Skip to main content

Advertisement

Log in

Tumor immune microenvironment is influenced by frameshift mutations and tumor mutational burden in gastric cancer

  • Research Article
  • Published:
Clinical and Translational Oncology Aims and scope Submit manuscript

Abstract

Purpose

Immunoscore can effectively predict prognosis in patients with colon cancer; however, its clinical application is limited. We modified the Immunoscore and created a tumor immune microenvironment (TIM) classification system for gastric carcinoma. Unlike previous studies that used small sample sizes or focused on particular immune-cell subtypes, our simplified system enables pathologists to classify gastric carcinomas intuitively using H&E-stained sections.

Methods

Samples from 326 patients with advanced gastric carcinoma were reviewed and analyzed by pathologists using simple determination and digital image analysis. Comprehensive results of cancer-panel sequencing, Epstein-Barr‒virus (EBV) status, and PD-L1, HER2, ATM, PTEN, MET, FGFR2, and EGFR immunohistochemistry were evaluated with respect to the TIM class.

Results

The TIM was classified as “hot” (n = 22), “immunosuppressed” (n = 178), “excluded” (n = 83), or “cold” (n = 43). TIM category was significantly associated with numbers of frameshift mutations (P < 0.001) and high tumor mutational burden (P < 0.004), and predicted overall survival. It was also significantly associated with age, histological type, degree of fibrosis, PD-L1 expression, loss of ATM and PTEN expression (P < 0.001), sex, EBV positivity, and HER2 overexpression (P < 0.04). “Hot” tumors were frequent in PD-L1 expressing and EBV-positive samples, and in those with ATM and PTEN loss. “Excluded” tumors were frequent in HER2-positive cases, whereas “cold” tumors were more frequent in younger patients with poorly cohesive histology and high fibrosis levels.

Conclusions

TIM classification system for gastric carcinoma has prognostic significance and results in classes that are associated with molecular characteristics.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

Availability of data and material

Data and material are available upon request.

Code availability

Not applicable.

References

  1. Yang L, Wang Y, Wang H. Use of immunotherapy in the treatment of gastric cancer. Oncol Lett. 2019;18(6):5681–90. https://doi.org/10.3892/ol.2019.10935 (Epub 2019/12/04).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ahn S, Lee J, Hong M, Kim ST, Park SH, Choi MG, et al. FGFR2 in gastric cancer: protein overexpression predicts gene amplification and high H-index predicts poor survival. Mod pathol. 2016;29(9):1095–103. https://doi.org/10.1038/modpathol.2016.96 (Epub 2016/05/28).

    Article  CAS  PubMed  Google Scholar 

  3. Coutzac C, Pernot S, Chaput N, Zaanan A. Immunotherapy in advanced gastric cancer, is it the future? Crit Rev Oncol Hematol. 2019;133:25–32. https://doi.org/10.1016/j.critrevonc.2018.10.007 (Epub 2019/01/22).

    Article  CAS  PubMed  Google Scholar 

  4. Ha SY, Lee J, Kang SY, Do IG, Ahn S, Park JO, et al. MET overexpression assessed by new interpretation method predicts gene amplification and poor survival in advanced gastric carcinomas. Mod Pathol. 2013;26(12):1632–41. https://doi.org/10.1038/modpathol.2013.108 (Epub 2013/06/29).

    Article  CAS  PubMed  Google Scholar 

  5. Nixon AB, Schalper KA, Jacobs I, Potluri S, Wang IM, Fleener C. Peripheral immune-based biomarkers in cancer immunotherapy: can we realize their predictive potential? J Immunother Cancer. 2019;7(1):325. https://doi.org/10.1186/s40425-019-0799-2.

    Article  PubMed  PubMed Central  Google Scholar 

  6. Bedognetti D, Ceccarelli M, Galluzzi L, Lu R, Palucka K, Samayoa J, et al. Toward a comprehensive view of cancer immune responsiveness: a synopsis from the SITC workshop. J Immunother Cancer. 2019;7(1):131. https://doi.org/10.1186/s40425-019-0602-4 (Epub 2019/05/23).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Pagès F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018;391(10135):2128–39. https://doi.org/10.1016/s0140-6736(18)30789-x (Epub 2018/05/15).

