The MURAL collection of prostate cancer patient-derived xenografts enables discovery through preclinical models of uro-oncology.
Gail P RisbridgerAshlee K ClarkLaura H PorterRoxanne ToivanenAndrew BakshiNatalie L ListerDavid PookCarmel J PezaroShahneen SandhuShivakumar KeerthikumarRosalia Quezada UrbanMelissa PapargirisJenna KraskaHeather B MadsenHong WangMichelle G RichardsBirunthi NiranjanSamantha O'DeaLinda TengWilliam WheelahanZhuoer LiNicholas ChooJohn F OuyangHeather ThorneLisa DevereuxRodney John HicksShomik SenguptaLaurence HarewoodMahesh IddawalaArun A AzadJeremy GoadJeremy GrummetJohn KourambasEdmond M KwanDaniel MoonDeclan G MurphyJohn PedersenDavid CloustonSam NordenAndrew RyanLuc FuricDavid L GoodeMark FrydenbergMitchell G LawrenceRenea A TaylorPublished in: Nature communications (2021)
Preclinical testing is a crucial step in evaluating cancer therapeutics. We aimed to establish a significant resource of patient-derived xenografts (PDXs) of prostate cancer for rapid and systematic evaluation of candidate therapies. The PDX collection comprises 59 tumors collected from 30 patients between 2012-2020, coinciding with availability of abiraterone and enzalutamide. The PDXs represent the clinico-pathological and genomic spectrum of prostate cancer, from treatment-naïve primary tumors to castration-resistant metastases. Inter- and intra-tumor heterogeneity in adenocarcinoma and neuroendocrine phenotypes is evident from bulk and single-cell RNA sequencing data. Organoids can be cultured from PDXs, providing further capabilities for preclinical studies. Using a 1 x 1 x 1 design, we rapidly identify tumors with exceptional responses to combination treatments. To govern the distribution of PDXs, we formed the Melbourne Urological Research Alliance (MURAL). This PDX collection is a substantial resource, expanding the capacity to test and prioritize effective treatments for prospective clinical trials in prostate cancer.
Keyphrases
- prostate cancer
- single cell
- radical prostatectomy
- clinical trial
- rna seq
- small molecule
- high throughput
- end stage renal disease
- ejection fraction
- chronic kidney disease
- cell therapy
- squamous cell carcinoma
- newly diagnosed
- randomized controlled trial
- palliative care
- stem cells
- gene expression
- prognostic factors
- radiation therapy
- machine learning
- papillary thyroid
- locally advanced
- mesenchymal stem cells
- data analysis
- copy number
- combination therapy
- bone marrow
- deep learning
- childhood cancer
- phase iii
- squamous cell