Clinicopathological significance of peritumoral alveolar macrophages in patients with resected early-stage lung squamous cell carcinoma

doi:10.21203/rs.3.rs-2213546/v1

Download PDF

Research Article

Clinicopathological significance of peritumoral alveolar macrophages in patients with resected early-stage lung squamous cell carcinoma

https://doi.org/10.21203/rs.3.rs-2213546/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 02 Mar, 2023

Read the published version in Cancer Immunology, Immunotherapy →

Version 1

posted

You are reading this latest preprint version

Introduction: This study aimed to clarify the correlation between the number of AMs and prognosis and to examine the gene expression of AMs in lung squamous cell carcinoma (SqCC).

Methods: We reviewed 124 stage I lung SqCC cases in our hospital and 139 stage I lung SqCC cases in The Cancer Genome Atlas (TCGA) cohort in this study. We counted the number of AMs in the peritumoral lung field (P-AMs), and in the lung field distant from the tumor (D-AMs). Moreover, we performed a novel ex vivo bronchoalveolar lavage fluid (BALF) analysis to select AMs from surgically resected lung SqCC cases and examined the expression of IL10, CCL2, IL6, TGFβ, and TNFα (n=3).

Results: Patients with high P-AMs had significantly shorter overall survival (OS) (p<0.01); however, patients with high D-AMs did not have significantly shorter OS. Moreover, in TCGA cohort, patients with high P-AMs had a significantly shorter OS (p<0.01). In multivariate analysis, a higher number of P-AMs was an independent poor prognostic factor (p=0.02). Ex vivoBALF analysis revealed that AMs collected from the tumor vicinity showed higher expression of IL10 and CCL2 than AMs from distant lung fields in all 3 cases (IL-10: 2.2, 3.0, and 10.0-fold; CCL-2: 3.0, 3.1, and 3.2-fold). Moreover, addition of recombinant CCL2 significantly increased the proliferation of RERF-LC-AI, a lung SqCC cell line.

Conclusion: The current results indicated the prognostic impact of the number of peritumoral AMs and suggested the importance of peritumoral tumor microenvironment in lung SqCC progression.

Peritumoral immune microenvironment

Alveolar macrophages

Lung squamous cell carcinoma

Ex vivo BALF

CCL2

Lung cancer accounts for the highest number of cancer-related deaths in the world [1]. The standard therapy for stage I lung cancer is surgery [2]. However, some lung cancer patients show recurrence after surgery, and the 5-year survival rate after surgery in stage I non-small cell lung cancer (NSCLC) is 57.1–73.5% [3].

The tumor microenvironment (TME) is one of the key determinants of cancer development, progression, and metastasis in many types of cancer, including lung cancer [4, 5]. The TME that focuses on immune cells is called tumor immune microenvironment (TIME) [6]. Within the TIME, tumor-associated macrophages (TAMs) have pro-tumoral functions, such as angiogenesis, immune suppression, cancer cell proliferation, invasion, and metastasis, via several paracrine mechanisms [7, 8, 9]. For example, chemokine (C-C motif) ligand (CCL) 2- chemokine (C-C motif) receptor (CCR) 2 promotes the recruitment of monocytes to primary and metastatic tumors [9]. Interleukin (IL)-10 polarizes macrophages to M2 phenotype and mediates the expression of programmed cell death ligand-1 in monocytes to inhibit the cytotoxic T cell response and promote immune suppression to mediate regulatory T cells [10]. Hence, TAMs are an important subject of study, and some drugs targeting TAMs are currently undergoing clinical trials [9].

The major role of alveolar macrophages (AMs) in the alveoli is to maintain lung homeostasis [11, 12]. In a mouse model after NSCLC seeding, AMs in peritumoral areas showed higher expression of several genes, including chemokines, than AMs in the contralateral lung field in early tumors. Moreover, the co-culture of AMs in peritumoral areas and mouse NSCLC spheroids significantly increased cancer cell invasion, suggesting that AMs contribute to tumor progression [13]. Recently, we reported that a higher number of AMs around adenocarcinoma was associated with poor prognosis [14]. In lung metastasis of hepatocellular carcinoma (HCC), AMs are recruited from circulating monocytes by CCL2-CCR2, unlike normal lungs, and AMs themselves can contribute to the formation of TIME to support lung metastasis and progression [15].

This study aimed to determine whether the number of AMs is a prognostic factor for lung SqCC and to elucidate its molecular mechanism. AMs are present in both peritumoral and distant areas from the tumor. We first counted the number of peritumoral (P-) and distant (D-) AMs and determined whether they were prognostic factors for pathological stage I lung SqCC. Then, we collected AMs from the tumor vicinity bronchoalveolar lavage (BAL) fluid (BALF) (AMs-VBALF) and AMs from distant lung field BALF (AMs-DBALF) using resected lungs, and compared the gene expression between them.

Patients

At our hospital, 241 consecutive patients with pathological stage I lung SqCC underwent surgical resection between January 2011 and December 2017. Furthermore, we excluded 117 patients based on the following criteria: (a) central type SqCC (displaced segmental bronchus due to tumor observed on preoperative imaging), (b) underwent sublobar resection, (c) incomplete resection, (d) neoadjuvant therapy, (e) no comprehensive consent, and (f) perioperative death. Finally, 124 patients were enrolled in the study (Supplemental Fig. 1A). This study was approved by the Institutional Review Board (IRB approval number; 2020 − 446).

