Comparative analysis of tertiary lymphoid structures for predicting survival of colorectal cancer: a whole-slide images-based study

Abstract

Background: Tertiary lymphoid structures (TLS) are major components in the immune microenvironment, correlating with a favorable prognosis in colorectal cancer. However, the methods used to define and characterize TLS were not united, hindering its clinical application. This study aims to seek a more stable method to characterize TLS and clarify their prognostic value in larger multicenter cohorts.

Methods: A total of 1609 patients from four hospitals and The Cancer Genome Atlas database were analyzed. We quantified the number and maximum length of TLS along the invasive margin of tumor using hematoxylin and eosin-stained whole-slide images (WSIs). Additionally, the length of the invasive margin was determined to calculate the TLS density. The prognostic value of TLS for overall survival was evaluated. In addition, we examined the association between TLS density and immune cell infiltration using immunohistochemistry-stained WSIs. The performance for predicting overall survival was measured using hazard ratios (HR) with 95% confidence intervals (CI).

Results: Among the three TLS quantification methods, TLS density has the strongest discriminative performance. Survival analysis indicated that higher TLS density correlated with better overall survival [HR for high vs. low 0.57 (95% CI 0.42-0.78) in the primary cohort; 0.49 (0.35-0.69) in the validation cohort; 0.35 (0.18-0.67) in TCGA cohort]. A high TLS density was associated with a high level of CD3⁺ T cell infiltration.

Conclusions: Based on this comparative multicenter analysis, TLS density was identified as a simple, robust, and effective immune prognostic index for colorectal cancer.

Keywords

colorectal cancer / tertiary lymphoid structures / digital pathology / prognosis / immune

Ethics statement

This study was ethically approved by the institutional review boards of the four hospitals and informed consent was waived because of the retrospective nature of the work.

Acknowledgments

This work was supported by the National Science Fund for Distinguished Young Scholars of China (Grant No. 81925023), Joint Funds of the National Natural Science Foundation of China (Grant No. U23A20478), and the National Science Foundation for Young Scientists of China(Grant No. 82202267, 82101996).

Author contributions

Ming He (Conceptualization, Writing—original draft, Writing—review & editing), Huifen Ye (Conceptualization, Writing—original draft, Writing—review & editing), Liu Liu (Conceptualization, Data curation, Writing—review & editing), Su Yao (Data curation, Writing—review & editing), Zhenhui Li (Data curation, Writing—review & editing), Xinjuan Fan (Data curation), Lili Feng (Writing—review & editing), Tong Tong (Writing—review & editing), Yanfen Cui (Methodology, Writing—review & editing), Xiaotang Yang (Writing—review & editing), Xiaomei Wu (Writing—review & editing), Yun Mao (Writing—review & editing), Ke Zhao (Conceptualization, Funding acquisition, Methodology, Writing—original draft, Writing—review & editing), and Zaiyi Liu (Conceptualization, Funding acquisition, Writing—review & editing)

Supplementary data

data are available at PCMEDI online.

Conflict of interest

None declared. As an Editorial Board Member of Precision Clinical Medicine, the corresponding author Z.L. was blinded from reviewing and making decisions on this manuscript.

References

[1] Galon J, Costes A,Sanchez-Cabo F et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 2006;313:1960-4. https://doi.org/10.1126/science. 1129139.

[2] Schumacher TN, Thommen DS. Tertiary lymphoid structures in cancer. Science 2022;375:eabf9419. https://doi.org/10.1126/science.abf9419.

[3] Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer 2020;20:662-80. https://doi.org/10.1038/s41568-020-0285-7.

[4] Pagès F, Mlecnik B, Marliot F et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet North Am Ed 2018;391:2128-39. https://doi.org/10.1016/S0140-6736(18)30789-X.

[5] Melero I, Rouzaut A, Motz GT et al. T-cell and NK-cell infiltration into solid tumors: a key limiting factor for efficacious cancer immunotherapy. Cancer Discov 2014;4:522-6. https://doi.org/10.1158/2159-8290.CD-13-0985.

[6] Sautès-Fridman C, Petitprez F, Calderaro J et al. Tertiary lymphoid structures in the era of cancer immunotherapy. Nat Rev Cancer 2019;19:307-25. https://doi.org/10.1038/s41568-019-0144-6.

