Frontiers in Genetics (Feb 2022)

Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning: A Systematic Study Across 23 Solid Tumor Types

  • Chiara Maria Lavinia Loeffler,
  • Chiara Maria Lavinia Loeffler,
  • Nadine T. Gaisa,
  • Nadine T. Gaisa,
  • Hannah Sophie Muti,
  • Hannah Sophie Muti,
  • Marko van Treeck,
  • Marko van Treeck,
  • Amelie Echle,
  • Amelie Echle,
  • Narmin Ghaffari Laleh,
  • Narmin Ghaffari Laleh,
  • Christian Trautwein,
  • Christian Trautwein,
  • Lara R. Heij,
  • Lara R. Heij,
  • Lara R. Heij,
  • Lara R. Heij,
  • Heike I. Grabsch,
  • Heike I. Grabsch,
  • Nadina Ortiz Bruechle,
  • Nadina Ortiz Bruechle,
  • Jakob Nikolas Kather,
  • Jakob Nikolas Kather,
  • Jakob Nikolas Kather,
  • Jakob Nikolas Kather

DOI
https://doi.org/10.3389/fgene.2021.806386
Journal volume & issue
Vol. 12

Abstract

Read online

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.

Keywords