The analysis of cell-free DNA (cfDNA) in blood plasma has proved to offer great clinical benefit for non-invasive prenatal testing (NIPT). Today, blood tests have been performed in thousands of pregnant women to screen for fetal abnormalities. However, there are other physiological conditions in which cfDNA could provide new opportunities for more effective clinical management, such as cancer, transplantation, autoimmune disease, trauma, and cardiovascular disease. This review focuses on the potential use of cfDNA analysis to non-invasively managing cancer and transplantation (Figure 1).
cfDNA for Cancer Management
Biological characteristics of cfDNA for improving cancer management
Tumor-specific genetic and epigenetic alterations can be detected in the body fluids of patients with cancer. This circulating tumor-specific DNA (ctDNA) provides information about the genomic composition of cancer. Similar to the challenges in NIPT, analyzing ctDNA requires methods sufficiently sensitive and specific to capture in blood cancer-specific alterations that are highly diluted with wild-type DNA sequences. Typically, each milliliter of blood plasma contains a few thousand copies of DNA fragments, less than 10% of which on average are derived from the tumor, depending on a patient’s stage of the disease (1). To achieve greater sensitivity and specificity, researchers have developed various molecular technologies to capture these DNA fragments, ranging from locus-specific assays to targeted whole-exome or whole-genome massively parallel sequencing.
ctDNA has a few biological properties that make it a promising biomarker for cancer monitoring. These fragments of DNA are thought to be by-products of apoptosis and necrosis released in blood during cancer development (2). The blood circulation is able to accumulate signals coming from different parts of the cancer, and when the cancer spreads, from different lesions in the body. In this way, analysis of ctDNA in blood plasma presents conceptual advantages over tumor tissue biopsies, as it poses less risk to patients and is less limited by spatial heterogeneity. For example, Forshew, et al. detected an unexpected EGFR mutation in the plasma of high-grade serous ovarian cancer patients at relapse that was only present at a low level in biopsies—1 out of 8 lesions archived from the tissue bank (3).
A study also reported that the concentrations of ctDNA decrease shortly after surgery, making it a very dynamic tool for rapid analysis of tumor mass changes and showing promise for assessing prognosis after surgery (4). Notably, somatic (i.e., tumor-specific) alterations on ctDNA are intrinsically specific to changes in tumors. Taking these two properties together, ctDNA has potential to track tumor progression and monitor treatment efficiency. In the same study, Diehl, et al. tracked ctDNA dynamics in 18 advanced colorectal cancer (CRC) patients and showed that ctDNA provides higher sensitivity than carcinoembryonic antigen, the standard biomarker for tracking tumor responses and prognosis in CRC patients (4). In another study, Dawson, et al. studied a group of metastatic breast cancer patients (n=52) and showed that the dynamics of ctDNA provided the earliest indication of responses compared with circulating tumor cells and serum marker CA 15-3 compared with CT imaging (5).
Because of its noninvasive nature, ctDNA analysis also facilitates more regular longitudinal follow-up, offering great opportunities for identifying treatment resistance early on. For example, studies have shown that in non-small cell lung cancer and CRC patients, known resistance-conferring mutations could be detected in plasma in good concordance with tumor biopsy data (6-8). Beyond known resistance mechanisms, ctDNA also offers the possibility of identifying previously unknown genetic alterations associated with resistance (9). Identifying early the molecular-based mechanisms of acquired resistance to targeted drugs helps clinicians adapt new therapeutic approaches with the aim of suppressing expansion of the resistance-conferring clones. Furthermore, ctDNA analysis holds the potential to provide genomic data that would enable physicians to implement alternative therapies before resistance manifests clinically.
Noninvasive cancer management using different body fluids and types of nucleic acids
Blood plasma has been identified previously as the most robust media for ctDNA analysis (10). Researchers considered serum, but it often contains high levels of nuclear wild-type DNA released during blood clotting. Depending on the isolation efficiency, this wild-type DNA leads to high variations in mutant allelic fractions (11).
Other body fluids also could be a source of ctDNA in difficult cancer types. For example, tumor-specific DNA has been detected in urine, a promising alternative media for ctDNA in urological cancers (12). Cerebrospinal fluid in patients with brain tumors seems to present relatively higher fractions of ctDNA compared to plasma (13). Apart from cell-free nucleic acids, recent research has shown that during tumor development, cancer cells also release DNA or RNA molecules in vesicles, for example exosomes (14). These vesicles are believed to be released by both dying and living cells, and might provide additional information for monitoring cancer progression.
The road to clinical implementation
Clinical applications of ctDNA in oncology are in constant development, and ctDNA may be informative at different stages of a patient’s treatment journey. However, before ctDNA enters routine practice, its robustness as a clinical biomarker needs to be considered carefully, as both technical and biological factors can introduce variability during analysis.
