Open Access

Siglec-7 is a predictive biomarker for the efficacy of cancer vaccination against metastatic colorectal cancer

  • Authors:
    • Kensuke Yamada
    • Shoichi Hazama
    • Nobuaki Suzuki
    • Ming Xu
    • Yuki Nakagami
    • Nobuyuki Fujiwara
    • Ryouichi Tsunedomi
    • Shin Yoshida
    • Shinobu Tomochika
    • Satoshi Matsukuma
    • Hiroto Matsui
    • Yukio Tokumitsu
    • Shinsuke Kanekiyo
    • Yoshitaro Shindo
    • Yusaku Watanabe
    • Michihisa Iida
    • Shigeru Takeda
    • Tatsuya Ioka
    • Tomio Ueno
    • Hiroyuki Ogihara
    • Yoshihiko Hamamoto
    • Yoshinobu Hoshii
    • Hiroo Kawano
    • Tomonobu Fujita
    • Yutaka Kawakami
    • Hiroaki Nagano
  • View Affiliations

  • Published online on: November 3, 2020     https://doi.org/10.3892/ol.2020.12271
  • Article Number: 10
  • Copyright: © Yamada et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

Cancer immunotherapy, including vaccination, is considered a major scientific and medical breakthrough. However, cancer immunotherapy does not result in durable objective responses against colorectal cancer (CRC). To improve the efficacy of immunotherapy, the present study investigated several biomarkers for selecting patients who were expected to respond well to immunotherapy. Firstly, a comprehensive proteomic analysis was performed using tumor tissue lysates from patients enrolled in a phase II study, in which five human leukocyte antigen (HLA)‑A*24:02‑restricted peptides were administered. Sialic acid‑binding immunoglobulin type lectin (Siglec)‑7 was identified as a potential predictive biomarker. Subsequently, this biomarker was validated using western blot analysis, and immunofluorescence using tissue samples from the patients enrolled in the phase II study. The expression levels of Siglec‑7 detected by immunofluorescence were quantified and their association with overall survival (OS) in patients treated with the peptide vaccine was examined. Furthermore, considering the important role of tumor‑infiltrating lymphocytes (TILs) for CRC prognosis, the densities of CD3+, CD4+, CD8+ and forkhead box P3 (FOXP3)+ T cells in CRC tissues were examined and compared with Siglec‑7 expression. The mean expression levels of Siglec‑7 were significantly higher in patients with poor prognosis, with an OS of ≤2 years, as shown in comprehensive proteomic analysis (P=0.016) and western blot analysis (P=0.025). Immunofluorescence analysis demonstrated that Siglec‑7 was expressed in intratumoral macrophages. The OS in patients with high Siglec‑7 expression was significantly shorter than in that in patients with low Siglec‑7 expression (P=0.017) in the HLA‑A*24:02‑matched patients. However, this difference was not observed in the HLA‑unmatched patients. There was no significant difference in OS between patients according to the numbers of TILs, nor significant correlation between TILs and Siglec‑7 expression. In conclusion, Siglec‑7 expression in macrophages in tumor tissue may be a novel predictive biomarker for the efficacy of immunotherapy against metastatic CRC.

Introduction

Colorectal cancer (CRC) is the third most common cause of cancer-related mortality among both men and women (1). In the past decade, chemotherapy and molecular targeted treatment have improved the overall survival (OS) in patients with metastatic CRC to ~30 months (2). These drugs, however, have some limitations, including drug resistance and side effects, so the development of new therapeutic options to prevent metastatic spread and eventually improve patient survival is necessary (3).

Cancer immunotherapy is considered as a major scientific and medical breakthrough (4), and several immune checkpoint-directed antibodies have increased the OS in patients with various cancers and are approved by the Food and Drug Administration (5,6). For example, PD-1 inhibitor nivolumab and pembrolizumab are used for deficient mismatch repair (dMMR) or microsatellite-instability-high (MSI-H) CRC world-wide (7,8).

However, immunotherapies, including immune checkpoint inhibitors to proficient-MMR CRC, have not achieved durable objective responses against CRC (9,10). Improving the efficacy of immunotherapies requires two approaches. One is the use of combination therapy to alter ‘cold tumors’ which are characterized by the absence of T cell infiltration, to ‘hot tumors’ characterized by the accumulation of proinflammatory cytokines and T cell infiltration (5,11). The other is the identification of biomarkers to select patients who are expected to respond well to immunotherapy.

The authors of the present study previously reported phase I and II studies in which five epitope peptides were administered to advanced-stage CRC patients (12,13). In these studies, a low neutrophil/lymphocyte ratio and a low plasma interleukin (IL)-6 level were the potential markers for improved survival time of vaccinated patients (14,15). Furthermore, it was also shown that several miRNAs and the integrity of plasma cell-free DNA were predictive biomarkers for active immunotherapy using epitope peptides (1518).

This study aimed to identify novel predictive biomarkers to select patients who are highly responsive to immunotherapy to improve the efficacy of immunotherapy. To this end, a comprehensive analysis of proteins in tumor tissues was performed and sialic acid-binding immunoglobulin type lectin (Siglec)-7 was identified as a potential predictive biomarker for immunotherapy.

Siglecs are a family of transmembrane receptors predominantly found in both innate and adaptive immune cells, involved in distinguishing between self and non-self-cells by recognizing sialic acids at the cellular surface (19,20). Siglec-7, the seventh member of the Siglec family, is mainly expressed on natural killer (NK) cells, monocytes, macrophages, and a minor subset of CD8+ T cells (21,22), and acts as an inhibitory receptor. The cytoplasmic portion of Siglec-7 contains immune receptor tyrosine-based inhibition motifs (ITIMs), which provide inhibitory signals by recruiting the SH2-domain-containing tyrosine phosphatase (SHP)-1 and SHP2 (22). SHP1 and SHP2 inhibit NK cell activation pathways such as the NKG2D pathway, suppressing NK cell cytotoxicity to tumor cells (23). However, it has never been evaluated for its possible role in cancer immunotherapy. In the present study, Siglec-7 was evaluated for its potential role as a novel biomarker for active immunotherapy.

