Abstract
BackgroundTumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). We aim at investigating radiomic features’ value in predicting the TMB status of BLCA patients.MethodsTotally, 75 patients with BLCA were enrolled. Radiomic features extracted from the volume of interest of preoperative pelvic contrast-enhanced computed tomography (CECT) were obtained for each case. Unsupervised hierarchical clustering analysis was performed based on radiomic features. Sequential univariate Logistic regression, the least absolute shrinkage and selection operator (LASSO) regression and the backward stepwise regression were used to develop a TMB-predicting model using radiomic features.ResultsThe unsupervised clustering analysis divided the total cohort into two groups, i.e., group A (32.0%) and B (68.0%). Patients in group A had a significantly larger proportion of having high TMB against those in group B (66.7% vs. 41.2%, p = 0.039), indicating the intrinsic ability of radiomic features in TMB-predicting. In univariate analysis, 27 radiomic features could predict TMB. Based on six radiomic features selected by logistic and LASSO regression, a TMB-predicting model was built and visualized by nomogram. The area under the ROC curve of the model reached 0.853. Besides, the calibration curve and the decision curve also revealed the good performance of the model.ConclusionsOur work firstly proved the feasibility of using radiomics to predict TMB for patients with BLCA. The predictive model based on radiomic features from pelvic CECT has a promising ability to predict TMB. Future study with a larger cohort is needed to verify our findings.
Highlights
Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA)
Based on the unsupervised nature of the clustering analysis, these findings indicated that radiomic features extracted from contrast-enhanced computed tomography (CECT) have an intrinsic ability in discriminating the TMB status of BLCA patients
We explored the relationship between the radiomic features or TMB status with the occurrence of the driver gene mutation
Summary
Tumor mutation burden (TMB) is an emerging prognostic biomarker of immunotherapy for bladder cancer (BLCA). As the most common tumor of the urinary system, the emergence of immunotherapy brings hope for patients of BLCA, it cannot be ignored that this novel treatment is not always effective in all patients. PD-L1 expression and tumor mutation burden (TMB) are the two most commonly used biomarkers [5, 6]. Unlike the detection of PD-L1 expression that focuses on the targeting protein of the ICIs, TMB predicts the therapeutic efficacy of ICIs through its strong correlation to the mutation-derived neoantigens which is a key factor for immune response activation [5]. A major obstacle that prevents the large-scale promotion of TMB detection in patients receiving ICIs is the high cost of the whole-exome sequencing (WES) test
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