| Autor: |
Bychkov D; Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland. dmitrii.bychkov@helsinki.fi.; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland. dmitrii.bychkov@helsinki.fi., Linder N; Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.; Department of Women's and Children's Health, International Maternal and Child Health, Uppsala University, Uppsala, Sweden., Tiulpin A; Physics and Technology, Research Unit of Medical Imaging, University of Oulu, Oulu, Finland.; Department of Diagnostic Radiology, Oulu University Hospital, Oulu, Finland.; Ailean Technologies Oy, Oulu, Finland., Kücükel H; Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland., Lundin M; Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland., Nordling S; Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland., Sihto H; Department of Pathology, Medicum, University of Helsinki, Helsinki, Finland., Isola J; Department of Cancer Biology, BioMediTech, University of Tampere, Tampere, Finland., Lehtimäki T; Helsinki University Hospital, Helsinki, Finland., Kellokumpu-Lehtinen PL; Department of Oncology, Tampere University Hospital, Tampere, Finland., von Smitten K; Eira Hospital, Helsinki, Finland., Joensuu H; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.; Department of Oncology, Helsinki University Hospital and University of Helsinki, Helsinki, Finland., Lundin J; Institute for Molecular Medicine Finland (FIMM), Nordic EMBL Partnership for Molecular Medicine, University of Helsinki, Helsinki, Finland.; iCAN Digital Precision Cancer Medicine Flagship, Helsinki, Finland.; Department of Global Public Health, Karolinska Institutet, Stockholm, Sweden. |
| Abstrakt: |
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. |
