University of Oulu

Bychkov, D., Linder, N., Tiulpin, A. et al. Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy. Sci Rep 11, 4037 (2021). https://doi.org/10.1038/s41598-021-83102-6

Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy

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Author: Bychkov, Dmitrii1,2; Linder, Nina1,2,3; Tiulpin, Aleksei4,5,6;
Organizations: 1Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland
2iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland
3Department of Women’s and Children’s Health, International Maternal and Child Health, Uppsala University, Uppsala, Sweden
4Physics and Technology, Research Unit of Medical Imaging, University of Oulu, Oulu, Finland
5Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland
6Ailean Technologies Oy, Oulu, Finland
7Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland
8Department of Cancer Biology, BioMediTech, University of Tampere, Tampere, Finland
9Helsinki University Hospital, Helsinki, Finland
10Department of Oncology, Tampere University Hospital, Tampere, Finland
11Eira Hospital, Helsinki, Finland
12Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland
13Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden
Format: article
Version: published version
Access: open
Online Access: PDF Full Text (PDF, 2.2 MB)
Persistent link: http://urn.fi/urn:nbn:fi-fe2021042611801
Language: English
Published: Springer Nature, 2021
Publish Date: 2021-04-26
Description:

Abstract

The treatment of patients with ERBB2 (HER2)-positive breast cancer with anti-ERBB2 therapy is based on the detection of ERBB2 gene amplification or protein overexpression. Machine learning (ML) algorithms can predict the amplification of ERBB2 based on tumor morphological features, but it is not known whether ML-derived features can predict survival and efficacy of anti-ERBB2 treatment. In this study, we trained a deep learning model with digital images of hematoxylin–eosin (H&E)-stained formalin-fixed primary breast tumor tissue sections, weakly supervised by ERBB2 gene amplification status. The gene amplification was determined by chromogenic in situ hybridization (CISH). The training data comprised digitized tissue microarray (TMA) samples from 1,047 patients. The correlation between the deep learning–predicted ERBB2 status, which we call H&E-ERBB2 score, and distant disease-free survival (DDFS) was investigated on a fully independent test set, which included whole-slide tumor images from 712 patients with trastuzumab treatment status available. The area under the receiver operating characteristic curve (AUC) in predicting gene amplification in the test sets was 0.70 (95% CI, 0.63–0.77) on 354 TMA samples and 0.67 (95% CI, 0.62–0.71) on 712 whole-slide images. Among patients with ERBB2-positive cancer treated with trastuzumab, those with a higher than the median morphology–based H&E-ERBB2 score derived from machine learning had more favorable DDFS than those with a lower score (hazard ratio [HR] 0.37; 95% CI, 0.15–0.93; P = 0.034).A high H&E-ERBB2 score was associated with unfavorable survival in patients with ERBB2-negative cancer as determined by CISH. ERBB2-associated morphology correlated with the efficacy of adjuvant anti-ERBB2 treatment and can contribute to treatment-predictive information in breast cancer.

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Series: Scientific reports
ISSN: 2045-2322
ISSN-E: 2045-2322
ISSN-L: 2045-2322
Volume: 11
Issue: 1
Article number: 4037
DOI: 10.1038/s41598-021-83102-6
OADOI: https://oadoi.org/10.1038/s41598-021-83102-6
Type of Publication: A1 Journal article – refereed
Field of Science: 3122 Cancers
Subjects:
Funding: The study was supported by the Sigrid Jusélius Foundation, the Biomedicum Helsinki Foundation, the Orion-Pharmos Research Foundation, Finska Läkaresällskapet, iCAN Digital Precision Cancer Medicine Flagship, and HiLIFE Helsinki Institute of Life Sciences.
Copyright information: © The Author(s) 2021. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
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