Detection and characterization of lung cancer using cell-free DNA fragmentomes

Liquid Biopsy Approaches for Cancer Characterization, Residual Disease Detection, and Therapy Monitoring

Liquid biopsy encompasses a variety of molecular approaches to detect circulating tumor DNA (ctDNA) and has become a powerful tool in the diagnosis and treatment of solid tumors. Current applications include comprehensive genomic profiling for identifying targetable mutations and therapeutic resistance mechanisms, with emerging applications in minimal residual disease detection and treatment response monitoring. Increasingly, the potential for liquid biopsy in guiding treatment decisions is under active investigation through prospective clinical trials using ctDNA-adaptive interventions in patients with early-stage and metastatic cancers. Limitations arise on the basis of the sensitivity and feasibility of individual liquid biopsy assays; nonetheless, emerging technologies set the stage for improving these shortcomings. As the global oncology community continues to ascertain the clinical value of liquid biopsy across the continuum of patient care, this minimally invasive approach heralds a significant advancement in the promise of precision oncology.

The growing field of liquid biopsy and its Snowball effect on reshaping cancer management

Liquid biopsy (LB) has emerged as a transformative tool in oncology, providing a minimally invasive approach for tumor detection, molecular characterization, and real-time treatment monitoring. By analyzing circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), extracellular vesicles (EVs), and microRNA (miRNA), LB enables comprehensive tumor profiling without the need for traditional tissue biopsies. Over the past decade, research in this field has expanded exponentially, leading to the integration of LB into clinical practice for specific cancer types, including lung and breast cancer. In 2024, the Journal of Liquid Biopsy (JLB) published innovative studies exploring the latest advancements in LB technologies, biomarkers, and their applications for cancer detection, minimal residual disease (MRD) monitoring, and therapy response assessment. This review synthesizes recent findings on the role of LB in cancer treatment and monitoring across different biomarkers, with a particular focus on newly published studies and their context within translational research. Additionally, it highlights emerging techniques such as fragmentomics, artificial intelligence, and multiomics, paving the way for more precise, personalized treatment decisions. Despite these advancements, challenges remain in standardizing methodologies, optimizing clinical validation, and integrating LB into routine oncological workflows. This mini-review highlights the evolving landscape of LB research and its potential to revolutionize cancer diagnosis, treatment monitoring, and therapeutic decision-making, ushering in a new era of precision oncology.

Genome-wide analyses of cell-free DNA for therapeutic monitoring of patients with pancreatic cancer

Determining response to therapy for patients with pancreatic cancer can be challenging. We evaluated methods for assessing therapeutic response using cell-free DNA (cfDNA) in plasma from patients with metastatic pancreatic cancer in the CheckPAC trial (NCT02866383). Patients were evaluated before and after initiation of therapy using tumor-informed plasma whole-genome sequencing (WGMAF) and tumor-independent genome-wide cfDNA fragmentation profiles and repeat landscapes (ARTEMIS-DELFI). Using WGMAF, molecular responders had a median overall survival (OS) of 319 days compared to 126 days for nonresponders [hazard ratio (HR) = 0.29, 95% confidence interval (CI) = 0.11-0.79, P = 0.011]. For ARTEMIS-DELFI, patients with low scores after therapy initiation had longer median OS than patients with high scores (233 versus 172 days, HR = 0.12, 95% CI = 0.046-0.31, P < 0.0001). We validated ARTEMIS-DELFI in patients with pancreatic cancer in the PACTO trial (NCT02767557). These analyses suggest that noninvasive mutation and fragmentation-based cfDNA approaches can identify therapeutic response of individuals with pancreatic cancer.

Peak analysis of cell-free RNA finds recurrently protected narrow regions with clinical potential

BACKGROUND

Cell-free RNAs (cfRNAs) can be detected in biofluids and have emerged as valuable disease biomarkers. Accurate identification of the fragmented cfRNA signals, especially those originating from pathological cells, is crucial for understanding their biological functions and clinical value. However, many challenges still need to be addressed for their application, including developing specific analysis methods and translating cfRNA fragments with biological support into clinical applications.

RESULTS

We present cfPeak, a novel method combining statistics and machine learning models to detect the fragmented cfRNA signals effectively. When test in real and artificial cfRNA sequencing (cfRNA-seq) data, cfPeak shows an improved performance compared with other applicable methods. We reveal that narrow cfRNA peaks preferentially overlap with protein binding sites, vesicle-sorting sites, structural sites, and novel small non-coding RNAs (sncRNAs). When applied in clinical cohorts, cfPeak identified cfRNA peaks in patients' plasma that enable cancer detection and are informative of cancer types and metastasis.

CONCLUSIONS

Our study fills the gap in the current small cfRNA-seq analysis at fragment-scale and builds a bridge to the scientific discovery in cfRNA fragmentomics. We demonstrate the significance of finding low abundant tissue-derived signals in small cfRNA and prove the feasibility for application in liquid biopsy.

