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Comprehensive analysis of immune subtypes reveals the prognostic value of cytotoxicity and FAP+ fibroblasts in stomach adenocarcinoma

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Abstract

Background

The heterogeneity limits the effective application of immune checkpoint inhibitors for patients with stomach adenocarcinoma (STAD). Precise immunotyping can help select people who may benefit from immunotherapy and guide postoperative management by describing the characteristics of tumor microenvironment.

Methods

Gene expression profiles and clinical information of patients were collected from ACRG and TCGA-STAD datasets. The immune subtypes (ISs) were identified by consensus clustering analysis. The tumor immune microenvironments (TIME) of each IS were characterized using a series of immunogenomics methods and further confirmed by multiplex immunohistochemistry (mIHC) staining in clinical samples. Two online datasets and one in-house dataset were utilized to construct and validate a prognostic immune-related gene (IRG) signature.

Results

STAD patients were stratified into five reproducible ISs. IS1 (immune deserve subtype) had low immune infiltration and the highest degree of HER2 gene mutation. With abundant CD8+ T cells infiltration and activated cytotoxicity reaction, patients in the IS2 (immune-activated subtype) had the best overall survival (OS). IS3 and IS4 subtypes were both in the reactive stroma state and indicated the worst prognosis. However, IS3 (immune-inhibited subtype) was characterized by enrichment of FAP+ fibroblasts and upregulated TGF-β signaling pathway, while IS4 (activated stroma subtype) was characterized by enrichment of ACTA2+ fibroblasts. In addition, mIHC staining confirmed that TGF-β upregulated FAP+ fibroblasts were independent risk factor of OS. IS5 (chronic inflammation subtype) displayed moderate immune cells infiltration and had a relatively good survival. Lastly, we developed a nine-IRG signature model with a robust performance on overall survival prognostication.

Conclusions

The immunotyping is indicative for characterize the TIME heterogeneity and the prediction of tumor prognosis for STADs, which may provide valuable stratification for the design of future immunotherapy.

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Availability of data and materials

All online data described in this article are available from the corresponding web servers and are freely available to any scientist wishing to use them for noncommercial purposes, without breaching participant confidentiality. Further information is available from the corresponding author on reasonable request. For the in-house data, please contact author for data requests.

Abbreviations

ACRG:

Asian Cancer Research Group

ARID1A:

AT-rich interaction domain 1A

AUC:

Area under the curve

CIN:

Chromosomal instability

CLDN18.2:

Claudin 18.2

CNV:

Copy number variation

CSMD3:

Sushi multiple domains 3

CT:

Cycle threshold

EBV:

Epstein–Barr virus

ERBB2:

Erb-B2 receptor tyrosine kinase 2

EZH2:

Enhancer of zeste homolog 2

FUSCC:

Fudan University Shanghai Cancer Center

GEO:

Gene expression omnibus

GO:

Gene ontology

GS:

Genomically stable

GSEA:

Gene set enrichment analysis

IGM:

Immune gene modules

IGP:

In-group proportion

IRG:

Immune‐related gene

ISs:

Immune subtypes

LASSO:

Least absolute shrinkage and selection operator

mIHC:

Multiplex immunohistochemistry

MMR:

Recruit mismatch repair

MSI:

Microsatellite instability

MSS:

Microsatellite stable

OS:

Overall survival

PAM:

Partition around medoids

PD-L1:

Programmed death-ligand 1

ROC:

Receiver operating characteristic

STAD:

Stomach adenocarcinoma

TCGA:

The cancer genome atlas

TIME:

Tumor immune microenvironment

TMA:

Tissue microarray

TME:

Tumor microenvironment

TSR:

Tumor–stroma ratio

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Acknowledgements

We would like to thank TCGA (http://cancergenome.nih.gov) and the GEO database (https://www.ncbi.nlm.nih.gov/geo/) for data collection.

