Sensitive tumour detection and classification using plasma cell-free DNA methylomes

Liquid biopsy into the clinics: Current evidence and future perspectives

As precision oncology has become a major part of the treatment landscape in oncology, liquid biopsies have developed as a particularly powerful tool as it surmounts several limitations of traditional tissue biopsies. These biopsies involve most commonly the isolation of circulating extracellular nucleic acids, including cell-free DNA (cfDNA) and circulating tumor DNA (ctDNA), as well as circulating tumor cells (CTCs), typically from blood. The clinical applications of liquid biopsies are diverse, encompassing the initial diagnosis and cancer detection, the application as a tool for prognostication in early and advanced tumor settings, the identification of potentially actionable alterations, the monitoring of response and resistance under systemic therapy and the detection of resistance mechanisms, the differentiation of distinct immune checkpoint blockade response patterns through serial samples, the prediction of immune checkpoint blockade responses based on initial liquid biopsy characteristics and the assessment of tumor heterogeneity. Moreover, molecular relapse monitoring in early-stage cancers and the personalization of adjuvant or additive therapy via MRD have become a major field of research in recent years. Compared to tissue biopsies, liquid biopsies are less invasive and can be collected serially, offering real-time molecular insights. Furthermore, liquid biopsies may allow for a more holistic evaluation of a patient's disease, as they assess material from all tumor sites and can theoretically reflect tumor heterogeneity. Furthermore, quicker turnaround-time also constitutes an advantage of liquid biopsies. Disadvantages or hurdles include the challenge of detecting low amounts of tumor deposits in peripheral blood or other fluids and the potential of different amounts tumor-shedding from different metastatic sites, as well as potentially false-positive from clonal hematopoietic mutations of indeterminate potential (CHIP) mutations. The clinical utility of liquid biopsies still must be validated in most settings and further research has to be done. Clinal trials including alternate bodily fluids and leveraging AI-technology are expected to revolutionize the field of liquid biopsies.

Ultrasensitive Detection of Tumor Suppressor Gene Methylation by Piezoelectric Sensing Based on Enrichment of Transcription Activator-Like Effectors

The detection of DNA methylation at cytosine/guanine dinucleotide (CpG) islands in promoter regions of tumor suppressor genes has great potential for early cancer screening, diagnosis, and prognosis monitoring. Nevertheless, achieving accurate, sensitive, cost-effective, and quantitative detection of target methylated DNA remains challenging. Herein, we propose a novel piezoelectric sensor (series piezoelectric quartz crystal (SPQC)) based on transcription activator-like effectors (TALEs) for detecting DNA methylation of Ras association domain family 1 isoform A (RASSF1A) tumor suppressor genes (R-5mC). The sensor employs TALEs-Ni magnetic beads to specifically recognize and separate the R-5mC, thereby improving the detection selectivity. The TALEs-Ni magnetic beads-R-5mC complex is sheared by a nucleic acid enzyme (DNAzyme) to release the single-stranded DNA (ST). ST initiates a catalyzed hairpin assembly (CHA) reaction on the surface of the electrode, which in turn triggers the hybridization chain reaction (HCR) and silver staining for enhanced detection sensitivity. The strategy exhibits a linear response in the detection of R-5mC in the range of 1 fM to 1 nM with a detection limit of 0.79 fM. R-5mC as low as 0.01% can be detected, even in the presence of large numbers of unmethylated DNA. The detection of R-5mC in circulating cell-free DNA (cfDNA) derived from clinical plasma specimens of lung cancer patients yielded satisfactory results.

An improved epigenetic counter to track mitotic age in normal and precancerous tissues

The cumulative number of stem cell divisions in a tissue, known as mitotic age, is thought to be a major determinant of cancer-risk. Somatic mutational and DNA methylation (DNAm) clocks are promising tools to molecularly track mitotic age, yet their relationship is underexplored and their potential for cancer risk prediction in normal tissues remains to be demonstrated. Here we build and validate an improved pan-tissue DNAm counter of total mitotic age called stemTOC. We demonstrate that stemTOC's mitotic age proxy increases with the tumor cell-of-origin fraction in each of 15 cancer-types, in precancerous lesions, and in normal tissues exposed to major cancer risk factors. Extensive benchmarking against 6 other mitotic counters shows that stemTOC compares favorably, specially in the preinvasive and normal-tissue contexts. By cross-correlating stemTOC to two clock-like somatic mutational signatures, we confirm the mitotic-like nature of only one of these. Our data points towards DNAm as a promising molecular substrate for detecting mitotic-age increases in normal tissues and precancerous lesions, and hence for developing cancer-risk prediction strategies.

Transcriptomic Profiling of Plasma Extracellular Vesicles Enables Reliable Annotation of the Cancer-Specific Transcriptome and Molecular Subtype

Longitudinal monitoring of patients with advanced cancers is crucial to evaluate both disease burden and treatment response. Current liquid biopsy approaches mostly rely on the detection of DNA-based biomarkers. However, plasma RNA analysis can unleash tremendous opportunities for tumor state interrogation and molecular subtyping. Through the application of deep learning algorithms to the deconvolved transcriptomes of RNA within plasma extracellular vesicles (evRNA), we successfully predicted consensus molecular subtypes in patients with metastatic colorectal cancer. Analysis of plasma evRNA also enabled monitoring of changes in transcriptomic subtype under treatment selection pressure and identification of molecular pathways associated with recurrence. This approach also revealed expressed gene fusions and neoepitopes from evRNA. These results demonstrate the feasibility of using transcriptomic-based liquid biopsy platforms for precision oncology approaches, spanning from the longitudinal monitoring of tumor subtype changes to the identification of expressed fusions and neoantigens as cancer-specific therapeutic targets, sans the need for tissue-based sampling.

SIGNIFICANCE

The development of an approach to interrogate molecular subtypes, cancer-associated pathways, and differentially expressed genes through RNA sequencing of plasma extracellular vesicles lays the foundation for liquid biopsy-based longitudinal monitoring of patient tumor transcriptomes.

A Pre-Leukemic DNA Methylation Signature in Healthy Individuals at Higher Risk for Developing Myeloid Malignancy

PURPOSE

DNA methylation alterations are widespread in acute myelogenous leukemia (AML) and myelodysplastic syndrome (MDS), some of which appear to have evolved independently of somatic mutations in epigenetic regulators. Although the presence of somatic mutations in peripheral blood can predict the risk of development of AML and MDS, its accuracy remains unsatisfactory.

EXPERIMENTAL DESIGN

We performed global DNA methylation profiling in a case control study nested within the Singapore Chinese Health Study to evaluate whether DNA methylation alterations were associated with AML/MDS development. Targeted deep sequencing and methylated DNA immunoprecipitation sequencing (MeDIP-seq) were performed on peripheral blood collected a median of 9.9 years before diagnosis of AML or MDS, together with age-matched still-healthy individuals as controls.

RESULTS

Sixty-six individuals who developed AML or MDS displayed significant DNA methylation changes in the peripheral blood compared with 167 age- and gender-matched controls who did not develop AML/MDS during the follow-up period. Alterations in methylation in the differentially methylation regions were associated with increased odds of developing AML/MDS.

CONCLUSIONS

The epigenetic changes may be acquired independently and before somatic mutations that are relevant for AML/MDS development. The association between methylation changes and the risk of pre-AML/MDS in these individuals was considerably stronger than somatic mutations, suggesting that methylation changes could be used as biomarkers for pre-AML/MDS screening.

