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Author |
Le Compte, M.; Cardenas De La Hoz, E.; Peeters, S.; Rodrigues Fortes, F.; Hermans, C.; Domen, A.; Smits, E.; Lardon, F.; Vandamme, T.; Lin, A.; Vanlanduit, S.; Roeyen, G.; van Laere, S.; Prenen, H.; Peeters, M.; Deben, C. |
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Title |
Single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer |
Type |
A1 Journal article |
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Year  |
2023 |
Publication |
npj Precision Oncology |
Abbreviated Journal |
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Volume |
7 |
Issue |
1 |
Pages |
128-14 |
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Keywords |
A1 Journal article; Center for Oncological Research (CORE); Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT); Antwerp Surgical Training, Anatomy and Research Centre (ASTARC) |
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Abstract |
Pancreatic ductal adenocarcinoma (PDAC) is one of the most lethal diseases, characterized by a treatment-resistant and invasive nature. In line with these inherent aggressive characteristics, only a subset of patients shows a clinical response to the standard of care therapies, thereby highlighting the need for a more personalized treatment approach. In this study, we comprehensively unraveled the intra-patient response heterogeneity and intrinsic aggressive nature of PDAC on bulk and single-organoid resolution. We leveraged a fully characterized PDAC organoid panel ( N = 8) and matched our artificial intelligence-driven, live-cell organoid image analysis with retrospective clinical patient response. In line with the clinical outcomes, we identified patient-specific sensitivities to the standard of care therapies (gemcitabine-paclitaxel and FOLFIRINOX) using a growth rate-based and normalized drug response metric. Moreover, the single-organoid analysis was able to detect resistant as well as invasive PDAC organoid clones, which was orchestrates on a patient, therapy, drug, concentration and time-specific level. Furthermore, our in vitro organoid analysis indicated a correlation with the matched patient progression-free survival (PFS) compared to the current, conventional drug response readouts. This work not only provides valuable insights on the response complexity in PDAC, but it also highlights the potential applications (extendable to other tumor types) and clinical translatability of our approach in drug discovery and the emerging era of personalized medicine. |
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Wos |
001118015800001 |
Publication Date |
2023-12-08 |
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ISSN |
2397-768x |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
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Times cited |
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Open Access |
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no |
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Call Number |
UA @ admin @ c:irua:201455 |
Serial |
9091 |
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Author |
Le Compte, M.; Cardenas De La Hoz, E.; Peeters, S.; Smits, E.; Lardon, F.; Roeyen, G.; Vanlanduit, S.; Prenen, H.; Peeters, M.; Lin, A.; Deben, C. |
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Title |
Multiparametric tumor organoid drug screening using widefield live-cell imaging for bulk and single-organoid analysis |
Type |
A1 Journal article |
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Year |
2022 |
Publication |
Jove-Journal Of Visualized Experiments |
Abbreviated Journal |
Jove-J Vis Exp |
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Volume |
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Issue |
190 |
Pages |
1-18 |
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Keywords |
A1 Journal article; Engineering sciences. Technology; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT); Antwerp Surgical Training, Anatomy and Research Centre (ASTARC); Center for Oncological Research (CORE) |
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Abstract |
Patient-derived tumor organoids (PDTOs) hold great promise for preclinical and translational research and predicting the patient therapy response from ex vivo drug screenings. However, current adenosine triphosphate (ATP)-based drug screening assays do not capture the complexity of a drug response (cytostatic or cytotoxic) and intratumor heterogeneity that has been shown to be retained in PDTOs due to a bulk readout. Live-cell imaging is a powerful tool to overcome this issue and visualize drug responses more in-depth. However, image analysis software is often not adapted to the three-dimensionality of PDTOs, requires fluorescent viability dyes, or is not compatible with a 384-well microplate format. This paper describes a semi-automated methodology to seed, treat, and image PDTOs in a high-throughput, 384-well format using conventional, widefield, live-cell imaging systems. In addition, we developed viability marker-free image analysis software to quantify growth rate-based drug response metrics that improve reproducibility and correct growth rate variations between different PDTO lines. Using the normalized drug response metric, which scores drug response based on the growth rate normalized to a positive and negative control condition, and a fluorescent cell death dye, cytotoxic and cytostatic drug responses can be easily distinguished, profoundly improving the classification of responders and non-responders. In addition, drug-response heterogeneity can by quantified from single-organoid drug response analysis to identify potential, resistant clones. Ultimately, this method aims to improve the prediction of clinical therapy response by capturing a multiparametric drug response signature, which includes kinetic growth arrest and cell death quantification. , |