    Article  PubMed  Google Scholar 

  8. Nagtegaal ID, Odze RD, Klimstra D, Paradis V, Rugge M, Schirmacher P, et al. The 2019 WHO classification of tumours of the digestive system. Histopathology. 2020;76(2):182–8. https://doi.org/10.1111/his.13975.

    Article  PubMed  Google Scholar 

  9. Lee JS, Won HS, Sun S, Hong JH, Ko YH. Prognostic role of tumor-infiltrating lymphocytes in gastric cancer: a systematic review and meta-analysis. Medicine. 2018;97(32):e11769. https://doi.org/10.1097/md.0000000000011769 (Epub 2018/08/11).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Jiang Y, Zhang Q, Hu Y, Li T, Yu J, Zhao L, et al. ImmunoScore signature: a prognostic and predictive tool in gastric cancer. Ann Surg. 2018;267(3):504–13. https://doi.org/10.1097/sla.0000000000002116 (Epub 2016/12/22).

    Article  PubMed  Google Scholar 

  11. Wen T, Wang Z, Li Y, Li Z, Che X, Fan Y, et al. A four-factor immunoscore system that predicts clinical outcome for stage II/III gastric cancer. Cancer Immunol Res. 2017;5(7):524–34. https://doi.org/10.1158/2326-6066.Cir-16-0381 (Epub 2017/06/18).

    Article  CAS  PubMed  Google Scholar 

  12. Mesker WE, Junggeburt JM, Szuhai K, de Heer P, Morreau H, Tanke HJ, et al. The carcinoma-stromal ratio of colon carcinoma is an independent factor for survival compared to lymph node status and tumor stage. Cell Oncol. 2007;29(5):387–98. https://doi.org/10.1155/2007/175276 (Epub 2007/08/30).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Cho J, Ahn S, Son DS, Kim NK, Lee KW, Kim S, et al. Bridging genomics and phenomics of gastric carcinoma. Int J Cancer. 2019;145(9):2407–17. https://doi.org/10.1002/ijc.32228 (Epub 2019/02/26).

    Article  CAS  PubMed  Google Scholar 

  14. Kim ST, Cristescu R, Bass AJ, Kim KM, Odegaard JI, Kim K, et al. Comprehensive molecular characterization of clinical responses to PD-1 inhibition in metastatic gastric cancer. Nat Med. 2018;24(9):1449–58. https://doi.org/10.1038/s41591-018-0101-z (Epub 2018/07/18).

    Article  CAS  PubMed  Google Scholar 

  15. Shin HT, Choi YL, Yun JW, Kim NKD, Kim SY, Jeon HJ, et al. Prevalence and detection of low-allele-fraction variants in clinical cancer samples. Nat Commun. 2017;8(1):1377. https://doi.org/10.1038/s41467-017-01470-y (Epub 2017/11/11).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The sequence alignment/map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. https://doi.org/10.1093/bioinformatics/btp352 (Epub 2009/06/10).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20(9):1297–303. https://doi.org/10.1101/gr.107524.110 (Epub 2010/07/21).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, et al. Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013;31(3):213–9. https://doi.org/10.1038/nbt.2514 (Epub 2013/02/12).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Ye K, Schulz MH, Long Q, Apweiler R, Ning Z. Pindel: a pattern growth approach to detect break points of large deletions and medium sized insertions from paired-end short reads. Bioinformatics. 2009;25(21):2865–71. https://doi.org/10.1093/bioinformatics/btp394 (Epub 2009/06/30).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Harada K, Baba Y, Shigaki H, Ishimoto T, Miyake K, Kosumi K, et al. Prognostic and clinical impact of PIK3CA mutation in gastric cancer: pyrosequencing technology and literature review. BMC Cancer. 2016;16:400. https://doi.org/10.1186/s12885-016-2422-y (Epub 2016/07/09).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Lee SH, Lee JW, Soung YH, Kim HS, Park WS, Kim SY, et al. BRAF and KRAS mutations in stomach cancer. Oncogene. 2003;22(44):6942–5. https://doi.org/10.1038/sj.onc.1206749.

    Article  CAS  PubMed  Google Scholar 

  22. Angell HK, Bruni D, Barrett JC, Herbst R, Galon J. The immunoscore: colon cancer and beyond. Clin Cancer Res. 2020;26(2):332–9. https://doi.org/10.1158/1078-0432.Ccr-18-1851 (Epub 2019/08/16).