Multimodal analysis of cfDNA methylomes for early detecting esophageal squamous cell carcinoma and precancerous lesions

Detecting early-stage esophageal squamous cell carcinoma (ESCC) and precancerous lesions is critical for improving survival. Here, we conduct whole-genome bisulfite sequencing (WGBS) on 460 cfDNA samples from patients with non-metastatic ESCC or precancerous lesions and matched healthy controls. We develop an expanded multimodal analysis (EMMA) framework to simultaneously identify cfDNA methylation, copy number variants (CNVs), and fragmentation markers in cfDNA WGBS data. cfDNA methylation markers are the earliest and most sensitive, detectable in 70% of ESCCs and 50% of precancerous lesions, and associated with molecular subtypes and tumor microenvironments. CNVs and fragmentation features show high specificity but are linked to late-stage disease. EMMA significantly improves detection rates, increasing AUCs from 0.90 to 0.99, and detects 87% of ESCCs and 62% of precancerous lesions with >95% specificity in validation cohorts. Our findings demonstrate the potential of multimodal analysis of cfDNA methylome for early detection and monitoring of molecular characteristics in ESCC.

Bridging horizons beyond CIRCULATE-Japan: a new paradigm in molecular residual disease detection via whole genome sequencing-based circulating tumor DNA assay

Circulating tumor DNA (ctDNA) is the fraction of cell-free DNA in patient blood that originates from a tumor. Advances in DNA sequencing technologies and our understanding of the molecular biology of tumors have increased interest in exploiting ctDNA to facilitate detection of molecular residual disease (MRD). Analysis of ctDNA as a promising MRD biomarker of solid malignancies has a central role in precision medicine initiatives exemplified by our CIRCULATE-Japan project involving patients with resectable colorectal cancer. Notably, the project underscores the prognostic significance of the ctDNA status at 4 weeks post-surgery and its correlation to adjuvant therapy efficacy at interim analysis. This substantiates the hypothesis that MRD is a critical prognostic indicator of relapse in patients with colorectal cancer. Despite remarkable advancements, challenges endure, primarily attributable to the exceedingly low ctDNA concentration in peripheral blood, particularly in scenarios involving low tumor shedding and the intrinsic error rates of current sequencing technologies. These complications necessitate more sensitive and sophisticated assays to verify the clinical utility of MRD across all solid tumors. Whole genome sequencing (WGS)-based tumor-informed MRD assays have recently demonstrated the ability to detect ctDNA in the parts-per-million range. This review delineates the current landscape of MRD assays, highlighting WGS-based approaches as the forefront technique in ctDNA analysis. Additionally, it introduces our upcoming endeavor, WGS-based pan-cancer MRD detection via ctDNA, in our forthcoming project, SCRUM-Japan MONSTAR-SCREEN-3.

Metabologenomic characterization uncovers a clinically aggressive IDH mutant glioma subtype

Mutations in the pivotal metabolic isocitrate dehydrogenase (IDH) enzymes are recognized to drive the molecular footprint of diffuse gliomas, and patients with IDH mutant gliomas have overall favorable outcomes compared to patients with IDH wild-type tumors. However, survival still varies widely among patients with IDH mutated tumors. Here, we aimed to characterize molecular signatures that explain the range of IDH mutant gliomas. By integrating matched epigenome-wide methylome, transcriptome, and global metabolome data in 154 patients with gliomas, we identified a group of IDH mutant gliomas with globally altered metabolism that resembled IDH wild-type tumors. IDH-mutant gliomas with altered metabolism have significantly shorter overall survival from their IDH mutant counterparts that is not fully accounted for by recognized molecular prognostic markers of CDKN2A/B loss and glioma CpG Island Methylator Phenotype (GCIMP) status. IDH-mutant tumors with dysregulated metabolism harbored distinct epigenetic alterations that converged to drive proliferative and stem-like transcriptional profiles, providing a window to target novel dependencies in gliomas.

Pushing the Boundaries of Liquid Biopsies for Early Precision Intervention

Liquid biopsies are emerging as powerful minimally invasive approaches that have the potential to solve several long-standing problems spanning the continuum of cancer care: early detection of cancer, minimal residual disease tracking, and refinement of the heterogeneity of clinical responses together with therapeutic response monitoring in the metastatic setting. Existing challenges driven by technical limitations and establishment of the clinical value of liquid biopsies represent fields of active research that call for convergence science approaches to bridge scientific discovery with clinical care.

Shedding light on ONOO- detection: the emergence of a fast-response fluorescent probe for biological systems

ONOO-, a bioactive molecule, plays a critical role in inflammation-related signaling pathways and pathological mechanisms. Numerous studies have established a direct correlation between elevated ONOO- levels and tumor progression. Therefore, investigating ONOO- levels in inflammation and tumors is of utmost importance. Fluorescence imaging presents a highly sensitive, non-invasive, easily operable, selective, and efficient method for ONOO- detection in situ. In this study, we designed and synthesized a rhodamine-based probe, NRho, which effectively identifies tumors, inflammatory cells, tissues, and organs by detecting ONOO- content. The synthesis process of NRho is simple, yielding a probe with favorable spectral characteristics and rapid response. Our cell imaging analysis has provided novel insights, revealing distinct ONOO- levels among different types of cancer cells, with hepatocellular carcinoma cells exhibiting higher ONOO- content than the others. This observation marks the proposal of such variations in ONOO- levels across cancer cell types. Furthermore, our study has showcased the practicality of our probe in live organ imaging, enabling the identification of tumors from living organs within a brief 5-minute incubation period. Additionally, our findings highlight the rapid detection capability of the probe NRho in various tissue samples, effectively identifying inflammation. This research holds important promise in advancing biomedical research and clinical diagnosis.

Cell‐free DNA‐associated multi‐feature applications in cancer diagnosis and treatment

Malignant tumours pose significant challenges in terms of high morbidity and mortality rates, primarily due to the lack of large‐scale applicable screening methods and efficient treatment strategies. However, the development of liquid biopsies, particularly circulating cell‐free DNA (cfDNA), offers promising solutions characterised by their non‐invasiveness and cost‐effectiveness, providing comprehensive tumour information on a global scale. The release of cfDNA is predominantly associated with cell death and turnover, while its elimination occurs through nuclease digestion, renal excretion into the urine and uptake by the liver and spleen. Extensive research into the biological properties of cfDNA has led to the identification of novel applications, including non‐invasive cancer screening, cancer subtype classification, tissue‐of‐origin detection and monitoring of treatment efficacy. Additionally, emerging fields such as methylation‐omics, fragment‐omics and nucleosome‐omics show immense potential as tissue‐ and disease‐specific markers. Therefore, this review aims to comprehensively introduce the latest detection techniques of cfDNA, along with detailed information on its characteristics and applications, providing valuable insights for cancer diagnosis and monitoring, which will assist us in purposefully enhancing relevant features for a more comprehensive application in clinical practice.

Lipidomics reveals new lipid-based lung adenocarcinoma early diagnosis model

Lung adenocarcinoma (LUAD) continues to pose a significant mortality risk with a lack of dependable biomarkers for early noninvasive cancer detection. Here, we find that aberrant lipid metabolism is significantly enriched in lung cancer cells. Further, we identified four signature lipids highly associated with LUAD and developed a lipid signature-based scoring model (LSRscore). Evaluation of LSRscore in a discovery cohort reveals a robust predictive capability for LUAD (AUC: 0.972), a result further validated in an independent cohort (AUC: 0.92). We highlight one lipid signature biomarker, PE(18:0/18:1), consistently exhibiting altered levels both in cancer tissue and in plasma of LUAD patients, demonstrating significant predictive power for early-stage LUAD. Transcriptome analysis reveals an association between increased PE(18:0/18:1) levels and dysregulated glycerophospholipid metabolism, which consistently displays strong prognostic value across two LUAD cohorts. The combined utility of LSRscore and PE(18:0/18:1) holds promise for early-stage diagnosis and prognosis of LUAD.

