Supplementary Figure 10: Performance of MARIA for predicting melanoma antigen presentation and vaccine T-cell responses.
From: Predicting HLA class II antigen presentation through integrated deep learning
Performance of MARIA in predicting CD4 T-cell responses to personalized vaccines. Plots depict results for two melanoma clinical trials of personalized cancer vaccines (Sahin et al. 2017 (a, top) or Ott et al. 2017 (b, bottom)), where a range of MARIA score cutoffs (x-axis) are related to the Positive predictive values (PPV), negative predictive values (NPV) and sensitivity (y-axis) for predicting post-vaccination CD4 T-cell responses. MARIA scores of 95% and 99.5% were used as cut-offs for ‘medium’ and ‘high’ confidence categories depicted in Fig. 5. (c) Potential CD4 T cell epitopes in Ott et al. cohort based on MARIA scores. Numbers of neoantigens in melanoma above MARIA-high cut-off. Each nonsynonymous mutation in 6 melanoma patients (Ott et al. 2017) was scored with MARIA on a basis of 15mer sliding windows. The best MARIA score of all potential 15mer windows was used to represent the neoantigen. ~7% of nonsynonymous mutations reached 99.5% MARIA-high cut-off. Except the patient 1, all patients had at least 20 neoantigens in the MARIA-high category (MARIA percentile >99.5th). (d) Weak association of NetMHCIIpan and CD4 T-cell post-vaccination responses. Each vaccine peptide sequence in Ott et al. was scored with NetMHCIIpan and was stratified into three categories based on the same cut-off used for MARIA (Fig. 6d): low (<95th), medium (95-99.5th) and high (>99.5th). NetMHCIIpan score categories were weakly associated with CD4 T-cell responses but did not reach statistical significance (chi-square test, P=0.3). Dashed red lines indicate average response rates of the whole cohort. (e) Precision-recall curves of MARIA and NetMHCIIpan for identifying melanoma HLA-II ligands. Curves depict the comparison of the precision (y-axis) of each of three methods (full MARIA model, NetMHCIIpan 3.1, and a ‘random’ MARIA model trained on shuffled data) when considering a range of recall/sensitivity thresholds (x-axis). At 20% recall, MARIA achieved 38% precision (PPV), assuming a 1% prevalence of true antigen presentation.
