, and the association between

expression levels of these immune effectors and patients’ OS, in order to build a

comprehensive immunoscore system based on the HRs of immune variables with

prognostic significance. Our prognostic model yielded an area under the ROC curve

(AUC) of 0.856 for prediction of mortality at 5 years, which was superior to TNM

stage with an AUC of 0.676.

An immunoscore system developed by Galon J et al. has demonstrated that immune

cell infiltration, compared to intrinsic tumor characteristics, has a better prognostic

value for colorectal cancer [8, 30]. However, only the amount and location of

CD3/CD8 lymphocyte infiltration, rather than the function of these immune cells,

were included in this system. Another five-feature–based immunoscore is available to

predict recurrence and survival of GC. Although CD45RO and CD66b were added to

the system, critical factors that could reflect the interaction between tumor cells and

immune cells were not included [32]. In addition, the tumor microenvironment (TME)

was divided into four stratifications according to the expression of PD-L1 in tumor

cells and presence of tumor-infiltrating lymphocytes (TILs), but the prognostic value

of this system was unclear for GC patients [33]. In contrast, our immunoscore system

contained not only the amount but also function of the intratumor immune cells that

could reflect interaction between tumor cells and immune cells, and it could fill in

several gaps of the TME classification. First, we noted that the expression of PD-L1

in TC was derived from the infiltration of CD8+ T cells, and patients with

PD-L1+CD8+, an adaptive subgroup, had the best prognosis in GC. In contrast,

PD-L1+ ICs were found to be associated with worse survival, rather than PD-L1+ TCs,

which was therefore more critical for predicting response to immune checkpoint

inhibitors. Second, PD-1 should be combined with CD8 when analyzing prognostic

significance. Lastly, although PD-L1, PD-1, and CD8 had independent prognostic

significance in GC, single factors were not sufficient for accurate prognoses of cancer

patients—the comprehensively immunoscore system was necessary.

Expression of PD-L1 in TCs was regulated by two major mechanisms [34], one is

extrinsic immune-induced PD-L1 expression[24], the other is intrinsic oncogenic

activation mechanism [35-38]. As the majority of GC are associated with

inflammation [3], TC PD-L1 expression in GC could be upregulated by extrinsic

inflammatory factors. Consistently, our IHC results showed that PD-L1 expression in

TC is mainly detected in areas surrounded by gatherings of immune cells in gastric

cancer tissues, with correlations between TC PD-L1 and more infiltration of CD8+ T

cell confirmed by the Spearman test. Patients with PD-L1+ tumor cells and CD8More T

cells have the best prognosis compared to other groups, indicating an adaptive

immune status existing in GC patients, consistent with reports in melanoma and

hepatocellular carcinoma[39, 40], although PD-L1 has been reported to correlate with

worse survival of GC patients [14]. A possible explanation for the contrary results is

the different patient populations enrolled. We enrolled 166 GC patients with stage

II-III who underwent resection without receiving preoperative neoadjuvant therapy,

thus PD-L1 staining were not influenced by the chemotherapy. In Thompson’s study,

however, only 34 patients with GC or gastro-oesophageal junction (GEJ) cancer were

enrolled, and 44% patients had received neoadjuvant chemotherapy and/or radiation

before the detection of PD-L1. Moreover, PD-L1 expression in TC was reported to

correlate with Epstein-Barr virus or microsatellite instability in previous studies[41],

both of which are associated with better survival in GC [42, 43]. Additionally, we also

detected PD-L2 level in 166 GC patients, however, its prognostic value was not

significant.

PD-L1 is more commonly expressed in TILs than that in TCs, and PD-L1 expressed

in TILs, but not in TCs, was relevant to response to the PD-L1 mAb (MPDL3280A)

treatment in a range of cancers [44]. In our study, PD-L1+ ICs, rather than PD-L1+

TCs, correlated with poorer OS. Immune cells positive for PD-L1 could be dendritic

cells (DCs) or macrophages in the tumor microenvironment, which exert

immunosuppressive function [45, 46]. Consistently, in the Keynote-012 clinical trial,

overall responses are higher in patients with PD-L1+ ICs, whereas the PD-L1+ TCs

were not useful in predicting the response, which indicated that PD-L1+ ICs were

more critical to the suppression of antitumor immunity. As for the prognostic impact

of PD-1, it was correlated with worse prognosis in Japanese patients with stage II/III

GC, but associated with better outcomes in Caucasians with stage I-IV GC [15, 47].

