Proteomics and personalized patient-derived xenograft models identify treatment opportunities for a progressive malignancy within a clinically actionable timeframe and change care

Abstract

AbstractIncreased 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.