Hepatic Clearance of Cell-Free DNA: Possible Impact on Early Metastasis Diagnosis

Immediate postoperative minimal residual disease detection with MAESTRO predicts recurrence and survival in head and neck cancer patients treated with surgery

Purpose

While circulating tumor DNA (ctDNA) is a promising biomarker for minimal residual disease (MRD) detection in head and neck squamous cell carcinoma (HNSCC), more sensitive assays are needed for accurate MRD detection at clinically-relevant timepoints. Ultrasensitive MRD detection immediately after surgery could guide adjuvant therapy decisions, but early ctDNA dynamics are poorly understood.

Experimental Design

We applied MAESTRO, a whole-genome, tumor-informed, mutation-enrichment sequencing assay, in a pooled testing format called MAESTRO-Pool, to plasma samples from HNSCC patients collected immediately after surgery and during surveillance. We evaluated whether early MRD detection could predict outcomes.

Results

Among 24 predominantly HPV-independent (95.8%) HNSCC patients, rapid ctDNA clearance occurred by the first postoperative sample (1-3 days postoperatively) in 9 patients without an event (recurrence or death). 13/15 patients with an event were MRD+ (PPV = 92.9%; NPV = 80%) with a median tumor fraction (TFx) of 54 ppm (range 6-1,177 ppm). In the first and last sample of the immediate postoperative window, 8/13 and 10/13 patients had TFx below 100 ppm, respectively, the detection limit of leading commercial assays. Early MRD detection correlated with worse overall survival (HR = 8.3; 95% CI: 1.1-66.1; P = 0.02) and event-free survival (HR = 27.4; 95% CI: 3.5-214.5; P < 0.0001) independent of high-risk pathology.

Conclusions

Immediate postoperative MRD detection by MAESTRO was predictive of recurrence and death. Given the ultralow TFxs observed, ultrasensitive assays will be essential for reliable MRD detection during early postoperative timepoints to enable personalized adjuvant therapy decision-making in HNSCC.

"Aging Clocks" Based on Cell-Free DNA

Aging is associated with systemic changes in the physiological and molecular parameters of the body. These changes are referred to as biomarkers of aging. Statistical models that link changes in individual biomarkers to biological age are called aging clocks. These tools facilitate a comprehensive evaluation of bodily health and permit the quantitative determination of the rate of aging. A particularly promising area for the development of aging clocks is the analysis of cell-free DNA (cfDNA), which is present in the blood and contains numerous potential biomarkers. This review explores in detail the fragmentomics, topology, and epigenetic landscape of cfDNA as possible biomarkers of aging. The review further underscores the potential of leveraging single-molecule sequencing of cfDNA in conjunction with long-read technologies to simultaneously profile multiple biomarkers, a strategy that could lead to the development of more precise aging clocks.

Modulating cell-free DNA biology as the next frontier in liquid biopsies

Technical advances over the past two decades have enabled robust detection of cell-free DNA (cfDNA) in biological samples. Yet, higher clinical sensitivity is required to realize the full potential of liquid biopsies. This opinion article argues that to overcome current limitations, the abundance of informative cfDNA molecules - such as circulating tumor DNA (ctDNA) - collected in a sample needs to increase. To accomplish this, new methods to modulate the biological processes that govern cfDNA production, trafficking, and clearance in the body are needed, informed by a deeper understanding of cfDNA biology. Successful development of such methods could enable a major leap in the performance of liquid biopsies and vastly expand their utility across the spectrum of clinical care.

Recent advances in ctDNA detection using electrochemical biosensor for cancer

In the quest for early cancer diagnosis, early identification and treatment are paramount. Recently, ctDNA detection has emerged as a viable avenue for early screening of cancer. The examination of ctDNA in fluid biopsies has gained substantial attention in tumor diagnosis and therapy. Both the scientific community and industry are actively exploring this field. However, developing cost-effective, portable, and real-time ctDNA measurement methods using conventional gene detection equipment poses a significant challenge. This challenge has led to the exploration of alternative approaches. Electrochemical biosensors, distinguished by their heightened sensitivity, remarkable specificity, affordability, and excellent portability, have emerged as a promising avenue for ctDNA detection. This review is dedicated to the specific focus on ctDNA detection, highlighting recent advancements in this evolving detection technology. We aimed to reference previous studies related to ctDNA-targeted cancer detection using electrochemical biosensors to advocate the utilization of electrochemical biosensors in healthcare diagnostics. Further research is imperative for the effective integration of ctDNA analysis into point-of-care cancer testing. Innovative approaches utilizing multiple markers need to be explored to advance this technology and make substantial contributions to societal well-being.

Priming agents transiently reduce the clearance of cell-free DNA to improve liquid biopsies

Liquid biopsies enable early detection and monitoring of diseases such as cancer, but their sensitivity remains limited by the scarcity of analytes such as cell-free DNA (cfDNA) in blood. Improvements to sensitivity have primarily relied on enhancing sequencing technology ex vivo. We sought to transiently augment the level of circulating tumor DNA (ctDNA) in a blood draw by attenuating its clearance in vivo. We report two intravenous priming agents given 1 to 2 hours before a blood draw to recover more ctDNA. Our priming agents consist of nanoparticles that act on the cells responsible for cfDNA clearance and DNA-binding antibodies that protect cfDNA. In tumor-bearing mice, they greatly increase the recovery of ctDNA and improve the sensitivity for detecting small tumors.

Bladder cancer

Bladder cancer is a global health issue with sex differences in incidence and prognosis. Bladder cancer has distinct molecular subtypes with multiple pathogenic pathways depending on whether the disease is non-muscle invasive or muscle invasive. The mutational burden is higher in muscle-invasive than in non-muscle-invasive disease. Commonly mutated genes include TERT, FGFR3, TP53, PIK3CA, STAG2 and genes involved in chromatin modification. Subtyping of both forms of bladder cancer is likely to change considerably with the advent of single-cell analysis methods. Early detection signifies a better disease prognosis; thus, minimally invasive diagnostic options are needed to improve patient outcomes. Urine-based tests are available for disease diagnosis and surveillance, and analysis of blood-based cell-free DNA is a promising tool for the detection of minimal residual disease and metastatic relapse. Transurethral resection is the cornerstone treatment for non-muscle-invasive bladder cancer and intravesical therapy can further improve oncological outcomes. For muscle-invasive bladder cancer, radical cystectomy with neoadjuvant chemotherapy is the standard of care with evidence supporting trimodality therapy. Immune-checkpoint inhibitors have demonstrated benefit in non-muscle-invasive, muscle-invasive and metastatic bladder cancer. Effective management requires a multidisciplinary approach that considers patient characteristics and molecular disease characteristics.

Bridging biological cfDNA features and machine learning approaches

Liquid biopsies (LBs), particularly using circulating tumor DNA (ctDNA), are expected to revolutionize precision oncology and blood-based cancer screening. Recent technological improvements, in combination with the ever-growing understanding of cell-free DNA (cfDNA) biology, are enabling the detection of tumor-specific changes with extremely high resolution and new analysis concepts beyond genetic alterations, including methylomics, fragmentomics, and nucleosomics. The interrogation of a large number of markers and the high complexity of data render traditional correlation methods insufficient. In this regard, machine learning (ML) algorithms are increasingly being used to decipher disease- and tissue-specific signals from cfDNA. Here, we review recent insights into biological ctDNA features and how these are incorporated into sophisticated ML applications.