Single-cell metabolism predicts drug response in patient-derived pancreatic cancer organoids (Conference Presentation)

Author(s):  
Joe T. Sharick ◽  
Alexander A. Parikh ◽  
Jillian K. Johnson ◽  
Lingjun Li ◽  
Cheri A. Pasch ◽  
...  
Cancers ◽  
2021 ◽  
Vol 13 (8) ◽  
pp. 1873
Author(s):  
Amani A. Gillette ◽  
Christopher P. Babiarz ◽  
Ava R. VanDommelen ◽  
Cheri A. Pasch ◽  
Linda Clipson ◽  
...  

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.


Pancreas ◽  
2007 ◽  
Vol 35 (4) ◽  
pp. 429-430
Author(s):  
H. Subramanian ◽  
R. Brand ◽  
P. Pradhan ◽  
N. Deep ◽  
V. Parikh ◽  
...  

Exploration ◽  
2021 ◽  
Vol 1 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Juan Liu ◽  
Chao Chen ◽  
Tuo Wei ◽  
Odile Gayet ◽  
Céline Loncle ◽  
...  

BioTechniques ◽  
2019 ◽  
Vol 67 (5) ◽  
pp. 210-217 ◽  
Author(s):  
Ndeye Khady Thiombane ◽  
Nicolas Coutin ◽  
Daniel Berard ◽  
Radin Tahvildari ◽  
Sabrina Leslie ◽  
...  

New technologies have powered rapid advances in cellular imaging, genomics and phenotypic analysis in life sciences. However, most of these methods operate at sample population levels and provide statistical averages of aggregated data that fail to capture single-cell heterogeneity, complicating drug discovery and development. Here we demonstrate a new single-cell approach based on convex lens-induced confinement (CLiC) microscopy. We validated CLiC on yeast cells, demonstrating subcellular localization with an enhanced signal-to-noise and fluorescent signal detection sensitivity compared with traditional imaging. In the live-cell CLiC assay, cellular proliferation times were consistent with flask culture. Using methotrexate, we provide drug response data showing a fivefold cell size increase following drug exposure. Taken together, CLiC enables high-quality imaging of single-cell drug response and proliferation for extended observation periods.


2022 ◽  
Vol 11 ◽  
Author(s):  
Dingju Wei ◽  
Meng Xu ◽  
Zhihua Wang ◽  
Jingjing Tong

Metabolic reprogramming is one of the hallmarks of malignant tumors, which provides energy and material basis for tumor rapid proliferation, immune escape, as well as extensive invasion and metastasis. Blocking the energy and material supply of tumor cells is one of the strategies to treat tumor, however tumor cell metabolic heterogeneity prevents metabolic-based anti-cancer treatment. Therefore, searching for the key metabolic factors that regulate cell cancerous change and tumor recurrence has become a major challenge. Emerging technology––single-cell metabolomics is different from the traditional metabolomics that obtains average information of a group of cells. Single-cell metabolomics identifies the metabolites of single cells in different states by mass spectrometry, and captures the molecular biological information of the energy and substances synthesized in single cells, which provides more detailed information for tumor treatment metabolic target screening. This review will combine the current research status of tumor cell metabolism with the advantages of single-cell metabolomics technology, and explore the role of single-cell sequencing technology in searching key factors regulating tumor metabolism. The addition of single-cell technology will accelerate the development of metabolism-based anti-cancer strategies, which may greatly improve the prognostic survival rate of cancer patients.


2021 ◽  
pp. clincanres.3925.2020
Author(s):  
Jaewon J Lee ◽  
Vincent Bernard ◽  
Alexander Semaan ◽  
Maria E Monberg ◽  
Jonathan Huang ◽  
...  

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Chayaporn Suphavilai ◽  
Shumei Chia ◽  
Ankur Sharma ◽  
Lorna Tu ◽  
Rafael Peres Da Silva ◽  
...  

AbstractWhile understanding molecular heterogeneity across patients underpins precision oncology, there is increasing appreciation for taking intra-tumor heterogeneity into account. Based on large-scale analysis of cancer omics datasets, we highlight the importance of intra-tumor transcriptomic heterogeneity (ITTH) for predicting clinical outcomes. Leveraging single-cell RNA-seq (scRNA-seq) with a recommender system (CaDRReS-Sc), we show that heterogeneous gene-expression signatures can predict drug response with high accuracy (80%). Using patient-proximal cell lines, we established the validity of CaDRReS-Sc’s monotherapy (Pearson r>0.6) and combinatorial predictions targeting clone-specific vulnerabilities (>10% improvement). Applying CaDRReS-Sc to rapidly expanding scRNA-seq compendiums can serve as in silico screen to accelerate drug-repurposing studies. Availability: https://github.com/CSB5/CaDRReS-Sc.


2022 ◽  
pp. 101441
Author(s):  
Christian Huisman ◽  
Mason A. Norgard ◽  
Peter R. Levasseur ◽  
Stephanie M. Krasnow ◽  
Monique G.P. van der Wijst ◽  
...  

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