Establishment and characterization of preclinical models of human gynecologic tract carcinosarcomas demonstrates targetable FGFR1 alterations
Bibliographic record
Abstract
OBJECTIVE: Gynecologic carcinosarcoma is an uncommon but aggressive malignancy that frequently requires systemic therapy but therapeutic options are limited. Development of preclinical models is therefore important for therapeutic advancement. METHODS: Carcinosarcoma tumor (6 uterine and 1 tubo-ovarian) from 7 surgical samples were implanted into immunocompromised mice for patient-derived xenograft (PDX) and/or cell line development. The histologic, immunophenotypic and genetic features were characterized. Based on the observed molecular profiles and targetable molecular alterations, in vivo studies were conducted to evaluate the efficacy of targeted therapy on tumor growth. RESULTS: We established 1 cell line and 6 PDX models which recapitulated the dominant phenotype of the respective parental tumors with preserved mesenchymal differentiation lineage in the sarcomatous component. Genomically, the PDX/cell line models preserved similar complex pattern of copy number alterations and similar mutation landscape when compared to the respective parental tumors. All 7 parental carcinosarcoma tumors and PDX/cell line models harbored pathogenic TP53 mutations. Moreover, we identified recurrent copy number gain/amplification involving several receptor tyrosine kinases (RTK), including amplification and protein over-expression of FGFR1. In vivo drug evaluation using a small molecule inhibitor (AZD4547) of FGFRs showed significant growth inhibition in the carcinosarcoma PDX tumor with the highest FGFR1 amplification and protein expression whereas AZD4547 showed no significant growth effects on carcinosarcoma lacking high level FGFR1 amplification, indicating oncogenic dependency on the amplified RTK pathway. CONCLUSIONS: These findings demonstrate the utility of patient-derived tumor models in the identification and the functional validation of potentially targetable molecular alterations in preclinical setting.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".