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March 21, 2026ESMO Open0 citationsOpen Access

503P Self-supervised AI reveals novel determinants of lethality in lung adenocarcinoma

JQJohn Le QuesneKRKai RakovicLFLucas Farndale

Key Points

  • This research aims to identify novel immunological determinants of lethality in lung adenocarcinoma using self-supervised AI analysis.
  • Applied self-supervised AI to analyze immune signatures in lung adenocarcinoma
  • Conducted multiplex immunofluorescence (mIF) for protein validation of immune markers
  • Utilized spatially resolved transcriptomics to evaluate tumor-stroma interactions
  • Developed a transcriptomic classification defining six lung adenocarcinoma variants
  • Identified high immune populations, elevated PD-L1, and low B-cells as critical factors in tumor virulence
  • Demonstrated exclusive presence of T-cells in tumor margins
  • Revealed significant overexpression of stromal extracellular matrix programs
  • Classified lung adenocarcinoma into six distinct variants with specific clinical features

Abstract

outcome cluster in every dataset is defined by signatures of high immune population, high PD-L1, and low B-cells, all of which were confirmed at the protein level by mIF.It also shows an exclusion of epithelial T-cells visible in routine H&E.Spatially resolved transcriptomics reveals overexpression of stromal extracellular matrix programs, supporting this physical exclusion of T-cells from the tumour regions.Our findings suggest a key role for B-cells in tumour virulence and checkpoint inhibitor sensitivity, and we are now studying their broader influence on the immune microenvironment. Conclusions:We propose a robust new transcriptomic based classification of LUAD which defines 6 LUAD variants, each with key clinicopathological features, and which highlights the defining role of B-cells in tumour virulence.

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Cite This Study

Quesne et al. (2026) studied this question.

synapsesocial.com/papers/69be34f26e48c4981c673166https://doi.org/10.1016/j.esmoop.2026.106818
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