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Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy

  • Authors:
    • Liping Wang
    • Xinliang Zhong
    • Mengxing Diao
    • Wei Deng
  • View Affiliations / Copyright

    Affiliations: Department of Medical Oncology, Southern University of Science and Technology Hospital, Shenzhen, Guangdong 518055, P.R. China, Department of Pathology, Southern University of Science and Technology Hospital, Shenzhen, Guangdong 518055, P.R. China
    Copyright: © Wang et al. This is an open access article distributed under the terms of Creative Commons Attribution License.
  • Article Number: 119
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    Published online on: January 27, 2026
       https://doi.org/10.3892/ol.2026.15472
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Abstract

Deficient mismatch repair (dMMR) in gastric cancer is associated with a high tumor mutation burden (TMB) and increased neoantigen load, which may enhance sensitivity to immunotherapy. Nevertheless, heterogeneous responses among patients with dMMR suggest complex tumor immune microenvironment (TIME) regulation. The present study aimed to integrate a case of dMMR gastric cancer with multi‑cohort bioinformatic analyses to assess TIME determinants of immunotherapy outcomes, focusing on hematological immune cell subsets. Pre‑ and post‑immunotherapy TIME changes were evaluated in a patient with dMMR gastric cancer using immunohistochemistry analysis [CD8+, CD4+, forkhead box P3 (FoxP3)+ regulatory T cells (Tregs), M1/M2 macrophages and programmed death‑ligand 1 (PD‑L1)]. Moreover, bioinformatics analyses assessed microsatellite instability (MSI) status, immune checkpoints [cytotoxic T‑lymphocyte‑associated protein 4 (CTLA4), CD274 and programmed cell death protein 1 (PDCD1)], TMB and TIME cell infiltration across The Cancer Genome Atlas (TCGA)‑stomach adenocarcinoma (STAD; n=383), Kim (n=45), Peking University Cancer Hospital (n=31) and Esophagogastric Cancer‑Memorial Sloan Kettering‑2017 (EGC‑MSK‑2017; n=28) cohorts using CIBERSORT (LM22) and R‑based statistical approaches. The case report demonstrated that, prior to treatment, the tumor was deemed ‘immune cold’, characterized by low levels of CD8+ T cells, M1 macrophages and Tregs. Post‑treatment, total T cell infiltration increased, driven primarily by CD4+ T cells, while CD8+ T cell levels remained low. FoxP3+ T cell density (0.18 cells/mm²) indicated potential immune suppression; B cell infiltration (CD20+, 17.07 cells/mm²) did not markedly increase, with no tertiary lymphoid structures (TLS) detected. Moreover, bioinformatics cross‑cohort analyses revealed favorable immunological features in microsatellite instability (MSI)‑high (MSI‑H) gastric cancer compared with in MSI‑low (MSI‑L)/microsatellite stability (MSS) gastric cancer. In TCGA‑STAD dataset, MSI‑H tumors had significantly higher TMB, neoantigen load, infiltration of memory‑activated CD4+ T cells, activated natural killer (NK) cells, mast cells, M1 macrophages and immune checkpoint markers (CTLA4, CD274 and PDCD1; all P<0.05); CD8+ T cells differed significantly between MSI‑H and MSI‑L (P<0.05), but not between MSI‑H and MSS cancer. MSI‑H was associated with a higher TMB in the Kim and EGC‑MSK‑2017 cohorts, a higher immunotherapy response rate in the Kim cohort (P<0.05) and trends toward improved OS in TCGA‑STAD [hazard ratio (HR), 0.68] and EGC‑MSK‑2017 (HR, 0.42) cohorts. All survival differences were non‑significant (P>0.05). In conclusion, the results demonstrated that in dMMR/MSI‑H tumors (which typically exhibit high TMB and neoantigen load), a TIME characterized by low CD8+ T cells, high Tregs, absent TLS and low PD‑L1, could be indicative of immunotherapy resistance. Cross‑cohort analysis further revealed that despite favorable immunological features (higher TMB, neoantigen load, memory‑activated CD4+ T cells, activated NK cells, mast cells, M1 macrophages and immune checkpoint expression), MSI‑H gastric cancers have heterogeneous immunotherapy outcomes. Collectively, dMMR/MSI‑H status alone is insufficient for outcome prediction, highlighting the need for individualized TIME evaluation in patients with gastric cancer receiving immunotherapy.

View Figures

Figure 1

Postoperative pathological findings
of the gastric tumor. (A) Tumor cells show diffuse/sheet-like
growth with focal glandular differentiation, increased
nuclear-to-cytoplasmic ratio and hyperchromatic nuclei.
Immunohistochemistry demonstrates (B) CK7 (+), (C) CK20 (−), (D)
EBER (−), (E) C-erbB-2 (0), (F) MLH1 (−), (G) PMS2 (−), (H) MSH2
(+) and (I) MSH6 (+), confirming deficient mismatch repair status.
Magnification, ×100. MLH1, MutL homolog; PMS2, postmeiotic
segregation increased (S. Cerevisiae) 2; MSH, MutS
homolog.

