Supplementary Table S5 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S5. Quantification of T cell Functionality Barcodes.</p

Supplementary Figure S3 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S3. A. SHAP plot showing the top 30 features driving the IA model. Features are ordered on the y-axis such that those with a larger impact on the model’s predictions appear at the top of the SHAP plot. SHAP values are shown on the x-axis, with a value of zero (center) indicating no impact on the model, and negative or positive SHAP values predicting long DFS or short DFS, respectively. Red or blue dots indicate presence or absence, respectively, of the corresponding feature in tissues. B. Box plot showing feature values for each of the top 15 features for the model derived from IA regions of the αCD40 cohort split by DFS group (n = 30 regions from short DFS patients per feature; n = 13 regions from long DFS patients per feature). Each dot represents the log10+1 normalized feature value for one tissue region, which was inputted into the classifier model. Boxes = Q1 to Q3; whiskers = smallest and largest datapoints within 1.5*IQR +/- Q3/Q1; solid line = median. Mann–Whitney U-test used to determine statistical significance. P-values corrected using the Benjamini–Hochberg procedure. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001.</p

Supplementary Figure S2 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S2. A. SHAP plots showing the top 30 features driving each histopathologic model. Features are ordered on the y-axis such that those with a larger impact on model’s predictions appear at the top of the SHAP plots. SHAP values are shown on the x-axis, with a value of zero (center) indicating no impact on the model, and negative or positive SHAP values predicting treatment-naive or αCD40-treated tissues, respectively. Red or blue dots indicate presence or absence, respectively, of the corresponding feature in the tissue. B-E. Box plots showing feature values for each of the top 15 features for models derived from T, IA, TAS, or NAP sites, respectively, split by treatment cohort. Each dot represents the log10+1 normalized feature value for one tissue region, inputted into the classifier model. Boxes = quartile 1 (Q1) to quartile 3 (Q3); whiskers = smallest and largest datapoints within 1.5*interquartile range (IQR) +/- Q3/Q1; solid line = median. Mann–Whitney U-test used to determine statistical significance. P-values corrected using the Benjamini–Hochberg procedure. *, P ≤ 0.05; **, P ≤ 0.01; ***, P ≤ 0.001. B. T site, n= 55 treatment-naive and n = 48 αCD40-treated regions per feature. C. IA site, n= 89 treatment-naive and n = 43 αCD40-treated regions per feature. D. TAS site, n = 25 treatment-naive and n = 27 αCD40-treated regions per feature. E. NAP site, n = 6 treatment-naive and n = 13 αCD40-treated regions per feature.</p

Supplementary Figure S4 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S4. A. Elbow plot showing optimal number of RCNs (k=7) for grouping cellular neighborhoods. B. Bar chart showing the number of cells assigned to each of the seven RCNs across all αCD40 IA regions. C. Bar chart showing the percentage (out of 100) of cells assigned to each of the seven RCNs across all αCD40 IA regions. D. Stacked bar chart showing fraction (out of 1.0) of RCNs present per αCD40 IA region. E. Stacked bar chart showing average proportion (out of 1.0) of RCNs present in IA regions for eachαCD40-treated patient. F. Scatterplot reconstructions for each αCD40 IA region. Each dot represents a cell present in the IA, and each cell is colored by its original cell state phenotype (top scatterplot) or RCN assignment (bottom scatterplot).</p

Supplementary Table S2 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S2. Tissue area sampled per patient out of total tissue area resected per patient (in mm2).</p

Supplementary Table S1 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S1. Statistical comparison between the Liudahl et al. original PDAC cohort and the selected subset used as Cohort 1 in this study. Mean value and standard error of the mean (SEM) shown for each variable. P-values computed using Fisher’s exact test for categorical variables, Wilcoxon rank-sum test for continuous variables, and log rank test for overall survival.</p

Supplementary Table S6 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S6. Quantification of Cell-Cell Spatial Interactions.</p

Supplementary Table S4 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S4. Quantification of Cell States.</p

Supplementary Table S3 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Table S3. Table of antibodies used in the mIHC panel.</p

Supplementary Figure S1 from Machine Learning Links T-cell Function and Spatial Localization to Neoadjuvant Immunotherapy and Clinical Outcome in Pancreatic Cancer

Supplementary Figure S1. A. Stacked bar chart showing percent tissue area (out of 100) sampled per resection from each patient. Bars are colored by histopathologic site of the regions sampled. B. Representative IHC staining of each antibody used in sequence in the panel. Scale bar = 50 μm. C. Two representative regions stained with CD3, CD8, and CD4 antibodies. For each region, top images show gates for CD8 on CD3+ population (left) and CD4 on CD3+ CD8- population (right), and bottom row shows pseudo-colored mIHC images. D. Hierarchical gating template used to phenotype cells using image gating cytometry in FCS Image Cytometry RUO.</p