Automatic Detection of the Circulating Cell-Free Methylated DNA Pattern of GCM2, ITPRIPL1 and CCDC181 for Detection of Early Breast Cancer and Surgical Treatment Response.

Sheng-Chao Wang,Wei-Wen Hsu,Muhamad Ansar,Cai-Cing Liu,Ruo-Kai Lin,Li-Min Liao,Yu-Mei Chung,Shih-Yun Lin,Chih-Ming Su,Chin-Sheng Hung

doi:10.3390/cancers13061375

Abstract

Simple SummaryBreast cancer is a multifactorial disease that arises from the cumulative accumulation of acquired genetic as well as epigenetic alterations. Epigenetic alteration constitutes a molecular signature that can serve as a promising biomarker for early detection. In this study, we carry out automatic detection of circulating cell-free methylated DNA, including GCM2, ITPRIPL1 and CCDC181, and find a pattern that can detect early breast cancer more accurately compared with currently used biomarkers. The pattern is also useful for the detection of the surgical responses of breast cancer patients. Circulating methylated CCDC181, GCM2 and ITPRIPL1 analysis could be combined with ultrasound to facilitate the early detection of breast cancer.The early detection of cancer can reduce cancer-related mortality. There is no clinically useful noninvasive biomarker for early detection of breast cancer. The aim of this study was to develop accurate and precise early detection biomarkers and a dynamic monitoring system following treatment. We analyzed a genome-wide methylation array in Taiwanese and The Cancer Genome Atlas (TCGA) breast cancer (BC) patients. Most breast cancer-specific circulating methylated CCDC181, GCM2 and ITPRIPL1 biomarkers were found in the plasma. An automatic analysis process of methylated ccfDNA was established. A combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was performed in R using Recursive Partitioning and Regression Trees to establish a new prediction model. Combined analysis of CCDC181, GCM2 and ITPRIPL1 (CGIm) was found to have a sensitivity level of 97% and an area under the curve (AUC) of 0.955 in the training set, and a sensitivity level of 100% and an AUC of 0.961 in the test set. The circulating methylated CCDC181, GCM2 and ITPRIPL1 was also significantly decreased after surgery (all p < 0.001). The aberrant methylation patterns of the CCDC181, GCM2 and ITPRIPL1 genes means that they are potential biomarkers for the detection of early BC and can be combined with breast imaging data to achieve higher accuracy, sensitivity and specificity, facilitating breast cancer detection. They may also be applied to monitor the surgical treatment response.

Highlights

Worldwide, breast cancer (BC) is the most commonly diagnosed cancer in women [1,2]
coiled-coil domain containing 181 (CCDC181), glial cells missing transcription factor 2 (GCM2), ITPRIPL1, ectonucleotide pyrophosphatase/phosphodiesterase 2 (ENPP2), LOC643719, zinc finger protein 177 (ZNF177), adenylate cyclase 4 (ADCY4) and Ras association domain family member 1 (RASSF1) were analyzed in the following assays
The functions of the CCDC181, GCM2, ITPRIPL1, ENPP2, LOC643719, ZNF177 and ADCY4 genes are described in Table S4 [77,78,79,80,81]

Summary

Introduction

Breast cancer (BC) is the most commonly diagnosed cancer in women [1,2]. Among 25–64-year-old women, breast cancer is the leading cause of cancer death in Taiwan [2]. Evidence shows that national mammography screening programs have sufficiently reduced BC-related mortality Both false-positive and false-negative BC diagnoses, excessive biopsies, irradiation linked to mammography application as well as suboptimal mammographybased screening in some populations, such as young females with highly dense breast tissue and Asian women, are limitations of mammography-based screening [5,6]. Breast ultrasonography fails to detect many tumors because the acoustic properties of healthy and cancerous tissues are very similar. It requires experienced radiologists, which significantly affects the sensitivity and specificity of the test [4]. Even a combined analysis of a serum panel of potential breast cancer markers, consisting of osteopontin, haptoglobin, haptoglobin, CA153, CEA, CA-125, prolactin, CA199, α-fetoprotein, leptin and migration inhibitory factor, is unable to predict the presence of early-stage breast cancer [9]

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