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Automated quantification of stromal tumour infiltrating lymphocytes is associated with prognosis in breast cancer

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Abstract

Stromal tumour infiltrating lymphocytes (sTIL) in haematoxylin and eosin (H&E) stained sections has been linked to better outcomes and better responses to neoadjuvant therapy in triple-negative and HER2-positive breast cancer (TNBC and HER2 +). However, the infiltrate includes different cell populations that have specific roles in the tumour immune microenvironment. Various studies have found high concordance between sTIL visual quantification and computational assessment, but specific data on the individual prognostic impact of plasma cells or lymphocytes within sTIL on patient prognosis is still unknown. In this study, we validated a deep-learning breast cancer sTIL scoring model (smsTIL) based on the segmentation of tumour cells, benign ductal cells, lymphocytes, plasma cells, necrosis, and ‘other’ cells in whole slide images (WSI). Focusing on HER2 + and TNBC patient samples, we assessed the concordance between sTIL visual scoring and the smsTIL in 130 WSI. Furthermore, we analysed 175 WSI to correlate smsTIL with clinical data and patient outcomes. We found a high correlation between sTIL values scored visually and semi-automatically (R = 0.76; P = 2.2e-16). Patients with higher smsTIL had better overall survival (OS) in TNBC (P = 0.0021). In the TNBC cohort, smsTIL was as an independent prognostic factor for OS. As part of this work, we introduce a new segmentation dataset of H&E-stained WSI.

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Data availability

Data on anonymized tiles and ground truth segmentations of breast cancer whole slide image sections have been deposited into the open-source database Zenodo (10.5281/zenodo.7775365).

References

  1. Hammerl D, Smid M, Timmermans AM, Sleijfer S, Martens JWM, Debets R (2018) Breast cancer genomics and immuno-oncological markers to guide immune therapies. Semin Cancer Biol 52:178–188. https://doi.org/10.1016/J.SEMCANCER.2017.11.003

    Article  CAS  PubMed  Google Scholar 

  2. Denkert C et al (2018) Tumour-infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol 19(1):40–50. https://doi.org/10.1016/S1470-2045(17)30904-X

    Article  PubMed  Google Scholar 

  3. Loi S et al (2013) Prognostic and predictive value of tumor-infiltrating lymphocytes in a phase III randomized adjuvant breast cancer trial in node-positive breast cancer comparing the addition of docetaxel to doxorubicin with doxorubicin-based chemotherapy: BIG 02–98. J Clin Oncol 31(7):860–867. https://doi.org/10.1200/JCO.2011.41.0902

    Article  CAS  PubMed  Google Scholar 

  4. Salgado R et al (2015) The evaluation of tumor-infiltrating lymphocytes (TILS) in breast cancer: recommendations by an International TILS Working Group 2014. Ann Oncol 26(2):259–271. https://doi.org/10.1093/annonc/mdu450. (Oxford University Press)

    Article  CAS  PubMed  Google Scholar 

  5. Mohaiminul Islam M, Huang S, Ajwad R, Chi C, Wang Y, Hu P (2020) An integrative deep learning framework for classifying molecular subtypes of breast cancer. Comput Struct Biotechnol J 18:2185–2199. https://doi.org/10.1016/j.csbj.2020.08.005

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bychkov D et al (2021) Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep 11(1). https://doi.org/10.1038/s41598-021-83102-6

  7. Beck AH et al (2011) Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med 3(108):108–113. https://doi.org/10.1126/scitranslmed.3002564

    Article  Google Scholar 

  8. Sarah Dudgeon AN, Wen S, Gallas BD A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study Title A Pathologist-Annotated Dataset for Validating Artificial Intelligence: A Project Description and Pilot Study Affiliations of Authors. iRhythm Technologies Inc

  9. Saltz J et al (2018) Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep 23(1):181-193.e7. https://doi.org/10.1016/j.celrep.2018.03.086

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Thagaard J et al (2021) Automated quantification of stil density with h&e-based digital image analysis has prognostic potential in triple-negative breast cancers. Cancers (Basel) 13(12). https://doi.org/10.3390/cancers13123050

  11. Bai Y et al (2021) An open-source, automated tumor-infiltrating lymphocyte algorithm for prognosis in triple-negative breast cancer. Clin Cancer Res 27(20):5557–5565. https://doi.org/10.1158/1078-0432.CCR-21-0325

