Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer.
Journal
Nature medicine
ISSN: 1546-170X
Titre abrégé: Nat Med
Pays: United States
ID NLM: 9502015
Informations de publication
Date de publication:
01 2023
01 2023
Historique:
received:
20
05
2021
accepted:
23
11
2022
pubmed:
20
1
2023
medline:
27
1
2023
entrez:
19
1
2023
Statut:
ppublish
Résumé
Triple-negative breast cancer (TNBC) is a rare cancer, characterized by high metastatic potential and poor prognosis, and has limited treatment options. The current standard of care in nonmetastatic settings is neoadjuvant chemotherapy (NACT), but treatment efficacy varies substantially across patients. This heterogeneity is still poorly understood, partly due to the paucity of curated TNBC data. Here we investigate the use of machine learning (ML) leveraging whole-slide images and clinical information to predict, at diagnosis, the histological response to NACT for early TNBC women patients. To overcome the biases of small-scale studies while respecting data privacy, we conducted a multicentric TNBC study using federated learning, in which patient data remain secured behind hospitals' firewalls. We show that local ML models relying on whole-slide images can predict response to NACT but that collaborative training of ML models further improves performance, on par with the best current approaches in which ML models are trained using time-consuming expert annotations. Our ML model is interpretable and is sensitive to specific histological patterns. This proof of concept study, in which federated learning is applied to real-world datasets, paves the way for future biomarker discovery using unprecedentedly large datasets.
Identifiants
pubmed: 36658418
doi: 10.1038/s41591-022-02155-w
pii: 10.1038/s41591-022-02155-w
doi:
Types de publication
Journal Article
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
135-146Informations de copyright
© 2023. The Author(s), under exclusive licence to Springer Nature America, Inc.
Références
Portha, H. et al. Nonmetastatic triple-negative breast cancer in 2016: definitions and management. Gynecol. Obstet. Fertil. 44, 492–504 (2016).
doi: 10.1016/j.gyobfe.2016.06.014
Hortobagyi, G. N. Treatment of breast cancer. N. Engl. J. Med. 339, 974–984 (1998).
doi: 10.1056/NEJM199810013391407
Waks, A. G. & Winer, E. P. Breast cancer treatment: a review. JAMA 321, 288–300 (2019).
doi: 10.1001/jama.2018.19323
Penault-Llorca, F. et al. 2014 update of the GEFPICS’ recommendations for HER2 status determination in breast cancers in France. Ann. Pathol. 34, 352–365 (2014).
doi: 10.1016/j.annpat.2014.08.018
Fujii, T. et al. New threshold of ER positivity in early stage HER2− breast cancer. J. Clin. Oncol. 34(15_suppl), 1067 (2016).
doi: 10.1200/JCO.2016.34.15_suppl.1067
Moo, T.-A., Sanford, R., Dang, C. & Morrow, M. Overview of breast cancer therapy. PET Clin. 13, 339–354 (2018).
doi: 10.1016/j.cpet.2018.02.006
Yin, L., Duan, J.-J., Bian, X.-W. & Yu, S.-C. Triple-negative breast cancer molecular subtyping and treatment progress. Breast Cancer Res. 22, 61 (2020).
doi: 10.1186/s13058-020-01296-5
Sakuma, K. et al. Pathological tumor response to neoadjuvant chemotherapy using anthracycline and taxanes in patients with triple-negative breast cancer. Exp. Ther. Med. 2, 257–264 (2011).
doi: 10.3892/etm.2011.212
Fraser Symmans, W. et al. Measurement of residual breast cancer burden to predict survival after neoadjuvant chemotherapy. J. Clin. Oncol. 25, 4414–4422 (2007).
doi: 10.1200/JCO.2007.10.6823
Pandy, J. G. P., Balolong-Garcia, J. C., Cruz-Ordinario, M. V. B. & Que, F. V. F. Triple negative breast cancer and platinum-based systemic treatment: a meta-analysis and systematic review. BMC Cancer 19, 1065 (2019).
doi: 10.1186/s12885-019-6253-5
Hwang, S.-Y., Park, S. & Kwon, Y. Recent therapeutic trends and promising targets in triple negative breast cancer. Pharmacol. Ther. 199, 30–57 (2019).
doi: 10.1016/j.pharmthera.2019.02.006
Vikas, P., Borcherding, N. & Zhang, W. The clinical promise of immunotherapy in triple-negative breast cancer. Cancer Manag. Res. 10, 6823–6833 (2018).
