Colorectal cancer spheroid biobanks: multi-level approaches to drug sensitivity studies.
Maria Laura De AngelisAlessandro BrusellesFederica FrancescangeliFlavia PucilliSara VitaleAnn ZeunerMarco TartagliaMarta BaiocchiPublished in: Cell biology and toxicology (2018)
Biobanking of molecularly characterized colorectal cancer stem cells (CSCs) generated from individual patients and growing as spheroids in defined serum-free media offer a fast, feasible, and multi-level approach for the screening of targeted therapies and drug resistance molecular studies. By combining in vitro and in vivo analyses of cetuximab efficacy with genetic data on an ongoing collection of stem cell-enriched spheroids, we describe the identification and preliminary characterization of microsatellite stable (MSS) CSCs that, despite the presence of the KRAS (G12D) mutation, display epidermal growth factor (EGF)-dependent growth and are strongly inhibited by anti-EGF-receptor (EGFR) treatment. In parallel, we detected an increased resistance to anti-EGFR therapy of microsatellite instable (MSI) CSC lines irrespective of KRAS mutational status. MSI CSC lines carried mutations in genes coding for proteins with a role in RAS and calcium signaling, highlighting the role of a genomically unstable context in determining anti-EGFR resistance. Altogether, these results argue for a multifactorial origin of anti-EGFR resistance that emerges as the effect of multiple events targeting direct and indirect regulators of the EGFR pathway. An improved understanding of key molecular determinants of sensitivity/resistance to EGFR inhibition will be instrumental to optimize the clinical efficacy of anti-EGFR agents, representing a further step towards personalized treatments.
Keyphrases
- small cell lung cancer
- epidermal growth factor receptor
- tyrosine kinase
- growth factor
- cancer stem cells
- stem cells
- end stage renal disease
- chronic kidney disease
- wild type
- ejection fraction
- gene expression
- emergency department
- squamous cell carcinoma
- newly diagnosed
- deep learning
- bone marrow
- machine learning
- radiation therapy
- bioinformatics analysis
- dna methylation
- wound healing
- metastatic colorectal cancer
- patient reported outcomes
- genetic diversity
- genome wide analysis