Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

Intratumoral spatial heterogeneity of tumor-infiltrating lymphocytes is a significant factor for precisely stratifying prognostic immune subgroups of microsatellite instability-high colorectal carcinomas

Abstract

Although the density of tumor-infiltrating lymphocytes (TILs) is known to be linked to prognosis in various cancers, the prognostic impact and immunologic significance of the spatial heterogeneity of TILs have been rarely investigated. In this study, CD3+ and CD8+ TILs were quantified in independent cohorts (discovery, n = 73; and external validation, n = 93) of colorectal carcinomas (CRCs) with microsatellite instability-high (MSI-H) utilizing whole-slide image analysis of CD3/CD8 immunohistochemistry. The Shannon and Simpson indices, which measure intratumoral patch-to-patch evenness of TIL densities, were used to quantitatively assess the spatial heterogeneity of TILs in each case. To uncover immune-related gene expression signatures of spatial heterogeneity-based TIL subgroups of MSI-H CRCs, representative cases were subjected to GeoMx digital spatial profiler (DSP) analysis. As expected, a low density of TILs was significantly associated with poor disease-free survival (DFS) in MSI-H CRCs. The TIL-low tumors were further classified into two subgroups based on the spatial heterogeneity of TILs: TIL-low/heterogeneity-high and TIL-low/heterogeneity-low subgroups. In both discovery and validation cohorts, the TIL-low/heterogeneity-high, TIL-low/heterogeneity-low, and TIL-high subgroups were significantly associated with poor, intermediate, and good DFS, respectively. In the DSP analysis, the TIL-low/heterogeneity-high subgroup showed higher spatial diversity in the expression of immune-related genes than that of the TIL-low/heterogeneity-low subgroup and exhibited upregulation of genes related to immune checkpoints, chemokine/cytokine receptors, and myeloid cells. TIL-low/heterogeneity-high tumors were also enriched with gene sets related to good response to immune checkpoint inhibitor therapy. In conclusion, TIL-low MSI-H CRCs are prognostically heterogeneous and can be divided into prognostically and immunologically distinct subgroups by considering the spatial heterogeneity of TILs. Our data suggest that intratumoral spatial heterogeneity of TILs can be used as a key element for clinically relevant immunologic subtyping of tumors.

This is a preview of subscription content, access via your institution

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1: Prognostic significance of the spatial heterogeneity of TILs in MSI-H CRCs.
Fig. 2: Validation of the prognostic impact of TIL spatial heterogeneity in MSI-H CRCs.
Fig. 3: GeoMx DSP analysis to identify TIL density-dependent immune-related gene expression signatures in MSI-H CRCs.
Fig. 4: GeoMx DSP analysis to identify TIL spatial heterogeneity-dependent immune-related gene expression signatures in MSI-H CRCs.
Fig. 5

Data availability

The raw dataset generated from GeoMx DSP analysis during the current study are available in the Mendeley data repository (https://doi.org/10.17632/jcyt9grfjv.1). The other datasets generated or analyzed in the study are available from the corresponding author upon reasonable request.

References

  1. Bruni D, Angell HK & Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer 20, 662–680 (2020).

  2. Fridman WH, Zitvogel L, Sautes-Fridman C & Kroemer G. The immune contexture in cancer prognosis and treatment. Nat Rev Clin Oncol 14, 717–734 (2017).

  3. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pages C et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313, 1960–1964 (2006).

  4. Idos GE, Kwok J, Bonthala N, Kysh L, Gruber SB & Qu C. The Prognostic Implications of Tumor Infiltrating Lymphocytes in Colorectal Cancer: A Systematic Review and Meta-Analysis. Sci Rep 10, 3360 (2020).

  5. Lanzi A, Pages F, Lagorce-Pages C & Galon J. The consensus immunoscore: toward a new classification of colorectal cancer. Oncoimmunology 9, 1789032 (2020).

  6. Bae JM, Yoo SY, Kim JH & Kang GH. Immune landscape and biomarkers for immuno-oncology in colorectal cancers. J Pathol Transl Med 54, 351–360 (2020).

