Author(s): Chen-Hsiung Yeh
Circulating cell-Free DNA (cfDNA) is emerging as a non-invasive liquid biopsy biomarker for personalized and precision cancer management. While extensive tissue-based DNA methylation profiling at global and gene levels have been documented, studies regarding methylation status of cfDNA at the sub-genome scale as well as correlation with that of tissue-derived genomic DNA have yet to be explored. The ability to specifically interrogate DNA methylation status of the transcriptionally active regions within chromosomes, i.e., euchromatin, not only fulfills the knowledge gap but also provides a much needed longitudinal and real-time insight for early cancer detection and intervention. We have developed a proprietary technology for selective enrichment of euchromatin cfDNA and analyzed the 5-methylcytosine (5-mC) content in these circulating nucleocomplexes. Paired tissue and plasma DNA (n=28) were obtained from breast cancer patients at various stages with ages and genders matched to the control arm (n=21). Quantitative measurement of DNA methylation was determined by ELISA-based assays. Our results revealed significant lower methylation levels from breast cancer cohort as compared to the cancer-free group (P<0.01). Most importantly, the methylation quantification dataset from euchromatin cfDNA strongly correlated with that of tissue genomic DNA (R²=0.72). This is the first report on euchromatin cfDNA methylation and provides promising outcomes for its future clinical application
Cancer is a multi-stage process fueled by a combination of
deregulated epigenetic changes and genetic alterations in DNA
sequence [1]. Comprehensive detection of DNA mutation is
relatively difficult and time-consuming because it could occur
randomly in any nucleotide of a particular gene. In contrast,
aberrant DNA methylation usually takes place in defined CpG
islands within the regulatory region of the genes and it’s easier to
measure methylation level in a quantitative manner [1, 2]. Given
the greater consistency of DNA methylation changes in cancer
compared to mutations, methylation is thus a promising target for
biomarker development.
Deregulated DNA methylation has been detected in a variety of
cancers, including esophagus, colon, breast, liver, kidney, and
lung [3-7]. DNA used for epigenetic analysis is usually extracted
from tumor tissues harvested after surgical operation or biopsy, the
invasive procedure and the existence of tumor heterogeneity thus
limit its clinical utility as a monitoring biomarker. Recently, both
genetic and epigenetic alterations found in genomic DNA extracted
from the primary tumor could be detected in peripheral blood as
cell-free DNA (cfDNA), opening the door for a non-invasive,
real-time and longitudinal epigenetic surrogate endpoint [8-11].
Most importantly, the methylation patterns detected in cfDNA
are in high concordance with patterns observed in corresponding
primary tumor tissues [12, 13].
Metabolic nucleic acids in the form of nucleoprotein complexes
when released into bloodstream are protected from circulating
DNase/RNase digestion, while naked forms of nucleic acids are rapidly degraded. Within the chromosome secondary and tertiary
structures, euchromatin regions which comprise the most active
portion of genome, such as enhancers, transcribed exons, or
active promoters, has been shown to display much higher protein/
DNA ratios than those of non-coding inactive heterochromatin
regions [14]. Because of the loosen structure of euchromatin, DNA
strands are widely exposed and readily accessible to transcriptional
machinery than those tightly packed heterochromatins. This
unique feature highlights an opportunity for selective separation
and enrichment of high-quality, biologically active and functional
euchromatin DNA complexes from circulation. In principle, our
proprietary approach requires no prior information about circulating
DNA/protein complex composition and is very sensitive since little
starting material is sufficient. Most importantly, taking into account
the fact that the vast majority of human genome doesn’t code
for proteins (over 98%), euchromatin-based selective enrichment
technology thus makes perfect sense for cfDNA clinical application
than other randomly blind-extraction methodologies.
Genome-wide and individual cfDNA methylation marker or
panel that differentiate control and breast cancer plasma have
been reported [15-18]. However, epigenetic study on sub-
chromosomal level (between global and specific gene/panel)
is still lacking. Given the fact that euchromatin region is rich
in gene concentration, and is often under active transcription,
and that more than 90% of the human genome is euchromatic,
it will be of great interest to selectively enrich well-protected,
higher-molecular-weight, functional euchromatin cfDNA for
the accurate quantification of methylation levels and profiles,
consequently, the improvement of sensitivity and specificity of current screening such as mammography and ultrasound [19].
