What is Dr. Zubair Khalid's research focus?

Dr. Zubair Khalid specializes in molecular virology, mRNA vaccine development, and computational biology, with a focus on avian pathogens like IBDV and Avian Reovirus.

Where is Dr. Zubair Khalid currently working?

Dr. Zubair Khalid is a Postdoctoral Research Associate at the University of Maryland (UMD), specifically within the Department of Animal and Avian Sciences.

Circulating Tumor DNA: Biology, Detection, and Clinical Use

By Dr. Zubair Khalid, DVM, MS, PhD · 2026-08-11

Introduction to Circulating Tumor DNA

What is ctDNA?

Circulating tumor DNA (ctDNA) is a fraction of extracellular DNA in the bloodstream that originates from tumor cells. When cancer cells die or release genetic material, their DNA enters the peripheral circulation, where it can be isolated from a simple blood draw. This makes ctDNA a form of liquid biopsy—a minimally invasive alternative to surgical tissue biopsy for obtaining tumor genetic information.

The term ctDNA refers specifically to the tumor-derived portion of total circulating DNA. It carries the same genetic mutations, copy number alterations, and epigenetic modifications present in the primary tumor or metastatic deposits. Because ctDNA reflects the genetic landscape of the tumor at the time of collection, it provides a real-time snapshot of cancer biology without requiring invasive tissue sampling.

ctDNA vs. cfDNA

Cell-free DNA (cfDNA) is the broader category of all extracellular DNA fragments circulating in blood plasma, serum, urine, cerebrospinal fluid, or other bodily fluids. cfDNA originates from multiple sources: apoptotic and necrotic cells of normal tissues, hematopoietic cells, and—in cancer patients—tumor cells. In healthy individuals, the vast majority of cfDNA comes from apoptotic white blood cells, with contributions from other regenerating tissues.

The key distinction is that ctDNA is a subset of cfDNA. In a cancer patient, cfDNA may contain both normal DNA fragments and tumor-derived fragments. The proportion of ctDNA within total cfDNA is called the variant allele fraction (VAF) or mutant allele fraction, and it can range from less than 0.01% in early-stage disease to over 50% in patients with high tumor burden. This distinction matters clinically: detecting a mutation in cfDNA requires the ability to distinguish tumor-derived fragments from the overwhelming background of normal cfDNA.

Mechanisms of ctDNA Release

Apoptosis and Necrosis

The dominant mechanism of ctDNA release is apoptosis, the programmed cell death pathway. During apoptosis, caspases—specifically caspase-activated DNase (CAD) and its activator caspase-3—cleave genomic DNA at internucleosomal linker regions. This produces DNA fragments of approximately 180–200 base pairs (bp) or integer multiples thereof (360 bp, 540 bp), corresponding to the length of DNA wrapped around a nucleosome core plus linker DNA. These apoptotic fragments are then packaged into apoptotic bodies or released as naked DNA into the circulation.

Necrosis, the uncontrolled cell death that occurs in response to injury, hypoxia, or inflammation, produces larger DNA fragments. Necrotic cells lose membrane integrity and release DNA that has been randomly cleaved, yielding fragments ranging from hundreds to thousands of base pairs. Tumors often contain regions of necrosis, particularly in rapidly growing masses that outstrip their blood supply. The relative contribution of apoptosis versus necrosis to ctDNA varies by tumor type, size, and microenvironment.

Active Release and Other Mechanisms

Beyond passive release from dying cells, evidence indicates that viable tumor cells can actively secrete DNA. This occurs through the release of extracellular vesicles—exosomes and microvesicles—that carry DNA cargo. Additionally, DNA can be actively extruded from the nucleus in complex with proteins, a process that may serve as a cell-to-cell communication mechanism. The relative contribution of active secretion to total ctDNA is debated, but it likely represents a minor fraction compared to apoptosis and necrosis.

Several factors influence the quantity of ctDNA in circulation:

Tumor burden: Larger tumors release more DNA, though the correlation is imperfect because different tumor types have different rates of cell turnover.
Tumor vascularity: Highly vascularized tumors shed DNA more efficiently into the bloodstream.
Cell turnover rate: Tumors with high proliferation and apoptosis rates release more ctDNA.
Treatment status: Chemotherapy and radiation can transiently increase ctDNA levels due to massive tumor cell death, followed by a decline if treatment is effective.
Renal clearance and hepatic metabolism: DNA is cleared from circulation by the liver and kidneys, with a half-life ranging from 16 minutes to 2.5 hours.

