iPSC-Derived Embryoid Bodies as Models of c-Met-Mutated Hereditary Papillary Renal Cell Carcinoma.
Jin Wook HwangChristophe DesterkeOlivier FéraudStéphane RichardSophie FerlicotVirginie VerkarreJean Jacques PatardJulien Loisel-DuwattezAdlen FoudiFrank GriscelliAnnelise Bennaceur-GriscelliAli G TurhanPublished in: International journal of molecular sciences (2019)
Hereditary cancers with cancer-predisposing mutations represent unique models of human oncogenesis, as a driving oncogenic event is present in germline. Currently, there are no satisfactory models to study these malignancies. We report the generation of IPSC from the somatic cells of a patient with hereditary c-met-mutated papillary renal cell carcinoma (PRCC). From these cells we have generated spontaneous aggregates organizing in structures which expressed kidney markers such as PODXL and Six2. These structures expressed PRCC markers both in vitro and in vivo in NSG mice. Gene-expression profiling showed striking molecular similarities with signatures found in a large cohort of PRCC tumor samples. This analysis, applied to primary cancers with and without c-met mutation, showed overexpression of the BHLHE40 and KDM4C only in the c-met-mutated PRCC tumors, as predicted by c-met-mutated embryoid bodies transcriptome. These data therefore represent the first proof of concept of "hereditary renal cancer in a dish" model using c-met-mutated iPSC-derived embryoid bodies, opening new perspectives for discovery of novel predictive progression markers and for drug-screening for future precision-medicine strategies.
Keyphrases
- tyrosine kinase
- renal cell carcinoma
- induced apoptosis
- genome wide
- wild type
- induced pluripotent stem cells
- papillary thyroid
- cell cycle arrest
- endothelial cells
- small molecule
- transcription factor
- high resolution
- squamous cell
- childhood cancer
- oxidative stress
- big data
- cell proliferation
- lymph node metastasis
- electronic health record
- emergency department
- case report
- single cell
- young adults
- endoplasmic reticulum stress
- dna repair
- cell death
- adipose tissue
- machine learning
- dna damage