Structural variation and its potential impact on genome instability: Novel discoveries in the EGFR landscape by long-read sequencing.

Structural variation (SV) is typically defined as variation within the human genome that exceeds 50 base pairs (bp). SV may be copy number neutral or it may involve duplications, deletions, and complex rearrangements. Recent studies have shown SV to be associated with many human diseases. However, studies of SV have been challenging due to technological constraints. With the advent of third generation (long-read) sequencing technology, exploration of longer stretches of DNA not easily examined previously has been made possible. In the present study, we utilized third generation (long-read) sequencing techniques to examine SV in the EGFR landscape of four haplotypes derived from two human samples. We analyzed the EGFR gene and its landscape (+/- 500,000 base pairs) using this approach and were able to identify a region of non-coding DNA with over 90% similarity to the most common activating EGFR mutation in non-small cell lung cancer. Based on previously published Alu-element genome instability algorithms, we propose a molecular mechanism to explain how this non-coding region of DNA may be interacting with and impacting the stability of the EGFR gene and potentially generating this cancer-driver gene. By these techniques, we were also able to identify previously hidden structural variation in the four haplotypes and in the human reference genome (hg38). We applied previously published algorithms to compare the relative stabilities of these five different EGFR gene landscape haplotypes to estimate their relative potentials to generate the EGFR exon 19, 15 bp canonical deletion. To our knowledge, the present study is the first to use the differences in genomic architecture between targeted cancer-linked phased haplotypes to estimate their relative potentials to form a common cancer-linked driver mutation.

A comparison of 100 human genes using an alu element-based instability model.

The human retrotransposon with the highest copy number is the Alu element. The human genome contains over one million Alu elements that collectively account for over ten percent of our DNA. Full-length Alu elements are randomly distributed throughout the genome in both forward and reverse orientations. However, full-length widely spaced Alu pairs having two Alus in the same (direct) orientation are statistically more prevalent than Alu pairs having two Alus in the opposite (inverted) orientation. The cause of this phenomenon is unknown. It has been hypothesized that this imbalance is the consequence of anomalous inverted Alu pair interactions. One proposed mechanism suggests that inverted Alu pairs can ectopically interact, exposing both ends of each Alu element making up the pair to a potential double-strand break, or "hit". This hypothesized "two-hit" (two double-strand breaks) potential per Alu element was used to develop a model for comparing the relative instabilities of human genes. The model incorporates both 1) the two-hit double-strand break potential of Alu elements and 2) the probability of exon-damaging deletions extending from these double-strand breaks. This model was used to compare the relative instabilities of 50 deletion-prone cancer genes and 50 randomly selected genes from the human genome. The output of the Alu element-based genomic instability model developed here is shown to coincide with the observed instability of deletion-prone cancer genes. The 50 cancer genes are collectively estimated to be 58% more unstable than the randomly chosen genes using this model. Seven of the deletion-prone cancer genes, ATM, BRCA1, FANCA, FANCD2, MSH2, NCOR1 and PBRM1, were among the most unstable 10% of the 100 genes analyzed. This algorithm may lay the foundation for comparing genetic risks posed by structural variations that are unique to specific individuals, families and people groups.