Biological Characteristics of ctDNA

Fragment Size and Integrity

The fragment size profile of ctDNA differs from that of normal cfDNA, and this difference has diagnostic utility. In healthy individuals, cfDNA shows a characteristic mononucleosomal peak at approximately 167 bp, representing DNA wrapped around a nucleosome plus linker. In cancer patients, ctDNA fragments are often shorter, with a peak around 145 bp, reflecting the altered chromatin structure of tumor cells. Some studies have also observed a higher proportion of longer fragments (>250 bp) in ctDNA, likely originating from necrotic tumor cells.

The fragment size distribution can be exploited to enrich for ctDNA before sequencing. Size selection methods that preferentially retain fragments in the 90–150 bp range can increase the mutant allele fraction by 2- to 4-fold, improving detection sensitivity. Additionally, the fragment size pattern itself can serve as a diagnostic feature: machine learning algorithms trained on fragment size profiles can distinguish cancer patients from healthy controls with high accuracy, even without identifying specific mutations.

The half-life of ctDNA is short, typically 16 minutes to 2.5 hours. This rapid clearance means that ctDNA levels reflect the current tumor status rather than historical disease. It also means that blood samples must be processed quickly after collection to prevent degradation of ctDNA and contamination from white blood cell lysis, which releases normal genomic DNA and dilutes the tumor-derived fraction.

Genetic and Epigenetic Alterations

ctDNA carries the full spectrum of genetic alterations found in the tumor of origin:

Single nucleotide variants (SNVs): Point mutations in oncogenes (e.g., EGFR, KRAS, BRAF) or tumor suppressor genes (e.g., TP53, PTEN).
Insertions and deletions (indels): Small insertions or deletions, such as the EGFR exon 19 deletion in non-small cell lung cancer.
Copy number alterations: Amplifications (e.g., ERBB2/HER2) or deletions of chromosomal regions.
Structural rearrangements: Gene fusions such as EML4-ALK in lung cancer.
Frameshift mutations: Insertions or deletions that shift the reading frame, often in genes with microsatellite regions.

Epigenetic alterations are also detectable in ctDNA. Aberrant DNA methylation at CpG islands in gene promoter regions—such as hypermethylation of the SEPT9 promoter in colorectal cancer or MGMT in glioblastoma—can be detected using methylation-specific PCR or bisulfite sequencing. Because methylation patterns are tissue-specific, they can also indicate the tissue of origin when the primary tumor is unknown.

The fragment length of ctDNA is not uniform across the genome. Nucleosome positioning differs between actively transcribed genes and silenced regions, and this affects which DNA fragments are protected from nuclease digestion. Analysis of fragment end motifs and nucleosome occupancy patterns can provide information about gene expression in the tumor, adding another layer of biological information extractable from ctDNA.

Detection and Quantification Methods

PCR-Based Methods

Polymerase chain reaction (PCR)-based methods are the most sensitive approaches for detecting known mutations in ctDNA. These methods require prior knowledge of the mutation to be detected, making them suitable for monitoring patients with known tumor mutations rather than for discovery.

Digital PCR (dPCR): This technique partitions a single reaction into thousands to millions of individual micro-reactions, each containing at most one DNA template molecule. After amplification, the fraction of positive partitions is counted, allowing absolute quantification of the number of mutant and wild-type molecules. The key advantage of dPCR is its ability to detect rare mutant alleles with a sensitivity of 0.01–0.1%, meaning it can identify one mutant molecule among 1,000–10,000 wild-type molecules. Droplet digital PCR (ddPCR) is the most common commercial implementation, using water-oil emulsion droplets as reaction vessels.

BEAMing (Beads, Emulsions, Amplification, Magnetics): This technique combines emulsion PCR with flow cytometry. DNA is amplified on magnetic beads within emulsion droplets, then fluorescently labeled probes distinguish mutant from wild-type sequences. BEAMing achieves sensitivity comparable to dPCR and was among the first methods used to detect KRAS mutations in plasma.

Amplification-refractory mutation system (ARMS): Also known as allele-specific PCR, this method uses primers designed to preferentially amplify the mutant allele. A primer with a mismatch at the 3′ end will not extend efficiently on wild-type templates, allowing selective amplification of mutant sequences. ARMS is less sensitive than dPCR (typically 1–5% mutant allele fraction) but is simpler and cheaper, making it suitable for clinical laboratories.