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Olkhanud PB et al (2011) Tumor-evoked regulatory B cells promote breast cancer metastasis by converting resting CD4+ T cells to T-regulatory cells. Cancer Res 71(10):3505–3515. https://doi.org/10.1158/0008-5472.CAN-10-4316

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Tower H, Ruppert M, Britt K (2019) The immune microenvironment of breast cancer progression. Cancers (Basel) 11(9):1–15. https://doi.org/10.3390/cancers11091375

    Article  CAS  Google Scholar 

  14. Ali HR et al (2014) Association between CD8+ T-cell infiltration and breast cancer survival in 12 439 patients. Ann Oncol 25(8):1536–1543. https://doi.org/10.1093/annonc/mdu191

    Article  CAS  PubMed  Google Scholar 

  15. Wang K, Shen T, Siegal GP, Wei S (2017) The CD4/CD8 ratio of tumor-infiltrating lymphocytes at the tumor-host interface has prognostic value in triple-negative breast cancer. Hum Pathol 69:110–117. https://doi.org/10.1016/j.humpath.2017.09.012

    Article  CAS  PubMed  Google Scholar 

  16. Garaud S et al (2019) Tumor-infiltrating B cells signal functional humoral immune responses in breast cancer. JCI Insight 4(18). https://doi.org/10.1172/jci.insight.129641

  17. Mohammed ZMA, Going JJ, Edwards J, Mcmillan DC (2013) The relationship between lymphocyte subsets and clinico-pathological determinants of survival in patients with primary operable invasive ductal breast cancer. Br J Cancer 109(6):1676–1684. https://doi.org/10.1038/bjc.2013.493

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Wei H, Fu P, Yao M, Chen Y, Du L (2016) Breast cancer stem cells phenotype and plasma cell-predominant breast cancer independently indicate poor survival. Undefined 212(4):294–301. https://doi.org/10.1016/J.PRP.2016.01.008

    Article  CAS  Google Scholar 

  19. bin Seow DY et al (2020) Tertiary lymphoid structures and associated plasma cells play an important role in the biology of triple-negative breast cancers. Breast Cancer Res Treat 180(2):369–377. https://doi.org/10.1007/S10549-020-05548-Y

    Article  Google Scholar 

  20. Kuroda H et al (2021) Prognostic value of tumor-infiltrating B lymphocytes and plasma cells in triple-negative breast cancer. Breast Cancer 28(4):904–914. https://doi.org/10.1007/s12282-021-01227-y

    Article  PubMed  Google Scholar 

  21. Yeong J et al (2018) High densities of tumor-associated plasma cells predict improved prognosis in triple negative breast cancer. Front Immunol 9(MAY). https://doi.org/10.3389/fimmu.2018.01209

  22. Li JJ, Tsang JY, Tse GM (2021) Tumor microenvironment in breast cancer—Updates on therapeutic implications and pathologic assessment. Cancers 13(16). https://doi.org/10.3390/cancers13164233. (MDPI)

  23. Moncusí A, Villanueva X, Noguera A, Bonilla M, Posso M, Macià F (2022) Informe 2022 del Registre de Tumors de l’Hospital del Mar (RTHMar), Barcelona. Barcelona: Parc de Salut MAR; 2022. Barcelonas

  24. Allison KH et al (2020) Estrogen and progesterone receptor testing in breast cancer: ASCO/CAP guideline update. J Clin Oncol 38:1346–1366. https://doi.org/10.1200/JCO.19

    Article  PubMed  Google Scholar 

  25. Wolff AC et al (2018) Human epidermal growth factor receptor 2 testing in breast cancer: American Society of Clinical Oncology/College of American Pathologists Clinical Practice Guideline focused update. J Clin Oncol 36:2105–2122. https://doi.org/10.1200/JCO

  26. Bankhead P et al (2017) QuPath: open source software for digital pathology image analysis. Sci Rep 7(1). https://doi.org/10.1038/s41598-017-17204-5

  27. Graham S et al (2019) Hover-Net: Simultaneous segmentation and classification of nuclei in multi-tissue histology images. Med Image Anal 58:101563. https://doi.org/10.1016/J.MEDIA.2019.101563

    Article  PubMed  Google Scholar 

  28. Dang VuQ et al (2019) Methods for segmentation and classification of digital microscopy tissue images. Front Bioeng Biotechnol 7:53. https://doi.org/10.3389/fbioe.2019.00053