Figure 2

Imaging and immune microenvironment
changes before and after treatment. CT images (A) before and (B)
after treatment. (C) Lymph node metastases after treatment. (D)
Brain metastases. Pathological examination of peritoneal puncture
lesions, showing (E) HE staining and (F) PMS2, (G) MSH6, (H) MSH2
and (I) MLH1 protein detection, consistent with postoperative
specimens (magnification, ×100). Tumor immune microenvironment
fusion images of (J) pre-treatment and (K) post-treatment
(magnification, ×200). Merge 1 shows PD-1 in green, programmed
death-ligand 1 in yellow, CD8 in pink, CD68 in cyan and CD163 in
red. Merge 2 shows CD3 in pink, CD4 in red, CD20 in green, CD56 in
cyan and forkhead box P3 in yellow. PMS2, postmeiotic segregation
increased (S. Cerevisiae) 2; MSH, MutS homolog; MLH1, MutL
homolog.

Figure 3

Clinical timeline of the disease
course of the patient. Overall survival was 11.6 months. PFS,
progression-free survival; PD, progressive disease; PD-1,
programmed death-1, RE, response Evaluation.

Figure 4

Association between MSI and gastric
cancer staging. (A) Association between MSI and clinical stage in
TCGA gastric cancer. (B) Association between MSI-H and immune
checkpoint expression (CTLA4, CD274 and PDCD1) in different
cohorts. (C) Association between MSI-H and TMB and neoantigen load
in different cohorts. MSI, microsatellite instability; TCGA, The
Cancer Genome Atlas; H, MSI-high; CTLA4, cytotoxic
T-lymphocyte-associated protein 4; PDCD1, programmed cell death
protein 1; TMB, tumor mutational burden; MSS, microsatellite
stability; MSI-L, MSI-low.

Figure 5

Prognostic differences between MSI-H
and MSS groups across different databases. Survival analysis
performed in the (A) TCGA, (B) PUCH and (C) EGC-MSK-2017 cohorts.
(D) Immunotherapy response rates of patients with MSI-H in the Kim,
PUCH and EGC-MSK-2017 cohorts. NR, not reported; R, response; MSI,
microsatellite instability; H, MSI-high; MSS, microsatellite
stability; TCGA, The Cancer Genome Atlas; HR, hazard ratio; CI,
confidence interval; dMMR, deficient mismatch repair; MSI-L,
MSI-low; pMMR proficient mismatch repair; DCB, Durable Clinical
Benefit; NDB, Non-Durable Benefit.

Figure 6

Differences in the immune
microenvironment between MSI-H and MSS. (A) TCGA analysis
demonstrated that patients with MSI-H exhibited significantly
higher infiltration of memory-activated CD4+ T cells,
activated NK cells, mast cells and M1 macrophages than those with
MSS; CD8+ T cells showed a similar significant
difference between the MSI-H and MSI-L groups (P<0.05). (B) Kim
cohort analysis demonstrated no significant differences in detected
immune-related indicators between MSI-H and MSS/MSI-L (P>0.05).
MSI, microsatellite instability; MSI-H, MSI-high; MSS,
microsatellite stability; TCGA, The Cancer Genome Atlas; NK,
natural killer; MSI-L, MSI-low; NK, natural killer; TME, tumor
microenvironment; M1, type 1 macrophages.
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Spandidos Publications style
Wang L, Zhong X, Diao M and Deng W: <p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>. Oncol Lett 31: 119, 2026.
APA
Wang, L., Zhong, X., Diao, M., & Deng, W. (2026). <p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>. Oncology Letters, 31, 119. https://doi.org/10.3892/ol.2026.15472
MLA
Wang, L., Zhong, X., Diao, M., Deng, W."<p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>". Oncology Letters 31.3 (2026): 119.
Chicago
Wang, L., Zhong, X., Diao, M., Deng, W."<p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>". Oncology Letters 31, no. 3 (2026): 119. https://doi.org/10.3892/ol.2026.15472
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Spandidos Publications style
Wang L, Zhong X, Diao M and Deng W: <p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>. Oncol Lett 31: 119, 2026.
APA
Wang, L., Zhong, X., Diao, M., & Deng, W. (2026). <p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>. Oncology Letters, 31, 119. https://doi.org/10.3892/ol.2026.15472
MLA
Wang, L., Zhong, X., Diao, M., Deng, W."<p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>". Oncology Letters 31.3 (2026): 119.
Chicago
Wang, L., Zhong, X., Diao, M., Deng, W."<p>Immune microenvironment and treatment response of dMMR gastric cancer: Heterogeneity and implications for immunotherapy</p>". Oncology Letters 31, no. 3 (2026): 119. https://doi.org/10.3892/ol.2026.15472
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