Patient-derived organoids model treatment response of metastatic gastrointestinal cancers.
Georgios VlachogiannisSomaieh HedayatAlexandra VatsiouYann JaminJavier Fernández-MateosKhurum KhanAndrea LampisKatherine EasonIan HuntingfordRosemary BurkeMihaela RataDow-Mu KohNina TunariuDavid CollinsSanna Hulkki-WilsonChanthirika RagulanInmaculada SpiteriSing Yu MoorcraftIan ChauSheela RaoDavid WatkinsNicos FotiadisMaria Antonietta BaliMahnaz Darvish DamavandiHazel LoteZakaria EltahirElizabeth C SmythRuwaida BegumPaul Andrew ClarkeJens C HahneMitchell DowsettJohann de BonoPaul WorkmanAnguraj SadanandamAngelo Paolo Dei TosOwen J SansomSuzanne EcclesNaureen StarlingChiara BraconiAndrea SottorivaSimon P RobinsonDavid CunninghamNicola ValeriPublished in: Science (New York, N.Y.) (2018)
Patient-derived organoids (PDOs) have recently emerged as robust preclinical models; however, their potential to predict clinical outcomes in patients has remained unclear. We report on a living biobank of PDOs from metastatic, heavily pretreated colorectal and gastroesophageal cancer patients recruited in phase 1/2 clinical trials. Phenotypic and genotypic profiling of PDOs showed a high degree of similarity to the original patient tumors. Molecular profiling of tumor organoids was matched to drug-screening results, suggesting that PDOs could complement existing approaches in defining cancer vulnerabilities and improving treatment responses. We compared responses to anticancer agents ex vivo in organoids and PDO-based orthotopic mouse tumor xenograft models with the responses of the patients in clinical trials. Our data suggest that PDOs can recapitulate patient responses in the clinic and could be implemented in personalized medicine programs.
Keyphrases
- clinical trial
- end stage renal disease
- ejection fraction
- squamous cell carcinoma
- newly diagnosed
- chronic kidney disease
- case report
- prognostic factors
- primary care
- randomized controlled trial
- peritoneal dialysis
- emergency department
- single cell
- phase ii
- machine learning
- big data
- bone marrow
- papillary thyroid
- open label
- lymph node metastasis
- mesenchymal stem cells
- deep learning
- patient reported
- childhood cancer
- placebo controlled