Opportunity in Complexity: Harnessing Molecular Biomarkers and Liquid Biopsies for Personalized Sarcoma Care

Due to their rarity and complexity, sarcomas represent a substantial therapeutic challenge. However, the incredible diversity within and across sarcoma subtypes presents an opportunity for personalized care to maximize efficacy and limit toxicity. A deeper understanding of the molecular alterations that drive sarcoma development and treatment response has paved the way for molecular biomarkers to shape sarcoma treatment. Genetic, transcriptomic, and protein biomarkers have become critical tools for diagnosis, prognostication, and treatment selection in patients with sarcomas. In the future, emerging biomarkers like circulating tumor DNA analysis offer the potential to improve early detection, monitoring response to treatment, and identifying mechanisms of resistance to personalize sarcoma treatment. Here, we review the current state of molecular biomarkers for sarcomas and highlight opportunities and challenges for the implementation of new technologies in the future.

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA reveals clinically relevant biomarkers but requires specialized protocols such as whole-genome bisulfite sequencing. Meanwhile, millions of cell-free DNA samples are being profiled by whole-genome sequencing. Here, we develop FinaleMe, a non-homogeneous Hidden Markov Model, to predict DNA methylation of cell-free DNA and, therefore, tissues-of-origin, directly from plasma whole-genome sequencing. We validate the performance with 80 pairs of deep and shallow-coverage whole-genome sequencing and whole-genome bisulfite sequencing data.

Genome-wide repeat landscapes in cancer and cell-free DNA

Genetic changes in repetitive sequences are a hallmark of cancer and other diseases, but characterizing these has been challenging using standard sequencing approaches. We developed a de novo kmer finding approach, called ARTEMIS (Analysis of RepeaT EleMents in dISease), to identify repeat elements from whole-genome sequencing. Using this method, we analyzed 1.2 billion kmers in 2837 tissue and plasma samples from 1975 patients, including those with lung, breast, colorectal, ovarian, liver, gastric, head and neck, bladder, cervical, thyroid, or prostate cancer. We identified tumor-specific changes in these patients in 1280 repeat element types from the LINE, SINE, LTR, transposable element, and human satellite families. These included changes to known repeats and 820 elements that were not previously known to be altered in human cancer. Repeat elements were enriched in regions of driver genes, and their representation was altered by structural changes and epigenetic states. Machine learning analyses of genome-wide repeat landscapes and fragmentation profiles in cfDNA detected patients with early-stage lung or liver cancer in cross-validated and externally validated cohorts. In addition, these repeat landscapes could be used to noninvasively identify the tissue of origin of tumors. These analyses reveal widespread changes in repeat landscapes of human cancers and provide an approach for their detection and characterization that could benefit early detection and disease monitoring of patients with cancer.

Protein biomarkers and alternatively methylated cell-free DNA detect early stage pancreatic cancer

OBJECTIVE

Pancreatic ductal adenocarcinoma (PDAC) is commonly diagnosed at an advanced stage. Liquid biopsy approaches may facilitate detection of early stage PDAC when curative treatments can be employed.

DESIGN

To assess circulating marker discrimination in training, testing and validation patient cohorts (total n=426 patients), plasma markers were measured among PDAC cases and patients with chronic pancreatitis, colorectal cancer (CRC), and healthy controls. Using CA19-9 as an anchor marker, measurements were made of two protein markers (TIMP1, LRG1) and cell-free DNA (cfDNA) pancreas-specific methylation at 9 loci encompassing 61 CpG sites.

RESULTS

Comparative methylome analysis identified nine loci that were differentially methylated in exocrine pancreas DNA. In the training set (n=124 patients), cfDNA methylation markers distinguished PDAC from healthy and CRC controls. In the testing set of 86 early stage PDAC and 86 matched healthy controls, CA19-9 had an area under the receiver operating characteristic curve (AUC) of 0.88 (95% CI 0.83 to 0.94), which was increased by adding TIMP1 (AUC 0.92; 95% CI 0.88 to 0.96; p=0.06), LRG1 (AUC 0.92; 95% CI 0.88 to 0.96; p=0.02) or exocrine pancreas-specific cfDNA methylation markers at nine loci (AUC 0.92; 95% CI 0.88 to 0.96; p=0.02). In the validation set of 40 early stage PDAC and 40 matched healthy controls, a combined panel including CA19-9, TIMP1 and a 9-loci cfDNA methylation panel had greater discrimination (AUC 0.86, 95% CI 0.77 to 0.95) than CA19-9 alone (AUC 0.82; 95% CI 0.72 to 0.92).

CONCLUSION

A combined panel of circulating markers including proteins and methylated cfDNA increased discrimination compared with CA19-9 alone for early stage PDAC.

Prediction of plasma ctDNA fraction and prognostic implications of liquid biopsy in advanced prostate cancer

No consensus strategies exist for prognosticating metastatic castration-resistant prostate cancer (mCRPC). Circulating tumor DNA fraction (ctDNA%) is increasingly reported by commercial and laboratory tests but its utility for risk stratification is unclear. Here, we intersect ctDNA%, treatment outcomes, and clinical characteristics across 738 plasma samples from 491 male mCRPC patients from two randomized multicentre phase II trials and a prospective province-wide blood biobanking program. ctDNA% correlates with serum and radiographic metrics of disease burden and is highest in patients with liver metastases. ctDNA% strongly predicts overall survival, progression-free survival, and treatment response independent of therapeutic context and outperformed established prognostic clinical factors. Recognizing that ctDNA-based biomarker genotyping is limited by low ctDNA% in some patients, we leverage the relationship between clinical prognostic factors and ctDNA% to develop a clinically-interpretable machine-learning tool that predicts whether a patient has sufficient ctDNA% for informative ctDNA genotyping (available online: https://www.ctDNA.org ). Our results affirm ctDNA% as an actionable tool for patient risk stratification and provide a practical framework for optimized biomarker testing.

Reduced representative methylome profiling of cell-free DNA for breast cancer detection

BACKGROUND

Whole-genome methylation sequencing of cfDNA is not cost-effective for tumor detection. Here, we introduce reduced representative methylome profiling (RRMP), which employs restriction enzyme for depletion of AT-rich sequence to achieve enrichment and deep sequencing of CG-rich sequences.

METHODS

We first verified the ability of RRMP to enrich CG-rich sequences using tumor cell genomic DNA and analyzed differential methylation regions between tumor cells and normal whole blood cells. We then analyzed cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals to detect breast cancer by building machine learning models.

RESULTS

RRMP captured 81.9% CpG islands and 75.2% gene promoters when sequenced to 10 billion base pairs, with an enrichment efficiency being comparable to RRBS. RRMP allowed us to assess DNA methylation changes between tumor cells and whole blood cells. Applying our approach to cfDNA from 29 breast cancer patients and 27 non-breast cancer individuals, we developed machine learning models that could discriminate between breast cancer and non-breast cancer controls (AUC = 0.85), suggesting possibilities for truly non-invasive cancer detection.

CONCLUSIONS

We developed a new method to achieve reduced representative methylome profiling of cell-free DNA for tumor detection.

Management of Renal Cell Carcinoma: Promising Biomarkers and the Challenges to Reach the Clinic

The incidence of renal cell carcinoma (RCC) is increasing worldwide, yet research within this field is lagging behind other cancers. Despite increased detection of early disease as a consequence of the widespread use of diagnostic CT scans, 25% of patients have disseminated disease at diagnosis. Similarly, around 25% progress to metastatic disease following curatively intended surgery. Surgery is the cornerstone in the treatment of RCC; however, when the disease is disseminated, immunotherapy or immunotherapy in combination with a tyrosine kinase inhibitor is the patient's best option. Immunotherapy is a potent treatment, with durable treatment responses and potential to cure the patient, but only half of the patients benefit from the administered treatment, and there are currently no methods that can identify which patients will respond to immunotherapy. Moreover, there is a need to identify the patients in greatest risk of relapsing after surgery for localized disease and direct adjuvant treatment there. Even though several molecular biomarkers have been published to date, we are still lacking routinely used biomarkers to guide optimal clinical management. The purpose of this review is to highlight some of the most promising biomarkers, discuss the efforts made within this field to date, and describe the barriers needed to be overcome to have reliable and robust predictive and prognostic biomarkers in the clinic for renal cancer.