    Article  CAS  PubMed  Google Scholar 

  23. Jiang W, Liu K, Guo Q, Cheng J, Shen L, Cao Y, et al (2017) Tumor-infiltrating immune cells and prognosis in gastric cancer: a systematic review and meta-analysis. Oncotarget 8(37):62312–62329. https://doi.org/10.18632/oncotarget.17602

  24. Liu K, Yang K, Wu B, Chen H, Chen X, Chen X, et al. Tumor-infiltrating immune cells are associated with prognosis of gastric cancer. Medicine. 2015;94(39): e1631. https://doi.org/10.1097/MD.0000000000001631.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rosso D, Rigueiro MP, Kassab P, Ilias EJ, Castro OA, Novo NF, et al. Correlation of natural killer cells with the prognosis of gastric adenocarcinoma. Arq Bras Cir Dig. 2012;25(2):114–7.

    Article  PubMed  Google Scholar 

  26. Mizukami Y, Kono K, Kawaguchi Y, Akaike H, Kamimura K, Sugai H, et al. Localisation pattern of Foxp3+ regulatory T cells is associated with clinical behaviour in gastric cancer. Br J Cancer. 2008;98(1):148–53. https://doi.org/10.1038/sj.bjc.6604149.

    Article  CAS  PubMed  Google Scholar 

  27. Thompson ED, Zahurak M, Murphy A, Cornish T, Cuka N, Abdelfatah E, et al. Patterns of PD-L1 expression and CD8 T cell infiltration in gastric adenocarcinomas and associated immune stroma. Gut. 2017;66(5):794–801. https://doi.org/10.1136/gutjnl-2015-310839.

    Article  CAS  PubMed  Google Scholar 

  28. Wang B, Xu D, Yu X, Ding T, Rao H, Zhan Y, et al. Association of intra-tumoral infiltrating macrophages and regulatory T cells is an independent prognostic factor in gastric cancer after radical resection. Ann Surg Oncol. 2011;18(9):2585–93. https://doi.org/10.1245/s10434-011-1609-3.

    Article  PubMed  Google Scholar 

  29. Haas M, Dimmler A, Hohenberger W, Grabenbauer GG, Niedobitek G, Distel LV. Stromal regulatory T-cells are associated with a favourable prognosis in gastric cancer of the cardia. BMC Gastroenterol. 2009;9:65. https://doi.org/10.1186/1471-230X-9-65.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Perrone G, Ruffini PA, Catalano V, Spino C, Santini D, Muretto P, et al. Intratumoural FOXP3-positive regulatory T cells are associated with adverse prognosis in radically resected gastric cancer. Eur J Cancer. 2008;44(13):1875–82. https://doi.org/10.1016/j.ejca.2008.05.017.

    Article  CAS  PubMed  Google Scholar 

  31. Zhou S, Shen Z, Wang Y, Ma H, Xu S, Qin J, et al. CCR7 expression and intratumoral FOXP3+ regulatory T cells are correlated with overall survival and lymph node metastasis in gastric cancer. PLoS ONE. 2013;8(9): e74430. https://doi.org/10.1371/journal.pone.0074430.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Dong J, Li J, Liu SM, Feng XY, Chen S, Chen YB, et al. CD33(+)/p-STAT1(+) double-positive cell as a prognostic factor for stage IIIa gastric cancer. Med Oncol. 2013;30(1):442. https://doi.org/10.1007/s12032-012-0442-2.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ishigami S, Natsugoe S, Tokuda K, Nakajo A, Che X, Iwashige H, et al. Prognostic value of intratumoral natural killer cells in gastric carcinoma. Cancer. 2000;88(3):577–83.

    Article  CAS  PubMed  Google Scholar 

  34. Ishigami S, Natsugoe S, Tokuda K, Nakajo A, Xiangming C, Iwashige H, et al. Clinical impact of intratumoral natural killer cell and dendritic cell infiltration in gastric cancer. Cancer Lett. 2000;159(1):103–8.