  7. Vilar E & Gruber SB. Microsatellite instability in colorectal cancer-the stable evidence. Nat Rev Clin Oncol 7, 153–162 (2010).

  8. Llosa NJ, Cruise M, Tam A, Wicks EC, Hechenbleikner EM, Taube JM et al. The vigorous immune microenvironment of microsatellite instable colon cancer is balanced by multiple counter-inhibitory checkpoints. Cancer Discov 5, 43–51 (2015).

  9. Makaremi S, Asadzadeh Z, Hemmat N, Baghbanzadeh A, Sgambato A, Ghorbaninezhad F et al. Immune Checkpoint Inhibitors in Colorectal Cancer: Challenges and Future Prospects. Biomedicines 9, 1075 (2021).

  10. Giannini R, Zucchelli G, Giordano M, Ugolini C, Moretto R, Ambryszewska K et al. Immune Profiling of Deficient Mismatch Repair Colorectal Cancer Tumor Microenvironment Reveals Different Levels of Immune System Activation. J Mol Diagn 22, 685–698 (2020).

  11. Yoon HH, Shi Q, Heying EN, Muranyi A, Bredno J, Ough F et al. Intertumoral Heterogeneity of CD3(+) and CD8(+) T-Cell Densities in the Microenvironment of DNA Mismatch-Repair-Deficient Colon Cancers: Implications for Prognosis. Clin Cancer Res 25, 125–133 (2019).

  12. Kim JH, Seo MK, Lee JA, Yoo SY, Oh HJ, Kang H et al. Genomic and transcriptomic characterization of heterogeneous immune subgroups of microsatellite instability-high colorectal cancers. J Immunother Cancer 9, e003414 (2021).

  13. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD et al. PD-1 blockade in tumors with mismatch-repair deficiency. N Engl J Med 372, 2509–2520 (2015).

  14. Andre T, Shiu KK, Kim TW, Jensen BV, Jensen LH, Punt C et al. Pembrolizumab in microsatellite-instability-high advanced colorectal cancer. N Engl J Med 383, 2207–2218 (2020).

  15. Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz HJ, Morse MA 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).

  16. Le DT, Kim TW, Van Cutsem E, Geva R, Jager D, Hara H et al. Phase II Open-Label Study of Pembrolizumab in Treatment-Refractory, Microsatellite Instability-High/Mismatch Repair-Deficient Metastatic Colorectal Cancer: KEYNOTE-164. J Clin Oncol 38, 11–19 (2020).

  17. Lee JA, Yoo SY, Oh HJ, Jeong S, Cho NY, Kang GH et al. Differential immune microenvironmental features of microsatellite-unstable colorectal cancers according to Fusobacterium nucleatum status. Cancer Immunol Immunother 70, 47–59 (2021).

  18. Kim JH, Hong JH, Choi YL, Lee JA, Seo MK, Lee MS et al. NTRK oncogenic fusions are exclusively associated with the serrated neoplasia pathway in the colorectum and begin to occur in sessile serrated lesions. J Pathol 255, 399–411 (2021).

  19. Boland CR, Thibodeau SN, Hamilton SR, Sidransky D, Eshleman JR, Burt RW et al. A National Cancer Institute Workshop on Microsatellite Instability for cancer detection and familial predisposition: development of international criteria for the determination of microsatellite instability in colorectal cancer. Cancer Res 58, 5248–5257 (1998).

  20. Bankhead P, Loughrey MB, Fernandez JA, Dombrowski Y, McArt DG, Dunne PD et al. QuPath: Open source software for digital pathology image analysis. Sci Rep 7, 16878 (2017).

  21. Loughrey MB, Bankhead P, Coleman HG, Hagan RS, Craig S, McCorry AMB et al. Validation of the systematic scoring of immunohistochemically stained tumour tissue microarrays using QuPath digital image analysis. Histopathology 73, 327–338 (2018).

