Sex-associated molecular differences for cancer immunotherapy

Abstract

Immune checkpoint blockade therapies have extended patient survival across multiple cancer lineages, but there is a heated debate on whether cancer immunotherapy efficacy is different between male and female patients. We summarize the existing meta-analysis to show inconsistent conclusions for whether gender is associated with the immunotherapy response. We analyze molecular profiling from ICB-treated patients to identify molecular differences for immunotherapy responsiveness. We perform comprehensive analyses for patients from The Cancer Genome Atlas (TCGA) and reveal divergent patterns for sex bias in immune features across multiple cancer types. We further validate our observations in multiple independent data sets. Considering that the majority of clinical trials are in melanoma and lung cancer, meta-analyses that pool multiple cancer types have limitations to discern whether cancer immunotherapy efficacy is different between male and female patients. Future studies should include omics profiling to investigate sex-associated molecular differences in immunotherapy.

Fig. 1. Clinical outcomes between male and female patients with ICB treatment.

The interaction between ICB treatment efficacy and gender for clinical trials summarized in Supplementary Fig. 1. Background and square color indicate OS advantage of ICB treatment in female (red) or male (blue). Square size indicates the proportion to the inverse of the variance of the estimates. Black vertical lines indicate the 95% confidence interval (CI). Cells filled with dark green indicates the data set used in this meta-analysis. Sample size for each trial was listed in Supplementary Table 1. ^a,b,cIndicate the different studies from the same first author and published in the same years. ccRCC clear cell renal cell carcinoma, GEJC gastric or gastroesophageal junction carcinoma, HNSC head and neck cancer, NSCLC non-small-cell lung cancer, SCLC, small-cell lung cancer, MESO mesothelioma.

Fig. 2. Clinical outcomes and molecular differences between male and female patients with ICB treatment.

a Univariate survival analysis in the Cox proportional hazard model for female patients with immunotherapy treatment compared with male patients in nine cancer types from five data sets. Square color indicates OS advantage in female (red) or male (blue). Square size indicates the significance of cox p-value. Black horizontal lines indicate the 95% CI. b The gender difference for molecular biomarkers reported for immunotherapy (x axis) across multiple cancer types (y axis) in seven immunotherapy data sets. Two-sided Wilcoxon–Mann–Whitney test was used for continuous variables and Fisher’s exact test was used for discrete variables. Blue cell: male-bias with p < 0.05, light blue cell: male-bias with 0.05 ≤ p < 0.2, pink cell: female-bias with 0.05 ≤ p < 0.2. Empty cells indicate the unavailability of data. Sample size for each data set was listed in Supplementary Table 2. ccRCC clear cell renal cell carcinoma, BLCA bladder cancer, COAD colorectal cancer, ESCA esophagogastric cancer, GBM glioma, HNSC, head and neck cancer, NSCLC non-small-cell lung cancer, PanCan multiple cancer types with small sample size. PD-L1/PD-L2 programmed cell death ligand 1/2, CTLA-4 cytotoxic T-lymphocyte antigen-4. GEP T cell-inflamed gene expression profile, CYT cytolytic activity.

Fig. 3. Differences in immune features between male and female patients from TCGA.

a Overview of the propensity score algorithm used to balance confounding effects, including age, race, tumor purity, tumor stage, subtype and smoking history, and to evaluate the sex-associated immune features, including TMB, neoantigen load, TCR/BCR, checkpoints, immune cell population and aneuploidy, across cancer types. b Differences of molecular biomarkers, including TMB, neoantigen load, GEP, CYT and PD-L1 protein expression, reported in immunotherapy data sets between male patients and female patients. Bar plots indicate the number of significant female-biased features minus the number of significant male-biased features. c Differences of relative abundance of six immune cell populations, including active CD4/CD8 T cells, effector memory CD4/CD8 T cells, myeloid-derived suppressor cell and regulatory T cells. d Differences of mRNA expression level of 34 immune checkpoints, including LAG3, CTLA-4, PDCD1 and CD274. X axis denotes immune features. Y axis of b–d denotes 22 cancer types analyzed by the propensity score algorithm and ordered by the number of significant female-biased features minus the number of significant male-biased features in b. Statistical analysis was performed using a propensity score algorithm to identify immune-associated features (see Supplementary methods). p-value was calculated by linear regression model and adjusted by Benjamini and Hochberg correction. FDR is labeled as red dots (female-bias) and blue dots (male-bias) in b–d. Sample size for each data set was listed in Supplementary Table 3.

Fig. 4. Immunotherapy clinical trials across cancer types.

Number of ICB clinical trials for anti-PD-1/PD-L1 and anti-CTLA-4 across cancer types as of April 16, 2019, with status of a completed and b active, not recruiting.