Artificial Intelligence-Assisted Amphiregulin and Epiregulin IHC Predicts Panitumumab Benefit in Wild-Type Metastatic Colorectal Cancer.

Journal: Clinical cancer research : an official journal of the American Association for Cancer Research
Published Date: Jun 15, 2021

Abstract

PURPOSE: High tumor mRNA levels of the EGFR ligands amphiregulin (AREG) and epiregulin (EREG) are associated with anti-EGFR agent response in metastatic colorectal cancer (mCRC). However, ligand RNA assays have not been adopted into routine practice due to issues with analytic precision and practicality. We investigated whether AREG/EREG IHC could predict benefit from the anti-EGFR agent panitumumab.

Authors

  • Christopher J M Williams
    Division of Pathology and Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Jenny F Seligmann
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Faye Elliott
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Michael Shires
    Division of Pathology and Data Analytics, University of Leeds, Leeds, United Kingdom.
  • Susan D Richman
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Sarah Brown
    Leeds Institute of Clinical Trials Research, University of Leeds, Leeds, United Kingdom.
  • Liping Zhang
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Shalini Singh
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Judith Pugh
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Xiao-Meng Xu
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Andrea Muranyi
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Christoph Guetter
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Auranuch Lorsakul
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Uday Kurkure
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Zuo Zhao
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Jim Martin
    1529Roche Tissue Diagnostics, Santa Clara, CA, USA.
  • Xingwei Wang
    Roche Tissue Diagnostics, Imaging and Algorithms, Digital Pathology, Santa Clara, California.
  • Kien Nguyen
    The Speech, Audio, Image and Video Technologies (SAIVT) research group, School of Electrical Engineering & Computer Science, Queensland University of Technology, Australia.
  • Wen-Wei Liu
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Dongyao Yan
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Nicholas P West
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Jennifer H Barrett
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Michael Barnes
    Department of Public Health, Brigham Young University, Provo, Utah, United States of America.
  • Isaac Bai
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona.
  • Matthew T Seymour
    Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Philip Quirke
    Pathology and Data Analytics, Leeds Institute of Medical Research at St James's, University of Leeds, Leeds, United Kingdom.
  • Kandavel Shanmugam
    Roche Tissue Diagnostics, Medical and Scientific Affairs, Tucson, Arizona. kandavel.shanmugam@roche.com.

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