Shallow genome-wide cell-free DNA sequencing holds great promise for noninvasive cancer monitoring by providing reliable copy number alteration (CNA) and fragmentomic profiles. Single-nucleotide variations (SNVs) are, however, much harder to identify with low sequencing depth due to sequencing errors. Here, we present Nanopore Rolling Circle Amplification (RCA)-enhanced Consensus Sequencing (NanoRCS), which leverages RCA and consensus calling based on genome-wide long-read nanopore sequencing to enable simultaneous multimodal tumor fraction (TF) estimation through SNVs, CNAs, and fragmentomics. The efficacy of NanoRCS is tested on 18 cancer patient samples and seven healthy controls, demonstrating its ability to reliably detect TFs as low as 0.24%. In vitro experiments confirm that SNV measurements are essential for detecting TFs below 3%. NanoRCS provides an opportunity for cost-effective and rapid sample processing, which aligns well with clinical needs, particularly in settings where quick and accurate cancer monitoring is essential for personalized treatment strategies.

Circulating tumour DNA (ctDNA) represents an increasingly important biomarker for the screening, diagnosis and management of patients in clinical practice in advanced/metastatic disease across multiple cancer types. In this context, ctDNA-based comprehensive genomic profiling is now available for patient management decisions, and several ctDNA-based companion diagnostic assays have been approved by regulatory agencies. However, although the assessment of ctDNA levels in Phase II-III drug development is now gathering momentum, it remains somewhat surprisingly limited in the early Phase I phases in light of the potential opportunities provided by such analysis. In this perspective review, we investigate the potential and hurdles of applying ctDNA testing for the inclusion and monitoring of patients in phase 1 clinical trials. This will enable more informed decisions regarding patient inclusion, dose optimization, and proof-of-mechanism of drug biological activity and molecular response, thereby supporting the evolving oncology drug development paradigm. Furthermore, we will highlight the use of cost-efficient, agnostic genome-wide techniques (such as low-pass whole genome sequencing and fragmentomics) and methylation-based methods to facilitate a more systematic integration of ctDNA in early clinical trial settings.

The quantification of circulating tumour DNA (ctDNA) in blood enables non-invasive surveillance of cancer progression. Here we show that a deep-learning model can accurately quantify ctDNA from the density distribution of cell-free DNA-fragment lengths. We validated the model, which we named 'Fragle', by using low-pass whole-genome-sequencing data from multiple cancer types and healthy control cohorts. In independent cohorts, Fragle outperformed tumour-naive methods, achieving higher accuracy and lower detection limits. We also show that Fragle is compatible with targeted sequencing data. In plasma samples from patients with colorectal cancer, longitudinal analysis with Fragle revealed strong concordance between ctDNA dynamics and treatment responses. In patients with resected lung cancer, Fragle outperformed a tumour-naive gene panel in the prediction of minimal residual disease for risk stratification. The method's versatility, speed and accuracy for ctDNA quantification suggest that it may have broad clinical utility.

The discovery of circulating fetal and tumor cell-free DNA (cfDNA) molecules in plasma has opened up tremendous opportunities in noninvasive diagnostics such as the detection of fetal chromosomal aneuploidies and cancers and in posttransplantation monitoring. The advent of high-throughput sequencing technologies makes it possible to scrutinize the characteristics of cfDNA molecules, opening up the fields of cfDNA genetics, epigenetics, transcriptomics, and fragmentomics, providing a plethora of biomarkers. Machine learning (ML) and/or artificial intelligence (AI) technologies that are known for their ability to integrate high-dimensional features have recently been applied to the field of liquid biopsy. In this review, we highlight various AI and ML approaches in cfDNA-based diagnostics. We first introduce the biology of cell-free DNA and basic concepts of ML and AI technologies. We then discuss selected examples of ML- or AI-based applications in noninvasive prenatal testing and cancer liquid biopsy. These applications include the deduction of fetal DNA fraction, plasma DNA tissue mapping, and cancer detection and localization. Finally, we offer perspectives on the future direction of using ML and AI technologies to leverage cfDNA fragmentation patterns in terms of methylomic and transcriptional investigations.

