Leveraging a Precision Medicine Platform to Predict Novel Therapies for Malignant Peripheral Nerve Sheath Tumors
Abstract
One of the most common cancers affecting adults with neurofibromatosis type l (NFl) is the malignant peripheral nerve sheath tumor (MPNST), a highly aggressive tumor. Sadly, for those individuals with NFl who develop these cancers, there are limited treatment options, and the vast majority of people with these malignancies will die within 5 years of diagnosis. Over the past decade, research has revealed that NFl-MPNST represents a group of diverse cancer types, characterized by different genetic changes. In this respect, there are likely different subtypes of MPNST that each harbor distinct sets of molecular changes and thus have different responses to rationally-chosen treatments. Unfortunately, current small-animal models of NFl-MPNST used to discover and evaluate new therapies are largely engineered with one set of genetic changes, limiting their ability to fully represent the human condition. For this reason, we believe that the failure to discover effective treatments is partly due to the inability of these preclinical models to accurately model the genetic landscape of the human tumors as well as the ability of the tumors to adapt to single drugs. To address this problem, we have started to develop and characterize a set of patient-derived MPNST mouse models (called PDX) obtained directly from actual human tumors. In this proposal, we plan to continue to generate new models for use in the NFl research community and extend these models to 3D cultures that enable rapid testing of preclinical drugs. This effort is essential for two reasons. First, PDX models can only be used for a limited period of time before they develop other genetic changes that cause them to differ from the human tumor from which they came and thus no longer mirror that tumor. As such, it is essential to continue to generate new models in order to maintain the resource for the NFl community. Second, we want to ensure that we can identify all of the relevant subtypes of this rare tumor. By growing the models in 3D cultures, we will be able to rapidly screen multiple drugs and drug combinations and determine which subtypes respond better to each given therapy. Additionally, we will analyze the changes brought about by the drugs by measuring changes in RNA and proteins. This will help us learn how the tumors adapt to drugs and identify other drugs that can prevent this adaptation if administered in combination. This work will give us the body of data needed to rationally design clinical trials in a personalized manner using information from patients tumors to choose which therapy the patient should receive. Currently, there are no effective therapies for metastatic NFl-MPNST and survival is dismal. Our hope is that, by the end of this funding period, we will be applying for funding for a clinical trial and thus, in the next few years, we will have the ability to explore this treatment paradigm in patients with NFl-MPNST-a patient population that desperately needs better therapies.
Document Details
- Document Type
- DoD Grant Award
- Publication Date
- Jan 04, 2024
- Source ID
- HT94252310253
Entities
People
- Angela Hirbe
Organizations
- United States Army
- Washington University in St. Louis