ReviewFrontiers in oncology2021
SEMMs: Somatically Engineered Mouse Models. A New Tool for
Review in Frontiers in oncology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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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.
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Who cites it
11 citing papers in PubMed, 23 citations in OpenAlex.
- Preclinical models of breast cancer metastasis: strengths, limitations, and clinical relevance.NPJ breast cancer · 2026Review
- From precursor to cancer: decoding the intrinsic and extrinsic pathways of pancreatic intraepithelial neoplasia progression.Carcinogenesis · 2024Review
- Clonal Lineage Tracing with Somatic Delivery of Recordable Barcodes Reveals Migration Histories of Metastatic Prostate Cancer.Cancer discovery · 2024Article
- Advances in Making Cancer Mouse Models More Accessible and Informative through Non-Germline Genetic Engineering.Cold Spring Harbor perspectives in medicine · 2024Review
- Transient and DNA-free in vivo CRISPR/Cas9 genome editing for flexible modeling of endometrial carcinogenesis.Cancer communications (London, England) · 2023Article
- Mechanisms driving the immunoregulatory function of cancer cells.Nature reviews. Cancer · 2023Review
- Applications of CRISPR-Cas9 for advancing precision medicine in oncology: from target discovery to disease modeling.Frontiers in genetics · 2023Review
- Magnetic resonance imaging and ultrasound elastography in the context of preclinical pharmacological research: significance for the 3R principles.Frontiers in pharmacology · 2023Review
- Population-wide gene disruption in the murine lung epithelium via AAV-mediated delivery of CRISPR-Cas9 components.Molecular therapy. Methods & clinical development · 2022Article
- Drug discovery oncology in a mouse: concepts, models and limitations.Future science OA · 2021Review
- Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Most experimental oncology therapies fail during clinical development despite years of preclinical testing rationalizing their use. This begs the question of whether the current preclinical models used for evaluating oncology therapies adequately capture patient heterogeneity and response to therapy. Most of the preclinical work is based on xenograft models where tumor mis-location and the lack of the immune system represent a major limitation for the translatability of many observations from preclinical models to patients. Genetically engineered mouse models (GEMMs) hold great potential to recapitulate more accurately disease models but their cost and complexity have stymied their widespread adoption in discovery, early or late drug screening programs. Recent advancements in genome editing technology made possible by the discovery and development of the CRISPR/Cas9 system has opened the opportunity of generating disease-relevant animal models by direct mutation of somatic cell genomes in an organ or tissue compartment of interest. The advent of CRISPR/Cas9 has not only aided in the production of conventional GEMMs but has also enabled the bypassing of the construction of these costly strains. In this review, we describe the Somatically Engineered Mouse Models (SEMMs) as a new category of models where a specific oncogenic signature is introduced in somatic cells of an intended organ in a post-natal animal. In addition, SEMMs represent a novel platform to perform
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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.