On the biological front, the concentration of DNA in plasma varies greatly depending on cancer type and stage of disease, from thousands of copies per mL of plasma in CRC, to a couple of copies per mL of plasma in glioblastoma (1). This biological variability has proven more challenging in certain tumor types, such as brain or kidney cancer, demonstrating the need for larger clinical cohorts to evaluate clinical utility.
On the other hand, technical bias mainly is derived from two sources: sample preparation at pre-analytical stages, and the use of different molecular assays that have different analytical performance. Studies investigating the effects of different blood sample handling procedures called for the need to standardise these procedures for clinical implementation (15). Different molecular assays, such as PCR- based or sequencing-based methods, are associated with different levels of false-positive and false-negative rates, especially in scenarios where the tumor-specific signal is present at extremely low levels. Thorough validation is critical to ensure the quality of the assay and the condition of the laboratory in which the assays are performed.
Once these challenges are addressed, profiling ctDNA will likely supplement traditional biopsy samplings to overcome inter- and intra-tumor heterogeneity. This will reveal a more comprehensive picture of the changing tumor genome during the treatment journey.
One of the main challenges in transplantation medicine is managing post-transplantation rejection. Biopsy for the most part is the current gold standard to identify graft rejection. Yet a noninvasive tool for longitudinal post-transplantation monitoring is critical for long-term survival of recipients. cfDNA offers the potential to deal with this challenge.
Biological characteristics of cfDNA for monitoring transplantation
Cell death is one possible source of cfDNA in the blood (2), and graft rejection after solid organ transplantation is a major cause of cell death in transplanted organs. Researchers have demonstrated that donor-specific DNA can be quantified in the recipients of kidney, liver, and heart transplants (16). The amount of DNA released differs, possibly relating to the size of the organ, and also is believed to be predominantly of hematopoietic origin (16). This makes cfDNA a promising tool to monitor post-bone marrow transplantation chimerism.
Strategies for monitoring transplantation by cfDNA
Analyzing cfDNA in a transplantation context presents challenges similar to NIPT and cancer diagnostics. Specifically, DNA from the transplanted organ is mixed in the order of a few percentage points with an overwhelming background of recipient-derived DNA. Several strategies can distinguish the two different sources of DNA. One uses a sex-mismatched transplantation model, in which the donor and recipient are of different sexes. In this case, a molecular assay can distinguish donor and recipient DNA based on Y chromosome markers (16). While this strategy can only be applied in sex-mismatched transplantations, it is highly specific.
Another strategy targets polymorphisms in the human leukocyte antigen (HLA) region to specifically detect donor-derived HLA alleles (17). This approach requires the laboratory to design molecular assays tailored to each donor-recipient pair, but can be applied in sex-matched transplantations.
A third strategy targets multiple single-nucleotide polymorphisms that are potentially different between the genomes of the donor and the recipient in order to distinguish their DNA and measure their respective fractions (18). In some situations this requires substantial sequencing efforts, but is applicable in most types of transplantations without the need for extensive assay design.
Clinical applications for monitoring transplantation
Clinical studies have shown that cfDNA presents important opportunities for noninvasive transplantation monitoring (Figure 1). Researchers have discovered that concentrations of total cfDNA and donor-specific DNA are significantly higher in recipients with graft rejection than those without (17, 18). In another study, longitudinal follow-up after heart transplantation revealed that the performance of plasma DNA in detecting rejection may be influenced by the length of time after transplant as well as the patient’s age, the latter possibly relating to age-related different immune response (19). This study also found that the performance of a plasma test is comparable to results obtained from invasive biopsy, but has the potential to detect rejection events several months earlier.
The ideal test for clinical use will offer quick turnaround time at an affordable cost. Although methods based on deep sequencing are relatively expensive now, they likely will become more affordable as the cost of sequencing drops (18). Other more cost-effective approaches also have been proposed to offer a clinically compatible turnaround time (20).
Similar to other clinical applications of cfDNA, transplantation will also require more extensive validation in larger patient cohorts, even as a growing body of evidence demonstrates that longitudinal follow-up can detect rejection episodes early at an actionable stage. Larger patient cohorts will be necessary to clearly define the timeline and threshold to guide intervention decisions. When interpreting results, other possible reasons for changes in cfDNA, such as cardiovascular disease, age-related immunosuppression, and malignancy, also need to be considered.
Meanwhile, plasma DNA analysis may be used as a longitudinal monitoring test to identify possible rejection and infection events early so that high-risk patients can be referred for additional diagnostic testing.