Integrating Plasma Cell-Free DNA Fragment End Motif and Size with Genomic Features Enables Lung Cancer Detection

Early detection of lung cancer is important for improving patient survival rates. Liquid biopsy using whole-genome sequencing of cell-free DNA (cfDNA) offers a promising avenue for lung cancer screening, providing a potential alternative or complementary approach to current screening modalities. Here, we aimed to develop and validate an approach by integrating fragment and genomic features of cfDNA to enhance lung cancer detection accuracy across diverse populations. Deep learning-based classifiers were trained using comprehensive cfDNA fragmentomic features from participants in multi-institutional studies, including a Korean discovery dataset (218 patients with lung cancer and 2,559 controls), a Korean validation dataset (111 patients with lung cancer and 1,136 controls), and an independent Caucasian validation cohort (50 patients with lung cancer and 50 controls). In the discovery dataset, classifiers using fragment end motif by size, a feature that captures both fragment end motif and size profiles, outperformed standalone fragment end motif and fragment size classifiers, achieving an area under the curve (AUC) of 0.917. The ensemble classifier integrating fragment end motif by size and genomic coverage achieved an improved performance, with an AUC of 0.937. This performance extended to the Korean validation dataset and demonstrated ethnic generalizability in the Caucasian validation cohort. Overall, the development of a deep learning-based classifier integrating cfDNA fragmentomic and genomic features in this study highlights the potential for accurate lung cancer detection across diverse populations. Significance: Evaluating fragment-based features and genomic coverage in cell-free DNA offers an accurate lung cancer screening method, promising improvements in early cancer detection and addressing challenges associated with current screening methods.

Cost-effective shallow genome-wide sequencing for profiling plasma cfDNA signatures to enhance lung cancer detection

BACKGROUND

Lung cancer (LC) screening via low-dose computed tomography (LDCT) faces challenges including high false-positive rates and low patient compliance. Circulating tumor DNA (ctDNA)-based tests offer a minimally invasive alternative but are limited by high costs and low sensitivity, particularly in early-stage detection. This study introduces a cost-effective, shallow genome-wide sequencing approach for LC detection by profiling multiple cell-free DNA (cfDNA) signatures.

METHODS

We developed a multimodal cfDNA assay with shallow sequencing coverage (0.5×) that integrates fragmentomic, nucleosome, end-motif, and copy number alteration analyses. A machine-learning model trained on a discovery cohort (99 LC patients, 168 healthy controls) and validated on an independent cohort (58 LC patients, 71 controls) demonstrated robust performance.

RESULTS

The ensemble model exhibited outstanding performance, achieving an AUC of 0.97 and a specificity of 92% in both the discovery and validation cohorts, with sensitivities of 94% and 90%, respectively. Notably, it outperformed hotspot mutation-based assays and the multi-cancer SPOT-MAS assay in sensitivity across all LC stages.

CONCLUSIONS

This assay provides a cost-effective, accurate, and minimally invasive method for LC detection, addressing the limitations of current screening methods. It represents a promising complementary tool to improve early detection and patient outcomes in LC.

Translating premalignant biology to accelerate non-small-cell lung cancer interception

Over the past decade, substantial progress has been made in the development of targeted and immune-based therapies for patients with advanced non-small-cell lung cancer. To further improve outcomes for patients with lung cancer, identifying and intercepting disease at the earliest and most curable stages are crucial next steps. With the recent implementation of low-dose computed tomography scan screening in populations at high risk, there is an emerging unmet need for new diagnostic, prognostic and therapeutic tools to help treat patients suspected of harbouring premalignant lesions and minimally invasive non-small-cell lung cancer. Continued advances in the identification of the earliest drivers of lung carcinogenesis are poised to address these unmet needs. Employing multimodal approaches to chart the temporal and spatial maps of the molecular events driving lung premalignant lesion progression will refine our understanding of early carcinogenesis. Elucidating the molecular drivers of premalignancy is critical to the development of biomarkers to detect those incubating a premalignant lesion, to stratify risk for progression to invasive cancer and to identify novel therapeutic targets to intercept that process. In this Review, we summarize emerging insights into the earliest cellular and molecular events associated with lung squamous and adenocarcinoma carcinogenesis and highlight the growing opportunity for translating these insights into clinical tools for early detection and disease interception to transform the outcomes for those at risk for lung cancer.

