bims-fragic Biomed News
on Fragmentomics
Issue of 2025–05–04
three papers selected by
Laura Mannarino, Humanitas Research
  1. Detection of brain cancer using genome-wide cell-free DNA fragmentomes.
  2. Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.
  3. Development and Validation of a Cell-Free DNA Fragmentomics-Based Model for Early Detection of Pancreatic Cancer.
  1. Cancer Discov. 2025 Apr 29.
    Detection of brain cancer using genome-wide cell-free DNA fragmentomes.
    Dimitrios Mathios, Noushin Niknafs, Akshaya V Annapragada, Ernest J Bobeff, Elaine J Chiao, Kavya Boyapati, Keerti Boyapati, Sarah Short, Adrianna L Bartolomucci, Stephen Cristiano, Shashikant Koul, Nicholas A Vulpescu, Leonardo Ferreira, Jamie E Medina, Daniel C Bruhm, Vilmos Adleff, Małgorzata Podstawka, Patrycja Stanisławska, Chul-Kee Park, Judy Huang, Gary L Gallia, Henry Brem, Debraj Mukherjee, Justin M Caplan, Jon Weingart, Christopher M Jackson, Michael Lim, Jillian Phallen, Robert B Scharpf, Victor E Velculescu.
      Diagnostic delays in patients with brain cancer are common and can impact patient outcome. Development of a blood-based assay for detection of brain cancers could accelerate brain cancer diagnosis. In this study, we analyzed genome-wide cell-free (cfDNA) fragmentomes, including fragmentation profiles and repeat landscapes, from the plasma of individuals with (n=148) or without (n=357) brain cancer. Machine learning analyses of cfDNA fragmentome features detected brain cancer across all grade gliomas (AUC=0.90, 95% CI: 0.87-0.93) and these results were validated in an independent prospectively collected cohort. cfDNA fragmentome changes in patients with gliomas represented a combination of fragmentation profiles from glioma cells and altered white blood cell populations in the circulation. These analyses reveal the properties of cfDNA in patients with brain cancer and open new avenues for noninvasive detection of these individuals.
    DOI:  https://doi.org/10.1158/2159-8290.CD-25-0074
  2. mSystems. 2025 Apr 29. e0027625
    Microbiome and fragmentation pattern of blood cell-free DNA and fecal metagenome enhance colorectal cancer micro-dysbiosis and diagnosis analysis: a proof-of-concept study.
    Zhongkun Zhou, Yunhao Ma, Dekui Zhang, Rui Ji, Yiqing Wang, Jianfang Zhao, Chi Ma, Hongmei Zhu, Haofei Shen, Xinrong Jiang, Yuqing Niu, Juan Lu, Baizhuo Zhang, Lixue Tu, Hua Zhang, Xin Ma, Peng Chen.
      Colorectal cancer (CRC) is the third most common cancer, and it can be prevented by performing early screening. As a hallmark of cancer, the human microbiome plays important roles in the occurrence and development of CRC. Recently, the blood microbiome has been proposed as an effective diagnostic tool for various diseases, yet its performance on CRC deserves further exploration. In this study, 133 human feces and 120 blood samples are collected, including healthy individuals, adenoma patients, and CRC patients. The blood cfDNA and fecal genome are subjected to shotgun metagenome sequencing. After removing human sequences, the microbial sequences in blood are analyzed. Based on the differential microbes and functions, random forest (RF) models are constructed for adenoma and CRC diagnosis. The results show that alterations of blood microbial signatures can be captured under low coverage (even at 3×). RF diagnostic models based on blood microbial markers achieve high area under the curve (AUC) values for adenoma patients (0.8849) and CRC patients (0.9824). When the fragmentation pattern is combined with microbial and KEGG markers, higher AUC values are obtained. Furthermore, compared to the blood microbiome, the fecal microbiome shows a different community composition, whereas their changes in KEGG pathways are similar. Pathogenic bacteria Fusobacterium nucleatum (F. nucleatum) in feces increased gradually from the healthy group to the adenoma and CRC groups. Additionally, F. nucleatum in feces and blood shows a positive correlation in CRC patients. Cumulatively, the integration of blood microbiome and fragmentation pattern is promising for CRC diagnosis.IMPORTANCEThe cell-free DNA of the human microbiome can enter the blood and can be used for cancer diagnosis, whereas its diagnostic potential in colorectal cancer and association with gut microbiome has not been explored. The microbial sequences in blood account for less than 1% of the total sequences. The blood microbial composition, KEGG functions, and fragmentation pattern are different among healthy individuals, adenoma patients, and CRC patients. Machine learning models based on these differential characteristics achieve high diagnostic accuracy, especially when they are integrated with fragmentation patterns. The great difference between fecal and blood microbiomes indicates that microbial sequences in blood may originate from various organs. Therefore, this study provides new insights into the community composition and functions of the blood microbiome of CRC and proposes an effective non-invasive diagnostic tool.
