Abstract 5533: Drug tolerance mechanisms in patient-derived xenograft models with <i>epidermal growth factor receptor</i> mutated tumors treated with tyrosine kinase inhibitors
Bibliographic record
Abstract
Abstract Background and Purpose: Epidermal growth factor receptor (EGFR) mutations on exon 18-21 are oncogenic drivers in 10-60% of patients with lung adenocarcinoma. First and second-generation EGFR tyrosine kinase inhibitors (TKIs) have high efficacy but resistance quickly develops, with 60-70% caused by the emergence of T790M secondary mutation. Osimertinib (osi) is effective against common EGFR mutations as well as T790M, yet most patients eventually also develop resistance. Understanding post-osi resistance mechanisms is a priority research goal. Our goal is to establish large cohort of patient-derived xenograft (PDX) and/or organoid models with EGFR mutations from patients who have progressed on osimertinib and use them to characterize the mechanisms of drug tolerance and resistance. Methods: We have established six PDX models from tumor biopsies and one organoid-derived xenograft (ODX) from a pleural effusion. To assess models response to osi, donor xenograft tumor fragments were implanted into replicate NOD SCID mice and once tumors reached an average tumor volume of 400 mm3, they were randomized into vehicle and osi treatment groups. Osi was dosed at 25mg/kg daily oral gavage for 30 days. Excised tumors were profiled by bulk RNAseq in two models PHLC4547 and 4672. Result: Models were established from tumors of three patients who have progressed on geftinib and osi treatment (PHLC4683Ex19delE746_A750del, PHLC4697L858R/L833V, PHLC4547L858R). Additionally, one progressed on gefitinib PHLC4223L858R and one on afatinib and gefitinib PHLC4192 Ex19delE746_A750del. One progressive on osi PHLC4672L858R/G719 and another patient who progressed on afatinib and osi treatment is PHLC4754L858R/T790M/C797S. PHLC4672 PDX demonstrated drug tolerance and stable tumor size to osi treatment, all other models showed resistance to osi. However, PHLC4547, 4223, 4683 and 4754 showed initial transient tolerance to osi and resistance quickly followed. PHLC4192 and PHLC4697 were completely osi-resistant. Transcriptomic profiling of osi-resistant PHLC4547 revealed no changes in gene expression profile during all three serial passages of osi-treated tumors. However, PHLC4672 with osi-tolerance revealed downregulation of Hallmark gene set enrichment in pathways of G2M checkpoint, E2F targets, mitotic spindle, glycolysis and estrogen response. The EGFR-TKI responses in patients and PDX models showed high concordance among the resistant models. Conclusion: The altered pathways in tolerance model appears to be unique to an osi-stable response compared to an osi-resistance response. PDX models from post-TKI patients may be useful to study the drug resistant mechanism. Citation Format: Arundhathi Arivajiagane, Quan Li, Nhu-An Pham, Ming Li, Katrina Hueniken, Takamasa Koga, Nikolina Radulovich, Ming-Sound Tsao. Drug tolerance mechanisms in patient-derived xenograft models with epidermal growth factor receptor mutated tumors treated with tyrosine kinase inhibitors [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 5533.
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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 | 0.001 |
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".