Drug response in organoids generated from frozen primary tumor tissues.
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ABSTRACT:
SUBMITTER: Walsh AJ
PROVIDER: S-EPMC4703961 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:A new workflow for protein-based tumor heterogeneity probing in tissues is here presented. Tumor heterogeneity is believed to be key for therapy failure and differences in prognosis in cancer patients. Comprehending tumor heterogeneity, especially at the protein level, is critical for tracking tumor evolution, and showing the presence of different phenotypical variants and their location with respect to tissue architecture. Although a variety of techniques is available for quantifying protein expression, the heterogeneity observed in the tissue is rarely addressed. The proposed method is validated in breast cancer fresh-frozen tissues derived from five patients. Protein expression is quantified on the tissue regions of interest (ROI) with a resolution of up to 100 μm in diameter. High heterogeneity values across the analyzed patients in proteins such as cytokeratin 7, β-actin and epidermal growth factor receptor (EGFR) using a Shannon entropy analysis are observed. Additionally, ROIs are clustered according to their expression levels, showing their location in the tissue section, and highlighting that similar phenotypical variants are not always located in neighboring regions. Interestingly, a patient with a phenotype related to increased aggressiveness of the tumor presents a unique protein expression pattern. In summary, a workflow for the localized extraction and protein analysis of regions of interest from frozen tissues, enabling the evaluation of tumor heterogeneity at the protein level is presented.
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Project description: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.
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Project description:Data Access Committee EGAC00001002819
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Project description:Patient-derived organoid models are proving to be a powerful platform for both basic and translational studies. Here we conduct a methodical analysis of pancreatic ductal adenocarcinoma (PDAC) tumor organoid drug response in paired patient-derived xenograft (PDX) and PDX-derived organoid (PXO) models grown under WNT-free culture conditions. We report a specific relationship between area under the curve value of organoid drug dose response and in vivo tumor growth, irrespective of the drug treatment. In addition, we analyzed the glycome of PDX and PXO models and demonstrate that PXOs recapitulate the in vivo glycan landscape. In addition, we identify a core set of 57 N-glycans detected in all 10 models that represent 50%-94% of the relative abundance of all N-glycans detected in each of the models. Last, we developed a secreted biomarker discovery pipeline using media supernatant of organoid cultures and identified potentially new extracellular vesicle (EV) protein markers. We validated our findings using plasma samples from patients with PDAC, benign gastrointestinal diseases, and chronic pancreatitis and discovered that 4 EV proteins are potential circulating biomarkers for PDAC. Thus, we demonstrate the utility of organoid cultures to not only model in vivo drug responses but also serve as a powerful platform for discovering clinically actionable serologic biomarkers.
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Project description:Metabolic diseases, including obesity, diabetes mellitus and cardiovascular disease, are a major threat to health in the modern world, but efforts to understand the underlying mechanisms and develop rational treatments are limited by the lack of appropriate human model systems. Notably, advances in stem cell and organoid technology allow the generation of cellular models that replicate the histological, molecular and physiological properties of human organs. Combined with marked improvements in gene editing tools, human stem cells and organoids provide unprecedented systems for studying mechanisms of metabolic diseases. Here, we review progress made over the past decade in the generation and use of stem cell-derived metabolic cell types and organoids in metabolic disease research, especially obesity and liver diseases. In particular, we discuss the limitations of animal models and the advantages of stem cells and organoids, including their application to metabolic diseases. We also discuss mechanisms of drug action, understanding the efficacy and toxicity of existing therapies, screening for new treatments and pursuing personalized therapies. We highlight the potential of combining stem cell-derived organoids with gene editing and functional genomics to revolutionize the approach to finding treatments for metabolic diseases.
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