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

  • 0Department of Breast Surgery, ISEIKAI International General Hospital, 4-14 Minamioogimachi, Kita-ku, Osaka City, Osaka, Japan. breast@iseikaihp.or.jp.
Breast Cancer Research and Treatment +

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March 7, 2025

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March 7, 2025

View abstract on PubMed

Summary

This summary is machine-generated.

A new model, Cir-Predict, accurately predicts circulating tumor DNA (ctDNA) detectability in breast cancer. This tool aids treatment decisions and understanding ctDNA detection mechanisms.

Area Of Science

  • Oncology
  • Molecular Biology
  • Genomics

Background

  • Circulating tumor DNA (ctDNA) detection is crucial for predicting recurrence and monitoring gene alterations in cancer.
  • Factors influencing ctDNA levels and detection rates are complex and vary across studies.
  • Accurate prediction of ctDNA detectability is needed for effective clinical application.

Purpose Of The Study

  • To develop and validate a predictive model for ctDNA detectability in breast tumor tissues.
  • To identify key genes and pathways associated with ctDNA detection.
  • To assess the model's performance and clinical relevance in breast cancer patients.

Main Methods

  • Differential gene expression analysis using DNA microarray in tumors with and without detectable ctDNA.
  • Construction of a prediction model (Cir-Predict) comprising 126 probe sets (111 genes).
  • Validation of the Cir-Predict model in independent training (n=35) and validation (n=13) cohorts of breast cancer patients.

Main Results

  • The Cir-Predict model achieved over 90% accuracy, sensitivity, and specificity in both training and validation sets.
  • Cir-Predict demonstrated significant independent association with ctDNA detection (P < 0.001, P = 0.014).
  • Cir-Predict positivity correlated with worse recurrence-free survival (P = 0.006) and was linked to pathways like tight junction and cell cycle.

Conclusions

  • Cir-Predict is a robust tool for predicting ctDNA detectability in breast cancer.
  • The model offers valuable insights for breast cancer treatment strategies.
  • Findings contribute to a better understanding of the biological mechanisms underlying ctDNA detection.

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