We validated 495 lung SqCC patients (The Cancer Genome Atlas [TCGA], Firehose Legacy). Furthermore, we included 139 patients based on the following criteria: (a) no stage I lung SqCC, (b) no available virtual slide, and (c) no peritumoral alveolar space on the virtual slide (Supplemental Fig. 1B).

Definition and evaluation of P-AMs and D-AMs

Peritumoral alveolar areas were defined within 2 mm of the tumor edges, and we randomly chose 9 square spots by loupe magnification. In the TCGA cohort, we defined peritumoral alveolar areas as adjacent to the tumor and randomly chose 3 square spots by loupe magnification because the virtual slide in TCGA had less peritumoral alveolar area than our hospital slide. We defined the most distant area from the tumor as the distant alveolar area and randomly chose 9 square spots by loupe magnification. The number of P-AMs and D-AMs was defined as the total number of AMs in square spots in peritumoral and distant alveolar areas, respectively divided by the total area of square spots (/mm²) (Fig. 1A-F). We defined AMs counts higher than the median as high P-AMs and high D-AMs in the respective cohort.

Evaluation of clinicopathological factors

The clinical characteristics of patients were reviewed from the available medical records. The following clinicopathological factors were investigated retrospectively to assess their impact on patient survival: age, sex, smoking history, interstitial pneumonitis (IP), pathologic stage, vessel invasion, lymphatic permeation, and pleural invasion (pl).

Histological diagnosis was based on the 4th edition of the World Health Organization histological classification [16], and the disease stages were categorized according to the guidelines of the 8th edition of the TNM classification [17].

Ex vivo BALF analysis

We performed ex vivo BALF analysis in three lung SqCC patients who underwent lobectomy between December 2020 and January 2021 (Supplemental Table 1). First, we injected saline into the sub-segmental bronchus and collected the saline using a 50 mL syringe; we repeated these procedures until returned volume reached 200 mL for each distant bronchus from the tumor (distant BALF) and the responsible bronchus of the tumor (tumor vicinity BALF) (Supplemental Fig. 2A-B). Next, we excluded debris through 40 µm cells strainers® (Corning Japan, Japan) and separated the pellet from the ex vivo BALF by centrifugation at 300 × g for 15 min. Then, we added phosphate-buffered saline (PBS) to the pellet to make a total of 16 mL fluid, injected the fluid into a BD vacutainer® CPT (Becton, Dickinson and Company, United States), and separated white blood cell components by centrifugation at 1800 × g for 20 min. Subsequently, we collected the buffy coat in BD vacutainer® CPT, added PBS to make a total of 40 mL fluid, and separated the pellet by centrifugation at 300 g × g for 15 min. Finally, we added 2 mL BD Pharm Lyse® (Becton, Dickinson and Company, United State) to the pellet for 15 min at 15–25 ℃, added PBS to make a total of 40 mL fluid, and separated the pellet by centrifugation at 200 × g for 5 min (Supplemental Fig. 2C).

Fluorescence activated cell sorting (FACS) analysis and cell sorting

First, we added 1mL PBS to the pellet of ex vivo BAL and divided the fluid into 100 µL (for FACS) and 900 µL (for cell sorting). Next, we established the cut-off value using the control (anti-IgG-APC) and anti-CD163-APC (Supplemental Fig. 3A-B). Then, we added 4 µL Biotin anti-CD-163 antibody® and 20 µL Anti-Biotin MicroBeads® to 900 µL cell suspension, and we selected CD163 positive AMs by magnetic cell separation® (Miltenyi Biotec, Bergisch Galdbach, Germany) (Supplemental Fig. 2C). We confirmed that the cells after magnetic cell separation included 88% CD163 positive AMs by flow cytometry (Supplemental Fig. 3C-D). Finally, 350 µL Lysis Buffer LBP was added, and the samples were frozen at -80 ℃.

Real-time reverse transcription–polymerase chain reaction

We extracted RNA from the samples using NucleoSpin RNA Plus® (Takara Bio Inc., Shiga, Japan). Using PrimeScript RT reagent Kit®, 0.1 µg of total RNA was reverse transcribed (Takara Bio Inc., Shiga, Japan), and the cDNA generated was amplified using Thermal Cycler Dice® (Takara Bio Inc., Shiga, Japan). The target gene expression was normalized to the GAPDH expression in the same sample. We used primers for IL10, CCL2, IL6, TGFβ, and TNFα (Supplemental Table 2).

TCGA analysis

We analyzed lung SqCC gene expression signatures using the TCGA dataset. All statistical analyses were performed using R (version 4.0.5) (https://www.r-project.org/). The R/Bioconductor package TCGAbiolinks (version 2.16.1) [18] was used to download the TCGA data from the NCI’s Genomic Data Commons, and Summarized Experiment (version 1.18.2) [19] was used to store the TCGA data. We collected TCGA RNA-seq V2 data of cases based on the American Joint Committee on Cancer stage I lung SqCC. We included 198 cases, and 59 cases were excluded because we could not evaluate P-AMs. A total of 139 cases were analyzed in this study (Supplemental Fig. 1B). We evaluated P-AMs using the digital histologic slides of each case published in TCGA. Differentially expressed genes (DEG) between cases with and without necrosis were identified using the DESeq2 package (version 1.28.1) [20]. DEGs were extracted by adjusted p-values (padj) < 0.05 and absolute values of log2 fold changes (|log2 FC|) > 0.58. For the detected 267 DEGs, a heatmap was drawn using gplots (version 3.0.4) [21] and gene filter (version 1.70.0) [22] packages. DEGs were assessed for gene ontology and pathway enrichment analysis using Metascape (http://metascape.org/gp/index.html#/main/step1) [23].

