ArticleNature communications2024
Integrative ensemble modelling of cetuximab sensitivity in colorectal cancer patient-derived xenografts.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Colorectal Cancer: Epidemiology, Risk Factors, Signaling Pathways, Clinical Features, Screening, Diagnosis, and Management.MedComm · 2026Review
- A translational colorectal cancer organoid biobank mirrors patients' tumor histology, molecular profiles, and treatment responses.Journal of experimental & clinical cancer research : CR · 2026Article
- Harnessing PDX and PDX 2.0: the next-generation paradigm for precision oncology and translational breakthroughs.Molecular cancer · 2026Review
- The Molecular and Functional Landscape of Resistance to FOLFIRI Chemotherapy in Metastatic Colorectal Cancer.Cancer discovery · 2026Article
- Single-cell sequencing and organoids: applications in organ development and disease.Molecular biomedicine · 2025Review
- Establishing a pediatric solid tumor PDX biobank for precision oncology research.Cancer biology & therapy · 2025Article
- Mitochondrial metabolic reprogramming in colorectal cancer: mechanisms of resistance and future clinical interventions.Cell death discovery · 2025Review
- Recent advances in the development and application of colorectal cancer mouse models.Frontiers in pharmacology · 2025Review
- DNA demethylation triggers cell free DNA release in colorectal cancer cells.Genome medicine · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
19 authors.
Funding
Abstract
Patient-derived xenografts (PDXs) are tumour fragments engrafted into mice for preclinical studies. PDXs offer clear advantages over simpler in vitro cancer models - such as cancer cell lines (CCLs) and organoids - in terms of structural complexity, heterogeneity, and stromal interactions. Here, we characterise 231 colorectal cancer PDXs at the genomic, transcriptomic, and epigenetic levels, along with their response to cetuximab, an EGFR inhibitor used clinically for metastatic colorectal cancer. After evaluating the PDXs' quality, stability, and molecular concordance with publicly available patient cohorts, we present results from training, interpreting, and validating the integrative ensemble classifier CeSta. This model takes in input the PDXs' multi-omic characterisation and predicts their sensitivity to cetuximab treatment, achieving an area under the receiver operating characteristics curve > 0.88. Our study demonstrates that large PDX collections can be leveraged to train accurate, interpretable drug sensitivity models that: (1) better capture patient-derived therapeutic biomarkers compared to models trained on CCL data, (2) can be robustly validated across independent PDX cohorts, and (3) could contribute to the development of future therapeutic biomarkers.
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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.