    Article  CAS  PubMed  Google Scholar 

  35. Galon J, Lanzi A (2020) Immunoscore and its introduction in clinical practice. Q J Nucl Med Mol Imaging 64(2):152–161. https://doi.org/10.23736/s1824-4785.20.03249-5 Epub 2020/02/29

  36. Zhao Z, Zhao D, Xia J, Wang Y, Wang B. Immunoscore predicts survival in early-stage lung adenocarcinoma patients. Front Oncol. 2020;10:691. https://doi.org/10.3389/fonc.2020.00691 (Epub 2020/05/28).

    Article  PubMed  PubMed Central  Google Scholar 

  37. Zeng D, Zhou R, Yu Y, Luo Y, Zhang J, Sun H, et al. Gene expression profiles for a prognostic immunoscore in gastric cancer. Br J Surg. 2018;105(10):1338–48. https://doi.org/10.1002/bjs.10871 (Epub 2018/04/25).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Galon J, Bruni D. Approaches to treat immune hot, altered and cold tumours with combination immunotherapies. Nat Rev Drug Discov. 2019;18(3):197–218. https://doi.org/10.1038/s41573-018-0007-y (Epub 2019/01/06).

    Article  CAS  PubMed  Google Scholar 

  39. Catalano I, Grassi E, Bertotti A, Trusolino L. Immunogenomics of colorectal tumors: facts and hypotheses on an evolving saga. Trends Cancer. 2019;5(12):779–88. https://doi.org/10.1016/j.trecan.2019.10.006 (Epub 2019/12/10).

    Article  CAS  PubMed  Google Scholar 

  40. Cristescu R, Lee J, Nebozhyn M, Kim KM, Ting JC, Wong SS, et al. Molecular analysis of gastric cancer identifies subtypes associated with distinct clinical outcomes. Nat Med. 2015;21(5):449–56. https://doi.org/10.1038/nm.3850 (Epub 2015/04/22).

    Article  CAS  PubMed  Google Scholar 

  41. Wong SS, Kim KM, Ting JC, Yu K, Fu J, Liu S, et al. Genomic landscape and genetic heterogeneity in gastric adenocarcinoma revealed by whole-genome sequencing. Nat Commun. 2014;5(1):5477. https://doi.org/10.1038/ncomms6477.

    Article  PubMed  Google Scholar 

Download references

Funding

This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF), funded by the Ministry of Science, Republic of Korea and ICT (grant numbers NRF-2017R1E1A1A01075005 and NRF-2017R1A2B4012436) and a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute (KHIDI), funded by the Ministry of Health & Welfare, Republic of Korea (grant number HR20C0025).

Author information

Authors and Affiliations

Authors

Contributions

Hyunjin Kim, You Jeong Heo, Soomin Ahn, and Kyoung-Mee Kim designed the study. Hyunjin Kim, You Jeong Heo, Yoon Ah Cho, So Young Kang, and Soomin Ahn conducted the data analysis. Hyunjin Kim, You Jeong Heo, and Kyoung-Mee Kim wrote the paper. Soomin Ahn and Kyoung-Mee Kim revised the paper. All authors read and approved the final manuscript.

Corresponding author

Correspondence to K. -M. Kim.

Ethics declarations

Conflict of interest

There is no conflicts of interest to declare.

Ethical approval

This study was approved by the Institutional Review Board of Samsung Medical Center (Seoul, Korea).

Consent to participate

All patients provided written informed consent before enrollment.

Consent for publication

For this type of study, formal consent was not required.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Below is the link to the electronic supplementary material.

12094_2021_2714_MOESM1_ESM.pdf

Online Resource Fig. S1 Heatmap showing associations between gene mutations and TIM classification. Online Resource Fig. S2 Kaplan-Meier curve depicting overall survival in Stage II and III tumors. (a) is according to the four TIM classes, and (b) is according to three TIM classes, where “excluded” and “immunosuppressed” are merged into “intermediate” (PDF 284 KB)

 Online Resource Table S1 Antibodies used in this study for immunohistochemistry.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kim, H., Heo, Y.J., Cho, Y.A. et al. Tumor immune microenvironment is influenced by frameshift mutations and tumor mutational burden in gastric cancer. Clin Transl Oncol 24, 556–567 (2022). https://doi.org/10.1007/s12094-021-02714-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12094-021-02714-6

Keywords

Navigation