Beyond NIPT, we envision that analysis of cfDNA will gradually make a significant impact on the management of different pathological scenarios. For cancer diagnostics, it will supplement tumor histological and imaging analysis to guide molecularly driven therapies for achieving more precise and effective medicine. For transplantation monitoring, it can potentially serve as a biomarker to detect rejection episodes early for timely intervention. Apart from cancer and transplantation, it also has potential in other conditions, such as autoimmune disease, sepsis, trauma, and neurodegenerative diseases. There will be more discoveries to come to further demonstrate its potential biological role and utility across multiple clinical contexts.
The authors acknowledge funding support from Cancer Research U.K., the University of Cambridge, and Memorial Sloan-Kettering Cancer Center.
Florent Mouliere, PhD, is a postdoctoral research associate at Cancer Research Cambridge Institute at the University of Cambridge. +Email: email@example.com
Dana W. Y. Tsui, PhD, is an assistant attending geneticist and a member of the Center for Molecular Oncology at Memorial Sloan Kettering Cancer Center in New York. +Email: firstname.lastname@example.org
1. Bettegowda C, Sausen M, Leary RJ, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 2014;6:224ra224.
2. Jahr S, Hentze H, Englisch S, et al. DNA fragments in the blood plasma of cancer patients: Quantitations and evidence for their origin from apoptotic and necrotic cells. Cancer Res 2001;61:1659–65.
3. Forshew T, Murtaza M, Parkinson C, et al. Noninvasive identification and monitoring of cancer mutations by targeted deep sequencing of plasma DNA. Sci Transl Med 2012;4:136ra168.
4. Diehl F, Schmidt K, Choti MA, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med 2008;14:985–90.
5. Dawson SJ, Tsui DW, Murtaza M, et al. Analysis of circulating tumor DNA to monitor metastatic breast cancer. N Engl J Med 2013;368:1199–209.
6. Kuang Y, Rogers A, Yeap BY, et al. Noninvasive detection of EGFR T790M in gefitinib or erlotinib resistant non-small cell lung cancer. Clin Cancer Res 2009;15:2630–6.
7. Diaz LA Jr., Williams RT, Wu J, et al. The molecular evolution of acquired resistance to targeted EGFR blockade in colorectal cancers. Nature 2012;486:537–40.
8. Thierry AR, Mouliere F, El Messaoudi S, et al. Clinical validation of the detection of KRAS and BRAF mutations from circulating tumor DNA. Nat Med 2014;20:430–5.
9. Murtaza M, Dawson SJ, Tsui DW, et al. Non-invasive analysis of acquired resistance to cancer therapy by sequencing of plasma DNA. Nature 2013;497:108–12.
10. Lui YY, Dennis YM. Circulating DNA in plasma and serum: Biology, preanalytical issues and diagnostic applications. Clin Chem Lab Med 2002;40:962–8.
11. Taback B, O’Day SJ, Hoon DS. Quantification of circulating DNA in the plasma and serum of cancer patients. Ann NY Acad Sci 2004;1022:17–24.
12. Togneri FS, Ward DG, Foster JM, et al. Genomic complexity of urothelial bladder cancer revealed in urinary cfDNA. Eur J Hum Genet 2016.
13. De Mattos-Arruda L, Mayor R, Ng CK, et al. Cerebrospinal fluid-derived circulating tumour DNA better represents the genomic alterations of brain tumours than plasma. Nat Commun 2015;6:8839.
14. Thakur BK, Zhang H, Becker A, et al. Double-stranded DNA in exosomes: A novel biomarker in cancer detection. Cell Res 2014;24:766–9.
15. Page K, Guttery DS, Zahra N, et al. Influence of plasma processing on recovery and analysis of circulating nucleic acids. PLoS One 2013;8:e77963.
16. Lui YY, Woo KS, Wang AY, et al. Origin of plasma cell-free DNA after solid organ transplantation. Clin Chem 2003;49:495–6.
17. Gadi VK, Nelson JL, Boespflug ND, et al. Soluble donor DNA concentrations in recipient serum correlate with pancreas-kidney rejection. Clin Chem 2006;52:379–82.
18. Snyder TM, Khush KK, Valantine HA, et al. Universal noninvasive detection of solid organ transplant rejection. Proc Natl Acad Sci U S A 2011;108:6229–34.
19. De Vlaminck I, Valantine HA, Snyder TM, et al. Circulating cell-free DNA enables noninvasive diagnosis of heart transplant rejection. Sci Transl Med 2014;6:241ra277.
20. Beck J, Bierau S, Balzer S, et al. Digital droplet PCR for rapid quantification of donor DNA in the circulation of transplant recipients as a potential universal biomarker of graft injury. Clin Chem 2013;59:1732–41.