Cancer continues to pose significant challenges to the medical community. Early detection, accurate molecular profiling, and adequate assessment of treatment response are critical factors in improving the quality of life and survival of cancer patients. Accumulating evidence shows that circulating tumor DNA (ctDNA) shed by tumors into the peripheral blood preserves the genetic and epigenetic information of primary tumors. Notably, DNA methylation, an essential and stable epigenetic modification, exhibits both cancer- and tissue-specific patterns. As a result, ctDNA methylation has emerged as a promising molecular marker for noninvasive testing in cancer clinics. In this review, we summarize the existing techniques for ctDNA methylation detection, describe the current research status of ctDNA methylation, and present the potential applications of ctDNA-based assays in the clinic. The insights presented in this article could serve as a roadmap for future research and clinical applications of ctDNA methylation.

Early detection of neurofibromatosis type 1 (NF1)-associated peripheral nerve sheath tumors (PNST) informs clinical decision-making, enabling early definitive treatment and potentially averting deadly outcomes. In this study, we describe a cell-free DNA (cfDNA) fragmentomic approach that distinguishes nonmalignant, premalignant, and malignant forms of PNST in the cancer predisposition syndrome, NF1.

cfDNA was isolated from plasma samples of a novel cohort of 101 patients with NF1 and 21 healthy controls and underwent whole-genome sequencing. We investigated diagnosis-specific signatures of copy-number alterations with in silico size selection as well as fragment profiles. Fragmentomics were analyzed using complementary feature types: bin-wise fragment size ratios, end motifs, and fragment non-negative matrix factorization signatures.

The novel cohort of patients with NF1 validated that our previous cfDNA copy-number alteration-based approach identifies malignant PNST (MPNST) but cannot distinguish between benign and premalignant states. Fragmentomic methods were able to differentiate premalignant states including atypical neurofibromas (AN). Fragmentomics also adjudicated AN cases suspicious for MPNST, correctly diagnosing samples noninvasively, which could have informed clinical management.

Novel cfDNA fragmentomic signatures distinguish AN from benign plexiform neurofibromas and MPNST, enabling more precise clinical diagnosis and management. This study pioneers the early detection of malignant and premalignant PNST in NF1 and provides a blueprint for decentralizing noninvasive cancer surveillance in hereditary cancer predisposition syndromes.

Current radiotherapy guidelines rely heavily on imaging-based monitoring. Liquid biopsy monitoring promises to complement imaging by providing frequent systemic information about the tumor. In particular, cell-free DNA (cfDNA) sequencing offers a tumor-agnostic approach, which lends itself to monitoring heterogeneous cohorts of cancer patients.

We collected plasma cfDNA from oligometastatic patients (OMD) and head-and-neck cancer patients (SCCHN) at six time points before, during, and after radiotherapy, and compared them to the plasma samples of healthy and polymetastatic volunteers. We performed low-pass (on average 7x) whole-genome sequencing on 93 plasma cfDNA samples and correlated copy number alterations and fragment length distributions to clinical and imaging findings.

We observed copy number alterations in 4/7 polymetastatic cancer patients, 1/7 OMD and 1/7 SCCHN patients, these patients' imaging showed progression following radiotherapy. Using unsupervised learning, we identified cancer-specific fragment length features that showed a strong correlation with copy number-based tumor fraction estimates. In 4/4 HPV-positive SCCHN patient samples, we detected viral DNA that enabled the monitoring of very low tumor fraction samples.

Our results indicate that an elevated tumor fraction is associated with tumor aggressiveness and systemic tumor spread. This information may be used to adapt treatment strategies. Further, we show that by detecting specific sequences such as viral DNA, the sensitivity of detecting cancer from cell-free DNA sequencing data can be greatly increased.