We also evaluated differences in gene expression levels of the receptor and ligand pathways of IL10 and CCL2 between groups with high P-AMs and low P-AMs using the Mann–Whitney U test and showed log2 FC of high/low P-AMs in a graph.

Proliferation assay

RERF-LC-AI cells (RIKEN BRC CELL BANK, Japan) were seeded in a 96-well plate (Life Technologies, Carlsbad, CA) at a concentration of 1,000 cells/well for 24 h, and the medium was removed and replaced with MEM alpha (Life Technologies, Carlsbad, CA) containing recombinant CCL2 (ProteinSimple, Inc., Japan) or IL10 (Proteintech Japan, Inc., Japan) (10 ng/mL). Cell proliferation was monitored by analyzing the occupied area (% confluence) of cell images after 72 h using Incu Cyte ZOOM® (Essen BioScience, Inc., Michigan, USA).

Statistical analysis

Overall survival (OS) and recurrence-free survival (RFS) curves were plotted according to the Kaplan–Meier method and compared using the log-rank test in a univariate analysis. Univariate and multivariate analyses were conducted to identify the predictors of RFS using the Cox proportional hazards model. Two category comparisons were performed using one of the following tests: chi-square test, Fisher’s exact test, Mann–Whitney U test, or paired t-test. Statistical significance was set at p < 0.05.

P-AMs and D-AMs

P-AM and D-AM counts for each patient are shown in Fig. 2A. The median values of P-AMs and D-AMs were 26.7/mm² and 11.1/mm², respectively. The number of P-AMs was significantly higher than that of D-AMs (P-AMs/mm² vs. D-AMs/mm², p < 0.01, Fig. 2B). There was no correlation between the number of P-AMs and D-AMs (r = 0.31) (Fig. 2C).

Characteristics of patients with high P-AMs

We defined high P-AMs (> 26.7/mm²), low P-AMs (≤ 26.7/mm²), high D-AMs (> 11.1/mm²), and low D-AMs (≤ 11.1/mm²). Compared with the low P-AMs group, the high P-AMs group was associated with T2 (p < 0.01), pl (p < 0.01), and vessel invasion (p = 0.04) (Table 1). There was no significant difference in IP between the high and low P-AMs groups (p = 0.08). On the contrary, the high D-AMs group had significantly more patients with T2 than the low D-AMs group (p = 0.02) (Supplementary Table 3).

Correlation between prognosis and high P-AMs

High P-AMs group had significantly shorter RFS and OS than the low P-AMs group (5-year RFS rate: high P-AMs, 56.6% vs. low P-AMs, 88.5%; RFS-HR, 0.24; p < 0.01; 5-year OS rate: high P-AMs, 62.7% vs. low P-AMs, 88.6%; OS-HR, 0.24; p < 0.01, Fig. 3A-B). In contrast, there was no significant difference in terms of RFS and OS between the high and low D-AMs groups (5-year RFS rate: high D-AMs, 65.5% vs. low D-AMs, 73.9%; RFS-HR, 0.77; p = 0.46; 5-year OS rate: high D-AMs, 71.1% vs. low D-AMs, 77.2%; OS-HR, 0.90; p = 0.80, Fig. 3C-D).

We validated the relationship between high P-AMs and lung SqCC prognosis in the TCGA cohort and in 139 stage I lung SqCC patients, and we obtained similar results (5-year OS rate: high P-AMs, 42.2% vs. low P-AMs, 74.6%; HR, 0.40; p = 0.01, Fig. 3E).

Prognostic factors for RFS

In the univariate analysis, IP, pl, and high P-AMs were significant poor prognostic factors for RFS (IP positive: HR, 4.33; p < 0.01; pl positive: HR, 2.10; p = 0.04; High P-AMs: HR, 4.17; p < 0.01,). Furthermore, in the multivariate analysis, IP and high P-AMs were independent poor prognostic factors for RFS (IP positive: HR, 3.16; p < 0.01; High P-AMs: HR, 3.00; p = 0.02, Table 2).

Ex vivo BAL analysis

We established a novel ex vivo BAL system for three SqCC patients with lung lobectomy (Supplementary Table 1) and sorted AMs-VBALF and AMs-DBALF (Supplemental Fig. 2A-C). We examined mRNA expression in AMs-VBALF and AMs-DBALF, including IL10, CCL2, IL6, TGFβ, and TNFα, which are known as important genes of TAMs in the formation of pro-tumor TIME [5, 6, 7]. Expressions of IL10 and CCL2 in AMs-VBALF were higher than those in of AMs-DBALF in all patients. The gene expression ratios (AMs-VBALF/AMs-DBALF) of IL-10 were 2.2, 3.0, and 10.0 and those of CCL2 were 3.0, 3.1, and 3.2. In contrast, the gene expression ratios of IL6 were 0.04, 1.3, and 0.04, those of TGFβ were 0.9, 0.6, and 0.8, and those of TNFα were 0.6, 0.8, and 0.9 (Fig. 4A).