Moreover, tumors that display global epigenetic alterations should
benefit from targeted therapies that restore these global patterns,
representing the first examples of personalized therapies developed
from epigenetic knowledge. However, treatment with currently
approved epigenetic drugs, e.g., DNA methyltransferase inhibitors
5-Aza-CR and 5-Aza-CdR, is rather broad, and yet to be defined
epigenetic cancer subtypes that might respond differently [20-
22]. In this regard, our euchromatin cfDNA methylation profiling
could further improve the treatment efficiency by selecting the
most responsive patients.
The objective of this study is to apply the euchromatin cfDNA
approach to compare the DNA methylation levels in breast cancer
and healthy cohorts, and to further correlate DNA methylation
profiles between matched tumor tissues and plasma samples.
The study cohort consisted of twenty-eight breast cancer patients who at diagnosis ranged from 32 to 88 years old (median age 55 years old), with 11% stage I, 36% stage II, 11% stage III and 42% stage IV cases. Twenty-one age- and gender-matched healthy subjects were also included. Patient clinical characteristics revealed 50% invasive ductal carcinoma, 39% ductal carcinoma in situ (DCIS) and 11% undefined tumor (Table 1).
Table 1: Patient cohort demographics and clinical characteristics| Parameter | Number (n=28) | Patient Percentage |
|---|---|---|
| Gender | Female | 100% |
| Median Age | 55 | (32-88) |
| Race | ||
| White | 17 | 61% |
| Black | 3 | 11% |
| Undefined | 8 | 28% |
| Breast Cancer | ||
| Invasive/ Infiltrating Ductal |
14 | 50% |
| DCIS | 11 | 39% |
| Undefined | 3 | 11% |
| Overall Clinical Stage | ||
| Stage I | 3 | 11% |
| Stage II | 10 | 36% |
| Stage III | 3 | 11% |
| Stage IV | 12 | 42% |
We first considered whether histologically normal tissue was epigenetically distinct from tumor mass. To do this, we compared tissue DNA methylation values for breast cancer (n = 28) and cancer- free (n = 21) groups. At the global methylation level, significant lower 5-mC concentration was detected in breast cancer cohort than in cancer-free group (mean ± SD; 54.93 ± 7.38 % vs. 87.94 ± 8.08 %, p <0.01), consistent with the observation of global hypomethylation in breast tumor (Figure 1). Next, we investigated euchromatin cfDNA methylation levels in plasma samples from both breast cancer (n = 28) and control (n = 21) arms. Quantification of cfDNA methylation averaged of 5.73% (± 0.85%) in breast cancer and 14.45% (± 1.98%) in cancer-free control (Figure 2). In agreement with tissue biopsy data, plasma samples showed significant lower euchromatin cfDNA methylation in breast cancer compared to cancer-free cohort with p value less than 0.001.
Figure 1: Global methylation of tissue genomic DNA from breast cancer patients and matched healthy cohort.
Figure 2: Global methylation of euchromatin cfDNA from breast cancer patients and matched healthy cohort
Finally, we evaluated the sensitivity of liquid biopsy to detect tumor-specific differential methylation. To assess the performance of liquid biopsies at capturing the euchromatin cfDNA methylation patterns reminiscent of the tumor, we next examined the DNA methylation profiles of each biopsy type and how similar they were to each other. To do this, methylation levels of cfDNA and corresponding tumor genomic DNA were quantified using 5-methylcytosine (5-mC) specific ELISA kits, and data were normalized to total DNA input. The most striking observation was the strong correlation between liquid and tissue biopsies (R 2=0.72; Figure 3). Comparing DNA methylation dataset of the plasma samples with the tumor tissues, it was notable that such high degree of association occurred in each individual patient. Patients with higher percentage of 5-mC in euchromatin cfDNA consistently showed higher levels of 5-mC in their matched tumor genomic DNA, the same patterns were also observed in the cases with lower concentrations of 5-mC. Collectively, our findings demonstrated that the majority of euchromatin cfDNA methylation detectable in liquid biopsy originated from tumor DNA, despite the fact that each patient had different stage of breast cancer and thus shed different cfDNA amount into the bloodstream.
Figure 3: Strong correlation of global methylation between matched plasma euchromatin cfDNA and tissue genomic DNA
1. Jones PA, Baylin SB (2007) The epigenomics of cancer. Cell
128: 683-692.
2. Baylin SB, Esteller M, Rountree MR, Bachman KE, Schuebel
K et al. (2001) Aberrant patterns of DNA methylation,
chromatin formation and gene expression in cancer. Hum
Mol Genet 10: 687- 692.
3. Jin Z, Cheng Y, Gu W, Zheng Y, Sato F et al. (2009) A
multicenter, double-blinded validation study of methylation
biomarkers for progression prediction in Barrett’s esophagus.
