Multiscale deep learning framework captures systemic immune features in lymph nodes predictive of triple negative breast cancer outcome in large-scale studies.
computational pathology
deep learning
digital pathology
germinal centre
lymph node
sinus
triple negative breast cancer
Journal
The Journal of pathology
ISSN: 1096-9896
Titre abrégé: J Pathol
Pays: England
ID NLM: 0204634
Informations de publication
Date de publication:
08 2023
08 2023
Historique:
revised:
27
02
2023
received:
09
09
2022
accepted:
11
04
2023
medline:
13
7
2023
pubmed:
26
5
2023
entrez:
25
5
2023
Statut:
ppublish
Résumé
The suggestion that the systemic immune response in lymph nodes (LNs) conveys prognostic value for triple-negative breast cancer (TNBC) patients has not previously been investigated in large cohorts. We used a deep learning (DL) framework to quantify morphological features in haematoxylin and eosin-stained LNs on digitised whole slide images. From 345 breast cancer patients, 5,228 axillary LNs, cancer-free and involved, were assessed. Generalisable multiscale DL frameworks were developed to capture and quantify germinal centres (GCs) and sinuses. Cox regression proportional hazard models tested the association between smuLymphNet-captured GC and sinus quantifications and distant metastasis-free survival (DMFS). smuLymphNet achieved a Dice coefficient of 0.86 and 0.74 for capturing GCs and sinuses, respectively, and was comparable to an interpathologist Dice coefficient of 0.66 (GC) and 0.60 (sinus). smuLymphNet-captured sinuses were increased in LNs harbouring GCs (p < 0.001). smuLymphNet-captured GCs retained clinical relevance in LN-positive TNBC patients whose cancer-free LNs had on average ≥2 GCs, had longer DMFS (hazard ratio [HR] = 0.28, p = 0.02) and extended GCs' prognostic value to LN-negative TNBC patients (HR = 0.14, p = 0.002). Enlarged smuLymphNet-captured sinuses in involved LNs were associated with superior DMFS in LN-positive TNBC patients in a cohort from Guy's Hospital (multivariate HR = 0.39, p = 0.039) and with distant recurrence-free survival in 95 LN-positive TNBC patients of the Dutch-N4plus trial (HR = 0.44, p = 0.024). Heuristic scoring of subcapsular sinuses in LNs of LN-positive Tianjin TNBC patients (n = 85) cross-validated the association of enlarged sinuses with shorter DMFS (involved LNs: HR = 0.33, p = 0.029 and cancer-free LNs: HR = 0.21 p = 0.01). Morphological LN features reflective of cancer-associated responses are robustly quantifiable by smuLymphNet. Our findings further strengthen the value of assessment of LN properties beyond the detection of metastatic deposits for prognostication of TNBC patients. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
376-389Subventions
Organisme : Cancer Research UK
ID : CRUK/07/012
Pays : United Kingdom
Organisme : Cancer Research UK
ID : KCL-BCN-Q3
Pays : United Kingdom
Organisme : Medical Research Council
ID : MR/X012476/1
Pays : United Kingdom
Organisme : Cancer Research UK
ID : CTRQQR-2021/100004
Pays : United Kingdom
Informations de copyright
© 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
Références
du Bois H, Heim TA, Lund AW. Tumor-draining lymph nodes: at the crossroads of metastasis and immunity. Sci Immunol 2021; 6: eabg3551.
Giuliano AE, Ballman KV, McCall L, et al. Effect of axillary dissection vs no axillary dissection on 10-year overall survival among women with invasive breast cancer and sentinel node metastasis: the ACOSOG Z0011 (alliance) randomized clinical trial. JAMA 2017; 318: 918-926.
Galimberti V, Cole BF, Viale G, et al. Axillary dissection versus no axillary dissection in patients with breast cancer and sentinel-node micrometastases (IBCSG 23-01): 10-year follow-up of a randomised, controlled phase 3 trial. Lancet Oncol 2018; 19: 1385-1393.
Sato J, Doi T, Kanno T, et al. Histopathology of incidental findings in cynomolgus monkeys (Macaca fascicularis) used in toxicity studies. J Toxicol Pathol 2012; 25: 63-101.
Litjens G, Bandi P, Ehteshami Bejnordi B, et al. 1399 H&E-stained sentinel lymph node sections of breast cancer patients: the CAMELYON dataset. Gigascience 2018; 7: giy065.
van der Laak J, Litjens G, Ciompi F. Deep learning in histopathology: the path to the clinic. Nat Med 2021; 27: 775-784.
Liu F, Hardiman T, Wu K, et al. Systemic immune reaction in axillary lymph nodes adds to tumor-infiltrating lymphocytes in triple-negative breast cancer prognostication. NPJ Breast Cancer 2021; 7: 86.
Grigoriadis A, Gazinska P, Pai T, et al. Histological scoring of immune and stromal features in breast and axillary lymph nodes is prognostic for distant metastasis in lymph node-positive breast cancers. J Pathol Clin Res 2018; 4: 39-54.
Quintana A, Peg V, Prat A, et al. Immune analysis of lymph nodes in relation to the presence or absence of tumor infiltrating lymphocytes in triple-negative breast cancer. Eur J Cancer 2021; 148: 134-145.
Seidl M, Bader M, Vaihinger A, et al. Morphology of immunomodulation in breast cancer tumor draining lymph nodes depends on stage and intrinsic subtype. Sci Rep 2018; 8: 5321.
