Poster
Machine learning based identification of predictive features of the tumor microenvironment and vasculature in NSCLC patients using the IMpower150 study
ASCO 2020
Study 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 non-squamous NSCLC.
To better understand pathologic signatures of benefit from IO and VEGF inhibition, we apply a machine-learning based approach to quantify cell composition and tissue architecture of the tumor micro-environment (TME) and vasculature. After model training and evaluation we apply model predictions to identify novel TME features predictive of clinical outcome in IMpower150.
To better understand pathologic signatures of benefit from IO and VEGF inhibition, we apply a machine-learning based approach to quantify cell composition and tissue architecture of the tumor micro-environment (TME) and vasculature. After model training and evaluation we apply model predictions to identify novel TME features predictive of clinical outcome in IMpower150.
Authors
Taylor-Weiner et al.