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

Abstract

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.