In the TCGA cohort of 495 all-stage SqCC patients, the high CCL2 expression group had significantly shorter OS than the low CCL2 expression group, but there was no significant difference between high and low IL10 expression (CCL2: HR, 0.69; p < 0.01; IL10: HR, 0.82; p = 0.15, Supplemental Fig. 4A-B).

Influence of CCL2 and IL10 on the proliferation of RERF-LC-AI

Next, we examined the effects of CCL2 and IL10 on the proliferation of the lung SqCC cell line RERF-LC-AI. In all three experiments, the addition of CCL2 significantly increased the proliferation of the cells compared to the control in experiment (Exp) 1–3 (Exp 1: 47.5% vs. 54.5%, p < 0.05; Exp 2: 76.9% vs. 87.5%, p < 0.01; Exp 3: 80.0% vs. 86.0%, p < 0.05) (Fig. 4B). However, the addition of IL10 did not increase the proliferation of the cell lines, except in Exp 1 (Exp 1: 60.7% vs. 66.9%, p < 0.05; Exp 2: 79.5% vs. 82.6%, p = 0.10; Exp 3: 66.3% vs. 71.2%, p = 0.10) (Fig. 4C). We also examined the effect of CCL2 and IL10 on the migration of RERF-LC-AI cells; however, CCL2 and IL10 did not increase the migration of cancer cells (data not shown).

In this study, we found that a higher number of P-AMs was an independent poor prognostic factor for stage I lung SqCC on multivariate analysis. Moreover, CCL2 and IL10 expressions in AMs-VBALF were much higher than those in AMs-DBALF, and CCL2 increased tumor cell proliferation. This is the first study to show that P-AMs may act in a tumor-promoting manner during lung SqCC progression.

Using two independent cohorts, we confirmed that a high number of P-AMs is a poor prognostic factor. In a recent study, the distribution of pSTAT3-positive tumor cells in small cell lung cancer (SCLC) was found to be predominant in the periphery of tumor nests, and IL-6 and CCL4/MIP-1β from macrophages activated STAT3 in SCLC, suggesting that TIME around the tumor is important for tumor progression in SCLC [24]. Recently, Noritake et al. reported that a high number of P-AMs was independently associated with poor prognosis in resected early lung adenocarcinoma. They found that the mRNA expression involved in chemotaxis and epithelial proliferation was enriched in patients with high P-AMs, suggesting that P-AMs may influence the peritumoral microenvironment [14]. Our current results also indicate that P-AMs are a poor prognostic factor in early-stage SqCC, suggesting that a higher number of P-AMs could be an unfavorable prognostic marker in early-stage SqCC as well as in early-stage adenocarcinoma and SCLC.

Recently, María et al. reported that AMs in the peritumoral region of the lung differed in gene expression compared with AMs in the contralateral lung in a mouse model. Fifteen days after NSCLC seeding, AMs in the peritumoral region displayed higher expression of peptidases (Mmp12, Mmp14, and Adamdec1), integrin-binding molecules (Tspan4), major histocompatibility complex class II molecules (H2-M2, H2-AA, H2-AB1, and H2-Q7), and T-cell chemoattractants (Ccl17 and Cxcl9) [10]. In the lung metastasis of HCC, CCR2-CCL2 was involved in the recruitment of P-AMs [14]. In the present study, using human samples, CCL2 levels were found to be elevated in AMs-VBALF in lung SqCC. Moreover, recombinant CCL2 significantly increased the proliferation of RERF-LC-AI cells. In addition, using the database of TCGA cohort, we found that the high CCL2 expression group had a significantly poorer prognosis than the low expression group (Supplemental Figure 4A). These findings suggest that AM-derived CCL2 in the peritumoral microenvironment may be involved in tumor cell progression.

We attempted to identify the gene ontology associated with macrophage recruitment between patient groups with high and low P-AMs. However, DEG gene ontology enrichment analysis from Z-score clustering analysis between high and low P-AMs did not show any gene ontology related to AMs (Supplemental Figure 5A-B). Therefore, we specifically investigated the receptors associated with CCL2 and IL10, which are highly expressed in AMs-VBALF. We found that CCR1, CCR2, CCR3, CCR4, and CCR10 were significantly expressed in the high P-AMs group (Supplemental Figure 5C). The CCL2-CCR2 axis between tumor cells and TAMs reportedly modifies TIME and amplifies tumor cell invasion [25]. These findings suggest that the positive feedback of the CCL2-CCR2 axis might contribute to the formation of the pro-tumoral microenvironment in the peritumoral area, similar to that in the intratumoral area.

In conclusion, we found that a high number of P-AMs was associated with significantly poorer prognosis than a low number of P-AMs in stage I postoperative lung SqCC patients. Moreover, AMs-VBALF-derived CCL2 promoted the proliferation of SqCC cell lines. These findings suggest that P-AMs are involved in tumor progression in early-stage squamous cell lung cancer. In recent years, several clinical trials targeting tumor-associated macrophages (TAMs), such as CCR2 and CSF-1R, have been ongoing [26]. There may be a need to target not only TAMs but also P-AMs as a possible future treatment.

