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.