Tumor infiltrating M2 macrophages could predict biochemical recurrence of localized prostate cancer after radical prostatectomy

Experimental Cell Research 384 (2019) 111588

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Tumor infiltrating M2 macrophages could predict biochemical recurrence of localized prostate cancer after radical prostatectomy

T

Qijie Zhanga,1, Jiadong Xiaa,1, Yi Wangb,1, Jiayi Zhanga, Chengjian Jia, Rong Conga, Yamin Wanga,∗∗, Ninghong Songa,∗ a b

Department of Urology, The First Affiliated Hospital of Nanjing Medical University, Nanjing, Jiangsu Province, China Department of Urology, Affiliated Hospital of Nantong University, Nantong, Jiangsu Province, China

A R T I C LE I N FO

A B S T R A C T

Keywords: M2 macrophages Localized prostate cancer Biochemical recurrence Prognosis CIBERSORT

Given the critical role of the tumor microenvironment in PCa progression, we aimed to assess the prognostic effect of tumor infiltrating M2 macrophages (TIMMs) on biochemical recurrence (BCR) in patients with localized prostate cancer (PCa) after radical prostatectomy. A total of 127 localized PCa patients from GSE116918, 268 patients from TCGA database and 77 patients from GSE70770 were enrolled in our study. TIMMs were evaluated by the CIBERSORT method. Patients with high TIMMs had a significantly poorer recurrence free survival (RFS) (P = 0.017, P = 0.0063 and P = 0.001) in the three sets. In the multivariate analysis, the presence of high TIMMs (HR = 3.026, P = 0.023; HR = 2.679, P = 0.017; HR = 2.648, P = 0.005) was identified as an independent prognostic factor for RFS in the three sets. Harrell's Concordance index (C-index) increased in all three sets after combining TIMMs with traditional risk factors (PSA, clinical stage(T) and Gleason score). Gene Set Enrichment Analysis showed that T cell receptor signaling pathway, B cell receptor signaling pathway and primary immunodeficiency were significantly enriched in the low TIMMs group. TIMMs could serve as an independent prognostic factor for BCR in localized PCa patients after RP. Incorporation of TIMMs into traditional risk classification might further stratify patients with different prognosis.

1. Introduction Prostate cancer (PCa) remains the second most frequently diagnosed cancer in men worldwide [1]. Evidence-based guideline recommends radical prostatectomy (RP) as the primary treatment for clinical localized prostate cancer. Although majority of the patients with localized diseases can benefit from this procedure, many of them still develop biochemical recurrence (BCR) and finally progress to castration-resistant PCa [2]. After RP, PSA typically drops to undetectable level, and BCR is defined as two consecutive PSA values higher than 0.2 ng/mL and rising [3]. PSA, Gleason score (GS) and clinical stage(T) were selected in risk groups for BCR after local therapy [4]. Currently, it is becoming increasingly clear that inflammation is involved in PCa pathogenesis and progression and that immune cells are the main driver of this effect. However, the traditional risk classification narrowly focuses on the tumor cells without incorporating the effects of the host immune response. Given the critical role of the tumor microenvironment in PCa progression, it may serve as a supplement to traditional risk

classification. As a major immune component in several cancers [5], tumor-associated macrophages (TAMs) usually have a tumor-suppressing M1 phenotype or tumor-promoting M2 phenotype. The presence of tumor infiltrating M2 macrophages (TIMMs) was associated with poor clinical outcome in many cancers, such as breast cancer, squamous cell carcinoma and renal cell carcinoma [6–8]. They can not only accelerate the progression of untreated tumors [9], but also influence the efficacy of anti-tumor therapy [10], including checkpoint blockade therapy [11]. It's worth noticing that TAMs have been shown to directly and indirectly regulate the programmed death 1 (PD-1)/programmed death ligand 1 (PD-L1) expression in the tumor microenvironment [12]. In PCa, patients with high numbers of TIMMs in tumor microenvironment had increased odds of dying [13]. However, evidence of TIMMs that predicts BCR after RP are yet to be discovered. In the present study, we evaluated TIMMs in localized PCa and explored its correlation with clinical parameters. The results may shed light on the importance of TIMMs in localized PCa patients and provide

∗

Corresponding author. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China. Corresponding author. Department of Urology, The First Affiliated Hospital of Nanjing Medical University, No. 300 Guangzhou Road, Nanjing, 210029, China. E-mail addresses: [email protected] (Y. Wang), [email protected] (N. Song). 1 Qijie Zhang, Jiadong Xia and Yi Wang contributed equally to this work. ∗∗

https://doi.org/10.1016/j.yexcr.2019.111588 Received 22 April 2019; Received in revised form 24 August 2019; Accepted 28 August 2019 Available online 29 August 2019 0014-4827/ © 2019 Elsevier Inc. All rights reserved.

