A pan-cancer organoid platform for precision medicine

Larsen, Brian M.; Kannan, Madhavi; Langer, Lee; Leibowitz, Benjamin D.; BenTaieb, Aïcha; Cancino, Andrea; Dolgalev, Igor; Drummond, B. J.; Dry, Jonathan R.; Ho, Chi Sing; Khullar, Gaurav; Krantz, Benjamin A.; Mapes, Brandon; McKinnon, Kelly E.; Metti, Jessica; Perera, Jason; Rand, Tim A.; Sánchez-Freire, Verónica; Shaxted, Jenna; Stein, Michelle M.; Streit, Michael; Tan, Yulong; Zhang, Yilin; Zhao, Ende; Venkataraman, Jagadish; Stumpe, Martin C.; Borgia, Jeffrey A.; Masood, Ashiq; Catenacci, Daniel V.T.; Mathews, Jeremy; Gursel, Demirkan; Wei, Jian Jun; Welling, Theodore H.; Simeone, Diane M.; White, Kevin P.; Khan, Aly A.; Igartua, Catherine

doi:10.1016/j.celrep.2021.109429

Cited by 71 publications

(56 citation statements)

References 68 publications

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“…A number of software tools have been developed to automate the process of brightfield/phase-contrast organoid image analysis. These platforms use conventional image processing methods, such as adaptive thresholding and mathematical morphology 20 , or convolutional neural networks [21][22][23] to identify organoids in sequences of microscopy images. Despite their advantages, many existing platforms require cellular nuclei to be transgenically labeled 22 , which increases experiment time and complexity and may modify cellular dynamics.…”

Section: Introductionmentioning

confidence: 99%

“…Despite their advantages, many existing platforms require cellular nuclei to be transgenically labeled 22 , which increases experiment time and complexity and may modify cellular dynamics. Other existing platforms require manual tuning of parameters for each image 20 , focus on singletimepoint analysis 23 , only provide population-averaged (bulk) measurements without single-organoid resolution, or are limited to bounding-box detection 21 , which fails to capture potentially useful morphological information at the individual organoid level. Changes in organoid shape, such as spiking or blebbing, can reveal important responses to external stimuli organoids and might be missed with bounding-box measurements 24 .…”

Section: Introductionmentioning

confidence: 99%

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OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics

Matthews

Schuster

Kashaf

et al. 2022

Preprint

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Organoids are three-dimensional in vitro tissue models that closely represent the native heterogeneity, microanatomy, and functionality of an organ or diseased tissue. Analysis of organoid morphology, growth, and drug response is challenging due to the diversity in shape and size of organoids, movement through focal planes, and limited options for live-cell staining. Here, we present OrganoID, an open-source image analysis platform that automatically recognizes, labels, and tracks single organoids in brightfield and phase-contrast microscopy. The platform identifies organoid morphology pixel by pixel without the need for fluorescence or transgenic labeling and accurately analyzes a wide range of organoid types in time-lapse microscopy experiments. OrganoID uses a modified u-net neural network with minimal feature depth to encourage model generalization and allow fast execution. The network was trained on images of human pancreatic cancer organoids and was validated on images from pancreatic, lung, colon, and adenoid cystic carcinoma organoids with a mean intersection-over-union of 0.76. OrganoID measurements of organoid count and individual area concurred with manual measurements at 96% and 95% agreement respectively. Tracking accuracy remained above 89% over the duration of a four-day validation experiment. Automated single-organoid morphology analysis of a dose-response experiment identified significantly different organoid circularity after exposure to different concentrations of gemcitabine. The OrganoID platform enables straightforward, detailed, and accurate analysis of organoid images to accelerate the use of organoids as physiologically relevant models in high-throughput research.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics

Matthews

Schuster

Kashaf

et al. 2022

Preprint

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“…A remarkable collection of epithelial organoids has been established from healthy, diseased and cancerous tissues, both of human and animal origin. These 3D organoids fill the experimental gap between cell lines and complex animal models, while accurately preserving patient-specific phenotypic and genetic characteristics [ 2 , 3 , 4 ]. Encouraging results from recent studies using patient-derived tumor organoids for personalized drug response profiling highlight the exceptional potential of these near-patient preclinical models in oncology research [ 5 , 6 , 7 , 8 ].…”

