Cell-free DNA in cancer research: Origins, fragmentomics and tumor DNA

Cell-free DNA carries information about the cells that release it in both its sequence and its fragmentation patterns. In cancer research, the challenge is to identify a tumor’s contribution to this mixture of DNA from different cells. Mutations, fragmentation patterns and methylation provide different clues, and understanding how those clues arise helps researchers interpret the evidence for cancer.

On this page

• Where cfDNA comes from and how ctDNA fits
• Cell-free DNA fragment size and fragmentomics
• Methylation and tissue of origin
• ctDNA tumor fraction and uneven shedding
• Studying cfDNA with dPCR and sequencing
• Products for cfDNA collection, preparation and analysis

Where cfDNA comes from and how ctDNA fits

Cell-free DNA (cfDNA) is DNA outside cells, found in plasma and other body fluids. Normal cells contribute to this pool as they die and their DNA breaks down. DNA can be released through cell-death processes such as apoptosis and necrosis, as well as through active secretion. (1) In plasma, much of this DNA comes from blood cells and their precursors. (2)

Circulating tumor DNA (ctDNA) is the tumor-derived component of cfDNA. In people with cancer, plasma can therefore contain DNA from both cancerous and noncancerous sources. Blood sampling gives access to this material repeatedly, making cfDNA one analyte studied through liquid biopsy. The cfDNA pool is continually replenished as cells release DNA and cleared as DNA is removed from circulation. Reported half-lives range from minutes to hours, although these estimates may vary with a person’s health status. Therefore, an individual measurement of cfDNA reflects both release and clearance rather than an accumulated record of everything a tumor has shed. (1)

Cell-free DNA fragment size and fragmentomics

How cfDNA fragments form

The way DNA is packaged inside cells influences the sizes of the fragments found in plasma. DNA wraps around histone proteins to form nucleosomes. As cellular DNA breaks down during cell death, the DNA between nucleosomes can be more accessible to DNA-cutting enzymes, called nucleases, while the wrapped DNA is relatively protected. This helps explain the common plasma cfDNA size peak around 166 base pairs, consistent with DNA associated with a nucleosome and its linker region. The sample also contains shorter fragments. Longer fragments spanning multiple nucleosomes can produce additional size peaks, creating a ladder-like pattern. (1, 2, 3)

Nucleosome positions vary among cell types, making some stretches of DNA more accessible to nucleases than others. Accessibility is only part of the explanation: nucleases also preferentially cut particular DNA sequences. Together, packaging and nuclease activity influence fragment lengths, the positions along the genome where DNA was cut and the short sequences at fragment ends, called end motifs. These fragmentation patterns can therefore retain information about the cells that released the DNA. (2, 3)

What fragmentomics can reveal

Fragmentomics studies these lengths, cutting positions and end sequences to investigate which cells contributed DNA to a sample. Comparing patterns across many molecules can provide information about their source cells without first identifying a tumor-specific sequence mutation. (2, 3, 4)

Mouliere and colleagues found ctDNA enriched among fragments 90–150 base pairs long. (4) They then tested size selection followed by shallow whole-genome sequencing in plasma samples from patients with high-grade serous ovarian cancer. Selecting shorter fragments increased the tumor-derived share of the DNA being analyzed and strengthened signals from tumor-associated copy-number alterations, the gains and losses of genomic DNA. In one illustrated sample, it revealed additional alterations that were not detected in the same sample without size selection. (4)

The length of one selected fragment still does not establish that it came from a tumor. Size selection also comes with a tradeoff: informative fragments outside the selected range may be lost. Size selection can help an experiment focused on tumor alterations, but changes the distribution available for a study of the original fragment population. (4)

Methylation and tissue of origin

Fragments also carry epigenetic information. DNA methylation patterns differ among cell types and can remain informative after DNA enters circulation. Comparing methylation patterns in cfDNA helps estimate which tissues contributed to the plasma mixture. (5) Tissue origin alone does not establish cancer. For example, a liver-specific methylation signal can also arise from noncancerous liver damage. (5) Cancer-focused assays look for cancer-associated methylation patterns as well as information about tissue origin. (2)

ctDNA tumor fraction and uneven shedding

Tumor fraction is the proportion of cfDNA that comes from tumor cells. It varies with disease burden, tumor site and biological properties such as vascularization and cell turnover. Tumor size alone does not determine how much DNA reaches the sample. (1, 6) Tumor fraction is also distinct from the allele fraction of an individual sequence variant: the latter describes that variant's representation at a particular genomic site. Using a variant’s allele fraction to estimate tumor fraction requires accounting for whether the variant is present in all tumor cells or only a subset, and how many copies of the affected DNA region those cells carry. (6)