Next-Generation Sequencing

Next-generation sequencing (NGS) methods enable the detection of multiple mutations simultaneously and can discover new mutations without prior knowledge.

Targeted amplicon sequencing: This approach amplifies specific genomic regions of interest—typically 50–500 genes commonly mutated in cancer—using multiplex PCR, then sequences the amplicons deeply. By sequencing at high depth (10,000–100,000× coverage), rare mutant alleles can be detected. The sensitivity depends on sequencing depth and error rate; with error-corrected methods, detection limits of 0.1–0.5% VAF are achievable.

Hybrid capture sequencing: This method uses biotinylated DNA or RNA probes complementary to regions of interest to enrich for target sequences from the total cfDNA library. Hybrid capture can cover larger genomic regions than amplicon-based approaches, including entire genes or even whole exomes. It is more expensive and requires more input DNA but provides more comprehensive coverage.

Error-corrected sequencing: A major challenge in ctDNA sequencing is distinguishing true mutations from sequencing errors. Two strategies address this:

Unique molecular identifiers (UMIs): Short random nucleotide sequences are ligated to each DNA fragment before amplification. After sequencing, reads sharing the same UMI are grouped into families, and a consensus sequence is generated. This "duplex sequencing" approach can reduce error rates from approximately 0.1% to 0.001%, enabling detection of mutations at very low VAFs.
Background polishing: Computational methods that model the error profile of specific sequencing platforms can subtract systematic errors, improving sensitivity.

Whole-genome sequencing (WGS): While expensive, WGS of cfDNA can detect copy number alterations, structural variants, and fragment size patterns without prior knowledge of tumor mutations. It is less sensitive for point mutations due to limited sequencing depth but provides genome-wide information.

Emerging Technologies

Several newer approaches are expanding the capabilities of ctDNA analysis:

Methylation-based detection: Methods such as bisulfite sequencing or enzymatic methylation conversion can detect tumor-specific methylation patterns. The CancerSEEK approach combines assessment of methylation patterns with mutation detection to achieve high sensitivity for multiple cancer types.
Fragmentomics: Analysis of fragment size distribution, end motifs, and nucleosome positioning patterns across the genome can identify the presence of cancer and its tissue of origin.
Protein-ctDNA combinations: Combining ctDNA mutation detection with protein biomarkers (e.g., CA-125, CEA) can improve sensitivity for early cancer detection.
Nanopore sequencing: Long-read sequencing technologies can detect methylation directly without bisulfite conversion and may enable real-time ctDNA analysis.

Clinical Applications of ctDNA Analysis

Early Cancer Detection

One of the most promising applications of ctDNA is the early detection of cancer in asymptomatic individuals. The biological rationale is that tumors release DNA into circulation even at small sizes, potentially years before clinical symptoms appear. However, the challenge is substantial: early-stage tumors release very small amounts of ctDNA, often below 0.01% of total cfDNA, requiring extremely sensitive detection methods.

Multi-cancer early detection (MCED) tests aim to detect multiple cancer types from a single blood sample. These tests typically combine mutation detection with methylation analysis and machine learning algorithms that can also predict the tissue of origin. The sensitivity of these tests is stage-dependent: they detect approximately 40–70% of stage I cancers and 80–95% of stage IV cancers, with higher sensitivity for cancers that shed more DNA (e.g., pancreatic, ovarian) and lower sensitivity for those that shed less (e.g., thyroid, prostate).

The specificity of MCED tests is critical to avoid false positives that lead to unnecessary invasive procedures. Current tests achieve specificity of 98–99%, meaning that in a screening population where cancer prevalence is low, most positive results will be false positives. This trade-off between sensitivity and specificity remains a central challenge in ctDNA-based screening.

Monitoring Treatment Response

ctDNA analysis provides a dynamic measure of tumor burden that can be assessed repeatedly over time. The short half-life of ctDNA means that changes in ctDNA levels reflect changes in tumor size within days, whereas radiographic assessment by CT or MRI may take weeks or months to show measurable changes.

