Cell-free DNA (cfDNA) carries a wealth of diagnostic information in numerous oncology workflows. Released from dying cells throughout the body, cfDNA reflects genetic mutations, chromosomal changes, and tissue-specific DNA patterns, providing a real-time snapshot of health and disease. However, this same sensitivity makes cfDNA workflows highly vulnerable to contamination and handling errors, which can compromise test accuracy, obscure vital signals, and affect clinical decisions. For laboratories striving to deliver precise, reproducible, and actionable results, mastering the complexities of cfDNA extraction and preparation is essential to unlocking the full potential of these advanced diagnostic applications.

cfDNA typically ranges between 150–200 base pairs (bp). Larger fragments, often greater than 500 bp, are usually derived from genomic DNA that enters the plasma when cells lyse during sample collection or processing. Studies across multiple clinical settings have reported that even trace amounts of gDNA can skew variant allele frequencies, reduce assay sensitivity, or introduce noise in sequencing reads. 

In oncology, the challenge is particularly pronounced. Circulating tumor DNA (ctDNA), the fraction of cfDNA carrying cancer-specific mutations, often accounts for less than 1% of the total DNA in circulation. Detecting mutations in clinically actionable genes such as EGFR or TP53 requires ultra-sensitive methods capable of discerning rare mutant signals from the overwhelming background of non-tumor DNA. A study published in JAMA Oncology highlighted this issue by comparing liquid biopsy results in lung cancer patients, showing that gDNA contamination led to false-negative findings in cases where ctDNA levels were already low. The study emphasized that stringent protocols for sample collection and fragment size selection were essential to enhancing mutation detection and improving diagnostic reliability in oncology workflows.

A similarly critical challenge arises in non-invasive prenatal testing (NIPT), where fetal cfDNA is typically shorter and far less abundant than maternal cfDNA. Excess gDNA in the sample can dilute the fetal fraction, making it difficult to accurately estimate the presence of chromosomal abnormalities such as trisomy 21 or trisomy 18. This dilution can lead to false negatives or inconclusive test results, which not only delay diagnosis but also increase patient anxiety and the need for invasive follow-up procedures. Research published in Prenatal Diagnosis examined the impact of gDNA on NIPT assays and found that improper sample handling, leading to increased maternal cell lysis, resulted in a significant drop in fetal fraction estimates. 

The role of size selection

A typical cfDNA profile after proper size selection reveals clear peaks at approximately 100 bp and 200 bp, representing mononucleosome-bound fragments. Additional peaks at 400 bp and 800 bp correspond to di- and tri-nucleosomes, offering deeper insights into tissue-of-origin signals or disease-related DNA fragmentation patterns. Analyzing these nucleosome patterns can help differentiate between cancer and non-cancer states, or between different stages of disease. This emerging field, often referred to as cfDNA fragmentomics, depends heavily on the removal of large DNA fragments to ensure that patterns are biologically interpretable rather than artifacts of contamination.

At the same time, larger fragments, often those above 1,000 bp, can complicate the picture. These oversized fragments are usually genomic DNA released from cells that break open during sample collection or processing. Even when present in small amounts, they can overshadow target DNA by diluting the sample and introducing unwanted background noise. This interference is especially problematic in workflows where the target DNA is already scarce, such as in liquid biopsy assays looking for rare tumor mutations or in prenatal tests that depend on accurately estimating the fetal fraction. Without proper clean-up, the presence of these fragments can obscure signals, reduce assay sensitivity, and ultimately impact the reliability of results.

This is where size selection becomes essential. By selectively removing unwanted larger fragments while retaining diagnostically relevant ones, laboratories can enrich their samples and ensure that sequencing efforts focus on the regions that matter most. Tools like CamSelect are designed to simplify this process, offering precise removal of everything above 1,000 bp, while also allowing fine control over smaller fragments, enabling users to enrich specific size ranges according to the assay’s needs. Whether the goal is to retain mononucleosomal fragments or carefully select dinucleosomal and trinucleosomal patterns, CamSelect supports laboratories in preserving the integrity of their sample and improving data quality.

Beyond diagnostics, cfDNA size profiling is opening new avenues for research and personalized medicine. Recent studies have explored how nucleosome positioning patterns in cfDNA can trace tissue-specific gene expression signatures, providing non-invasive insights into disease states like autoimmune disorders and liver injury. Other research has combined fragment size analysis with methylation profiling to enhance the resolution of cancer detection, demonstrating how refining cfDNA workflows can unlock multi-layered information from a simple blood draw. Moreover, as sequencing costs decrease and computational tools improve, laboratories are increasingly integrating cfDNA fragment analysis into machine learning pipelines to predict treatment responses or stratify patients based on genetic instability patterns.