Autofluorescence Imaging of Treatment Response in Neuroendocrine Tumor Organoids.
Amani A GilletteChristopher P BabiarzAva R VanDommelenCheri A PaschLinda ClipsonKristina A MatkowskyjDustin A DemingMelissa C SkalaPublished in: Cancers (2021)
Gastroenteropancreatic neuroendocrine tumors (GEP-NET) account for roughly 60% of all neuroendocrine tumors. Low/intermediate grade human GEP-NETs have relatively low proliferation rates that animal models and cell lines fail to recapitulate. Short-term patient-derived cancer organoids (PDCOs) are a 3D model system that holds great promise for recapitulating well-differentiated human GEP-NETs. However, traditional measurements of drug response (i.e., growth, proliferation) are not effective in GEP-NET PDCOs due to the small volume of tissue and low proliferation rates that are characteristic of the disease. Here, we test a label-free, non-destructive optical metabolic imaging (OMI) method to measure drug response in live GEP-NET PDCOs. OMI captures the fluorescence lifetime and intensity of endogenous metabolic cofactors NAD(P)H and FAD. OMI has previously provided accurate predictions of drug response on a single cell level in other cancer types, but this is the first study to apply OMI to GEP-NETs. OMI tested the response to novel drug combination on GEP-NET PDCOs, specifically ABT263 (navitoclax), a Bcl-2 family inhibitor, and everolimus, a standard GEP-NET treatment that inhibits mTOR. Treatment response to ABT263, everolimus, and the combination were tested in GEP-NET PDCO lines derived from seven patients, using two-photon OMI. OMI measured a response to the combination treatment in 5 PDCO lines, at 72 h post-treatment. In one of the non-responsive PDCO lines, heterogeneous response was identified with two distinct subpopulations of cell metabolism. Overall, this work shows that OMI provides single-cell metabolic measurements of drug response in PDCOs to guide drug development for GEP-NET patients.
Keyphrases
- neuroendocrine tumors
- single cell
- high resolution
- signaling pathway
- endothelial cells
- end stage renal disease
- ejection fraction
- newly diagnosed
- combination therapy
- young adults
- adverse drug
- label free
- prognostic factors
- deep learning
- photodynamic therapy
- artificial intelligence
- single molecule
- mesenchymal stem cells
- patient reported