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. Author manuscript; available in PMC: 2020 Apr 1.
Published in final edited form as: Cancer Immunol Res. 2019 Sep 12;7(10):1591–1604. doi: 10.1158/2326-6066.CIR-19-0155

Figure 3: Performance and validation of the gradient boosting model (GBM) approach for predicting neoantigen/mHA immunogenicity.

Figure 3:

(A) Schema of the cross-validation approach used for GBM model building. (B) Performance of the final GBM model in validation set, showing actual (x-axis) versus predicted (y-axis) immunogenicity scores. Size of each point represents number of antigens at each coordinate. Red line represents the line of best fit, with p-value of fit shown above the graph. (C,E) Schema for in vivo validation experiments, with tumor vaccine studies performed in (C) BBN963 basal-like bladder cancer and (E) the P815 mastocytoma syngeneic transplant model. (D,F) Kaplan-Meier survival curves for animals bearing (D) BBN963 basal-like bladder cancer and (F) P815 mastocytoma syngeneic transplants. Animals treated with predicted high (red) or low (blue) immunogenicity antigens, no-peptide control (black), or bone marrow only control (grey). Data in (D) represents two independent experiments. Data in (F) represents one independent experiment. Statistics performed with log-ranked testing (**p<0.01; ***p<0.001).