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


Autoři: George W. Cook aff001;  Michael G. Benton aff002;  Wallace Akerley aff003;  George F. Mayhew aff004;  Cynthia Moehlenkamp aff004;  Denise Raterman aff004;  Daniel L. Burgess aff004;  William J. Rowell aff005;  Christine Lambert aff005;  Kevin Eng aff005;  Jenny Gu aff005;  Primo Baybayan aff005;  John T. Fussell aff001;  Heath D. Herbold aff001;  John M. O’Shea aff006;  Thomas K. Varghese aff007;  Lyska L. Emerson aff008
Působiště autorů: Sentry Genomics, Baton Rouge, LA, United States of America aff001;  Department of Chemical Engineering, Louisiana State University, Baton Rouge, LA, United States of America aff002;  Huntsman Cancer Institute, University of Utah School of Medicine, Department of Oncological Sciences, Salt Lake City, UT, United States of America aff003;  Roche Sequencing Solutions, Madison, WI, United States of America aff004;  Pacific Biosciences, Menlo Park, CA, United States of America aff005;  Huntsman Cancer Institute, Biorepository Molecular Pathology, Salt Lake City, UT, United States of America aff006;  Huntsman Cancer Institute, University of Utah School of Medicine, Department of Surgery, Division of Thoracic Surgery, Salt Lake City, UT, United States of America aff007;  Huntsman Cancer Institute, University of Utah School of Medicine, Department of Pathology, Salt Lake City, UT, United States of America aff008
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0226340

Souhrn

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.

Klíčová slova:

Alu elements – Cancer genomics – Genetic networks – Haplotypes – Human genomics – Protein structure networks – Sequence alignment – Sequence motif analysis


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