A new study from the European Molecular Biology Laboratory (EMBL), in collaboration with the German Cancer Research Centre (DKFZ), has found that long-read genomic sequencing is more effective than short-read sequencing in identifying chromosomal structural rearrangements in cancer genomes. The researchers used Oxford Nanopore long-read sequencing to analyze a medulloblastoma, a childhood brain tumor, and developed new long-read sequence analysis methods to identify novel mutational patterns in cancer genomes.
The team identified a complex pattern tied to a specific form of mutation that went undetected with short-read sequencing. The leaders of the research group hailed long-read sequencing technology as providing a new way to see genome information, both in structural variation and DNA modifications such as methylation.
Long-read sequencing could potentially offer a more comprehensive and accurate way to detect mutations in cancer genomes, leading to better diagnoses and more effective therapies. The technology and analysis tools developed through the collaboration between EMBL and DKFZ could help researchers and clinicians detect more complex mutations, leading to a better understanding and treatment of cancer.
The research group also discovered a pattern of somatic structural variation called templated insertion (TI) threads, which occurs in 3% of cancers, with a prevalence of up to 74% in liposarcoma and frequent colocalization with chromothripsis. This could help researchers develop better targeted therapies for cancers that exhibit this pattern of mutation.
Cancer genomes harbor a broad spectrum of structural variants (SVs) driving tumorigenesis, and long-read sequencing provides a more accurate and comprehensive way to detect them. Short-read sequencing technology has been the primary method for exploring the mutational landscapes of cancer, but it’s believed to miss some mutation patterns.
Long-read sequencing provides sequences that are easier to assemble, like a puzzle with fewer, larger pieces. This is particularly important for complex DNA rearrangements, the characterization of which remains an important challenge, with short-read sequencing data only partially resolving their sequence structures.
Initial efforts to classify somatic SVs uncovered common somatic rearrangement patterns, but more complex patterns have largely resisted systematic classification based on breakpoint junction connectivity. This problem is exacerbated by repetitive sequences in the genome, in which SV breakpoints are readily missed by Illumina whole-genome sequencing (WGS). This leaves the possibility that essential patterns of structural rearrangement have not yet been discovered and are elusive due to the predominant use of short-read sequencing in cancer genomics. This research underscores the importance of continued innovation and collaboration in cancer research, as new technologies and analytical methods can uncover novel insights into the disease.