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Background: Breast cancer (BC) is the most frequent female cancer and preferentially metastasizes to bone. The transcription factor TGFB-induced factor homeobox 1 (TGIF) is involved in bone metabolism. However, it is not yet known whether TGIF is associated with BC bone metastasis or patient outcome and thus of potential interest. Methods: TGIF expression was analyzed by immunohistochemistry in 1197 formalin-fixed, paraffin-embedded tissue samples from BC patients treated in the GAIN (German Adjuvant Intergroup Node-Positive) study with two adjuvant dose-dense schedules of chemotherapy with or without bisphosphonate ibandronate. TGIF expression was categorized into negative/low and moderate/strong staining. Endpoints were disease-free survival (DFS), overall survival (OS) and time to primary bone metastasis as first site of relapse (TTPBM). Results: We found associations of higher TGIF protein expression with smaller tumor size (p= 0.015), well differentiated phenotype (p< 0.001) and estrogen receptor (ER)-positive BC (p< 0.001). Patients with higher TGIF expression levels showed a significantly longer disease-free (DFS: HR 0.75 [95%CI 0.59–0.95], log-rank p=0.019) and overall survival (OS: HR 0.69 [95%CI 0.50–0.94], log-rank p= 0.019), but no association with TTPBM (HR 0.77 [95%CI 0.51–1.16]; p= 0.213). Univariate analysis in molecular subgroups emphasized that elevated TGIF expression was prognostic for both DFS and OS in ER-positive BC patients (DFS: HR 0.68 [95%CI 0.51–0.91]; log-rank p= 0.009, interaction p= 0.130; OS: HR 0.60 [95%CI 0.41–0.88], log-rank p= 0.008, interaction p= 0.107) and in the HER2-negative subgroup (DFS:HR 0.67 [95%CI 0.50–0.88], log-rank p= 0.004, interaction p= 0.034; OS: HR 0.57 [95%CI 0.40–0.81], log-rank p= 0.002, interaction p= 0.015). Conclusions: Our results suggest that moderate to high TGIF expression is a common feature of breast cancer cells and that this is not associated with bone metastases as first site of relapse. However, a reduced expression is linked to tumor progression, especially in HER2-negative breast cancer.
Following publication of the original article, the authors noticed an incorrect affiliation for Christine Stürken and Udo Schumacher. The correct affiliations are as follows: Christine Stürken: Institute of Anatomy and Experimental Morphology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany. Udo Schumacher: Institute of Anatomy and Experimental Morphology, University Medical Center Hamburg-Eppendorf, Martinistrasse 52, 20246 Hamburg, Germany. The affiliations have been correctly published in this correction and the original article has been updated.
In gastric cancer (GC), there are four molecular subclasses that indicate whether patients respond to chemotherapy or immunotherapy, according to the TCGA. In clinical practice, however, not every patient undergoes molecular testing. Many laboratories have used well-implemented in situ techniques (IHC and EBER-ISH) to determine the subclasses in their cohorts. Although multiple stains are used, we show that a staining approach is unable to correctly discriminate all subclasses. As an alternative, we trained an ensemble convolutional neuronal network using bagging that can predict the molecular subclass directly from hematoxylin–eosin histology. We also identified patients with predicted intra-tumoral heterogeneity or with features from multiple subclasses, which challenges the postulated TCGA-based decision tree for GC subtyping. In the future, deep learning may enable targeted testing for molecular subtypes and targeted therapy for a broader group of GC patients. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.