A blind retrospective analysis of a novel predictive marker to ICB response in NSCLC, calculated directly from histopathological slides.
Authors: Tirosh Omer , Gugel Leon , Dinstag Gal , Kinar Yaron , Elia Anna , Tabi Michael , Gottlieb Tzivia , Pikarsky Eli , Popovtzer Aron , Aharonov Ranit , Arnon Johnathan 
Published: June 2024

2665 Background: Immune checkpoint blockers (ICB), and primarily PD-1/PD-L1 inhibitors, are in the forefront of contemporary clinical oncology and have become an integral part of treatment of many malignancies, including non-small cell lung cancer (NSCLC). Nevertheless, tumor response to ICB varies widely. Predictive markers commonly used to distinguish patients likely to respond to ICB, such as PD-L1 expression and tumor mutational burden (TMB) have limited predictive value, which calls for the development of practical and more accurate tests. We present results of a blind retrospective analysis of a novel predictive marker of ICB response in NSCLC, relying solely on histopathological slides. Methods: We obtained high resolution Hematoxylin and Eosin (H&E) slides from tumor-tissue samples of 50 cases of metastatic NSCLC patients treated with first-line PD-1 inhibitors. We retrospectively applied our ENLIGHT-DeepPT (ENLIGHT-DP for short) pipeline to generate, in a blinded manner, an individual response score to PD-1 inhibition for each slide. ENLIGHT-DP is composed of two main steps: (i) predict mRNA expression directly from an H&E slide using DeepPT, our digital-pathology based algorithm; and (ii) use these values as input to ENLIGHT, our transcriptome-based precision oncology platform, which generates a score that predicts response to targeted therapies and ICB (based on a 10-gene signature in this case). We then unblinded the clinical outcome (RECIST1.1), and evaluated ENLIGHT-DP’s performance vs. standard markers. Results: ENLIGHT-DP’s score is predictive of response in this cohort, which had an overall response rate of 68% (34 of 50), with ROC AUC = 0.69 (p = 0.01, one-sided permutation test). Using a predefined threshold for binary classification of response derived from independent data, all 15 patients that were predicted to respond by ENLIGHT-DP indeed responded (100% PPV, 44% sensitivity). In comparison, predicting response according to PD-L1 > 1% achieves 68% PPV and 62% sensitivity, while PD-L1 > 50% achieves 65% PPV and 38% sensitivity, i.e, both thresholds exhibit no predictive power (PPV <= baseline response rate). Patients with high TMB (>10) had 82% PPV and 26% sensitivity, showing lower predictive benefit than ENLIGHT-DP. ENLIGHT-DP was particularly good at stratifying patients with PD-L1 < 1% (18 patients, ROC AUC = 0.8, p = 0.03). Conclusions: ENLIGHT-DP demonstrates high predictive power for response to ICB in NSCLC relying solely on accessible H&E slides, outperforming the commonly used PD-L1 and TMB markers. ENLIGHT-DP is also able to identify responders within patients with PD-L1 < 1%, for whom ICB is usually considered ineffective. Importantly, our approach does not require training on prior treatment outcomes, and can therefore be generalized to drugs for which such data is unavailable or scarce.