ReviewPharmacology & therapeutics2016
Personalized in vitro cancer models to predict therapeutic response: Challenges and a framework for improvement.
Review in Pharmacology & therapeutics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed, 74 citations in OpenAlex.
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- The adverse outcome pathway for breast cancer: a knowledge management framework bridging biomedicine and toxicology.Discover oncology · 2023Review
- Portable optical spectroscopic assay for non-destructive measurement of key metabolic parameters onBiomedical optics express · 2023Article
- The effect of matrices on the gene expression profile of patient-derived head and neck carcinoma cells for in vitro therapy testing.Cancer cell international · 2023Article
- Engineered Vasculature for Cancer Research and Regenerative Medicine.Micromachines · 2023Review
- Development of an adverse outcome pathway network for breast cancer: a comprehensive representation of the pathogenesis, complexity and diversity of the disease.Archives of toxicology · 2022Review
- Microfluidics in vascular biology research: a critical review for engineers, biologists, and clinicians.Lab on a chip · 2022Review
- Microphysiological model of renal cell carcinoma to inform anti-angiogenic therapy.Biomaterials · 2022Article
- Patient-derived tumor spheroid cultures as a promising tool to assist personalized therapeutic decisions in breast cancer.Translational cancer research · 2022Article
- Article
- Patient-Derived Explants of Colorectal Cancer: Histopathological and Molecular Analysis of Long-Term Cultures.Cancers · 2021Article
- Recapitulating the Cancer Microenvironment Using Bioprinting Technology for Precision Medicine.Micromachines · 2021Review
- Cancer models in preclinical research: A chronicle review of advancement in effective cancer research.Animal models and experimental medicine · 2021Review
- FutureTox IV Workshop Summary: Predictive Toxicology for Healthy Children.Toxicological sciences : an official journal of the Society of Toxicology · 2021Review
- Toward improvedAPL bioengineering · 2021Article
- A Personalized Therapeutics Approach Using anFrontiers in oncology · 2021Article
- Organotypic Models of Lung Cancer.Current topics in microbiology and immunology · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 1 institution in 1 country.
Funding
Abstract
Personalized cancer therapy focuses on characterizing the relevant phenotypes of the patient, as well as the patient's tumor, to predict the most effective cancer therapy. Historically, these methods have not proven predictive in regards to predicting therapeutic response. Emerging culture platforms are designed to better recapitulate the in vivo environment, thus, there is renewed interest in integrating patient samples into in vitro cancer models to assess therapeutic response. Successful examples of translating in vitro response to clinical relevance are limited due to issues with patient sample acquisition, variability and culture. We will review traditional and emerging in vitro models for personalized medicine, focusing on the technologies, microenvironmental components, and readouts utilized. We will then offer our perspective on how to apply a framework derived from toxicology and ecology towards designing improved personalized in vitro models of cancer. The framework serves as a tool for identifying optimal readouts and culture conditions, thus maximizing the information gained from each patient sample.
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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.