Materials and methods

Summary of the phase II study

To assess the clinical benefits of cancer vaccination treatment, a phase II study was conducted using five human leukocyte antigen (HLA)-A*24:02-restricted peptides, including kinase of the outer chloroplast membrane 1 (KOC1) (24), translocase of outer mitochondrial membrane 34 (TOMM34) (25), ring finger protein 43 (RNF43) (26), vascular endothelial growth factor receptor (VEGFR) 1 and 2 (27,28). This phase II study was a non-randomized, HLA-A status double-blind study. The detailed protocol of this phase II study was previously described (13). Briefly, the therapy consisted of a cocktail of five therapeutic epitope peptides in addition to oxaliplatin-containing chemotherapy. The cocktail containing 3 mg of each of the five peptides was mixed with 1.5 ml of incomplete Freund's adjuvant and administered subcutaneously into the thigh or axilla regions every week for 13 weeks, followed by the vaccination once every 2 weeks. Patients ≥20 years old with histologically confirmed advanced CRC who were chemotherapy-naïve, who had adequate functions of critical organs, and had a life expectancy of ≥3 months were eligible. Between February 2009 and November 2012, 96 chemotherapy-naïve CRC patients were enrolled with masked HLA-A*24:02 status.

Sample collection

From the 96 patients who were enrolled in the phase II trial (50 were HLA-A*24:02-matched and 46 were unmatched), 63 formalin-fixed paraffin-embedded (FFPE) tissue samples of primary CRC were obtained (32 were HLA-A*24:02-matched and 31 were unmatched) (Fig. 1). In 14 of the 32 HLA-A*24:02-matched patients, fresh tissues were also snap-frozen in liquid nitrogen and preserved at −80°C until further examination. Primary CRC tissues were obtained by surgery prior to the vaccine treatment at Yamaguchi University Hospital and affiliated hospitals. All samples were obtained with the patients' written informed consent. This study was conducted according to the Declaration of Helsinki and was approved by the Institutional Ethics Review Boards of Yamaguchi University (approval no. H20-102; Clinical Trials Registry: UMIN000001791).

Comprehensive proteomic analysis of tumor tissue

A comprehensive analysis of the protein levels in tumor tissue lysate was performed using the SOMAscan (SomaLogic, Inc.) to quantify 1,129 biologically relevant proteins as previously described (29). Frozen CRC tissue samples were available from patients who survived for either more than three years or less than two years (Fig. 1). According to the manufacturer's protocol (SomaLogic, Inc.), the total protein of the frozen CRC tissue sample was extracted with lysis buffer T-PER Tissue Protein Extraction Reagent (Thermo Fisher Scientific, Inc.) supplemented with Halt Protease Inhibitor Cocktail (Thermo Fisher Scientific, Inc.) through a Qiagen TissueLyser (Qiagen). Samples were sent to SomaLogic and analyzed using the SOMAscan assay. In this assay, protein signals were converted to nucleotide signals using chemically modified nucleotides so that quantification could be done using relative fluorescence signal on microarrays. For this reason, SOMAscan measurements were presented as relative fluorescence units (RFUs).

Western blot analysis

Western blot analysis was performed as previously described (30), using the same extracts as those used in the comprehensive analysis of SOMAscan. Briefly, protein samples (10 µg) were separated on 10% SDS-PAGE and transferred onto a PVDF membrane (Bio-Rad Laboratories, Inc.). Membranes were blocked by pre-incubation with 3% skim milk for 30 min at room temperature and then were incubated with anti-Siglec-7 antibody (ProteinTech Group, Inc.) at 4ºC overnight. After washing 3 times with Tris-buffered saline with Tween-20 (TBST) buffer, the membranes were incubated with the corresponding secondary antibody for 1 h at room temperature. Immunoreactions were detected using an enhanced chemiluminescence (ECL) western blotting detection system and an Amersham Imager 600 (GE Healthcare Life Sciences). Densitometry analysis was performed using ImageJ software (National Institutes of Health) (31). Since the protein levels of VCP, one of the housekeeping proteins, are more stable compared to other housekeeping proteins, such as glyceraldehyde 3-phosphate dehydrogenase (GAPDH) and actin, VCP was chosen as the loading control (30,32,33).

Immunohistochemistry

Immunohistochemistry was carried out on 4-µm-thick FFPE sections. For staining Siglec-7, sections were deparaffinized through xylene and graded alcohols, and antigen retrieval was performed in 10 mM Tris-EDTA buffer pH 9.0 (Dako) in a microwave at 95°C for 20 min. Endogenous peroxidase activity in the sections was blocked with 3% hydrogen peroxidase for 20 min, and nonspecific protein binding was blocked with Protein Block Serum-Free (Dako) for 10 min. The staining procedures were performed in a Dako Autostainer (Dako) according to the manufacturer's protocol. Sections were incubated with an anti-Siglec-7 antibody (rabbit polyclonal, 13939-1-AP, ProteinTech Group, Inc.; dilution 1:800) at room temperature for 1 h. After washing 3 times with phosphate-buffered saline (PBS), the sections were incubated with the corresponding secondary antibody for 30 min. The reactions were visualized with 3,3′-diaminobenzidine chromogen (DAB; Dako) and counterstained with Mayer's hematoxylin. Images were acquired using the All-in-one fluorescence microscope BZ-X710 (Keyence).

Considering the important role of tumor-infiltrating lymphocytes (TILs) for the CRC prognosis, the densities of CD3+, CD4+, CD8+, and forkhead box P3 (FOXP3)+ T cells in CRC tissues were also examined. Immunohistochemistry for TILs was performed as previously described (34,35). Briefly, using the Ventana Discovery XT staining system (Ventana), the sections were incubated with anti-CD3 antibody (mouse monoclonal, 518110079; Ventana), anti-CD4 (mouse monoclonal, 518108816; Ventana), anti-CD8 (mouse monoclonal, IR623; Dako; dilution 1:50), and anti-FOXP3 (mouse monoclonal, ab20034; Abcam; dilution 1:100). The microscopic images were acquired using a high-resolution digital slide scanner NanoZoomer-XR C12000 (Hamamatsu Photonics).

