Circulating cell-free DNA variables as marker of ovarian cancer patients: A pilot study

Abstract

BACKGROUND:

Minimal invasive blood-based molecular markers are evaluated as promising biomarkers in malignant diseases these days.

OBJECTIVE:

In this pilot study, we investigated the potential of cell-free DNA (cfDNA) concentration and cell-free DNA Integrity (cfDI) as blood-based diagnostic markers for ovarian cancer patients in a retrospective study cohort.

METHODS:

cfDNA concentration and cfDI were determined in the plasma of 37 ovarian cancer patients and 28 healthy controls, by measuring ALU and LINE1 repetitive DNA elements using quantitative real-time PCR.

RESULTS:

A high correlation was observed between the results of ALU and LINE1. The correlated co-efficiency between the values of cfDNA concentration and cfDI was 0.86 and 0.71. As for the results between cases and controls, no or just borderline significant difference was observed in cfDI after age adjustment ( $P=$ 0.40 for ALU and $P=$ 0.05 for LINE1) while cfDNA concentration showed a significant difference between ovarian cancer patients and healthy controls groups ( $P=$ 0.03 for ALU and $P=$ 3.00 E-03 for LINE1). cfDNA concentration of ALU and LINE1 had an AUC of 0.81 (0.70–0.91). ALU and LINE1 cfDI reached an AUC of 0.60 (95% CI: 0.46–0.73). The combination of these markers reached the best diagnostic power with an AUC of 0.84.

CONCLUSIONS:

cfDNA variables might be potentially diagnostic biomarkers in ovarian cancer, in combination with additional molecular markers. However, further studies are needed to confirm the diagnostic ability of cfDNA variables (cfDNA concentration and cfDI).

Keywords

Ovarian cancer diagnostic marker cell free DNA concentration cell free DNA integrity

1. Introduction

Ovarian cancer (OC) has a high mortality and morbidity rate among gynecological cancers with more than 22280 new cases estimated to be diagnosed and 14240 estimated number of deaths in the United States in 2016 alone [1]. Overall, it accounts for an estimated 239,000 new cases and 152,000 deaths worldwide annually [2]. As a heterogeneous disease, the origins of ovarian cancer are complex. Basically it can be generally categorized into three types according to its initial origins: epithelial, stromal and germ cell [3]. Epithelial ovarian cancer is the most common type of ovarian cancer with the proportion of 90% [4].

The survival rate of ovarian cancer patients is low, the 5-year relative survival rate is around 49% in general [5]. In stage I ovarian cancer patients where the cancer has not spread outside of the ovary, the 5-year survival rate is 90%. However, only 15% of all ovarian cancers can be identified at this early stage as the symptoms are not noticeable. The current screening measurements, including ultrasound and serum cancer antigen 125 (CA125) test, are used widely nowadays [6]. Ultrasound, especially transvaginal ultrasound can help to find abnormalities in the ovary, however, it cannot confirm whether these abnormalities are benign or malignant. CA125 is at the moment the most extensively studied serum biomarker for possible use in the early detection of OC while its sensitivity and specificity are limited [7]. So there is an urgent need of new non-invasive diagnostic markers for early stage ovarian cancer diagnosis to increase survival rates and benefit patient’s prognosis.

Recently, liquid biopsy has gained increasing importance as circulating molecular biomarkers in the peripheral blood hold the advantages of easy accessibility, reproducibility and early detection [8]. Circulating DNA, microRNAs and circulating tumor cells have been investigated for the diagnosis and prognosis of different type of cancers [9, 10, 11, 12, 13, 14].

Circulating DNA is characterized as cell-free DNA (cfDNA) observed in serum or plasma [15, 16]. Elevated cfDNA concentrations have been observed in serveral types of cancer [17]. cfDNA integrity (cfDI) is one measure defining the fragmentation of cfDNA. It is calculated as the ratio of longer DNA fragment concentrations to shorter ones of the same specific genetic locus. As the most abundant classes of repetitive DNA elements, ALU and LINE1 cover approximately 10% and 17% of the genome [18]. Studies have been conducted to evaluate the potential application of cfDI from ALU and LINE1 variables as a diagnostic biomarker in many different types of cancer like breast or prostate cancer [19].

