RT Journal Article
SR Electronic
T1 Pan-cancer image-based detection of clinically actionable genetic alterations
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 833756
DO 10.1101/833756
A1 Jakob Nikolas Kather
A1 Lara R. Heij
A1 Heike I. Grabsch
A1 Loes F. S. Kooreman
A1 Chiara Loeffler
A1 Amelie Echle
A1 Jeremias Krause
A1 Hannah Sophie Muti
A1 Jan M. Niehues
A1 Kai A. J. Sommer
A1 Peter Bankhead
A1 Jefree J. Schulte
A1 Nicole A. Cipriani
A1 Nadina Ortiz-Brüchle
A1 Akash Patnaik
A1 Andrew Srisuwananukorn
A1 Hermann Brenner
A1 Michael Hoffmeister
A1 Piet A. van den Brandt
A1 Dirk Jäger
A1 Christian Trautwein
A1 Alexander T. Pearson
A1 Tom Luedde
YR 2019
UL http://biorxiv.org/content/early/2019/11/08/833756.abstract
AB Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular biology assays.1 These tests can be a bottleneck in oncology workflows because of high turnaround time, tissue usage and costs.2 Here, we show that deep learning can predict point mutations, molecular tumor subtypes and immune-related gene expression signatures3,4 directly from routine histological images of tumor tissue. We developed and systematically optimized a one-stop-shop workflow and applied it to more than 4000 patients with breast5, colon and rectal6, head and neck7, lung8,9, pancreatic10, prostate11 cancer, melanoma12 and gastric13 cancer. Together, our findings show that a single deep learning algorithm can predict clinically actionable alterations from routine histology data. Our method can be implemented on mobile hardware14, potentially enabling point-of-care diagnostics for personalized cancer treatment in individual patients.