Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.

Journal: Cell reports. Medicine
PMID: 37729872

Abstract

Clear cell renal cell carcinoma (ccRCC) is molecularly heterogeneous, immune infiltrated, and selectively sensitive to immune checkpoint inhibition (ICI). However, the joint tumor-immune states that mediate ICI response remain elusive. We develop spatially aware deep-learning models of tumor and immune features to learn representations of ccRCC tumors using diagnostic whole-slide images (WSIs) in untreated and treated contexts (n = 1,102 patients). We identify patterns of grade heterogeneity in WSIs not achievable through human pathologist analysis, and these graph-based "microheterogeneity" structures associate with PBRM1 loss of function and with patient outcomes. Joint analysis of tumor phenotypes and immune infiltration identifies a subpopulation of highly infiltrated, microheterogeneous tumors responsive to ICI. In paired multiplex immunofluorescence images of ccRCC, microheterogeneity associates with greater PD1 activation in CD8 lymphocytes and increased tumor-immune interactions. Our work reveals spatially interacting tumor-immune structures underlying ccRCC biology that may also inform selective response to ICI.

Authors

  • Jackson Nyman
    Dana-Farber Cancer Institute, Boston, Massachusetts.
  • Thomas Denize
    Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
  • Ziad Bakouny
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA.
  • Chris Labaki
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Beth Israel Deaconess Medical Center, Boston, MA, USA.
  • Breanna M Titchen
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Graduate Program in Biological and Biomedical Sciences, Boston, MA, USA.
  • Kevin Bi
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
  • Surya Narayanan Hari
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
  • Jacob Rosenthal
    Dana-Farber Cancer Institute, Boston, MA, USA.
  • Nicita Mehta
    Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
  • Bowen Jiang
    School of Automation, Beijing Institute of Technology, Beijing, China.
  • Bijaya Sharma
    ImmunoProfile, Department of Pathology, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA.
  • Kristen Felt
    ImmunoProfile, Department of Pathology, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA.
  • Renato Umeton
    Dana-Farber Cancer Institute, Boston, MA, USA.
  • David A Braun
    Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
  • Scott Rodig
    Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
  • Toni K Choueiri
    Lank Center for Genitourinary Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.
  • Sabina Signoretti
    Broad Institute, Cambridge, MA, USA; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Eliezer M Van Allen
    Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Harvard University, Boston, Massachusetts.

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