Discrimination of pancreato-biliary cancer and pancreatitis patients by non-invasive liquid biopsy

BACKGROUND

Current diagnostics for the detection of pancreato-biliary cancers (PBCs) need to be optimized. We therefore propose that methylated cell-free DNA (cfDNA) derived from non-invasive liquid biopsies serves as a novel biomarker with the ability to discriminate pancreato-biliary cancers from non-cancer pancreatitis patients.

METHODS

Differentially methylated regions (DMRs) from plasma cfDNA between PBCs, pancreatitis and clinical control samples conditions were identified by next-generation sequencing after enrichment using methyl-binding domains and database searches to generate a discriminatory panel for a hybridization and capture assay with subsequent targeted high throughput sequencing.

RESULTS

The hybridization and capture panel, covering around 74 kb in total, was applied to sequence a cohort of 25 PBCs, 25 pancreatitis patients, 25 clinical controls, and seven cases of Intraductal Papillary Mucinous Neoplasia (IPMN). An unbiased machine learning approach identified the 50 most discriminatory methylation markers for the discrimination of PBC from pancreatitis and controls resulting in an AUROC of 0.85 and 0.88 for a training (n = 45) and a validation (n = 37) data set, respectively. The panel was also able to distinguish high grade from low grade IPMN samples.

CONCLUSIONS

We present a proof of concept for a methylation biomarker panel with better performance and improved discriminatory power than the current clinical marker CA19-9 for the discrimination of pancreato-biliary cancers from non-cancerous pancreatitis patients and clinical controls. This workflow might be used in future diagnostics for the detection of precancerous lesions, e.g. the identification of high grade IPMNs vs. low grade IPMNs.

Diagnostic Utility of Next-Generation Sequencing in Circulating Free DNA and a Comparison With Matched Tissue in Gallbladder Carcinoma

Mutation detection for therapy monitoring in cell-free DNA (cfDNA) is used clinically for some malignancies. Gallbladder carcinoma (GBC) presents a diagnostic challenge and has limited late-stage treatment options. To our knowledge, this novel study examines, for the first time, genomic alterations in cfDNA from GBC to assess diagnostic accuracy and therapeutic options. The concordance of somatic genomic changes in cfDNA and DNA from paired tumor tissue was analyzed. Paired serum and tissue samples from 40 histologically proven GBC, 20 cholecystitis, and 4 normal (noninflamed gallbladder) controls were included. Targeted next-generation sequencing with a 22-gene panel (Colon and Lung Cancer Research Panel v2, Thermo Scientific) in cfDNA and tumor tissue with high depth and uniform coverage on ION Personal Genome Machine (ION, PGM) was performed. A spectrum of 223 mutations in cfDNA and 225 mutations in formalin-fixed paraffin-embedded tissue DNA were identified in 22 genes. Mutations ranged from 1 to 17 per case. In cfDNA frequent alterations were in TP53 (85.0%), EGFR (52.5%), MET (35%) CTNNB1, SMAD4, BRAF (32.5%), PTEN (30%), FGFR3 and PIK3CA (27.5%), NOTCH1 (25.0%), and FBXW7 and ERBB4 (22.5%). At least one clinically actionable mutation was identified in all cfDNA samples. Paired samples shared 149 of 225 genetic abnormalities (66.2%). Individual gene mutation concordance ranged from 44.44% to 82.0% and was highest for EGFR (82.0%), BRAF and NOTCH1 (80.0%), TP53 (73.08%), MET (72.22%), and ERBB4 (71.42%) with a significant level of correlation (Spearman r = 0.91, P ≤ .0001). The sensitivity and specificity of the TP53 gene at the gene level was the highest (94.44% and 100.0%, respectively). Overall survival was higher for ERBB4 and ERBB2 mutant tumors. The adenocarcinoma subtype revealed specific genetic changes in ERBB4, SMAD4, ERBB2, PTEN, KRAS, and NRAS. NGS-based cfDNA mutation profiling can be used to diagnose GBC before surgery to guide treatment decisions. Targeted therapy identified in GBC included SMAD4, ERBB2, ERBB4, EGFR, KRAS, BRAF, PIK3CA, MET, and NRAS.

Circulating cell-free DNA-based multi-cancer early detection

Patients benefit considerably from early detection of cancer. Existing single-cancer tests have various limitations, which could be effectively addressed by circulating cell-free DNA (cfDNA)-based multi-cancer early detection (MCED). With sensitive detection and accurate localization of multiple cancer types at a very low and fixed false-positive rate (FPR), MCED has great potential to revolutionize early cancer detection. Herein, we review state-of-the-art approaches for cfDNA-based MCED and their limitations and discuss both technical and clinical challenges in the development and application of MCED tests. Given the constant improvements in technology and understanding of cancer biology, we propose that a cfDNA-based targeted sequencing assay that integrates multimodal features should be optimized for MCED.

Blood-based multi-cancer detection: A state-of-the-art update

The early detection of cancer is a key goal of the National Cancer Plan formally released by the National Institutes of Health's (NIH) National Cancer Institute (NCI) in April 2023. To support this effort, many laboratories and vendors are developing multi-cancer detection (MCD) assays that interrogate blood and other bodily fluids for cancer-related biomarkers, most commonly circulating tumor DNA (ctDNA). While this approach holds promise for non-invasively detecting early signals of multiple different cancers and potentially reducing cancer-related mortality, there is a dearth of prospective clinical data to inform the deployment of MCD assays for cancer screening in the general adult population. In this review we highlight differing technologies that underpin various MCD assays in clinical development, the importance of achieving adequate performance specifications for MCD assays, ongoing clinical studies investigating the utility of MCD assays in cancer screening and detection, and efforts by the NCI's Division of Cancer Prevention (DCP) to establish a network infrastructure that has the capacity to comprehensively address the scientific and logistical challenges of evaluating blood-based MCD approaches and other cancer screening tools.

Magnetic nanoparticles for ferroptosis cancer therapy with diagnostic imaging

Ferroptosis offers a novel method for overcoming therapeutic resistance of cancers to conventional cancer treatment regimens. Its effective use as a cancer therapy requires a precisely targeted approach, which can be facilitated by using nanoparticles and nanomedicine, and their use to enhance ferroptosis is indeed a growing area of research. While a few review papers have been published on iron-dependent mechanism and inducers of ferroptosis cancer therapy that partly covers ferroptosis nanoparticles, there is a need for a comprehensive review focusing on the design of magnetic nanoparticles that can typically supply iron ions to promote ferroptosis and simultaneously enable targeted ferroptosis cancer nanomedicine. Furthermore, magnetic nanoparticles can locally induce ferroptosis and combinational ferroptosis with diagnostic magnetic resonance imaging (MRI). The use of remotely controllable magnetic nanocarriers can offer highly effective localized image-guided ferroptosis cancer nanomedicine. Here, recent developments in magnetically manipulable nanocarriers for ferroptosis cancer nanomedicine with medical imaging are summarized. This review also highlights the advantages of current state-of-the-art image-guided ferroptosis cancer nanomedicine. Finally, image guided combinational ferroptosis cancer therapy with conventional apoptosis-based therapy that enables synergistic tumor therapy is discussed for clinical translations.

Recommendations for the collection and annotation of biosamples for analysis of biomarkers in neurofibromatosis and schwannomatosis clinical trials

INTRODUCTION

Neurofibromatosis 1 and schwannomatosis are characterized by potential lifelong morbidity and life-threatening complications. To date, however, diagnostic and predictive biomarkers are an unmet need in this patient population. The inclusion of biomarker discovery correlatives in neurofibromatosis 1/schwannomatosis clinical trials enables study of low-incidence disease. The implementation of a common data model would further enhance biomarker discovery by enabling effective concatenation of data from multiple studies.

METHODS

The Response Evaluation in Neurofibromatosis and Schwannomatosis biomarker working group reviewed published data on emerging trends in neurofibromatosis 1 and schwannomatosis biomarker research and developed recommendations in a series of consensus meetings.

