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Publications

Journal Article Ppublish

PubMed: 40499545 DOI: 10.1016/j.xcrm.2025.102188

Cell states and neighborhoods in distinct clinical stages of primary and metastatic esophageal adenocarcinoma.

Cell Rep Med 6(6):102188

Authors: Josephine Yates, Camille Mathey-Andrews, Jihye Park, Amanda Garza, Andréanne Gagné, Samantha Hoffman, Kevin Bi, Breanna Titchen, Connor Hennessey, Joshua Remland, Matthew Carnes, Erin Shannon, Sabrina Camp, Siddhi Balamurali, Shweta Kiran Cavale, Zhixin Li, Akhouri Kishore Raghawan, Agnieszka Kraft, Genevieve Boland, Andrew J Aguirre, Nilay S Sethi, Valentina Boeva, Eliezer M Van Allen

Affiliations

  • Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Surgery, Massachusetts General Hospital, Boston, MA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA.
  • Penn Medicine, University of Pennsylvania, Philadelphia, PA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.
  • Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland.
  • Department of Surgery, Division of Gastrointestinal and Surgical Oncology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA.
  • Institute for Machine Learning, Department of Computer Science, ETH Zürich, Zurich, Switzerland; ETH AI Center, ETH Zurich, Zurich, Switzerland; Swiss Institute for Bioinformatics (SIB), Lausanne, Switzerland; Cochin Institute, Inserm U1016, CNRS UMR 8104 Paris Descartes University UMR-S1016, Paris 75014, France.
  • Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA; Cancer Program, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Division of Medical Sciences, Harvard University, Boston, MA, USA; Parker Institute for Cancer Immunotherapy, Dana-Farber Cancer Institute, Boston, MA, USA.

Abstract

Esophageal adenocarcinoma (EAC) is a highly lethal cancer of the upper gastrointestinal tract with rising incidence in western populations. To decipher EAC disease progression and therapeutic response, we perform multiomic analyses of a cohort of primary and metastatic EAC tumors, incorporating single-nuclei transcriptomic and chromatin accessibility sequencing along with spatial profiling. We recover tumor microenvironmental features previously described to associate with therapy response. We subsequently identify five malignant cell programs, including undifferentiated, intermediate, differentiated, epithelial-to-mesenchymal transition, and cycling programs, which are associated with differential epigenetic plasticity and clinical outcomes, and for which we infer candidate transcription factor regulons. Furthermore, we reveal diverse spatial localizations of malignant cells expressing their associated transcriptional programs and predict their significant interactions with microenvironmental cell types. We validate our findings in three external single-cell RNA sequencing (RNA-seq) and three bulk RNA-seq studies. Altogether, our findings advance the understanding of EAC heterogeneity, disease progression, and therapeutic response.

MeSH

Cells, Biological Science Disciplines, Neoplasms, Adenocarcinoma, Disease Progression, Gastrointestinal Neoplasms, Therapeutics, Computational Biology