Skip to main content

Advertisement

SpringerLink
Log in

The Crohn’s-like lymphoid reaction density: a new artificial intelligence quantified prognostic immune index in colon cancer

  • Original Article
  • Published:
Cancer Immunology, Immunotherapy Aims and scope Submit manuscript

Abstract

Background

The Crohn’s-like lymphoid reaction (CLR) is manifested as peritumoral lymphocytes aggregation in colon cancer, which is a major component of the host immune response to cancer. However, the lack of a unified and objective CLR evaluation standard limits its clinical application. We, therefore, developed a deep learning model for the fully automated CLR density quantification on routine hematoxylin and eosin (HE)-stained whole-slide images (WSIs) and further investigated its prognostic validity for patient stratification.

Methods

The CLR density was calculated by using a deep learning method on HE-stained WSIs. A training (N = 279) and a validation (N = 194) cohorts were used to evaluate the prognostic value of CLR density for overall survival (OS).

Result

The fully automated quantified CLR density was an independent prognostic factor, with high CLR density associated with increased OS in the discovery (HR 0.58, 95% CI 0.38–0.89, P = 0.012) and validation cohort (0.45, 0.23–0.88, 0.020). Integrating CLR density into a Cox model with other risk factors showed improved prognostic capability.

Conclusion

We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer. The CLR density was demonstrated its predictive value for OS in two independent cohorts. This approach allows for the objective and standardized quantification while reducing pathologists’ workload. Therefore, this fully automated standardized method of CLR evaluation had potential clinical value.

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

Access this article

Log in via an institution

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

References

  1. Xia L, Oyang L, Lin J, Tan S, Han Y, Wu N et al (2021) The cancer metabolic reprogramming and immune response. Mol Cancer 20:28. https://doi.org/10.1186/s12943-021-01316-8

    Article  PubMed  PubMed Central  Google Scholar 

  2. Dieu-Nosjean MC, Giraldo NA, Kaplon H, Germain C, Fridman WH, Sautès-Fridman C (2016) Tertiary lymphoid structures, drivers of the anti-tumor responses in human cancers. Immunol Rev 271:260–275. https://doi.org/10.1111/imr.12405

    Article  CAS  PubMed  Google Scholar 

  3. Ueno H, Hashiguchi Y, Shimazaki H, Shinto E, Kajiwara Y, Nakanishi K et al (2013) Objective criteria for crohn-like lymphoid reaction in colorectal cancer. Am J Clin Pathol 139:434–441. https://doi.org/10.1309/AJCPWHUEFTGBWKE4

    Article  CAS  PubMed  Google Scholar 

  4. Dieu-Nosjean MC, Antoine M, Danel C, Heudes D, Wislez M, Poulot V et al (2008) Long-term survival for patients with non-small-cell lung cancer with intratumoral lymphoid structures. J Clin Oncol 26:4410–4417. https://doi.org/10.1200/JCO.2007.15.0284

    Article  CAS  PubMed  Google Scholar 

  5. Ishigami E, Sakakibara M, Sakakibara J, Masuda T, Fujimoto H, Hayama S et al (2019) Coexistence of regulatory B cells and regulatory T cells in tumor-infiltrating lymphocyte aggregates is a prognostic factor in patients with breast cancer. Breast Cancer-tokyo 26:180–189. https://doi.org/10.1007/s12282-018-0910-4

    Article  Google Scholar 

  6. Maoz A, Dennis M, Greenson JK (2019) The crohn’s-like lymphoid reaction to colorectal cancer-tertiary lymphoid structures with immunologic and potentially therapeutic relevance in colorectal cancer. Front Immunol 10:1884. https://doi.org/10.3389/fimmu.2019.01884

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Graham DM, Appelman HD. Crohn’s-like lymphoid reaction and colorectal carcinoma: a potential histologic prognosticator. Mod Pathol (1990) 3:332–35. https://pubmed.ncbi.nlm.nih.gov/2362940

