Understanding genetic variation in cancer using targeted nanopore sequencing | Shruti...
NCM 2019
Shruti Iyer (Cold Spring Harbor Laboratory & Stony Brook University) began her plenary talk by discussing how "cancer is a disease of the genome", in which the accumulation of genetic and epigenetic alterations results in a loss of control over normal cell growth. These genetic alterations, she described, can vary from single point mutations up to larger structural variants, which affect one or multiple genes: the field of cancer genomics aims to identify and characterise these variations.
Shruti noted how several thousand tumours have been sequenced via next-generation sequencing, enabling the discovery of different signatures and mutation rates across different cancer types, plus insights into the clonal structure and evolution of tumours. Malignant cells can comprise as little as 10% of a sample; furthermore, heterogeneity is "very much a part of cancer", with subpopulations within this exhibiting different alleles or genomic features. The combination of this intricacy and lack of depth, Shruti explained, means that the ability to detect these variants via whole genome sequencing to the typical depth of coverage of 30x is very low. Shruti described how targeted methods, such as exome capture, have helped this field to move forward by enriching for regions of interest and improve their coverage; this has enabled the detection of many small variants associated with cancers, but there has been a "blind spot" when it comes to detection of structural variants (SVs).
SVs, Shruti explained, are defined as variants spanning over 50 bp; they encompass insertions, deletions, duplications and translocations. Due to their large size, these variants tend to be disruptive. SVs contribute to copy number changes, which can amplify or delete oncogenes and tumour suppressor genes. SVs can also lead to gene fusions, which can modify the sequence and function of the protein produced; for example, by fusing a highly expressed transcript to one with lower expression levels. SVs can therefore act as prognostic indicators: greater genome instability is generally associated with poorer patient outcome. However, Shruti described how, despite the significance of SVs, relatively little about all but large copy number variations is known, and that "this is largely because of the way these variants are studied".
Some methods of analysing SVs, such as cytogenetics and microarrays - can provide a "bird's eye view" of SVs, but lack resolution. High-resolution methods, on the other hand, generally involve short-read sequencing; short reads cannot span SVs, resulting in misalignments and low sensitivity - "up to 80% false positive rates". Shruti quoted that ~700 genes have been identified as "inaccessible to sequencing with short reads", with ~200 of these being medically relevant. An individual human genome, aligned to the human reference genome, has ~20,000 SVs; Shruti highlighted how we are "really missing a lot of things by not looking for them in the right way. How can these important variants be resolved? "Spoiler alert: long reads can help!" Shruti described how SVs can be detected using long reads with a sensitivity and specificity of over 95%.
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