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Abnormal plasma DNA profiles in early ovarian cancer using a non-invasive prenatal testing platform: implications for cancer screening

  • Paul A. Cohen1, 7, 9Email author,
  • Nicola Flowers3,
  • Stephen Tong4, 5,
  • Natalie Hannan4, 5,
  • Mark D. Pertile3, 8 and
  • Lisa Hui2, 4, 5, 6
BMC Medicine201614:126

https://doi.org/10.1186/s12916-016-0667-6

Received: 28 May 2016

Accepted: 4 August 2016

Published: 24 August 2016

Abstract

Background

Non-invasive prenatal testing (NIPT) identifies fetal aneuploidy by sequencing cell-free DNA in the maternal plasma. Pre-symptomatic maternal malignancies have been incidentally detected during NIPT based on abnormal genomic profiles. This low coverage sequencing approach could have potential for ovarian cancer screening in the non-pregnant population. Our objective was to investigate whether plasma DNA sequencing with a clinical whole genome NIPT platform can detect early- and late-stage high-grade serous ovarian carcinomas (HGSOC).

Methods

This is a case control study of prospectively-collected biobank samples comprising preoperative plasma from 32 women with HGSOC (16 ‘early cancer’ (FIGO I–II) and 16 ‘advanced cancer’ (FIGO III–IV)) and 32 benign controls. Plasma DNA from cases and controls were sequenced using a commercial NIPT platform and chromosome dosage measured.

Sequencing data were blindly analyzed with two methods: (1) Subchromosomal changes were called using an open source algorithm WISECONDOR (WIthin-SamplE COpy Number aberration DetectOR). Genomic gains or losses ≥ 15 Mb were prespecified as “screen positive” calls, and mapped to recurrent copy number variations reported in an ovarian cancer genome atlas. (2) Selected whole chromosome gains or losses were reported using the routine NIPT pipeline for fetal aneuploidy.

Results

We detected 13/32 cancer cases using the subchromosomal analysis (sensitivity 40.6 %, 95 % CI, 23.7–59.4 %), including 6/16 early and 7/16 advanced HGSOC cases. Two of 32 benign controls had subchromosomal gains ≥ 15 Mb (specificity 93.8 %, 95 % CI, 79.2–99.2 %). Twelve of the 13 true positive cancer cases exhibited specific recurrent changes reported in HGSOC tumors. The NIPT pipeline resulted in one “monosomy 18” call from the cancer group, and two “monosomy X” calls in the controls.

Conclusions

Low coverage plasma DNA sequencing used for prenatal testing detected 40.6 % of all HGSOC, including 38 % of early stage cases. Our findings demonstrate the potential of a high throughput sequencing platform to screen for early HGSOC in plasma based on characteristic multiple segmental chromosome gains and losses. The performance of this approach may be further improved by refining bioinformatics algorithms and targeting selected cancer copy number variations.

Keywords

Non-invasive prenatal testingHigh-grade serous carcinomaOvarian cancer screeningCirculating tumor DNALow coverage sequencingCopy number variationsGenomic profilingLiquid biopsy

Background

The detection and monitoring of specific cancer mutations by sequencing circulating DNA holds much promise, but has yet to be widely translated into clinical care. In contrast, sequencing plasma DNA during pregnancy to detect fetal chromosomal abnormalities (non-invasive prenatal testing, NIPT) has been rapidly implemented globally due to its high accuracy and proven clinical validity [1].

Circulating DNA of tumor origin can interfere with NIPT performance and produce abnormal genomic profiles that suggest occult malignancy in pregnant women [2]. Amant et al. [3] recently reported the pre-symptomatic identification of cancer in three pregnant women undergoing NIPT, suggesting that genomic profiling for copy number variations (CNVs) may be a feasible approach for cancer screening. However, the sensitivity and specificity of clinical NIPT platforms for cancer remains unknown.

