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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 |
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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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Open Access |
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no |
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Call Number |
UA @ admin @ c:irua:201455 |
Serial |
9091 |
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