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Purpose <p>The detection of circulating tumor DNA (ctDNA) is a valuable method to predict the risk of recurrence and to detect real-time gene changes. The amount of ctDNA is affected by many factors. Moreover, the detection rate of ctDNA varies from report to report.</p> Methods <p>The present study evaluated differentially expressed genes using a DNA microarray assay for gene expression in tumors with and without detected ctDNA and constructed a prediction model for the detectability of ctDNA in breast tumor tissues. The model, named Cir-Predict, consisted of 126 probe sets (111 genes) and was constructed in a training set of breast cancer patients (<i>n</i> = 35) and validated in a validation set (<i>n</i> = 13).</p> Results <p>The accuracy, sensitivity, and specificity in training and validation sets were over 90%, and Cir-Predict was significantly associated with ctDNA detection independently of the other conventional clinicopathological parameters in training and validation sets (<i>P</i> &lt; 0.001, <i>P</i> = 0.014, respectively). Cir-Predict (+) was significantly associated with worse recurrence-free survival (<i>P</i> = 0.006). Pathway analysis revealed that nine pathways including tight junction and cell cycle tended to be related to ctDNA detectability.</p> Conclusion <p>Cir-Predict not only provides information useful for breast cancer treatment, but also helps the understanding of the mechanism by which ctDNA is detected.</p>

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Development of a prediction model for ctDNA detection (Cir-Predict) in breast cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年3月7日

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  • Chiaki Nakauchi,
  • Nanae Masunaga,
  • Naofumi Kagara,
  • Chiya Oshiro,
  • Masafumi Shimoda,
  • Kenzo Shimazu

摘要

Purpose

The detection of circulating tumor DNA (ctDNA) is a valuable method to predict the risk of recurrence and to detect real-time gene changes. The amount of ctDNA is affected by many factors. Moreover, the detection rate of ctDNA varies from report to report.

Methods

The present study evaluated differentially expressed genes using a DNA microarray assay for gene expression in tumors with and without detected ctDNA and constructed a prediction model for the detectability of ctDNA in breast tumor tissues. The model, named Cir-Predict, consisted of 126 probe sets (111 genes) and was constructed in a training set of breast cancer patients (n = 35) and validated in a validation set (n = 13).

Results

The accuracy, sensitivity, and specificity in training and validation sets were over 90%, and Cir-Predict was significantly associated with ctDNA detection independently of the other conventional clinicopathological parameters in training and validation sets (P < 0.001, P = 0.014, respectively). Cir-Predict (+) was significantly associated with worse recurrence-free survival (P = 0.006). Pathway analysis revealed that nine pathways including tight junction and cell cycle tended to be related to ctDNA detectability.

Conclusion

Cir-Predict not only provides information useful for breast cancer treatment, but also helps the understanding of the mechanism by which ctDNA is detected.