TY - JOUR
T1 - Predicting Mutational Status of Driver and Suppressor Genes Directly from Histopathology With Deep Learning
T2 - A Systematic Study Across 23 Solid Tumor Types
AU - Loeffler, Chiara Maria Lavinia
AU - Gaisa, Nadine T
AU - Muti, Hannah Sophie
AU - van Treeck, Marko
AU - Echle, Amelie
AU - Ghaffari Laleh, Narmin
AU - Trautwein, Christian
AU - Heij, Lara R
AU - Grabsch, Heike I
AU - Ortiz Bruechle, Nadina
AU - Kather, Jakob Nikolas
N1 - Copyright © 2022 Loeffler, Gaisa, Muti, van Treeck, Echle, Ghaffari Laleh, Trautwein, Heij, Grabsch, Ortiz Bruechle and Kather.
PY - 2022/2/16
Y1 - 2022/2/16
N2 - 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.
AB - 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.
KW - deep learning
KW - artificail intelligence (AI)
KW - cancer pathway
KW - cancer pathway genes
KW - genetic
KW - TCGA
KW - COMPREHENSIVE MOLECULAR CHARACTERIZATION
KW - INTEGRATED GENOMIC CHARACTERIZATION
KW - SIGNALING PATHWAYS
KW - LANDSCAPE
U2 - 10.3389/fgene.2021.806386
DO - 10.3389/fgene.2021.806386
M3 - Article
C2 - 35251119
SN - 1664-8021
VL - 12
JO - Frontiers in Genetics
JF - Frontiers in Genetics
M1 - 806386
ER -