Alvaro de Mingo Pulido

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.

Supplementary Table S1. Patient cohort: Clinical data and assays Supplementary Table S2. Sample cohort: Clinical data and assays Supplementary Table S3. Pre-ICB bulk RNA-seq cohort, 63 samples Supplementary Table S4. Pre-Post ICB matched bulk RNA-seq, 28 samples, 14 patient pairs Supplementary Table S5. Number of reads for each bulk RNA-seq sample (85 samples) Supplementary Table S6. Tissue single-cell RNA-seq quality control and sample metadata (54 samples) Supplementary Table S7. Blood single-cell RNA-seq quality control and sample metadata (55 samples) Supplementary Table S8. Multiplex immunofluorescence sample cohort (44 samples) Supplementary Table S9. GeoMx sample cohort (15 samples) Supplementary Table S10. CosMx sample cohort (13 samples) Supplementary Table S11: Genes differentially expressed between MCC that respond to anti-PD-1/PD-L1 therapy (n=31) and MCC that do not respond to anti-PD-1/PD-L1 therapy (n=32) in the pre-ICB cohort (n=63). Supplementary Table S12. Enrichr results of differentially expressed genes (FDR < 0.1) in responders and non-responders prior to immunotherapy in the bulk RNA-seq dataset Supplementary Table S13. Wilcoxon markers of single-cell RNA-seq clusters for all cells in the tissue dataset Supplementary Table S14. Pseudobulk markers of tumor cells comparing immunotherapy responders to non-responders Supplementary Table S15. GSEA results of differentially expressed genes from responders and non-responders in tumor cells pseudobulk dataset. Supplementary Table S16. SCENIC tumor markers of response and non-response to immunotherapy Supplementary Table S17. Genes in single-cell derived gene signatures Supplementary Table S18. Average antibody derived tag expression for cells in CD8 T cell clusters Supplementary Table S19. Wilcoxon markers of Tcirc and Trm CD8 T cells Supplementary Table S20. Wilcoxon markers of CD8 T cell clusters Supplementary Table S21. TCR clonotype linkage of CD8 T cell clusters Supplementary Table S22. Gini index of each CD8 cluster split by sample Supplementary Table S23. Small T and large T antigens peptide pools for TCR epitope screen Supplementary Table S24. GLIPH2 results of CD8 TCRs Supplementary Table S25. CD8 T cells with TCRs that match public databases with known antigens (VDJdb, McPAS-TCR, TRAdb) Supplementary Table S26. Wilcoxon RNA markers of each cell type, split by δ chain, in the γδ/NK cell subset. Supplementary Table S27. Wilcoxon antibody derived tag markers of each cell type, split by δ chain and cellular source, in the γδ/NK cell subset. Supplementary Table S28. Wilcoxon antibody derived tag markers of δ1 versus δ2 T cells from the tissue dataset Supplementary Table S29. Genes with significant non-linear fits along Vδ1 pseudotime Supplementary Table S30. GeoMx: Differentially expressed genes between responders and non-responders in tumor regions of interest Supplementary Table S31. GeoMx: Differentially expressed genes between responders and non-responders in stroma regions of interest Supplementary Table S32. CosMx: Differentially expressed genes among cell types identified by InSituType. Supplementary Table S33. CosMx: SpatialTime matrices of degree of co-localization between cell types Supplementary Table S34. CellChat receptor ligand interaction with CD8 exhausted T cells (Tex) as targets Supplementary Table S35. Differential expressed genes between bulk RNA-seq samples pre/post immunotherapy treatment split between responders (n=7) and non-responders (n=7) Supplementary Table S36. Enrichr results of differentially expressed genes (FDR <0.1) post-immunotherapy responders in the pre/post matched bulk RNA-seq dataset

Legends for Supplementary Figures and Supplementary Tables.

Role of nitrosylation in regulating the immune microenvironment in the Nras mutated melanoma generated in the immunocompetent mouse model

Sustained ERK phosphorylation induced by nitrosylation.

Correlation between nitrosylation, MEKi resistance, and cell migration.

Densitometric analyses of ERK and MEK mutants in the nitrosylomic and cellular fractions of HEK293 cells.

Validation of NOS-inhibition-mediated sensitization of NRAS-mutant melanoma cells to targeted MEK inhibition.