Early kinetics of circulating tumor DNA (ctDNA) in plasma predict response to pembrolizumab but typically requires sequencing of matched tumor tissue or fixed gene panels. We analyzed genome-wide methylation and fragment-length profiles using cell-free methylated DNA immunoprecipitation and sequencing (cfMeDIP-seq) in 204 plasma samples from 87 patients before and during treatment with pembrolizumab from a pan-cancer phase II investigator-initiated trial (INSPIRE). We trained a pan-cancer methylation signature using independent methylation array data from The Cancer Genome Atlas to quantify cancer-specific methylation (CSM) and fragment-length score (FLS) for each sample. CSM and FLS are strongly correlated with tumor-informed ctDNA levels. Early kinetics of CSM predict overall survival and progression-free survival, independently of tumor type, PD-L1, and tumor mutation burden. Early kinetics of FLS are associated with overall survival independently of CSM. Our tumor-naïve mutation-agnostic ctDNA approach integrating methylomics and fragmentomics could predict outcomes in patients treated with pembrolizumab.

Analysis of methylation and fragment length in plasma using cfMeDIP-seq provides a tumor-naive approach to measure ctDNA with results comparable with a tumor-informed bespoke ctDNA. Early kinetics within the first weeks of treatment in methylation and fragment quantity can predict outcomes with pembrolizumab in patients with various advanced solid tumors. This article is featured in Selected Articles from This Issue, p. 897.

In solid tumor oncology, circulating tumor DNA (ctDNA) is poised to transform care through accurate assessment of minimal residual disease (MRD) and therapeutic response monitoring. To overcome the sparsity of ctDNA fragments in low tumor fraction (TF) settings and increase MRD sensitivity, we previously leveraged genome-wide mutational integration through plasma whole-genome sequencing (WGS). Here we now introduce MRD-EDGE, a machine-learning-guided WGS ctDNA single-nucleotide variant (SNV) and copy-number variant (CNV) detection platform designed to increase signal enrichment. MRD-EDGESNV uses deep learning and a ctDNA-specific feature space to increase SNV signal-to-noise enrichment in WGS by ~300× compared to previous WGS error suppression. MRD-EDGECNV also reduces the degree of aneuploidy needed for ultrasensitive CNV detection through WGS from 1 Gb to 200 Mb, vastly expanding its applicability within solid tumors. We harness the improved performance to identify MRD following surgery in multiple cancer types, track changes in TF in response to neoadjuvant immunotherapy in lung cancer and demonstrate ctDNA shedding in precancerous colorectal adenomas. Finally, the radical signal-to-noise enrichment in MRD-EDGESNV enables plasma-only (non-tumor-informed) disease monitoring in advanced melanoma and lung cancer, yielding clinically informative TF monitoring for patients on immune-checkpoint inhibition.

With the rapid development of science and technology, cell-free DNA (cfDNA) is rapidly becoming an important biomarker for tumor diagnosis, monitoring and prognosis, and this cfDNA-based liquid biopsy technology has great potential to become an important part of precision medicine. cfDNA is the total amount of free DNA in the systemic circulation, including DNA fragments derived from tumor cells and all other somatic cells. Tumor cells release fragments of DNA into the bloodstream, and this source of cfDNA is called circulating tumor DNA (ctDNA). cfDNA detection has become a major focus in the field of tumor research in recent years, which provides a new opportunity for non-invasive diagnosis and prognosis of cancer. In this paper, we discuss the limitations of the study on the origin and dynamics analysis of ctDNA, and how to solve these problems in the future. Although the future faces major challenges, it also contains great potential.