Until now, there are limited studies regarding the difference of cfDNA concentration and cfDI between ovarian cancer patients and healthy controls. Here, we compare the difference of these cfDNA variables between the two groups. In addition, the potential diagnostic ability of cfDNA variables was also measured as the main purpose of this pilot study.

2. Methods and materials

2.1 Study subjects

This study was approved by the Ethical Committee of the University of Heidelberg (Heidelberg, Germany). All subjects were females and of Caucasian origin. Ovarian cancer patients with a primary tumor diagnosed between April 2010 and April 2014 were enrolled in the study. Patients’ blood was collected at the time point of the enrollment. Healthy controls were verified not to have ovarian disease, autoimmune diseases or inflammation diseases at the time of blood collection. In total, 37 ovarian cancer patients and 28 healthy controls were included in this study. All subjects’ demographic and clinical data are presented in Table 1.

Table 1
Distribution of clinical features of healthy controls and ovarian cancer patients

Characteristics	Controls $n=$ 28	Ovarian cancer patients $n=$ 37
Age, y
Mean	46.8	63
Median	47	62
Range	30–63	37–80
BMI, kg/m ${}^{2}$
Mean $\pm$ SD	25.7 $\pm$ 5.4	26.8 $\pm$ 5.4
Parous women
0	7	3
1	6	3
$\geqslant$ 2	15	11
NA		20
Diabetes
0	28	5
1	0	20
NA		12
Menopausal status
Pre		3
Post		19
Peri		3
NA		12
Histology
Serous		28
Endomertrial adenocarcinoma		2
NA		7
FIGO stage
I		2
II		1
III		23
IV		3
NA		8
Grading
G1		0
G2		9
G3		17
NA		11

Table 2

Mean and median cfDNA concentration and cfDI in ovarian cancer patients and healthy controls calculated from ALU and LINE1 variables, and $P$ -value of Wilcoxon rank sum tests comparing the differences of two groups

		Ovarian cancer		Healthy control		Comparison
Variable	Index	Mean	Median	Mean	Median	$P$ -value
ALU	cfDI	0.52 $\pm$ 0.19	0.49	0.61 $\pm$ 0.15	0.59	3.75E-02
	cfDNAconc (ng/ $\mu$ l)	3.66 $\pm$ 10.64	1.05	0.62 $\pm$ 0.50	0.50	1.97E-04
LINE1	cfDI	0.56 $\pm$ 0.20	0.54	0.71 $\pm$ 0.16	0.72	2.52E-03
	cfDNAconc (ng/ $\mu$ l)	2.21 $\pm$ 4.96	0.84	0.36 $\pm$ 0.37	0.24	4.29E-06

2.2 Sample collection and cfDNA extraction

Peripheral blood was collected from all patients in 9 ml EDTA tubes (S-Monovette R, Sarstedt, Nümbrecht, Germany). Blood was centrifuged at 1300 g for 20 min at 10 ${{}^{\circ}}$ C within two hours of blood withdrawal. The supernatant was transferred and centrifuged at 15500 g for 10 min at 10 ${}^{\circ}$ C. This high-speed step was done to make sure that the plasma was free of cells or cell debris. The plasma supernatant was snap frozen in liquid nitrogen and stored at $-$ 80 ${}^{\circ}$ C until further use. cfDNA was extracted from 800 $\mu$ l plasma using the QIAamp DNA Blood Mini Kit (Qiagen, Hilden, Germany) with minor modifications as described before [11]. Extracted cfDNA was eluted in 30 $\mu$ l of AE elution buffer. The eluate was re-applied onto the column, and the final eluate was collected and stored at $-$ 20 ${}^{\circ}$ C. cfDNA of blood samples from OC patients and healthy contros was extracted together to avoid batch effects.