    Article  Google Scholar 

  29. Hendry S et al (2017) Assessing tumor-infiltrating lymphocytes in solid tumors: a practical review for pathologists and proposal for a standardized method from the international immuno-oncology biomarkers working group: part 2: TILs in melanoma, gastrointestinal tract carcinomas, non-small cell lung carcinoma and mesothelioma, endometrial and ovarian carcinomas, squamous cell carcinoma of the head and neck, genitourinary carcinomas, and primary brain tumors. Adv Anat Pathol 24(6):311–335. https://doi.org/10.1097/PAP.0000000000000161. (Lippincott Williams and Wilkins)

    Article  PubMed  PubMed Central  Google Scholar 

  30. AiErken NJ et al (2017) High PD-L1 expression is closely associated with tumor-infiltrating lymphocytes and leads to good clinical outcomes in Chinese triple negative breast cancer patients. Int J Biol Sci 13(9):1172–1179. https://doi.org/10.7150/ijbs.20868

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Schoenfeld D (1981) The asymptotic properties of nonparametric tests for comparing survival distributions. Biometrika 68(1):316–319. https://doi.org/10.2307/2335833

    Article  Google Scholar 

  32. Schoenfeld DA (1983) Sample-size formula for the proportional-hazards regression model. Biometrics 39(2):499–503. https://doi.org/10.2307/2531021

    Article  CAS  PubMed  Google Scholar 

  33. Jang N, Kwon HJ, Park MH, Kang SH, Bae YK (2018) Prognostic value of tumor-infiltrating lymphocyte density assessed using a standardized method based on molecular subtypes and adjuvant chemotherapy in invasive breast cancer. Ann Surg Oncol 25(4):937–946. https://doi.org/10.1245/s10434-017-6332-2

    Article  PubMed  Google Scholar 

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Acknowledgements

The authors would like to acknowledge Xavier Duran for his statistical guidance.

Funding

This research was funded by Rio Hortega post-residency grant, Instituto de Salud Carlos III CM20/00019 and supported by grants from Instituto de Salud Carlos III/FEDER (PT17/0015/0011) and the ‘Xarxa de Bancs de tumors’ sponsored by Pla Director d’Oncologia de Catalunya (XBTC).

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Author notes

  1. Mònica Gonzàlez-Farré and Joan Gibert contributed equally to this work.

  2. Ivonne Vázquez and Laura Comerma contributed equally to this work.

Authors and Affiliations

  1. Department of Pathology, Hospital del Mar, Passeig Marítim de la Barceloneta 25-29, 08003, Barcelona, Spain

    Mònica Gonzàlez-Farré, Joan Gibert, Pablo Santiago-Díaz, Jordina Santos, Pilar García, Jordi Massó, Beatriz Bellosillo, Belén Lloveras, Ivonne Vázquez & Laura Comerma

  2. Cancer Research Program, IMIM (Hospital del Mar Medical Research Institute), 08003, Barcelona, Spain

    Mònica Gonzàlez-Farré, Joan Gibert, Beatriz Bellosillo, Belén Lloveras, Joan Albanell & Laura Comerma

  3. Department of Medicine and Life Sciences (MELIS), University Pompeu Fabra, Doctor Aiguader 88, 08003, Barcelona, Spain

    Beatriz Bellosillo, Belén Lloveras & Joan Albanell

  4. Department of Medical Oncology, Hospital del Mar, 08003, Barcelona, Spain

    Joan Albanell

  5. Center for Biomedical Network Research On Cancer (CIBERONC), 28029, Madrid, Spain

    Joan Albanell

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  1. Mònica Gonzàlez-Farré
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Contributions

Conceptualization: MG, JG, LC and IV. Methodology: MG, JG, PS, JS, PG, JM, LC and IV. Formal analysis and investigation: MG, JG, LC and IV. Writing-original draft preparation: MG and JG. Writing-review and editing: LC, IV, BB, BL and JA. Funding acquisition: MG, LC and JA.

Corresponding author

Correspondence to Mònica Gonzàlez-Farré.

Ethics declarations

This study was approved by the Ethics Commission of Hospital del Mar (2020/794537/I). Biological samples were obtained from ‘Parc de Salut MAR Biobank (MARBiobanc)’(Barcelona).

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The authors have no conflicts of interest to declare that are relevant to the content of this article.

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Gonzàlez-Farré, M., Gibert, J., Santiago-Díaz, P. et al. Automated quantification of stromal tumour infiltrating lymphocytes is associated with prognosis in breast cancer. Virchows Arch 483, 655–663 (2023). https://doi.org/10.1007/s00428-023-03608-4

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  • DOI: https://doi.org/10.1007/s00428-023-03608-4

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