ArticleNature communications2023
Tracing cancer evolution and heterogeneity using Hi-C.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 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
11 citing papers in PubMed, 12 citations in OpenAlex.
- Chromosomal instability in cancer: sources, consequences and new therapeutic opportunities.Signal transduction and targeted therapy · 2026Review
- Genomic-guided precision oncology in canine cancer: from comparative genomics to liquid biopsy and molecular monitoring.Journal of veterinary science · 2026Review
- Origins and consequences of oncogenic 3D chromatin remodelling.Nature reviews. Cancer · 2026Review
- Hi-C for genome-wide detection of enhancer-hijacking rearrangements in routine lymphoid cancer biopsies.Cell genomics · 2026Article
- Connecting multiway enhancer-promoter interactions to changes in gene expression in cancer.bioRxiv : the preprint server for biology · 2026Article
- Drug resistance and tumor heterogeneity: cells and ensembles.Biophysical reviews · 2025Review
- [New methods at the transition from research to routine diagnostics].Pathologie (Heidelberg, Germany) · 2025Article
- Single cell variant to enhancer to gene map for coronary artery disease.medRxiv : the preprint server for health sciences · 2024Article
- The translocation activity of Rad54 reduces crossover outcomes during homologous recombination.Nucleic acids research · 2024Article
- 3C methods in cancer research: recent advances and future prospects.Experimental & molecular medicine · 2024Review
- Removing unwanted variation between samples in Hi-C experiments.Briefings in bioinformatics · 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
11 authors at 6 institutions in 2 countries.
Funding
Abstract
Chromosomal rearrangements can initiate and drive cancer progression, yet it has been challenging to evaluate their impact, especially in genetically heterogeneous solid cancers. To address this problem we developed HiDENSEC, a new computational framework for analyzing chromatin conformation capture in heterogeneous samples that can infer somatic copy number alterations, characterize large-scale chromosomal rearrangements, and estimate cancer cell fractions. After validating HiDENSEC with in silico and in vitro controls, we used it to characterize chromosome-scale evolution during melanoma progression in formalin-fixed tumor samples from three patients. The resulting comprehensive annotation of the genomic events includes copy number neutral translocations that disrupt tumor suppressor genes such as NF1, whole chromosome arm exchanges that result in loss of CDKN2A, and whole-arm copy-number neutral loss of homozygosity involving PTEN. These findings show that large-scale chromosomal rearrangements occur throughout cancer evolution and that characterizing these events yields insights into drivers of melanoma progression.
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.