2.3 Measurement of cfDI and cfDNA concentration

Concentration and integrity of cfDNA in blood plasma were analyzed by measuring the abundances of short and long fragments of two repetitive DNA elements, ALU (ALU-111bp, ALU-260bp) and LINE1 (LINE1-97bp, LINE1-266bp) as described before [11, 20]. All primer sequences and amplicon lengths are given in Table 1S as described before [11]. The fragment concentrations were measured in triplicates by quantitative PCR using ABsolute qPCR SYBR Green Mix (Thermo Scientific, Carlsbad, USA) and the LightCycler480 system (Roche Diagnostics, Mannheim, Germany). The cfDNA eluate was diluted 1:20 before use to achieve optimal PCR efficiency. Concentrations of the long and short fragments were calculated using the absolute quantification method according to the Light Cylcer 480 software instructions by a standard curve from five different dilutions with known DNA concentrations in each PCR plate. cfDI was calculated as the ratio of long divided by short fragments concentrations for each of the elements: ALU-260/111, LINE1-266/97. As short amplicons were nested within the long fragments, cfDI ranged from 0 to 1. Short fragment concentrations were regarded as overall cfDNA concentrations.

2.4 Statistical analysis

All statistical analyses were carried out using the PASW Statistics 23.0 (SPSS, Chicago, IL, USA) package. cfDNA concentrations were log ${}_{2}$ -transformed for further analysis as not normally distributed data. Differences of cfDNA concentrations and cfDI between case and control groups were evaluated by the Mann-Whitney U test. Receiver operating characteristic (ROC) analysis was applied to assess the discriminatory power of cfDI and cfDNA concentration between the cases and controls and the corresponding area under the curve (AUC) was calculated. Logistic regression analysis was applied for age-adjusted comparison of cfDI and log ${}_{2}$ cfDNA concentrations between different groups. $P$ -values less than 0.05 were considered as statistically significant.

3. Results

3.1 Clinical characteristics of the study

The clinical characteristics of ovarian cancer patients are described in Table 1. The range of age is 30–63 in control group and 37–80 in ovarian cancer patients group. We observed that age distributions between healthy controls and ovarian cancer patients were not similar (Median: 47 vs. 62, range: 30–63 vs. 37–80) because of our strict inclusion criteria. The mean $\pm$ SD of BMI index is 25.7 $\pm$ 5.4 in healthy control group and 26.8 $\pm$ 5.4 in ovarian cancer patients group. The parous status were similar between the two groups.

Table 3
$P$ -values of logistic regression analysis for age-adjusted comparisons of cfDI and cfDNA concentrations between ovarian cancer patients and healthy controls

Comparison	ALU		LINE1
	cfDI	cfDNAconc (ng/ $\mu$ l)	cfDI	cfDNAconc (ng/ $\mu$ l)
Control vs ovarian cancer	0.404	0.03	0.05	0.003

Table 4

Area under curve (AUC) using cell-free DNA variables calculated from ALU and LINE1 targets, to estimate the strength of the model to discriminate two groups revealed by receiver operating characteristic (ROC)

	AUC (95% CI)
Index	Before age adjustment	After age adjustment
ALU cfDNA concentration	0.77 (0.66–0.89)	0.80 (0.70–0.91)
LINE1 cfDNA concentration	0.84 (0.73–0.94)	0.77 (0.65–0.89)
cfDNA concentration combined	0.84 (0.74–0.94)	0.81 (0.70–0.91)
ALU cfDI	0.65 (0.52–0.79)	0.60 (0.46–0.74)
LINE1 cfDI	0.72 (0.60–0.85)	0.52 (0.38–0.66)
cfDI combined	0.72 (0.59–0.84)	0.60 (0.46–0.73)
All markers combined	0.87 (0.77–0.96)	0.84 (0.74–0.94)

Figure 1.

Correlation of (a) cell-free DNA concentration and (b) cell-free DNA integrity (cfDI) estimated from ALU and LINE1 repetitive elements.