Funding

This work was supported in part by National cancer center research fund (2020-A-9) and JSPS KAKENHI (21H02931).

Competing Interests

T. Sakai reports grants from AMGEN outside the submitted work; personal fees from AstraZeneca, Chugai Pharmaceutical, Novartis, MSD, Merck and Themo Fisher Scientific outside the submitted work. Y. Shibata reports grants from Ono Pharmaceutical Co., Ltd. and MSD outside the submitted work; personal fees from Pfizer Inc., Bristol-Myers Squibb K.K., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Merck, Takeda Pharmaceutical, Chugai Pharmaceutical. H. Izumi reports grants from Amgen Inc, Takeda Pharmaceutical Co., AstraZeneca, Eisai and Ono Pharmaceutical Company outside the submitted work; personal fees from Amgen Inc, Ono Pharmaceutical Company, Chugai Pharmaceutical Co., AstraZeneca, Merck Biopharma Co., Takeda Pharmaceutical Co. and Eisai. H. Udagawa reports grants from Takeda Pharmaceutical, and Boehringer Ingelheim GmbH outside the submitted work. Y. Zenke reports grants and personal fees from AstraZeneca, MSD, and Amgen; personal fees from Bristol-Myers Squibb, Lilly, Chugai, Ono Pharmaceutical, Boehringer Ingelheim, Pfizer, Taiho Pharmaceutical, Takeda Pharmaceutical, Novartis, Nippon Kayaku, and Kyowa Kirin; and grants from Merck and Daiichi Sankyo outside the submitted work. S. Matsumoto reports grants and personal fees from Merck; personal fees from Eli Lilly, AstraZeneca, Chugai Pharmaceutical, ThermoFisher Scientific, Riken Genesis, Guardant Health, Novartis Pharma, and Amgen; and grants from Janssen Pharmaceutical outside the submitted work. K. Yoh reports grants and personal fees from AstraZeneca, Boehringer Ingelheim, Chugai, Daiichi Sankyo, Lilly, Taiho, and Takeda; personal fees from Bristol-Myers Squibb, Janssen, Kyowa Kirin, and Novartis; and grants from AbbVie, MSD, and Pfizer outside the submitted work. M Tsuboi reports from Boehringer-Ingelheim Japan, MSD, AstraZeneca KK, Ono Pharmaceutical CO.,LTD, Bristol-Myers Squibb KK, Eli Lilly Japan, Novartis, BMG Inc. and MiRXES Japan outside the submitted work; and personal fees from AstraZeneca, Chugai Pharmaceutical CO.,LTD, MSD, Novartis, Johnson & Johnson Japan, AstraZeneca KK, Eli Lilly Japan, Chugai Pharmaceutical CO.,LTD, Taiho Pharma, Medtronic Japan, Ono Pharmaceutical CO.,LTD, MSD, Bristol-Myers Squibb KK and Teijin Pharma outside the submitted work. K. Goto reports grants from Merck, Takeda Pharmaceutical, Amgen, Astellas, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Janssen, Kyowa Kirin, Lilly, Medical & Biological Laboratories, MSD, Novartis, Ono, Pfizer, Sumitomo Dainippon, Bayer Yakuhin Ltd., Haihe Biopharma Co. Ltd., Ignyta Inc., Kissei Pharmaceutical Co. Ltd., Life Technologies Japan Ltd., Loxo Oncology Inc., Merus N.V., NEC Corporation., Pfizer Japan Inc., Spectrum Pharmaceuticals Inc., Sysmex Corporation, Turning Point Therapeutics Inc., and Taiho and nonfinancial support from Guardant Health Inc. during the conduct of the study as well as grants and personal fees from Amgen, Amgen K.K., Amgen Astellas BioPharma K.K., AstraZeneca K.K., Boehringer Ingelheim Japan Inc., BristolMyers Squibb K.K., Blueprint Medicines Corporation, Chugai Pharmaceutical Co. Ltd., Daiichi Sankyo Co. Ltd., Eisai Co. Ltd., Eli Lilly Japan K.K., Janssen Pharmaceutical K.K., Merck Biopharma Co. Ltd., MSD K.K., Novartis Pharma K.K., Ono Pharmaceutical Co. Ltd., Taiho Pharmaceutical Co. Ltd., and Takeda Pharmaceutical Co. Ltd. and personal fees from Amoy Diagnostics Co. Ltd., Bayer U.S., Guardant Health Inc., Thermo Fisher Scientific K.K., Medpace Japan K.K., and Otsuka Pharmaceutical Co. Ltd. outside the submitted work. G. Ishii report grands from DAIICHI SANKYO, INC., ONO PHARMACEUTICAL CO.,LTD., Noile-Immune Biotech Takeda Pharmaceutical Company Limited, Sumitomo Dainippon Pharma Co.,Ltd., Nihon Medi-Physics Co.,Ltd. Indivumed GmbH H.U. Group Research Institute; personal fees from Roche Diagnostics K.K., CHUGAI PHARMACEUTICAL CO., LTD., Novartis International AG, Oncolys BioPharma Inc., DAIICHI SANKYO, INC., Eli Lilly Japan K.K., Takeda Pharmaceutical Company Limited, AstraZeneca, RIKEN GENESIS CO., LTD. No disclosures were reported by the other authors.

