High Cell-Free DNA Integrity Is Associated with Poor Breast Cancer Survival

Abstract

Background: A recent point of focus in breast cancer (BC) research has been the utilization of cell-free DNA (cfDNA) and its concentration (cfDConc) and integrity (cfDI) as potential biomarkers. Though the association of cfDConc and poor survival is already recognized, studies on the prognostic value of cfDI have had contradictory results. Here, we provide further evidence to support the use of cfDI as a potential biomarker.

Methods: We selected 204 Eastern Finnish BC cases with non-metastatic disease and isolated cfDNA from the serum collected at the time of diagnosis before any treatment was given. The cfDConc and cfDI were measured with a fluorometer and electrophoresis and analyzed with 25 years of survival data.

Results: High cfDConc was not an independent prognostic factor in our analyses while high cfDI was found to be an independent prognostic factor for poor OS (p = 0.020, hazard ratio (HR) = 1.57, 95% confidence interval (CI) 1.07-2.29, Cox) and BCSS (p = 0.006, HR = 1.93, 95% CI 1.21-3.08)). Inclusion of cfDI in the multivariate logistic regression model improved the predictive performance.

Conclusions: Our results show high cfDI is an independent prognostic factor for poor OS and BCSS and improves the predictive performance of logistic regression models, thus supporting its prognostic potential.

Keywords: DNA fragmentation; biomarker; diagnostics; liquid biopsy.

Figure 1

Survival functions in the ER+ and ER− patients. (a) ER− BC was associated with poor short-term overall survival and (b) breast cancer-specific survival especially during the first five years of follow-up compared to ER+ BC. (c) The difference in survival was lifted off over time, mainly due to different recurrence rates on long-term follow-up.

Figure 2

Kaplan–Meier plots representing the association between cfDConc and BC survival. (a–c) Patients with high cfDConc had generally worse survival, although the only significant difference in survival was observed for OS at the 10-year follow-up (p = 0.022). (d–f) A similar survival trend was observed in the ER+ patients with a significant association with OS at the 10-year follow-up (p = 0.017). (g–i) The association with poor survival was less prominent in the ER− patient cohort.

Figure 3

Kaplan–Meier plots representing the association between cfDI and survival. (a) High cfDI was associated with poor OS (p = 0.002) and (b) BCSS (p = 0.008). (c) The association with RFS was less prominent and only visible at the 10- and 15-year follow-ups (p = 0.045 and p = 0.037). (d,e) In ER+ patients, high cfDI was associated with poor OS and BCSS, but (f) association with RFS was not observed. (g) In ER− patients, high cfDI was associated with poor OS and (h) poor BCSS at the 5- and 10-year follow-ups (p = 0.024 and p = 0.045). (i) Association between high cfDI and poor RFS was the most prominent in ER− patients. All p-values were obtained by the Logrank test.

Figure 4

Cox regression analysis plots representing the association between high cfDI and BC survival. (a) High cfDI was an independent prognostic factor for poor OS (p = 0.006) and (b) BCSS (p = 0.020) but not for the (c) poor RFS (p = 0.478). (d–f) Similar results were observed in ER+ patients although the only significant difference in survival was observed for OS. (g–i) In the ER− patients high cfDI was associated with poor BCSS but was not significant, whereas associations with OS and RFS remained less prominent. Grey lines represent the survival function of high cfDI group in areas where the plot overlaps with the legend.

Figure 5

Performance of the univariate and multivariate logistic regression models with cfDI. The top row (a–c) represents the ROC curves derived from the univariate logistic analyses with cfDI as a predictor. The ROC curves illustrate that cfDI predicts OS and BCSS with an acceptable accuracy but is less accurate biomarker for BC recurrence. The bottom row (d–f) represents the performance of the multivariate logistic regression model with and without cfDI and illustrates how including the cfDI in the model improves the general model performance.