Predictive modeling of resistance to SMO inhibition in a patient-derived orthotopic xenograft model of SHH medulloblastoma.

ABSTRACT:

Background

Inhibition of the sonic hedgehog (SHH) pathway with Smoothened (SMO) inhibitors is a promising treatment strategy in SHH-activated medulloblastoma, especially in adult patients. However, the problem is that tumors frequently acquire resistance to the treatment. To understand the underlying resistance mechanisms and to find ways to overcome the resistance, preclinical models that became resistant to SMO inhibition are needed.

Methods

To induce SMO inhibitor resistant tumors, we have treated a patient-derived xenograft (PDX) model of SHH medulloblastoma, sensitive to SMO inhibition, with 20 mg/kg Sonidegib using an intermitted treatment schedule. Vehicle-treated and resistant models were subjected to whole-genome and RNA sequencing for molecular characterization and target engagement. In vitro drug screens (76 drugs) were performed using Sonidegib-sensitive and -resistant lines to find other drugs to target the resistant lines. One of the top hits was then validated in vivo.

Results

Nine independent Sonidegib-resistant PDX lines were generated. Molecular characterization of the resistant models showed that eight models developed missense mutations in SMO and one gained an inactivating point mutation in MEGF8, which acts downstream of SMO as a repressor in the SHH pathway. The in vitro drug screen with Sonidegib-sensitive and -resistant lines identified good efficacy for Selinexor in the resistant line. Indeed, in vivo treatment with Selinexor revealed that it is more effective in resistant than in sensitive models.

Conclusions

We report the first human SMO inhibitor resistant medulloblastoma PDX models, which can be used for further preclinical experiments to develop the best strategies to overcome the resistance to SMO inhibitors in patients.

SUBMITTER: Krausert S 

PROVIDER: S-EPMC9034118 | biostudies-literature | 2022 Jan-Dec

REPOSITORIES: biostudies-literature