Section: Introductionmentioning

confidence: 99%

“…The scaffold provides structural support for organoid assembly, while reproducing key cell-matrix interactions [ 23 ]. Natural scaffolds such as Matrigel, Geltrex or Cultrex, all murine-derived basement membrane matrices, are ubiquitously used for organoid culturing and functional applications [ 2 , 3 , 24 ]. However their variable and poorly defined composition, along with their animal origin, impede reproducibility regarding organoid differentiation and organoid responses to chemical compounds [ 23 , 25 , 26 ].…”

Section: Introductionmentioning

confidence: 99%

“…Recently emerged synthetic hydrogels, on the other hand, provide a controlled environment with well-defined physical, mechanical and biological properties [ 23 , 25 ]. Besides the organoid culture method applied, current drug testing protocols also vary in timing of treatment initiation, seeding of single organoid cells versus assembled organoids, organoid size, drug dosing, drug exposure time and read-outs [ 2 , 7 , 8 , 12 , 13 , 14 , 24 , 27 , 28 , 29 ]. Many of these factors have been implied to affect drug testing results [ 8 , 30 , 31 , 32 ].…”

Section: Introductionmentioning

confidence: 99%

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Modeling Prostate Cancer Treatment Responses in the Organoid Era: 3D Environment Impacts Drug Testing

et al. 2021

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Organoid-based studies have revolutionized in vitro preclinical research and hold great promise for the cancer research field, including prostate cancer (PCa). However, experimental variability in organoid drug testing complicates reproducibility. For example, we observed PCa organoids to be less affected by cabazitaxel, abiraterone and enzalutamide as compared to corresponding single cells prior to organoid assembly. We hypothesized that three-dimensional (3D) organoid organization and the use of various 3D scaffolds impact treatment efficacy. Live-cell imaging of androgen-induced androgen receptor (AR) nuclear translocation and taxane-induced tubulin stabilization was used to investigate the impact of 3D scaffolds, spatial organoid distribution and organoid size on treatment effect. Scaffolds delayed AR translocation and tubulin stabilization, with Matrigel causing a more pronounced delay than synthetic hydrogel as well as incomplete tubulin stabilization. Drug effect was further attenuated the more centrally organoids were located in the scaffold dome. Moreover, cells in the organoid core revealed a delayed treatment effect compared to cells in the organoid periphery, underscoring the impact of organoid size. These findings indicate that analysis of organoid drug responses needs careful interpretation and requires dedicated read-outs with consideration of underlying technical aspects.

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Emerging In Vitro and In Vivo Models: Hope for the Better Understanding of Cancer Progression and Treatment

Yadav,

Mahajan,

Singh

et al. 2024

Advanced Biology

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Various cancer models have been developed to aid the understanding of the underlying mechanisms of tumor development and evaluate the effectiveness of various anticancer drugs in preclinical studies. These models accurately reproduce the critical stages of tumor initiation and development to mimic the tumor microenvironment better. Using these models for target validation, tumor response evaluation, resistance modeling, and toxicity comprehension can significantly enhance the drug development process. Herein, various in vivo or animal models are presented, typically consisting of several mice and in vitro models ranging in complexity from transwell models to spheroids and CRISPR‐Cas9 technologies. While in vitro models have been used for decades and dominate the early stages of drug development, they are still limited primary to simplistic tests based on testing on a single cell type cultivated in Petri dishes. Recent advancements in developing new cancer therapies necessitate the generation of complicated animal models that accurately mimic the tumor's complexity and microenvironment. Mice make effective tumor models as they are affordable, have a short reproductive cycle, exhibit rapid tumor growth, and are simple to manipulate genetically. Human cancer mouse models are crucial to understanding the neoplastic process and basic and clinical research improvements. The following review summarizes different in vitro and in vivo metastasis models, their advantages and disadvantages, and their ability to serve as a model for cancer research.

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A pan-cancer organoid platform for precision medicine

Abstract: Highlights d Tumor organoid cultures from >1,000 patients reveal genomic/transcriptomic fidelity d Establishment of chemically defined minimal medias for each solid tumor type d Pan-cancer neural network predicts drug response from label-free light microscopy

Cited by 71 publications

References 68 publications

OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics

OrganoID: a versatile deep learning platform for tracking and analysis of single-organoid dynamics

Modeling Prostate Cancer Treatment Responses in the Organoid Era: 3D Environment Impacts Drug Testing

Emerging In Vitro and In Vivo Models: Hope for the Better Understanding of Cancer Progression and Treatment

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