Here, we demonstrated for the first time that patients with high intensity of PD-1

correlated with poorer survival in Chinese GC patients, whereas, there was no

correlation between PD-1 and patient’s survival if patients were divided by positive

and negative expression of PD-1. Of note, our result showed that patients with

CD8MorePD-1low had significantly better OS compared with those with CD8lessPD-1hi,

which may explain the controversial prognostic prediction of CD8 in GC [6, 7, 14,

25]. CD8+ T cells with PD-1hi/CTLA4high are partially exhausted T cells, and strongly

correlate with response to PD1 mAb therapy [48]. This provided molecular evidence

that the intensity of PD-1 expression could impact the function of intratumor CD8+ T

cells, which supported our conclusion that CD8 and PD-1 should be combined to

evaluate their prognostic significance for GC patients.

Taken together, we built an immunoscore system based on hazard ratios of four

immune variables. This comprehensive immunoscore system can improve the

accuracy of TNM staging for predicting survival, and is an essential complement for

the AJCC staging system for operable GC patients with stage II-III. Future studies

should measure other critical immune markers for inclusion in the system, such as

Bim et al. [49]. Further development of immune signatures will facilitate choosing

appropriate immunotherapy options for GC patients.

Reference List

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Table 1. Univariate and Multivariate analysis for overall survival in two cohorts of GC patients cancerimmunolres.aacrjournals.org Univariate analysis Multivariate analysis HR 95% CI P value HR 95% CI P value Score

TC PD-L1 Published OnlineFirstJune15,2017;DOI:10.1158/2326-6066.CIR-16-0381 Positive 1 1 0 Negative 2.474 1.449-4.224 0.001 1.841 1.044-3.246 0.035 1 IC PD-L1 Negative 1 1 0 Positive 1.944 1.165-3.243 0.011 3.156 1.775-5.611 0.000091 2 IC PD-1

on September 28, 2021. © 2017American Association for Cancer Research. Negative 1 1 0 Positive 2.236 1.405-3.559 0.001 2.354 1.437-3.859 0.001 1 CD8 More 1 1 0 Less 1.513 0.928-2.465 0.097 1.72 1-2.96 0.05 1 Lauren Classification Intestinal type 1 1 0 Diffuse type 2.099 1.208-3.648 0.009 1.747 1.237-2.466 0.002 1 Mix 2.838 1.382-5.826 0.004 1.747 1.237-2.466 0.002 1 Differentiation Well 1 Moderate 1.199 0.39-3.682 0.751 Poor 2.215 0.802-6.118 0.125 AJCC stage 2 1 1 0 3 4.778 1.916-11.917 0.001 5.496 2.124-14.218 0.000442 3 Abbreviations:HR, hazard ratio; CI,confidence interval; TC, tumor cell; IC, immune cell.

23 Published OnlineFirst June 15, 2017; DOI: 10.1158/2326-6066.CIR-16-0381

Figure Legend

Fig. 1 Expression of PD-L1, PD-1, and CD8 by immune cells that infiltrated tumor tissues of GC patients Representative images of PD-L1, PD-1, and CD8 staining in GC (gastric cancer) samples are shown at ×200 (100 μm) original magnification. Negative expression of (A) PD-L1 and (D, E) PD-1. Positive expression of (B) PD-L1 in IC and (F) PD-1 in IC in areas of lymphocyte aggregates. (C) PD-L1+ IC and (G) PD-1hi IC scattered in tumor tissues. (H) CD8Less and (I) CD8More infiltrated in tumor tissues from different patients.

Fig. 2 Frequencies of immune marker expression in tumor-infiltrating cells by flow cytometry and in situ multi-color immunofluorescence. Flow cytometry dot plots (A-E), IHC staining from serial section (F-H), and in situ multi-color immunofluorescence staining by confocal analysis (I-M) were conducted at the same time to confirm the frequencies of these immune factors. Shown is representative data from a patient with CD8More, PD-1hi, and PD-L1+ IC expression.

Fig. 3 Overall survival of GC patients based on the expression of immune checkpoint proteins in immune cells and the infiltration of CD8+ T cells (A and D) Overall survival of patients grouped by expression of PD-L1 by tumor-infiltrating immune cells; (B and E) OS of patients divided by high or low expression of PD-1; (C and F) OS of patients grouped by expression of more or less CD8. (A, B, and C) Patients in the discovery cohort, and (D, E, and F) patients in the validation cohort. Probabilities of OS were estimated using the Kaplan-Meier method and compared using the log-rank statistic.

