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Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures
Published version of a preprint
Abstract
Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNγ. Activation of transforming growth factor β, epithelial–mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33–0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46–0.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07–0.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.
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“Recent studies reported that alteration of tumour immune microenvironment in gastric cancer is a pivotal strategy for predicting the immunotherapeutic responses and prognosis, which might provide new treatment strategies for patients [26, 27].”
From Phosphoserine phosphatase as a prognostic biomarker in patients with gastric cancer and its potential association with immune cells (2022) · cites this paper as [27]
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“13); (vi) DNA replication–dependent histones (13); (vii) selectmembers of theDDR-relevant gene set (39); (viii) Angiogenesis signature previously reported (40); (ix) cellcycle genes (KEGG); (x) WNT targets (41); (xi) cell-cycle regulators (42); (xii)mismatch repair (KEGG); (xiii) nucleotide excision repair (KEGG); (xi…”
BackgroundResting dendritic cells induce peripheral CD8+ T cell tolerance through PD-1 and CTLA-4
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Ulloa-Montoya, F., Louahed, J., Dizier, B., Gruselle, O., Spiessens, B., Lehmann, F. F., Suciu, S., Kruit, W. H. J., Eggermont, A. M. M., Vansteenkiste, J., et al. (2013) · Journal of Clinical Oncology · doi:10.1200/jco.2012.44.3762 · Cited by 436 · cited 2×
“Genomic and clinical data sets with immune-checkpoint blockade Five genomic and transcriptomic data sets from patients with metastatic urothelial cancer (13) treatedwith ananti–PD-L1 agent (atezolizumab), patients with metastatic melanoma (43) treated with anti–PD-1 (pembrolizumab), patients with advanced melanoma tre…”
MethodClinical and genomic landscape of gastric cancer with a mesenchymal phenotype
Oh, S. C., Sohn, B. H., Cheong, J., Kim, S.-B., Lee, J. E., Park, K. C., Lee, S. H., Park, Y.-Y., Park, Y.-Y., Lee, H., et al. (2018) · Nature Communications · doi:10.1038/s41467-018-04179-8 · Cited by 430 · cited 1×
“Genomic analysis has been the primary methodology used in international efforts to discover novel biological targets in gastric cancer (1, 2), although this method has not led to the successful discovery of distinct mechanisms.”
MethodBiostatistics series module 3: Comparing groups: Numerical variables
Hazra, A., & Gogtay, N. (2016) · Indian Journal of Dermatology · doi:10.4103/0019-5154.182416 · Cited by 354 · cited 1×
“For comparisons of more than two groups, Kruskal–Wallis tests and one-way analysis of variance were used as nonparametric and parametric methods, respectively (48).”
MethodControl of the Immune Response by Pro-Angiogenic Factors
Voron, T., Marcheteau, E., Pernot, S., Colussi, O., Tartour, É., Taı̈eb, J., & Terme, M. (2014) · Frontiers in Oncology · doi:10.3389/fonc.2014.00070 · Cited by 337 · cited 2×
“These data offer mechanistic insights into the responses to immune-checkpoint blockade, suggesting that response to PD-L1 and PD-1 blockade is not only related to enhanced cytolytic activity, antigen processing, and IFNg pathway components (13, 75), but is also associated with inhibition of fibroblast activation, angi…”
BackgroundRavi, R., Noonan, K. A., Pham, V., Bedi, R., Zhavoronkov, A., Ozerov, I. V., Makarev, E., V. Artemov, A., Wysocki, P. T., Mehra, R., et al. (2018) · Nature Communications · doi:10.1038/s41467-017-02696-6 · Cited by 325
Mast cells, angiogenesis, and tumour growth
Ribatti, D., & Crivellato, E. (2010) · Biochimica et Biophysica Acta (BBA) - Molecular Basis of Disease · doi:10.1016/j.bbadis.2010.11.010 · Cited by 237 · cited 2×
“S2A–D): TMEcluster-A was characterized by increases in the infiltration of cancer-associated fibroblasts, M2 macrophages, resting DCs, and resting MCs (43-46) and exhibited variable decreases in other TME cell types; TMEcluster-B exhibited high infiltration of M0 macrophages,”
BackgroundImmune Activation and Benefit From Avelumab in EBV-Positive Gastric Cancer
Panda, A., Mehnert, J. M., Hirshfield, K. M., Riedlinger, G., Damare, S., Saunders, T., Kane, M., Sokol, L. O., Stein, M. N., Poplin, E. A., et al. (2017) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djx213 · Cited by 209 · cited 6×
“Several studies have indicated that EBVþ gastric cancer does not exhibit higher TMB or MSI, but can respond to immune-checkpoint therapy (15, 63), suggesting that the TMEscore may be more useful for predicting clinical benefits in patients with gastric cancer treated with immunotherapy than TMB or MSI.”
BackgroundMethodInfluentialGene expression profiles for a prognostic immunoscore in gastric cancer
Zeng, D., Zhou, R., Yu, Y., Luo, Y., Zhang, J., Sun, H., Bin, J., Liao, Y., Rao, J., Zhang, Y., et al. (2018) · British journal of surgery · doi:10.1002/bjs.10871 · Cited by 199 · cited 3×
“S2E; Supplementary Table S6), which we established based on a lasso immune signature score model in a previous study (7) to predict survival outcomes in patients with gastric cancer.”
