For individuals with a family history of cancer, early detection of shared mutations in genes known to drive cancer is critical for disease prevention and early diagnosis. However, many cancers that appear at increased rates within families have no known genetic explanation. This may be because the heritability of many cancers is due to a class of mutations called structural variations (SVs), which are made up of longer stretches of DNA that are added, missing, or rearranged within a gene. Cancer-causing SVs are not easily detected by standard genetic sequencing methods that only read and assemble short stretches of DNA, so the role of SVs in cancer heritability is not well understood. Identifying heritable SVs that lead to cancer will immediately allow for improved cancer screening and diagnostics within families with inexplicably high rates of cancer. It may also provide general insight into the heritability of many cancers. Researchers in the Schatz and Van Allen labs have teamed up to develop and utilize AI-assisted, genetic sequencing technology that can read and assemble long stretches of DNA and detect SVs with accuracy unparalleled by other sequencing technologies. The team has already recruited several families with high rates of cancers that have no known genetic causes and plans to continuously add more to increase the breadth of data they collect. They will analyze both tumor and healthy tissue samples from members of this cohort to identify SVs that are more likely to occur in those with cancer. They expect this research will clarify the role of many SVs in heritable cancers. Not only would this increase the diagnostic power of genetic sequencing, it may also help researchers discover new drug targets and uncover novel disease pathways.
PUBLISHED RESEARCH
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