A Mayo Clinic-led study has shown how to detect a broad range of DNA mutations in noncancerous human tissues and across the genome. The analysis, drawing on more than 90 trillion DNA letters of sequencing data, could help scientists study when these changes arise, from early development through aging, and how they contribute to cancer and other diseases.
Published in Cell, the study is the flagship paper in a package of roughly a dozen publications from the NIH Common Fund’s Somatic Mosaicism across Human Tissues, or SMaHT, Network. Researchers evaluated approaches for detecting somatic mutations ranging from single-letter DNA changes to large deletions, insertions and rearrangements.
“We often think of the human genome as a single, fixed sequence, but every person is a mosaic of cells that acquire genetic changes over time,” explained Alexej Abyzov, corresponding author of the Cell study. “As we have increasingly recognized in recent years, these mutations are relevant not only to cancer but also to a broad range of age-related diseases.”
Unlike inherited genetic changes, somatic mutations arise after conception and accumulate over a lifetime. As cells divide, they can pass those changes to new cells, creating genetic differences within the same person known as somatic mosaicism. Some somatic mutations can alter how cells behave and contribute to cancer, aging, and neurodevelopmental, cardiovascular and autoimmune diseases.
These mutations can be difficult to detect when they are rare or occur in complex, repetitive regions of the genome, so the SMaHT Network compared short- and long-read whole-genome sequencing, duplex sequencing, single-cell sequencing, and computational approaches. Long-read sequencing produced one of the clearest findings, detecting about twice as many large DNA changes as short-read sequencing and finding insertions that shorter reads often missed. A more complete genome reference also raised precision in one benchmark from 79% to 89%, and single-cell sequencing of 102 lung and colon cells revealed cell-to-cell differences that helped trace their history back to early development.
Dr. Abyzov also co-led a companion SMaHT paper published in The American Journal of Human Genetics, in which deep whole-genome sequencing accurately detected rare, single-letter DNA changes present at levels below 1% in a tissue sample. Analyzing DNA from different organs helped detect mutations that arose early in development and spread to multiple tissues; combining lung and colon data uncovered 13 additional shared mutations, improving detection by 145% for these very rare mutations compared with analyzing either tissue alone.
SMaHT aims to catalog somatic mutations across 10 to 15 tissue types from 150 donors while improving the tools used to detect them. NIH is planning a second stage focused on understanding how these mutations influence biological processes and disease. Dr. Abyzov says the research could eventually help identify people at higher cancer risk and track precancerous changes in tissues before they develop symptoms.
Published in Cell, the study is the flagship paper in a package of roughly a dozen publications from the NIH Common Fund’s Somatic Mosaicism across Human Tissues, or SMaHT, Network. Researchers evaluated approaches for detecting somatic mutations ranging from single-letter DNA changes to large deletions, insertions and rearrangements.
“We often think of the human genome as a single, fixed sequence, but every person is a mosaic of cells that acquire genetic changes over time,” explained Alexej Abyzov, corresponding author of the Cell study. “As we have increasingly recognized in recent years, these mutations are relevant not only to cancer but also to a broad range of age-related diseases.”
Unlike inherited genetic changes, somatic mutations arise after conception and accumulate over a lifetime. As cells divide, they can pass those changes to new cells, creating genetic differences within the same person known as somatic mosaicism. Some somatic mutations can alter how cells behave and contribute to cancer, aging, and neurodevelopmental, cardiovascular and autoimmune diseases.
These mutations can be difficult to detect when they are rare or occur in complex, repetitive regions of the genome, so the SMaHT Network compared short- and long-read whole-genome sequencing, duplex sequencing, single-cell sequencing, and computational approaches. Long-read sequencing produced one of the clearest findings, detecting about twice as many large DNA changes as short-read sequencing and finding insertions that shorter reads often missed. A more complete genome reference also raised precision in one benchmark from 79% to 89%, and single-cell sequencing of 102 lung and colon cells revealed cell-to-cell differences that helped trace their history back to early development.
Dr. Abyzov also co-led a companion SMaHT paper published in The American Journal of Human Genetics, in which deep whole-genome sequencing accurately detected rare, single-letter DNA changes present at levels below 1% in a tissue sample. Analyzing DNA from different organs helped detect mutations that arose early in development and spread to multiple tissues; combining lung and colon data uncovered 13 additional shared mutations, improving detection by 145% for these very rare mutations compared with analyzing either tissue alone.
SMaHT aims to catalog somatic mutations across 10 to 15 tissue types from 150 donors while improving the tools used to detect them. NIH is planning a second stage focused on understanding how these mutations influence biological processes and disease. Dr. Abyzov says the research could eventually help identify people at higher cancer risk and track precancerous changes in tissues before they develop symptoms.