Immunofluorescence

Immunofluorescence was carried out on 4-µm-thick FFPE sections the same way as immunohistochemistry. Sections were deparaffinized and antigen retrieval was performed in 10 mM Tris-EDTA buffer pH 9.0 (Dako) in a microwave at 95°C for 20 min. Nonspecific protein binding was blocked with Protein Block Serum-Free (Dako) for 10 min. Sections were incubated with an antibody mixture (1:800 diluted anti-Siglec-7 antibody, and 1:400 diluted anti-CD68 antibody; mouse monoclonal, Ab783; Abcam) at 4°C overnight. The next day, after washing 3 times with PBS, sections were incubated with secondary antibody mixture (1:1,000 diluted anti-mouse Alexa Fluor 568 and 1:1,000 diluted anti-rabbit Alexa Fluor 488; Thermo Fisher Scientific) for 60 min at room temperature. Slides were counterstained with DAPI blue to visualize nuclei. All staining procedures were performed manually, and stained sections were visualized and photographed using the All-in-one fluorescence microscope BZ-X710 (KEYENCE; magnification, ×200). From each section, 10 fields near the center of the tumor with the highest density of Siglec-7-positive cells and CD68-positive cells were manually selected by observers. Images were analyzed with an algorithm for positive pixel count using ImageJ software (NIH) to quantify the expression levels of Siglec-7 and CD68. The threshold intensity was set at 40 for Siglec-7 and CD68 staining. The results were presented as a percent of the total positive area to the area of the examined fields.

Measurement of TILs

Based on the immunohistochemistry for TILs, the number of TILs was measured as previously described (34,35). Briefly, intratumoral-infiltrating CD3+, CD4+, CD8+ and FOXP3+ cells were defined as TILs and their numbers were measured. Those found in the peritumoral stroma and extratumoral lymphoid structures were excluded from this analysis. A computerized image analysis system Tissue Studio (Definiens) was used to score all tumor lesions. The numbers of TILs were recorded in square millimeters as the mean number of positive cells per tumor tissue unit.

Statistical analysis

In comprehensive protein analysis, differential expression of proteins was detected using the log2 and Fisher ratio using Microsoft Excel 2010 (Microsoft Corporation) (36). The log2 ratio for a protein k was calculated according to the following formula:

log2 ratio=log2(x¯k(good prognosis group)x¯k(poor prognosis group)),

where x¯k is the kth protein of the sample mean of the good or poor prognosis group. The Fisher ratio F for a protein k was calculated using the following formula:

formula: F(k)=[x¯k(good prognosis group)-x¯k(poor prognosis group)]212[sk2(good prognosis group)+sk2(poor prognosis group)],

where sk2 is the kth protein of the sample variance of the good or poor prognosis group.

Differences between the two groups were estimated using the Welch's t-test, which was selected for this study because recent statistical recommendations and simulation studies suggest using this test under either homoscedasticity or heteroscedasticity conditions (37). The categorical variables were compared using the χ2 or Fisher's exact tests. The strength of a correlation between two groups was assessed by the Spearman's rank correlation coefficient. The optimal cut-off values of the expression levels of Siglec-7, CD3, CD4, CD8, and FOXP3 were determined using either the median value or the time-dependent receiver operating characteristic (ROC) curve analysis using the Kaplan-Meier (KM) estimation method and Youden's index (sensitivity + specificity - 1) (38). The survival curves were estimated using the KM method and tested using the log-rank test. All statistical analyses were performed using R language for 64-bit Windows (version 3.6.1, R Development Core Team). P<0.05 was considered to indicate a statistically significant difference.

Results

Selection of candidate protein to predict the efficacy of vaccination

Comprehensive analysis of the expression profiles of 1,129 proteins in 13 frozen CRC tissue samples from HLA-A*24:02-matched patients was performed. The patients were divided into good and poor prognosis groups; in 7 cases with good prognosis, the patients had OS of 3 years or more and in 6 cases with poor prognosis, the patients had OS of 2 years or less. Comparing the protein expression levels of the two groups, 23 proteins satisfied the absolute log2 ratio ≥1 and the Fisher ratio ≥1. Of the 23 proteins, Table I shows the 10 proteins with the highest Fisher ratio. The expression level of Sonic hedgehog (SHH) in the good prognosis group was significantly higher than that in the poor prognosis group (P=0.022). In contrast, the expression levels of Siglec-7 and fibronectin were significantly higher in the poor prognosis group than those in the good prognosis group (P=0.016 and 0.025, respectively). Among them, Siglec-7 was selected as a candidate protein because of the lowest P-value.

Table I.

Predictive markers from comprehensive proteomic analysis of tumor tissue.

Table I.

Predictive markers from comprehensive proteomic analysis of tumor tissue.

Good prognosis (n=7)Poor prognosis (n=6) Welchs t-test



RankTarget proteinMeanSDMeanSD|Log2 ratio|Fisher ratioP-value
1Sonic Hedgehog492.5226.9230.382.71.13.660.022
2ICOSLG14492.514465.941296.134872.01.52.290.089
3Lysozyme9204.35769.519217.012240.31.12.200.079
4Siglec-7934.8511.92272.11121.81.32.000.016
5Siglec-9416.4167.91158.81044.21.51.620.089
6Fibronectin4794.13336.513751.48483.41.51.600.025
7FCGR3B1494.4827.53934.33370.61.41.420.089
8TIMP16566.04235.214537.211608.01.11.320.117
9LBP3525.22064.212051.214610.81.81.220.152
10C1q14432.510855.032501.822469.41.21.200.085

[i] Proteins eligible for predictive biomarkers were narrowed down by the absolute log2 ratio ≥1 and ranked according to the Fisher ratio between the good and poor prognosis groups. Good prognosis, with overall survival of 3 years or more; poor prognosis, with overall survival of 2 years or less; SD, standard deviation; ICOSLG, inducible T cell costimulator ligand; Siglec-7, sialic acid-binding immunoglobulin-like lectin 7; Siglec-9, sialic acid-binding immunoglobulin-like lectin 9; FCGR3B, Fc fragment of IgG receptor IIIb; TIMP1, tissue inhibitor of metalloproteinase 1; LBP, lipopolysaccharide binding protein; C1q, complement component 1, q subcomponent.