  22. Simpson E. Measurement of diversity. Nature 163, 688 (1949).

  23. Shannon CE. A mathematical theory of communication. The Bell System Technical Journal 27, 379–423 (1948).

  24. Morris EK, Caruso T, Buscot F, Fischer M, Hancock C, Maier TS et al. Choosing and using diversity indices: insights for ecological applications from the German Biodiversity Exploratories. Ecol Evol 4, 3514–3524 (2014).

  25. Evans JA, Carlotti E, Lin ML, Hackett RJ, Haughey MJ, Passman AM et al. Clonal transitions and phenotypic evolution in Barrett’s esophagus. Gastroenterology 162, 1197–1209 e1113 (2022).

  26. Nelson LS, Mansfield JR, Lloyd R, Oguejiofor K, Salih Z, Menasce LP et al. Automated prognostic pattern detection shows favourable diffuse pattern of FOXP3(+) Tregs in follicular lymphoma. Br J Cancer 113, 1197–1205 (2015).

  27. Levy-Jurgenson A, Tekpli X, Kristensen VN & Yakhini Z. Spatial transcriptomics inferred from pathology whole-slide images links tumor heterogeneity to survival in breast and lung cancer. Sci Rep 10, 18802 (2020).

  28. Andersson A, Larsson L, Stenbeck L, Salmen F, Ehinger A, Wu SZ et al. Spatial deconvolution of HER2-positive breast cancer delineates tumor-associated cell type interactions. Nat Commun 12, 6012 (2021).

  29. Campisciano G, Florian F, D’Eustacchio A, Stankovic D, Ricci G, De Seta F et al. Subclinical alteration of the cervical-vaginal microbiome in women with idiopathic infertility. J Cell Physiol 232, 1681–1688 (2017).

  30. Nagendra H. Opposite trends in response for the Shannon and Simpson indices of landscape diversity. Applied Geography 22, 175–186 (2002).

  31. Pielou EC. The measurement of diversity in different types of biological collections. Journal of Theoretical Biology 13, 131–144 (1966).

  32. Merritt CR, Ong GT, Church SE, Barker K, Danaher P, Geiss G et al. Multiplex digital spatial profiling of proteins and RNA in fixed tissue. Nat Biotechnol 38, 586–599 (2020).

  33. Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y et al. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods 12, 453–457 (2015).

  34. Bhattacharya S, Dunn P, Thomas CG, Smith B, Schaefer H, Chen J et al. ImmPort, toward repurposing of open access immunological assay data for translational and clinical research. Sci Data 5, 180015 (2018).

  35. Yin XM. Signal transduction mediated by Bid, a pro-death Bcl-2 family proteins, connects the death receptor and mitochondria apoptosis pathways. Cell Res 10, 161–167 (2000).

  36. Gene Ontology Consortium. The Gene Ontology resource: enriching a GOld mine. Nucleic Acids Res 49, D325–D334 (2021).

  37. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM et al. Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet 25, 25–29 (2000).

  38. Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc Natl Acad Sci U S A 102, 15545–15550 (2005).

  39. Kanehisa M & Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res 28, 27–30 (2000).

  40. Chen J, Bardes EE, Aronow BJ & Jegga AG. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res 37, W305-W311 (2009).

  41. Lal N, Beggs AD, Willcox BE & Middleton GW. An immunogenomic stratification of colorectal cancer: Implications for development of targeted immunotherapy. Oncoimmunology 4, e976052 (2015).

  42. Gide TN, Quek C, Menzies AM, Tasker AT, Shang P, Holst J et al. Distinct Immune Cell Populations Define Response to Anti-PD-1 Monotherapy and Anti-PD-1/Anti-CTLA-4 Combined Therapy. Cancer Cell 35, 238–255 e236 (2019).

  43. Mollica Poeta V, Massara M, Capucetti A & Bonecchi R. Chemokines and chemokine receptors: new targets for cancer immunotherapy. Front Immunol 10, 379 (2019).

