m6A methylation regulators as predictors for treatment of advanced urothelial carcinoma with anti-PDL1 agent

Abstract

Purpose: Immune checkpoint blockade agents were shown to provide a survival advantage in urothelial carcinoma, while some patients got minimal benefit or side effects. Therefore, we aimed to investigate the prognostic value of m6A methylation regulators, and developed a nomogram for predicting the response to atezolizumab in urothelial carcinoma patients.

Methods: A total of 298 advanced urothelial carcinoma patients with response data in the IMvigor210 cohort were included. Differential expressions of 23 m6A methylation regulators in different treatment outcomes were conducted. Subsequently, a gene signature was developed in the training set using the least absolute shrinkage and selection operator (LASSO) regression. Based on the multivariable logistic regression, a nomogram was constructed by incorporating the gene signature and independent clinicopathological predictors. The performance of the nomogram was assessed by its discrimination, calibration, and clinical utility with internal validation.

Results: Six m6A methylation regulators, including IGF2BP1, IGF2BP3, YTHDF2, HNRNPA2B1, FMR1, and FTO, were significantly differentially expressed between the responders and non-responders. These six regulators were also significantly correlated with the treatment outcomes. Based on the LASSO regression analysis, the gene signature consisting of two selected m6A methylation regulators (FMR1 and HNRNPA2B1) was constructed and showed favorable discrimination. The nomogram integrating the gene signature, TMB, and PD-L1 expression on immune cells, showed favorable calibration and discrimination in the training set (AUC 0.768), which was confirmed in the validation set (AUC 0.755). Decision curve analysis confirmed the potential clinical usefulness of the nomogram.

Conclusions: This study confirmed the prognostic value of FMR1 and HNRNPA2B1, and constructed a nomogram for individualized prediction of the response to atezolizumab in patients with urothelial carcinoma, which may aid in making treatment strategies.

Keywords: PD1/PDL1; m6A methylation regulators; outcome; prediction; urothelial carcinoma.

Figure 1

Relationship between the expression of m6A RNA methylation regulators and treatment response in urothelial carcinoma patients. (A) The heatmap shows the expression patterns of the 23 m6A methylation regulators between the response group and non-response group. (B) The violin plots exhibit the differential expression of the 23 m6A methylation regulators in the response group (red) and the non-response group (blue). (C) Spearman correlation analyses of the expression of the 23 m6A methylation regulators. *P < 0.05, ***P < 0.001.

Figure 2

Construction and assessment of the m6A-related gene signature. (A) Univariable logistic regression analyses evaluating the predictive ability of m6A methylation regulators for treatment response of urothelial carcinoma patients. (B) Tuning parameter (λ) selection in the LASSO model used 10-fold cross-validation via minimum criteria. Binomial deviances from the LASSO regression cross-validation procedure were plotted as a function of log(λ). The numbers along the upper x-axis represent the average number of predictors. The red dots indicate the average deviance values for each model with a given λ, and the vertical bars through the red dots show the upper and lower values of the deviances. The dotted vertical lines are drawn at the optimal values where the model provides its best fit to the data. The optimal λ value of 0.053 with log (λ) = -2.936 was chosen. (C) LASSO coefficient profiles of the 23 m6A methylation regulators. The dotted vertical line is drawn at the value selected using 10-fold cross-validation in Figure 2B, where optimal λ resulted in 2 nonzero coefficients. (D) ROC curves of the gene signature in the training and validation sets.

Figure 3

Weight Gene Co-expression Network Analysis. (A) Analysis of the scale-free index for various soft power thresholds. (B) Analysis of the mean connectivity of various soft power thresholds. (C) Dendrogram of the genes clustered based on a dissimilarity measure (1-TOM). (D) Average gene significances and errors in the modules associated with treatment response. The turquoise module was the most significantly correlated to treatment response. FMR1 and HNRNPA2B1 are in this module.

Figure 4

The relationship of FMR1 and HNRNPA2B1 with different types of immune cells. (A–H) Correlation plots show the relationship between FMR1 and different types of immunocytes. (I–K) Correlation plots show the relationship between HNRNPA2B1 and different types of immunocytes.

Figure 5

Nomogram to predict the response of atezolizumab treatment for patients with advanced urothelial carcinoma and its performance evaluation. (A) Points were assigned for gene score, IC and TMB by drawing a line upward from the corresponding values to the “Points” line. The sum of these three points, plotted on the “Total points” line, corresponds to predictions of the treatment response. (B) ROC curves of the nomogram. (C) Calibration curves of the nomogram. The observed treatment outcome is shown compared with the nomogram using the training set and validation set, respectively. The calibration curves depict the calibration of the nomogram in terms of the agreement between the predicted treatment outcomes and the observed treatment outcomes. The 45-degree dotted gray line represents a perfect prediction, and the solid lines represent the predictive performance of the nomogram. The distance between the solid line and the ideal line represents the superior predictive accuracy of the nomogram.

Figure 6

Clinical Usefulness of the Nomogram. (A, B) Kaplan-Meier survival curves of patients categorized into response and non-response groups in the training set (A) and validation set (B), respectively. (C, D) DCA of the nomogram in the training set (A) and validation set (B), respectively. The x-axis represents the threshold probability. The y-axis measures the net benefit. The black line depicts the net benefit of the strategy of treating no patients. The gray line depicts the net benefit of the strategy of treating all patients. The red line represents the nomogram. The net benefit was calculated by subtracting the proportion of all patients who are false positive from the proportion who are true positive, weighting by the relative harm of forgoing treatment compared with the negative consequences of unnecessary treatment. The threshold probability is where the expected benefit of treatment is equal to the expected benefit of avoiding treatment.