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Abstract Translation of precision oncology data into feasible precision therapies for hard-to-cure childhood, adolescent and young adult malignancies remains a significant challenge. Identifying therapeutic targets at the protein and pathway level and demonstrating treatment response in personalized models hold great promise, particularly for combination therapies, but may be considered complex and time consuming. Here, we present the case of an adolescent with metastatic, progressive spindle epithelial tumor with thymus-like differentiation (SETTLE) and evaluate how proteomics combined with rapid patient-derived models can identify treatment options not apparent at the genome or transcript level. Mass spectrometric proteome analysis of macro-dissected tumor and adjacent normal from formalin fixed paraffin embedded sections was completed within two weeks of biopsy and identified key proteins involved in one-carbon metabolism, including SHMT2 and DHFR as possible targets for single or combination therapy. Elevated SHMT2 levels were validated by immunohistochemistry and compared to levels across AYA tumors. Based on the suitability for an innovative therapy trial, we prioritized single-agent sertraline, a commercially available anti-depressant medication that inhibits SHMT2, and confirmed a positive drug response in both chicken chorioallantoic membrane (CAM) and larval zebrafish xenografts generated from the patient. Retrospective expansion in a murine xenograft enabled metabolic tracing on isolated SETTLE- patient-derived xenograft cells using 13C6-glucose confirming SHMT2 activity and response to in vitro treatment. Following failure of cytotoxic chemotherapy and second-line sorafenib treatment, a monotherapy trial of sertraline was initiated by the patient but stopped after 8 weeks after evidence of progressive disease. Possible combination therapies were evaluated further in the patient-derived models. Combining sertraline with the common antibiotic trimethoprim, resulted in enhanced growth inhibition of SETTLE cells in the larval zebrafish xenografts. Significance: Overall, we demonstrate that proteomics and personalized xenograft models may provide supportive pre-clinical data in a clinically meaningful timeframe to support medical decision-making and impact clinical practice. Citation Format: Georgina D. Barnabas, Tariq A. Bhat, Verena Goebeler, Pascal Leclair, Nadine Azzam, Nicole Melong, Jason N. Berman, Jennifer A. Chan, Donna L. Senger, Seth Parker, Christopher A. Maxwell, Gregor S. Reid, Jonathan Bush, Caron Strahlendorf, Rebecca Deyell, C James Lim, Philipp F. Lange. Prioritizing treatment targets for an adolescent with metastatic processive malignancy using proteomics and personalized xenograft models within an actionable timeframe abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 937.
Barnabas et al. (Fri,) studied this question.