Cell-free DNA (cfDNA) sequencing has demonstrated great potential for early cancer detection. However, most large-scale studies have focused only on either targeted methylation sites or whole-genome sequencing, limiting comprehensive analysis that integrates both epigenetic and genetic signatures. In this study, we present a platform that enables simultaneous analysis of whole-genome methylation, copy number, and fragmentomic patterns of cfDNA in a single assay. Using a total of 950 plasma (361 healthy and 589 cancer) and 240 tissue samples, we demonstrate that a multifeature cancer signature ensemble (CSE) classifier integrating all features outperforms single-feature classifiers. At 95.2% specificity, the cancer detection sensitivity with methylation, copy number, and fragmentomic models was 77.2%, 61.4%, and 60.5%, respectively, but sensitivity was significantly increased to 88.9% with the CSE classifier (p value < 0.0001). For tissue of origin, the CSE classifier enhanced the accuracy beyond the methylation classifier, from 74.3% to 76.4%. Overall, this work proves the utility of a signature ensemble integrating epigenetic and genetic information for accurate cancer detection.

Circulating cell-free DNA (cfDNA) and vascular endothelial growth factor-C (VEGF-C) can be utilized to detect cancer and predict its prognosis. However, their potential application in laryngeal squamous cell carcinoma (LSCC) is unclear.

This study aimed to identify the diagnostic and prognostic value of cfDNA and VEGF-C in LSCC patients.

The plasma cfDNA of 148 LSCC patients and 43 non-tumor patients were isolated. Quantitative real-time PCR (qRT-PCR) was performed to assess long and short DNA fragments in plasma by amplifying the ALU repeats. ALU-qPCR results (ALU247/ALU115) were used to calculate cfDNA integrity index. Vascular endothelial growth factor-C (VEGF-C) level was detected by ELISA assay. Correlation between cfDNA and clinical features was analyzed. For detecting the sensitivity and specificity of cfDNA and VEGF-C alone or in combination for diagnosing LSCC, receiver operator characteristic (ROC) was established. For evaluating the overall survival (OS) of LSCC, Kaplan-Meier curves were established.

LSCC patients had significantly higher levels of plasma cfDNA (ALU115, ALU247, and cfDNA integrity index) and VEGF-C than those without cancer (p < 0.05), showing area under the curve (AUC) values of 0.79, 0.74, 0.62 and 0.80, when cutoff value was correspondingly defined at 2.14 ng/mL, 1.39 ng/mL, 0.73 and 412.90 pg/mL, respectively. The AUC for distinguishing LSCC patients from non-tumor patients by plasma cfDNA combined with VEGF-C was 0.89 (95% CI: 0.83-0.94). A significant correlation was found between plasma cfDNA levels and Ki-67, tumor size, pT stage, and smoking history (p < 0.05). Based on survival analysis, low VEGF-C concentration groups had longer OS than those with high VEGF-C concentration (p = 0.02).

Indicators such as plasma cfDNA and VEGF-C may be used to diagnose and monitor LSCC for its noninvasiveness and rapid accessibility.

Cell-free DNA molecules are released into the plasma via apoptotic or necrotic events and active release mechanisms, which carry the genetic and epigenetic information of its origin tissues. However, cfDNA is the mixture of various cell fragments, and the efficient enrichment of cfDNA fragments with diagnostic value remains a great challenge for application in the clinical setting. Evidence from recent years shows that cfDNA fragmentomics' characteristics differ in normal and diseased individuals without the need to distinguish the source of the cfDNA fragments, which makes it a promising novel biomarker. Moreover, cfDNA fragmentomics can identify tissue origins by inferring epigenetic information. Thus, further insights into the fragmentomics of plasma cfDNA shed light on the origin and fragmentation mechanisms of cfDNA during physiological and pathological processes in diseases and enhance our ability to take the advantage of plasma cfDNA as a molecular diagnostic tool. In this review, we focus on the cfDNA fragment characteristics and its potential application, such as fragment length, end motifs, jagged ends, preferred end coordinates, as well as nucleosome footprints, open chromatin region, and gene expression inferred by the cfDNA fragmentation pattern across the genome. Furthermore, we summarize the methods for deducing the tissue of origin by cfDNA fragmentomics.