Project Summary Despite advances in treatment options, 5-year overall survival (OS) for non-small cell lung cancer (NSCLC) patients remains around 20% [1]. Subpopulations of tumor initiating cells (TICs) representing <1.5% of the overall tumor population exhibit the capacity for self-renewal, drug-resistance, and are believed to drive disease progression [2]. Although surface markers including CD133, CD44, CD166, and EPCAM have been proposed to isolate lung TICs, results are inconsistent. Micro-heterogeneity within the tumor microenvironment (TME) is believed to regulate balance between progenitor-like and differentiated tumor cell phenotypes, and consequently supports heterogeneous drug responses. This proposed research attempts to definitively characterize expression profiles of TICs, and study the relationship between the tumor micro-environment and TIC dynamics in the context of drug response, with the goal of identifying critical pathways that mediate transitions to a progenitor-like state. Aim 1 - Lineage tracing studies suggest that TICs exhibit clonal dominance in culture, whereby a small fraction of tumor cells tend to drive outgrowth of the overall population. Having already established a protocol using cell line models, I will transfect patient-derived NSCLC cells with RNA-expressed barcodes and analyze growing populations using serial passaging assays under normal and drug-treated conditions. Using transcriptional analysis of time-series single-cell RNA Sequencing (scRNA-Seq) data in combination with custom computational tools, I aim to identify gene expression profiles and surface markers unique to progenitor-like subclones that drive population growth under treatment selection pressure. Aim 2 - TICs are dependent on niche signalling from a heterogeneous tumor microenvironment (TME) to support the progenitor phenotype. We hypothesize that micro-heterogeneity within the TME regulates the ratio of progenitor-to-differentiated tumor cells and influences drug sensitivity. I will first develop an in-vitro spheroid culture platform combining clonally barcoded patient-derived tumor and stromal cells exposed to cytotoxic therapy, processing them with the 10X Genomics Spatial Transcriptomics platform. This data will enable assessment of essential TME crosstalk signalling and its impact on spatial cancer projenitor-like transcriptional signatures defined from Aim 1. We will confirm these insights by integrating scRNA-Seq and Spatial Transcriptomics data from naive and post-treatment patient-derived lung samples used for Aim 1 to characterize patient-specific TIC niches. Through the robust profiling of the TIC transcriptional profile and its associated microenvironment using multimodal sequencing approaches, we hope to potentially identify new targets or prognostic biomarkers to aid in the treatment of NSCLC.