Proteomics and personalized patient-derived xenograft models identify treatment opportunities for a progressive malignancy within a clinically actionable timeframe and change care
Bibliographic record
Abstract
Abstract Increased access to high-throughput DNA sequencing platforms has transformed the diagnostic landscape of pediatric malignancies by identifying and integrating actionable genomic or transcriptional features that refine diagnosis, classification, and treatment. Yet less than 10% of treated patients show a positive response and translating precision oncology data into feasible and effective therapies for hard-to-cure childhood, adolescent, and young adult malignancies remains a significant challenge. Combining the identification of therapeutic targets at the protein and pathway levels with demonstration of treatment response in personalized models holds great promise. Here we present the case for combining proteomics with patient-derived xenograft (PDX) models to identify personalized treatment options that were not apparent at genomic and transcriptomic levels. Proteome analysis with immunohistochemistry (IHC) validation of formalin-fixed paraffin-embedded sections from an adolescent with primary and metastatic spindle epithelial tumor with thymus-like elements (SETTLE) was completed within two weeks of biopsy. The results identified an elevated protein level of SHMT2 as a possible target for therapy with the commercially available anti-depressant sertraline. Within 2 months and ahead of a molecular tumor board, we confirmed a positive drug response in a personalized chick chorioallantoic membrane (CAM) model of the SETTLE tumor (CAM-PDX). Following the failure of cytotoxic chemotherapy and second-line therapy, a treatment of sertraline was initiated for the patient. After 3 months of sertraline treatment the patient showed decreased tumor growth rates, albeit with clinically progressive disease. Significance: Overall, we demonstrate that proteomics and fast-track personalized xenograft models can provide supportive pre-clinical data in a clinically meaningful timeframe to support medical decision-making and impact the clinical practice. By this we show that proteome-guided and functional precision oncology are feasible and valuable complements to the current genome-driven precision oncology practices.
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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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".