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Fig. 1. Flow diagram of the GEO datasets selection process.

a more precise and effective prediction system for BCR after RP.

including age, PSA, GS and clinical stage(T) (all P > 0.05).

2. Results

2.2. Association of TIMMs with RFS

2.1. Identification and characteristics of eligible dataset

Kaplan-Meier survival curves were generated to analyze the association of TIMMs with RFS. The cut-off of TIMMs in the discovery set (GSE116918) and the validation set (TCGA and GSE70770) was 0.0646, 0.1311 and 0.1240(relative leukocyte fraction), separately. Patients with high TIMMs had a significantly poorer RFS than those with low TIMMs in the discovery set (GSE116918), the validation set (TCGA) and the validation set (GSE70770)(P = 0.017,P = 0.0063, P = 0.001, separately)(Fig. 2). In Cox regression analysis, after correction for age, PSA, GS and clinical stage(T), high TIMMs was an independent

The process of dataset selection was displayed in Fig. 1. Finally, two datasets from GEO dataset, GSE116918 and GSE70770, and TCGA database were eligible and enrolled in our study. The discovery set (GSE116918) and the validation set (TCGA and GSE70770) included 127, 268 and 77 localized PCa patients underwent RP, separately. The baseline characteristic of each set was shown in Table 1. There was no strong relationships between TIMMs and clinicopathological features,

Table 1 Correlations between TIMMs and clinical characteristics in the discovery and validation sets of localized PCa patients.

Age (years) ≦65 > 65 PSA < 10 10-20 > 20 GS ≦6 7 ≧8 Clinical stage (T) < T2b ≧T2b

Discovery set (GSE116918)

Validation set (TCGA)

Validation set (GSE70770)

TIMMs

TIMMs

TIMMs

low

high

P

low

high

P

low

high

P

NA 67 (52.8%) 44 (34.6%) NA 33 (26.0%) 40 (31.5%) 38 (29.9%) NA 27 (21.3%) 52 (40.9%) 32 (25.2%) NA 45 (35.4%) 66 (52.0%)

NA 8 (6.3%) 8 (6.3%) NA 3 (2.4%) 9 (7.1%) 4 (3.1%) NA 6 (4.7%) 8 (6.3%) 2 (1.6%) NA 6 (4.7%) 10 (7.9%)

0.431 NA NA 0.178 NA NA NA 0.308 NA NA NA 0.817 NA NA

NA 165 (61.6%) 49 (18.3%) NA NA NA NA NA 31 (11.6%) 121 (45.2%) 62 (23.1%) NA 139 (51.9%) 75 (28.0%)

NA 43 (16.0%) 11 (4.1%) NA NA NA NA NA 2 (0.7%) 30 (11.2%) 22 (8.2%) NA 41 (15.3%) 13 (4.8%)

0.691 NA NA NA NA NA NA 0.051 NA NA NA 0.125 NA NA

NA NA NA NA 40 (51.9%) 16 (20.8%) NA NA 17 (22.1%) 32 (41.5%) 7 (9.1%) NA 52 (67.5%) 4 (5.2%)

NA NA NA NA 12 (15.6%) 9 (11.7%) NA NA 2 (2.6%) 17 (22.1%) 2 (2.6%) NA 20 (26.0%) 1 (1.3%)

NA NA NA 0.233 NA NA NA 0.125 NA NA NA 0.706 NA NA

Abbreviation: TIMMs, tumor infiltrating M2 macrophages; GS, Gleason score; PSA, prostate specific antigen; NA, not available. 2

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Fig. 2. Kaplan-Meier analysis of RFS in localized PCa patients from the discovery set (A), validation set (TCGA) (B), and validation set (GSE70770) (C).