Fig. 4 CD8 infiltration should be combined with PD-1 expression in prognostic analysis The Kaplan-Meier curves for OS are shown for patients grouped according to the combination of CD8+ T cell infiltration (CD8More/CD8Less) and PD-1expression by immune cells (PD-1hi/ PD-1low). (A) Discovery cohort: CD8MorePD-1Low vs. CD8LessPD-1Low, P = 0.412; CD8MorePD-1Low vs. CD8MorePD-1High, P = 0.048; CD8MorePD-1Low vs. CD8LessPD-1High, P = 0.001; CD8Less PD-1Low vs. CD8MorePD-1High, P = 0.147; CD8LessPD-1Low vs. CD8LessPD-1High, P = 0.005; and CD8More PD-1High vs. CD8Less PD-1High, P = 0.216. (B) Validation cohort: CD8More PD-1Low vs. CD8Less PD-1Low, P = 0.137; CD8More PD-1Low vs. CD8More PD-1High, P = 0.103; CD8More PD-1Low vs. CD8Less PD-1High, P = 0.007; CD8Less PD-1Low vs. CD8More PD-1High, P = 0.566; CD8Less PD-1Low vs. CD8Less PD-1High, P = 0.025; and CD8More PD-1High vs. CD8Less PD-1High, P = 0.235. Probabilities of OS were estimated using the

Kaplan-Meier method and compared using the log-rank statistic. Tumor-infiltrating cells were isolated from GC tissues and analyzed. (C) Gates used for CD3 and CD8 double-positive cells. (D) Representative dot plot for CD8 and PD-1 double-positive population. (E) and (F) are representative images of overlapping CD8 and PD-1 staining in tumor serial sections from one GC sample. (G, H) and (I-L) are representative images of co-expression of CD8 and PD-1 by multicolor IHC (CD8 in red, PD-1 in purple) and in situ immunofluorescence (400 ×) staining.

Fig. 5 Expression of PD-L1 by tumor cells and its correlation with CD8 expression (A) H&E staining (panel 1), and representative images of positive PD-L1 expression (panel 2) in tumor cells surrounded by tumor-infiltrating CD8+ T cells (panel 3), at ×200 (100 μm) original magnification. (B) Overall survival of patients grouped by positive or negative expression by tumor cells of PD-L1 in the discovery cohort and (C) in the validation cohort. (D) Overall survival of patients grouped by the combination of tumor cell positivie or negative expression PD-L1 and “more” or “less” percentages of infiltrating CD8+ T cells, in all 166 GC patients: TC PD-L1+ CD8More vs. TC PD-L1+ CD8Less, P = 0.317; TC PD-L1+ CD8More vs. TC PD-L1- CD8More, P = 0.02; TC PD-L1+ CD8More vs. TC PD-L1- CD8Less, P = 0.002; TC PD-L1+ CD8Less vs. TC PD-L1- CD8More , P = 0.104; TC PD-L1+ CD8Less vs. TC PD-L1- CD8Less, P = 0.031; TC PD-L1- CD8More vs. TC PD-L1- CD8Less, P = 0.681. Probabilities of OS were estimated using the Kaplan-Meier method and compared using the log-rank statistics.

Fig. 6 Survival curves based on immunoscore system Expression of PD-L1/PD-1 and infiltration of CD8+ T cells have separated the patients with stage II and III (P < 0.0001) score 0 vs. score 1-2 (P = 0.07), score 0 vs. score 3-5 (P < 0.0001), score 1-2 vs. score 3-5 (P < 0.0001) (A), with stage II (P =0.014) score 0 vs. score 1-2 (P = 0.447), score 0 vs. score 3-5 (P = 0.098), score 1-2 vs. score 3-5 (P = 0.011) (B), with stage III (P <0.0001) score 0 vs. score 1-2 (P = 0.10), score 0 vs. score 3-5 (P = 0.001), score 1-2 vs. score 3-5 (P < 0.0001 ) (C) into different risk subgroups. (D) ROC analysis of 5-year survival rate based on established Model and TNM stage for gastric cancer patients (n = 166). AUC of Score = 0.856, AUC of Stage = 0.676, AUC, area under the curve.

A Four-Factor Immunoscore System that Predicts Clinical Outcome for Stage II/III Gastric Cancer

Ti Wen, Zhenning Wang, Yi Li, et al.

Cancer Immunol Res Published OnlineFirst June 15, 2017.

Updated version Access the most recent version of this article at: doi:10.1158/2326-6066.CIR-16-0381

Supplementary Access the most recent supplemental material at: Material http://cancerimmunolres.aacrjournals.org/content/suppl/2017/06/16/2326-6066.CIR-16-0381.DC1

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