Remarkable progress in molecular analyses has improved our understanding of the evolution of cancer cells toward immune escape1-5. However, the spatial configurations of immune and stromal cells, which may shed light on the evolution of immune escape across tumor geographical locations, remain unaddressed. We integrated multiregion exome and RNA-sequencing (RNA-seq) data with spatial histology mapped by deep learning in 100 patients with non-small cell lung cancer from the TRACERx cohort6. Cancer subclones derived from immune cold regions were more closely related in mutation space, diversifying more recently than subclones from immune hot regions. In TRACERx and in an independent multisample cohort of 970 patients with lung adenocarcinoma, tumors with more than one immune cold region had a higher risk of relapse, independently of tumor size, stage and number of samples per patient. In lung adenocarcinoma, but not lung squamous cell carcinoma, geometrical irregularity and complexity of the cancer-stromal cell interface significantly increased in tumor regions without disruption of antigen presentation. Decreased lymphocyte accumulation in adjacent stroma was observed in tumors with low clonal neoantigen burden. Collectively, immune geospatial variability elucidates tumor ecological constraints that may shape the emergence of immune-evading subclones and aggressive clinical phenotypes.

Original languageEnglish
Pages (from-to)1054-1062
Number of pages9
JournalNature Medicine
Issue number7
Early online date27 May 2020
Publication statusPublished - 1 Jul 2020


  • Adenocarcinoma of Lung/genetics
  • Antigens, Neoplasm/genetics
  • Biomarkers, Tumor/genetics
  • Carcinoma, Non-Small-Cell Lung/genetics
  • Deep Learning
  • Exome/genetics
  • Female
  • Humans
  • Male
  • Middle Aged
  • Mutation/genetics
  • Neoplasm Recurrence, Local/genetics
  • Neoplasm Staging
  • RNA-Seq
  • Recurrence
  • Tumor Microenvironment/genetics
  • Whole Exome Sequencing


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