Circulating Tumor DNA (ctDNA)

ctDNA carries tumor-specific somatic mutations—single nucleotide variants, insertions, deletions, and structural rearrangements—that are absent from the patient's germline genome. These mutations serve as cancer-specific barcodes that distinguish ctDNA from normal cfDNA shed by healthy cells. Common targets include TP53, KRAS, EGFR, and PIK3CA hotspot mutations. The presence of these variants is the primary signal used for minimal residual disease detection and treatment response monitoring.

ctDNA exhibits a distinct fragmentation pattern compared to normal cfDNA. Key characteristics include:

• Shorter fragment lengths: Tumor-derived fragments are typically 20-50 bp shorter than non-tumor fragments, often peaking below 150 bp
• Altered end motifs: The nucleotide sequences at fragment termini differ between tumor and normal cfDNA
• Nucleosome positioning: Tumor-specific nucleosome occupancy patterns reflect the epigenetic state of the cell of origin These fragmentomic features enable tissue-of-origin inference even in the absence of driver mutations.

ctDNA typically constitutes less than 1% of total cfDNA in early-stage disease, and can drop below 0.01% after treatment. This extreme dilution demands:

• Ultra-deep sequencing (often >10,000x coverage)
• Molecular barcoding with unique molecular identifiers to suppress errors
• Stringent limit of detection validation, often targeting allele frequencies as low as 0.005% The low abundance is the central analytical challenge driving the need for specialized error-correction methods like duplex sequencing.

ctDNA has a short half-life in circulation, typically ranging from 16 minutes to 2.5 hours. This rapid turnover means:

• ctDNA levels reflect real-time tumor burden rather than historical disease
• Changes in ctDNA concentration can be detected days to weeks before radiographic progression
• The short half-life enables pharmacodynamic monitoring of drug response within hours of treatment initiation Clearance occurs primarily through nuclease degradation in the blood and renal excretion, with the liver playing a secondary role.

Beyond mutations, ctDNA retains epigenetic marks that identify its source tissue. DNA methylation patterns at CpG dinucleotides are highly tissue-specific and remain stable in circulation. Deconvolution algorithms can analyze genome-wide methylation profiles to determine whether ctDNA originated from lung, breast, colon, or other tissues. This is critical for cancer of unknown primary diagnosis and for distinguishing multiple primary tumors in patients with complex oncological histories.

ctDNA captures the full clonal architecture of a tumor, including both truncal mutations present in all cancer cells and subclonal mutations found only in specific cell populations. By analyzing variant allele frequency distributions across multiple mutations, it is possible to reconstruct the subclonal architecture of the tumor non-invasively. This reveals:

• Emergence of therapy-resistant subclones
• Temporal heterogeneity as tumors evolve under treatment pressure
• The presence of multiple independent tumor lineages

The fraction of sequencing reads at a genomic locus carrying a variant allele, expressed as a percentage. In ctDNA analysis, VAF is critical for estimating tumor fraction and tracking clonal dynamics:

• High VAF (>10%): Suggests a truncal mutation or high tumor burden
• Low VAF (<1%): Requires deep sequencing and error suppression to distinguish from noise
• VAF fluctuations: Used to monitor treatment response and clonal evolution over time

A random nucleotide barcode ligated to individual DNA molecules before PCR amplification. UMIs enable computational deduplication by grouping reads sharing the same barcode into consensus families. This suppresses polymerase errors and amplification bias, allowing accurate counting of original template molecules. Without UMIs, low-frequency ctDNA variants are indistinguishable from PCR artifacts.

A computational strategy to exclude somatic variants originating from clonal hematopoiesis of indeterminate potential (CHIP) rather than solid tumors. Age-related clonal expansions in hematopoietic stem cells shed cfDNA carrying mutations in genes like DNMT3A, TET2, and ASXL1. Without matched white blood cell sequencing or a curated CHIP blacklist, these variants are falsely attributed to the tumor.

The study of cfDNA fragmentation patterns to infer the tissue of origin and epigenetic state. Key features include:

• Fragment length: Tumor-derived ctDNA is typically shorter (~134-144 bp) than non-tumor cfDNA (~166 bp)
• End motifs: The nucleotide sequences at fragment termini reflect nuclease preferences
• Nucleosome footprints: Protection patterns reveal open chromatin and gene regulatory elements Fragmentomic signals enable cancer detection even without specific mutations.

The distribution of 5-methylcytosine modifications across CpG dinucleotides, serving as a tissue-specific epigenetic fingerprint. Each cell type has a unique methylation landscape, allowing algorithms to deconvolve the cell of origin of cfDNA fragments. Cancer-specific methylation changes—such as promoter hypermethylation of tumor suppressor genes—provide a complementary biomarker layer independent of sequence mutations.