Language
English
Publication Date
4-14-2025
Journal
Genome Research
DOI
10.1101/gr.280041.124
PMID
40113261
PMCID
PMC12047254
PubMedCentral® Posted Date
4-1-2025
PubMedCentral® Full Text Version
Post-print
Abstract
Cancer is fundamentally a disease of the genome, characterized by extensive genomic, transcriptomic, and epigenomic alterations. Most current studies predominantly use short-read sequencing, gene panels, or microarrays to explore these alterations; however, these technologies can systematically miss or misrepresent certain types of alterations, especially structural variants, complex rearrangements, and alterations within repetitive regions. Long-read sequencing is rapidly emerging as a transformative technology for cancer research by providing a comprehensive view across the genome, transcriptome, and epigenome, including the ability to detect alterations that previous technologies have overlooked. In this Perspective, we explore the current applications of long-read sequencing for both germline and somatic cancer analysis. We provide an overview of the computational methodologies tailored to long-read data and highlight key discoveries and resources within cancer genomics that were previously inaccessible with prior technologies. We also address future opportunities and persistent challenges, including the experimental and computational requirements needed to scale to larger sample sizes, the hurdles in sequencing and analyzing complex cancer genomes, and opportunities for leveraging machine learning and artificial intelligence technologies for cancer informatics. We further discuss how the telomere-to-telomere genome and the emerging human pangenome could enhance the resolution of cancer genome analysis, potentially revolutionizing early detection and disease monitoring in patients. Finally, we outline strategies for transitioning long-read sequencing from research applications to routine clinical practice.
Keywords
Humans, Neoplasms, Genomics, High-Throughput Nucleotide Sequencing, Sequence Analysis, DNA, Computational Biology, Genome, Human
Published Open-Access
yes
Recommended Citation
Li, Qiuhui; Keskus, Ayse G; Wagner, Justin; et al., "Unraveling the Hidden Complexity of Cancer Through Long-Read Sequencing" (2025). Faculty and Staff Publications. 5121.
https://digitalcommons.library.tmc.edu/baylor_docs/5121
Included in
Genetic Phenomena Commons, Genetic Processes Commons, Genetic Structures Commons, Medical Genetics Commons, Medical Molecular Biology Commons, Medical Specialties Commons