Spatial Alignment of Organoids Tracking Subclonal Chemotherapy Resistance in Pancreatic and Ampullary Cancer.
Md Shahadat HossanEthan Samuel LinEleanor RiedlAustin StramEric MehlhaffLuke KoeppelJamie WarnerInem UkoLori Mankowski GettleSam LubnerStephanie M McGregorWei ZhangWilliam MurphyJeremy D KratzPublished in: Bioengineering (Basel, Switzerland) (2023)
Pancreatic and ampullary cancers remain highly morbid diseases for which accurate clinical predictions are needed for precise therapeutic predictions. Patient-derived cancer organoids have been widely adopted; however, prior work has focused on well-level therapeutic sensitivity. To characterize individual oligoclonal units of therapeutic response, we introduce a low-volume screening assay, including an automated alignment algorithm. The oligoclonal growth response was compared against validated markers of response, including well-level viability and markers of single-cell viability. Line-specific sensitivities were compared with clinical outcomes. Automated alignment algorithms were generated to match organoids across time using coordinates across a single projection of Z-stacked images. After screening for baseline size (50 μm) and circularity (>0.4), the match efficiency was found to be optimized by accepting the diffusion thresholded with the root mean standard deviation of 75 μm. Validated well-level viability showed a limited correlation with the mean organoid size (R = 0.408), and a normalized growth assayed by normalized changes in area (R = 0.474) and area (R = 0.486). Subclonal populations were defined by both residual growth and the failure to induce apoptosis and necrosis. For a culture with clinical resistance to gemcitabine and nab-paclitaxel, while a therapeutic challenge induced a robust effect in inhibiting cell growth (GΔ = 1.53), residual oligoclonal populations were able to limit the effect on the ability to induce apoptosis (GΔ = 0.52) and cell necrosis (GΔ = 1.07). Bioengineered approaches are feasible to capture oligoclonal heterogeneity in organotypic cultures, integrating ongoing efforts for utilizing organoids across cancer types as integral biomarkers and in novel therapeutic development.
Keyphrases
- papillary thyroid
- deep learning
- machine learning
- squamous cell
- oxidative stress
- endoplasmic reticulum stress
- high throughput
- cell death
- squamous cell carcinoma
- bariatric surgery
- signaling pathway
- cell therapy
- magnetic resonance imaging
- locally advanced
- optical coherence tomography
- stem cells
- magnetic resonance
- induced pluripotent stem cells
- convolutional neural network
- lymph node metastasis
- high glucose
- genetic diversity