Immunotherapy continues to gain traction as a promising approach to cancer treatment. Chimeric antigen receptor (CAR) T-cell therapy, in particular, works by genetically modifying T-cells to recognize and eliminate cancer cells. While this strategy has shown notable success in treating blood cancers, its effectiveness in solid tumors remains limited.
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One major challenge lies within the solid tumor microenvironment (TME), where immunosuppressive cytokines can hinder the tumor-killing activity of T-cells. As a result, the TME plays a critical role in shaping treatment outcomes, and a deeper understanding of its influence is key to improving the efficacy of CAR T-cell therapy.
The advantage of employing three-dimensional (3D) patient-derived organoids (PDOs) lies in their ability to replicate the physical and chemical cues present within the TME, which are often absent in traditional two-dimensional (2D) monolayer cultures.
Research indicates that PDOs exhibit drug response patterns similar to those of the original tumors, underscoring their potential to enhance therapeutic outcomes. Thus, PDOs represent a superior preclinical model compared to 2D systems due to their inherent heterogeneity, long-term stability, suitability for high-throughput screening, and improved ability to capture tumor characteristics.
Despite the advantages associated with PDOs, significant barriers hinder their widespread adoption in drug discovery, primarily due to the high costs and labor-intensive processes required for their growth and maintenance.
To overcome the challenges of using PDOs in large-scale applications, a semi-automated bioreactor was developed for the efficient expansion of assay-ready organoids.
This solution enables the reproducible production of millions of assay-ready patient-derived organoids.
In this study, a workflow was established to quantify T-cell efficacy in solid tumors using PDOs.
By utilizing bioreactor-expanded patient-derived colorectal cancer organoids (CRCs), activated human peripheral blood mononuclear cells (PBMCs) were introduced to the CRCs in a 384-well microtiter plate, monitoring them every four hours for three days via high-content imaging.
To assess T-cell-induced morphological changes, each organoid was segmented in the transmitted light (TL) channel using a deep learning U-net model, and relevant features were extracted. These features were classified with a trained Random Forest model to distinguish morphologically altered organoids from intact ones.
The resulting features were classified with a trained Random Forest model to differentiate morphologically altered organoids from intact ones. This approach revealed that, compared to control wells, the proportion of modified organoids in wells treated with stimulated PBMCs increased rapidly, reaching a fluctuating plateau within approximately two days, indicating the potential of a live T-cell efficacy assessment method, with or without labeling.
The study highlights the potential of combining bioreactor-expanded organoids with advanced image analysis for large-scale T-cell–based screening.
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Acknowledgments
Produced from material originally authored by Zhisong Tong from Molecular Devices.
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