Ovarian cancer is the leading cause of gynecologic cancer-related deaths in developed countries [4] and there is a pressing need for an effective screening test [5, 6]. High-grade serous ovarian cancer (HGSOC) accounts for most deaths from the disease [7] and demonstrates a marked chromosomal instability [8]. We hypothesized that these tumor-derived chromosome abnormalities would be detectable in the plasma of HGSOC patients collected prior to primary surgery. The aims of this study were to investigate whether a clinical NIPT platform could detect HGSOC in the non-pregnant population based on an abnormal plasma DNA profile, and to compare the detection rates for early and advanced stage HGSOC.

Methods

We performed a case control study of 64 plasma samples obtained from the Western Australia Gynecologic Oncology Biospecimen Bank. These were prospectively collected between January 2013 and August 2015 with informed consent from patients prior to undergoing surgery. Ethical approval was granted for this study.

The 32 cancer cases comprised 16 women with International Federation of Gynecology and Obstetrics (FIGO) stage I and II HGSOC (‘early cancer’), and 16 women with FIGO stages III and IV HGSOC (‘advanced cancer’). The control group included women with benign gynecologic disease undergoing surgery (n = 24), or germline BRCA1 and BRCA2 mutation carriers without evidence of malignancy who were undergoing risk-reduction surgery (n = 8).

DNA libraries, prepared from cell-free DNA extracted from plasma, were sequenced on a commercial whole genome NIPT platform using the standard workflow employed for aneuploidy screening (percept™ prenatal test, Victorian Clinical Genetics Services, Parkville VIC Australia, based on Illumina’s verifi™ NIPT methodology [2]). Each research sample was sequenced alongside 14 clinical samples, with 36-cycle single-end sequencing on an Illumina NextSeq500. The read depth was low coverage at 0.2× to 0.3× based on 18–28 M × 36 bp single end reads. Laboratory and analysis staff were blinded to the case/control allocation of samples. Two types of data analyses were performed.
  1. (1)

    We used the open source algorithm WISECONDOR (WIthin-SamplE COpy Number aberration DetectOR) to detect whole chromosome and subchromosomal abnormalities not identifiable by the standard NIPT pipeline [9]. Segmental changes > 15 Mb were prespecified as abnormal calls (“positive cancer screen”).

     
  2. (2)

    We also analyzed the sequence data using the routine clinical percept™ pipeline, developed to detect fetal aneuploidy for chromosomes 21, 18, 13, X, and Y.

     

Paired tumor DNA was unavailable to correlate with plasma sequencing data. We therefore compared the results of the WISECONDOR analysis with somatic CNVs reported in the Integrated Genomic Analyses of Ovarian Carcinoma (IGAOC) derived from 489 HGSOC tumor genomes by The Cancer Genome Atlas Research Network [8]. Our data were examined for recurrent regional aberrations affecting extended chromosome regions that were reported as statistically significant by the IGAOC (8 gains and 22 losses).

Results

We detected 6/16 early stage and 7/16 advanced stage HGSOC cases using the WISECONDOR analysis, giving an overall detection rate of 13/32 (sensitivity 40.6 %, 95 % CI, 23.7–59.4 %). There were two false positive calls in the control group (specificity 93.8 %, 95 % CI, 79.2–99.2 %) (Table 1).
Table 1

Sequencing copy number variation calls using percept™ pipeline and WISECONDOR algorithm

Group

Stage

WISECONDOR call

Percept™ call

Total

Abnormal

Normal

Low risk

No call

High risk

Early HGSOC

FIGO I–II

6

10

14

2

0

16

Advanced HGSOC

FIGO III–IV

7

9

12

3

1a

16

Benign

N/A

2

30

29

1

2b

32

aMonosomy 18 call

bTwo monosomy X calls

HGSOC, high grade serous ovarian carcinoma; FIGO, International Federation of Gynecology and Obstetrics

