Abstract 813: <i>In vitro</i> and <i>in vivo</i> modelling of drug tolerance and minimal residual disease to discover effective therapeutic combination strategies for EGFR-mutated lung cancer
Bibliographic record
Abstract
Abstract Resistance to EGFR-targeted therapy is a major barrier to improving survival rates for non-small cell lung cancer (NSCLC) patients whose tumors have activating EGFR mutations. Resistance to EGFR tyrosine kinase inhibitors (TKIs) emerges from drug-tolerant persister cells (DTPs) that survive TKI therapy and manifest in patients as minimal residual disease, which inevitably drives fatal tumor recurrences. DTPs are a rare population of cancer cells in a reversible, slowly proliferating state that survives targeted therapy. Upon TKI withdrawal, they exit the DTP state to recommence rapid proliferation and aggressive tumor growth. Since DTPs provide a reservoir of surviving cells from which outright resistance driven by acquired genetic alterations arises, we hypothesize that discovering DTP survival mechanisms will inform vulnerabilities whose inhibition could enhance the efficacy of EGFR TKIs. However, such mechanisms are poorly understood in EGFR-mutant lung adenocarcinoma (LUAD). To address this knowledge gap, this study aims to generate robust in vitro and in vivo LUAD models of Osimertinib (Osi)-induced drug tolerance. Genetic and functional characterization of these models has the potential to identify mechanisms enabling drug tolerance that could be targeted to prevent TKI resistance from developing. To date, the response of 3 EGFR-mutant LUAD models to Osi treatment has been characterized both in vitro and in vivo [PC9, HCC4006, and 1 patient-derived xenograft (PDX)-derived cell line, X137CL]. Osi treatment of these cells increased apoptosis, but also arrested treated cells in G0-G1 phases of the cell cycle, consistent with the slow cycling nature of DTPs. Osi treatment of the xenografts formed by these lines induced strong tumor regressions and tumor replicates relapsed upon drug cessation, indicating these models are appropriate for studying DTPs in vivo. These models will be leveraged for molecular, functional, and pharmacological studies to identify shared and distinct mechanisms driving drug tolerance, in in vitro and in vivo conditions, including comparative single-cell transcriptomic profiling and pathway analysis. Candidate genes and pathways discovered will be functionally validated using CRISPR screens. Our comparative analyses of DTP versus untreated tumors will reveal novel interventions for preventing tumor relapse following TKI therapy in EGFR-mutant lung cancers. Citation Format: Kristyna A. Gorospe, Ming Li, Arundhathi Arivajiagane, Nhu-An Pham, Roya Navab, Kelsie L. Thu, Ming-Sound Tsao. In vitro and in vivo modelling of drug tolerance and minimal residual disease to discover effective therapeutic combination strategies for EGFR-mutated lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 813.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".