doi: 10.2147/CMAR.S185176
Schmid, P. et al. Event-free survival with pembrolizumab in early triple-negative breast cancer. N. Engl. J. Med. 386, 556–567 (2022).
doi: 10.1056/NEJMoa2112651
Gass, P. et al. Prediction of pathological complete response and prognosis in patients with neoadjuvant treatment for triple-negative breast cancer. BMC Cancer 18, 1051 (2018).
doi: 10.1186/s12885-018-4925-1
Abuhadra, N. et al. Beyond TILs: predictors of pathologic complete response (pCR) in triple-negative breast cancer (TNBC) patients with moderate tumor-infiltrating lymphocytes (TIL) receiving neoadjuvant therapy. J. Clin. Oncol. 37(15_suppl), 572 (2019).
doi: 10.1200/JCO.2019.37.15_suppl.572
Jung, Y. Y. et al. Histomorphological factors predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer. J. Breast Cancer 19, 261–267 (2016).
doi: 10.4048/jbc.2016.19.3.261
Mao, Y. et al. The prognostic value of tumor-infiltrating lymphocytes in breast cancer: a systematic review and meta-analysis. PLoS One 11, e0152500 (2016).
doi: 10.1371/journal.pone.0152500
Stanton, S. E. & Disis, M. L. Clinical significance of tumor-infiltrating lymphocytes in breast cancer. J. Immunother. Cancer 4, 59 (2016).
doi: 10.1186/s40425-016-0165-6
Denkert, C. et al. Tumour infiltrating lymphocytes and prognosis in different subtypes of breast cancer: a pooled analysis of 3771 patients treated with neoadjuvant therapy. Lancet Oncol. 19, 40–50 (2018).
doi: 10.1016/S1470-2045(17)30904-X
Luen, S. J. et al. Prognostic implications of residual disease tumor-infiltrating lymphocytes and residual cancer burden in triple-negative breast cancer patients after neoadjuvant chemotherapy. Ann. Oncol. 30, 236–242 (2019).
doi: 10.1093/annonc/mdy547
Penault-Llorca, F. & Radosevic-Robin, N. Ki67 assessment in breast cancer: an update. Pathology 49, 166–171 (2017).
doi: 10.1016/j.pathol.2016.11.006
Pistelli, M. et al. Prognostic factors in early-stage triple-negative breast cancer: lessons and limits from clinical practice. Anticancer Res. 33, 2737–2742 (2013).
Ahn, K. J., Park, J. & Choi, Y. Lymphovascular invasion as a negative prognostic factor for triple-negative breast cancer after surgery. Radiat. Oncol. J. 35, 332–339 (2017).
doi: 10.3857/roj.2017.00416
Dimitriou, N., Arandjelović, O. & Caie, P. D. Deep learning for whole slide image analysis: an overview. Front. Med. 6, 264 (2019).
doi: 10.3389/fmed.2019.00264
Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).
doi: 10.1038/s41591-019-0508-1
Echle, A. et al. Deep learning in cancer pathology: a new generation of clinical biomarkers. Br. J. Cancer 124, 686–696 (2021).
doi: 10.1038/s41416-020-01122-x
Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).
doi: 10.1038/s41591-019-0583-3
Saillard, C. et al. Predicting survival after hepatocellular carcinoma resection using deep learning on histological slides. Hepatology 72, 2000–2013 (2020).
doi: 10.1002/hep.31207
Couture, H. D. et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer 4, 30 (2018).
doi: 10.1038/s41523-018-0079-1
Turkki, R. et al. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res. Treat. 177, 41–52 (2019).
doi: 10.1007/s10549-019-05281-1
Naik, N. et al. Deep learning-enabled breast cancer hormonal receptor status determination from base-level H&E stains. Nat. Commun. 11, 5727 (2020).
doi: 10.1038/s41467-020-19334-3
Saltz, J. et al. Spatial organization and molecular correlation of tumor-infiltrating lymphocytes using deep learning on pathology images. Cell Rep. 23, 181–193 (2018).
doi: 10.1016/j.celrep.2018.03.086
Binder, A. et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nat. Mach. Intell. 3, 355–366 (2021).
doi: 10.1038/s42256-021-00303-4
Cancer du sein triple négatif: la has autorise le trodelvy en accès précoce. Haute Autorité de Santé https://www.has-sante.fr/jcms/p_3284628/fr/cancer-du-sein-triple-negatif-la-has-autorise-le-trodelvy-en-acces-precoce (2021).
