Abstract
The status of lymph node (LN) metastases plays a decisive role in the selection of surgical procedures and post-operative treatment. Several histopathologic features, known as predictors of LN metastasis, are commonly available post-operatively. Medical imaging improved pre-operative diagnosis, but the results are not fully satisfactory due to substantial false positives. Thus, a reliable and robust method for pre-operative assessment of LN status is urgently required. We developed a prediction model in a training set from the TCGA-BLCA cohort including 196 bladder urothelial carcinoma samples with confirmed LN metastasis status. Least absolute shrinkage and selection operator (LASSO) regression was harnessed for dimension reduction, feature selection, and LNM signature building. Multivariable logistic regression was used to develop the prognostic model, incorporating the LNM signature, and a genomic mutation of MLL2, and was presented with a LNM nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was evaluated by the testing set from the TCGA cohort and independent validation was assessed by two independent cohorts. The LNM signature, which consisted of 48 selected features, was significantly associated with LN status (p < 0.005 for both the training and testing sets of the TCGA cohort). Predictors contained in the individualized prediction nomogram included the LNM signature and MLL2 mutation status. The model demonstrated good discrimination, with an area under the curve (AUC) of 98.7% (85.3% for testing set) and good calibration with p = 0.973 (0.485 for testing set) in the Hosmer-Lemeshow goodness of fit test. Decision curve analysis demonstrated that the LNM nomogram was clinically useful. This study presents a pre-operative nomogram incorporating a LNM signature and a genomic mutation, which can be conveniently utilized to facilitate pre-operative individualized prediction of LN metastasis in patients with bladder urothelial carcinoma.
Highlights
Bladder cancer is the 9th most prevalent cause of cancer worldwide and the 2nd most common genitourinary malignancy, with transitional cell carcinomas comprising about 90% of primary bladder tumors [1]
Tumors with lymph node (LN)+ demonstrated poorer prognosis than LN– (p = 0.002, hazard ratio (HR) = 1.95, 95% confidence interval (CI) = [1.18–3.23], Figure 1A) and a tendency could be observed where LN+ tumors presented a higher recurrence rate (p = 0.083, HR = 1.58, 95% CI = [0.88–2.82], Figure 1B)
Total points calculated by lymph node metastasis (LNM) nomogram for each sample in the testing set was determined to be a significant predictor when performing logistic regression (p = 0.032), and no departure from perfect fit was identified (p = 0.485) (Figure 6A, see Supplementary Figures S1A–T for total point of each testing sample)
Summary
Bladder cancer is the 9th most prevalent cause of cancer worldwide and the 2nd most common genitourinary malignancy, with transitional cell carcinomas comprising about 90% of primary bladder tumors [1]. Previous research has revealed that Lymph node (LN) involvement—which is frequently found in bladder cancer—possesses prognostic implications, and both the pathological stage of primary bladder tumor and the presence of LN metastasis are considered the most important determinants of survival in bladder cancer patients undergoing radical cystectomy [3]. Computed tomography (CT) revealed a high false negative rate of 21% [11]. Next-generation sequencing technology has brought massively high throughput sequencing data to bear on research questions with low cost, which enables us to decipher the difference of bladder cancer in terms of status of LN metastasis in a genomic level. This study aims to develop and validate a pre-operative nomogram that incorporates a LNmetastasis signature and genomic mutations for individualized pre-operative prediction of LN metastasis in patients with bladder cancer
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