BackgroundMethodSelf-citationFu, H., Zhu, Y., Wang, Y., Liu, Z., Zhang, J., Xie, H., Fu, Q., Dai, B., Ye, D., & Xu, J. (2018) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-17-2687 · Cited by 172 · cited 1×
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
BackgroundSelf-citationImmune Response and the Tumor Microenvironment: How They Communicate to Regulate Gastric Cancer
Lee, K., Hwang, H. K., & Nam, K. T. (2014) · Gut and Liver · doi:10.5009/gnl.2014.8.2.131 · Cited by 159 · cited 1×
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
BackgroundGenomic landscape associated with potential response to anti-CTLA-4 treatment in cancers
Ock, C.-Y., Hwang, J., Keam, B., Kim, S.-B., Shim, J., Jang, H.-J., Park, S., Sohn, B. H., Cha, M., Ajani, J. A., et al. (2017) · Nature Communications · doi:10.1038/s41467-017-01018-0 · Cited by 136 · cited 1×
“Consistent with a previous study about an immune signature score (74), we observed a significantly higher TMEscores in responders than in nonresponders undergoing checkpoint blockade therapy.”
ResultGentles, A. J., Bratman, S. V., Lee, L. J., Harris, J. P., Feng, W., Nair, R. V., Shultz, D. B., Nair, V. S., Hoang, C. D., West, R. B., et al. (2015) · JNCI Journal of the National Cancer Institute · doi:10.1093/jnci/djv211 · Cited by 76 · cited 1×
“Although several studies using these methodologies have explored the clinical utility of TME infiltrates (7, 19), and although several mechanisms associated with the role of TME in immunotherapy response and resistance have been experimentally identified for some tumor types (4, 13, 14, 20, 21), to date, the comprehen…”
BackgroundLesterhuis, W. J., Rinaldi, C., Jones, A. C., Rozali, E. N., Dick, I. M., Khong, A., Boon, L., Robinson, B. W. S., Nowak, A. K., Bosco, A., et al. (2015) · Scientific Reports · doi:10.1038/srep12298 · Cited by 69
Hwang, M. L., Lukens, J. R., & Bullock, T. N. J. (2007) · The Journal of Immunology · doi:10.4049/jimmunol.179.9.5829 · Cited by 40
maxstat: Maximally Selected Rank Statistics
Hothorn, T. (2001) · doi:10.32614/cran.package.maxstat · Cited by 24
Cited by
Sorted by citations
Zeng, D., Ye, Z., Shen, R., Yu, G., Wu, J., Xiong, Y., Zhou, R., Qiu, W., Huang, N., Sun, L., et al. (2021) · Frontiers in Immunology · doi:10.3389/fimmu.2021.687975 · Cited by 1,274
Self-citationZhang, B., Wu, Q., Li, B., Wang, D., Wang, L., & Zhou, Y. L. (2020) · Molecular Cancer · doi:10.1186/s12943-020-01170-0 · Cited by 940
Sathe, A., Grimes, S. M., Lau, B. T., Chen, J., Suarez, C. J., Huang, R. J., Poultsides, G. A., & Ji, H. P. (2020) · Clinical Cancer Research · doi:10.1158/1078-0432.ccr-19-3231 · Cited by 395
“This resembles findings from recent studies in gastric cancer where molecular subtypes could be differentiated by gene signatures based on a combination of immunosuppression, immune activation, and stromal activation processes rather than the leucocyte fractions within the TME (48, 49).”
ResultGambardella, V., Castillo, J., Tarazona, N., Gimeno-Valiente, F., Martínez‐Ciarpaglini, C., Cabeza-Segura, M., Roselló, S., Roda, D., Huerta, M., Cervantes, A. M. R., et al. (2020) · Cancer Treatment Reviews · doi:10.1016/j.ctrv.2020.102015 · Cited by 337
Wu, J., Li, L., Zhang, H., Zhao, Y., Zhang, H., Wu, S., & Xu, B. (2021) · Oncogene · doi:10.1038/s41388-021-01853-y · Cited by 255
Metadata
- Authors
- Zeng, D., Li, M., Zhou, R., Zhang, J., Sun, H., Shi, M., Bin, J., Liao, Y., Rao, J., & Liao, W.
- Affiliations
- Nanfang Hospital (CN)
- Southern Medical University (CN)
- ORCID
- Venue
- Cancer Immunology Research
- Published
- 2019-05-01
- Volume
- 7
- Issue
- 5
- Pages
- 737-750
- ISSN
- 2326-6066 (ISSN-L)
- 2326-6074
- OpenAlex
- W2920600229
- Semantic Scholar
- 180648d1827a7e8c086f03b00ca69831a1ca538e
- Cited by
- 986
- References
- 76
- Citations by year
- 2026: 38 · 2025: 87 · 2024: 164 · 2023: 211 · 2022: 251 · 2021: 162 · 2020: 64 · 2019: 9
- FWCI
- 53.10
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Cancer Immunology Research (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Natural Science Foundation of China (81772580)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Oncology › Gastric Cancer Management and Outcomes
- Life Sciences › Immunology and Microbiology › Immunology › Immune cells in cancer
- Keywords
- gastric cancer, tumor microenvironment, prognostic factors, immunotherapy response, immune cell infiltration, T cell suppression, epithelial-mesenchymal transition, angiogenesis, TGF-beta signaling, IFN-gamma response, gene expression signatures, IMvigor210
- MeSH terms
- Antineoplastic Agents, Immunological, Algorithms, Humans, Immunotherapy, Phenotype, Prognosis, Stomach Neoplasms, Gene Expression Regulation, Neoplastic, Tumor Microenvironment
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