    Keywords:  cell-free DNA; colorectal cancer; diagnosis; fragmentation pattern; microbiome
    DOI:  https://doi.org/10.1128/msystems.00276-25
  3. J Clin Oncol. 2025 May 01. JCO2400287
    Development and Validation of a Cell-Free DNA Fragmentomics-Based Model for Early Detection of Pancreatic Cancer.
    Lingdi Yin, Cheng Cao, Jianzhen Lin, Zheng Wang, Yunpeng Peng, Kai Zhang, Cheng Xu, Ruowei Yang, Dongqin Zhu, Fufeng Wang, Shuang Chang, Hua Bao, Shanshan Yang, Ningyou Li, Xue Wu, Yang Shao, Zheng Wu, Shuai Wu, Ning Pu, Zhihang Xu, Feng Guo, Xu Feng, Jianmin Chen, Bin Xiao, Min Tu, Qiang Li, Jishu Wei, Junli Wu, Wentao Gao, Yi Miao, Liang Liu, Zipeng Lu, Kuirong Jiang.
       PURPOSE: Pancreatic ductal adenocarcinoma (PDAC), known for its high fatality rate, is often diagnosed in its advanced stages where surgical options are not viable. This highlights the critical need for innovative and effective early detection techniques. This study focuses on the potential of cell-free DNA (cfDNA) fragmentomics integrating advanced machine learning to identify early-stage PDAC with high accuracy.
    METHODS: Our study included a broad cohort of 1,167 participants, from which plasma was collected and subjected to shallow whole-genome sequencing. After rigorous quality assessments, 166 individuals diagnosed with PDAC and 167 healthy participants were in the training cohort, whereas the validation cohort consisted of 112 patients with PDAC and 111 healthy individuals. A separate group of 67 individuals with nonmalignant pancreatic cysts was also included to validate the model's accuracy. Finally, two additional external validation cohorts and one additional independent early-stage data set were included to evaluate the robustness of model. Our analysis used fragmentomic profiling, integrating copy-number variations, fragment size, mutational signatures, and methylation patterns analyzed using machine learning.
    RESULTS: The model demonstrated remarkable accuracy in distinguishing patients with PDAC from controls, with an AUC of 0.992 in the training data set and 0.987 in the validation data set. At a cutoff of 0.52, the training set reached a sensitivity of 93.4% and a specificity of 95.2%. In the validation data set, the sensitivity was 97.3% with a specificity of 92.8%, while the external data set demonstrated a sensitivity of 90.91% and a specificity of 94.5%.
    CONCLUSION: This study underscores the effectiveness of using cfDNA fragmentomics and machine learning for early detection of PDAC. Our approach promises significant potential in reducing PDAC mortalities through early intervention and could serve as a breakthrough in oncologic diagnostics.
    DOI:  https://doi.org/10.1200/JCO.24.00287
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