Genetic Engineering & Biotechnology News

OCT15 2017

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8 | OCTOBER 15, 2017 | GENengnews.com | Genetic Engineering & Biotechnology News Copy Number Variation Probes Inform Diverse Applications See Copy Number Variation on page 10 An overarching challenge has been to accurately identify and describe CNVs, particularly because many of them occur in repetitive genomic regions, which are relatively resilient to sequencing with the short reads that are routinely used. Moreover, no single sequencing approach can accurately identify all CNV types. Developing Better Methods "We tried to develop more powerful and less expensive ways of identifying copy number variants," says Michael H. Wigler, Ph.D., professor at Cold Spring Harbor Labo- ratories. Investigators in Dr. Wigler's lab recently developed SMASH (short multiply aggregated sequence homologies), a new protocol to measure CNV. In this approach, genomic DNA is randomly sheared into fragments with a mean length of about 40 base-pairs that are subsequently joined together into chimeric stretches to build next-generation sequencing libraries. Compared to whole-genome sequencing, SMASH generates multiple independent mappings in each sequence, increasing the information density per read. "SMASH was our best offering on how to reduce the cost of large-scale copy number measurement by sequence," says Dr. Wigler. Another advantage of SMASH is that the mechanical and/or enzymatic shearing process eliminate the GC bias. "Sequencing platforms may change and may get cheaper, but this will always be the cheapest way to do it, and that is because we break the DNA up into minimum size pieces," says Dr. Wigler. Another effort in Dr. Wigler's lab led to the development of single nucleus sequencing (SNS), a method that allows the characterization of the copy number profile of a single cell. In a proof-of-principle analysis that used breast cancer cells, Dr. Wigler and colleagues showed that SNS can identify clonal expansions during tumor growth and also compare the copy number profile of metastatic cells with those of the primary tumor. Building on this work, Dr. Wigler and colleagues re- cently proposed an approach for the early detection of cancer using a blood test. This approach proposes that screening for cancer cells in the blood, to detect disease before clinical on- set, should be based on a universal signal that emerges from the genomic DNA of cancer cells based on their shared CNV profile, which is acquired during the clonal expansion of the malignancy. After enrichment for atypical rare cells from the blood, the cells are separated based on surface proteins, and their copy number is profiled using a computational ap- proach. Analysis of the individual cells can inform investiga- tors about the tissue of origin and help guide diagnosis and treatment. The alternative approach, currently, is to profile sequence variants using cell-free DNA in the blood. "Our method is somewhat orthogonal to that, and we don't know which one will work better yet," says Dr. Wigler. One of the advantages of examining individual circulating tumor cells is what Dr. Wigler calls "phase." "When we see a lot of mutations in single-cell analysis, we know that they are all on the same cell, and that provides certainty that the cell is malignant," notes Dr. Wigler. Mutations that are seen on cell-free DNA isolated from the blood might originate from the same or from different normal cells that have undergone somatic mutations. "There are reasons why one might want to do both methods," says Dr. Wigler. This method boasts accurate risk assessment for early cancer detection and to examine response to therapy. "I think in the future this will take survival beyond the metastatic stage, and doing this within the next few years is very plausible," says Dr. Wigler. In-Depth Analysis of CNVs "For the first time, we are getting near-complete genomes that are phased with respect to structural variations," says Charles Lee, Ph.D., professor and director of The Jackson Laboratory for Genomic Medicine and president of the Hu- man Genome Organization. A recent effort by the Human Genome Structural Variation Consortium, which Dr. Lee cochairs, has focused on placing structural variants on the distinct haplotypes, which is required for their proper ge- notyping. Structural variants often mutate faster than single nucleotide polymorphisms. "Which makes structural vari- ants difficult to impute, since linkage disequilibrium between them and other structural variants and single nucleotide variants break down faster," says Dr. Lee. As a result, many CNVs have to be identified directly and then be placed on the proper haplotypes. "When the 1000 Genomes Project first came out, we were picking up only deletions, which was the state of the art, but now we are able to also see duplications, mobile element insertions, and even segments of mitochondrial DNA that became inserted into the chromosomal DNA," says Dr. Lee. In a study that examined over 2,500 human genomes (across 26 human populations) from the 1000 Genomes Project, Dr. Lee and colleagues used short-read DNA sequencing frag- ments—approximately 125 base-pairs long—and cataloged over 68,000 structural variants falling into eight classes. Drug Discovery Feature The evolution of the MUC7 gene, which encodes a saliva protein, was studied by scientists at the University at Buffalo. To capture the gene's proline-, threonine-, and serine-rich tandem repeat copy number variations (CNVs), the scientists analyzed more than 2,500 genomes. This extensive work revealed that MUC7 subexonic CNVs fall into eight divergent haplotype clusters. It also showed that the version of the gene found in sub-Saharan African populations was very different from the versions found in other populations. This finding could be due to archaic introgression, the introduction of genetic material from an unknown human relative. Bob Wilder/University at Buffalo Richard A. Stein, M.D., Ph.D. A major contributor to inter-individual genomic variability is copy number variation (CNV). CNVs change the diploid status of the DNA, involve one or multiple genes, and may disrupt coding regions, affect regulatory elements, or change gene dosage. While some of these changes may have no phenotypic consequences, others underlie disease, explain evolutionary processes, or impact the response to medication.

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