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Abstract
In the last four years, advances in Deep Learning technology have enabled the inference of selected mutational alterations directly from routine histopathology slides. In particular, recent studies have shown that genetic changes in clinically relevant driver genes are reflected in the histological phenotype of solid tumors and can be inferred by analysing routine Haematoxylin and Eosin (H&E) stained tissue sections with Deep Learning. However, these studies mostly focused on selected individual genes in selected tumor types. In addition, genetic changes in solid tumors primarily act by changing signaling pathways that regulate cell behaviour. In this study, we hypothesized that Deep Learning networks can be trained to directly predict alterations of genes and pathways across a spectrum of solid tumors. We manually outlined tumor tissue in H&E-stained tissue sections from 7,829 patients with 23 different tumor types from The Cancer Genome Atlas. We then trained convolutional neural networks in an end-to-end way to detect alterations in the most clinically relevant pathways or genes, directly from histology images. Using this automatic approach, we found that alterations in 12 out of 14 clinically relevant pathways and numerous single gene alterations appear to be detectable in tissue sections, many of which have not been reported before. Interestingly, we show that the prediction performance for single gene alterations is better than that for pathway alterations. Collectively, these data demonstrate the predictability of genetic alterations directly from routine cancer histology images and show that individual genes leave a stronger morphological signature than genetic pathways.
Highlights
Genetic changes can influence the cell and tissue morphology of solid tumors (Figure 1A)
We aimed to extend the evaluation of Deep Learning-based detection of genetic alterations from FFPE slides to a broad range of tumor types, beyond the findings of previous studies which were limited in their selection of genetic alterations (Kather et al, 2020)
We systematically tested whether the mutation status of the preselected 69 genes with potential clinical relevance with a mutation prevalence above 5% according to the MSK-IMPACT Clinical Sequencing Cohort (MSKCC) and OnkoKB database is directly predictable from histology slides
Summary
Genetic changes can influence the cell and tissue morphology of solid tumors (Figure 1A). Multiple studies suggested that many genetic alterations are predictable from routine histology alone across different tumor types (Fu et al, 2020; Kather et al, 2020; Schmauch et al, 2020; Loeffler et al, 2021; Muti et al, 2021). It has been shown that both, PTEN loss and PIK3CA mutation can lead to the activation of the PI3K or MAPK pathway in cancer of the breast, colorectum, stomach or lung (Dhillon et al, 2007; Jiang et al, 2020). This phenomenon, can be of therapeutic relevance, as targeted therapies may affect one specific gene, and affect other downstream genes. Instead of focusing on a single gene, in some cases it might even be sufficient to identify pathway activation or inhibition to predict treatment response or failure (Schumacher et al, 2019; Ben-Hamo et al, 2020)
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