  8. Väyrynen JP, Sajanti SA, Klintrup K, Mäkelä J, Herzig KH, Karttunen TJ et al (2014) Characteristics and significance of colorectal cancer associated lymphoid reaction. Int J Cancer 134:2126–2135. https://doi.org/10.1002/ijc.28533

    Article  CAS  PubMed  Google Scholar 

  9. Ogino S, Nosho K, Irahara N, Meyerhardt JA, Baba Y, Shima K et al (2009) Lymphocytic reaction to colorectal cancer is associated with longer survival, independent of lymph node count, microsatellite instability, and CpG island methylator phenotype. Clin Cancer Res 15:6412–6420. https://doi.org/10.1158/1078-0432.CCR-09-1438

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kim JH, Kim KJ, Bae JM, Rhee YY, Cho NY, Lee HS et al (2015) Comparative validation of assessment criteria for Crohn-like lymphoid reaction in colorectal carcinoma. J Clin Pathol 68:22–28. https://doi.org/10.1136/jclinpath-2014-202603

    Article  PubMed  Google Scholar 

  11. Lewis B, Lin J, Wu X, Xie H, Shen B, Lai K et al (2013) Crohn’s disease-like reaction predicts favorable prognosis in colitis-associated colorectal cancer. Inflamm Bowel Dis 19:2190–2198. https://doi.org/10.1097/MIB.0b013e31829e13e1

    Article  PubMed  Google Scholar 

  12. Murphy J, O’Sullivan GC, Lee G, Madden M, Shanahan F, Collins JK et al (2000) The inflammatory response within Dukes’ B colorectal cancers: implications for progression of micrometastases and patient survival. Am J Gastroenterol 95:3607–3614. https://doi.org/10.1111/j.1572-0241.2000.03377.x

    Article  CAS  PubMed  Google Scholar 

  13. Hynes SO, Coleman HG, Kelly PJ, Irwin S, O’Neill RF, Gray RT et al (2017) Back to the future: routine morphological assessment of the tumour microenvironment is prognostic in stage II/III colon cancer in a large population-based study. Histopathology 71:12–26. https://doi.org/10.1111/his.13181

    Article  PubMed  Google Scholar 

  14. Konishi T, Shimada Y, Lee LH, Cavalcanti MS, Hsu M, Smith JJ et al (2018) Poorly differentiated clusters predict colon cancer recurrence: an in-depth comparative analysis of invasive-front prognostic markers. Am J Surg Pathol 42:705–714. https://doi.org/10.1097/PAS.0000000000001059

    Article  PubMed  PubMed Central  Google Scholar 

  15. Stower H (2019) Machines match humans in image-based diagnoses. Nat Med 25:1798. https://doi.org/10.1038/s41591-019-0679-9

    Article  CAS  PubMed  Google Scholar 

  16. Hu Y, Su F, Dong K, Wang X, Zhao X, Jiang Y et al (2021) Deep learning system for lymph node quantification and metastatic cancer identification from whole-slide pathology images. GASTRIC CANCER [Online ahead of print]. doi: https://doi.org/10.1007/s10120-021-01158-9

  17. Yamashita R, Long J, Longacre T, Peng L, Berry G, Martin B et al (2021) Deep learning model for the prediction of microsatellite instability in colorectal cancer: a diagnostic study. Lancet Oncol 22:132–141. https://doi.org/10.1016/S1470-2045(20)30535-0

    Article  PubMed  Google Scholar 

  18. Kather JN, Krisam J, Charoentong P, Luedde T, Herpel E, Weis CA et al (2019) Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. Plos Med 16:e1002730. https://doi.org/10.1371/journal.pmed.1002730

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Zhao K, Li Z, Yao S, Wang Y, Wu X, Xu Z et al (2020) Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine 61:103054. https://doi.org/10.1016/j.ebiom.2020.103054

    Article  PubMed  PubMed Central  Google Scholar 

  20. Zhou Y, Xu X, Song L, Wang C, Gou J, Yi Z et al (2020) The application of artificial intelligence and radiomics in lung cancer. Precis Clin Med 3:214–227. https://doi.org/10.1093/pcmedi/pbaa028

