Abstract PR010: Exploratory analysis of tumor tertiary lymphoid structures using a novel artificial intelligence–based approach in patients with muscle-invasive urothelial carcinoma from the CheckMate 274 trial
Authors: Witjes Johannes Alfred , Galsky Matthew D. , Gschwend Jürgen E. , Bajorin Dean F. , Milowsky Matthew I. , Jha Gautam , Li Roger , Li Jun , Matos-Cruz Vanessa , Ely Scott , Lee George , Baxi Vipul , Zhang Joshua , Gupta Saurabh , David Justin M. 
Published: May 2024

Abstract
Introduction: The phase 3 randomized CheckMate 274 trial in patients (pts) with high-risk muscle-invasive urothelial carcinoma (MIUC) demonstrated improved disease-free survival (DFS) with nivolumab (NIVO) vs placebo (PBO) in intent-to-treat (ITT) and tumor programmed death ligand 1 expression ≥1% pts (primary endpoints). With extended follow-up, continued efficacy was observed, and immature overall survival favored NIVO in both populations. Extending from previous exploratory tumor biomarker analyses (Necchi et al. ESMO 2022), we conducted a post hoc analysis of tertiary lymphoid structures (TLS), which are ectopic lymphoid structures often found within the solid tumor microenvironment. A subset of TLS has been associated with improved outcomes to neoadjuvant immunotherapy in UC. Using artificial intelligence (AI)–based algorithms, we explored the biomarker utility of TLS from CheckMate 274. Methods: We used a reproducible AI-based algorithm to identify and classify TLS in situ using hematoxylin and eosin (H&E) images from pretreatment tumor tissue specimens (n=272/699 treated pts). Images were scored for the presence or absence of lymphoid aggregate (LA), immature TLS (iTLS), and mature TLS (mTLS) and scored as either positive (+) or negative (−) for the respective TLS category. Relationships between TLS categories and DFS were explored (minimum follow-up, 5.9 months). Results: Most tumors exhibited some form of LA/TLS (245/272; 90.1%), with a higher incidence of LA (237/272; 87.1%) compared with mTLS (89/272; 32.7%). There was heterogeneity of TLS within the tumors, with most tumors demonstrating more than 1 form of TLS (198/272; 72.8%). In the PBO arm, median DFS was modest for most TLS categories (5.7–8.3 months) but was substantially improved for mTLS+ tumors (22.1 months; 95% CI, 5.4–not estimable [NE]); n=41), representing a 2.7- to 3.9-fold increase relative to any other category. In contrast, treatment with NIVO resulted in 2.1- to 4.0-fold improvements in median DFS relative to PBO across all but the mTLS+ category (22.9 months; 95% CI,11.9–NE; n=48), which was similar to the PBO arm. The largest increases in median DFS (95% CI) for NIVO vs PBO were observed for LA+ (27.6 [16.1–NE; n=114] vs 8.3 [5.6–11.9; n=123] months; HR, 0.56; 95% CI, 0.39–0.80; P=0.0015), iTLS+ (27.0 [16.1–NE; n=91] vs 8.2 [5.4–21.2; n=105] months; HR, 0.61; 95% CI, 0.41–0.91; P=0.0143), and mTLS− tumors (24.6 [11.4–NE; n=81] vs 6.1 [5.2–8.5; n=102] months; HR, 0.53; 95% CI, 0.36–0.78; P=0.0012]). Conclusion: These results suggest that a novel AI-based TLS maturity classification using H&E images is feasible to investigate the tumor microenvironment in MIUC, with potential broad application in other studies.
Citation Format: Johannes Alfred Witjes, Matthew D. Galsky, Jürgen E. Gschwend, Dean F. Bajorin, Matthew I. Milowsky, Gautam Jha, Roger Li, Jun Li, Vanessa Matos-Cruz, Scott Ely, George Lee, Vipul Baxi, Joshua Zhang, Saurabh Gupta, Justin M. David. Exploratory analysis of tumor tertiary lymphoid structures using a novel artificial intelligence–based approach in patients with muscle-invasive urothelial carcinoma from the CheckMate 274 trial [abstract]. In: Proceedings of the AACR Special Conference on Bladder Cancer: Transforming the Field; 2024 May 17-20; Charlotte, NC. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(10_Suppl):Abstract nr PR010.