RESULTS

Liquid biopsy has emerged as a promising assay for neurofibromatosis 1/schwannomatosis biomarker discovery and validation. In addition, we review recommendations for a range of biomarkers in clinical trials, neurofibromatosis 1/schwannomatosis-specific data annotations, and common data models for data integration.

CONCLUSION

These Response Evaluation in Neurofibromatosis and Schwannomatosis consensus guidelines are intended to provide best practices for the inclusion of biomarker studies in neurofibromatosis 1/schwannomatosis clinical trials, data, and sample annotation and to lay a framework for data harmonization and concatenation between trials.

Blood FOLR3 methylation dysregulations and heterogeneity in non-small lung cancer highlight its strong associations with lung squamous carcinoma

BACKGROUND

Non-small cell lung cancer (NSCLC) accounts for the vast majority of lung cancers. Early detection is crucial to reduce lung cancer-related mortality. Aberrant DNA methylation occurs early during carcinogenesis and can be detected in blood. It is essential to investigate the dysregulated blood methylation markers for early diagnosis of NSCLC.

METHODS

NSCLC-associated methylation gene folate receptor gamma (FOLR3) was selected from an Illumina 850K array analysis of peripheral blood samples. Mass spectrometry was used for validation in two independent case-control studies (validation I: n = 2548; validation II: n = 3866). Patients with lung squamous carcinoma (LUSC) or lung adenocarcinoma (LUAD), normal controls (NCs) and benign pulmonary nodule (BPN) cases were included. FOLR3 methylations were compared among different populations. Their associations with NSCLC clinical features were investigated. Receiver operating characteristic analyses, Kruskal-Wallis test, Wilcoxon test, logistics regression analysis and nomogram analysis were performed.

RESULTS

Two CpG sites (CpG_1 and CpG_2) of FOLR3 was significantly lower methylated in NSCLC patients than NCs in the discovery round. In the two validations, both LUSC and LUAD patients presented significant FOLR3 hypomethylations. LUSC patients were highlighted to have significantly lower methylation levels of CpG_1 and CpG_2 than BPN cases and LUAD patients. Both in the two validations, CpG_1 methylation and CpG_2 methylation could discriminate LUSC from NCs well, with areas under the curve (AUCs) of 0.818 and 0.832 in validation I, and 0.789 and 0.780 in validation II. They could also differentiate LUAD from NCs, but with lower efficiency. CpG_1 and CpG_2 methylations could also discriminate LUSC from BPNs well individually in the two validations. With the combined dataset of two validations, the independent associations of age, gender, and FOLR3 methylation with LUSC and LUAD risk were shown and the age-gender-CpG_1 signature could discriminate LUSC and LUAD from NCs and BPNs, with higher efficiency for LUSC.

CONCLUSIONS

Blood-based FOLR3 hypomethylation was shown in LUSC and LUAD. FOLR3 methylation heterogeneity between LUSC and LUAD highlighted its stronger associations with LUSC. FOLR3 methylation and the age-gender-CpG_1 signature might be novel diagnostic markers for the early detection of NSCLC, especially for LUSC.

Epigenomic Characterization of Lymphoid Neoplasms

Lymphoid neoplasms represent a heterogeneous group of disease entities and subtypes with markedly different molecular and clinical features. Beyond genetic alterations, lymphoid tumors also show widespread epigenomic changes. These severely affect the levels and distribution of DNA methylation, histone modifications, chromatin accessibility, and three-dimensional genome interactions. DNA methylation stands out as a tracer of cell identity and memory, as B cell neoplasms show epigenetic imprints of their cellular origin and proliferative history, which can be quantified by an epigenetic mitotic clock. Chromatin-associated marks are informative to uncover altered regulatory regions and transcription factor networks contributing to the development of distinct lymphoid tumors. Tumor-intrinsic epigenetic and genetic aberrations cooperate and interact with microenvironmental cells to shape the transcriptome at different phases of lymphoma evolution, and intraclonal heterogeneity can now be characterized by single-cell profiling. Finally, epigenetics offers multiple clinical applications, including powerful diagnostic and prognostic biomarkers as well as therapeutic targets.

Multimodal epigenetic sequencing analysis (MESA) of cell-free DNA for non-invasive colorectal cancer detection

BACKGROUND

Detecting human cancers through cell-free DNA (cfDNA) in blood is a sensitive and non-invasive option. However, capturing multiple forms of epigenetic information remains a technical and financial challenge.

METHODS

To address this, we developed multimodal epigenetic sequencing analysis (MESA), a flexible and sensitive approach to capturing and integrating a diverse range of epigenetic features in cfDNA using a single experimental assay, i.e., non-disruptive bisulfite-free methylation sequencing, such as Enzymatic Methyl-seq. MESA enables simultaneous inference of four epigenetic modalities: cfDNA methylation, nucleosome occupancy, nucleosome fuzziness, and windowed protection score for regions surrounding gene promoters and polyadenylation sites.

RESULTS

When applied to 690 cfDNA samples from 3 colorectal cancer clinical cohorts, MESA's novel modalities, which include nucleosome fuzziness, and genomic features, including polyadenylation sites, improve cancer detection beyond the traditional epigenetic markers of promoter DNA methylation.

CONCLUSIONS

Together, MESA stands as a major advancement in the field by utilizing comprehensive and complementary epigenetic profiles of cfDNA for effective non-invasive cancer detection.

Plasma Cell-Free Tumor Methylome as a Biomarker in Solid Tumors: Biology and Applications

DNA methylation is a fundamental mechanism of epigenetic control in cells and its dysregulation is strongly implicated in cancer development. Cancers possess an extensively hypomethylated genome with focal regions of hypermethylation at CPG islands. Due to the highly conserved nature of cancer-specific methylation, its detection in cell-free DNA in plasma using liquid biopsies constitutes an area of interest in biomarker research. The advent of next-generation sequencing and newer computational technologies have allowed for the development of diagnostic and prognostic biomarkers that utilize methylation profiling to diagnose disease and stratify risk. Methylome-based predictive biomarkers can determine the response to anti-cancer therapy. An additional emerging application of these biomarkers is in minimal residual disease monitoring. Several key challenges need to be addressed before cfDNA-based methylation biomarkers become fully integrated into practice. The first relates to the biology and stability of cfDNA. The second concerns the clinical validity and generalizability of methylation-based assays, many of which are cancer type-specific. The third involves their practicability, which is a stumbling block for translating technologies from bench to clinic. Future work on developing pan-cancer assays with their respective validities confirmed using well-designed, prospective clinical trials is crucial in pushing for the greater use of these tools in oncology.

Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA

People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS.

SIGNIFICANCE

By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.

Plasma cell-free DNA hydroxymethylation profiling reveals anti-PD-1 treatment response and resistance biology in non-small cell lung cancer

BACKGROUND

Treatment with immune checkpoint inhibitors (ICIs) targeting programmed death-1 (PD-1) can yield durable antitumor responses, yet not all patients respond to ICIs. Current approaches to select patients who may benefit from anti-PD-1 treatment are insufficient. 5-hydroxymethylation (5hmC) analysis of plasma-derived cell-free DNA (cfDNA) presents a novel non-invasive approach for identification of therapy response biomarkers which can tackle challenges associated with tumor biopsies such as tumor heterogeneity and serial sample collection.

METHODS

151 blood samples were collected from 31 patients with non-small cell lung cancer (NSCLC) before therapy started and at multiple time points while on therapy. Blood samples were processed to obtain plasma-derived cfDNA, followed by enrichment of 5hmC-containing cfDNA fragments through biotinylation via a two-step chemistry and binding to streptavidin coated beads. 5hmC-enriched cfDNA and whole genome libraries were prepared in parallel and sequenced to obtain whole hydroxymethylome and whole genome plasma profiles, respectively.