Cancer Res 69: 4112-4115.
4. Arai E, Ushijima S, Fujimoto H, Hosoda F, Shibata T et
al. (2009) Genome-wide DNA methylation profiles in both
precancerous conditions and clear cell renal cell carcinomas
are correlated with malignant potential and patient outcome.
Carcinogenesis 30: 214-221.
5. Arai E, Ushijima S, Gotoh M, Ojima H, Kosuge T et al. (2009)
Genome-wide DNA methylation profiles in liver tissue at
the precancerous stage and in hepatocellular carcinoma. Int
J Cancer 125: 2854-2862.
6. Belinsky SA, Klinge DM, Dekker JD, Smith MW, Bocklage
TJ et al. (2005) Gene promoter methylation in plasma and
sputum increases with lung cancer risk. Clin Cancer Res 11:
6505-6511.
7. Hoque MO, Begum S, Topaloglu O, Chatterjee A, Rosenbaum
E et al. (2006) Quantitation of promoter methylation of
multiple genes in urine DNA and bladder cancer detection.
J Natl Cancer Inst 98: 996-1004.
8. Diehl F, Li M, Dressman D, He Y, Shen D et al. (2005)
Detection and quantification of mutations in the plasma of
patients with colorectal tumors. Proc Natl Acad Sci U S A
102: 16368-16373.
9. Allegra CJ, Jessup JM, Somerfield MR, Hamilton SR,
Hammond EH et al. (2009) American Society of Clinical
Oncology provisional clinical opinion: testing for KRAS gene
mutations in patients with metastatic colorectal carcinoma
to predict response to anti-epidermal growth factor receptor
monoclonal antibody therapy. J Clin Oncol 27: 2091-2096.
10. Levenson VV (2010) DNA methylation as a universal
biomarker. Expert Rev Mol Diagn 10: 481-488.
11. Warton K, Samimi G (2015) Methylation of cell-free
circulating DNA in the diagnosis of cancer. Front Mol Biosci
2: 1-10.
12. Board RE, Knight L, Greystoke A, Blackhall FH, Hughes A
et al. (2008) DNA methylation in circulating tumour DNA as
a biomarker for cancer. Biomark Insights 2: 307-319.
13. Gormally E, Caboux E, Vineis P, Hainaut P (2007) Circulating
free DNA in plasma or serum as biomarker of carcinogenesis:
practical aspects and biological significance. Mutat Res 635:
105-117.
14. Schwartz YB, Kahn TG, Pirrotta V (2005) Characteristic
low density and shear sensitivity of cross-linked chromatin
containing Polycomb complexes. Mol Cell Biol 25: 432-439.
15. Zhang Y, Jeltsch A (2010) The application of next generation
sequencing in DNA methylation analysis. Genes 1: 85-101.
16. Liggett TE, Melnikov AA, Marks JR, Levenson VV (2011)
Methylation patterns in cell-free plasma DNA reflect removal
of the primary tumor and drug treatment of breast cancer
patients. Int J Cancer 128: 492-499.
17. Hoque MO, Feng Q, Toure P, Dem A, Critchlow CW et al.
(2006). Detection of aberrant methylation of four genes in
plasma DNA for the detection of breast cancer. J Clin Oncol
24: 4262-4269.
18. Radpour R, Barekati Z, Kohler C, Lv Q, Burki N et al. (2011)
Hypermethylation of tumor suppressor genes involved in
critical regulatory pathways for developing a blood-based
test in breast cancer. PLoS ONE 6: e16080.
19. International Human Genome Sequencing Consortium (2004)
Finishing the euchromatic sequence of the human genome.
Nature 431: 931-945.
20. Oki Y, Issa JP (2006) Review: recent clinical trials in
epigenetic therapy. Rev Recent Clin Trials 1: 169-182.
21. Kelly TK, De Carvalho DD, Jones PA (2010) Epigenetic
modifications as therapeutic targets. Nat Biotechnol 28: 1069-
1078.
22. Keating GM (2012) Azacitidine: a review of its use in the
management of myelodysplastic syndromes/acute myeloid
leukaemia. Drugs 72:1111-1136.
23. Pan H, Chen L, Dogra S, The AL, Tan JH et al. (2012).
Measuring the methylome in clinical samples: improved
processing of the Infinium Human Methylation 450 Bead
Chip Array. Epigenetics 7:1173-1187.
24. Hovestadt V, Jones DT, Picelli S, Wang W, Kool M et al.
(2014) Decoding the regulatory landscape of medulloblastoma
using DNA methylation sequencing. Nature 5:537-541.