3.2 Altered cfDI and cfDNA concentrations in ovarian cancer patients

In ovarian cancer patients, we observed a significantly higher cfDNA concentration compared to healthy controls as described in Table 2 and Fig. 1. In detail, the concentrations (mean $\pm$ SD) of ALU in ovarian cancer patients were 3.66 $\pm$ 10.64 ng/ $\mu$ l with a median level of 1.05 ng/ $\mu$ l while in healthy control, the value is 0.62 $\pm$ 0.50 ng/ $\mu$ l with a median level of 0.50 ng/ $\mu$ l ( $P=$ 1.97 E-04). Also for LINE1, the patients had a similar significantly higher level of cfDNA concentrations with a median of 0.84 compared to healthy controls with a median of 0.24) ( $P=$ 4.29 E-06). In contrast, patients had a significantly lower cfDI (median ALU cfDI $=$ 0.49, median LINE1 cfDI $=$ 0.54) compared to healthy controls (median ALU cfDI $=$ 0.62, median LINE1 cfDI $=$ 0.56) ( $P<$ 0.05 for each).

The $P$ -value of logistic regression analysis for age-adjusted comparison of cfDI and log ${}_{2}$ cfDNA concentration between healthy controls and ovarian cancer patients were shown in Table 3. After age-adjustment, cfDNA concentration exhibited significant values ( $P=$ 0.03 for ALU, and $P=$ 3.00 E-03 for LINE1) while cfDI was not significant ( $P=$ 0.40 for ALU and $P=$ 0.05 for LINE1).

3.3 Consistency of cfDI and cfDNA concentration of ALU and LINE1 repetitive results

In order to test the consistency of the ALU and LINE1 repetitive results, Spearman’s rank correlation method was applied. The results between the independently measured ALU and LINE1 elements were consistent with high correlation both for log ${}_{2}$ cfDNA concentration and cfDI (Fig. 2). The correlation coefficient between the values of cfDNA concentration and cfDI was 0.86 and 0.71.

Figure 2.

Box and whisker plots of cell-free DNA concentration between controls and ovarian cancer patients estimated from ALU and LINE1 targets.

3.4 cfDNA variables associate with clinical factors

Since the age distributions between ovarian cancer patients and healthy controls were not similar, we assessed the correlation between age and cfDNA variables by Spearman correlation analysis. In ALU repetitive DNA elements, the spearman’s $\rho=$ 0.40, $P=$ 1.00 E-03 for cfDNA concentration and $\rho=-$ 0.29, $P=$ 0.02 for cfDI. Similarity, in LINE1 repetitive DNA elements, the spearman’s $\rho=$ 0.43, $P=$ 3.93 E-04 for cfDNA concentration and $\rho=-$ 0.28, $P=$ 0.02 for cfDI. Next, we investigated the cfDNA variables in the subgroup of 4 clinical factors (parous status, menopausal status, FIGO stages, and diabetic status). Results revealed that there were no significant differences ( $P>$ 0.05) between these groups, except cfDI of patients with or without diabetes. Patients with diabetes had a low cfDI of LINE1 ( $P=$ 0.013), however no significant difference was observed for ALU cfDI ( $P=$ 0.12)(Supplemental Figs 1--8).

Figure 3.

Receiver operating characteristic (ROC) analysis using cell-free DNA concentration and cell-free DNA integrity calculated from ALU and LINE1 targets, to estimate the strength of the model to discriminate between OC patients and healthy controls, along with area under the curve (AUC) after age adjustment.

Figure 4.

Receiver operating characteristic (ROC) analysis using cell-free DNA concentration and cell-free DNA integrity, calculated from ALU and LINE1 variables in combination, to estimate the strength of the model to discriminate between OC patients and healthy controls, along with area under the curve (AUC) after age adjustment.

3.5 Diagnostic ability of cfDNA variables in ovarian cancer patients

The discriminatory power of cfDNA concentration and cfDI was examined by receiver operating characteristic analysis (Table 4). ROC analysis revealed that cfDI can distinguish patients from healthy controls with an AUC of only 0.65 for ALU and only 0.72 for LINE1(Supplemental Fig. 9). cfDNA concentration can distinguish patients from healthy controls with an AUC of 0.77 for ALU and 0.84 for LINE1(Supplemental Fig. 10). Combining ALU and LINE1 data for cfDI, a discriminatory power with an AUC of 0.72 was reached (Supplemental Fig. 11). The combination of ALU and LINE1 cfDNA concentration and integrity resulted in an AUC of 0.87 which indicates a promising biomarker for ovarian cancer diagnosis indicated that the measurement of these repetitive DNA elements parameters might be promising biomarker for ovarian cancer diagnosis (Supplemental Fig. 12).