    Article  Google Scholar 

  21. Rozek LS, Schmit SL, Greenson JK, Tomsho LP, Rennert HS, Rennert G et al (2016) Tumor-infiltrating lymphocytes, crohn’s-like lymphoid reaction, and survival from colorectal cancer. J Natl Cancer Inst. https://doi.org/10.1093/jnci/djw027

    Article  PubMed  PubMed Central  Google Scholar 

  22. Toh J, Lim SH, MacKenzie S, de Souza P, Bokey L, Chapuis P et al (2020) Association between microsatellite instability status and peri-operative release of circulating tumour cells in colorectal cancer. Cells-Basel 9:425. https://doi.org/10.3390/cells9020425

    Article  CAS  Google Scholar 

  23. Greenson JK, Bonner JD, Ben-Yzhak O, Cohen HI, Miselevich I, Resnick MB et al (2003) Phenotype of microsatellite unstable colorectal carcinomas: well-differentiated and focally mucinous tumors and the absence of dirty necrosis correlate with microsatellite instability. Am J Surg Pathol 27:563–570. https://doi.org/10.1097/00000478-200305000-00001

    Article  PubMed  Google Scholar 

  24. Greenson JK, Huang SC, Herron C, Moreno V, Bonner JD, Tomsho LP et al (2009) Pathologic predictors of microsatellite instability in colorectal cancer. Am J Surg Pathol 33:126–133. https://doi.org/10.1097/PAS.0b013e31817ec2b1

    Article  PubMed  PubMed Central  Google Scholar 

  25. Jenkins MA, Hayashi S, O’Shea AM, Burgart LJ, Smyrk TC, Shimizu D et al (2007) Pathology features in Bethesda guidelines predict colorectal cancer microsatellite instability: a population-based study. Gastroenterology 133:48–56. https://doi.org/10.1053/j.gastro.2007.04.044

    Article  CAS  PubMed  Google Scholar 

  26. Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz HJ, Morse MA et al (2017) 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. https://doi.org/10.1016/S1470-2045(17)30422-9

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Yeh YC, Nitadori J, Kadota K, Yoshizawa A, Rekhtman N, Moreira AL et al (2015) Using frozen section to identify histological patterns in stage I lung adenocarcinoma of ≤ 3 cm: accuracy and interobserver agreement. Histopathology 66:922–938. https://doi.org/10.1111/his.12468

    Article  PubMed  PubMed Central  Google Scholar 

  28. Steu S, Baucamp M, von Dach G, Bawohl M, Dettwiler S, Storz M et al (2008) A procedure for tissue freezing and processing applicable to both intra-operative frozen section diagnosis and tissue banking in surgical pathology. VIRCHOWS ARCH 452:305–312. https://doi.org/10.1007/s00428-008-0584-y

    Article  PubMed  Google Scholar 

  29. Hildebrand LA, Pierce CJ, Dennis M, Paracha M, Maoz A (2021) Artificial intelligence for histology-based detection of microsatellite instability and prediction of response to immunotherapy in colorectal cancer. Cancers (Basel) 13:391. https://doi.org/10.3390/cancers13030391

    Article  CAS  Google Scholar 

  30. Hansen TF, Kjær-Frifeldt S, Lindebjerg J, Rafaelsen SR, Jensen LH, Jakobsen A et al (2018) Tumor-stroma ratio predicts recurrence in patients with colon cancer treated with neoadjuvant chemotherapy. ACTA ONCOL 57:528–533. https://doi.org/10.1080/0284186X.2017.1385841

    Article  CAS  PubMed  Google Scholar 

  31. van Pelt GW, Sandberg TP, Morreau H, Gelderblom H, van Krieken J, Tollenaar R et al (2018) The tumour-stroma ratio in colon cancer: the biological role and its prognostic impact. Histopathology 73:197–206. https://doi.org/10.1111/his.13489

