Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information
Yinxi Wang, Maya Alsheh Ali, Johan Vallon‐Christersson, Keith Humphreys, Johan Hartman, Mattias Rantalainen
Abstract
Background Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer. Methods Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N=931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N=1,358) and SCAN-B-Lund (N=1,262). Results We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p=0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p=3.99*10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20mm). Conclusion We developed a novel method for an automated, scalable and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer. Significance Transcriptional intra-tumour heterogeneity predicted by deep learning models enables prediction of patient survival from routine histopathology whole slide images in breast cancer.