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      Genome-wide cell-free DNA fragmentation in patients with cancer

      1 , 2 , 1 , 1 , 1 , 2 , 1 , 1 , 3 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 1 , 9 , 9 , 4 , 3 , 5 , 6 , 7 , 8 , 10 , 4 , 11 , 4 , 12 , 13 , 14 , 12 ,   3 , 1 , 2 , # , 1 , #

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          Cell-free DNA (cfDNA) in the blood provides a noninvasive diagnostic avenue for patients with cancer 1 . However, characteristics of the origins and molecular features of cfDNA are poorly understood. We developed an approach to evaluate fragmentation patterns of cfDNA across the genome and found that cfDNA profiles of healthy individuals reflected nucleosomal patterns of white blood cells, while patients with cancer had altered fragmentation profiles. We applied this method to analyze fragmentation profiles of 236 patients with breast, colorectal, lung, ovarian, pancreatic, gastric, or bile duct cancer and 245 healthy individuals. A machine learning model incorporating genome-wide fragmentation features had sensitivities of detection ranging from 57% to >99% among the seven cancer types at 98% specificity, with an overall AUC of 0.94. Fragmentation profiles could be used to identify the tissue of origin of the cancers to a limited number of sites in 75% of cases. Combining our approach with mutation based cfDNA analyses detected 91% of cancer patients. The results of these analyses highlight important properties of cfDNA and provide a proof of principle approach for screening, early detection, and monitoring of human cancer.

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          Most cited references20

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          The chromatin accessibility landscape of primary human cancers

          We present the genome-wide chromatin accessibility profiles of 410 tumor samples spanning 23 cancer types from The Cancer Genome Atlas (TCGA). We identify 562,709 transposase-accessible DNA elements that substantially extend the compendium of known cis-regulatory elements. Integration of ATAC-seq (the assay for transposase-accessible chromatin using sequencing) with TCGA multi-omic data identifies a large number of putative distal enhancers that distinguish molecular subtypes of cancers, uncovers specific driving transcription factors via protein-DNA footprints, and nominates long-range gene-regulatory interactions in cancer. These data reveal genetic risk loci of cancer predisposition as active DNA regulatory elements in cancer, identify gene-regulatory interactions underlying cancer immune evasion, and pinpoint noncoding mutations that drive enhancer activation and may affect patient survival. These results suggest a systematic approach to understanding the noncoding genome in cancer to advance diagnosis and therapy.
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            Lengthening and shortening of plasma DNA in hepatocellular carcinoma patients.

            The analysis of tumor-derived circulating cell-free DNA opens up new possibilities for performing liquid biopsies for the assessment of solid tumors. Although its clinical potential has been increasingly recognized, many aspects of the biological characteristics of tumor-derived cell-free DNA remain unclear. With respect to the size profile of such plasma DNA molecules, a number of studies reported the finding of increased integrity of tumor-derived plasma DNA, whereas others found evidence to suggest that plasma DNA molecules released by tumors might be shorter. Here, we performed a detailed analysis of the size profiles of plasma DNA in 90 patients with hepatocellular carcinoma, 67 with chronic hepatitis B, 36 with hepatitis B-associated cirrhosis, and 32 healthy controls. We used massively parallel sequencing to achieve plasma DNA size measurement at single-base resolution and in a genome-wide manner. Tumor-derived plasma DNA molecules were further identified with the use of chromosome arm-level z-score analysis (CAZA), which facilitated the studying of their specific size profiles. We showed that populations of aberrantly short and long DNA molecules existed in the plasma of patients with hepatocellular carcinoma. The short ones preferentially carried the tumor-associated copy number aberrations. We further showed that there were elevated amounts of plasma mitochondrial DNA in the plasma of hepatocellular carcinoma patients. Such molecules were much shorter than the nuclear DNA in plasma. These results have improved our understanding of the size profile of tumor-derived circulating cell-free DNA and might further enhance our ability to use plasma DNA as a molecular diagnostic tool.
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              Is Open Access

