A three-gene expression score for predicting clinical benefit to anti-PD-1 blockade in advanced renal cell carcinoma

In the advanced renal cell carcinoma (RCC) scenario, there are no consistent biomarkers to predict the clinical benefit patients derived from immune checkpoint blockade (ICB). Taking this into consideration, herein, we conducted a retrospective study in order to develop and validate a gene expression score for predicting clinical benefit to the anti-PD-1 antibody nivolumab in the context of patients diagnosed with advanced clear cell RCC enrolled in the CheckMate-009, CheckMate-010, and CheckMate-025 clinical trials. First, a three-gene expression score (3GES) with prognostic value for overall survival integrating HMGA1, NUP62, and ARHGAP42 transcripts was developed in a cohort of patients treated with nivolumab. Its prognostic value was then validated in the TCGA-KIRC cohort. Second, the predictive value for nivolumab was confirmed in a set of patients from the CheckMate-025 phase 3 clinical trial. Lastly, we explored the correlation of our 3GES with different clinical, molecular, and immune tumor characteristics. If the results of this study are definitively validated in other retrospective and large-scale, prospective studies, the 3GES will represent a valuable tool for guiding the design of ICB-based clinical trials in the aRCC scenario in the near future.

Clinical, molecular, and immune correlates of the Immunotherapy Response Score in patients with advanced urothelial carcinoma under atezolizumab monotherapy: analysis of the phase II IMvigor210 trial

BACKGROUND

In the advanced urothelial carcinoma (aUC) scenario there are no consistent immune checkpoint blockade predictive biomarkers. Recently a novel pan-tumor molecular tissue-based biomarker, the Immunotherapy Response Score (IRS), has been proposed. We conducted a retrospective study to validate the prognostic/predictive utility of the IRS in patients with aUC under atezolizumab monotherapy and to characterize its underlying molecular/immune features in the context of the IMvigor210 phase II trial.

PATIENTS AND METHODS

This is a post hoc pooled analysis of 261 patients with available clinical, molecular, and immune tumor data treated with atezolizumab monotherapy in the IMvigor210 phase II clinical trial. Efficacy endpoints were overall survival (OS), disease control rate (DCR), and overall response rate (ORR). Survival estimates were calculated by the Kaplan-Meier method, and groups were compared with the log-rank test. The Cox proportional hazards regression model was used to evaluate factors independently associated with OS. Factors associated with disease control (DC) and response were tested with logistic regression in univariable and multivariable analyses. Comparisons between patient and disease characteristics were carried out using chi-square or Fisher's exact tests. All P values were two-sided, and those <0.05 were considered statistically significant.

RESULTS

High IRS was significantly associated with a better OS in univariable [hazard ratio (HR) = 0.49, P < 0.001] and multivariable (HR = 0.60, P = 0.018) analyses. DCR and ORR were significantly higher among high IRS patients (DCR for high IRS versus low IRS patients: 57% versus 32%, P < 0.001; ORR: 42% versus 10%, P < 0.001). High IRS patients presented a higher probability of DC and response in univariable [DC: odds ratio (OR) = 2.72, P < 0.001; response: OR = 3.92, P < 0.001] and multivariable (DC: OR = 2.72, P < 0.001; response: OR = 3.92, P < 0.001) analyses.

CONCLUSIONS

This study validates IRS as a strong independent prognostic and predictive biomarker for OS and DC/response in patients with aUC treated with atezolizumab monotherapy in the IMvigor210 phase II clinical trial.

MSIMEP: Predicting microsatellite instability from microarray DNA methylation tumor profiles

Deficiency in DNA MMR activity results in tumors with a hypermutator phenotype, termed microsatellite instability (MSI). Beyond its utility in Lynch syndrome screening algorithms, today MSI has gained importance as predictive biomarker for various anti-PD-1 therapies across many different tumor types. Over the past years, many computational methods have emerged to infer MSI using either DNA- or RNA-based approaches. Considering this together with the fact that MSI-high tumors frequently exhibit a hypermethylated phenotype, herein we developed and validated MSIMEP, a computational tool for predicting MSI status from microarray DNA methylation tumor profiles of colorectal cancer samples. We demonstrated that MSIMEP optimized and reduced models have high performance in predicting MSI in different colorectal cancer cohorts. Moreover, we tested its consistency in other tumor types with high prevalence of MSI such as gastric and endometrial cancers. Finally, we demonstrated better performance of both MSIMEP models vis-à-vis a MLH1 promoter methylation-based one in colorectal cancer.