Scott M. Lippman

Fig S10. A. Univariate analysis of potential checkpoint inhibitors in the discovery samples. B. Proportion of patients responding in each MHC reliance group when discovery samples are partitioned according to low versus high expression of 7 different checkpoint genes. C. Univariate analysis of potential checkpoint inhibitors in the validation samples. D. Proportion of patients responding in each MHC reliance group when validation samples are partitioned according to low versus high expression of 7 different checkpoint genes

Fig S6. Difference in feature importance rankings of composite model features in linear (left) versus nonlinear (right) models.

Fig S8. A. MHC-I reliant patients in TCGA with melanoma, non-small cell lung cancer or renal cell carcinoma stratified by CD4/CD8 T Cell infiltration ratio. B. MHC-II reliant patients in TCGA with melanoma, non-small cell lung cancer or renal cell carcinoma stratified by CD4/CD8 T Cell infiltration ratio. P Values generated via log-rank test. C. Immune infiltrate adjusted HLA DRB1 expression is associated with prolonged survival in MHC-II Reliant TCGA patients. D. Immune infiltrate adjusted HLA DRB1 expression is not protective in MHC-I Reliant TCGA patients.

Supplementary Figure 1: cancer-associated HRAS mutations in HNSCC using data from The Cancer Genome Atlas (TCGA) and Represetive images of the clonogenic assays.

Fig S11. A. Checkpoint expression and neoantigen levels by MHC Reliance category in discovery samples. B. Checkpoint expression and neoantigen levels by MHC Reliance category in validation samples. C. Pearson correlation between various checkpoint genes across discovery samples. D. Pearson correlation between various checkpoint genes across validation samples.

Fig S4. A-C. Pearson correlation of three different IC-Index scores with TMB. D. Purity vs ploidy across all patients, colored by response with pearson correlation shown. E-G. ROC plots comparing the performance of composite IC-Index to TMB and clinical predictors of ICB response. H-I. ROC comparing the performance of composite IC-Index to transcriptomic predictors of ICB response J. Histogram of composite IC-Index across patients, colored by ICB response status.

Fig S3. A-C. SHAP derived nonlinear feature importance beeswarm plots of germline, somatic, and composite models. D-E. Correlation of immunogenicity features with TMB. F-G. Coefficients of features from linear regression-based germline and somatic models

Fig S1. A. eQTL-score schematic. SNPs alleles are oriented so that they are all in alignment in terms of direction of effects on gene expression. Number of alleles affecting gene expression are then summed into a continuous score B. Pearson correlation of Gene level eQTL-scores in TCGA and Discovery cohorts. P<=0.05 is indicated by an X.

Supplementary Figure 2: metformin sensitivity in HRAS-mutant HNSCC cells