Supplementary Figures S1-S8 from Immunogenomic Analyses of Advanced Serous Ovarian Cancer Reveal Immune Score is a Strong Prognostic Factor and an Indicator of Chemosensitivity
Authors: Hao Dapeng , Liu Jie , Chen Meng , Li JingJing , Wang Li , Li Xiaobo , Zhao Qi , Di Li-jun 
Published: April 2023

<p>Supplementary Figure 1 to Supplementary Figure 8 Supplementary Figure 1 | Association of immune score with TILs (A) and cytolytic activity (B). Supplementary Figure 2 | Prognostic evaluation of immune cells in ovarian cancer datasets. (A) Forest plot visualizing hazard ratios (HRs) of infiltrating CD8+ T cells in all cohort datasets. The red diamond shows the fixed-effects meta-analysis summary of HRs over the 16 datasets (HR = 0.92[0.87-0.97]; p = 0.003). HR is determined by univariate Cox proportional hazards regression analysis. (B) Same as (A) but for Plasmacytoid dendritic cells (HR = 1.07[1.01-1.13], p = 0.02). Supplementary Figure 3 | The association of immune score with somatic mutations in TCGA dataset. (A) A volcano plot representation between immune score and gene mutations showing the magnitude and significance of immune score-gene associations. The x-axis measures the average difference of the immune score in patients with somatic mutations of a gene minus the immune score in patients with the wild type gene. The y-axis indicates the statistical significance of immune score between mutated tumors and wild type tumors by Wilcoxon Rank Sum test. Only genes with non-silient mutations in at least five different tumor samples are analyzed. All the associations in the figure are with a FDR > 0.5. (B) The correlation between mutation burden and immune score across tumor samples (Pearson's r = 0.08, p = 0.19). (C) The correlation between neo-antigen burden and immune score across tumor samples (Pearson's r = 0.05, p = 0.43). Supplementary Figure 4 | Genomic assocaitions with immune score in TCGA melanoma cohort. (A) Oncoplot of the copy number status of IFN-γ pathway genes. Copy number loss or gain was determined by GISTIC 2.0 and defined as score < 0 or >0 respectively. Green bar and grey bar indicate the samples with or without copy number loss of IFN-γ pathway genes, respectively. (B) Association between immune score and copy number status of IFN-γ pathway genes. Samples are divided into groups with or without copy number loss of IFN-γ pathway genes, as shown by the green and grey bar in A. (C) Association between immune score and the expression of IRF-1 in melanoma. (D) Association between immune score and the expression of IRF-1 in TCGA ovarian cancer dataset (related to Figure 2D). Significance is determined by Wilcoxon Rank Sum test. Supplementary Figure 5 | Correlation matrix between immune score and the expression of chemokines and antigen-presenting genes in the 16 cohort datasets of ovarian cancer. Color of units represents the Pearson correlation coefficient between immune score and gene expression. Correlation for genes not included in the data platform is shown as NA (black). Supplementary Figure 6 | GSEA reveals the assocation of response to chemotherapy and infiltrating activated CD4+ T cells in TCGA dataset. Supplementary Figure 7 | Prognostic evaluation of immune score for BRCA mutated samples. (A) Kaplan-Meier survival curves of TCGA ovarian cancer patients, divided by BRCA1/2 mutation status and immune score. (B) Kaplan-Meier survival curves of BRCA1/2 wild type patients and patients with BRCA1/2 mutations and diploid copy number status inTCGA ovarian cancer dataset. Supplementary Figure 8 | Immune score is predictive of response to immunotherapy. (A) Immune score for the cohort received anti-CTLA-4 immunotherapy. (B) Kaplan-Meier survival curves of patients with high vs. low immune score in the cohort received anti-CTLA-4 immunotherapy. (C) Receiver operating characteristics (ROC) for immune score and CTLA4 expression in predicting the responders of immunotherapy.</p>