Author Contributions

Material preparation, data collection and analysis were performed by Yu Tanaka, Ayako Suzuki and Hiroko Hashimoto. The experimental designs was developed by Hiroko Hashimoto and Genichiro Ishii. The first draft of the manuscript was written by Yu Tanaka. The conduct of this study was supervised by Genichiro Ishii. All authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Date Availability

The datasets generated during and/or analyzed during the current study are available in the The Cancer Genome Atlas repository, “https://portal.gdc.cancer.gov/”

Ethics approval

This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of National cancer center hospital east (2020-343).

Consent to participate

This study was conducted on patients who provided blanket consent.

Hyuna S, Jacques F, Rebecca L S, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021 Feb 4. “https://doi.org/ 10.3322/caac.21660.”
National Comprehensive Cancer Network. NCCN Clinical Practice Guidelines in Oncology Non-Small Cell Lung Cancer. Version 5. 2022 (https://www.nccn.org/professionals/physician_gls/pdf/nscl.pdf) Accessed 2022 October 7.
Samantha T, Joachim P et al. Validation of the 8th lung cancer TNM classification and clinical staging system in a German cohort of surgically resected patients. Innov Surg Sci. 2020 Aug 12;5(1-2):1-9. “https://doi.org/10.1515/iss-2020-0010.”
Åsa K Ö, Ann R H, Daniel N, et al. The immune microenvironment in non-small cell lung cancer is predictive of prognosis after surgery. Mol Oncol. 2019 May;13(5):1166-1179. “https://doi.org/10.1002/1878-0261.12475.”
G Ishii, A Ochiai, S Neri. Phenotypic and functional heterogeneity of cancer-associated fibroblast within the tumor microenvironment. Adv Drug Deliv Rev . 2016 Apr 1;99(Pt B):186-196. “https://doi.org/10.1016/j.addr.2015.07.007.”
Dan Su, Gao Wu, Ran Xiong, et al. Tumor Immune Microenvironment Characteristics and Their Prognostic Value in Non-Small-Cell Lung Cancer. Front Oncol. 2021 Mar 3;11:634059. “https://doi.org/10.3389/fonc.2021.634059.”
Alberto M, Federica M, Alberto M, et al. Tumour-associated macrophagess as treatment targets in oncology. Nat Rev Clin Oncol. 2017 Jul;14(7):399-416. “https://doi.org/10.1038/nrclinonc.2016.217.”
Yi-C W, Yu-S W, Chia-Y H, et al. USP24 induces IL-6 in tumor-associated microenvironment by stabilizing p300 and β-TrCP and promotes cancer malignancy. Nat Commun. 2018 Sep 28;9(1):3996. “https://doi.org/10.1038/s41467-018-06178-1.”
Luca C, Stamatina F, Andrew H S, et al. Human Tumor-Associated Macrophages and Monocyte Transcriptional Landscapes Reveal Cancer-Specific Reprogramming, Biomarkers, and Therapeutic Targets. Cancer Cell. 2019 Apr 15;35(4):588-602. “https://doi.org/10.1016/j.ccell.2019.02.009.”
Li Y, Yi Z. Tumor-associated macrophagess: from basic research to clinical application. J Hematol Oncol. 2017 Feb 28;10(1):58. “https://doi.org/10.1186/s13045-017-0430-2.”
Adam J Byrne, Sara A Mathie, Lisa G Gregory, et al. Pulmonary macrophages: key players in the innate defence of the airways. Thorax. 2015 Dec;70(12):1189-96. “https://doi.org/10.1136/thoraxjnl-2015-207020.”
Pamelia N Lim, Maritza M Cervantes, Linh K Pham et al. Alveolar macrophages: novel therapeutic targets for respiratory diseases. Expert Rev Mol Med. 2021 Nov 26;23:e18. “https://doi.org/10.1017/erm.2021.21.”
María Casanova-Acebes, Erica Dalla, Andrew M Leader, et al. Tissue-resident macrophagess provide a pro-tumorigenic niche to early NSCLC cells. Nature. 2021 Jun 16. “https://doi.org/10.1038/s41586-021-03651-8.”
Noritake O, K Aokage, A Suzuki, et al. Prognostic Impact of the Number of Peritumoral Alveolar Macrophages in Patients With Stage I Lung Adenocarcinoma. J Cancer Res Clin Oncol. 2022 Jul 2. “https://doi.org/10.1007/s00432-022-04056-4.”
Nosaka T, Baba T, Tanabe Y, et al. Alveolar Macrophages Drive Hepatocellular Carcinoma Lung Metastasis by Generating Leukotriene B 4. J Immunol. 2018 Mar 1;200(5):1839-1852. “10.4049/jimmunol.1700544. ”https://doi.org/ 10.4049/jimmunol.1700544.”
William D Travis, Elisabeth Brambilla, Andrew G Nicholson, et al. The 2015 World Health Organization Classification of Lung Tumors: Impact of Genetic, Clinical and Radiologic Advances Since the 2004 Classification, J Thorac Oncol. 2015 Sep;10(9):1243-1260. “https://doi.org/ 10.1097/JTO.0000000000000630.”
Peter Goldstraw, Kari Chansky, John Crowley et al. The IASLC Lung Cancer Staging Project: Proposals for Revision of the TNM Stage Groupings in the Forthcoming (Eighth) Edition of the TNM Classification for Lung Cancer. J Thorac Oncol. 2016 Jan;11(1):39-51. “https://doi.org/10.1016/j.jtho.2015.09.009.”
Colaprico A, Silva TC, Olsen C, et al: TCGAbiolinks: An R/Bioconductor package for integrative analysis of TCGA data. Nucleic Acids Res 2016;44;e7
Morgan M, Obenchain V, Hester J, et al (2020): Summarized Experiment: Summarized Experiment container. R package version 1.18.2.
M.I. Love, W. Huber, S. Anders: Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 Genome Biol., 15 (2014), p. 550
G.R. Warnes, B. Bolker, L. Bonebakker, et al. gplots: various R programming tools for plotting data R package version, 3 (2016), p. 4
Gentleman R, Carey V, Huber W, et al (2020): genefilter: methods for filtering genes from high-throughput experiments. R package version 1.70.0.
Y. Zhou, B. Zhou, L. Pache, M, et al. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets Nat. Commun., 10 (1) (2019). “https://doi.org/10.1038/s41467-019-09234-6.”
Toyohisa Iriki, Koji Ohnishi, Yukio Fujiwara et al. The cell-cell interaction between tumor-associated macrophagess and small cell lung cancer cells is involved in tumor progression via STAT3 activation. Lung Cancer. 2017 Apr;106:22-32. “https://doi.org/10.1016/j.lungcan.2017.01.003.”
Qiongyu Hao, Jaydutt V Vadgama, Piwen Wang. CCL2/CCR2 signaling in cancer pathogenesis. Cell Commun Signal. 2020 May 29;18(1):82. “https://doi.org/10.1186/s12964-020-00589-8.”
Clément Anfray, Aldo Ummarino, Fernando Torres Andón et al. Current Strategies to Target Tumor-Associated-Macrophages to Improve Anti-Tumor Immune Responses. Cells. 2020 Jan; 9(1): 46. “https://doi.org/10.3390/cells9010046.”

