13073_2020_743_MOESM1_ESM.docx (2.55 MB)
Download file

Additional file 1 of MHC-I genotype and tumor mutational burden predict response to immunotherapy

Download (2.55 MB)
journal contribution
posted on 20.05.2020, 03:48 by Aaron M. Goodman, Andrea Castro, Rachel Marty Pyke, Ryosuke Okamura, Shumei Kato, Paul Riviere, Garrett Frampton, Ethan Sokol, Xinlian Zhang, Edward D. Ball, Hannah Carter, Razelle Kurzrock
Additional file 1: Table S1. List of patients who underwent immunotherapy at UCSD (N = 83). Table S2. Univariate analysis of factors affecting outcome for patients with TMB > 10 mutations/mb treated with immune checkpoint blockade (N = 39 with TMB ≥ 10 mutations/mb). Table S3. Multivariate analysis of factors affecting outcome for patients treated with immunotherapy (N = 39 with TMB ≥10 mutations/mb). Table S4. Validation cohort of 32 patients with NSCLC treated with pembrolizumab. Table S5. Validation cohort patient demographics by PHBR score (< 0.5 vs. ≥0.5) for 32 patients with NSCLC treated with pembrolizumab. Table S6. Univariate analysis of factors affecting outcome for validation patients treated with immune checkpoint blockade (N = 32). Table S7. Overall response rate and PFS, segregated by TMB low/high and PHBR low/high among validation patients (N = 32). Table S8. Covariates retained after the backwards selection process. The coefficients and respective p-values for the covariates including TMB and PHBR in the final model are shown. Figure S1. Overview of tumor type distribution for the discovery cohort. Figure S2. CONSORT Diagram. Figure S3. Overview of minimum PHBR score distribution and TMB distribution for the discovery (A-B) and validation (C-D) cohorts. Figure S4. Kaplan and Meier PFS and OS for patients treated with immunotherapy, excluding patients with TMB = 0. Figure S5. Additional PFS and OS for patients treated with immunotherapy (N = 77 with TMB available. Figure S6. Correlation between PHBR score and TMB (N = 77 with TMB available. Figure S7. Area under the receiver operating characteristic curve (AUROC) for predicting OBR in the discovery cohort using the covariates obtained from the backward selection process, with the addition of PHBR (A), TMB (B) and the combination of PHBR and TMB (C). Figure S8. Kaplan Meier PFS dichotomized by both PHBR < 0.5 and ≥ 0.5 and TMB < 10 and ≥ 10 mutations/mb for histologies with ≥5 patients; NSCLC (A), SCC (B), Head and Neck (C), and Breast (D). Figure S9. Kaplan Meier PFS dichotomized by both PHBR < 0.5 and ≥ 0.5 and TMB < 10 and ≥ 10 mutations/mb excluding NSCLC (A), SCC (B), Head and Neck (C), Breast (D) and both NSCLC and SCC, the most common histologies in our cohort (E). Fig. S10: Area under the receiver operating characteristic curve (AUROC) for predicting OBR from PHBR and TMB in the discovery cohort training on NSCLC and SCC patients (A) and testing on patients in the remaining tumor types (B).

Funding

National Cancer Institute

History

Usage metrics

Categories

Exports