RESULTS

Comparison of on-treatment time point to matched pretreatment samples from same patients revealed that anti-PD-1 treatment induced distinct changes in plasma cfDNA 5hmC profiles of responding patients, as judged by Response evaluation criteria in solid tumors, relative to non-responders. In responders, 5hmC accumulated over genes involved in immune activation such as inteferon (IFN)-γ and IFN-α response, inflammatory response and tumor necrosis factor (TNF)-α signaling, whereas in non-responders 5hmC increased over epithelial to mesenchymal transition genes. Molecular response to anti-PD-1 treatment, as measured by 5hmC changes in plasma cfDNA profiles were observed early on, starting with the first cycle of treatment. Comparison of pretreatment plasma samples revealed that anti-PD-1 treatment response and resistance associated genes can be captured by 5hmC profiling of plasma-derived cfDNA. Furthermore, 5hmC profiling of pretreatment plasma samples was able to distinguish responders from non-responders using T cell-inflamed gene expression profile, which was previously identified by tissue RNA analysis.

CONCLUSIONS

These results demonstrate that 5hmC profiling can identify response and resistance associated biological pathways in plasma-derived cfDNA, offering a novel approach for non-invasive prediction and monitoring of immunotherapy response in NSCLC.

cfDNA UniFlow: a unified preprocessing pipeline for cell-free DNA data from liquid biopsies

BACKGROUND

Cell-free DNA (cfDNA), a broadly applicable biomarker commonly sourced from urine or blood, is extensively used for research and diagnostic applications. In various settings, genetic and epigenetic information is derived from cfDNA. However, a unified framework for its processing is lacking, limiting the universal application of innovative analysis strategies and the joining of data sets.

FINDINGS

Here, we describe cfDNA UniFlow, a unified, standardized, and ready-to-use workflow for processing cfDNA samples. The workflow is written in Snakemake and can be scaled from stand-alone computers to cluster environments. It includes methods for processing raw genome sequencing data as well as specialized approaches for correcting sequencing errors, filtering, and quality control. Sophisticated methods for detecting copy number alterations and estimating and correcting GC-related biases are readily incorporated. Furthermore, it includes methods for extracting, normalizing, and visualizing coverage signals around user-defined regions in case-control settings. Ultimately, all results and metrics are aggregated in a unified report, enabling easy access to a wide variety of information for further research and downstream analysis.

CONCLUSIONS

We provide an automated pipeline for processing cell-free DNA sampled from liquid biopsies, including a wide variety of additional functionalities like bias correction and signal extraction. With our focus on scalability and extensibility, we provide a foundation for future cfDNA research and faster clinical applications. The source code and extensive documentation are available on our GitHub repository (https://github.com/kircherlab/cfDNA-UniFlow).

FinaleMe: Predicting DNA methylation by the fragmentation patterns of plasma cell-free DNA

Analysis of DNA methylation in cell-free DNA (cfDNA) reveals clinically relevant biomarkers but requires specialized protocols and sufficient input material that limits its applicability. Millions of cfDNA samples have been profiled by genomic sequencing. To maximize the gene regulation information from the existing dataset, we developed FinaleMe, a non-homogeneous Hidden Markov Model (HMM), to predict DNA methylation of cfDNA and, therefore, tissues-of-origin directly from plasma whole-genome sequencing (WGS). We validated the performance with 80 pairs of deep and shallow-coverage WGS and whole-genome bisulfite sequencing (WGBS) data.

Terminal modifications independent cell-free RNA sequencing enables sensitive early cancer detection and classification

Cell-free RNAs (cfRNAs) offer an opportunity to detect diseases from a transcriptomic perspective, however, existing techniques have fallen short in generating a comprehensive cell-free transcriptome profile. We develop a sensitive library preparation method that is robust down to 100 µl input plasma to analyze cfRNAs independent of their 5'-end modifications. We show that it outperforms adapter ligation-based method in detecting a greater number of cfRNA species. We perform transcriptome-wide characterizations in 165 lung cancer, 30 breast cancer, 37 colorectal cancer, 55 gastric cancer, 15 liver cancer, and 133 cancer-free participants and demonstrate its ability to identify transcriptomic changes occurring in early-stage tumors. We also leverage machine learning analyses on the differentially expressed cfRNA signatures and reveal their robust performance in cancer detection and classification. Our work sets the stage for in-depth study of the cfRNA repertoire and highlights the value of cfRNAs as cancer biomarkers in clinical applications.

A Novel Tissue-Free Method to Estimate Tumor-Derived Cell-Free DNA Quantity Using Tumor Methylation Patterns

Estimating the abundance of cell-free DNA (cfDNA) fragments shed from a tumor (i.e., circulating tumor DNA (ctDNA)) can approximate tumor burden, which has numerous clinical applications. We derived a novel, broadly applicable statistical method to quantify cancer-indicative methylation patterns within cfDNA to estimate ctDNA abundance, even at low levels. Our algorithm identified differentially methylated regions (DMRs) between a reference database of cancer tissue biopsy samples and cfDNA from individuals without cancer. Then, without utilizing matched tissue biopsy, counts of fragments matching the cancer-indicative hyper/hypo-methylated patterns within DMRs were used to determine a tumor methylated fraction (TMeF; a methylation-based quantification of the circulating tumor allele fraction and estimate of ctDNA abundance) for plasma samples. TMeF and small variant allele fraction (SVAF) estimates of the same cancer plasma samples were correlated (Spearman's correlation coefficient: 0.73), and synthetic dilutions to expected TMeF of 10-3 and 10-4 had estimated TMeF within two-fold for 95% and 77% of samples, respectively. TMeF increased with cancer stage and tumor size and inversely correlated with survival probability. Therefore, tumor-derived fragments in the cfDNA of patients with cancer can be leveraged to estimate ctDNA abundance without the need for a tumor biopsy, which may provide non-invasive clinical approximations of tumor burden.

Promising and Minimally Invasive Biomarkers: Targeting Melanoma

The therapeutic landscape of malignant melanoma has been radically reformed in recent years, with novel treatments emerging in both the field of cancer immunotherapy and signalling pathway inhibition. Large-scale tumour genomic characterization has accurately classified malignant melanoma into four different genomic subtypes so far. Despite this, only somatic mutations in BRAF oncogene, as assessed in tumour biopsies, has so far become a validated predictive biomarker of treatment with small molecule inhibitors. The biology of tumour evolution and heterogeneity has uncovered the current limitations associated with decoding genomic drivers based only on a single-site tumour biopsy. There is an urgent need to develop minimally invasive biomarkers that accurately reflect the real-time evolution of melanoma and that allow for streamlined collection, analysis, and interpretation. These will enable us to face challenges with tumour tissue attainment and process and will fulfil the vision of utilizing "liquid biopsy" to guide clinical decisions, in a manner akin to how it is used in the management of haematological malignancies. In this review, we will summarize the most recent published evidence on the role of minimally invasive biomarkers in melanoma, commenting on their future potential to lead to practice-changing discoveries.

A novel DNA methylation signature revealed GDF6 and RCC1 as potential prognostic biomarkers correlated with cell proliferation in clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) accounts for the majority (80%-90%) of renal cell carcinoma (RCC) patients at the time of diagnosis, and approximately 15% of ccRCC patients will develop distant metastasis or recurrence during their lifetime. Increasing number of studies have revealed that the aberrant DNA methylations is closely correlated with the tumorigenesis in ccRCC.

RESULTS

In this study, we utilized a LASSO (least absolute shrinkage and selection operator) model to identify a combination of 13 probes-based DNA methylation signature that associated with the progression-free survival (PFS) of ccRCC patients. First, differentially methylated regions (CpGs) related to PFS and phenotypes were identified. Next, prognostic DNA methylation probes were selected from the differentially methylated probes (DMPs) and calculated risk scores to stratify patients with ccRCC. The performance of this signature was validated in an independent testing set using various analyses, including Kaplan-Meier analysis for PFS and receiver operating characteristic (ROC) curve analysis. Based on our 13-DNA methylation probes signature, ccRCC patients were successfully stratified into high- and low-risk groups. Combining DNA methylation signature with clinical variables such as T stage, M stage and tumor grade could further improve the accuracy of prediction. Moreover, we highlight two molecular biomarkers (RCC1 and GDF6) corresponding to our probes. Invitro experiments showed that knockdown of RCC1 or GDF6 in ccRCC cell lines reduced cell proliferation, which indicated that both biomarkers are associated with tumorigenesis.

