Molecular Systems BiologyCancer BiologyPeer-reviewed journal article
Engineered paediatric tumours retain patient tumour genotype and phenotype for precision medicine.
MoonSun JungiD, Valentina Poltavets, Joanna N. SkhinasiD, Gábor TaxiD, Alvin KamiliiD, Jinhan XieiD, Sarah GhamrawiiD, Philipp GraberiD, Jie MaoiD, Marie WongiD, Lujing CuiiD, Kathleen KimptoniD, Pooja VenkatiD, Chelsea MayohiD, Angela LiniD, Emmy D.G. FleureniD, Ashleigh M. FordhamiD, Zara A. BargeriD, John P. GradyiD, David Morgan ThomasiD, Eric Yiwei DuiD, Nicole S. GrafiD, Mark J. CowleyiD, Andrew J. GiffordiD, Jamie I. FletcheriD, , Maria KavallarisiD
Abstract
Precision medicine for paediatric and adult cancers that includes drug sensitivity profiling, can identify effective therapies for individual patients. However, obtaining adequate biopsy samples for high-throughput (HTP) screening remains challenging, with tumours needing to be expanded in culture or patient-derived xenografts - this is time-consuming and often unsuccessful. Herein, we have developed paediatric patient-derived tumour models using an engineered extracellular matrix (ECM) tissue mimic hydrogel system and HTP 3D bioprinting. Gene expression analysis from neuroblastoma and sarcoma patients identified key components of the ECM in these tumour types. Engineered hydrogels with ECM-mimic peptides were used to create patient-specific tumour organoids, modelling tumour growth conditions. Expanded tumour organoids recapitulated the genetic and phenotypic characteristics of the original tumours and retained tumourgenicity. Screening of these models identified individualised drug sensitivities. Our approach offers a timely and clinically relevant technology platform for precision medicine in paediatric cancers, potentially transforming preclinical testing across cancer types.
- organism: human
- assay: bulk RNA-seq
- research focus: cell type annotation
- research focus: drug response / drug discovery
- approach: experimental assay
- Citations
- 10
- FWCI
- 2.29
- Field percentile
- top 11%
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