Naylor, P., Boyd, J., Laé, M., Reyal, F. & Walter, T. Predicting residual cancer burden in a triple negative breast cancer cohort. In Proc. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 933–937 (IEEE, 2019).
McMahan, B., Moore, E., Ramage, D., Hampson, S. & Aguera y Arcas, B. Communication-efficient learning of deep networks from decentralized data. In Proc. 20th International Conference of Artificial Intelligence and Statistics (AISTATS) 54,1273–1282 (JMLR, 2017).
Rieke, N. et al. The future of digital health with federated learning. NPJ Digital Med. 3, 119 (2020).
doi: 10.1038/s41746-020-00323-1
Sheller, M. J., Reina, G. A., Edwards, B., Martin, J. & Bakas, S. Multi-institutional deep learning modeling without sharing patient data: a feasibility study on brain tumor segmentation. In International MICCAI Brain Lesion Workshop, 92–104 (Springer, 2018).
Chang, K. et al. Distributed deep learning networks among institutions for medical imaging. J. Am. Med. Inform. Assoc. 25, 945–954 (2018).
doi: 10.1093/jamia/ocy017
Lee, J. et al. Privacy preserving patient similarity learning in a federated environment: development and analysis. JMIR Med. Inform. 6, e7744 (2018).
doi: 10.2196/medinform.7744
Lu, M. Y. et al. Federated learning for computational pathology on gigapixel whole slide images. Med. Image Anal. 76, 102298 (2022).
doi: 10.1016/j.media.2021.102298
Sheller, M. J. et al. Federated learning in medicine: facilitating multi-institutional collaborations without sharing patient data. Sci. Rep. 10, 1–12 (2020).
doi: 10.1038/s41598-020-69250-1
Warnat-Herresthal, S. et al. Swarm learning for decentralized and confidential clinical machine learning. Nature 594, 12598 (2021).
doi: 10.1038/s41586-021-03583-3
Sadilek, A. et al. Privacy-first health research with federated learning. NPJ Digital Med. 4, 132 (2021).
doi: 10.1038/s41746-021-00489-2
Saldanha, O. L. et al. Swarm learning for decentralized artificial intelligence in cancer histopathology. Nat. Med. 28, 1232–1239 (2022).
doi: 10.1038/s41591-022-01768-5
Zou, H. & Hastie, T. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Series B Stat. Methodol. 67, 301–320 (2005).
doi: 10.1111/j.1467-9868.2005.00503.x
Ma, D. et al. Integrated molecular profiling of young and elderly patients with triple-negative breast cancer indicates different biological bases and clinical management strategies. Cancer 126, 3209–3218 (2020).
doi: 10.1002/cncr.32922
Tang, Z. et al. Prognostic factors and models for elderly (≥70 years old) primary operable triple-negative breast cancer: analysis from the national cancer database. Front. Endocrinol. (Lausanne) 13, 856268 (2022).
doi: 10.3389/fendo.2022.856268
Dietterich, T. G. Ensemble methods in machine learning. In International Workshop on Multiple Classifier Systems, 1–15 (Springer, 2000).
Karimireddy, S. P. et al. Stochastic controlled averaging for federated learning. In Proc. International Conference on Machine Learning, 5132–5143 (PMLR, 2020).
Desa, D. E. et al. Second harmonic generation directionality is associated with neoadjuvant chemotherapy response in breast cancer core needle biopsies. J. Biomed. Opt. 24, 086503 (2019).
doi: 10.1117/1.JBO.24.8.086503
Lehmann, B. D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Investig. 121, 2750–2767 (2011).
doi: 10.1172/JCI45014
Masuda, H. et al. Differential response to neoadjuvant chemotherapy among 7 triple-negative breast cancer molecular subtypes. Clin. Cancer Res. 19, 5533–5540 (2013).
doi: 10.1158/1078-0432.CCR-13-0799
Schmauch, B. et al. A deep learning model to predict RNA-Seq expression of tumours from whole slide images. Nat. Commun. 11, 3877 (2020).
doi: 10.1038/s41467-020-17678-4
Colin, I., Bellet, A., Salmon, J. & Clémençon, S. Extending gossip algorithms to distributed estimation of U-statistics. In Proc. 28th International Conference on Neural Information Processing Systems 1, 271–279 (MIT Press, 2015).
