Actionable biological programs to enhance EGFR-targeted therapy response unveiled by single-cell lineage tracing in clinically relevant lung cancer models

Developing high-resolution approaches to capture both tumor architectural clonality and transcriptional state(s) in individual cells within heterogeneous tumor cell populations could shed light on the evolution of pre-existing and newly emergent tumor subclones and their phenotypes, elucidating their trajectories in response to selective pressures such as drug treatment. Reports to date have focused primarily on analyzing the drug-induced evolution of lung cancer cells in in vitro preclinical models, with limited complexity. These reports lack characterization of actionable biological programs in more tumor-proximal models to induce durable responses and aim to identify features of acquired resistance. Here, we challenged this paradigm and employed a lineage tracing single-cell RNAseq method to track the evolution of primary non-small cell lung cancer (NSCLC) patient-derived organoids (PDOs) and tumor xenografts in response to the standard-of- care EGFR inhibitor osimertinib, with a focus on understanding features of precursors to the persistent and resistant states. By identifying pre-treatment features of tumor cells that persist after treatment, we aimed to identify actionable biomarkers to reduce the occurrence of acquired resistance. Our single-cell lineage tracing-RNAseq system revealed the presence of a discrete set of lineages with distinct transcriptional phenotypes over the course of the treatment. We identified two lineage populations that became predominant during drug treatment and resisted therapy in the PDOs and tumor xenografts. These lineages were present before treatment and harbored Hedgehog pathway and FOXD1 transcriptional programs, respectively. These specific transcriptomic lineages were otherwise undetectable by lower-resolution profiling. Functional studies confirmed the protective role that the baseline expression of the Hedgehog pathway and FOXD1 programs in the lineage tumor cell sub-populations exerts upon targeted therapy. The potential clinical relevance of these regulatory programs was validated by cross-analysis of single-cell transcriptomic data obtained from human NSCLC specimens. Overall, our approach identified pre-existing seeds of resistance before therapy and convergent, adaptive mechanisms supporting tumor residual disease and resistant states. This study highlights the utility of high-resolution tracing of tumor clonal heterogeneity with matched single-cell profiling to reveal occult cell states and molecular mechanisms of therapy resistance and develop counteracting strategies.