[7] Lin Q Tao P, Wang J et al. Tumor-associated tertiary lymphoid structure predicts postoperative outcomes in patients with primary gastrointestinal stromal tumors. Oncoimmunology 2020;9:1747339. https://doi.org/10.1080/2162402X.2020.1747339.

[8] Helmink BA, Reddy SM, Gao J et al. B cells and tertiary lymphoid structures promote immunotherapy response. Nature 2020;577:549-55. https://doi.org/10.1038/s41586-019-1922-8.

[9] Calderaro J, Petitprez F, Becht E et al. Intra-tumoral tertiary lymphoid structures are associated with a low risk of early recurrence of hepatocellular carcinoma. J Hepatol 2019;70:58-65. https://doi.org/10.1016/j.jhep.2018.09.003.

[10] Lynch KT, Young SJ, Meneveau MO et al. Heterogeneity in tertiary lymphoid structure B-cells correlates with patient survival in metastatic melanoma. J Immunother Cancer 2021;9:e002273. https://doi.org/10.1136/jitc-2020-002273.

[11] Silina K, Rulle U, Kalniņa Z et al. Manipulation of tumourinfiltrating B cells and tertiary lymphoid structures: a novel anti-cancer treatment avenue? Cancer Immunol Immunother 2014;63:643-62. https://doi.org/10.1007/s00262-014-1544-9.

[12] Hiraoka N, Ino Y, Yamazaki-Itoh R et al. Intratumoral tertiary lymphoid organ is a favourable prognosticator in patients with pancreatic cancer. BrJ Cancer 2015;112:1782-90. https://doi.org/10.1038/bjc.2015.145.

[13] Dieu-Nosjean MC, Antoine M, Danel C et al. Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. JCO 2008;26:4410-7. https://doi.org/10.1200/JCO.2007.15.0284.

[14] Herbst RS, Soria JC, Kowanetz M et al. Predictive correlates of response to the anti-PD-L1 antibody MPDL3280A in cancer patients. Nature 2014;515:563-7. https://doi.org/10.1038/nature14011.

[15] Vanhersecke L, Brunet M, Guégan JP et al. Mature tertiary lymphoid structures predict immune checkpoint inhibitor efficacy in solid tumors independently of PD-L1 expression. Nat Cancer 2021;2:794-802. https://doi.org/10.1038/s43018-021-00232-6.

[16] Graham DM, Appelman HD. Crohn's-like lymphoid reaction and colorectal carcinoma: a potential histologic prognosticator. Mod Pathol Off J U S Can Acad Pathol Inc 1990;3:332-5.

[17] Buckowitz A, Knaebel HP, Benner A et al. Microsatellite instability in colorectal cancer is associated with local lymphocyte infiltration and low frequency of distant metastases. Br J Cancer 2005;92:1746-53. https://doi.org/10.1038/sj.bjc.6602534.

[18] Ueno H, Hashiguchi Y, Shimazaki H et al. Objective criteria for Crohn-like lymphoid reaction in colorectal cancer. Am J Clin Pathol 2013;139:434-41. https://doi.org/10.1309/AJCPWHUEFTGBWKE4.

[19] Väyrynen JP, Sajanti SA, Klintrup K et al. Characteristics and significance of colorectal cancer associated lymphoid reaction: Colorectal cancer associated lymphoid reaction. Intl Journal of Cancer 2014;134:2126-35. https://doi.org/10.1002/ijc.28533.

[20] Zhao M, Yao S, Li Z et al. The Crohn's-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer. Cancer Immunol Immunother 2022;71:122131. https://doi.org/10.1007/s00262-021-03079-z.

[21] Xu Z, Li Y, Wang Y et al. A deep learning quantified stromaimmune score to predict survival of patients with stage II-III colorectal cancer. Cancer Cell Int 2021;21:585. https://doi.org/10.1186/s12935-021-02297-w.

[22] Ye Y, Wu X, Wang H et al. Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer. Ann Med 2023;55:2215541. https://doi.org/10.1080/07853890.2023.2215541.

[23] Kather JN, Krisam J, Charoentong P et al. Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med 2019;16:e1002730. https://doi.org/10.1371/journal.pmed.1002730.

[24] Rakaee M, Kilvaer TK, Jamaly S et al. Tertiary lymphoid structure score: a promising approach to refine the TNM staging in resected non-small cell lung cancer. BrJ Cancer 2021;124:1680-9. https://doi.org/10.1038/s41416-021-01307-y.