CONCLUSIONS

The 13-probes-based DNA methylation signature has the potential to serve as an independent tool for survival outcome improvement and treatment strategy selection for ccRCC patients. In addition, our findings suggest that RCC1 and GDF6 may serve as promising markers for ccRCC.

Real-time analysis of the cancer genome and fragmentome from plasma and urine cell-free DNA using nanopore sequencing

Cell-free DNA (cfDNA) can be isolated and sequenced from blood and/or urine of cancer patients. Conventional short-read sequencing lacks deployability and speed and can be biased for short cfDNA fragments. Here, we demonstrate that with Oxford Nanopore Technologies (ONT) sequencing we can achieve delivery of genomic and fragmentomic data from liquid biopsies. Copy number aberrations and cfDNA fragmentation patterns can be determined in less than 24 h from sample collection. The tumor-derived cfDNA fraction calculated from plasma of lung cancer patients and urine of bladder cancer patients was highly correlated (R = 0.98) with the tumor fraction calculated from short-read sequencing of the same samples. cfDNA size profile, fragmentation patterns, fragment-end composition, and nucleosome profiling near transcription start sites in plasma and urine exhibited the typical cfDNA features. Additionally, a high proportion of long tumor-derived cfDNA fragments (> 300 bp) are recovered in plasma and urine using ONT sequencing. ONT sequencing is a cost-effective, fast, and deployable approach for obtaining genomic and fragmentomic results from liquid biopsies, allowing the analysis of previously understudied cfDNA populations.

The changing face of circulating tumor DNA (ctDNA) profiling: Factors that shape the landscape of methodologies, technologies, and commercialization

Liquid biopsies, in particular the profiling of circulating tumor DNA (ctDNA), have long held promise as transformative tools in cancer precision medicine. Despite a prolonged incubation phase, ctDNA profiling has recently experienced a strong wave of development and innovation, indicating its imminent integration into the cancer management toolbox. Various advancements in mutation-based ctDNA analysis methodologies and technologies have greatly improved sensitivity and specificity of ctDNA assays, such as optimized preanalytics, size-based pre-enrichment strategies, targeted sequencing, enhanced library preparation methods, sequencing error suppression, integrated bioinformatics and machine learning. Moreover, research breakthroughs have expanded the scope of ctDNA analysis beyond hotspot mutational profiling of plasma-derived apoptotic, mono-nucleosomal ctDNA fragments. This broader perspective considers alternative genetic features of cancer, genome-wide characterization, classical and newly discovered epigenetic modifications, structural variations, diverse cellular and mechanistic ctDNA origins, and alternative biospecimen types. These developments have maximized the utility of ctDNA, facilitating landmark research, clinical trials, and the commercialization of ctDNA assays, technologies, and products. Consequently, ctDNA tests are increasingly recognized as an important part of patient guidance and are being implemented in clinical practice. Although reimbursement for ctDNA tests by healthcare providers still lags behind, it is gaining greater acceptance. In this work, we provide a comprehensive exploration of the extensive landscape of ctDNA profiling methodologies, considering the multitude of factors that influence its development and evolution. By illuminating the broader aspects of ctDNA profiling, the aim is to provide multiple entry points for understanding and navigating the vast and rapidly evolving landscape of ctDNA methodologies, applications, and technologies.

Circulating tumor DNA as liquid biopsy in lung cancer: Biological characteristics and clinical integration

Lung cancer maintains high morbidity and mortality rate globally despite significant advancements in diagnosis and treatment in the era of precision medicine. Pathological analysis of tumor tissue, the current gold standard for lung cancer diagnosis, is intrusive and intrinsically confined to evaluating the limited amount of tissues that could be physically extracted. However, tissue biopsy has several limitations, including the invasiveness of the procedure and difficulty in obtaining samples for patients at advanced stages., there Additionally,has been no major breakthrough in tumor biomarkers with high specificity and sensitivity, particularly for early-stage lung cancer. Liquid biopsy has been considered a feasible auxiliary tool for tearly dianosis, evaluating treatment responses and monitoring prognosis of lung cancer. Circulating tumor DNA (ctDNA), an ideal biomarker of liquid biopsy, has emerged as one of the most reliable tools for monitoring tumor processes at molecular levels. Herein, this review focuses on tumor heterogeneity to elucidate the superiority of liquid biopsy and retrospectively discussdeciphersolution. We systematically elaborate ctDNA biological characteristics, introduce methods for ctDNA detection, and discuss the current role of plasma ctDNA in lung cancer management. Finally, we summarize the drawbacks of ctDNA analysis and highlight its potential clinical application in lung cancer.

Liquid biopsy biomarkers to guide immunotherapy in breast cancer

Immune checkpoint inhibitors (ICIs) therapy has emerged as a promising treatment strategy for breast cancer (BC). However, current reliance on immunohistochemical (IHC) detection of PD-L1 expression alone has limited predictive capability, resulting in suboptimal efficacy of ICIs for some BC patients. Hence, developing novel predictive biomarkers is indispensable to enhance patient selection for immunotherapy. In this context, utilizing liquid biopsy (LB) can provide supplementary or alternative value to PD-L1 IHC testing for identifying patients most likely to benefit from immunotherapy and exhibit favorable responses. This review discusses the predictive and prognostic value of LB in breast cancer immunotherapy, as well as its limitations and future directions. We aim to promote the individualization and precision of immunotherapy in BC by elucidating the role of LB in clinical practice.

Single-molecule systems for detection and monitoring of plasma circulating nucleosomes and oncoproteins in Diffuse Midline Glioma

The analysis of cell-free tumor DNA (ctDNA) and proteins in the blood of cancer patients potentiates a new generation of non-invasive diagnostics and treatment monitoring approaches. However, confident detection of these tumor-originating markers is challenging, especially in the context of brain tumors, in which extremely low amounts of these analytes circulate in the patient's plasma. Here, we applied a sensitive single-molecule technology to profile multiple histone modifications on millions of individual nucleosomes from the plasma of Diffuse Midline Glioma (DMG) patients. The system reveals epigenetic patterns that are unique to DMG, significantly differentiating this group of patients from healthy subjects or individuals diagnosed with other cancer types. We further develop a method to directly capture and quantify the tumor-originating oncoproteins, H3-K27M and mutant p53, from the plasma of children diagnosed with DMG. This single-molecule system allows for accurate molecular classification of patients, utilizing less than 1ml of liquid-biopsy material. Furthermore, we show that our simple and rapid detection strategy correlates with MRI measurements and droplet-digital PCR (ddPCR) measurements of ctDNA, highlighting the utility of this approach for non-invasive treatment monitoring of DMG patients. This work underscores the clinical potential of single-molecule-based, multi-parametric assays for DMG diagnosis and treatment monitoring.

Cell-free multi-omics analysis reveals potential biomarkers in gastrointestinal cancer patients' blood

During cancer progression, tumorigenic and immune signals are spread through circulating molecules, such as cell-free DNA (cfDNA) and cell-free RNA (cfRNA) in the blood. So far, they have not been comprehensively investigated in gastrointestinal cancers. Here, we profile 4 categories of cell-free omics data from patients with colorectal cancer and patients with stomach adenocarcinoma and then assay 15 types of genomic, epigenomic, and transcriptomic variations. We find that multi-omics data are more appropriate for detection of cancer genes compared with single-omics data. In particular, cfRNAs are more sensitive and informative than cfDNAs in terms of detection rate, enriched functional pathways, etc. Moreover, we identify several peripheral immune signatures that are suppressed in patients with cancer. Specifically, we establish a γδ-T cell score and a cancer-associated-fibroblast (CAF) score, providing insights into clinical statuses like cancer stage and survival. Overall, we reveal a cell-free multi-molecular landscape that is useful for blood monitoring in personalized cancer treatment.