Table 2 presents the specific CNVs detected in the 13 true positive cancer cases and the two false positive controls. Twelve of the 13 true positive cancer calls had a CNV that was reported in The Cancer Genome Atlas Network as statistically significant (FDR q value < 0.25) at high frequency (>50 % of tumors). The most common DNA amplifications observed in the 13 true positive calls affected chromosome arms 3q (n = 5), 8q (n = 7), 20q (n = 4), and 12p (n = 3). The most common DNA losses were seen on chromosome arms 5q (n = 3), 8p (n = 3), 13q (n = 4), and 15q (n = 3). Figure 1 shows the WISECONDOR plots of sequenced cfDNA showing copy number variations of chromosome 3 in the plasma of five subjects with high-grade serous ovarian carcinomas.
Table 2

“Screen positive” copy number variations (CNVs) in 13 cancer cases and two controls mapped to reported gains and losses in the Integrated Genomic Analysis of Ovarian Cancer (IGAOC) study [11]

Subject number

Age (years)

Study group

FIGO Stage

Percept™ call for aneuploidy

Detected CNVs ≥ 15 Mb mapped according to IGAOCa

 

Highly specific

Moderately specific

Non-specific

1

76

Early stage cancer

2C

No call

Chr 3q gain

Chr 12p gain

Chr 20q terminal gain

Chr 5q segmental loss

Chr 8p loss

Chr 9p loss

 

Chr 5p gain

Chr 7q segmental loss

2

65

Early stage cancer

2C

No call

Chr 3q terminal gain

Chr 20 gain

Chr 4q loss

Chr 7p loss

Chr 13q segmental loss

Chr 15q segmental loss

Chr 6p gain

Chr 2q interstitial gain

Chr 18q segmental gain

3

48

Early stage cancer

1C

Low risk

Chr 12p terminal gain

  

4

71

Early stage cancer

2C

Low risk

Chr 3q interstitial gain

Chr 8q gain

  

5

38

Early stage cancer

1C

Low risk

Chr 8q terminal gain

 

Chr 3p terminal gain

6

47

Early stage cancer

2A

Low risk

Chr 8q terminal gain

  

7

54

Advanced stage cancer

4

No call

Chr 3q terminal gain

Chr 8 gain

Chr 5q loss

Chr 13 loss

Chr 15 loss

Chr 17 loss

Chr 18 loss

Chr 22 loss

Chr 14 loss

Chr 5p gain

Chr 9p gain

8

57

Advanced stage cancer

3B

Low risk

Chr 8q terminal gain

Chr 8p terminal loss

Chr 1q interstitial gain

Chr 6p gain

Chr 1p interstitial gain

Chr 11q segmental gain

9

60

Advanced stage cancer

3A1

Low risk

Chr 20 gain

  

10

83

Advanced stage cancer

3A

Low risk

  

Chr 11q interstitial gain

11

33

Advanced stage cancer

3C

No call

Chr 8q terminal gain

Chr 12p terminal gain

Chr 4q segmental loss

Chr 5q interstitial loss

Chr 6q terminal loss

Chr 8p loss

Chr 9p terminal loss

Chr 13 segmental loss

Chr 15 segmental loss

Chr 17q segmental loss

Chr 22 loss

Chr 6p segmental gains

Chr 7q segmental gains

Chr 1p segmental gains

Chr 2 segmental gains

Chr 5p gain

Chr 11q interstitial gain

Chr 18q segmental gain

Chr 1p segmental loss

Chr 10p loss

Chr 11q terminal loss

Chr 21 loss

12

58

Advanced stage cancer

3C

No call

Chr 3q gain

Chr 4p loss

Chr 9q loss

Chr 13 loss

Chr 1q gain

Chr 6p gain

Chr 7q terminal gain

Chr 11p loss

Chr 5p loss

Chr 7p terminal loss

Chr 10p gain

Chr 18 gain

13

66

Advanced stage cancer

3C

Monosomy 18

Chr 20q gain

Chr 8q terminal segmental gain

  

14

44

Benign control

NA

Low risk

Chr 20q segmental gain

  