Lassau, N. et al. Integrating deep learning CT-scan model, biological and clinical variables to predict severity of COVID-19 patients. Nat. Commun. 12, 634 (2021).
doi: 10.1038/s41467-020-20657-4
Singletary, S. E. et al. Revision of the American Joint Committee on Cancer staging system for breast cancer. J. Clin. Oncol. 20, 3628–3636 (2002).
doi: 10.1200/JCO.2002.02.026
Park, Y. H. et al. Clinical relevance of TNM staging system according to breast cancer subtypes. Ann. Oncol. 22, 1554–1560 (2011).
doi: 10.1093/annonc/mdq617
Salgado, R. et al. The evaluation of tumor infiltrating lymphocytes (TILs) in breast cancer: recommendations by an international TILs working group 2014. Ann. Oncol. 26, 259–271 (2015).
doi: 10.1093/annonc/mdu450
Levsky, J. M. & Singer, R. H. Fluorescence in situ hybridization: past, present and future. J. Cell Sci. 116, 2833–2838 (2003).
doi: 10.1242/jcs.00633
Geršak, K., Gazic, B., Klevisar Ivancic, A., Ruzic Gorenjec, N. & Grasic Kuhar, C. Intra-and inter-observer variability in tumor infiltrating lymphocyte scoring in breast cancer core needle biopsy. J. Clin. Oncol. 39(15_suppl), e12626 (2021).
doi: 10.1200/JCO.2021.39.15_suppl.e12626
Titford, M. The long history of hematoxylin. Biotech. Histochemistry 80, 73–78 (2005).
doi: 10.1080/10520290500138372
Nietner, T., Jarutat, T. & Mertens, A. Systematic comparison of tissue fixation with alternative fixatives to conventional tissue fixation with buffered formalin in a xenograft-based model. Virchows Arch. 461, 259–269 (2012).
doi: 10.1007/s00428-012-1248-5
Courtiol, P., Tramel, E. W., Sanselme, M. & Wainrib, G. Classification and disease localization in histopathology using only global labels: a weakly-supervised approach. Preprint at https://doi.org/10.48550/arXiv.1802.02212 (2018).
Norgeot, B. et al. Minimum information about clinical artificial intelligence modeling: the MI-CLAIM checklist. Nat. Med. 26, 1320–1324 (2020).
doi: 10.1038/s41591-020-1041-y
Ronneberger, O., Fischer, P. & Brox, T. U-net: convolutional networks for biomedical image segmentation. In Proc.International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 234–241 (Springer, 2015).
He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In Proc. IEEE Conference on Computer Vision and Pattern Recognition, 770–778 (IEEE, 2016).
Vahadane, A. et al. Structure-preserving color normalization and sparse stain separation for histological images. IEEE Trans. Med. Imaging 35, 1962–1971 (2016).
doi: 10.1109/TMI.2016.2529665
Abdi, H. & Williams, L. J. Principal component analysis. Wiley Interdiscip. Rev. Comput. Stat. 2, 433–459 (2010).
doi: 10.1002/wics.101
Maurice Fréchet, M. Sur quelques points du calcul fonctionnel. Rendiconti Circolo Matematico di Palermo 22, 1–72 (1906).
doi: 10.1007/BF03018603
Dowson, D. C. & Landau, B. V. The Fréchet distance between multivariate normal distributions. J. Multivar. Anal. 12, 450–455 (1982).
doi: 10.1016/0047-259X(82)90077-X
Heusel, Martin, Hubert Ramsauer, Thomas Unterthiner, Bernhard Nessler, & Sepp Hochreiter. Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30 (Curran Associates, Inc., 2017).
Macenko, M. et al. A method for normalizing histology slides for quantitative analysis. In 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 1107–1110 (IEEE, 2009).
Tarek Shaban, M., Baur, C., Nÿavab, N. & Albarqouni, S. Staingan: stain style transfer for digital histological images. In Proc. 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), 953–956 (IEEE, 2019).
Tomczak, A. et al. Multi-task multi-domain learning for digital staining and classification of leukocytes. IEEE Trans. Med. Imaging 40, 2897–2910 (2020).
doi: 10.1109/TMI.2020.3046334
Andreux, M., Manoel, A., Menuet, R., Saillard, C. & Simpson, C. Federated survival analysis with discrete-time Cox models. Preprint at https://doi.org/10.48550/arXiv.2006.0899 (2020).
