Supplementary Figures S1-S25 from Preexisting Skin-Resident CD8 and γδ T-cell Circuits Mediate Immune Response in Merkel Cell Carcinoma and Predict Immunotherapy Efficacy

Supplementary Fig. S1: Patient level heatmaps by assay. Supplementary Fig. S2: Overview of modalities in database. Supplementary Fig. S3: Computational pipeline for single cell RNA-seq data processing. Supplementary Fig. S4: Quality control metrics of tissue single cell data set. Supplementary Fig. S5: Cell type clusters in single-cell PBMC dataset. Supplementary Fig. S6: Myeloid cells in Merkel cell carcinoma. Supplementary Fig. S7: Response to ICB is associated with tumor cell upregulation of an interferon-γ signature. Supplementary Fig. S8: GSEA on tumor cells controlling for proliferation. Supplementary Fig. S9: Dot plot of RNA markers in CD8 clusters (tissue). Supplementary Fig. S10: Clonotype distribution between CD8 T cell lineages. Supplementary Fig. S11: CD8 lineage characterization. Supplementary Fig. S12: Clonality and neoantigen metrics of metrics of CD8 T cells. Supplementary Fig. S13: Comparisons of cell type signatures and cell proportions separated by sample origin. Supplementary Fig. S14: Epitope screening results. Supplementary Fig. S15: Public epitopes. Supplementary Fig. S16: Expression of naïve-like markers on Vδ1 T cells. Supplementary Fig. S17: GeoMx quality control for regions of interest. Supplementary Fig. S18: GeoMx image and differential expression analysis. Supplementary Fig. S19: CosMx quality control for fields of view. Supplementary Fig. S20: CosMx degree of co-localization sensitivity test shows B cell, T cell, pDC, and DC interactions. Supplementary Fig. S21: Pre-post genes. Supplementary Fig. S22: MIXCR analysis of pre-post ICB clonal dynamics. Supplementary Fig. S23: Single cell analysis of pre-post ICB clonal dynamics. Supplementary Fig. S24: Functional convergence of CD8 and γT cells. Supplementary Fig. S25: Graphical model of immune checkpoint response in MCC.