医学
乳腺癌
肿瘤科
内科学
生物标志物
癌症
阶段(地层学)
队列
生物
古生物学
生物化学
化学
作者
Xiangui Zhang,Rui Feng,Yaqian Xu,Yang Liu,Fei Xie,Houpu Yang,Siyuan Wang,Yuan Peng,Miao Liu,Chaobin Wang,Shu Wang
出处
期刊:Gland surgery
[AME Publishing Company]
日期:2024-05-01
卷期号:13 (5): 684-696
摘要
Background: Circulating tumor DNA (ctDNA) is a potential biomarker not only capable of monitoring the treatment response during neoadjuvant therapy (NAT) or rescue therapy, but also identifying minimal residual disease (MRD) and detecting early relapses after primary treatment. However, it remains uncertain whether the detection of ctDNA at diagnosis, before any treatment, can predict the prognosis for patients with early breast cancer. The objective of our study was to evaluate the predictive value of baseline ctDNA for prognosis in patients with early breast cancer. Methods: A total of 90 patients with early breast cancer and 24 healthy women were recruited between August 2016 and October 2016. Peripheral blood samples were collected from patients at diagnosis, before any treatment. Blood samples were processed and subjected to targeted deep sequencing with a next-generation sequencing (NGS) panel of 1,021 cancer-related genes. The recurrence-free survival (RFS) and invasive disease-free survival (iDFS) were reported. Results: The 90 patients with breast cancer included 6 patients with ductal carcinoma in situ (DCIS) and 84 patients with invasive breast cancer. Within the cohort of patients with invasive breast cancer, ctDNA were detected in 57 patients, with a ctDNA detection rate of 67.9%. Meanwhile, no ctDNA was detected in DCIS patients. Among 84 patients with invasive breast cancer, patients with high-level ctDNA had a significantly lower RFS compared to patients with low-level ctDNA (log-rank P=0.0036). Conclusions: Our study suggested that ctDNA at diagnosis, before any treatment, could potentially serve as a biomarker to predict the prognosis for patients with early breast cancer. However, further follow-up and more studies with large sample sizes are required to confirm these findings.
科研通智能强力驱动
Strongly Powered by AbleSci AI