  44. Peranzoni E, Ingangi V, Masetto E, Pinton L & Marigo I. Myeloid cells as clinical biomarkers for immune checkpoint blockade. Front Immunol 11, 1590 (2020).

  45. Masugi Y, Abe T, Ueno A, Fujii-Nishimura Y, Ojima H, Endo Y et al. Characterization of spatial distribution of tumor-infiltrating CD8(+) T cells refines their prognostic utility for pancreatic cancer survival. Mod Pathol 32, 1495–1507 (2019).

  46. Konig L, Mairinger FD, Hoffmann O, Bittner AK, Schmid KW, Kimmig R et al. Dissimilar patterns of tumor-infiltrating immune cells at the invasive tumor front and tumor center are associated with response to neoadjuvant chemotherapy in primary breast cancer. Bmc Cancer 19, 120 (2019).

  47. Park S, Ock CY, Kim H, Pereira S, Park S, Ma M et al. Artificial Intelligence-Powered Spatial Analysis of Tumor-Infiltrating Lymphocytes as Complementary Biomarker for Immune Checkpoint Inhibition in Non-Small-Cell Lung Cancer. J Clin Oncol 40, 1916–1928 (2022).

  48. Azarianpour S, Corredor G, Bera K, Leo P, Fu P, Toro P et al. Computational image features of immune architecture is associated with clinical benefit and survival in gynecological cancers across treatment modalities. J Immunother Cancer 10 (2022).

  49. Chen DS & Mellman I. Elements of cancer immunity and the cancer-immune set point. Nature 541, 321–330 (2017).

  50. Liu YT & Sun ZJ. Turning cold tumors into hot tumors by improving T-cell infiltration. Theranostics 11, 5365–5386 (2021).

Download references

Acknowledgements

We thank Seoul National University Hospital Medical Research Collaborating Center for partially supporting statistical analysis in the study.

Funding

This study was supported by a grant from the SNUH Research Fund (04-2020-0550; to JHK), a faculty research grant of Yonsei University College of Medicine (6-2021-0144; to MJ), a grant from Seoul National University College of Medicine (800-20210387; to JHK), a grant from the Korea Health Technology R&D Project through the Korea Health Industry Development Institute funded by the Korea government (Ministry of Health and Welfare) (HI21C0977; to MJ), and the National Research Foundation of Korea grants funded by the Korea government (Ministry of Science and ICT) (NRF-2016R1C1B2010627; NRF-2019R1F1A1059535; to JHK).

Author information

Authors and Affiliations

Authors

Contributions

MJ and JHK performed study concept and design. MJ, JAL, S-YY, JMB, GHK, and JHK collected tissue samples and clinicopathologic data. MJ, JAL, S-YY, and JHK provided acquisition, analysis, and interpretation of data. MJ and JHK conducted statistical analyses. MJ and JHK wrote and revised the manuscript. All authors read and approved the final version of the manuscript.

Corresponding author

Correspondence to Jung Ho Kim.

Ethics declarations

Competing interests

The authors declare no competing interests.

Ethics approval and consent to participate

This study was conducted in compliance with the ethical guidelines of the 2013 Declaration of Helsinki and was approved by the Institutional Review Boards of Seoul National University Hospital (IRB No. 1804-036-935; and 1805-018-944) and Severance Hospital (IRB No. 4-2021-1130). The study samples collected from Seoul National University Hospital were previously registered in the Cancer Tissue Bank of Seoul National University Hospital with informed consent obtained from all patients about the research use of their tissues. The study samples collected from Severance Hospital were exempted from informed consent acquisition from patients by the Institutional Review Board because the study used only retrospective, anonymized tissue samples.

Additional information

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

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Jung, M., Lee, J.A., Yoo, SY. et al. Intratumoral spatial heterogeneity of tumor-infiltrating lymphocytes is a significant factor for precisely stratifying prognostic immune subgroups of microsatellite instability-high colorectal carcinomas. Mod Pathol (2022). https://doi.org/10.1038/s41379-022-01137-0

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41379-022-01137-0

Search

Quick links