For a given cfDNA input, a low tumor fraction means fewer tumor-derived molecules are available for analysis. Consequently, a negative finding may reflect limited tumor material in the sample as well as the sensitivity and scope of the assay. Detection is a joint outcome of biology, sampling and measurement; it is not a direct measure of shedding alone. (1) The distinction between mutations and variant allele fraction becomes especially useful when comparing quantitative results.

Studying cfDNA with dPCR and sequencing

A study may ask how much of a known variant is present, which alterations occur across selected genes or how fragment patterns differ between samples. For variant analysis, origin remains a separate question. In clonal hematopoiesis, blood-cell populations descended from a common precursor carry shared acquired mutations. DNA released by these cells can contribute those variants to plasma cfDNA. Comparing plasma variants with DNA from matched white blood cells helps distinguish this contribution from tumor-derived variants. (6)

Digital PCR for measuring known variants in cfDNA

Tumor-derived variants can be rare within the mixture of cfDNA in plasma. Digital PCR with mutation-specific assays can detect and quantify selected variants against this background, making it useful for measuring known cancer-associated mutations and tracking changes in their abundance over time. (1, 7)

Sequencing for cfDNA variants and fragment patterns

Sequencing can examine many DNA regions in one analysis, including variants that were not specified in advance. (6) Because cfDNA is fragmented and often available only in small amounts, library preparation needs to convert those fragments efficiently into sequencing libraries. (3) Rare variants can also be difficult to distinguish from technical errors. Molecular barcodes help by identifying reads from the same original DNA molecule, so those reads can be compared to reduce errors introduced during amplification and sequencing. (1, 8)

Sequencing can also reveal fragment-size patterns. With suitable library preparation, paired-end reads can be mapped to the genome to infer fragment lengths. Studying these patterns requires preparation that preserves the fragment features of interest. (2, 3, 4)

Products for cfDNA collection, preparation and analysis

The plasma mixture can change after collection if blood cells release intracellular DNA. That adds DNA that was not part of the circulating cfDNA pool when the sample was collected. Stabilizing blood cells limits this contamination. Choosing suitable collection and preparation methods also reduces the risk of losing the fragments of interest or altering the features the study aims to measure. (1, 2) QIAGEN products support collection, recovery and analysis in cfDNA studies.

References

1. Bronkhorst AJ, Ungerer V, Holdenrieder S. The emerging role of cell-free DNA as a molecular marker for cancer management. Biomol Detect Quantif. 2019;17:100087. doi.org/10.1016/j.bdq.2019.100087
2. Lo YMD, Han DSC, Jiang P, Chiu RWK. Epigenetics, fragmentomics, and topology of cell-free DNA in liquid biopsies. Science. 2021;372(6538):eaaw3616. doi.org/10.1126/science.aaw3616
3. Snyder MW, Kircher M, Hill AJ, Daza RM, Shendure J. Cell-free DNA comprises an in vivo nucleosome footprint that informs its tissues-of-origin. Cell. 2016;164(1–2):57–68. doi.org/10.1016/j.cell.2015.11.050
4. Mouliere F, et al. Enhanced detection of circulating tumor DNA by fragment size analysis. Sci Transl Med. 2018;10(466):eaat4921. doi.org/10.1126/scitranslmed.aat4921
5. Moss J, et al. Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nat Commun. 2018;9:5068. doi.org/10.1038/s41467-018-07466-6
6. Razavi P, et al. High-intensity sequencing reveals the sources of plasma circulating cell-free DNA variants. Nat Med. 2019;25(12):1928–1937. doi.org/10.1038/s41591-019-0652-7
7. Diehl F, et al. Circulating mutant DNA to assess tumor dynamics. Nat Med. 2008;14(9):985–990. doi.org/10.1038/nm.1789
8. Kinde I, Wu J, Papadopoulos N, Kinzler KW, Vogelstein B. Detection and quantification of rare mutations with massively parallel sequencing. Proc Natl Acad Sci U S A. 2011;108(23):9530–9535. doi.org/10.1073/pnas.1105422108