Clinical, pathological and genetic predictors of patient-derived xenograft (PDX) engraftment in <i>EGFR</i>-mutated lung adenocarcinoma (LUAD).
Bibliographic record
Abstract
3110 Background: PDX are useful preclinical models to study drug response and resistance. Different specimen types have been used to generate PDX models including histological (surgery and CT-guided biopsy) and cytological preparations (EBUS and pleural effusions). We hypothesize that engraftment is not stochastic and is affected by many factors including sample type and tumor pathological and molecular properties. To improve sample selection and cost-effectiveness of PDX experiments, we investigated clinical, histological and genetic correlates of engraftment in EGFR-mutated LUAD. Methods: We assessed PDX engraftment from 96 surgical resections, 13 CT-guided biopsies, 21 EBUS and 14 pleural effusions of EGFR-mutated LUAD. Sixty-five samples, including 6 engrafted (XG) and 54 non-engrafted (noXG) were evaluated by exome sequencing. Results: Engraftment was successful in 9/96 (9%) surgical resections, 6/13 (46%) CT-guided biopsies, and 0/35 cytological samples. Biopsies taken at time of treatment failure (compared to treatment naive biopsies) correlated with greater engraftment (p=0.007, AUC = 0.68). Multivariable regression analysis of clinical variables at the time of sampling identified advanced (vs early) stage (p = 0.003) and histological (vs cytological) preparations (p < 0.001) as the strongest predictors of engraftment (AUC = 0.79). Among tumor histologic features, solid (vs lepidic, acinar and papillary) pattern was associated with greater engraftment (p < 0.001). Presence of EGFR-T790M (p = 0.004) and TP53 (p = 0.009) mutations were associated with greater engraftment; all XG samples carried TP53 mutations. EGFR-Ex19del (p = 0.076) showed a trend towards engraftment whereas EGFR-L858R (p = 0.086) trended towards non-engraftment. Conclusions: Advanced stage, post-therapy tumors, T790M+ and TP53+ EGFR-mutated LUAD samples obtained for histological processing are more likely to engraft as PDXs. Despite low engraftment rates, these models are useful to study novel therapeutic strategy and elucidation of resistance mechanisms.
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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 | 0.001 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".