In metastatic cancer patients receiving systemic therapy, a decline in ctDNA levels within 2–4 weeks of treatment initiation is associated with better response and longer progression-free survival. Conversely, rising ctDNA levels during treatment indicate disease progression, often weeks before radiographic evidence. This early signal can guide treatment decisions, allowing patients to switch therapies sooner when the current regimen is failing.

The concept of molecular response uses ctDNA dynamics to classify patients into responders and non-responders. For example, in patients with metastatic colorectal cancer receiving chemotherapy plus anti-EGFR therapy, a ≥50% decrease in ctDNA VAF after 2 weeks of treatment predicts improved outcomes. In melanoma patients on immunotherapy, ctDNA clearance at 6 weeks is associated with higher response rates and longer survival.

Identifying Resistance Mutations

As tumors evolve under treatment pressure, they acquire new mutations that confer resistance to therapy. ctDNA analysis can detect these resistance mutations earlier than tissue biopsy, enabling timely treatment adjustment.

The classic example is EGFR-mutant non-small cell lung cancer (NSCLC) treated with tyrosine kinase inhibitors (TKIs) such as erlotinib or osimertinib. Resistance commonly arises through the EGFR T790M mutation (in patients on first- or second-generation TKIs) or C797S mutation (in patients on osimertinib). ctDNA testing can detect these resistance mutations in plasma, and the presence of T790M in ctDNA is now an accepted indication for switching to osimertinib without requiring a tissue re-biopsy.

Similarly, in colorectal cancer, KRAS mutations that emerge during anti-EGFR therapy can be detected in ctDNA weeks before radiographic progression. In breast cancer, ESR1 mutations that confer resistance to aromatase inhibitors can be detected in ctDNA, guiding the switch to fulvestrant or other agents.

Minimal Residual Disease Monitoring

After curative-intent treatment such as surgical resection, patients may harbor microscopic residual disease that is undetectable by imaging. This state is called minimal residual disease (MRD) or molecular residual disease. ctDNA analysis can detect the presence of residual tumor DNA, identifying patients at high risk of recurrence who may benefit from adjuvant therapy.

The typical approach involves:

Tumor profiling: Sequencing the primary tumor to identify patient-specific mutations.
Personalized assay design: Creating a custom assay to detect those specific mutations in plasma.
Serial monitoring: Testing plasma at regular intervals after treatment (e.g., every 3–6 months).
Clinical action: Escalating therapy if ctDNA becomes detectable, or de-escalating if it remains undetectable.

In stage II colon cancer, patients with detectable ctDNA after surgery have a significantly higher risk of recurrence (approximately 80% at 3 years) compared to those with undetectable ctDNA (approximately 10%). This information can guide decisions about adjuvant chemotherapy: patients with detectable ctDNA may benefit from more aggressive treatment, while those without may safely avoid chemotherapy-related toxicity.

Challenges and Limitations in ctDNA Analysis

Sensitivity and Specificity

The fundamental challenge in ctDNA analysis is detecting extremely rare mutant molecules amid a vast excess of normal cfDNA. A typical blood draw of 10 mL yields approximately 10,000–100,000 genome equivalents of cfDNA. If the tumor contributes 0.1% of this DNA, there are only 10–100 mutant molecules available for analysis. This limits sensitivity, particularly for early-stage disease or low tumor burden.

Several factors affect sensitivity:

Input volume: More blood yields more DNA, but there are practical limits to blood collection.
Pre-analytical handling: Delayed processing leads to white blood cell lysis, diluting ctDNA with normal genomic DNA.
Mutation type: Some mutations are easier to detect than others. SNVs require error correction, while copy number changes require sufficient coverage to detect small differences.
Tumor shedding: Different tumor types and stages shed different amounts of DNA. Prostate and thyroid cancers shed relatively little, while pancreatic and lung cancers shed more.

Specificity is challenged by sequencing errors, which can be mistaken for true mutations. Error-corrected sequencing methods reduce but do not eliminate this problem. Additionally, clonal hematopoiesis—the presence of mutations in white blood cells that are unrelated to the tumor—can produce false-positive results.

Clonal Hematopoiesis of Indeterminate Potential (CHIP)

Clonal hematopoiesis of indeterminate potential (CHIP) is a major source of false-positive ctDNA results. As people age, hematopoietic stem cells accumulate mutations, and some of these clones expand in the bone marrow. The most commonly mutated genes in CHIP are DNMT3A, TET2, and ASXL1, but mutations in TP53, KRAS, and JAK2 also occur.

