Morphological diversity of cancer cells predicts prognosis across tumor types.
Researchers
Journal
Modalities
Models
Abstract
Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin and eosin (H&E)-stained histopathology images.We analyzed publicly available digitized whole slide H&E images for a total of 2000 patients. Four tumor types were included: lung, head and neck, colon and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on H&E images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intra-nuclear variability and inter-nuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine learning model to predict patient prognosis.A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range: 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range: 1.62-3.23, P < 0.035) in validation cohorts and further improved prognostication when combined with clinical risk factors.Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.© The Author(s) 2023. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected].