Deep learning identifies morphological features in breast cancer predictive of cancer ERBB2 status and trastuzumab treatment efficacy
Bychkov, Dmitrii; Linder, Nina; Tiulpin, Aleksei; Kücükel, Hakan; Lundin, Mikael; Nordling, Stig; Sihto, Harri; Isola, Jorma; Lehtimäki, Tiina; Kellokumpu-Lehtinen, Pirkko Liisa; von Smitten, Karl; Joensuu, Heikki; Lundin, Johan (2021-02)
Bychkov, Dmitrii
Linder, Nina
Tiulpin, Aleksei
Kücükel, Hakan
Lundin, Mikael
Nordling, Stig
Sihto, Harri
Isola, Jorma
Lehtimäki, Tiina
Kellokumpu-Lehtinen, Pirkko Liisa
von Smitten, Karl
Joensuu, Heikki
Lundin, Johan
02 / 2021
4037
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202105195194
https://urn.fi/URN:NBN:fi:tuni-202105195194
Kuvaus
Peer reviewed
Tiivistelmä
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.
Kokoelmat
- TUNICRIS-julkaisut [17020]