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Spatially aware deep learning reveals tumor heterogeneity patterns that encode distinct kidney cancer states.
Nyman, Jackson; Denize, Thomas; Bakouny, Ziad; Labaki, Chris; Titchen, Breanna M; Bi, Kevin; Hari, Surya Narayanan; Rosenthal, Jacob; Mehta, Nicita; Jiang, Bowen; Sharma, Bijaya; Felt, Kristen; Umeton, Renato; Braun, David A; Rodig, Scott; Choueiri, Toni K; Signoretti, Sabina; Van Allen, Eliezer M.
Affiliation
  • Nyman J; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Graduate Program in Systems Biology, Cambridge, MA, USA; Broad Institute, Cambridge, MA, USA.
  • Denize T; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
  • Bakouny Z; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Medicine, Memorial Sloan Kettering Cancer Center, New York
  • Labaki C; 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.
  • Titchen BM; 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.
  • Bi K; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
  • Hari SN; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA.
  • Rosenthal J; Department of Informatics & Analytics, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA.
  • Mehta N; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
  • Jiang B; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Stanford University, Stanford, CA, USA.
  • Sharma B; ImmunoProfile, Department of Pathology, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA.
  • Felt K; ImmunoProfile, Department of Pathology, Brigham & Women's Hospital and Dana-Farber Cancer Institute, Boston, MA, USA.
  • Umeton R; Department of Informatics & Analytics, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Pathology and Laboratory Medicine, Weill Cornell Medicine, New York, NY, USA; Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA; Department of Biostat
  • Braun DA; Center of Molecular and Cellular Oncology, Yale Cancer Center, Yale School of Medicine, New Haven, CT, USA.
  • Rodig S; Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA.
  • Choueiri TK; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Brigham and Women's Hospital, Boston, MA, USA.
  • Signoretti S; 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.
  • Van Allen EM; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Broad Institute, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA. Electronic address: eliezerm_vanallen@dfci.harvard.edu.
Cell Rep Med ; 4(9): 101189, 2023 09 19.
Article in En | MEDLINE | ID: mdl-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.
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carcinoma / Carcinoma, Renal Cell / Deep Learning / Kidney Neoplasms Limits: Humans Language: En Journal: Cell Rep Med Year: 2023 Document type: Article
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Full text: 1 Collection: 01-internacional Database: MEDLINE Main subject: Carcinoma / Carcinoma, Renal Cell / Deep Learning / Kidney Neoplasms Limits: Humans Language: En Journal: Cell Rep Med Year: 2023 Document type: Article