Andreux, M., Ogier du Terrail, J., Beguier, C. & Tramel, E. W. Siloed federated learning for multi-centric histopathology datasets. In Domain Adaptation and Representation Transfer, and Distributed and Collaborative Learning, 129–139 (Springer, 2020).
doi: 10.1007/978-3-030-60548-3_13
Ioffe, S. & Szegedy, C. Batch normalization: accelerating deep network training by reducing internal covariate shift. In Proc.International Conference on Machine Learning, 484–456 (PMLR, 2015).
Tomczak, K., Czerwińska, P. & Wiznerowicz, M. The Cancer Genome Atlas (TCGA): an immeasurable source of knowledge. Contemp. Oncol. 19, 68–77 (Termedia, 2015).
Chen, X., Fan, H., Girshick, R. & He, K. Improved baselines with momentum contrastive learning. Preprint at https://doi.org/10.48550/arXiv.2003.04297 (2020).
Maron, O. & Lozano-Pérez, T. A framework for multiple-instance learning. In Advances in Neural Information Processing Systems, 570–576 (MIT Press, 1998).
Durand, T, Thome, N & Cord, M. WELDON: weakly supervised learning of deep convolutional neural networks. In Pattern Recognition CVPR 2016, 4743–4752 (IEEE, 2016).
Ilse, M., Tomczak, J. & Welling, M. Attention-based deep multiple instance learning. In Proc. International Conference on Machine Learning, 2127–2136 (PMLR, 2018).
Kingma, D. P. & Ba, J. Adam: a method for stochastic optimization. In Proc. 3rd International Conference on Learning Representations, (ICLR, 2015).
Galtier M. N. & Marini C. Substra: a framework for privacy-preserving, traceable and collaborative machine learning. Preprint at https://doi.org/10.48550/arXiv.1910.11567 (2019).
Androulaki, E. et al. Hyperledger fabric: a distributed operating system for permissioned blockchains. In Proc. Thirteenth EuroSys Conference 1–15 (ACM SIGOPS, 2018).
McMahan, B., Moore, E., Ramage, D., Hampson, S. & Aguera y Arcas, B. Communication-efficient learning of deep networks from decentralized data. In Proc. 20th International Conference of Artificial Intelligence and Statistics (AISTATS) 54, 1273–1282 (JMLR, 2017).
Steinberg, D. & Colla, P. Cart: classification and regression trees. Top. Ten Algorithms Data Min. 9, 179 (2009).
doi: 10.1201/9781420089653.ch10
Chen, T. & Guestrin, C. Xgboost: a scalable tree boosting system. In Proc. 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 785–794 (ACM, 2016).
doi: 10.1145/2939672.2939785
Lahat, D., Adali, T. & Jutten, C. Multimodal data fusion: an overview of methods, challenges, and prospects. Proc. IEEE 103, 1449–1477 (2015).
doi: 10.1109/JPROC.2015.2460697
Baltrušaitis, T., Ahuja, C. & Morency, L.-P. Multimodal machine learning: a survey and taxonomy. IEEE Trans. Pattern Anal. Mach. Intell. 41, 423–443 (2018).
doi: 10.1109/TPAMI.2018.2798607
Bergeron, A. et al. Triple negative breast lobular carcinoma: a luminal androgen receptor carcinoma with specific esrra mutations. Mod. Pathol. 34, 1282–1296 (2021).
doi: 10.1038/s41379-021-00742-9
Aickin, M. & Gensler, H. Adjusting for multiple testing when reporting research results: the Bonferroni vs Holm methods. Am. J. Public Health 86, 726–728 (1996).
doi: 10.2105/AJPH.86.5.726
Seabold, S. & Perktold, J. Statsmodels: econometric and statistical modeling with python. In Proc. 9th Python in Science Conference 57, 10–25080 (Scipy, 2010).
Wilcoxon, F. Individual comparisons by ranking methods. In Breakthroughs in Statistics 202, 196–983. (Springer, 1992).
Virtanen, P. et al. Scipy 1.0: fundamental algorithms for scientific computing in python. Nat. Methods 17, 261–272 (2020).
doi: 10.1038/s41592-019-0686-2
Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).
doi: 10.18637/jss.v036.i03
Fisher, R. A. On the interpretation of χ2 from contingency tables, and the calculation of p. J. R. Stat. Soc. 85, 87–94 (1922).
doi: 10.2307/2340521