ReviewCurrent treatment options in oncology2020
Human Colon Organoids and Other Laboratory Strategies to Enhance Patient Treatment Selection.
Review in Current treatment options in oncology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- Organoids: technology refining, current applications and future directions.Molecular biomedicine · 2026Review
- Genotoxic Bacteria and Oncogenic Viruses in Colorectal Cancer: Evidence, Gaps, and a Proposed Interaction Model.International journal of molecular sciences · 2026Review
- Non-small cell lung cancer organoids: Advances and challenges in current applications.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Building consensus on the application of organoid-based drug sensitivity testing in cancer precision medicine and drug development.Theranostics · 2024Review
- Anti-cancer effect of afatinib, dual inhibitor of HER2 and EGFR, on novel mutation HER2 E401G in models of patient-derived cancer.BMC cancer · 2023Article
- Review
- Impact of baseline culture conditions of cancer organoids when determining therapeutic response and tumor heterogeneity.Scientific reports · 2022Article
- Biomarkers and cell-based models to predict the outcome of neoadjuvant therapy for rectal cancer patients.Biomarker research · 2021Review
- Comparative Analysis of Patient-Matched PDOs Revealed a Reduction in OLFM4-Associated Clusters in Metastatic Lesions in Colorectal Cancer.Stem cell reports · 2021Article
- Proteomics of Colorectal Cancer: Tumors, Organoids, and Cell Cultures-A Minireview.Frontiers in molecular biosciences · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
opinion statementThough many advancements in personalized medicine have been made, better methods are still needed to predict treatment benefit for patients with colorectal cancer. Patient-derived cancer organoids (PDCOs) are a major advance towards true personalization of treatment strategies. A growing body of literature is demonstrating the feasibility of PDCOs as an accurate and high-throughput preclinical tool for patient treatment selection. Many studies demonstrate that these cultures are readily generated and represent the tumors they were derived from phenotypically and based on their mutation profile. This includes maintenance of the driver muatations giving the cancer cells a selective growth advantage, and also heterogeneity, including molecular and metabolic heterogeneity. Additionally, PDCOs are now being utilized to develop patient biospecimen repositories, perform high to moderate-throughput drug screening, and to potentially predict treatment response for individual patients that are undergoing anti-cancer treatments. In order to develop PDCOs as a true clinical tool, further studies are required to determine the reproducibility and accuracy of these models to predict patient response.
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.