Identification and Validation Model for Informative Liquid Biopsy-Based microRNA Biomarkers: Insights from Germ Cell Tumor In Vitro, In Vivo and Patient-Derived Data.
João LoboAd J M GillisAnnette van den BergLambert C J DorssersGafanzer BelgeKlaus-Peter DieckmannHenk P RoestLuc J W van der LaanJourik GietemaRobert J HamiltonCarmen JerónimoRui M HenriqueDaniela SalvatoriLeendert H J LooijengaPublished in: Cells (2019)
Liquid biopsy-based biomarkers, such as microRNAs, represent valuable tools for patient management, but often do not make it to integration in the clinic. We aim to explore issues impeding this transition, in the setting of germ cell tumors, for which novel biomarkers are needed. We describe a model for identifying and validating clinically relevant microRNAs for germ cell tumor patients, using both in vitro, in vivo (mouse model) and patient-derived data. Initial wide screening of candidate microRNAs is performed, followed by targeted profiling of potentially relevant biomarkers. We demonstrate the relevance of appropriate (negative) controls, experimental conditions (proliferation), and issues related to sample origin (serum, plasma, cerebral spinal fluid) and pre-analytical variables (hemolysis, contaminants, temperature), all of which could interfere with liquid biopsy-based studies and their conclusions. Finally, we show the value of our identification model in a specific scenario, contradicting the presumed role of miR-375 as marker of teratoma histology in liquid biopsy setting. Our findings indicate other putative microRNAs (miR-885-5p, miR-448 and miR-197-3p) fulfilling this clinical need. The identification model is informative to identify the best candidate microRNAs to pursue in a clinical setting.
Keyphrases
- germ cell
- ultrasound guided
- cell proliferation
- ionic liquid
- mouse model
- long non coding rna
- end stage renal disease
- fine needle aspiration
- chronic kidney disease
- ejection fraction
- spinal cord
- case report
- mass spectrometry
- machine learning
- deep learning
- prognostic factors
- artificial intelligence
- drinking water
- patient reported outcomes
- data analysis
- clinical evaluation