Funding

This work was supported by National Natural Science Foundation of China (82273370, 82202899, 82172702, 81972249, 81902430), Shanghai Clinical Science and Technology Innovation Project of Municipal Hospital (SHDC12020102), Clinical Research Project of Shanghai Shenkang Hospital Development Center (SHDC2020CR4068), Natural Science Foundation of Shanghai (22ZR1413000, 21ZR1414900), Artificial Intelligence Medical Hospital Cooperation Project of Xuhui District (2021-017), Shanghai Science and technology development fund (19MC1911000), Shanghai Municipal Key Clinical Specialty (shslczdzk01301), Clinical Research Project of Shanghai Municipal Health Committee (20194Y0348), Shanghai “Rising Stars of Medical Talents” Youth Development Program Youth Medical Talents—Specialist Program (SHWSRS(2020)_087), and the Interdisciplinary Program of Shanghai Jiao Tong University (YG2019QNA40).

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Author notes

  1. Xin Wang, Sun Hui and Cong Tan contributed equally to the paper.

Authors and Affiliations

  1. Department of Pathology, Fudan University Shanghai Cancer Center, Shanghai, 200032, People’s Republic of China

    Xin Wang, Sun Hui, Cong Tan, Xu Wang, Weiwei Weng, Meng Zhang, Shujuan Ni, Lei Wang, Dan Huang, Midie Xu & Weiqi Sheng

  2. Department of Oncology, Shanghai Medical College, Fudan University, Shanghai, 200032, People’s Republic of China

    Xin Wang, Sun Hui, Cong Tan, Xu Wang, Weiwei Weng, Meng Zhang, Shujuan Ni, Lei Wang, Dan Huang, Midie Xu & Weiqi Sheng

  3. Institute of Pathology, Fudan University, Shanghai, 200032, People’s Republic of China

    Xin Wang, Sun Hui, Cong Tan, Xu Wang, Weiwei Weng, Meng Zhang, Shujuan Ni, Lei Wang, Dan Huang, Midie Xu & Weiqi Sheng

  4. Department of Oncology, Xinhua Hospital, School of Medicine, Shanghai Jiaotong University, Shanghai, 200092, People’s Republic of China

    Zhenzhong Deng

  5. Shanghai Urological Cancer Institute, Cancer Institute, Fudan University Shanghai Cancer Center, Fudan University, Shanghai, 200032, People’s Republic of China

    Wenfeng Wang

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  1. Xin Wang
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Contributions

XiW, MX, and CT conceived the study, performed the literature search and bioinformatics analysis, and prepared the figures. XiW, HS, XuW, We W, MZ, SN, ZD, and WaW helped with data collection, analysis, and interpretation. MX, Wa W, and WS wrote and revised the manuscript. XiW, HS, and CT contributed equally to this work. WS, MX and Wa W share the corresponding authorship of this study. All authors read and approved the final manuscript.

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Correspondence to Wenfeng Wang, Midie Xu or Weiqi Sheng.

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Ethics approval and consent to participate for the study were obtained from the Clinical Research Ethics Committee of Fudan University Shanghai Cancer Center (FUSCC), Ethical code: 050432-4-1212B.

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262_2023_3368_MOESM1_ESM.tif

Supplementary Figure 1. Consensus clustering results and validation between datasets. (A, B) Sample cumulative distribution function (CDF) curve (A) and delta area curve (B) of consensus clustering in the ACRG dataset, indicating the relative change in area under the CDF curve for each category number k compared with k – 1. The horizontal axis represents the category number k and the vertical axis represents the relative change in area under CDF curve. (C) Consensus matrix with consensus k=5 in the ACRG dataset. (D, E) CDF curve (D) and delta area curve (E) of the TCGA-STAD dataset. (F) Correlation of average IGMs scores of the ACRG and the TCGA-STAD dataset. (G) IGP evaluates the similarity and reproducibility of the proposed IGMs between discovery and validation cohorts. (TIF 2366 KB)

262_2023_3368_MOESM2_ESM.tif

Supplementary Figure 2. Abundance of immune cells (A) and ssGSEA heatmap (B) within ISs of the TCGA-STAD dataset. (TIF 11550 KB)

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Wang, X., Hui, S., Tan, C. et al. Comprehensive analysis of immune subtypes reveals the prognostic value of cytotoxicity and FAP+ fibroblasts in stomach adenocarcinoma. Cancer Immunol Immunother 72, 1763–1778 (2023). https://doi.org/10.1007/s00262-023-03368-9

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