Defining and targeting the lung cancer progenitor cell niche using a high-resolution, multi-omics approach

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.

Grant Number: 5F30CA265288-04
NIH Institute/Center: NIH
Principal Investigator: Daniel Charytonowicz