Poster Presentation 28th Lorne Cancer Conference 2016

Modelling personalized medicine in high-risk neuroblastoma (#291)

Mitchell Lockwood 1 , Alvin Kamili 1 , Carol Wadham 1 , Emily Mould 1 , Anna Mariana 1 2 , Tim W Failes 1 2 , Greg M Arndt 1 2 , C. Patrick Reynolds 3 , Murray D Norris 1 , Michelle Haber 1 , Toby N Trahair 1 4 , Jamie I Fletcher 1
  1. Children’s Cancer Institute, Lowy Cancer Research Centre, Randwick, NSW, Australia
  2. ACRF Drug Discovery Centre for Childhood Cancer, Lowy Cancer Research Centre, Randwick, NSW, Australia
  3. Texas Tech University Health Sciences Centre, Lubbock, Texas, USA
  4. Kids Cancer Centre, Sydney Children's Hospital, Randwick, NSW, Australia

Neuroblastoma is the most common extra-cranial paediatric solid tumour and is projected to account for 6% of Australian childhood cancer cases in 2015. Under current treatments, those children with high-risk neuroblastoma face a survival rate of less than 50%. A personalised medicine approach may improve this poor survival rate. The aim of this project is to use patient-derived models of high-risk neuroblastoma to assess whether personalised medicine through molecular profiling of tumours and unbiased examination of ex vivo drug sensitivity is a valid strategy for this disease. Methods: Patient-derived xenograft(PDX) models of high-risk neuroblastoma and their matching patient-derived cell lines (PDCLs) were obtained from Children’s Oncology Group (USA). Molecular profiles, consisting of gene expression and gene sequencing, were generated for each model and used to select targeted therapies for subsequent testing. An unbiased drug screening was carried out in parallel with the PDCLs to find sensitivity to anti-cancer therapies. Results: Of the 5 targeted therapies selected by gene expression profiling none could be successfully validated. In contrast, each of the three of the anti-cancer therapies selected from the unbiased drug screen were validated. Gene sequencing matched the ALK F1174L mutation in one model to the targeted therapy crizotinib, which was subsequently validated in the PDCL and PDX models. Conclusion: Unbiased drug screening and gene sequencing appear to be promising areas of therapy selection for personalised medicine in these models, whereas gene expression will require optimisation if it is to be incorporated in future personalised medicine investigations.