Immunotherapies have achieved remarkable successes in the treatment of cancer, but major challenges remain1,2. An inherent weakness of current treatment approaches is that therapeutically targeted pathways are not restricted to tumours, but are also found in other tissue microenvironments, complicating treatment3,4. Despite great efforts to define inflammatory processes in the tumour microenvironment, the understanding of tumour-unique immune alterations is limited by a knowledge gap regarding the immune cell populations in inflamed human tissues. Here, in an effort to identify such tumour-enriched immune alterations, we used complementary single-cell analysis approaches to interrogate the immune infiltrate in human head and neck squamous cell carcinomas and site-matched non-malignant, inflamed tissues. Our analysis revealed a large overlap in the composition and phenotype of immune cells in tumour and inflamed tissues. Computational analysis identified tumour-enriched immune cell interactions, one of which yields a large population of regulatory T (Treg) cells that is highly enriched in the tumour and uniquely identified among all haematopoietically-derived cells in blood and tissue by co-expression of ICOS and IL-1 receptor type 1 (IL1R1). We provide evidence that these intratumoural IL1R1+ Treg cells had responded to antigen recently and demonstrate that they are clonally expanded with superior suppressive function compared with IL1R1− Treg cells. In addition to identifying extensive immunological congruence between inflamed tissues and tumours as well as tumour-specific changes with direct disease relevance, our work also provides a blueprint for extricating disease-specific changes from general inflammation-associated patterns.

7We introduce a non-parametric method for unbiased cell population discovery in single-cell flow 8 and mass cytometry that annotates cell populations with biologically interpretable phenotypes 9 through a new procedure called Full Annotation Using Shape-constrained Trees (FAUST). 10 We used FAUST to discover novel (and validate known) cell populations associated with 11 treatment outcome across three cancer immunotherapy clinical trials. In a Merkel cell carcinoma 12 anti-PD-1 trial, we detected a PD-1 expressing CD8+ T cell population -undetected both by 13 manual gating and existing computational discovery approaches -in blood at baseline that was 14 associated with outcome and correlated with PD-1 IHC and T cell clonality in the tumor. We also 15 validated a previously reported cellular correlate in a melanoma trial, and detected it de novo 16 in two independent trials. We show that FAUST's phenotypic annotations enable cross-study 17 data integration and multivariate analysis in the presence of heterogeneous data and diverse 18 immunophenotyping staining panels, demonstrating FAUST is a powerful method for unbiased 19 discovery in single-cell data. 20 1 Introduction 21 Cytometry is used throughout the biological sciences to interrogate the state of an individual's 22 immune system at a single-cell level. Modern instruments can measure approximately thirty (via 23 fluorescence) or forty (via mass) protein markers per individual cell [1] and increasing throughput 24 can quantify millions of cells per sample. In typical clinical trials, multiple biological samples are 25 measured per subject in a longitudinal design. Consequently, a single clinical trial can produce 26 hundreds of high-dimensional samples that together contain measurements on millions of cells. 27To analyze these data, cell sub-populations of interest must be identified within each sample. 28 The manual process of identifying cell sub-populations is called "gating". An investigator gates a 29 single sample by sequentially inspecting bi-variate scatter plots of protein expression and grouping 30 cells with similar expression profiles together. Each sample is gated according to the same scheme, 31 ⇤ Corresponding author 2 FAUST Method 3 and samples are usually compared on the basis of the frequencies of cells found within each cell 32 sub-population. 33Manual gating introduces the potential for bias into cytometry data analysis [1, 2]. One source 34 of bias is the choice of gating strategy, since it is fixed in advance and is only one of many possible 35 strategies to identify a cell phenotype. A different strategy can lead to different gate placements 36 and consequently different cell counts. A more serious source of bias arises from the fact that 37 manual gating only identifies cell populations deemed important a-priori by the investigator. Since 38 the number of possible populations grows exponentially with the number of measured protein 39 markers, manual identification cannot be used to perform unbiased discovery and analysis...

Unique circulating regulatory T cell phenotypes distinguish hospitalized patients with SARS-CoV-2.

Highlights d An interpretable machine-learning method for cytometry data analysis is developed d Using this, candidate biomarkers of response to therapy are identified and visualized d The method is used to validate our findings on two additional cytometry datasets d It is shown how to integrate findings across datasets with heterogeneous marker panels