Table 1 and 2 are available in the Supplementary Files section

Competing interest reported. T. Sakai reports grants from AMGEN outside the submitted work; personal fees from AstraZeneca, Chugai Pharmaceutical, Novartis, MSD, Merck and Themo Fisher Scientific outside the submitted work. Y. Shibata reports grants from Ono Pharmaceutical Co., Ltd. and MSD outside the submitted work; personal fees from Pfizer Inc., Bristol-Myers Squibb K.K., AstraZeneca K.K., Ono Pharmaceutical Co., Ltd., Eli Lilly Japan K.K., Merck, Takeda Pharmaceutical, Chugai Pharmaceutical. H. Izumi reports grants from Amgen Inc, Takeda Pharmaceutical Co., AstraZeneca, Eisai and Ono Pharmaceutical Company outside the submitted work; personal fees from Amgen Inc, Ono Pharmaceutical Company, Chugai Pharmaceutical Co., AstraZeneca, Merck Biopharma Co., Takeda Pharmaceutical Co. and Eisai. H. Udagawa reports grants from Takeda Pharmaceutical, and Boehringer Ingelheim GmbH outside the submitted work. Y. Zenke reports grants and personal fees from AstraZeneca, MSD, and Amgen; personal fees from Bristol-Myers Squibb, Lilly, Chugai, Ono Pharmaceutical, Boehringer Ingelheim, Pfizer, Taiho Pharmaceutical, Takeda Pharmaceutical, Novartis, Nippon Kayaku, and Kyowa Kirin; and grants from Merck and Daiichi Sankyo outside the submitted work. S. Matsumoto reports grants and personal fees from Merck; personal fees from Eli Lilly, AstraZeneca, Chugai Pharmaceutical, ThermoFisher Scientific, Riken Genesis, Guardant Health, Novartis Pharma, and Amgen; and grants from Janssen Pharmaceutical outside the submitted work. K. Yoh reports grants and personal fees from AstraZeneca, Boehringer Ingelheim, Chugai, Daiichi Sankyo, Lilly, Taiho, and Takeda; personal fees from Bristol-Myers Squibb, Janssen, Kyowa Kirin, and Novartis; and grants from AbbVie, MSD, and Pfizer outside the submitted work. M Tsuboi reports from Boehringer-Ingelheim Japan, MSD, AstraZeneca KK, Ono Pharmaceutical CO.,LTD, Bristol-Myers Squibb KK, Eli Lilly Japan, Novartis, BMG Inc. and MiRXES Japan outside the submitted work; and personal fees from AstraZeneca, Chugai Pharmaceutical CO.,LTD, MSD, Novartis, Johnson & Johnson Japan, AstraZeneca KK, Eli Lilly Japan, Chugai Pharmaceutical CO.,LTD, Taiho Pharma, Medtronic Japan, Ono Pharmaceutical CO.,LTD, MSD, Bristol-Myers Squibb KK and Teijin Pharma outside the submitted work. K. Goto reports grants from Merck, Takeda Pharmaceutical, Amgen, Astellas, AstraZeneca, Boehringer Ingelheim, Bristol-Myers Squibb, Chugai, Daiichi Sankyo, Eisai, Janssen, Kyowa Kirin, Lilly, Medical & Biological Laboratories, MSD, Novartis, Ono, Pfizer, Sumitomo Dainippon, Bayer Yakuhin Ltd., Haihe Biopharma Co. Ltd., Ignyta Inc., Kissei Pharmaceutical Co. Ltd., Life Technologies Japan Ltd., Loxo Oncology Inc., Merus N.V., NEC Corporation., Pfizer Japan Inc., Spectrum Pharmaceuticals Inc., Sysmex Corporation, Turning Point Therapeutics Inc., and Taiho and nonfinancial support from Guardant Health Inc. during the conduct of the study as well as grants and personal fees from Amgen, Amgen K.K., Amgen Astellas BioPharma K.K., AstraZeneca K.K., Boehringer Ingelheim Japan Inc., BristolMyers Squibb K.K., Blueprint Medicines Corporation, Chugai Pharmaceutical Co. Ltd., Daiichi Sankyo Co. Ltd., Eisai Co. Ltd., Eli Lilly Japan K.K., Janssen Pharmaceutical K.K., Merck Biopharma Co. Ltd., MSD K.K., Novartis Pharma K.K., Ono Pharmaceutical Co. Ltd., Taiho Pharmaceutical Co. Ltd., and Takeda Pharmaceutical Co. Ltd. and personal fees from Amoy Diagnostics Co. Ltd., Bayer U.S., Guardant Health Inc., Thermo Fisher Scientific K.K., Medpace Japan K.K., and Otsuka Pharmaceutical Co. Ltd. outside the submitted work. G. Ishii report grands from DAIICHI SANKYO, INC., ONO PHARMACEUTICAL CO.,LTD., Noile-Immune Biotech Takeda Pharmaceutical Company Limited, Sumitomo Dainippon Pharma Co.,Ltd., Nihon Medi-Physics Co.,Ltd. Indivumed GmbH H.U. Group Research Institute; personal fees from Roche Diagnostics K.K., CHUGAI PHARMACEUTICAL CO., LTD., Novartis International AG, Oncolys BioPharma Inc., DAIICHI SANKYO, INC., Eli Lilly Japan K.K., Takeda Pharmaceutical Company Limited, AstraZeneca, RIKEN GENESIS CO., LTD. No disclosures were reported by the other authors.