Table 2 Univariate Cox regression analyses for recurrence free survival in the discovery and validation sets of localized PCa patients. Variable

Recurrence-free survival Discovery set (GSE116918)

Age PSA

GS

Clinical stage (T) TIMMs

≦65 > 65 < 10 10–20 > 20 ≦6 7 ≧8 < T2b ≧T2b low high

Validation set (TCGA)

Validation set (GSE70770)

HR(95%CI)

P value

HR(95%CI)

P value

HR(95%CI)

P value

1.000 1.480 1.000 1.627 1.978 1.000 1.584 1.885 1.000 1.518 1.000 3.026

NA 0.384 NA 0.428 0.266 NA 0.438 0.327 NA 0.393 NA 0.023

1.000 0.230 NA NA NA 1.000 1.855 9.661 1.000 2.382 1.000 2.618

NA 0.044 NA NA NA NA 0.558 0.027 NA 0.015 NA 0.009

NA NA 1.000 (reference) 1.562 (0.799–3.056) NA 1.000 (reference) 13.947 (1.895–102.654) 26.317 (3.196–216.714) 1.000 (reference) 1.710 (0.521–5.616) 1.000 (reference) 2.933 (1.497–5.746)

NA NA NA 0.193 NA NA 0.010 0.002 NA 0.376 NA 0.002

(reference) (0.612–3.582) (reference) (0.489–5.416) (0.594–6.584) (reference) (0.496–5.060) (0.530–6.707) (reference) (0.582–3.962) (reference) (1.162–7.883)

(reference) (0.055–0.961)

(reference) (0.235–14.665) (1.297–71.969) (reference) (1.184–4.790) (reference) (1.278–5.364)

Abbreviation: TIMMs, tumor infiltrating M2 macrophages; GS, Gleason score; PSA, prostate specific antigen; NA, not available. Bold in the Table 2 means P value <0.05. Table 3 Multivariate Cox regression analyses for recurrence free survival in the discovery and validation sets of localized PCa patients. Variable

Recurrence-free survival Discovery set (GSE116918)

TIMMs PSA

GS

Clinical stage (T) Age

low high < 10 10–20 > 20 ≦6 7 ≧8 < T2b ≧T2b ≦65 > 65

Validation set (TCGA)

Validation set (GSE70770)

HR(95%CI)

P value

HR(95%CI)

P value

HR(95%CI)

P value

1.000 (reference) 3.026 (1.162–7.883) NA NA NA NA NA NA NA NA NA NA

NA 0.023 NA NA NA NA NA NA NA NA NA NA

1.000 2.679 NA NA NA 1.000 1.691 8.315 1.000 1.879 1.000 0.184

NA 0.017 NA NA NA NA 0.620 0.042 NA 0.085 NA 0.021

1.000 (reference) 2.648 (1.333–5.260) NA NA NA 1.000 (reference) 11.903 (1.609–88.044) 26.766 (3.234–221.537) NA NA NA NA

NA 0.005 NA NA NA NA 0.015 0.002 NA NA NA NA

(reference) (1.794–3.547)

(reference) (0.213–13.447) (1.080–64.037) (reference) (0.916–3.855) (reference) (0.043–0.776)

Abbreviation: TIMMs, tumor infiltrating M2 macrophages; GS, Gleason score; PSA, prostate specific antigen; NA, not available. Bold in the Table 3 means P value <0.05.

2.3. Complementary to traditional prognostic model with TIMMs

prognostic factor that was associated with RFS (Discovery set: HR = 3.026, 95%CI 1.162–7.883, P = 0.023; validation set-TCGA: HR = 2.679, 95%CI 1.794–3.547, P = 0.017; validation set-GSE70770: HR = 2.648, 95%CI 1.333–5.260, P = 0.005)(Tables 2 and 3).

To provide a more precise and effective prediction model for BCR after RP, we combined TIMMs with traditional risk factors (PSA, clinical 3

