Personalized Medicine for Neuroblastoma: Moving from Static Genotypes to Dynamic Simulations of Drug Response.
Jeremy Z R HanJordan F HastingsMonica PhimmachanhDirk FeyWalter KolchDavid R CroucherPublished in: Journal of personalized medicine (2021)
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.
Keyphrases
- drug induced
- liver injury
- childhood cancer
- healthcare
- squamous cell carcinoma
- small cell lung cancer
- poor prognosis
- molecular dynamics
- cell death
- gene expression
- palliative care
- dna methylation
- electronic health record
- adverse drug
- locally advanced
- case report
- young adults
- risk factors
- binding protein
- emergency department
- machine learning
- human health
- quality improvement
- free survival
- pain management
- combination therapy
- climate change
- single cell
- anti inflammatory
- data analysis