Tumor heterogeneity plays a critical role in tumor evolution. Our prior in vitro studies highlighted the extent to which microenvironmental forces, such as hypoxia, influence this evolution. Up until recently, a lack of approaches for analyzing the spatial patterning of molecular phenotypes has made it challenging to examine the influence of tumor micro-heterogeneity on clinical outcomes. We recently developed an approach to study multiplexed immunohistochemistry (IHC) data to establish NSCLC histology-specific molecular subtypes and identify regions of hypoxia in tumor tissues. We were then able to examine the impact of hypoxia on protein networks in situ. In addition, we were able to use our new computational approaches to stratify patient outcomes, noting that spatially derived features better predicted 5-year survival (AUC = 0.72) compared to non-spatially derived features (AUC = 0.49) in a held-out test set. Additionally, the total area of defined hypoxic regions was better able to separate the patient population in a Kaplan-Meier survival analysis (p=0.024) compared to standard percent positivity measures (p=0.268). Our results highlight the utility of highly multiplexed spatial analysis in understanding complex cancer phenotypes and demonstrate the clinical significance of the spatial organization of phenotypic cancer drivers.
Dr. Parag Mallick is an Associate Professor at Stanford University. Originally trained as an engineer and biochemist, his research spans computational and experimental systems biology, cancer biology and nanotechnology. Dr. Mallick received his undergraduate degree in Computer Science from Washington University in St. Louis. He then obtained his Ph.D. from UCLA in Chemistry & Biochemistry, where he worked with Dr. David Eisenberg. He completed Post-Doctoral studies at The Institute for Systems Biology, in Seattle, WA with Dr. Ruedi Aebersold. Beyond studying fundamental disease mechanisms, his group has been pioneering novel approaches for enabling personalized and predictive medicine. Most recently, his group has been developing model-based and physics-based approaches to machine learning that enable learning over domains that span a wide range of time and length scales.