A deep learning image-based intrinsic molecular subtype classifier of breast tumors reveals tumor heterogeneity that may affect survival.

Journal: Breast cancer research : BCR
Published Date:

Abstract

BACKGROUND: Breast cancer intrinsic molecular subtype (IMS) as classified by the expression-based PAM50 assay is considered a strong prognostic feature, even when controlled for by standard clinicopathological features such as age, grade, and nodal status, yet the molecular testing required to elucidate these subtypes is not routinely performed. Furthermore, when such bulk assays as RNA sequencing are performed, intratumoral heterogeneity that may affect prognosis and therapeutic decision-making can be missed.

Authors

  • Mustafa I Jaber
    NantOmics LLC, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
  • Bing Song
    ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
  • Clive Taylor
    Department of Pathology, Keck School of Medicine, University of Southern California, HMR 2011 Zonal Ave., Health Sciences Campus, Los Angeles, CA, 90033, USA.
  • Charles J Vaske
    ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA.
  • Stephen C Benz
    ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA.
  • Shahrooz Rabizadeh
    NantOmics LLC, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
  • Patrick Soon-Shiong
    ImmunityBio, 9920 Jefferson Blvd., Culver City, CA, 90232, USA.
  • Christopher W Szeto
    ImmunityBio, 2901 Mission St. Ext., Santa Cruz, CA, 95066, USA. Christopher.Szeto@ImmunityBio.com.