Prognostic Analysis of Diagnostic Ureteroscopic Biopsy for Intravesical Recurrence of Upper Urinary Tract Urothelial Carcinoma

Cutoff values of PD-L1 expression in urinary cytology samples for predicting response to immune checkpoint inhibitor therapy in upper urinary tract urothelial carcinoma

BACKGROUND

The objective of this study was to determine the cutoff value of PD-L1 expression that can predict response to immune checkpoint inhibitor (ICI) immunotherapy for upper tract urothelial carcinoma (UTUC).

METHODS

The concordance of PD-L1 expression between paired surgical resection specimens (SRSs) and urine cell blocks (UCBs) (cohort 1) was studied in a retrospective set of 58 UTUC patients to determine its suitability as a predictor of ICI immunotherapy efficacy. PD-L1 expression in UCBs obtained before neoadjuvant ICI immunotherapy was verified in a prospective set of 12 UTUC patients (cohort 2). PD-L1 (SP263 clone) expression was assessed for percentage (tumor proportional score) of tumor cell (TC) showing PD-L1 staining.

RESULTS

The authors found an overall agreement of 94.4% (51 of 54) between UCBs and SRSs in cohort 1 (positive percent agreement = 100%, negative percent agreement = 93.8%, r value = 0.63). PD-L1 expression in <10% and ≥10% of tumor cells (TCs) of UCBs were the best predictors of negative (<25%) and positive (≥25%) expression in TCs of SRSs, respectively (concordance = 98.1%, r value = 0.93). These findings were verified in cohort 2: at the 10% cutoff for PD-L1 expression, the best response predictive value was 83.3% (5 of 6) in PD-L1-positive patients, and the nonresponse predictive value was 50% (3 of 6) in PD-L1-negative patients. The sensitivity, specificity, and area under the receiver operating characteristic curve values for predicting ICI immunotherapy efficacy based on PD-L1-expressing TCs in UCBs were 62.5%, 75%, and 0.688, respectively.

CONCLUSIONS

Immunocytochemistry of UCBs is reliable for determining PD-L1 expression, which can predict the efficacy of ICI immunotherapy at a cutoff of 10%.

The application value of multi-parameter cystoscope in improving the accuracy of preoperative bladder cancer grading

Purpose

To develop and validate a preoperative cystoscopic-based predictive model for predicting postoperative high-grade bladder cancer (BCa), which could be used to guide the surgical selection and postoperative treatment strategies.

Materials and methods

We retrospectively recruited 366 patients with cystoscopy biopsy for pathology and morphology evaluation between October 2010 and January 2021. A binary logistic regression model was used to assess the risk factors for postoperative high-grade BCa. Diagnostic performance was analyzed by plotting receiver operating characteristic curve and calculating area under the curve (AUC), sensitivity, specificity. From January 2021 to July 2021, we collected 105 BCa prospectively to validate the model's accuracy.

Results

A total of 366 individuals who underwent transurethral resection of bladder tumor (TURBT) or radical cystectomy following cystoscopy biopsy were included for analysis. 261 (71.3%) had a biopsy pathology grade that was consistent with postoperative pathology grade. We discovered five cystoscopic parameters, including tumor diameter, site, non-pedicled, high-grade biopsy pathology, morphology, were associated with high-grade BCa. The established multi-parameter logistic regression model (“JSPH” model) revealed AUC was 0.917 (P < 0.001). Sensitivity and specificity were 86.2% and 84.0%, respectively. And the consistency of pre- and post-operative high-grade pathology was improved from biopsy-based 70.5% to JSPH model-based 85.2%. In a 105-patients prospective validation cohort, the consistency of pre- and post-operative high-grade pathology was increased from 63.1 to 84.2% after incorporation into JSPH model for prediction.

Conclusion

The cystoscopic parameters based “JSPH model” is accurate at predicting postoperative pathological high-grade tumors prior to operations.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01054-z.