Dataset Information

A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA.

ABSTRACT:

Background

Breast cancer (BrC) is the most frequent neoplasm in women. New biomarkers, including aberrant DNA methylation, may improve BrC management. Herein, we evaluated the detection and prognostic performance of seven genes' promoter methylation (APC, BRCA1, CCND2, FOXA1, PSAT1, RASSF1A and SCGB3A1).

Methods

Methylation levels were assessed in primary BrC tissues by quantitative methylation-specific polymerase chain reaction (QMSP) and in circulating cell-free DNA (ccfDNA) by multiplex QMSP from two independent cohorts of patients (Cohort #1, n = 137; and Cohort #2, n = 44). Receiver operating characteristic (ROC) curves were constructed, and log-rank test and Cox regression were performed to assess the prognostic value of genes' methylation levels.

Results

The gene-panel APC, FOXA1, RASSF1A, SCGB3A1 discriminated normal from cancerous tissue with high accuracy (95.55%). In multivariable analysis, high PSAT1-methylation levels [>percentile 75 (P75)] associated with longer disease-free survival, whereas higher FOXA1-methylation levels (>P75) associated with shorter disease-specific survival. The best performing panel in ccfDNA (APC, FOXA1 and RASSF1A) disclosed a sensitivity, specificity and accuracy over 70%.

Conclusions

This approach enables BrC accurate diagnosis and prognostic stratification in tissue samples, and allows for early detection in liquid biopsies, thus suggesting a putative value for patient management.

SUBMITTER: Salta S

PROVIDER: S-EPMC6262630 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Publications

A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA.

Salta Sofia S P Nunes Sandra S Fontes-Sousa Mário M Lopes Paula P Freitas Micaela M Caldas Margarida M Antunes Luís L Castro Fernando F Antunes Pedro P Palma de Sousa Susana S Henrique Rui R Jerónimo Carmen C

Journal of clinical medicine 20181107 11

<h4>Background</h4>Breast cancer (BrC) is the most frequent neoplasm in women. New biomarkers, including aberrant DNA methylation, may improve BrC management. Herein, we evaluated the detection and prognostic performance of seven genes' promoter methylation (<i>APC</i>, <i>BRCA1</i>, <i>CCND2</i>, <i>FOXA1</i>, <i>PSAT1</i>, <i>RASSF1A</i> and <i>SCGB3A1</i>).<h4>Methods</h4>Methylation levels were assessed in primary BrC tissues by quantitative methylation-specific polymerase chain reaction (QM ...[more]

PMID: 30405052

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Project description:Mammography is used to detect breast cancer (BC), but its sensitivity is limited, especially for dense breasts. Circulating cell-free DNA (cfDNA) methylation tests is expected to compensate for the deficiency of mammography. We derived a specific panel of markers based on computational analysis of the DNA methylation profiles from The Cancer Genome Atlas (TCGA). Through training (n = 160) and validation set (n = 69), we developed a diagnostic prediction model with 26 markers, which yielded a sensitivity of 89.37% and a specificity of 100% for differentiating malignant disease from normal lesions [AUROC = 0.9816 (95% CI: 96.09-100%), and AUPRC = 0.9704 (95% CI: 94.54-99.46%)]. A simplified 4-marker model including cg23035715, cg16304215, cg20072171, and cg21501525 had a similar diagnostic power [AUROC = 0.9796 (95% CI: 95.56-100%), and AUPRC = 0.9220 (95% CI: 91.02-94.37%)]. We found that a single cfDNA methylation marker, cg23035715, has a high diagnostic power [AUROC = 0.9395 (95% CI: 89.72-99.27%), and AUPRC = 0.9111 (95% CI: 88.45-93.76%)], with a sensitivity of 84.90% and a specificity of 93.88%. In an independent testing dataset (n = 104), the obtained diagnostic prediction model discriminated BC patients from normal controls with high accuracy [AUROC = 0.9449 (95% CI: 90.07-98.91%), and AUPRC = 0.8640 (95% CI: 82.82-89.98%)]. We compared the diagnostic power of cfDNA methylation and mammography. Our model yielded a sensitivity of 94.79% (95% CI: 78.72-97.87%) and a specificity of 98.70% (95% CI: 86.36-100%) for differentiating malignant disease from normal lesions [AUROC = 0.9815 (95% CI: 96.75-99.55%), and AUPRC = 0.9800 (95% CI: 96.6-99.4%)], with better diagnostic power and had better diagnostic power than that of using mammography [AUROC = 0.9315 (95% CI: 89.95-96.34%), and AUPRC = 0.9490 (95% CI: 91.7-98.1%)]. In addition, hypermethylation profiling provided insights into lymph node metastasis stratifications (p < 0.05). In conclusion, we developed and tested a cfDNA methylation model for BC diagnosis with better performance than mammography.

Project description:Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance. Methods: To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort. Results: We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded area under the receiver operating characteristic (AUROC) curves of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Validation of a 5-marker panel created from combining hypermethylated and hypomethylated CpGs in an independent cfDNA set by bisulfite pyrosequencing yielded an AUROC of 0.956, compared to the discovery AUROC of 0.996. Conclusion: Our finding that 5mC markers derived from primary tissue did not perform well in cfDNA, compared to those identified directly from cfDNA, reveals potential advantages of starting with cfDNA to discover high performing markers for liquid biopsy development.

Project description:Background & Aims. Hepatocellular carcinoma (HCC), the most prevalent form of liver cancer, is growing in incidence but treatment options remain limited, particularly for late stage disease. As liver cirrhosis is the principal risk state for HCC development, markers to detect early HCC within this patient population are urgently needed. Perturbation of epigenetic marks, such as DNA methylation (5mC), is a hallmark of human cancers, including HCC. Identification of regions with consistently altered 5mC levels in circulating cell free DNA (cfDNA) during progression from cirrhosis to HCC could therefore serve as markers for development of minimally-invasive screens of early HCC diagnosis and surveillance. Methods. To discover DNA methylation derived biomarkers of HCC in the background of liver cirrhosis, we profiled genome-wide 5mC landscapes in patient cfDNA using the Infinium HumanMethylation450k BeadChip Array. We further linked these findings to primary tissue data available from TCGA and other public sources. Using biological and statistical frameworks, we selected CpGs that robustly differentiated cirrhosis from HCC in primary tissue and cfDNA followed by validation in an additional independent cohort. Results. We identified CpGs that segregate patients with cirrhosis, from patients with HCC within a cirrhotic liver background, through genome-wide analysis of cfDNA 5mC landscapes. Lasso regression analysis pinpointed a panel of probes in our discovery cohort that were validated in two independent datasets. A panel of five CpGs (cg04645914, cg06215569, cg23663760, cg13781744, and cg07610777) yielded AUROCs of 0.9525, 0.9714, and 0.9528 in cfDNA discovery and tissue validation cohorts 1 and 2, respectively. Conclusions. 5mC markers derived from cfDNA robustly identify HCC within a cirrhotic liver background indicating that further validation is warranted. Our finding that 5mC markers derived from primary tissue did not perform well in cfDNA, compared to those identified directly from cfDNA, reveals potential advantages of starting with cfDNA to discover high performing markers for liquid biopsy development.

Dataset Information

A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA.

Background

Methods

Results

Conclusions

Publications

A DNA Methylation-Based Test for Breast Cancer Detection in Circulating Cell-Free DNA.

Similar Datasets

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