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Circulating cell-free DNA in breast cancer: size profiling, levels, and methylation patterns lead to prognostic and predictive classifiers

Abstract

Blood circulating cell-free DNA (ccfDNA) is a suggested biosource of valuable clinical information for cancer, meeting the need for a minimally-invasive advancement in the route of precision medicine. In this paper, we evaluated the prognostic and predictive potential of ccfDNA parameters in early and advanced breast cancer. Groups consisted of 150 and 16 breast cancer patients under adjuvant and neoadjuvant therapy respectively, 34 patients with metastatic disease and 35 healthy volunteers. Direct quantification of ccfDNA in plasma revealed elevated concentrations correlated to the incidence of death, shorter PFS, and non-response to pharmacotherapy in the metastatic but not in the other groups. The methylation status of a panel of cancer-related genes chosen based on previous expression and epigenetic data (KLK10, SOX17, WNT5A, MSH2, GATA3) was assessed by quantitative methylation-specific PCR. All but the GATA3 gene was more frequently methylated in all the patient groups than in healthy individuals (all p < 0.05). The methylation of WNT5A was statistically significantly correlated to greater tumor size and poor prognosis characteristics and in advanced stage disease with shorter OS. In the metastatic group, also SOX17 methylation was significantly correlated to the incidence of death, shorter PFS, and OS. KLK10 methylation was significantly correlated to unfavorable clinicopathological characteristics and relapse, whereas in the adjuvant group to shorter DFI. Methylation of at least 3 or 4 genes was significantly correlated to shorter OS and no pharmacotherapy response, respectively. Classification analysis by a fully automated, machine learning software produced a single-parametric linear model using ccfDNA plasma concentration values, with great discriminating power to predict response to chemotherapy (AUC 0.803, 95% CI [0.606, 1.000]) in the metastatic group. Two more multi-parametric signatures were produced for the metastatic group, predicting survival and disease outcome. Finally, a multiple logistic regression model was constructed, discriminating between patient groups and healthy individuals. Overall, ccfDNA emerged as a highly potent predictive classifier in metastatic breast cancer. Upon prospective clinical evaluation, all the signatures produced could aid accurate prognosis.

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

The study was approved by the Scientific Board of the University General Hospital of Evros (PGNE), following assessment by Ethics Committee (decision 14/895/28.11.11), and was conducted according to the ethical principles of the 1964 Declaration of Helsinki and its later amendments.

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All patients participated after signing a voluntary informed consent.

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Acknowledgements

We would like to thank Dr. Vassilis Vassilakakis and Dr. Maria Markaki for preliminary data processing.

Funding

Ms Maria Panagopoulou received a scholarship for the implementation of her PhD Thesis, co-funded through the Act: “PROGRAM FOR THE GRANTING OF SCHOLARSHIPS FOR POSTGRADUATE STUDIES OF SECOND CYCLE STUDIES”. State Scholarship Foundation in Greece (IKY) (Operational Program “Human Resources Development—Education and Lifelong Learning”, Partnership Agreement PA 2014-2020).

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Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval

The study was approved by the Scientific Board of the University General Hospital of Evros (PGNE), following assessment by Ethics Committee (decision 14/895/28.11.11), and was conducted according to the ethical principles of the 1964 Declaration of Helsinki and its later amendments.

Informed consent

All patients participated after signing a voluntary informed consent.

Correspondence to Ekaterini Chatzaki.

Supplementary information

Supplementary data to survival analysis

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