Collections of signatures of chromosomal instability across thousands of human tumors are published in a pair of papers in this week's Nature, providing resources for studying genomic alterations in cancer and their use in precision medicine. In the first paper, a team led by Cancer Research UK scientists examined the extent, diversity, and origin of chromosomal instability in 7,880 tumors representing 33 cancer types to build a compendium of 17 copy number signatures that characterize specific types of chromosomal instability. The signatures, they write, predict drug responses and point to new drug targets. In the second paper, a group from University College London and collaborators develop a computational framework to examine the patterns of copy number alterations in cancer and that is applicable to a range of data types including whole-genome sequencing, and single-cell DNA sequencing. Deploying the framework to nearly 10,000 cancers representing 33 cancer types reveals a set of 21 copy number signatures that could explain patters in copy number in 97 percent of samples studied. The investigators find that the signatures could be clinically relevant in assessing patient prognosis and that they could potentially be used to improve the accuracy of existing tests. GenomeWeb has more on these studies, here.
A paper describing the use of Oxford Nanopore RNA sequencing to directly identify adenosine-to-inosine editing sites is published in Nature Methods this week. Traditionally, inosines — a prevalent RNA modification formed when an adenosine is deaminated by the ADAR family of enzymes — are identified indirectly as variants from Illumina RNA sequencing data because they are interpreted as guanosines by cellular machineries. But this indirect approach performs poorly in protein-coding regions where exons are typically short, in non-model organisms with sparsely annotated single-nucleotide polymorphisms, and in disease contexts where unknown DNA mutations are pervasive. To overcome this, a group led by Nanyang Technological University scientists combined nanopore native RNA sequencing with machine learning to create a computational method, called Dinopore, that can discriminate between unmodified adenosines, inosines, and adenosine-to-guanosine genetic variants without the use of any genomic DNA information, as well as estimate the modification rate at each editing site. "Excitingly, inosine is just one of over 150 known chemical modifications in the transcriptome," the researchers write. "Future work is needed to extend the use of Dinopore to other RNA modifications."
Two novel PCR assays for the detection of SARS-CoV-2-specific cellular immunity are described in this week's Nature Biotechnology. While there is a pressing need to be able to measure levels and durations of protective immune responses to SARS-CoV-2 to prevent breakthrough infections, traditional approaches such as flow cytometry are complex and lack scalability. To address this problem, an international team led by Mount Sinai investigators developed a pair of quantitative PCR assays for SARS-CoV-2-specific T cell activation in whole blood. The assays, they write, are rapid, internally normalized, and probe-based: one requires RNA extraction while the other avoids any sample prep. Both rely on the quantification of CXCL10 messenger RNA, a chemokine whose expression is strongly correlated with activation of antigen-specific T cells. "Our assays may allow large-scale monitoring of the magnitude and duration of functional T cell immunity to SARS-CoV-2, thus helping to prioritize revaccination strategies in vulnerable populations," the researchers write.