Immunotherapy Induced T cell dynamics in Non-Small Cell Lung Cancer (NSCLC)

Immune checkpoint inhibitors (ICIs) have improved outcomes for a subset of patients with non–small cell lung cancer (NSCLC), yet current biomarkers such as PD‑L1 expression and tumor mutational burden (TMB) offer only limited predictive value for determining who will benefit. Existing approaches fail to capture the dynamic, T cell–mediated biology that underlies effective antitumor immunity. This project proposes a multi‑omic, T cell–centric biomarker framework integrating deep T cell receptor (TCR) repertoire profiling with highly multiplexed, spatially resolved interrogation of the tumor microenvironment (TME) using the HOST (High‑plex Optical Spatial Tissue) platform. We hypothesize that the expansion and persistence of tumor‑reactive T cell clonotypes, coupled with favorable spatial and phenotypic TME features, will predict clinical response to ICI‑containing regimens more accurately than current biomarkers. In a prospective cohort of approximately 100 NSCLC patients initiating ICI‑based therapy, we will longitudinally profile peripheral blood TCR dynamics, assess baseline and on‑treatment TME architecture via multiplex immunofluorescence, and derive mechanistic and predictive HOST‑based features. Specific aims include: (1) identifying baseline TCR and spatial TME features associated with response; (2) characterizing dynamic immune activation patterns during therapy; and (3) integrating these data to construct and analytically validate a composite immune activation signature. By combining longitudinal clonal tracking with high‑resolution spatial mapping of immune contexture, this study seeks to establish a scalable biomarker strategy capable of improving patient stratification, informing therapeutic decision‑making, and guiding future immunotherapy combination trials.