Pham HHN, Futakuchi M, Bychkov A, et al. Detection of lung cancer lymph node metastases from whole-slide histopathologic images using a two-step deep learning approach. Am J Pathol 2019; 189: 2428-2439.
Bera K, Schalper KA, Rimm DL, et al. Artificial intelligence in digital pathology - new tools for diagnosis and precision oncology. Nat Rev Clin Oncol 2019; 16: 703-715.
Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation. In Medical Image Computing and Computer-Assisted Intervention - MICCAI 2015, Navab N, Hornegger J, Wells W, et al. (eds). Springer International Publishing: Cham, 2015; 234-241.
Shelhamer E, Long J, Darrell T. Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 2017; 39: 640-651.
Su R, Zhang D, Liu J, et al. MSU-net: multi-scale U-net for 2D medical image segmentation. Front Genet 2021; 12: 639930.
Vollebergh MA, Lips EH, Nederlof PM, et al. Genomic patterns resembling BRCA1- and BRCA2-mutated breast cancers predict benefit of intensified carboplatin-based chemotherapy. Breast Cancer Res 2014; 16: R47.
Alberts E, Wall I, Calado DP, et al. Immune crosstalk between lymph nodes and breast carcinomas, with a focus on B cells. Front Mol Biosci 2021; 8: 673051.
McShane LM, Altman DG, Sauerbrei W, et al. REporting recommendations for tumour MARKer prognostic studies (REMARK). Br J Cancer 2005; 93: 387-391.
Otsu N. A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern 1979; 9: 62-66.
Li Y, Ping W. Cancer metastasis detection with neural conditional random field. ArXiv 2018; abs/1806.07064. [see also https://openreview.net/forum?id=S1aY66iiM].
Oktay O, Schlemper J, Folgoc LL, et al. Attention U-net: learning where to look for the pancreas. ArXiv 2018; abs/1804.03999. [see also https://openreview.net/forum?id=Skft7cijM].
Kurian NC, Lohan A, Verghese G, et al. Deep multi-scale U-Net architecture and label-noise robust training strategies for histopathological image segmentation. In 2022 IEEE 22nd International Conference on Bioinformatics and Bioengineering (BIBE). IEEE: Taichung, 2022; 91-96.
Bankhead P, Loughrey MB, Fernández JA, et al. QuPath: open source software for digital pathology image analysis. Sci Rep 2017; 7: 16878.
Sørensen TJ. A Method of Establishing Groups of Equal Amplitude in Plant Sociology based on Similarity of Species Content and its Application to Analyses of the Vegetation on Danish Commons. I kommission hos E. Munksgaard: København, 1948.
Gourgou-Bourgade S, Cameron D, Poortmans P, et al. Guidelines for time-to-event end point definitions in breast cancer trials: results of the DATECAN initiative (definition for the assessment of time-to-event endpoints in CANcer trials)†. Ann Oncol 2015; 26: 873-879.
Team RC. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, 2013.
Kleppe A, Skrede O-J, De Raedt S, et al. Designing deep learning studies in cancer diagnostics. Nat Rev Cancer 2021; 21: 199-211.
Steenbruggen TG, Steggink LC, Seynaeve CM, et al. High-dose chemotherapy with hematopoietic stem cell transplant in patients with high-risk breast cancer and 4 or more involved axillary lymph nodes: 20-year follow-up of a phase 3 randomized clinical trial. JAMA Oncol 2020; 6: 528-534.
Habenicht LM, Kirschbaum SB, Furuya M, et al. Tumor regulation of lymph node lymphatic sinus growth and lymph flow in mice and in humans. Yale J Biol Med 2017; 90: 403-415.
Batista FD, Harwood NE. The who, how and where of antigen presentation to B cells. Nat Rev Immunol 2009; 9: 15-27.
Kastenmüller W, Torabi-Parizi P, Subramanian N, et al. A spatially-organized multicellular innate immune response in lymph nodes limits systemic pathogen spread. Cell 2012; 150: 1235-1248.
Komohara Y, Ohnishi K, Takeya M. Possible functions of CD169-positive sinus macrophages in lymph nodes in anti-tumor immune responses. Cancer Sci 2017; 108: 290-295.
Pucci F, Garris C, Lai CP, et al. SCS macrophages suppress melanoma by restricting tumor-derived vesicle-B cell interactions. Science 2016; 352: 242-246.
Marini N, Otálora S, Podareanu D, et al. Multi_Scale_Tools: a python library to exploit multi-scale whole slide images. Front Comput Sci 2021; 3: 684521.
López-Pérez M, Amgad M, Morales-Álvarez P, et al. Learning from crowds in digital pathology using scalable variational gaussian processes. Sci Rep 2021; 11: 11612.
Schmid P, Cortes J, Pusztai L, et al. Pembrolizumab for early triple-negative breast cancer. N Engl J Med 2020; 382: 810-821.
Sun P, He J, Chao X, et al. A computational tumor-infiltrating lymphocyte assessment method comparable with visual reporting guidelines for triple-negative breast cancer. EBioMedicine 2021; 70: 103492.
Acs B, Salgado R, Hartman J. What do we still need to learn on digitally assessed biomarkers? EBioMedicine 2021; 70: 103520.
Warnat-Herresthal S, Schultze H, Shastry KL, et al. Swarm learning for decentralized and confidential clinical machine learning. Nature 2021; 594: 265-270.
Shmatko A, Ghaffari Laleh N, Gerstung M, et al. Artificial intelligence in histopathology: enhancing cancer research and clinical oncology. Nat Cancer 2022; 3: 1026-1038.