15

53

Benign control

NA

Low risk

Chr 20q gain

  

aCNVs are categorized according to IGAOC analysis [8]. The IGAOC found 8 significantly gained chromosome arms (5 present in > 50 % of tumor samples), and 22 significantly deleted chromosome arms (18 present in > 50 %). We used the following definitions: highly specific CNV, statistically significant gain or loss (q value < 0.25) with frequency in > 50 %; Moderately specific CNV, statistically significant gain or loss (q value < 0.25) with frequency in < 50 %; non-specific CNV, gain or loss with q value > 0.25

Fig. 1

WISECONDOR plots of sequenced cfDNA showing copy number variations of chromosome 3 in the plasma of five subjects with high-grade serous ovarian carcinomas. From top, Subject 1 diagnosed with a stage 2C, Subject 2 stage 2C, Subject 3 stage 4, Subject 4 stage 3C, Subject 5 stage 3C, and an Ideogram of chromosome 3. Y axis of plots depicts Z-score; red and blue lines are Z-score plotted by windowed and individual bin methods, respectively. Pink and purple bars indicate deviation detected by windowed method or called by windowed method, respectively [12]. Subjects 1, 2, 3, and 5 show whole arm and/or segmental gains of chromosome 3q. Subject 4 shows segmental copy number losses within chromosome 3p and 3q

The percept™ pipeline resulted in one “monosomy 18” call from the cancer group, and two “monosomy X” calls in the controls (Table 2). In five cancer cases and one control case, the pipeline failed to produce a result because of unexpected profiles on normalizing chromosomes.

A post hoc analysis of our results showed that many smaller focal aberrations identified by the IGAOC were also present in the “screen positive” cancer cases. Most of the cancer cases had multiple focal changes, whereas none of the benign controls, including the two false positive calls, had more than one focal change (Additional file 1).

The two false positives in the control groups in the WISECONDOR analysis had single segmental gains on 20q. The clinical history of these controls included a benign fallopian tube cyst in a patient with endometriosis and a hemorrhagic follicular cyst in a patient with a prior history of breast ductal carcinoma in situ which had been completely excised prior to plasma collection. Both patients were alive with no clinical evidence of malignant or systemic disease at the time of writing.

Discussion

In this proof of concept study, low coverage plasma DNA sequencing and analysis for chromosomal CNVs ≥ 15 Mb detected 40 % of HGSOC. Surprisingly, we detected similar proportions of early and advanced stage HGSOC cancers with this approach. This finding was unexpected because one would assume a higher detection rate in the advanced stage cases, given the lower tumor bulk of early disease. This suggests that the detection of ovarian tumor CNVs in plasma is not directly related to cancer stage; other biological factors such as fractional concentration of tumor DNA in plasma, tumor genetic heterogeneity, vascularity, and cell turnover may also be important influences on detection rates.

A limitation of our study was the inability to correlate the plasma sequencing data with paired tumor DNA due to the absence of suitable archived specimens. However, the principle that tumor DNA is detectable in plasma using NIPT sequencing platforms has been previously established [2, 3]. Furthermore, the majority of genomic aberrations detected in our cases included common imbalances previously reported in a cohort of 489 HGSOC specimens [8], supporting our assumption that the DNA aberrations detected in plasma originated from ovarian tumors.

Prior “liquid biopsy” studies in ovarian cancer have relied on the identification of tumor-specific mutations in advanced disease and the postoperative monitoring of patient-specific mutations in plasma via deep sequencing [10, 11]. Our results are notable for demonstrating that it is possible to detect early stage ovarian cancer in the absence of patient-specific tumor DNA using an existing low coverage sequencing platform. Thus, high throughput whole genome plasma sequencing, with or without the addition of other biomarkers, is an exciting avenue for future studies of cancer screening. It may have utility as a cost-effective method of monitoring high risk patients for whom tumor tissue is unavailable, such as presymptomatic BRCA1/2 mutation carriers, or to assess the preoperative risk of malignancy in patients presenting with ovarian masses.

