Use of patient derived orthotopic xenograft models for real-time therapy guidance in a pediatric sporadic malignant peripheral nerve sheath tumor
Ther Adv Med Oncol. 2020 Jul 3;12:1758835920929579. doi: 10.1177/1758835920929579. eCollection 2020.
ABSTRACT
BACKGROUND: The aim of this study was to test the feasibility and utility of developing patient-derived orthotopic xenograft (PDOX) models for patients with malignant peripheral nerve sheath tumors (MPNSTs) to aid therapeutic interventions in real time.
PATIENT & METHODS: A sporadic relapsed MPNST developed in a 14-year-old boy was engrafted in mice, generating a PDOX model for use in co-clinical trials after informed consent. SNP-array and exome sequencing was performed on the relapsed tumor. Genomics, drug availability, and published literature guided PDOX treatments.
RESULTS: A MPNST PDOX model was generated and expanded. Analysis of the patient’s relapsed tumor revealed mutations in the MAPK1, EED, and CDK2NA/B genes. First, the PDOX model was treated with the same therapeutic regimen as received by the patient (everolimus and trametinib); after observing partial response, tumors were left to regrow. Regrown tumors were treated based on mutations (palbociclib and JQ1), drug availability, and published literature (nab-paclitaxel; bevacizumab; sorafenib plus doxorubicin; and gemcitabine plus docetaxel). The patient had a lung metastatic relapse and was treated according to PDOX results, first with nab-paclitaxel, second with sorafenib plus doxorubicin after progression, although a complete response was not achieved and multiple metastasectomies were performed. The patient is currently disease free 46 months after first relapse.
CONCLUSION: Our results indicate the feasibility of generating MPNST-PDOX and genomic characterization to guide treatment in real time. Although the treatment responses observed in our model did not fully recapitulate the patient’s response, this pilot study identify key aspects to improve our co-clinical testing approach in real time.
PMID:32670419 | PMC:PMC7339074 | DOI:10.1177/1758835920929579
