ArticleTrends in molecular medicine2015
A co-clinical platform to accelerate cancer treatment optimization.
Article in Trends in molecular medicine, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 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
16 citing papers in PubMed.
- Understanding and Overcoming Osteosarcoma Heterogeneity.Biomolecules · 2026Review
- TRPM8 levels determine tumor vulnerability to channel agonists.Molecular oncology · 2025Article
- Acoustofluidic assembly of primary tumor-derived organotypic cell clusters for rapid evaluation of cancer immunotherapy.Journal of nanobiotechnology · 2023Article
- Review
- Optimizing an effective combination of the new microtubule-targeting agent lisavanbulin with standard-of-care therapy for glioblastoma in patient-derived xenograft preclinical models.Neuro-oncology · 2022Article
- Exploring liver cancer biology through functional genetic screens.Nature reviews. Gastroenterology & hepatology · 2021Review
- Bioengineered 3D models of human pancreatic cancer recapitulate in vivo tumour biology.Nature communications · 2021Article
- Co-Clinical Trials: An Innovative Drug Development Platform for Cholangiocarcinoma.Pharmaceuticals (Basel, Switzerland) · 2021Review
- Review
- Patient-derived xenograft mouse models: A high fidelity tool for individualized medicine.Oncology letters · 2019Review
- Article
- The Mouse Hospital and Its Integration in Ultra-Precision Approaches to Cancer Care.Frontiers in oncology · 2018Article
- The promise of circulating tumor cells for precision cancer therapy.Biomarkers in medicine · 2016Review
- Mouse Models of Breast Cancer: Platforms for Discovering Precision Imaging Diagnostics and Future Cancer Medicine.Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2016Review
- Review
- Zebrafish: a new companion for translational research in oncology.Clinical cancer research : an official journal of the American Association for Cancer Research · 2015Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
Sophistication in DNA and RNA sequencing technology is unraveling the tremendous genetic and molecular complexity of human cancer. However, the rate at which this knowledge is being translated into patient care is too slow. To this end, we have designed and implemented a new translational platform, 'The Co-Clinical Trial Project', where data obtained in genetically engineered mouse models (GEMMs) of human cancer treated with protocols identical to those of ongoing clinical trials or with therapies already established in patients serve to rapidly: (i) stratify patients in terms of response and resistance on the basis of genetic and molecular criteria; (ii) identify mechanisms responsible for tumor resistance; and (iii) evaluate the effectiveness of drug combinations to overcome such resistance based on mechanistic understanding.
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.