When white blood cells die, their DNA enters the cfDNA pool. If a white blood cell clone carries a mutation in a gene that is also commonly mutated in cancer (e.g., TP53), that mutation will appear in the cfDNA and can be misinterpreted as evidence of tumor DNA. This is particularly problematic because TP53 is mutated in approximately 50% of all cancers, making it a common target in ctDNA panels.

The prevalence of CHIP increases with age: it is present in approximately 10% of individuals over 65 and 20% over 80. In cancer patients, CHIP can confound MRD monitoring, producing persistent "positive" ctDNA results that reflect benign blood cell clones rather than residual tumor.

Strategies to address CHIP include:

Paired sequencing: Sequencing white blood cell DNA alongside cfDNA to identify and subtract CHIP mutations.
Variant filtering: Excluding known CHIP-associated mutations from ctDNA analysis unless they are also present in the tumor.
Fragment size analysis: Tumor-derived fragments tend to be shorter than those from white blood cells, providing a way to distinguish their origin.

Key Evidence and Landmark Studies

Lung Cancer and EGFR Mutations

The clinical utility of ctDNA was first demonstrated convincingly in non-small cell lung cancer (NSCLC) with EGFR mutations. Early studies showed that EGFR mutations could be detected in plasma with high concordance to tumor tissue, and that the presence of the T790M resistance mutation in plasma predicted response to third-generation TKIs.

A pivotal development was the demonstration that ctDNA-based detection of T790M could replace tissue re-biopsy for patients progressing on first-line EGFR TKIs. This finding changed clinical practice, establishing ctDNA testing as a standard component of lung cancer management. Subsequent studies showed that ctDNA dynamics during EGFR TKI treatment—specifically, the rate of decline in mutant allele fraction—predicts the duration of response and time to progression.

Colorectal Cancer Monitoring

Colorectal cancer has been a model system for ctDNA applications. Studies in patients with resected stage II and III colon cancer demonstrated that detectable ctDNA after surgery identifies patients at high risk of recurrence, and that serial ctDNA monitoring can detect recurrence months before radiographic evidence.

The DYNAMIC trial in stage II colon cancer used ctDNA results to guide adjuvant chemotherapy decisions. Patients with detectable ctDNA received adjuvant chemotherapy, while those with undetectable ctDNA did not. The trial showed that this ctDNA-guided approach reduced chemotherapy use without compromising recurrence-free survival, demonstrating that ctDNA can inform treatment decisions in a way that improves patient outcomes.

In metastatic colorectal cancer, ctDNA analysis of KRAS mutations has been used to monitor treatment response and detect emerging resistance to anti-EGFR therapy. The appearance of KRAS mutations in plasma during treatment predicts radiographic progression and can guide the timing of treatment changes.

Common Pitfalls and Best Practices for Students

Misconceptions about ctDNA

Several misunderstandings are common among students learning about ctDNA:

"ctDNA is the same as cfDNA." This is incorrect. ctDNA is a subset of cfDNA that originates specifically from tumor cells. In healthy individuals, ctDNA is absent or undetectable. In cancer patients, ctDNA may constitute a small or large fraction of total cfDNA.

"ctDNA is only released when tumor cells die." While apoptosis and necrosis are the primary sources, active secretion by viable tumor cells also contributes. This distinction matters for interpreting ctDNA dynamics during treatment.

"Detecting a mutation in ctDNA means the patient has cancer." This is not necessarily true. CHIP can produce mutations in cfDNA that are not tumor-derived. Additionally, some benign conditions can release DNA with mutations. Clinical interpretation requires context.

"ctDNA testing can replace tissue biopsy entirely." While ctDNA provides valuable information, it does not provide information about tumor histology, architecture, or protein expression. Some mutations may not be detectable in ctDNA due to low shedding. Tissue biopsy remains necessary in many situations.

"Higher ctDNA levels always mean worse prognosis." While ctDNA levels generally correlate with tumor burden, the relationship is not perfect. Some aggressive tumors shed little DNA, while some benign conditions can elevate cfDNA levels.