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Wos |
000928020400010 |
Publication Date |
2022-12-24 |
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Series Volume |
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Series Issue |
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Edition |
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ISSN |
1940-087x |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
1.2 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
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Most recent IF: 1.2 |
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Call Number |
UA @ admin @ c:irua:193168 |
Serial |
7271 |
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Author |
Deben, C.; Cardenas De La Hoz, E.; Le Compte, M.; Van Schil, P.; Hendriks, J.M.H.; Lauwers, P.; Yogeswaran, S.K.; Lardon, F.; Pauwels, P.; van Laere, S.; Bogaerts, A.; Smits, E.; Vanlanduit, S.; Lin, A. |
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Title |
OrBITS : label-free and time-lapse monitoring of patient derived organoids for advanced drug screening |
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A1 Journal article |
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Year |
2022 |
Publication |
Cellular Oncology (2211-3428) |
Abbreviated Journal |
Cell Oncol |
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Volume |
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Issue |
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Pages |
1-16 |
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Keywords |
A1 Journal article; Plasma Lab for Applications in Sustainability and Medicine – Antwerp (PLASMANT); Antwerp Surgical Training, Anatomy and Research Centre (ASTARC); Center for Oncological Research (CORE) |
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Abstract |
Background Patient-derived organoids are invaluable for fundamental and translational cancer research and holds great promise for personalized medicine. However, the shortage of available analysis methods, which are often single-time point, severely impede the potential and routine use of organoids for basic research, clinical practise, and pharmaceutical and industrial applications. Methods Here, we developed a high-throughput compatible and automated live-cell image analysis software that allows for kinetic monitoring of organoids, named Organoid Brightfield Identification-based Therapy Screening (OrBITS), by combining computer vision with a convolutional network machine learning approach. The OrBITS deep learning analysis approach was validated against current standard assays for kinetic imaging and automated analysis of organoids. A drug screen of standard-of-care lung and pancreatic cancer treatments was also performed with the OrBITS platform and compared to the gold standard, CellTiter-Glo 3D assay. Finally, the optimal parameters and drug response metrics were identified to improve patient stratification. Results OrBITS allowed for the detection and tracking of organoids in routine extracellular matrix domes, advanced Gri3D (R)-96 well plates, and high-throughput 384-well microplates, solely based on brightfield imaging. The obtained organoid Count, Mean Area, and Total Area had a strong correlation with the nuclear staining, Hoechst, following pairwise comparison over a broad range of sizes. By incorporating a fluorescent cell death marker, infra-well normalization for organoid death could be achieved, which was tested with a 10-point titration of cisplatin and validated against the current gold standard ATP-assay, CellTiter-Glo 3D. Using this approach with OrBITS, screening of chemotherapeutics and targeted therapies revealed further insight into the mechanistic action of the drugs, a feature not achievable with the CellTiter-Glo 3D assay. Finally, we advise the use of the growth rate-based normalised drug response metric to improve accuracy and consistency of organoid drug response quantification. Conclusion Our findings validate that OrBITS, as a scalable, automated live-cell image analysis software, would facilitate the use of patient-derived organoids for drug development and therapy screening. The developed wet-lab workflow and software also has broad application potential, from providing a launching point for further brightfield-based assay development to be used for fundamental research, to guiding clinical decisions for personalized medicine. |
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Wos |
000898426100001 |
Publication Date |
2022-12-12 |
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Edition |
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ISSN |
2211-3428; 2211-3436 |
ISBN |
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Additional Links |
UA library record; WoS full record; WoS citing articles |
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Impact Factor |
6.6 |
Times cited |
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Open Access |
OpenAccess |
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Notes |
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Approved |
Most recent IF: 6.6 |
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Call Number |
UA @ admin @ c:irua:192698 |
Serial |
7272 |
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