Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response
Jeremy Z R Han1, Jordan F Hastings1, Monica Phimmachanh1
1Garvan Institute of Medical Research, Sydney, NSW 2010, Australia.
Abstract:
High-risk neuroblastoma is an aggressive childhood cancer that is characterized by high rates of chemoresistance and frequent metastatic relapse. A number of studies have characterized the genetic and epigenetic landscape of neuroblastoma, but due to a generally low mutational burden and paucity of actionable mutations, there are few options for applying a comprehensive personalized medicine approach through the use of targeted therapies. Therefore, the use of multi-agent chemotherapy remains the current standard of care for neuroblastoma, which also conceptually limits the opportunities for developing an effective and widely applicable personalized medicine approach for this disease. However, in this review we outline potential approaches for tailoring the use of chemotherapy agents to the specific molecular characteristics of individual tumours by performing patient-specific simulations of drug-induced apoptotic signalling. By incorporating multiple layers of information about tumour-specific aberrations, including expression as well as mutation data, these models have the potential to rationalize the selection of chemotherapeutics contained within multi-agent treatment regimens and ensure the optimum response is achieved for each individual patient.
Insights
Personalized medicine for high-risk neuroblastoma may be achieved by simulating chemotherapy responses. This approach uses patient-specific tumor data to tailor drug selection for better outcomes in this aggressive childhood cancer.
Area of Science:
- Pediatric Oncology
- Cancer Genomics
- Pharmacodynamics
Background:
- High-risk neuroblastoma is an aggressive childhood cancer with poor outcomes.
- Chemoresistance and relapse are common challenges in neuroblastoma treatment.
- Limited actionable mutations restrict targeted therapy options, making multi-agent chemotherapy the standard.
Purpose of the Study:
- To explore personalized medicine strategies for high-risk neuroblastoma.
- To outline methods for tailoring chemotherapy based on individual tumor molecular characteristics.
- To improve treatment selection and patient response rates.
Main Methods:
- Reviewing existing genetic and epigenetic data of neuroblastoma.
- Developing patient-specific computational models.
- Simulating drug-induced apoptotic signaling pathways.
- Integrating multi-omics data (expression and mutation data).
Main Results:
- Computational models can predict responses to chemotherapy agents.
- Personalized simulations offer a rational basis for selecting chemotherapeutics.
- This approach has the potential to optimize multi-agent treatment regimens.
Conclusions:
- Tailoring chemotherapy for neuroblastoma using patient-specific simulations is a promising personalized medicine strategy.
- Integrating molecular data into predictive models can enhance treatment efficacy.
- This approach may overcome limitations of current standard-of-care chemotherapy.
