Targeting chromosome 12q amplification in relapsed glioblastoma: the use of computational biological modeling to identify effective therapy-a case report

by Selleckchem | Apr 24 2023

Background: Relapsed glioblastoma (GBM) is often an imminently fatal condition with limited therapeutic options. Computation biological modeling, i.e., biosimulation, of comprehensive genomic information affords the opportunity to create a disease avatar that can be interrogated in silico with various drug combinations to identify the most effective therapies.

Case description: We report the outcome of a GBM patient with chromosome 12q amplification who achieved substantial disease remission from a novel therapy using this approach. Following next generation sequencing (NGS) was performed on the tumor specimen. Mutation and copy number changes were input into a computational biologic model to create an avatar of disease behavior and the malignant phenotype. In silico responses to various drug combinations were biosimulated in the disease network. Efficacy scores representing the computational effect of treatment for each strategy were generated and compared to each other to ascertain the differential benefit in drug response from various regimens. Biosimulation identified CDK4/6 inhibitors, nelfinavir and leflunomide to be effective agents singly and in combination. Upon receiving this treatment, the patient achieved a prompt and clinically meaningful remission lasting 6 months.

Conclusions: Biosimulation has utility to identify active treatment combinations, stratify treatment options and identify investigational agents relevant to patients' comprehensive genomic abnormalities. Additionally, the combination of abemaciclib and nelfinavir appear promising for GBM and potentially other cancers harboring chromosome 12q amplification.

Comments：

The case report describes a successful outcome in a patient with relapsed GBM who received a novel therapy identified through biosimulation. The patient's tumor was analyzed using next generation sequencing, and the mutation and copy number changes were used to create a disease avatar in silico. The disease avatar was then used to simulate the response of various drug combinations, and efficacy scores were generated for each strategy. CDK4/6 inhibitors, nelfinavir and leflunomide were identified as effective agents alone or in combination, and the patient achieved a substantial remission lasting 6 months upon receiving this treatment.

The study highlights the potential of biosimulation to identify active treatment combinations and stratify treatment options based on patients' comprehensive genomic abnormalities. The study also identifies a promising combination of abemaciclib and nelfinavir for GBM and potentially other cancers harboring chromosome 12q amplification.

Overall, the study suggests that biosimulation can be a valuable tool in the development of personalized cancer therapies and underscores the importance of incorporating genomic information into cancer treatment decision-making.