[25] Maoz A, Dennis M, Greenson JK. The Crohn's-like lymphoid reaction to colorectal cancer-Tertiary lymphoid structures with immunologic and potentially therapeutic relevance in colorectal cancer. Front Immunol 2019;10:1884. https://doi.org/10.3389/fimmu.2019.01884.

[26] Barmpoutis P, Di Capite M, Kayhanian H et al. Tertiary lymphoid structures (TLS) identification and density assessment on H&Estained digital slides of lung cancer. PLoS One 2021;16:e0256907. https://doi.org/10.1371/journal.pone.0256907.

[27] Di Caro G, Bergomas F, Grizzi F et al. Occurrence of tertiary lymphoid tissue is associated with T-cell infiltration and predicts better prognosis in early-stage colorectal cancers. Clin Cancer Res OffJ Am Assoc Cancer Res 2014;20:2147-58. https://doi.org/10.1158/1078-0432.CCR-13-2590.

[28] Peske JD, Thompson ED, Gemta L et al. Effector lymphocyteinduced lymph node-like vasculature enables naive T-cell entry into tumours and enhanced anti-tumour immunity. Nat Commun 2015;6:7114. https://doi.org/10.1038/ncomms8114.

[29] Reticker-Flynn NE, Zhang W, Belk JA et al. Lymph node colonization induces tumor-immune tolerance to promote distant metastasis. Cell 2022;185:1924-42..https://doi.org/10.1016/j.cell.2022.04.019.

[30] Yang J, Lu Z, Li L et al. Relationship of lymphovascular invasion with lymph node metastasis and prognosis in superficial esophageal carcinoma: systematic review and meta-analysis. BMC Cancer 2020;20:176. https://doi.org/10.1186/s12885-020-6656-3.

[31] Petitprez F, de Reyniès A, Keung EZ et al. B cells are associated with survival and immunotherapy response in sarcoma. Nature 2020;577:556-60. https://doi.org/10.1038/s41586-019-1906-8.

[32] Cabrita R, Lauss M, Sanna A et al. Tertiary lymphoid structures improve immunotherapy and survival in melanoma. Nature 2020;577:561-5. https://doi.org/10.1038/s41586-019-1914-8.

[33] Posch F, Silina K, Leibl S et al. Maturation of tertiary lymphoid structures and recurrence of stage II and III colorectal cancer. Oncoimmunology 2018;7:e1378844. https://doi.org/10.1080/2162402X.2017.1378844.

[34] Jenkins MA, Hayashi S, O'Shea AM et al. Pathology features in Bethesda guidelines predict colorectal cancer microsatellite instability: a population-based study. Gastroenterology 2007;133:48-56. https://doi.org/10.1053/j.gastro.2007.04.044.

[35] Alexander J, Watanabe T, Wu TT et al. Histopathological identification of colon cancer with microsatellite instability. Am J Pathol 2001;158:527-35. https://doi.org/10.1016/S0002-9440(10)63994-6.

[36] Morad G, Helmink BA, Sharma P et al. Hallmarks of response, resistance, and toxicity to immune checkpoint blockade. Cell 2021;184:5309-37. https://doi.org/10.1016/j.cell.2021.09.020.

[37] Gunter MJ, Alhomoud S, Arnold M et al. Meeting report from the joint IARC-NCI international cancer seminar series: a focus on colorectal cancer. Ann Oncol 2019;30:510-9. https://doi.org/10.1093/annonc/mdz044.

[38] Ganesh K, Stadler ZK, Cercek A et al. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol 2019;16:361-75. https://doi.org/10.1038/s41575-019-0126-x.

[39] Narayanan S, Kawaguchi T, Yan L et al. Cytolytic activity score to assess anticancer immunity in colorectal cancer. Ann Surg Oncol 2018;25:2323-31. https://doi.org/10.1245/s10434-018-6506-6.

[40] Chen S, Song D, Chen L et al. Artificial intelligence-based non-invasive tumor segmentation, grade stratification and prognosis prediction for clear-cell renal-cell carcinoma. Precis Clin Med 2023;6:pbad019. https://doi.org/10.1093/pcmedi/pbad019.

[41] Wang Z, Lin R, Li Y et al. Deep learning-based multi-modal data integration enhancing breast cancer disease-free survival prediction. Precis Clin Med 2024;7:pbae012. https://doi.org/10.1093/pcmedi/pbae012.