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.
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Data underpinning this study are under restricted access and are not freely available as they contain patients’ data, and specific clearance from the ethics committee is required in each center. Data can, however, be made available upon reasonable request from Centre Léon Bérard, Institut Curie, Institut Gustave Roussy and Institut Universitaire du Cancer Toulouse (IUCT) Oncopole. Specific conditions and restrictions of access to the datasets are to be discussed directly with the main investigators in each center: P.E.H. for Centre Léon Bérard, G.B. or A. Leopold for Institut Curie, C.F. for IUCT Oncopole and M.L.-T. for Institut Gustave Roussy.
All federated learning experiments relied on Substra, an open source software available at https://github.com/Substra/substra. The scripts to run these experiments are not public as they rely heavily on the intellectual property of Owkin, Inc. and on proprietary libraries that are used outside this research project. Those libraries include the TilingTool, a tool to preprocess and tile raw whole-slide images; ClassicAlgos, a library of weakly supervised machine learning models; and Ruche, a library to launch specific federated learning algorithms using the Substra framework. Detailed algorithmic information is presented in the Methods section and should be sufficient to replicate all experiments. For any questions regarding the replication of the experiments, the corresponding author J.O.d.T. should be contacted.
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This project is supported by Bpifrance as part of the Healthchain project, which resulted from the Digital Investments Program for the major challenges of the future Request For Proposal (RFP). As part of the Healthchain project, a consortium coordinated by Owkin, Inc. (a private company) has been established, including the Substra association, Apricity (a private company), the Assistance Publique des Hôpitaux de Paris, the University Hospital Center of Nantes, the Léon Bérard Center, the French National Center for Scientific Research, the Ecole Polytechnique, the Institut Curie and the University of Paris Descartes. We thank all the Owkin, Inc. teams involved in the project, most notably the Research & Development (R&D) data science team led by Eric Durand, the Information Technology (IT) team led by Jocelyn Dachary, the legal team, the partnerships team and Owkin Inc. founders Thomas Clozel and Gilles Wainrib for their support team.
The authors declare the existence of a financial competing interest. As described in the Acknowledgements section, this work received the financial support of the French Banque Publique d’Investissement (BPI) through the HealthChain project (https://www.substra.ai/en/healthchain-project). Some authors are or were employed by Owkin, Inc. during their time on the project (J.O.d.T., C.B., M.A., C. Maussion, B.S., E.B., M.Z., G.W., K.M., C.G., I.D., A.- L.M., C. Marini, M.G., J.M., R.D., A.S. and F.B.). In addition, G.W. is the cofounder of Owkin, Inc. P.E.H. reports grants, personal fees and nonfinancial support from Pfizer; grants and nonfinancial support from Novartis; grants and nonfinancial support from Roche; grants, personal fees and nonfinancial support from AstraZeneca; personal fees and nonfinancial support from Mylan; grants, personal fees, and nonfinancial support from Pierre Fabre; personal fees and nonfinancial support from Amgen; and personal fees and nonfinancial support from Seagen outside the submitted work.
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Ogier du Terrail, J., Leopold, A., Joly, C. et al. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat Med 29, 135–146 (2023). https://doi.org/10.1038/s41591-022-02155-w