Confirmation of candidate protein expression using western blot analysis

To validate the results obtained in comprehensive analysis of SOMAscan, western blot analysis was performed using the same 13 samples as the ones used in SOMAscan analysis (Fig. 2A). As shown in Fig. 2A, the protein band in lane #9 was lower than those in the other lanes. Siglec-7 has three isoforms, and the shorter isoform may have been highly expressed in the tumor tissue of patient #9 compared with the other patients. The levels of Siglec-7 showed a positive correlation between SOMAscan measurements and western blot measurements (rs=0.758, P=0.00268; Fig. 2B). The mean expression levels of Siglec-7 were significantly higher in the poor prognosis group based on both SOMAscan analysis (P=0.016) and in western blot analysis (P=0.025) (Fig. 2C and D). These results indicated that the levels of Siglec-7 protein in the CRC tissue were significantly higher in patients with poor prognosis than in those with good prognosis in HLA-A*24:02-matched cohort.

Localization of Siglec-7 in tumor tissue

To identify the localization of Siglec-7 in CRC tissue, immunohistochemistry and immunofluorescence were performed. Immunohistochemistry showed that Siglec-7 was expressed in stromal cells located between or around tumor cells (Fig. 3A). Immunofluorescence showed that Siglec-7 was expressed in stromal cells which also expressed CD68 (Fig. 3B). These results indicated that Siglec-7 was expressed in intratumoral macrophages.

Validation of Siglec-7 as a predictive biomarker of vaccination

The levels of Siglec-7 expression in 63 CRC tissue samples from 32 HLA-A*24:02-matched patients and 31 HLA-A*24:02-unmatched patients were examined using immunofluorescence (Fig. 1; Table II). The levels of Siglec-7 expression ranged from 0.00001 to 7.81% (median, 0.0279%), and from 0.0400 to 0.457% (median, 0.120%) in HLA-A*24:02-matched and -unmatched patients, respectively. The comprehensive proteomic analysis in the present study was based on the survival of stage IV patients. Since the median OS among stage IV CRC patients is approximately 3 years, the optimal cut-off value was determined using ROC curve analysis at 36 months. This analysis was performed in HLA-A*24:02-matched patients because HLA-restricted peptides vaccines are theoretically effective for these patients. The cut-off value was presented as a percentage of the total positive area of Siglec-7 to the area of the examined fields. A percent of 0.213 was selected as the cut-off value for Siglec-7 expression. In the HLA-A*24:02-matched patients, the OS in patients with high Siglec-7 expression was significantly shorter than that in patients with low Siglec-7 expression (P=0.017; Fig. 3C). In contrast, in the HLA-A*24:02-unmatched patients, there was no significant difference in OS between patients with high or low Siglec-7 expression (P=0.910; Fig. 3D). In patients with low Siglec-7 expression, there was a significant difference in OS between HLA-A*24:02-matched and -unmatched patients (P=0.041; Fig. S1A), whereas there was no significant difference in patients with high Siglec-7 expression (P=0.179; Fig. S1B). The levels of Siglec-7 expression in tumor tissue were correlated with that of CD68 (rs=0.786, P<0.001; Fig. S2A). However, there was no significant difference in OS between patients with high and low levels of CD68 expression in HLA-A*24:02-matched patients (P=0.528; Fig. S2B). These results indicated that Siglec-7 expression in tumor microenvironment might be a predictive biomarker of the efficacy of cancer vaccine therapy.

Table II.

Characteristics of patients in the phase II study whose tissues were analyzed by immunofluorescence.

Table II.

Characteristics of patients in the phase II study whose tissues were analyzed by immunofluorescence.

HLA-A*24:02

CharacteristicsMatched (n=32)Unmatched (n=31)P-value
Age
  Mean67.964.30.069
  Range47-8247-77
Sex
  Male13180.211
  Female1913
Unresectable site
  Liver18240.300
  Lung11  9
  Dissemination  3  2
  Bone  0  2
  Lymph node  3  9
  Other  3  1
Number of metastatic organs
  One26190.068
  Two  6  8
  Three  0  4
Location of tumor
  Colon22240.572
  Rectum10  7

[i] HLA, human leukocyte antigen.

Relationship of TIL infiltration and prognosis with Siglec-7 expression

Because TILs have been reported as biomarkers for CRC, they were analyzed using immunohistochemistry in CRC tissue samples from 32 HLA-A*24:02-matched patients, the same as those used for Siglec-7 analysis (Fig. S3). Using ROC curve analysis at 36 months, the optimal cut-off values were determined as 440.1, 133.8, 52.6 and 17.8 for CD3+, CD4+, CD8+ and FOXP3+ cell densities, respectively. There was no significant difference in OS between patients with high and low numbers of TILs including CD4+, CD8+ and FOXP3+ T cells (P=0.319, 0.605 and 0.242, respectively; Fig. 4), although there was a trend for better OS in patients with high infiltration of CD3+ lymphocytes (P=0.065; Fig. 4A). Next, the correlation between Siglec-7 expression and TILs in CRC tissues was examined. There were no significant associations between the levels of Siglec-7 expression detected in immunofluorescence and the numbers of CD3+, CD4+, CD8+ and FOXP3+ T cells in immunohistochemistry (P=0.565, 0.154, 0.982 and 0.676, respectively; Fig. 5). These findings indicated that lymphocytes and monocytes/macrophage infiltration might be independent.

Discussion

The purpose of the present study was to explore proteins as novel biomarkers to predict the efficacy of immunotherapy before treatment. First, it was demonstrated that high levels of Siglec-7 expression in tumor tissues were associated with shorter OS in patients treated with peptide vaccines for metastatic CRC. Second, it was shown that Siglec-7 was expressed in macrophages in CRC tissue. Further, there was no significant correlation between the level of Siglec-7 expression and the number of TILs in CRC tissue. These results indicated that high levels of Siglec-7 expression in intratumoral macrophages can be a negative biomarker of the vaccine treatment efficacy against metastatic CRC. To our knowledge, this is the first report showing the relationship between Siglec-7 and CRC prognosis.

