Abstract 685: eTRACER illuminates the spatiotemporal landscape of immune escape in EGFR-mutant lung adenocarcinoma.

Abstract EGFR-mutant lung adenocarcinoma (LUAD), prevalent among East Asian non-smoking women, typically exhibits a strong initial response to tyrosine kinase inhibitors. However, resistance inevitably develops and these tumors often show poor responsiveness to immune checkpoint blockade. Deciphering the mechanisms of cellular evolution underlying immune evasion in EGFR-mutant LUAD could provide critical insights for developing effective immunotherapies. Here we developed eTRACER, an endogenous gene 3’UTR-based CRISPR-Cas9 lineage tracer that avoids lineage information-disruptive large deletions of traditional techniques and enables the reconstruction of high-fidelity spatiotemporal phylogeny through integration of single-cell RNA-seq, chromatin accessibility and/or spatial transcriptome. We applied eTRACER to systematically decode clonal and phenotypic fate maps of EGFR-mutant LUAD under CD8+ T cell-mediated cytotoxicity in mice. Quantitative analyses of temporally-resolved single-cell lineages across baseline, early- and late-immune escape stages revealed the dynamics of cell-state transitions from the Hypoxic and Proliferative states to the epithelial-mesenchymal transition (EMT) state leading to immune evasion. Spatially-resolved lineage tracing unveiled evident stratification of distinct tumor cell states and location-primed transitions to the EMT state. Multiomic study uncovers the cooperation between the intrinsic role of the activator protein 1 (AP-1) in cancer cells and the spatially stratified interactions between macrophages and tumor cells in promoting the transition to the EMT state. Collectively, we have developed a powerful lineage tracing system eTRACER and uncovered the spatiotemporal cell-fate dynamics underlying immune evasion in EGFR-mutant LUAD. These findings hold important implication for future design of effective immunotherapy. Citation Format: Hongbin Ji, Zheng Hu, Jinhua Yang, Liangzhen Hou, Xue Wang. eTRACER illuminates the spatiotemporal landscape of immune escape in EGFR-mutant lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 685.

Authors

Institutions

Publication Details

Journal
Cancer Research
Published
2026-04-03
DOI
https://doi.org/10.1158/1538-7445.am2026-685
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Abstract 685: eTRACER illuminates the spatiotemporal landscape of immune escape in EGFR-mutant lung adenocarcinoma.

Liangzhen Hou, Hongbin Ji, Zheng Hu, Jinhua Yang
Cancer Research
Single-cell and spatial transcriptomics
article

Abstract 685: eTRACER illuminates the spatiotemporal landscape of immune escape in EGFR-mutant lung adenocarcinoma.

Liangzhen Hou, Hongbin Ji, Zheng Hu, Jinhua Yang
article en

Abstract

Abstract EGFR-mutant lung adenocarcinoma (LUAD), prevalent among East Asian non-smoking women, typically exhibits a strong initial response to tyrosine kinase inhibitors. However, resistance inevitably develops and these tumors often show poor responsiveness to immune checkpoint blockade. Deciphering the mechanisms of cellular evolution underlying immune evasion in EGFR-mutant LUAD could provide critical insights for developing effective immunotherapies. Here we developed eTRACER, an endogenous gene 3’UTR-based CRISPR-Cas9 lineage tracer that avoids lineage information-disruptive large deletions of traditional techniques and enables the reconstruction of high-fidelity spatiotemporal phylogeny through integration of single-cell RNA-seq, chromatin accessibility and/or spatial transcriptome. We applied eTRACER to systematically decode clonal and phenotypic fate maps of EGFR-mutant LUAD under CD8+ T cell-mediated cytotoxicity in mice. Quantitative analyses of temporally-resolved single-cell lineages across baseline, early- and late-immune escape stages revealed the dynamics of cell-state transitions from the Hypoxic and Proliferative states to the epithelial-mesenchymal transition (EMT) state leading to immune evasion. Spatially-resolved lineage tracing unveiled evident stratification of distinct tumor cell states and location-primed transitions to the EMT state. Multiomic study uncovers the cooperation between the intrinsic role of the activator protein 1 (AP-1) in cancer cells and the spatially stratified interactions between macrophages and tumor cells in promoting the transition to the EMT state. Collectively, we have developed a powerful lineage tracing system eTRACER and uncovered the spatiotemporal cell-fate dynamics underlying immune evasion in EGFR-mutant LUAD. These findings hold important implication for future design of effective immunotherapy. Citation Format: Hongbin Ji, Zheng Hu, Jinhua Yang, Liangzhen Hou, Xue Wang. eTRACER illuminates the spatiotemporal landscape of immune escape in EGFR-mutant lung adenocarcinoma [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2026; Part 1 (Regular Abstracts); 2026 Apr 17-22; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2026;86(7 Suppl):Abstract nr 685.

Cancer ResearchVol. 86(7_Supplement)
Westlake University (CN), Center for Excellence in Molecular Cell Science (CN), Shenzhen Institutes of Advanced Technology (CN), Shanghai Cell Therapy Research Institute (CN)
Good health and well-being
Openalex Percentile: Top 22%
Single-cell and spatial transcriptomics
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.