Early detection of hepatocellular carcinoma via no end-repair enzymatic methylation sequencing of cell-free DNA and pre-trained neural network

BACKGROUND

Early detection of hepatocellular carcinoma (HCC) is important in order to improve patient prognosis and survival rate. Methylation sequencing combined with neural networks to identify cell-free DNA (cfDNA) carrying aberrant methylation offers an appealing and non-invasive approach for HCC detection. However, some limitations exist in traditional methylation detection technologies and models, which may impede their performance in the read-level detection of HCC.

METHODS

We developed a low DNA damage and high-fidelity methylation detection method called No End-repair Enzymatic Methyl-seq (NEEM-seq). We further developed a read-level neural detection model called DeepTrace that can better identify HCC-derived sequencing reads through a pre-trained and fine-tuned neural network. After pre-training on 11 million reads from NEEM-seq, DeepTrace was fine-tuned using 1.2 million HCC-derived reads from tumor tissue DNA after noise reduction, and 2.7 million non-tumor reads from non-tumor cfDNA. We validated the model using data from 130 individuals with cfDNA whole-genome NEEM-seq at around 1.6X depth.

RESULTS

NEEM-seq overcomes the drawbacks of traditional enzymatic methylation sequencing methods by avoiding the introduction of unmethylation errors in cfDNA. DeepTrace outperformed other models in identifying HCC-derived reads and detecting HCC individuals. Based on the whole-genome NEEM-seq data of cfDNA, our model showed high accuracy of 96.2%, sensitivity of 93.6%, and specificity of 98.5% in the validation cohort consisting of 62 HCC patients, 48 liver disease patients, and 20 healthy individuals. In the early stage of HCC (BCLC 0/A and TNM I), the sensitivity of DeepTrace was 89.6 and 89.5% respectively, outperforming Alpha Fetoprotein (AFP) which showed much lower sensitivity in both BCLC 0/A (50.5%) and TNM I (44.7%).

CONCLUSIONS

By combining high-fidelity methylation data from NEEM-seq with the DeepTrace model, our method has great potential for HCC early detection with high sensitivity and specificity, making it potentially suitable for clinical applications. DeepTrace: https://github.com/Bamrock/DeepTrace.

A non-invasive method for concurrent detection of multiple early-stage cancers in women

Untargeted serum metabolomics was combined with machine learning-powered data analytics to develop a test for the concurrent detection of multiple cancers in women. A total of fifteen cancers were tested where the resulting metabolome data was sequentially analysed using two separate algorithms. The first algorithm successfully identified all the cancer-positive samples with an overall accuracy of > 99%. This result was particularly significant given that the samples tested were predominantly from early-stage cancers. Samples identified as cancer-positive were next analysed using a multi-class algorithm, which then enabled accurate discernment of the tissue of origin for the individual samples. Integration of serum metabolomics with appropriate data analytical tools, therefore, provides a powerful screening platform for early-stage cancers.

Liquid biopsy: creating opportunities in brain space

In recent years, liquid biopsy has emerged as an alternative method to diagnose and monitor tumors. Compared to classical tissue biopsy procedures, liquid biopsy facilitates the repetitive collection of diverse cellular and acellular analytes from various biofluids in a non/minimally invasive manner. This strategy is of greater significance for high-grade brain malignancies such as glioblastoma as the quantity and accessibility of tumors are limited, and there are collateral risks of compromised life quality coupled with surgical interventions. Currently, blood and cerebrospinal fluid (CSF) are the most common biofluids used to collect circulating cells and biomolecules of tumor origin. These liquid biopsy analytes have created opportunities for real-time investigations of distinct genetic, epigenetic, transcriptomics, proteomics, and metabolomics alterations associated with brain tumors. This review describes different classes of liquid biopsy biomarkers present in the biofluids of brain tumor patients. Moreover, an overview of the liquid biopsy applications, challenges, recent technological advances, and clinical trials in the brain have also been provided.

Assessing the capacity of methylated DNA markers of cervical squamous cell carcinoma to discriminate oropharyngeal squamous cell carcinoma in human papillomavirus mediated disease

OBJECTIVE

Early identification of human papillomavirus associated oropharyngeal squamous cell carcinoma (HPV(+)OPSCC) is challenging and novel biomarkers are needed. We hypothesized that a panel of methylated DNA markers (MDMs) found in HPV(+) cervical squamous cell carcinoma (CSCC) will have similar discrimination in HPV(+)OPSCC tissues.

MATERIALS AND METHODS

Formalin-fixed, paraffin-embedded tissues were obtained from patients with primary HPV(+)OPSCC or HPV(+)CSCC; control tissues included normal oropharynx palatine tonsil (NOP) and cervix (NCS). Using a methylation-specific polymerase chain reaction, 21 previously validated cervical MDMs were evaluated on tissue-extracted DNA. Discrimination between case and control cervical and oropharynx tissue was assessed using area under the curve (AUC).

RESULTS

34 HPV(+)OPSCC, 36 HPV(+)CSCC, 26 NOP, and 24 NCS patients met inclusion criteria. Within HPV(+)CSCC, 18/21 (86%) of MDMs achieved an AUC ≥ 0.9 and all MDMs exhibited better than chance classifications relative to control cervical tissue (all p < 0.001). In contrast, within HPV(+)OPSCC only 5/21 (24%) MDMs achieved an AUC ≥ 0.90 but 19/21 (90%) exhibited better than chance classifications relative to control tonsil tissue (all p < 0.001). Overall, 13/21 MDMs had statistically significant lower AUCs in the oropharyngeal cohort compared to the cervical cohort, and only 1 MDM exhibited a statistically significant increase in AUC.

CONCLUSIONS

Previously validated MDMs exhibited robust performance in independent HPV(+)CSCC patients. However, most of these MDMs exhibited higher discrimination for HPV(+)CSCC than for HPV(+)OPSCC. This suggests that each SCC subtype requires a unique set of MDMs for optimal discrimination. Future studies are necessary to establish an MDM panel for HPV(+)OPSCC.

Aberrant methylation scanning by quantitative DNA melting analysis with hybridization probes as exemplified by liquid biopsy of SEPT9 and HIST1H4F in colorectal cancer

OBJECTIVE

The generally accepted method of quantifying hypermethylated DNA by qPCR using methylation-specific primers has the risk of underestimating DNA methylation and requires data normalization. This makes the analysis complicated and less reliable.

METHODS

The end-point PCR method, called qDMA-HP (for quantitative DNA Melting Analysis with hybridization probes), which excludes the normalization procedure, is multiplexed and quantitative, has been proposed. qDMA-HP is characterized by the following features: (i) asymmetric PCR with methylation-independent primers; (ii) fluorescent dual-labeled, self-quenched probes (commonly known as TaqMan probes) covering several interrogated CpGs; (iii) post-PCR melting analysis of amplicon/probe hybrids; (iv) quantitation of unmethylated and methylated DNA alleles by measuring the areas under the corresponding melt peaks.

RESULTS

qDMA-HP was tested in liquid biopsy of colorectal cancer by evaluating SEPT9 and HIST1H4F methylations simultaneously in the single-tube reaction. Differences in the methylation levels in healthy donors versus cancer patients were statistically significant (p < 0.0001), AUCROC values were 0.795-0.921 for various marker combinations.

CONCLUSIONS

This proof-of-concept study shows that qDMA-HP is a simple, normalization-independent, quantitative, multiplex and "closed tube" method easily adapted to clinical settings. It is demonstrated, for the first time, that HIST1H4F is a perspective marker for liquid biopsy of colorectal cancer.

Cancer signature ensemble integrating cfDNA methylation, copy number, and fragmentation facilitates multi-cancer early detection

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.