Practical Tips for Analysis

When interpreting ctDNA data, consider the following:

Check the variant allele fraction: A VAF of 0.1% means one mutant molecule per 1,000 wild-type molecules. Consider whether this is biologically plausible given the clinical context.
Evaluate fragment size: Tumor-derived fragments are typically shorter. If a mutation is detected in long fragments, consider whether it might originate from a non-tumor source.
Compare with tumor tissue: If tumor tissue sequencing is available, check whether the ctDNA mutation matches. Discordance may indicate tumor heterogeneity or a different tumor clone.
Consider the timing: ctDNA levels change rapidly. A sample taken immediately after chemotherapy may show elevated levels due to treatment-induced cell death, not disease progression.
Account for CHIP: If the mutation is in a gene commonly mutated in CHIP (e.g., DNMT3A, TET2, TP53), consider whether paired white blood cell sequencing is needed.
Understand the assay limitations: Different detection methods have different sensitivities and specificities. A negative result does not exclude the presence of ctDNA below the detection limit.
Use appropriate controls: Always include positive and negative controls in ctDNA assays to ensure quality and reproducibility.

Frequently Asked Questions

What is circulating tumor DNA?

Circulating tumor DNA (ctDNA) is the fraction of cell-free DNA in the bloodstream that originates from tumor cells. It is released through apoptosis, necrosis, and active secretion, and carries the genetic and epigenetic alterations present in the tumor of origin. ctDNA can be detected and analyzed through a blood sample, providing a minimally invasive method for assessing tumor genetics.

How is circulating tumor DNA different from cell-free DNA?

Cell-free DNA (cfDNA) is the total pool of extracellular DNA in the blood, originating from all cell types in the body. In healthy individuals, most cfDNA comes from apoptotic white blood cells. ctDNA is the tumor-derived subset of cfDNA. The distinction is clinically important because detecting tumor-specific mutations in cfDNA requires distinguishing tumor-derived fragments from the background of normal DNA.

What is the half-life of circulating tumor DNA?

The half-life of ctDNA is short, typically 16 minutes to 2.5 hours. This rapid clearance means that ctDNA levels reflect the current tumor status rather than historical disease. It also requires that blood samples be processed quickly after collection to prevent degradation and contamination from white blood cell lysis.

How is circulating tumor DNA detected?

ctDNA is detected using PCR-based methods such as digital PCR and BEAMing, which are highly sensitive for known mutations, or next-generation sequencing methods such as targeted amplicon sequencing and hybrid capture, which can detect multiple mutations simultaneously. Emerging technologies include methylation-based detection, fragmentomics, and nanopore sequencing.

Can circulating tumor DNA be used for early cancer detection?

Yes, ctDNA is being developed for early cancer detection through multi-cancer early detection (MCED) tests. These tests combine mutation detection with methylation analysis and machine learning to detect cancer signals in blood. However, sensitivity is limited for early-stage disease, and specificity must be high to avoid false positives in screening populations.

What are the limitations of circulating tumor DNA testing?

Limitations include low abundance of ctDNA in early-stage disease, biological variability in tumor shedding, technical challenges in detecting rare mutations, and confounding factors such as clonal hematopoiesis. Additionally, ctDNA does not provide information about tumor histology or protein expression, and some tumors shed very little DNA into circulation.

What is the clinical utility of circulating tumor DNA in monitoring treatment?

ctDNA monitoring provides a dynamic measure of tumor burden that changes within days of treatment initiation. Declining ctDNA levels indicate treatment response, while rising levels indicate progression, often weeks before radiographic evidence. ctDNA can also detect resistance mutations that emerge during treatment, guiding therapy switches.

Key Takeaways

Circulating tumor DNA (ctDNA) is the tumor-derived fraction of cell-free DNA in blood, released through apoptosis, necrosis, and active secretion.
ctDNA has a short half-life (16 minutes to 2.5 hours), making it a real-time indicator of tumor burden.
Detection methods range from highly sensitive PCR-based approaches (digital PCR, BEAMing) to comprehensive sequencing methods (targeted NGS, whole-genome sequencing).
Clinical applications include early cancer detection, minimal residual disease monitoring, treatment response assessment, and resistance mutation identification.
Major challenges include low ctDNA abundance in early disease, biological variability, and confounding from clonal hematopoiesis of indeterminate potential (CHIP).
ctDNA analysis has transformed cancer management, particularly in lung cancer (EGFR mutations) and colorectal cancer (MRD monitoring), and continues to expand into new clinical contexts.
Understanding the biology of ctDNA release, its fragment characteristics, and the technical limitations of detection methods is essential for correct interpretation of ctDNA results.