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to guideline recommendation, PCa patients were classified into low, intermediate and high risk group based on the level of PSA, GS and clinical stage(T) [4]. Patients with a high risk of BCR should be treated with adjuvant or other systemic therapies, while low-risk patients should be enrolled into active surveillance. Unfortunately, the performance of this risk stratification was barely satisfactory. Quite a portion of low and intermediate risk patients developed BCR [2]. So it's necessary and urgent to update this risk stratification and provide a more precise predictive tool for BCR. Till now, considerable efforts and attempts have been made to better predict BCR. As for miRNAs, the predictive potential has received much attention after the increasing understanding of the molecular mechanism in PCa. Many miRNAs have been found to have predictive value for BCR. For example, Wei et al. [17] discovered that miR-1 can serve as an independent BCR predictor along with clinicopathological variables. Similarly, Xu et al. [18] reported that downregulation of miR129 was associated with poor BCR-free survival. The role of long noncoding RNA (lncRNA) can not be ignored. A seven long lncRNAs signature was identified for prediction of BCR in PCa [19]. Moreover, some mRNAs and methylation levels were feasible for individualized BCR risk assessment of PCa following RP, such as CRTC2 [20], SAMD5 [21] and PITX2 [22]. The PROSTATE scoring system, which was first proposed for the prediction of positive surgical margin after RP in 2016, can also serve as a potential predicative tool for BCR after RP in clinical practice [23]. It is worth noting that pre-treatment 18F-choline PET/CT is prognostic for BCR following radical local therapy of high-risk prostate cancer [24]. Different from previous researches, we aimed to explore the prognostic value of tumor-infiltrating inflammatory cells for BCR. The role of the microenvironment in carcinogenesis has been unraveled recently with several breakthroughs among which lies the role of the host inflammatory response. So, it may most likely play an important role in predicting BCR. Unfortunately, as we know, there was few studies focusing on the relationship between them. In 2018, Cao et al. [25] ever reported that neutrophil-to-lymphocyte ratio (NLR) was an independent predictor of BCR in patients receiving RP, which to some extent preliminarily revealed the potential of tumor microenvironment in predicting BCR. The NLR, which combines peripheral blood neutrophil and lymphocyte counts, has been proposed as an indicator of the host inflammatory status and general immune response to various stress stimuli. In our study, we found that high TIMMs were significantly associated with worse RFS in both discovery set and validation sets, which convinced the result as soon as possible. In cox regression analysis, TIMMs were proved to be an independent predictor for BCR as well. This finding indicated that TIMMs may serve as a supplement to original risk stratification. After combining TIMMs with

Table 4 Comparison of the prognostic accuracies of traditional risk factors (PSA, clinical stage(T) and GS) and TIMMs. Model

C-index

Discovery set (GSE116918) TIMMs PSA + GS + Clinical stage (T) PSA + GS + Clinical stage (T)+TIMMs Validation set (TCGA) TIMMs GS + Clinical stage (T) GS + Clinical stage (T)+TIMMs Validation set (GSE70770) TIMMs PSA + GS + Clinical stage (T) PSA + GS + Clinical stage (T)+TIMMs

0.568 0.597 0.670 0.581 0.748 0.761 0.623 0.718 0.755

Abbreviation: C-index, Harrell's Concordance index; TIMMs, tumor infiltrating M2 macrophages; GS, Gleason score; PSA, prostate specific antigen.

stage(T) and GS). The C-index was 0,579 when assessed with traditional prognostic model and increased to 0.670 when TIMMs were added in the discovery set (Table 4). The result was verified in the validation sets. 2.4. Identification of KEGG pathways We compared the expression profile of localized PCa patients with high TIMMs and low TIMMs group. Stratified GSEA revealed that some immune-related pathways, such as T cell receptor signaling pathway, B cell receptor signaling pathway and primary immunodeficiency, were significantly enriched in the low TIMMs group from the validation set (TCGA)(Fig. 3). However, similar pathways related to immune system were not noticed in the discovery set and another validation set (GSE70770). 3. Discussion A significant proportion of patients who receive RP for primary treatment of prostate cancer will unfortunately experience BCR with average 3.5 years disease-free survival [14]. The 5-year and 10-year BCR-free survival rate of prostate cancer patients is estimated as 80% and 68%, separately [15]. Progression to BCR is a vital turning point in the development of PCa and early sign of BCR is considered as a sign of clinical recurrence, metastasis and cancer-specific mortality. Also, Chow et al. [16] reported that late BCR after RP was associated with slower rate of progression. Therefore, accurate prediction of BCR would be beneficial to prostate cancer patients after RP. Currently, according

Fig. 3. Immune-related KEGG pathways enriched in the low-TIMMs group. 4

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analysis was determined by the “surv_cutpoint” function of the “survminer” R package [28].

clinicopathological features (PSA, clinical stage(T) and GS), C-index got a significant improvement in both discovery and validation set, which meant TIMMs can improve the prognostic accuracy for current prognostic model. Considering the tumor-suppressing effects of M1 phenotype in some other cancers, relevant analyses were performed in our search as well, which is not shown in the article. We found that there was no obvious correlation between the expression of M1 and M2 phenotype in PCa. Moreover, M1 phenotype can not predict BCR, which is an interesting phenomenon and needs further research. TIMMs havs been reported to promote tumor growth by suppressing the anti-tumor immune response [26]. Through Gene Set Enrichment Analysis, we found that the presence of low TIMMs was associated with some immune-related pathways, including T cell receptor signaling pathway and B cell receptor signaling pathway. Both two pathways were important components of the immune system. These findings suggested that high TIMMs may correlate with immunosuppression in the environment of PCa, which was consistent with the results from previous studies. We guess TIMMs may play some roles in the development of BCR in PCa by regulating immune response. However, as mentioned before, these immune-related pathways were only enriched in the low TIMMs group from the validation set (TCGA). Such situation didn't exist in the other two sets, which may be related to some factors such as sample size. Therefore, the role of TIMMs in PCa and its mechanism in the development of BCR remained to be further studied. It is worth noting that our study suffers from several limitations. Firstly, three online datasets were enrolled in this study. Some of them were with incomplete clinical data, such as age and PSA. Secondly, in lack of experiment verification, TIMMs were calculated by CIBERSORT software in both the discovery set and the validation sets. Thirdly, the retrospective design and relatively small sample size limited our study as well. So, a multicenter, prospective and well-designed study is needed to validate these results in a larger population in the future. Despite aforementioned limitations, the prognostic value of TIMMs for BCR in localized PCa can't be denied.

