Mian, Omar Y.

Bladder cancer, the sixth most common cancer in the United States, is most commonly of the urothelial carcinoma histologic subtype. The clinical spectrum of bladder cancer is divided into 3 categories that differ in prognosis, management, and therapeutic aims: (1) non–muscle-invasive bladder cancer (NMIBC); (2) muscle invasive, nonmetastatic disease; and (3) metastatic bladder cancer. These NCCN Guidelines Insights detail recent updates to the NCCN Guidelines for Bladder Cancer, including changes in the fifth edition of the WHO Classification of Tumours: Urinary and Male Genital Tumours and how the NCCN Guidelines aligned with these updates; new and emerging treatment options for bacillus Calmette-Guérin (BCG)–unresponsive NMIBC; and updates to systemic therapy recommendations for advanced or metastatic disease.

686 Background: Immune checkpoint inhibitors (ICI) have been used to treat advanced muscle invasive and/or metastatic bladder cancer. However, ICIs are only effective in 30-40% of cases. In light of potentially significant IRAE’s, tools to predict individual patient level benefits are desirable. AI technology offers the opportunity to apply computational methods to predict outcomes from readily available digitized pathology slides. We investigated the utility of an AI platform integrating computational biomarkers based on morphological characterization of digitized H&E slide whole images (WSI) to predict response to ICI. Methods: We analyzed H&E-stained whole slide images (WSI) of bladder tumor tissue collected from transurethral resection of bladder tumor (TURBT) from 116 patients with advanced or metastatic bladder cancer at a single institution between 2015-2020. Adjacent multiplex IHC stained specimens were analyzed myeloid and lymphoid marker panels. 20 patients from the overall cohort treated with ICI were designated ‘responders’ (complete response, partial response, stable disease) and 20 patients were identified as non-responders (progressive disease) based on ICI best clinical/radiographic response. WSIs were divided into small non-overlapping image patches. These image tiles were processed into multiple AI encoder models to extract morphological features. The tile morphological features, represented by high dimensional vectors, were combined by an aggregation model to represent the whole slide morphology and then used to classify the patients into responders vs non-responders to immunotherapy. Area-under-the-receiver operating characteristic (AUC) was used to measure the performance of response prediction. Results: Our method shows AUC of 0.708 at classification of the patients (n=40) into responder and non-responder groups. With a cutoff that identifies the top 50% patients (n=20) of high probability of responding to immunotherapy predicted by our models, 75% of them are responders. In the bottom 50% patients (n=20) of the low scores, 75% of them are non-responders. . Multiplex IHC (myeloid and lymphoid panel) was performed using adjacent sections and were characterized yielding deeper mechanistic insight into the inflammatory populations driving predictive morphologic patterns. Conclusions: By applying innovative AI morphological analysis on patient WSIs, we generated a computational biomarker that shows promising ability to predict patient response to immunotherapy in bladder cancer. This approach merits future prospective validation and additional patient level analysis in broader cohorts. [Table: see text]

The NCCN Guidelines for Bladder Cancer provide recommendations for the diagnosis, evaluation, treatment, and follow-up of patients with bladder cancer and other urinary tract cancers (upper tract tumors, urothelial carcinoma of the prostate, primary carcinoma of the urethra). These NCCN Guidelines Insights summarize the panel discussion behind recent important updates to the guidelines regarding the treatment of non–muscle-invasive bladder cancer, including how to treat in the event of a bacillus Calmette-Guérin (BCG) shortage; new roles for immune checkpoint inhibitors in non–muscle invasive, muscle-invasive, and metastatic bladder cancer; and the addition of antibody–drug conjugates for metastatic bladder cancer.