ReviewCancer letters2014
One mouse, one patient paradigm: New avatars of personalized cancer therapy.
Review in Cancer letters, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 154 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
154 citing papers in PubMed.
- The Avatar Acceptability Study: Survivor, Parent and Community Willingness to Use Patient-Derived Xenografts to Personalize Cancer Care.EBioMedicine · 2018Trial
- Revisited analysis of a SHIVA01 trial cohort using functional mutational analyses successfully predicted treatment outcome.Molecular oncology · 2018Trial
- Advantages and research progress of three-dimensional culture systems for lung cancer drug screening (Review).Oncology letters · 2026Review
- Surface-engineered dual drug-loaded tumor-targeted liposomal nanoparticles to overcome the therapeutic resistance in glioblastoma multiforme.Communications medicine · 2026Article
- LINC00467 mediates PARP inhibitor resistance in high-grade serous ovarian cancer by regulating miR-485-5p/RAD50 axis.Journal of ovarian research · 2026Article
- Review
- Article
- Exploiting acquired vulnerability to develop novel treatments for cholangiocarcinoma.Cancer cell international · 2024Review
- Characterizing the Cell-Free Transcriptome in a Humanized Diffuse Large B-Cell Lymphoma Patient-Derived Tumor Xenograft Model for RNA-Based Liquid Biopsy in a Preclinical Setting.International journal of molecular sciences · 2024Article
- Diverse landscape of genetically engineered mouse models: Genomic and molecular insights into prostate cancer.Cancer letters · 2024Review
- Patient-Derived Microphysiological Systems for Precision Medicine.Advanced healthcare materials · 2024Review
- The paradoxical role of cytokines and chemokines at the tumor microenvironment: a comprehensive review.European journal of medical research · 2024Review
- Patient-Derived Xenograft Models in Cancer Research: Methodology, Applications, and Future Prospects.Methods in molecular biology (Clifton, N.J.) · 2024Review
- Genomic profiling of subcutaneous patient-derived xenografts reveals immune constraints on tumor evolution in childhood solid cancer.Nature communications · 2023Article
- Patient Derived Xenografts (PDX) Models as an Avatar to Assess Personalized Therapy Options in Uveal Melanoma: A Feasibility Study.Current oncology (Toronto, Ont.) · 2023Article
- Drosophila innate immunity suppresses the survival of xenografted mammalian tumor cells.Scientific reports · 2023Article
- Zebrafish Cancer Avatars: A Translational Platform for Analyzing Tumor Heterogeneity and Predicting Patient Outcomes.International journal of molecular sciences · 2023Review
- Personalized functional profiling usingCancer biology & therapy · 2022Article
- Recent Advances of Organ-on-a-Chip in Cancer Modeling Research.Biosensors · 2022Review
- Article
94 more citing papers are in PubMed but not listed here.
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
Over the last few decades, study of cancer in mouse models has gained popularity. Sophisticated genetic manipulation technologies and commercialization of these murine systems have made it possible to generate mice to study human disease. Given the large socio-economic burden of cancer, both on academic research and the health care industry, there is a need for in vivo animal cancer models that can provide a rationale that is translatable to the clinic. Such a bench-to-bedside transition will facilitate a long term robust strategy that is economically feasible and clinically effective to manage cancer. The major hurdles in considering mouse models as a translational platform are the lack of tumor heterogeneity and genetic diversity, which are a hallmark of human cancers. The present review, while critical of these pitfalls, discusses two newly emerging concepts of personalized mouse models called "Mouse Avatars" and Co-clinical Trials. Development of "Mouse Avatars" entails implantation of patient tumor samples in mice for subsequent use in drug efficacy studies. These avatars allow for each patient to have their own tumor growing in an in vivo system, thereby allowing the identification of a personalized therapeutic regimen, eliminating the cost and toxicity associated with non-targeted chemotherapeutic measures. In Co-clinical Trials, genetically engineered mouse models (GEMMs) are used to guide therapy in an ongoing human patient trial. Murine and patient trials are conducted concurrently, and information obtained from the murine system is applied towards future clinical management of the patient's tumor. The concurrent trials allow for a real-time integration of the murine and human tumor data. In combination with several molecular profiling techniques, the "Mouse Avatar" and Co-clinical Trial concepts have the potential to revolutionize the drug development and health care process. The present review outlines the current status, challenges and the future potential of these two new in vivo approaches in the field of personalized oncology.
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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.