In the comprehensive proteomic analysis, the good and poor prognosis groups showed significant differences in expression levels of Siglec-7, SHH, and fibronectin. SHH is a ligand for the Hedgehog signaling pathway, which is critical for embryonic development and carcinogenesis (39). Although increased expression of SHH has been associated with poor prognosis in patients with various malignancies, including CRC (40,41), the present study obtained opposite results in this aspect. Furthermore, fibronectin is a ligand for many members of the integrin receptor family and it is involved in cell adhesion, migration, growth, and differentiation (42). Because the relationship between fibronectin and CRC has been already reported (43,44), it was difficult to find additional roles for this protein as a biomarker in cancer vaccination against CRC. For these reasons, SHH and fibronectin were excluded as candidates for predictive biomarkers.

Low levels of Siglec-7 expression in tumor tissue was associated with better prognosis in HLA-A*24:02-matched patients, but not in the unmatched patients. HLA-restricted epitope peptides show theoretical antitumoral effects only in HLA-matched patients. And only HLA-A*24:02-matched patients were considered to be treated with vaccines in the present study. Therefore, the resulting difference in OS based on Siglec-7 expression was only in HLA-A*24:02-matched patients, indicating that Siglec-7 was not a prognostic marker for CRC but a predictive biomarker for cancer vaccination.

Siglec-7, a member of the CD33-related Siglecs, is mainly expressed in NK cells and monocytes/macrophages (22). The distribution of Siglec-7+ cells has been reported to differ between peripheral blood and colonic lamina propria (45). In the peripheral blood, 75% of Siglec-7+ cells were NK cells and 8% were monocytes. In colonic lamina propria, in contrast, 76% of Siglec-7+ cells were monocyte/macrophage lineages and 4% were NK cells. In this study, Siglec-7 was observed mostly in CD68+ cells in CRC tissue, thereby it was suggested that intratumoral macrophages expressed Siglec-7. The role of Siglec-7 in macrophage has been poorly explored, whereas Siglec-9, another CD33-related Siglec that shares 84% sequence homology with Siglec-7, was reported to play an inhibitory role in macrophages (46). Specifically, Siglec-9 mediated reduction in proinflammatory cytokine tumor necrosis factor (TNF)-α production and potent increment in anti-inflammatory cytokine IL-10 production via ITIMs (47). Therefore, it was hypothesized that Siglec-7-expressing macrophages may mediate the reduction in secretion of proinflammatory cytokine TNF-α and increase in secretion of anti-inflammatory cytokine IL-10, resulting in immunosuppression of the tumor microenvironment. MSI status, another factor related to the tumor microenvironment, was also analyzed in the present study, and only one patient had MSI-high CRC (data not shown). Although the level of Siglec-7 expression was low in the MSI-high CRC, the relationship between Siglec-7 expression in CRC tissue and MSI status was not analyzed because it was statistically inappropriate.

Cancer vaccination shows antitumoral effects by introducing tumor antigen-specific cytotoxic T lymphocytes (CTLs). Described as the cancer-immunity cycle (48), injected HLA-restricted epitope peptides are captured and presented to T cells by dendritic cells via HLA molecules. Then, activated tumor antigen-specific CTLs infiltrate the tumor, recognizing and killing target cancer cells. However, CTLs may have their function inhibited by PD-L1 and immunosuppressive mediators such as IL-10 and transforming growth factor-β in the tumor microenvironment (49,50). Siglec-7 may pose an obstacle to CTLs by mediating immunosuppression of tumor microenvironment via regulation of TNF-α and IL-10 secretions, resulting in suppressed efficacy of vaccine treatment against metastatic CRC. These mechanisms may explain the association between high levels of Siglec-7 expression in intratumoral macrophages and poor prognosis in HLA-A*24:02-matched patients.

TILs, especially CD3+ and CD8+ T cells, are prognostic biomarkers for CRC (28,51). For instance, a scoring system based on CD3+ and CD8+ T cells densities within the tumor and its invasive margin, the immunoscore, was demonstrated to be a strong prognostic factor for CRC patients (52,53). In the present study, Siglec-7 expression was not associated with CD3+, CD4+, CD8+ and FOXP3+ T cells. It was suggested that Siglec-7 was an independent biomarker from TILs. The analysis of Siglec-7 might have led to these results by assessing macrophages rather than lymphocytes in the tumor microenvironment.

The present study, however, had several limitations. The first one is the small number of patients enrolled in this study. Second, multivariate analysis, including clinicopathological factors to adjust for confounding factors, was not performed because it was statistically inappropriate due to the small number of patients. The third limitation concerns the lack of mechanistic studies. Nonetheless, understanding the functions of Siglec-7 in the tumor environment might lead to novel immunotherapeutic strategies such as the alteration of cold tumor to hot tumor. For example, because Siglecs are endocytic receptors suitable for drug delivery, the alteration may be achieved by administering a Siglec-7-specific antibody conjugated to toxins or chemotherapeutic agents to deplete Siglec-7-expressing macrophages (54). Finally, the relationship between Siglec-7 expression and other immunologically important molecules including PD-1, PD-L1 and HLA expressions were not evaluated.

In conclusion, Siglec-7 expression in macrophages in tumor tissue might be a novel predictive biomarker for the efficacy of immunotherapy against metastatic CRC. Further studies are needed to confirm the utility of Siglec-7 as a predictive biomarker and to analyze the role of Siglec-7 in the tumor microenvironment.

Supplementary Material

Supporting Data

Acknowledgements

The authors would like to thank Ms. Hiroko Takenouchi (Department of Translational Research and Developmental Therapeutics against Cancer, Yamaguchi University School of Medicine) for her technical support.

Funding

This study was performed as a research program of the Project for Development of Innovative Research on Cancer Therapeutics (P-DIRECT; grant no. 11039020) and The Japan Agency for Medical Research and Development (AMED; grant no. 15cm0106085h0005). This study was supported in part by a grant for Leading Advanced Projects for Medical Innovation (LEAP; grant no. 16am0001006h0003) from the Japan Agency for Medical Research and Development.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Authors' contributions

KY, SH and HN designed the study. KY, NS, MX, NF, RT, SY, STo, SM, HM, YT, SK, YS, YW, MI, STa, TI, TU, YHo, HK, TF and YK contributed to patient recruitment and collection of data, and analysis and interpretation of data. KY, YN, HO and YHa performed the statistical analysis. KY, SH and HN wrote the manuscript. All authors read and approved the final manuscript.

