Uncovering the hidden threat: single-organoid analysis reveals clinically relevant treatment-resistant and invasive subclones in pancreatic cancer

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 show 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 strong 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.

Capturing the heterogeneity of the pancreatic ductal adenocarcinoma tumor microenvironment: novel triple co-culture spheroids for drug screening and angiogenic evaluation

“…Indeed, (epi)genetic changes (in different cell lines) contribute to the creation of unique TMEs that cause growth, angiogenesis, drug resistance, among others, where the speci c communication between cancer cells and CAFs is a key component (28). The observed difference in drug response in our TCC spheroid models matched the patient-speci c drug resistance pro les that were recently shown in PDOs (29). In this regard, we suggest to use at least two spheroid combinations in any study, with BxPC-3:RLT-PSC:HMEC-1 being a suitable model to represent a highly resistant tumor, while, MiaPaCa-2:hPSC21:HMEC-1 spheroids are ideal as more drug-sensitive tumors.…”