037 A systems immunology approach to classify melanoma tumor infiltrating lymphocytes (TILs) informs and models overall survival

Abstract

Faithful modeling of human tumor infiltrating leukocytes (TILs) is necessary to advance therapeutic strategies and inform patient outcomes. However, TILs have traditionally been assigned to a state using a small number of markers, often redundant to several cell states, such as PD-1. We utilize robust classification methods to interrogate TILs status. First, we generated viral T cell activation, memory, resident memory, and exhaustion signatures and scored bulk and single cell TILs spanning a variety of tumor types including melanoma. We also queried single cell TILs clusters associated with response/nonresponse along multiple kinetic T cell differentiation trajectories in response to several infections, tumor exhaustion models, and human vaccines. In addition, we compared TILs programs to T cell programs from > 25 pre-clinical mouse models to see which model transcriptionally resembled TILs the most. Finally, we score metastatic melanoma tumors from patients both naïve to and receiving immune checkpoint blockade to test if high, medium, or low expression of T cell state signatures can predict survival outcomes. We believe these methods are less susceptible to bias and more accurately characterize the differentiation of TILs. Additionally, usage of these methods to characterize TILs associated with survival/improved response to immune checkpoint blockade may suggest novel combination therapies that are desirable adjuncts to immune checkpoint blockade.