Potential reasons for the false positive WISECONDOR results in the two controls include technical issues with the archived plasma samples or reference chromosome set. The two “monosomy X” calls in the NIPT pipeline in the controls (aged 43 and 54 years) might be explained by normal age-related X chromosome loss [12] or low grade mosaicism [13]. It is plausible that, with larger cohorts, algorithms could be devised that increase test specificity. Further work is also required to assess the technical issues with archived plasma samples and to develop the clinical potential of this approach.

Conclusions

A low coverage plasma DNA sequencing protocol used in a high throughput prenatal screening platform detected more than one in three women with early stage ovarian cancer based on common segmental chromosome gains and losses. Further refinement of this approach may have utility for future studies of ovarian cancer screening.

Abbreviations

CNV: 

copy number variation

HGSOC: 

high grade serous ovarian carcinoma

NIPT: 

non-invasive prenatal testing

WISECONDOR: 

within sample copy number aberration detector

Declarations

Acknowledgements

The National Health and Medical Research Council provided salary support to ST (#1050765) and LH (#1105603). PC is supported by the Jakovich Family and the St John of God Foundation. NJH is supported by a University of Melbourne CR Roper Fellowship. None of the funders had any involvement in the study design, data collection, data analysis, manuscript preparation or publication decisions. The authors would like to thank Professor Yee Leung, Drs. Stuart Salfinger, Jason Tan, and Ganendra Raj Kader Ali Mohan at the Western Australia Gynecologic Cancer Service, and Dr Nik Zeps and Sanela Bilic at St John of God Subiaco Hospital and the Western Australia Gynecologic Oncology Biospecimen Bank. We gratefully acknowledge the patients who participated in this study.

Funding

Funding for this study was provided by the Norman Beischer Medical Research Foundation.

Availability of data and materials

The datasets during the current study are available from the corresponding author on reasonable request.

Authors’ contributions

PC contributed to the conception and study design, acquisition, analysis, and interpretation of data, drafting, and final approval of the manuscript, and is accountable for all aspects of the work. NF contributed to the acquisition, analysis, and interpretation of data, drafting and final approval of the manuscript, and is accountable for all aspects of the work. ST contributed to the conception and study design, acquisition, analysis, and interpretation of data, drafting and final approval of the manuscript, and is accountable for all aspects of the work. NH contributed to the acquisition, analysis and interpretation of data, drafting and final approval of the manuscript, and is accountable for all aspects of the work. MP contributed to the study design, acquisition, analysis, and interpretation of data, drafting and final approval of the manuscript, and is accountable for all aspects of the work. LH contributed to the conception and study design, acquisition, analysis, and interpretation of data, drafting and final approval of the manuscript, and is accountable for all aspects of the work. All authors read and approved the final manuscript.

Competing interests

The authors declare that they have no competing interests.

Ethical approval and consent to participate

Ethical approval for the study was granted by the Human Research Ethics Committees at the Mercy Hospital for Women, 163 Studley Road, Heidelberg, Victoria 3084 Australia (ref R15/41) and St John of God Hospital, 12 Salvado Road Subiaco, Perth, Western Australia 6008 (ref 916). All plasma samples used in this study were obtained from the Western Australia Gynecologic Oncology Biospecimen Bank. These were prospectively collected between January 2013 and August 2015 with informed consent from patients prior to undergoing surgery.

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

Authors’ Affiliations

(1)
St John of God Hospital Bendat Family Comprehensive Cancer Centre
(2)
Department of Perinatal Medicine, Mercy Hospital for Women
(3)
Victorian Clinical Genetics Services
(4)
Translational Obstetrics Group, Mercy Hospital for Women
(5)
Department of Obstetrics and Gynaecology, University of Melbourne
(6)
Murdoch Children’s Research Institute
(7)
School of Women’s and Infants’ Health, University of Western Australia
(8)
Department of Paediatrics, University of Melbourne
(9)
Institute for Health Research, University of Notre Dame Australia

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Copyright

© The Author(s). 2016

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