Ethics approval and consent to participate

The study was carried out according to the Declaration of Helsinki on experimentation on human subjects and was approved by the Institutional Ethics Review Boards of Yamaguchi University (approval number: H20-102; Clinical Trials Registry: UMIN000001791). Written informed consent for participation in this study was obtained from each patient.

Patient consent for publication

Written informed consent for publication was obtained from each patient at the time of enrollment.

Competing interests

The authors declare that they have no competing interests.

References

1 

Siegel RL, Miller KD and Jemal A: Cancer statistics, 2020. CA Cancer J Clin. 70:7–30. 2020. View Article : Google Scholar : PubMed/NCBI

2 

Loupakis F, Cremolini C, Masi G, Lonardi S, Zagonel V, Salvatore L, Cortesi E, Tomasello G, Ronzoni M, Spadi R, et al: Initial therapy with FOLFOXIRI and bevacizumab for metastatic colorectal cancer. N Engl J Med. 371:1609–1618. 2014. View Article : Google Scholar : PubMed/NCBI

3 

Sanchez-Castañón M, Er TK, Bujanda L and Herreros-Villanueva M: Immunotherapy in colorectal cancer: What have we learned so far? Clin Chim Acta. 460:78–87. 2016. View Article : Google Scholar : PubMed/NCBI

4 

Myint ZW and Goel G: Role of modern immunotherapy in gastrointestinal malignancies: A review of current clinical progress. J Hematol Oncol. 10:862017. View Article : Google Scholar : PubMed/NCBI

5 

Hazama S, Tamada K, Yamaguchi Y, Kawakami Y and Nagano H: Current status of immunotherapy against gastrointestinal cancers and its biomarkers: Perspective for precision immunotherapy. Ann Gastroenterol Surg. 2:289–303. 2018. View Article : Google Scholar : PubMed/NCBI

6 

Kono K: Advances in cancer immunotherapy for gastroenterological malignancy. Ann Gastroenterol Surg. 2:244–245. 2018. View Article : Google Scholar : PubMed/NCBI

7 

Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, et al: Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 357:409–413. 2017. View Article : Google Scholar : PubMed/NCBI

8 

Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz HJ, Morse MA, Desai J, Hill A, Axelson M, Moss RA, et al: Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): An open-label, multicentre, phase 2 study. Lancet Oncol. 18:1182–1191. 2017. View Article : Google Scholar : PubMed/NCBI

9 

Nagorsen D and Thiel E: Clinical and immunologic responses to active specific cancer vaccines in human colorectal cancer. Clin Cancer Res. 12:3064–3069. 2006. View Article : Google Scholar : PubMed/NCBI

10 

Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, et al: PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med. 372:2509–2520. 2015. View Article : Google Scholar : PubMed/NCBI

11 

Duan Q, Zhang H, Zheng J and Zhang L: Turning cold into hot: Firing up the tumor microenvironment. Trends Cancer. 6:605–618. 2020. View Article : Google Scholar : PubMed/NCBI

12 

Hazama S, Nakamura Y, Takenouchi H, Suzuki N, Tsunedomi R, Inoue Y, Tokuhisa Y, Iizuka N, Yoshino S, Takeda K, et al: A phase I study of combination vaccine treatment of five therapeutic epitope-peptides for metastatic colorectal cancer; safety, immunological response, and clinical outcome. J Transl Med. 12:632014. View Article : Google Scholar : PubMed/NCBI

13 

Hazama S, Nakamura Y, Tanaka H, Hirakawa K, Tahara K, Shimizu R, Ozasa H, Etoh R, Sugiura F, Okuno K, et al: A phase II study of five peptides combination with oxaliplatin-based chemotherapy as a first-line therapy for advanced colorectal cancer (FXV study). J Transl Med. 12:1082014. View Article : Google Scholar : PubMed/NCBI

14 

Hazama S, Takenouchi H, Tsunedomi R, Iida M, Suzuki N, Iizuka N, Inoue Y, Sakamoto K, Nakao M, Shindo Y, et al: Predictive biomarkers for the outcome of vaccination of five therapeutic epitope peptides for colorectal cancer. Anticancer Res. 34:4201–4205. 2014.PubMed/NCBI

15 

Shindo Y, Hazama S, Nakamura Y, Inoue Y, Kanekiyo S, Suzuki N, Takenouchi H, Tsunedomi R, Nakajima M, Ueno T, et al: miR-196b, miR-378a and miR-486 are predictive biomarkers for the efficacy of vaccine treatment in colorectal cancer. Oncol Lett. 14:1355–1362. 2017. View Article : Google Scholar : PubMed/NCBI

16 

Kijima T, Hazama S, Tsunedomi R, Tanaka H, Takenouchi H, Kanekiyo S, Inoue Y, Nakashima M, Iida M, Sakamoto K, et al: MicroRNA-6826 and −6875 in plasma are valuable non invasive biomarkers that predict the efficacy of vaccine treatment against metastatic colorectal cancer. Oncol Rep. 37:23–30. 2017. View Article : Google Scholar : PubMed/NCBI

17 

Tanaka H, Hazama S, Iida M, Tsunedomi R, Takenouchi H, Nakajima M, Tokumitsu Y, Kanekiyo S, Shindo Y, Tomochika S, et al: miR-125b-1 and miR-378a are predictive biomarkers for the efficacy of vaccine treatment against colorectal cancer. Cancer Sci. 108:2229–2238. 2017. View Article : Google Scholar : PubMed/NCBI

18 

Kitahara M, Hazama S, Tsunedomi R, Takenouchi H, Kanekiyo S, Inoue Y, Nakajima M, Tomochika S, Tokuhisa Y, Iida M, et al: Prediction of the efficacy of immunotherapy by measuring the integrity of cell-free DNA in plasma in colorectal cancer. Cancer Sci. 107:1825–1829. 2016. View Article : Google Scholar : PubMed/NCBI

19 

Crocker PR, Paulson JC and Varki A: Siglecs and their roles in the immune system. Nat Rev Immunol. 7:255–266. 2007. View Article : Google Scholar : PubMed/NCBI

20 

Fraschilla I and Pillai S: Viewing Siglecs through the lens of tumor immunology. Immunol Rev. 276:178–191. 2017. View Article : Google Scholar : PubMed/NCBI