              Fragment Length of Circulating Tumor DNA

              Malignant tumors shed DNA into the circulation. The transient half-life of circulating tumor DNA (ctDNA) may afford the opportunity to diagnose, monitor recurrence, and evaluate response to therapy solely through a non-invasive blood draw. However, detecting ctDNA against the normally occurring background of cell-free DNA derived from healthy cells has proven challenging, particularly in non-metastatic solid tumors. In this study, distinct differences in fragment length size between ctDNAs and normal cell-free DNA are defined. Human ctDNA in rat plasma derived from human glioblastoma multiforme stem-like cells in the rat brain and human hepatocellular carcinoma in the rat flank were found to have a shorter principal fragment length than the background rat cell-free DNA (134–144 bp vs. 167 bp, respectively). Subsequently, a similar shift in the fragment length of ctDNA in humans with melanoma and lung cancer was identified compared to healthy controls. Comparison of fragment lengths from cell-free DNA between a melanoma patient and healthy controls found that the BRAF V600E mutant allele occurred more commonly at a shorter fragment length than the fragment length of the wild-type allele (132–145 bp vs. 165 bp, respectively). Moreover, size-selecting for shorter cell-free DNA fragment lengths substantially increased the EGFR T790M mutant allele frequency in human lung cancer. These findings provide compelling evidence that experimental or bioinformatic isolation of a specific subset of fragment lengths from cell-free DNA may improve detection of ctDNA.

                Author and article information

                30 August 2019
                29 May 2019
                June 2019
                29 November 2019
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                [1 ]The Sidney Kimmel Comprehensive Cancer Center, Johns Hopkins University School of Medicine, Baltimore, MD 21287, USA
                [2 ]Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21287, USA
                [3 ]Department of Molecular Medicine, Aarhus University Hospital, DK-8200 Aarhus, Denmark
                [4 ]Department of Medical Oncology, University Medical Center, Utrecht University, Utrecht, The Netherlands
                [5 ]Department of Surgery, Herning Regional Hospital, DK-7400 Herning, Denmark
                [6 ]Department of Surgery, Leiden University Medical Center, Leiden, The Netherlands
                [7 ]Department of Radiation Oncology, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands
                [8 ]Department of Gastrointestinal Oncology, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands
                [9 ]Division of Hematology and Oncology, Moores Cancer Center, University of California, San Diego, La Jolla, CA 92093, USA
                [10 ]Department of Medical Oncology, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
                [11 ]Department of Pathology, VU University Medical Center, Amsterdam 1081HV, The Netherlands
                [12 ]Department of Pathology, The Netherlands Cancer Institute, Amsterdam 1066CX, The Netherlands
                [13 ]Department of Oncology, Herlev and Gentofte Hospital, Copenhagen University Hospital, 2720 Herlev, Denmark
                [14 ]Department of Surgical Gastroenterology 360, Hvidovre Hospital, 2650 Hvidovre, Denmark
                Author notes

                These authors contributed equally to this effort

                Author contributions

                S.C., A.L., J.P., J.F., V. Adleff, R.B.S., and V.E.V. designed and planned the study, and developed optimized experimental protocols. A.L., J.P., V. Adleff, J.E.M., and D.N.P. performed experiments. S.Ø.J., V. Anagnostou, P.F., J.N., K.M., J.B., B.D.W., H.H., K.L.V., M.W.Ø., A.H.M., C.J.H.V., M.V., A.C., C.J.A.P., G.R.V., N.C.T.V., M.K., R.J.A.F., J.S.J., H.J.N., G.A.M., and C.L.A. organized patient enrollment, sample collection, and clinical data curation. S.C., A.L., J.P., J.F., V. Adleff, D.C.B., J.E.M., J.R.W., N.N., G.A.M., C.L.A., R.B.S., and V.E.V. analyzed and interpreted data. S.C., A.L., J.P., J.F., R.B.S., and V.E.V. wrote the manuscript and incorporated feedback from all authors. S.C., A.L., J.P., and J.F. contributed equally to this study.

                [# ] To whom correspondence should be addressed: velculescu@ 123456jhmi.edu , rscharpf@ 123456jhu.edu

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