Computational Immunohistochemistry- We perform deep learning of spatial immune
signatures to aid in stromal immune cell identification. With the ability to provide ground truth to
the spatial configuration of the tumor immune microenvironment, the ability for
a deep learning algorithm to identify specific immune cell populations on
routine H&E staining may help unravel novel immunologic mechanisms and
better elucidate the role of immunologic biomarkers on specific immune cell
populations. We focus on identifying
these immune cell populations in human and murine tumors to allow for
identification through computer vision techniques without the need for physical
staining.
Predictive spatial immune signatures - Current immune
biomarker development focuses on gross quantitation of biomarker levels on a
slide, or single-cell techniques with an overabundance of data. Lost in these analyses is the spatial context
of clinically relevant biomarker expression.
We study the role of geospatial immune cell localization to better
understand how immunologic structure may inform function.
ImmunoScape – We collect PBMC, sera, plasma, microbiome (stool,
skin) from consented cancer patients on immunotherapy with clinical annotation
to uncover novel immunobiology related to therapeutic response and toxicity