A new data-driven cell population discovery and annotation method for single-cell data, FAUST, reveals correlates of clinical response to cancer immunotherapy

We introduce a non-parametric method for unbiased cell population discovery in single-cell flow and mass cytometry that annotates cell populations with biologically interpretable phenotypes through a new procedure called Full Annotation Using Shape-constrained Trees (FAUST). We used FAUST to discover novel (and validate known) cell populations associated with treatment outcome across three cancer immunotherapy clinical trials. In a Merkel cell carcinoma anti-PD-1 trial, we detected a PD-1 expressing CD8+ T cell population - undetected both by manual gating and existing computational discovery approaches - in blood at baseline that was associated with outcome and correlated with PD-1 IHC and T cell clonality in the tumor. We also validated a previously reported cellular correlate in a melanoma trial, and detected it de novo in two independent trials. We show that FAUST9s phenotypic annotations enable cross-study data integration and multivariate analysis in the presence of heterogeneous data and diverse immunophenotyping staining panels, demonstrating FAUST is a powerful method for unbiased discovery in single-cell data.