4.4. Gene set enrichment analysis (GSEA) GSEA software [29], which was downloaded from Broad Institute (http://www.broadinstitute.org/gsea/index.jsp), was used to identify the pathways that were significantly enriched between low TIMMs and high TIMMs tumor samples.Gene set permutations were used 1000 times for each analysis. The normalized enrichment score (NES) value was calculated for each gene set. The nominal P < 0.05 and NES > 1.5 were used to find the pathway with significant enrichment. 4.5. Statistics The t-test or Wilcoxon rank-sum test was used for continous variables, and the chi-square test or Fisher exact for categorical variables. Spearman's correlation test was applied for correlational analyses.Survival curves were generated and analyzed by Kaplan-Meier statistics using the log-rank test. The Cox proportional hazards regression model was applied to perform univariate and multivariate analyses, and those parameters that demonstrated a statistically significant effect on recurrence free survival (RFS) in the univariate analysis were included in the multivariate analysis. Harrell's index of concordance (Cindex) was calculated to compare the accuracy of the prognostic model models [30]. All statistics were performed in IBM SPSS Statistics 22.0 and R software 3.5.3. Two-tailed P value < 0.05 was considered as statistically significant for all statistical analyses. 5. Conclusion TIMMs could serve as an independent prognostic factor for BCR in localized PCa. Incorporation of TIMMs into traditional risk classification might further stratify patients with different prognosis. These findings could be utilized to improve clinical decision-making regarding treatment and follow-up regime. But, well-designed prospective studies are needed for in-depth research on TIMMs and BCR prognosis.

4. Materials and methods 4.1. Searching strategy for GEO datasets and TCGA database

Author contributions All relevant datasets were identified by comprehensively searching NCBI Gene Expression Omnibus (GEO) datasets before March 10, 2019. The searching strategy was consisted of the following keywords: “Homo sapiens” and “Series” and “Expression profiling by array” and “prostate cancer” and (“recurrence” or “recurrent”). Besides, expression data along with all available clinical information were retrieved for PCa patients from the cancer genome atlas (TCGA).

Data curation, Jiayi Zhang; Formal analysis, Yi Wang and Yamin Wang; Funding acquisition, Ninghong Song and Jiadong Xia; Methodology, Chengjian Ji; Project administration, Ninghong Song; Software, Qijie Zhang; Supervision, Ninghong Song; Writing – original draft, Qijie Zhang and Jiadong Xia; Writing – review & editing, Yi Wang and Rong Cong.

4.2. Inclusion and exclusion criteria

Funding

The inclusion criteria were as follows: (1) biospecimens were collected from patients with localized PCa undergoing RP; (2) containing at least 50 samples in each dataset; (3) including both clinical parameters (PSA, clinical stage(T) or GS) and outcomes (BCR). The exclusion criteria were as follows: (1) duplicates of the previous eligible datasets; (2) patients receiving chemotherapy or radiotherapy before or after the surgery.

This research was funded by Medical key talent of Jiangsu Province: ZDRCA2016009 and the National Natural Science Foundation of China (81871151,81501245,81971377).

4.3. Data extraction

Abbreviations

Detailed information was extracted from each eligible GEO dataset and TCGA database: age, PSA, clinical stage(T), GS, time to biochemical recurrence, survival status and genomic data. The CIBERSORT method [27], which combines support vector regression with prior knowledge of expression profiles from purified leukocyte subsets, was applied to above eligible datasets separately to analyze the associations between clinical outcomes and TIMMs. The cut-off of TIMMs for survival

TIMMs BCR PCa RFS RP GS

Conflicts of interest The authors declare no conflict of interest.

5

tumor infiltrating M2 macrophages biochemical recurrence prostate cancer recurrence free survival radical prostatectomy Gleason score

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