Novel Predictive Spatial Biomarker in Non-Small Cell Lung Carcinoma: The Diversity of Niches Unlocking Treatment Sensitivity (DONUTS)

Abstract

Probabilistic spatial modelling techniques developed on large-scale tumor-immune Atlases ([~]35M individually mapped cells; 50,000 high power fields) were used to characterize predictive features of treatment-responsive lung cancer. We identified CD8+FoxP3+ cell density as a robust pre-treatment biomarker for outcomes across disease stages and therapy types. In parallel, single-cell RNAseq studies of CD8+FoxP3+ T-cells revealed an activated, early effector phenotype, substantiating an anti-tumor role, and contrasting with CD4+FoxP3+ T-regulatory cells. A spatial biomarker was developed using an empirical probabilistic model to define the immediate cell neighbors or niche surrounding CD8+FoxP3+ cells and proximity to the tumor-stromal boundary. The resultant Diversity of Niches Unlocking Treatment Sensitivity (DONUTS) are more prevalent than the CD8+FoxP3+ cells themselves, mitigating sampling error in small biopsies. Further, the DONUTS only require four markers, are additive to PD-L1, and associate with tertiary lymphoid structure counts. Taken together, the DONUTS represent a next-generation predictive biomarker poised for clinical implementation. HIGHLIGHTSO_LILarge-scale tumor-immune Atlases drive robust computational biomarker development C_LIO_LICD8+FoxP3+ cells are anti-tumor T-cells and predict response to therapy C_LIO_LIThe niches or spatial donuts around CD8+FoxP3+ cells boost biomarker performance C_LIO_LICD8+FoxP3+ donuts are hallmarks of a larger immune organization that includes TLS C_LI