    Article  PubMed  Google Scholar 

  32. Park JH, Richards CH, McMillan DC, Horgan PG, Roxburgh C (2014) The relationship between tumour stroma percentage, the tumour microenvironment and survival in patients with primary operable colorectal cancer. ANN ONCOL 25:644–651. https://doi.org/10.1093/annonc/mdt593

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ueno H, Jones AM, Wilkinson KH, Jass JR, Talbot IC (2004) Histological categorisation of fibrotic cancer stroma in advanced rectal cancer. Gut 53:581–586. https://doi.org/10.1136/gut.2003.028365

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C et al (2006) Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science 313:1960–1964. https://doi.org/10.1126/science.1129139

    Article  CAS  PubMed  Google Scholar 

  35. Pagès F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C et al (2018) International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet 391:2128–2139. https://doi.org/10.1016/S0140-6736(18)30789-X

    Article  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by the Key R&D Program of Guangdong Province, China [2021B0101420006], National Science Fund for Distinguished Young Scholars [81925023], National Natural Science Foundation of China [82001986, 62002082 and 82071892], Outstanding Youth Science Foundation of Yunnan Basic Research Project [202101AW070001], Project funded by China Postdoctoral Science Foundation [2021M690753], and High-level Hospital Construction Project [DFJH201805 and DFJH201914].

Funding

Funding sources can be found in the acknowledgements section.

Author information

Author notes

  1. Minning Zhao, Su Yao and Zhenhui Li have contributed equally to this work.

Authors and Affiliations

  1. The Second School of Clinical Medicine, Southern Medical University, Guangzhou, China

    Minning Zhao & Zaiyi Liu

  2. Department of Radiology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, 106 Zhongshan Er Road, Guangzhou, 510080, China

    Minning Zhao, Zeyan Xu, Xipeng Pan, Huan Lin, Ke Zhao, Changhong Liang & Zaiyi Liu

  3. Department of Pathology, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Su Yao

  4. Department of Radiology, Yunnan Cancer Center, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China

    Zhenhui Li

  5. Department of Pathology, Yunnan Cancer Center, Yunnan Cancer Hospital, the Third Affiliated Hospital of Kunming Medical University, Kunming, China

    Lin Wu

  6. China School of Medicine, South China University of Technology, Guangzhou, China

    Zeyan Xu, Huan Lin & Ke Zhao

  7. School of Bioengineering, Chongqing University, Chongqing, China

    Yao Xu

  8. School of Life Science and Technology, Xidian University, Xian, China

    Shangqing Yang

  9. Department of Pathology, The Sixth Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China

    Shenyan Zhang

  10. Department of General Surgery, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Yong Li

Authors
  1. Minning Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Su Yao
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Zhenhui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lin Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Zeyan Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xipeng Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Huan Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Yao Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Shangqing Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Shenyan Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Yong Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Ke Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Changhong Liang
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Zaiyi Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Study design: MNZ, SY, and ZYL. Performed the research and collected data: MNZ, SY, ZHL, LW, ZYX, XPP, HL, YX, SQY, and SYZ. Analyzed the data: KZ and MNZ. Manuscript drafting: MNZ and KZ. Labeled images: SY and LW. Provided discussion, critical feedback and manuscript editing: ZYL, CHL, KZ, and YS.

Corresponding authors

Correspondence to Ke Zhao, Changhong Liang or Zaiyi Liu.

Ethics declarations

Conflict of interest

The authors declared no conflicts of interest.

Data Sharing

The data sets for training the model was available online (https://doi.org/10.5281/zenodo.4024676, https://doi.org/10.5281/zenodo.4023999).

Additional information

Publisher's Note

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

Précis: We developed a new immune indicator (CLR density) quantified by a deep learning method to evaluate the lymphocytes aggregation in colon cancer, and demonstrated its predictive value for OS. This automated approach has the value of clinical work.

Supplementary Information

Below is the link to the electronic supplementary material.

Supplementary file1 (PDF 212 KB)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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 71, 1221–1231 (2022). https://doi.org/10.1007/s00262-021-03079-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00262-021-03079-z

Keywords

Search

Navigation