![]() RS: By looking at the fragmentation profiles, we were able to detect chromosome arm-level changes in the short to long fragment ratios, reflecting gain or loss of chromosomal copy number. Their cfDNA does not seem to be derived from only blood cells, but instead from a mixture of blood cells and the cancer tissue of origin. In contrast, fragmentation profiles from individuals with cancer were distinct from that of healthy individuals. In our study, we found these profiles to be remarkably consistent across 215 healthy individuals. VV: Since most cfDNA is derived from turnover of blood cells, the fragmentation profiles of healthy individuals reflect the chromatin structure of hematopoietic cells. PIW: What happens to the cfDNA fragmentation profile in cancer patients? Since we performed low-coverage whole genome sequencing (1-2X coverage), we found 5 Mb bins to provide sufficient numbers per window and resolution of the genome. So for each segment of DNA across the genome, we have a number that tells us something about how DNA is getting broken up there. Robert Scharpf: The “fragmentation profile” is something we developed to measure these potential differences in the packaging of DNA, defined as the ratio of the number of short fragments to the number of long fragments in non-overlapping bins across the genome. Different parts of the genome may be more susceptible to fragmentation depending on its packaging within the nucleus-and packaging can vary among different cell types or as a result of disease such as cancer. VV: DNA is broken into pieces, or fragmented, as part of normal processes when cells die or when cfDNA is cleared from the blood. PIW: What is DNA fragmentation and how are you defining a DNA fragmentation profile? In cancer patients, however, there is also a small amount of cfDNA derived from cancer cells that can be detected in the blood. Most cfDNA is derived from non-cancerous cells, typically as a result of cell death of blood cells. Victor Velculescu: DNA released into blood circulation is considered cfDNA. Wang: Starting with the very basic: what exactly is cell-free DNA (cfDNA)? They show that their machine learning-based method works to detect breast, colorectal, lung, ovarian, pancreatic, and gastric or bile duct cancer. ![]() Their study, published in Nature earlier this year, demonstrates the feasibility of liquid biopsies using DNA fragmentation profiles. Velculescu, MD, PhD, professor of oncology, pathology, and medicine at the Johns Hopkins University School of Medicine. Scharpf, PhD, associate professor of oncology at the Sidney Kimmel Cancer Center and Biostatistics at the Johns Hopkins Bloomberg School of Public Health and Victor E. I spoke with two researchers who have developed an approach to detect cancer from cell-free DNA by looking at DNA fragmentation: Robert B. While this scenario may have sounded like science fiction not long ago, liquid biopsies are now an exciting, tangible, area of cancer research. The authors have declared no competing interest.Imagine, taking a simple blood draw to find out if you have cancer, and if so, the cancer’s location and molecular type. To assist with the design of future experiments, we created a first-of-its-kind Atlas that catalogues the subcellular distribution and abundance of 5,898 isomiRs, tRFs, and rRFs across three cell lines. The findings suggest the existence of a complex subcellular trafficking program, and hint at expanded functions for these RNA molecules that differ by compartment. The findings have implications for previous and future molecular studies of the function of isomiRs, tRFs, and rRFs. The subcellular localization of these RNAs depends on their exact sequence and differs even for molecules that arise from the same parental miRNA, tRNA, or rRNA.įor a given RNA, its subcellular localization additionally depends on cell type. Our analysis revealed complex localization patterns involving numerous isomiRs, tRFs, and rRFs. ![]() We corrected the measured abundances by accounting for cross-fraction contamination and technical errors through a rigorous mathematical model. Of biological replicates from three cell lines from the same tissue. We analyzed the distribution of microRNA isoforms (isomiRs), tRNA-derived fragments (tRFs), and rRNA-derived fragments (rRFs) in the ![]()
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