A study published in Nature (2019) explores genome-wide cell-free DNA (cfDNA) fragmentation in cancer patients. The research team developed a method to analyze cfDNA fragmentation patterns across the genome, revealing that healthy individuals' cfDNA profiles reflect nucleosomal patterns of white blood cells, while cancer patients show altered fragmentation. The method was applied to 236 cancer patients and 245 healthy individuals, with a machine learning model achieving high sensitivity and specificity in detecting cancer. Fragmentation profiles could identify cancer origins in 75% of cases, and combining this with mutation-based cfDNA analysis detected 91% of cancer patients. The study highlights the importance of cfDNA fragmentation patterns for cancer screening, early detection, and monitoring. The findings suggest that cfDNA fragmentation profiles can serve as biomarkers for cancer detection, with the DELFI method enabling the analysis of tens to hundreds of tumor-specific abnormalities from minute cfDNA amounts. The study also shows that cfDNA fragmentation profiles are influenced by genomic and epigenomic alterations, and that combining DELFI with mutation detection improves cancer detection sensitivity. The results demonstrate that cfDNA fragmentation analysis can be useful for detecting tumor-derived cfDNA and monitoring patients during treatment. The study also shows that cfDNA fragmentation profiles can help determine the tissue of origin of ctDNA, with a 75% accuracy in identifying the tissue of origin. The study concludes that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals, and that the DELFI method can be used for screening and management of patients with cancer. The study was supported by various funding sources and includes detailed methods, patient and sample characteristics, and statistical analyses. The study provides a proof of principle approach for screening, early detection, and monitoring of human cancer.A study published in Nature (2019) explores genome-wide cell-free DNA (cfDNA) fragmentation in cancer patients. The research team developed a method to analyze cfDNA fragmentation patterns across the genome, revealing that healthy individuals' cfDNA profiles reflect nucleosomal patterns of white blood cells, while cancer patients show altered fragmentation. The method was applied to 236 cancer patients and 245 healthy individuals, with a machine learning model achieving high sensitivity and specificity in detecting cancer. Fragmentation profiles could identify cancer origins in 75% of cases, and combining this with mutation-based cfDNA analysis detected 91% of cancer patients. The study highlights the importance of cfDNA fragmentation patterns for cancer screening, early detection, and monitoring. The findings suggest that cfDNA fragmentation profiles can serve as biomarkers for cancer detection, with the DELFI method enabling the analysis of tens to hundreds of tumor-specific abnormalities from minute cfDNA amounts. The study also shows that cfDNA fragmentation profiles are influenced by genomic and epigenomic alterations, and that combining DELFI with mutation detection improves cancer detection sensitivity. The results demonstrate that cfDNA fragmentation analysis can be useful for detecting tumor-derived cfDNA and monitoring patients during treatment. The study also shows that cfDNA fragmentation profiles can help determine the tissue of origin of ctDNA, with a 75% accuracy in identifying the tissue of origin. The study concludes that genome-wide cfDNA fragmentation profiles are different between cancer patients and healthy individuals, and that the DELFI method can be used for screening and management of patients with cancer. The study was supported by various funding sources and includes detailed methods, patient and sample characteristics, and statistical analyses. The study provides a proof of principle approach for screening, early detection, and monitoring of human cancer.