Supplementaryfigureandtable.pdf
List of supplemental information: Supplemental Figure 1. Selection flow of study patients. A: Hospital cohort. B: TCGA cohort. Supplemental Figure 2. Procedure for ex vivo BAL and AMs selection. A: Procedure of distant ex vivo BAL. After lobectomy, a 50 mL syringe was wedged into the bronchus, distant from the lung tumor, and the injected saline was aspirated. B: Procedure of peritumoral ex vivo BAL. After procedure of distant ex vivo BAL, a 50 mL syringe was wedged into the bronchus where the lung tumor existed, and the injected saline was aspirated. C: Selection of CD163-positive AMs from ex vivo BALF. Supplemental Figure 3.Flow cytometric analysis before and after CD163 positive selection. A: Region setting. B: Flow cytometric analysis of ex vivo BALF using control (IgG)-APC. C: Flow cytometric analysis of ex vivo BALF after CD163 positive selection using CD163-Ab. D: Flow cytometric analysis of ex vivo BALF after CD163 negative selection using CD163-Ab. Supplemental Figure 4. Kaplan–Meier curve of overall survival by gene expression in TCGA cohort. A: Overall survival of patients with high expression of CCL2 in the TCGA cohort. B: Overall survival of patients with high expression of IL10 in the TCGA cohort. HR and p-values were determined using the Cox proportional hazards model. Supplemental Figure 5. Gene ontology analyses by the number of peritumoral AMs in the TCGA database. A: Heatmap of DEGs between high and low P-AMs. B: Gene ontology and pathway enrichment analysis between high and low P-AMs. C: Fold expression changes in IL10 and CCL2-associated genes between high and low P-AMs.
Tab1.png
Table 1. Clinicopathological features of high and low peritumoral alveolar macrophages.
Tab2.png
Table 2. Univariate and multivariate analyses of recurrence-free survival (n=124).

Download PDF

Journal Publication

published 02 Mar, 2023

Read the published version in Cancer Immunology, Immunotherapy →

Version 1

posted

You are reading this latest preprint version

Clinicopathological significance of peritumoral alveolar macrophages in patients with resected early-stage lung squamous cell carcinoma

Status:

Journal Publication

Version 1

Abstract

Figures

Introduction

Materials And Methods

Patients

Definition and evaluation of P-AMs and D-AMs

Evaluation of clinicopathological factors

Ex vivo BALF analysis

Fluorescence activated cell sorting (FACS) analysis and cell sorting

Real-time reverse transcription–polymerase chain reaction

TCGA analysis

Proliferation assay

Statistical analysis

Results

P-AMs and D-AMs

Characteristics of patients with high P-AMs

Correlation between prognosis and high P-AMs

Prognostic factors for RFS

Ex vivo BAL analysis

Influence of CCL2 and IL10 on the proliferation of RERF-LC-AI

Discussion

Declarations

References

Tables

Additional Declarations

Supplementary Files

Status:

Journal Publication

Version 1