21 

Matsumoto T, Takahashi N, Kojima T, Yoshioka Y, Ishikawa J, Furukawa K, Ono K, Sawada M, Ishiguro N and Yamamoto A: Soluble Siglec-9 suppresses arthritis in a collagen-induced arthritis mouse model and inhibits M1 activation of RAW264.7 macrophages. Arthritis Res Ther. 18:1332016. View Article : Google Scholar : PubMed/NCBI

22 

Nicoll G, Ni J, Liu D, Klenerman P, Munday J, Dubock S, Mattei MG and Crocker PR: Identification and characterization of a novel siglec, siglec-7, expressed by human natural killer cells and monocytes. J Biol Chem. 274:34089–34095. 1999. View Article : Google Scholar : PubMed/NCBI

23 

Daly J, Carlsten M and O'Dwyer M: Sugar free: Novel immunotherapeutic approaches targeting siglecs and sialic acids to enhance natural killer cell cytotoxicity against cancer. Front Immunol. 10:10472019. View Article : Google Scholar : PubMed/NCBI

24 

Suda T, Tsunoda T, Daigo Y, Nakamura Y and Tahara H: Identification of human leukocyte antigen-A24-restricted epitope peptides derived from gene products upregulated in lung and esophageal cancers as novel targets for immunotherapy. Cancer Sci. 98:1803–1808. 2007. View Article : Google Scholar : PubMed/NCBI

25 

Shimokawa T, Matsushima S, Tsunoda T, Tahara H, Nakamura Y and Furukawa Y: Identification of TOMM34, which shows elevated expression in the majority of human colon cancers, as a novel drug target. Int J Oncol. 29:381–386. 2006.PubMed/NCBI

26 

Uchida N, Tsunoda T, Wada S, Furukawa Y, Nakamura Y and Tahara H: Ring finger protein 43 as a new target for cancer immunotherapy. Clin Cancer Res. 10:8577–8586. 2004. View Article : Google Scholar : PubMed/NCBI

27 

Ishizaki H, Tsunoda T, Wada S, Yamauchi M, Shibuya M and Tahara H: Inhibition of tumor growth with antiangiogenic cancer vaccine using epitope peptides derived from human vascular endothelial growth factor receptor 1. Clin Cancer Res. 12:5841–5849. 2006. View Article : Google Scholar : PubMed/NCBI

28 

Wada S, Tsunoda T, Baba T, Primus FJ, Kuwano H, Shibuya M and Tahara H: Rationale for antiangiogenic cancer therapy with vaccination using epitope peptides derived from human vascular endothelial growth factor receptor 2. Cancer Res. 65:4939–4946. 2005. View Article : Google Scholar : PubMed/NCBI

29 

Nakashima-Nakasuga C, Hazama S, Suzuki N, Nakagami Y, Xu M, Yoshida S, Tomochika S, Fujiwara N, Matsukuma S, Matsui H, et al: Serum LOX-1 is a novel prognostic biomarker of colorectal cancer. Int J Clin Oncol. 25:1308–1317. 2020. View Article : Google Scholar : PubMed/NCBI

30 

Fujiwara N, Usui T, Ohama T and Sato K: Regulation of beclin 1 protein phosphorylation and autophagy by protein phosphatase 2A (PP2A) and death-associated protein kinase 3 (DAPK3). J Biol Chem. 291:10858–10866. 2016. View Article : Google Scholar : PubMed/NCBI

31 

Schneider CA, Rasband WS and Eliceiri KW: NIH Image to ImageJ: 25 years of image analysis. Nat Methods. 9:671–675. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Enjoji S, Yabe R, Tsuji S, Yoshimura K, Kawasaki H, Sakurai M, Sakai Y, Takenouchi H, Yoshino S, Hazama S, et al: Stemness is enhanced in gastric cancer by a SET/PP2A/E2F1 axis. Mol Cancer Res. 16:554–563. 2018. View Article : Google Scholar : PubMed/NCBI

33 

Yabe R, Tsuji S, Mochida S, Ikehara T, Usui T, Ohama T and Sato K: A stable association with PME-1 may be dispensable for PP2A demethylation - implications for the detection of PP2A methylation and immunoprecipitation. FEBS Open Bio. 8:1486–1496. 2018. View Article : Google Scholar : PubMed/NCBI

34 

Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, Tosolini M, Camus M, Berger A, Wind P, et al: Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 313:1960–1964. 2006. View Article : Google Scholar : PubMed/NCBI

35 

Kuwahara T, Hazama S, Suzuki N, Yoshida S, Tomochika S, Nakagami Y, Matsui H, Shindo Y, Kanekiyo S, Tokumitsu Y, et al: Intratumoural-infiltrating CD4+ and FOXP3+ T cells as strong positive predictive markers for the prognosis of resectable colorectal cancer. Br J Cancer. 121:659–665. 2019. View Article : Google Scholar : PubMed/NCBI

36 

Iizuka N, Oka M, Yamada-Okabe H, Nishida M, Maeda Y, Mori N, Takao T, Tamesa T, Tangoku A, Tabuchi H, et al: Oligonucleotide microarray for prediction of early intrahepatic recurrence of hepatocellular carcinoma after curative resection. Lancet. 361:923–929. 2003. View Article : Google Scholar : PubMed/NCBI

37 

Zimmerman DW: Some properties of preliminary tests of equality of variances in the two-sample location problem. J Gen Psychol. 123:217–231. 1996. View Article : Google Scholar

38 

Kamarudin AN, Cox T and Kolamunnage-Dona R: Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med Res Methodol. 17:532017. View Article : Google Scholar : PubMed/NCBI

39 

Yoshikawa K, Shimada M, Miyamoto H, Higashijima J, Miyatani T, Nishioka M, Kurita N, Iwata T and Uehara H: Sonic hedgehog relates to colorectal carcinogenesis. J Gastroenterol. 44:1113–1117. 2009. View Article : Google Scholar : PubMed/NCBI

40 

Xu M, Li X, Liu T, Leng A and Zhang G: Prognostic value of hedgehog signaling pathway in patients with colon cancer. Med Oncol. 29:1010–1016. 2012. View Article : Google Scholar : PubMed/NCBI

