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<p>Li-Fraumeni syndrome (LFS) confers high lifetime cancer risk due to germline <i>TP53</i> pathogenic variants (PV). A comprehensive surveillance regimen termed the ‘Toronto Protocol’, has been adopted for early tumor detection, demonstrating improved survival among <i>TP53</i> PV carriers. However, the protocol’s “one-size-fits-all” approach fails to consider individual cancer risk. To personalize screening, we developed a support vector machine model to predict early onset of primary tumors (age &lt; 6) using peripheral blood methylation data of <i>TP53</i> PV carriers (<i>n</i> = 237). Validation (<i>n</i> = 64) and external testing (<i>n</i> = 79) showed AUROC = 0.928 [0.835–1.000], F1-score = 0.692 [0.435–0.867], and NPV = 0.984 [0.946–1.000]. The model achieved 91% accuracy, correctly classifying 90% of patients with cancer before the age of six and 87% of cancer-free individuals in the external test set. Our tool enables risk stratification for early-onset malignancies, to optimize clinical surveillance and improve patient outcomes.</p>

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Peripheral blood DNA methylation predicts the early onset of primary tumor in TP53 mutation carriers

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月26日

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  • Vallijah Subasri,
  • Benjamin Brew,
  • Brianne Laverty,
  • Lauren Erdman,
  • Tanya Guha,
  • Jordan R. Hansford,
  • Elizabeth Cairney,
  • Carol Portwine,
  • Christine Elser,
  • Jonathan L. Finlay,
  • Kim E. Nichols,
  • Jo Anson,
  • Wendy Kohlmann,
  • Haifan Gong,
  • Jodi Lees,
  • Noa Alon,
  • Ledia Brunga,
  • Anita Villani,
  • Kelvin C. de Andrade,
  • Payal P. Khincha,
  • Sharon A. Savage,
  • Joshua D. Schiffman,
  • Trevor J. Pugh,
  • David Malkin,
  • Anna Goldenberg

摘要

Li-Fraumeni syndrome (LFS) confers high lifetime cancer risk due to germline TP53 pathogenic variants (PV). A comprehensive surveillance regimen termed the ‘Toronto Protocol’, has been adopted for early tumor detection, demonstrating improved survival among TP53 PV carriers. However, the protocol’s “one-size-fits-all” approach fails to consider individual cancer risk. To personalize screening, we developed a support vector machine model to predict early onset of primary tumors (age < 6) using peripheral blood methylation data of TP53 PV carriers (n = 237). Validation (n = 64) and external testing (n = 79) showed AUROC = 0.928 [0.835–1.000], F1-score = 0.692 [0.435–0.867], and NPV = 0.984 [0.946–1.000]. The model achieved 91% accuracy, correctly classifying 90% of patients with cancer before the age of six and 87% of cancer-free individuals in the external test set. Our tool enables risk stratification for early-onset malignancies, to optimize clinical surveillance and improve patient outcomes.