After age adjustment ROC analysis revealed that cfDI can distinguish patients from healthy controls (but not as strong as cfDNA concentration) with an AUC of only 0.60 for ALU and only 0.52 for LINE1 (Fig. 3). ROC analysis fort cfDNA concentration can distinguish patients from healthy controls with an AUC of 0.80 for ALU and 0.77 for LINE1 (Fig. 3). Combining ALU and LINE1 data for cfDI, discriminatory power with an AUC of 0.60 was reached and for cfDNA concentration an AUC of 0.81 (Fig. 4). Using the combination of both repetitive elements (ALU and LINE1) with both measured characteristics, cfDNA concentration and integrity, the AUC can reach 0.84 which indicates a promising biomarker for ovarian cancer diagnosis (Fig. 4).

4. Discussion

In this study, we identified an elevated level of cfDNA concentration and a decreased level of cfDI of the two repetitive elements ALU and LINE1 in OC patients in comparison to healthy controls. After age-adjustment, only cfDNA concentration between different groups was a significant marker while cfDI was not significant any more. By using ROC analysis, we identified that the combination of cfDI and cfDNA concentration of both repetitive elements can be a diagnostic biomarkers in ovarian cancer patients with large potential.

Liquid biopsy is becoming more and more important in precision medicine [21]. Many traditional biomarkers, such as microRNA, circulating DNA, circulating tumor cells have been investigated as diagnostic or prognostic markers in various kind of tumors. cfDNA concentration and cfDI are two prominent biomarkers among them. Overall cancer patients have higher cell-free DNA concentration levels than healthy controls and also showed a much wide range of values [8]. As to cfDI, several studies have identified an altered integrity of cfDNA in malignant cancer patients compared to healthy controls [22]. However these results are heterogeneous. Some studies observed a reduced cfDI in cancer patients [23, 24, 25], while others reported an increased cfDI [26, 27].

Results of this study, showing a higher cfDNA concentration in OC patients compared to healthy controls (age-adjust $P=$ 0.03 for ALU and $P=$ 3.00 E-03 for LINE1) were consistent with most of the other studies before. In other reports related on cfDNA biomarkers in ovarian cancer, Kamat et al. reported elevated cfDNA levels in advanced stage of ovarian cancer patients compared to controls by real-time PCR early in 2006 [28]. In 2010, Kamat et al. showed again significantly elevated cfDNA in patients’ plasma of epithelial ovarian cancer with the cfDNA level of 10.11 genome equivalents (GE)/ml in epithelial ovarian cancer patients, 1.91 GE/ml in controls and 2,365 GE/ml in benign ovarian neoplasms [29]. Similar results were observed in serum of ovarian cancer patients by Shao et al. [30]. The serum levels of cfDNA were 197.20 $\mu$ g/l, 199.90 $\mu$ g/l and 811.40 $\mu$ g/l in healthy control, benign tumor and ovarian cancer groups, respectively. However not all cfDNA concentration levels were higher in patients compared to controls. No et al. reported that the cfDNA concentration of B2M, RAB25, CLDN4 and ABCF2 were similar between benign and malignant ovarian tumors [31].

