Validation of ENLIGHT, an AI predictor of immune checkpoint blockade (ICB) response and resistance, across the treatment span.

Abstract

2632 Background: Advanced computational AI algorithms, such as ENLIGHT and DeepPT (Med 2023, Nature Cancer 2024), represent a promising approach to identify predictive biomarkers for cancer therapeutics. Evaluation of ICB response prediction via these algorithms through the full span of pre-treatment, on-treatment, and at progression time points provides a dynamic perspective of response prediction abilities. Methods: A post-hoc analysis of two pan-cancer clinical trials was performed: i) BIO2 is a biobanking protocol of ICB-naïve patients (pts) treated with pembrolizumab (NCT02644369); and ii) The IRIS study (NCT04243720) which enrolled pts who have progressed immediately post ICB. In BIO2, complete, partial response or stable disease for >6 months was classified as responders (R), the rest as non-responders (NR). In IRIS, acquired and primary resistance were defined according to trial protocol. ENLIGHT matching scores were calculated using either transcriptomics from NGS (EMS-NGS), or transcriptomics imputed directly from H&E slides using DeepPT (EMS-DP). The predictive value of EMS was compared to PD-L1 IHC, tumor mutational burden (TMB) and tumor infiltrating lymphocytes (TILs) abundance by IHC, and its trajectory across timepoints was studied. Results: 76 pts from BIO2 (23:53, R:NR), and 37 pts from IRIS (18:19, AR:PR), comprising of 14 tumor types, were analyzed. We first established the value of ENLIGHT as a predictive biomarker using the BIO2 pre-treatment samples. EMS-NGS was a superior predictive biomarker compared with PD-L1 IHC, TMB and TIL abundance, while EMS-DP was comparable (Table). The EMS-NGS scores of responders were significantly higher than non-responders pre-treatment (medians: 0.92 vs. 0.62, p = 1.4e-4). Analyzing the trajectory of the EMS-NGS scores across two additional timepoints reveals that while the scores of non-responding patients remained low (median: 0.62, 0.69, 0.67 for pre-, on–treatment and post-progression, respectively), it is higher among responders (median: 0.92, 0.78 for pre- and on–treatment, respectively). Finally, EMS-NGS was higher among pts with acquired vs primary resistance in IRIS (medians: 0.75 vs 0.59, p = 0.17). Conclusions: In two pan-cancer cohorts, EMS-NGS outperformed conventional biomarkers in predicting ICB response. EMS-DP was comparable to conventional biomarkers and could be calculated directly from H&E slides in a fast, low-cost manner. EMS-NGS values were concordant with response or resistance throughout the ICB treatment course, reflecting the level of the tumor’s vulnerability to ICB inhibition. Further validation of ENLIGHT in larger ICB-treated pts is warranted given these promising results. Clinical trial information: NCT02644369 , NCT04243720 . ROC AUC (p) Sensitivity PPV (cf 30% baseline response rate) F1 Score EMS-NGS 0.74 (0.0003) 61 48 54 EMS-DP 0.64 (0.02) 57 45 50 PD-L1 IHC 0.7 (0.003) 70 40 51 TMB 0.64 (0.03) 39 69 50 TILs 0.6 (0.065) 39 52 44