Machine Learning-Based Identification Of Predictive Features Of The Tumor Micro-Environment And Vasculature In Nsclc Patients Using The Impower150 Study.

3130 Background: IMpower150 is a phase 3 study measuring the effect of carboplatin and paclitaxel (CP) combined with atezolizumab (A) and/or bevacizumab (B) in patients with advanced nonsquamous NSCLC, testing the hypothesis that anti-PD-L1 therapy may be enhanced by the blockade of VEGF. Here, we apply a machine-learning based approach to quantify the tumor micro-environment (TME) and vasculature and identify associations with clinical outcome in IMpower150. Methods: Digitized H&E images were registered onto the PathAI research platform (n=1027). Over 200K annotations from 90 pathologists were used to train convolutional neural networks (CNNs) that classify human-interpretable features (HIFs) of cells and tissue structures from images. Blood vessel compression (BVC) indices were calculated using the long versus short axes for each predicted blood vessel. HIFs were clustered to reduce redundancy, and selected features were associated with progression free survival (PFS) within each arm (ABCP, ACP, and BCP) using Cox proportional hazard models. Results: We used the trained CNNs to generate 4,534 features summarizing each patient’s histopathology and TME. After association with survival and correction for multiple comparisons we identified clusters that were significantly associated with survival in at least one arm. Among patients receiving treatments that target PD-L1 (ABCP and ACP), high lymphocyte to fibroblast ratio (LFR) was associated with improved PFS (HR=0.64 (0.51, 0.81), p < 0.001) and showed no significant association with PFS among patients treated with BCP alone (HR=1.13 (0.85, 1.51), p=0.4). Among BCP treated patients, a higher average BVC within the tumor tissue was associated with improved PFS (HR=0.67 (0.50,0.90), p=0.01) and worse PFS among patients treated with ACP (HR=1.50 (1.10,2.06), p=0.009). Conclusions: We developed a deep learning-based assay for quantifying pathology features of the TME and vasculature from H&E images. Application of this system to Impower150 identified an association between high LFR and improved PFS among patients receiving PD-L1 targeting therapy, and between low BVC and improved PFS among patients receiving BCP. These findings support the importance of the TME and vasculature in determining response to PD-L1 and VEGF-targeting therapies.