ArticlePLoS computational biology2019
Morphophenotypic classification of tumor organoids as an indicator of drug exposure and penetration potential.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Pooled it
- Evaluating carboplatin and PARP inhibitor combination efficacy using high-grade serous carcinoma spheroids and organoids.Cancer biology & therapy · 2026Article
- Longitudinal drug response assessment of tumor organoids based on optical attenuation coefficient and multi-dimensional morphological characterization.Biomedical optics express · 2026Article
- Physics-informed deep learning enables reliable and scalable organoid quantification for drug screening via OCT.NPJ digital medicine · 2026Article
- Tumor organoid platform design for drug response modeling: culture architecture, microenvironmental complexity, and AI-assisted readouts.Archives of pharmacal research · 2026Review
- An organoid-guided roadmap for precision delivery of epigallocatechin gallate in oral submucous fibrosis.Frontiers in bioengineering and biotechnology · 2026Review
- Plasma exosomes from individuals with type 2 diabetes drive breast cancer aggression in patient-derived organoids.Communications biology · 2025Article
- Agent-based modeling in cancer biomedicine: applications and tools for calibration and validation.Cancer biology & therapy · 2024Review
- A deep-learning model for characterizing tumor heterogeneity using patient-derived organoids.Scientific reports · 2024Article
- An agent-based modelling framework to study growth mechanisms inRoyal Society open science · 2024Article
- Histology-guided mathematical model of tumor oxygenation: sensitivity analysis of physical and computational parameters.bioRxiv : the preprint server for biology · 2024Article
- Dynamics of Fibril Collagen Remodeling by Tumor Cells: A Model of Tumor-Associated Collagen Signatures.Cells · 2023Article
- Article
- Organoid Cultures In Silico: Tools or Toys?Bioengineering (Basel, Switzerland) · 2022Review
- Review
- Pancreatic cancer derived 3D organoids as a clinical tool to evaluate the treatment response.Frontiers in oncology · 2022Article
- Bridging cell-scale simulations and radiologic images to explain short-time intratumoral oxygen fluctuations.PLoS computational biology · 2021Article
- Collective Cell Migration in a Fibrous Environment: A Hybrid Multiscale Modelling Approach.Frontiers in applied mathematics and statistics · 2021Article
- Navigating Multi-Scale Cancer Systems Biology Towards Model-Driven Clinical Oncology and Its Applications in Personalized Therapeutics.Frontiers in oncology · 2021Review
- Comparison of Cell and Organoid-Level Analysis of Patient-Derived 3D Organoids to Evaluate Tumor Cell Growth Dynamics and Drug Response.SLAS discovery : advancing life sciences R & D · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The dynamics of tumor progression is driven by multiple factors, which can be exogenous to the tumor (microenvironment) or intrinsic (genetic, epigenetic or due to intercellular interactions). While tumor heterogeneity has been extensively studied on the level of cell genetic profiles or cellular composition, tumor morphological diversity has not been given as much attention. The limited analysis of tumor morphophenotypes may be attributed to the lack of accurate models, both experimental and computational, capable of capturing changes in tumor morphology with fine levels of spatial detail. Using a three-dimensional, agent-based, lattice-free computational model, we generated a library of multicellular tumor organoids, the experimental analogues of in vivo tumors. By varying three biologically relevant parameters-cell radius, cell division age and cell sensitivity to contact inhibition, we showed that tumor organoids with similar growth dynamics can express distinct morphologies and possess diverse cellular compositions. Taking advantage of the high-resolution of computational modeling, we applied the quantitative measures of compactness and accessible surface area, concepts that originated from the structural biology of proteins. Based on these analyses, we demonstrated that tumor organoids with similar sizes may differ in features associated with drug effectiveness, such as potential exposure to the drug or the extent of drug penetration. Both these characteristics might lead to major differences in tumor organoid's response to therapy. This indicates that therapeutic protocols should not be based solely on tumor size, but take into account additional tumor features, such as their morphology or cellular packing density.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.