As to cfDI, only a few studies focus on the cfDI in ovarian cancer so far. In Wang’s research about cfDI in malignant gynecological neoplasms, no detailed cfDI results were shown between ovarian cancer patients and controls [32]. In our results we observed a significant decreased level of cfDI in ovarian cancer patients in comparison to healthy controls. The results we obtained were similar to other studies in breast cancer, reported from our group before [11, 20]. However, the differences of cfDI were not significant while only LINE1 cfDI showed a borderline significance ( $P$ value of 0.05) after age adjustment which indicated that cfDI alone may not be a suitable diagnostic biomarker in ovarian cancer. In a recent paper published by Yu et al., the authors observed an increased cfDNA concentration and a decreased cfDI in OC patients compared to healthy controls. The diagnostic value was 0.86 for cfDNA concentration and 0.72 for cfDI, separately [33]. The results were similar to our results, however, the study only evaluated 20 cases and 20 controls. In 2018, Zhang et al. reported an increased cfDI and cfDNA concentration in 24 OC patients compared to 12 healthy controls in a retrospective study [34]. So taking into consideration these different reports, the cfDI results in ovarian cancer patients are heterogeneous.

In order to detect potential clinical-used biomarkers, it is of great importance to apply a standardized sample processing protocol with all samples. Many factors during the process can influence the outcome of cfDNA results [35]. In our study we conducted a protocol which has been validated many times before in other studies of our group [11, 20, 36]. Very important handling steps are the processing of blood within 2 hours after withdrawal and a two-step centrifugation process for the plasma purification, including a high-speed centrifugation step in order to remove cell debris of all plasma samples were thawed only once directly before use. Using this standardized procedure, we observed a good correlation from results between ALU and LINE1. Our concordant observations from independent cfDI measurements of these two elements minimize the possibility of false-positive results. In addition, cfDI shows values between 0 to 1 in contrast to cfDNA concentration that can vary a lot more and sometimes be extremely low and therefore hard to detect.

However, also the biggest limitation of this study should also be noted here. Because of the strict inclusion and exclusion criteria, the sample size of the study is very small which resulted insufficient power of the study. Nevertheless, it is not reasonable to analyze cfDNA related variables in different clinical subgroups of OC. Also depending on the strict inclusion criteria, the age distribution was not matched between the OC patients and the healthy controls. We also observed that age was correlated with cfDNA variables. Nevertheless, it would be worthwhile to investigate cfDNA variables combined with other blood-based biomarker in further prospective studies with excellent plasma material in a larger cohort. In addition, it would be interesting to evaluate if cfDNA variables can also contribute to the discrimination of different stages of OC patients.

In summary, our study shows increased cfDNA concentrations and decreased cfDI in ovarian cancer patients compared to healthy controls. After age adjustment, only cfDNA concentrations showed a significant difference while cfDI were not significantly different. Combining all markers, the best diagnostic power was reached. More studies are needed to confirm the putative diagnostic value of cfDNA variables in ovarian cancer patients, especially as part of a molecular, blood-based multi-marker assay.

Footnotes

Acknowledgments

We thank Sabine Serick, Katharina Cuk, Sarah Schott, Frederik Marme, Christof Sohn and all clinical colleagues and patients for their contribution to this sample collection.

Conflict of interest

The authors declare that they have no conflict of interest.

Supplementary data

The supplementary files are available to download from https://dx-doi-org.web.bisu.edu.cn/10.3233/CBM-191018.

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Circulating cell-free DNA variables as marker of ovarian cancer patients: A pilot study

Abstract

BACKGROUND:

OBJECTIVE:

METHODS:

RESULTS:

CONCLUSIONS:

Keywords

1. Introduction

2. Methods and materials

2.1 Study subjects

Table 1 Distribution of clinical features of healthy controls and ovarian cancer patients

2.3 Measurement of cfDI and cfDNA concentration

2.4 Statistical analysis

3. Results

3.1 Clinical characteristics of the study

Table 3 P -values of logistic regression analysis for age-adjusted comparisons of cfDI and cfDNA concentrations between ovarian cancer patients and healthy controls

3.3 Consistency of cfDI and cfDNA concentration of ALU and LINE1 repetitive results

4. Discussion

Footnotes

Acknowledgments

Conflict of interest

Supplementary data

References

Table 1
Distribution of clinical features of healthy controls and ovarian cancer patients

Table 3
$P$ -values of logistic regression analysis for age-adjusted comparisons of cfDI and cfDNA concentrations between ovarian cancer patients and healthy controls