41 

Maréchal R, Bachet JB, Calomme A, Demetter P, Delpero JR, Svrcek M, Cros J, Bardier-Dupas A, Puleo F, Monges G, et al: Sonic hedgehog and Gli1 expression predict outcome in resected pancreatic adenocarcinoma. Clin Cancer Res. 21:1215–1224. 2015. View Article : Google Scholar : PubMed/NCBI

42 

Pankov R and Yamada KM: Fibronectin at a glance. J Cell Sci. 115:3861–3863. 2002. View Article : Google Scholar : PubMed/NCBI

43 

Yi W, Xiao E, Ding R, Luo P and Yang Y: High expression of fibronectin is associated with poor prognosis, cell proliferation and malignancy via the NF-κB/p53-apoptosis signaling pathway in colorectal cancer. Oncol Rep. 36:3145–3153. 2016. View Article : Google Scholar : PubMed/NCBI

44 

Inufusa H, Nakamura M, Adachi T, Nakatani Y, Shindo K, Yasutomi M and Matsuura H: Localization of oncofetal and normal fibronectin in colorectal cancer. Correlation with histologic grade, liver metastasis, and prognosis. Cancer. 75:2802–2808. 1995. View Article : Google Scholar : PubMed/NCBI

45 

Miyazaki K, Sakuma K, Kawamura YI, Izawa M, Ohmori K, Mitsuki M, Yamaji T, Hashimoto Y, Suzuki A, Saito Y, et al: Colonic epithelial cells express specific ligands for mucosal macrophage immunosuppressive receptors siglec-7 and −9. J Immunol. 188:4690–4700. 2012. View Article : Google Scholar : PubMed/NCBI

46 

Dharmadhikari G, Stolz K, Hauke M, Morgan NG, Varki A, de Koning E, Kelm S and Maedler K: Siglec-7 restores β-cell function and survival and reduces inflammation in pancreatic islets from patients with diabetes. Sci Rep. 7:453192017. View Article : Google Scholar : PubMed/NCBI

47 

Ando M, Tu W, Nishijima K and Iijima S: Siglec-9 enhances IL-10 production in macrophages via tyrosine-based motifs. Biochem Biophys Res Commun. 369:878–883. 2008. View Article : Google Scholar : PubMed/NCBI

48 

Chen DS and Mellman I: Oncology meets immunology: The cancer-immunity cycle. Immunity. 39:1–10. 2013. View Article : Google Scholar : PubMed/NCBI

49 

Dong H, Strome SE, Salomao DR, Tamura H, Hirano F, Flies DB, Roche PC, Lu J, Zhu G, Tamada K, et al: Tumor-associated B7-H1 promotes T-cell apoptosis: A potential mechanism of immune evasion. Nat Med. 8:793–800. 2002. View Article : Google Scholar : PubMed/NCBI

50 

Vermaelen K: Vaccine strategies to improve anti-cancer cellular immune responses. Front Immunol. 10:82019. View Article : Google Scholar : PubMed/NCBI

51 

Mlecnik B, Tosolini M, Kirilovsky A, Berger A, Bindea G, Meatchi T, Bruneval P, Trajanoski Z, Fridman WH, Pagès F, et al: Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J Clin Oncol. 29:610–618. 2011. View Article : Google Scholar : PubMed/NCBI

52 

Galon J, Mlecnik B, Bindea G, Angell HK, Berger A, Lagorce C, Lugli A, Zlobec I, Hartmann A, Bifulco C, et al: Towards the introduction of the ‘Immunoscore’ in the classification of malignant tumours. J Pathol. 232:199–209. 2014. View Article : Google Scholar : PubMed/NCBI

53 

Pagès F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, Lugli A, Zlobec I, Rau TT, Berger MD, et al: International validation of the consensus Immunoscore for the classification of colon cancer: A prognostic and accuracy study. Lancet. 391:2128–2139. 2018. View Article : Google Scholar : PubMed/NCBI

54 

O'Reilly MK and Paulson JC: Siglecs as targets for therapy in immune-cell-mediated disease. Trends Pharmacol Sci. 30:240–248. 2009. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

January-2021
Volume 21 Issue 1

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Yamada K, Hazama S, Suzuki N, Xu M, Nakagami Y, Fujiwara N, Tsunedomi R, Yoshida S, Tomochika S, Matsukuma S, Matsukuma S, et al: Siglec-7 is a predictive biomarker for the efficacy of cancer vaccination against metastatic colorectal cancer. Oncol Lett 21: 10, 2021.
APA
Yamada, K., Hazama, S., Suzuki, N., Xu, M., Nakagami, Y., Fujiwara, N. ... Nagano, H. (2021). Siglec-7 is a predictive biomarker for the efficacy of cancer vaccination against metastatic colorectal cancer. Oncology Letters, 21, 10. https://doi.org/10.3892/ol.2020.12271
MLA
Yamada, K., Hazama, S., Suzuki, N., Xu, M., Nakagami, Y., Fujiwara, N., Tsunedomi, R., Yoshida, S., Tomochika, S., Matsukuma, S., Matsui, H., Tokumitsu, Y., Kanekiyo, S., Shindo, Y., Watanabe, Y., Iida, M., Takeda, S., Ioka, T., Ueno, T., Ogihara, H., Hamamoto, Y., Hoshii, Y., Kawano, H., Fujita, T., Kawakami, Y., Nagano, H."Siglec-7 is a predictive biomarker for the efficacy of cancer vaccination against metastatic colorectal cancer". Oncology Letters 21.1 (2021): 10.
Chicago
Yamada, K., Hazama, S., Suzuki, N., Xu, M., Nakagami, Y., Fujiwara, N., Tsunedomi, R., Yoshida, S., Tomochika, S., Matsukuma, S., Matsui, H., Tokumitsu, Y., Kanekiyo, S., Shindo, Y., Watanabe, Y., Iida, M., Takeda, S., Ioka, T., Ueno, T., Ogihara, H., Hamamoto, Y., Hoshii, Y., Kawano, H., Fujita, T., Kawakami, Y., Nagano, H."Siglec-7 is a predictive biomarker for the efficacy of cancer vaccination against metastatic colorectal cancer". Oncology Letters 21, no. 1 (2021): 10. https://doi.org/10.3892/ol.2020.12271