Predictors of disease aggressiveness influence outcome from immunotherapy treatment in renal clear cell carcinoma

Abstract

Renal clear cell carcinoma (RCC) is the most common type of kidney cancer and has a high propensity for metastasis. While treatment with immune checkpoint inhibitors, such as anti-PD-1, have shown modest improvements in survival for RCC, it is difficult to identify responders from non-responders. Attempts to elucidate the mechanisms associated with differential response to checkpoint inhibitors have been limited by small sample size making it difficult to detect meaningful associations. We utilized existing large datasets from The Cancer Genome Atlas (TCGA) to first find predictors of disease aggressiveness in the tumor microenvironment (TME) and hypothesized that these same predictors may influence response to immunotherapy. We found primary metastatic (M1-stage IV) tumors exhibit high immune infiltration, and high TP53-inactivation induced senescence activity compared to non-metastatic (M0-Stage I/II) tumors. Moreover, some TME features inferred from deconvolution algorithms, which differ between M0 and M1 tumors, also influence overall survival. A focused analysis identified interactions between tumor TP53-inactivation induced senescence activity and expression of inflammatory molecules in pre-treatment RCC tumors, which predict both change in tumor size and response to checkpoint blockade therapy. We also noted frequency of inactivating mutations in the protein polybromo-1 (PBRM1) gene was found to be negatively associated with TP53-inactivation induced senescence enrichment. Our findings suggest a mechanism by which tumor TP53-inactivation induced senescence can modulate the TME and thereby influence outcome from checkpoint blockade therapy.

Keywords: cellular senescence; deconvolution; metastasis; molecular modeling; renal clear cell carcinoma; tumor immunobiology.

Figure 1.

Outline of methods and relationship between senescence and immune cells in the tumor microenvironment. A) TCGA renal clear cell carcinoma RNA-seq and whole exome sequencing data were used in combination with tumor staging and survival information to discern differences between metastatic (M1-stage IV, n = 79) and non-metastatic (M0-stage II/III, n = 297) tumors. Five different analyses were performed to determine immune infiltration, pathway activity, and mutation status. Outputs from these analyses were used in regression models to characterize and assess the role of the tumor microenvironment in metastatic disease. B) A diagram outlining a potential mechanism by which high oncogenic activity can lead to senescence induction, which has both pro and anti-tumorigenic effects.

Figure 2.

Renal clear cell carcinoma tumor immune microenvironment characterization A) Shown are 12 out of 13 BASE-generated immune cell types found to be predictors of metastasis status for RCC based on penalized regression modeling. B) Results from backward selection applied to penalized regression predictors show three immune cell types, CD8 + T cell-1, DC Active (Activated DC), and CD4+ Memory T cell are significant predictors of overall survival in an age and sex adjusted cox proportional hazards model. Sensitivity analysis shows the effect of immune predictors on overall survival in all M0 tumors (stage I/II, n = 297, stage III, n = 123). C) Immune scores for three predictors, CD8 + T cell-1, CD4 + T cell-2, and Macrophage-3, which interact with metastasis status in regression models of survival, were binned as high or low. The effect of these three immune infiltration scores on survival is shown. D) We performed GSEA analysis for all senescence pathways in the MSigDB database. The normalized enrichment score (NES) is displayed on the x-axis. Pathways shown are significant for M1 post-FDR correction (Q-values are shown with Q < 0.2).

Figure 3.

TP53-inactivation induced senescence pathways correlate with survival and T cell infiltration. The effect of high vs. low single-sample gene set enrichment (ssGSEA) scores for the SENESCENCE_TP53_TARGETS_UP (A) and SENESCENCE_TP53_TARGETS_DN (E) pathways in the TCGA RCC (M0 and M1) cohort was examined using Kaplan-Meier (KM) survival curves. High vs. low ssGSEA scores for SENESCENCE_TP53_TARGETS_UP (B) and SENESCENCE_TP53_TARGETS_DN (F) in all individuals who received immunotherapy in the Miao et al., 2018 dataset is also shown as well as KM curves comparing high and low ssGSEA scores for SENESCENCE_TP53_TARGETS_UP (C, D) and SENESCENCE_TP53_TARGETS_DN (G, H) in the discovery and the validation cohorts. The cut-offs for high and low scores was set at the median for the score distribution in each cohort. We also examined the association of SENESCENCE_TP53_TARGETS_DN enrichment with CD4 + T cell-2 infiltration scores in all cohorts (I-L) using the spearman rank correlation test.