Genomic and fragmentomic landscapes of cell-free DNA for early cancer detection

Genomic analyses of cell-free DNA (cfDNA) in plasma are enabling noninvasive blood-based biomarker approaches to cancer detection and disease monitoring. Current approaches for identification of circulating tumour DNA typically use targeted tumour-specific mutations or methylation analyses. An emerging approach is based on the recognition of altered genome-wide cfDNA fragmentation in patients with cancer. Recent studies have revealed a multitude of characteristics that can affect the compendium of cfDNA fragments across the genome, collectively called the 'cfDNA fragmentome'. These changes result from genomic, epigenomic, transcriptomic and chromatin states of an individual and affect the size, position, coverage, mutation, structural and methylation characteristics of cfDNA. Identifying and monitoring these changes has the potential to improve early detection of cancer, especially using highly sensitive multi-feature machine learning approaches that would be amenable to broad use in populations at increased risk. This Review highlights the rapidly evolving field of genome-wide analyses of cfDNA characteristics, their comparison to existing cfDNA methods, and recent related innovations at the intersection of large-scale sequencing and artificial intelligence. As the breadth of clinical applications of cfDNA fragmentome methods have enormous public health implications for cancer screening and personalized approaches for clinical management of patients with cancer, we outline the challenges and opportunities ahead.

Clinical Validation of a Noninvasive Multi-Omics Method for Multicancer Early Detection in Retrospective and Prospective Cohorts

Recent studies highlight the promise of blood-based multicancer early detection (MCED) tests for identifying asymptomatic patients with cancer. However, most focus on a single cancer hallmark, thus limiting effectiveness because of cancer's heterogeneity. Here, a blood-based multi-omics test named SeekInCare for MCED is reported. SeekInCare incorporates multiple genomic and epigenetic hallmarks, including copy number aberration, fragment size, end motif, and oncogenic virus, via shallow whole-genome sequencing from cell-free DNA, alongside seven protein tumor markers in one tube of blood. Artificial intelligence algorithms were developed to distinguish patients with cancer from individuals without cancer and to predict the likely affected organ. The retrospective study included 617 patients with cancer and 580 individuals without cancer, covering 27 cancer types. SeekInCare achieved 60.0% sensitivity at 98.3% specificity, resulting in an area under the curve of 0.899. Sensitivities were 37.7%, 50.4%, 66.7%, and 78.1% in patients with stage I, II, III, and IV disease, respectively. Additionally, SeekInCare was evaluated in a prospective cohort consisting of 1203 individuals who received the test as a laboratory-developed test (median follow-up time, 753 days) in which it achieved 70.0% sensitivity at 95.2% specificity. The performances of SeekInCare in both retrospective and prospective studies demonstrate that SeekInCare is a blood-based MCED test, showing comparable performance to the other tests currently in development. These findings support its potential clinical utility as a cancer screening test in high-risk populations.

Accurate and Efficient Detection of Nasopharyngeal Carcinoma Using Multi-Dimensional Features of Plasma Cell-Free DNA

BACKGROUND

The incidence of Nasopharyngeal carcinoma (NPC) is rising in recent years, especially in some non-developed parts of the world. Hence, cost-efficient means for sensitive detection of NPC are vital.

METHODS

We recruited 646 participants, including healthy individuals, patients with benign nasopharyngeal diseases, and NPC patients for plasma cell-free DNA(cfDNA), which underwent low-depth whole-genome sequencing (WGS) to extract multi-dimensional molecular features, including fragmentation pattern, end motif, copy number variation(CNV), and transcription factors(TF). Based on these features, we employed a machine learning algorithm to build prediction models for NPC detection.

RESULTS

We achieved a sensitivity of 95.8% and a specificity of 99.4% to discriminate NPC patients from healthy individuals.

CONCLUSIONS

This study can be a proof-of-concept for these multi-dimensional molecular features to be implemented as a noninvasive approach for the detection and even early detection of NPC.

Analysis of the clinical application value of cfDNA methylation and fragmentation in early diagnosis of esophageal cancer

BACKGROUND

This study explores the clinical value of cfDNA methylation and fragmentation for the early diagnosis of esophageal cancer using liquid biopsy.

METHODS

Whole genome bisulfite sequencing and low-pass whole genome sequencing were utilized to detect cfDNA biomarkers, comparing 30 esophageal cancer patients with 10 healthy controls.

RESULTS

Significant differences in cfDNA methylation and fragmentation were observed between cancerous and non-cancerous samples (p < 0.05). A volcano plot identified 822 differentially methylated markers (817 upregulated, 5 downregulated), with SOX17, SOX1, ZNF382, ZNF667-AS1, and TFPI2 highly associated with esophageal cancer. Fragmentation markers (EDM, FSD, FSR, TFBS, CNV) showed 95 % specificity and sensitivity, with EDM demonstrating the best performance.

CONCLUSION

Our study highlights the clinical potential of cfDNA methylation and fragmentation biomarkers for the early diagnosis of esophageal cancer.

Deciphering the interplay: circulating cell-free DNA, signaling pathways, and disease progression in idiopathic pulmonary fibrosis

Idiopathic pulmonary fibrosis (IPF) is a lung disease with an unknown etiology and a short survival rate. There is no accurate method of early diagnosis, and it involves computed tomography (CT) or lung biopsy. Since diagnostic methods are not accurate due to their similarity to other lung pathologies, discovering new biomarkers is a key issue for diagnosticians. Currently, the use of ccf-DNA (circulating cell-free deoxyribonucleic acid) is an important focus due to its association with IPF-induced alterations in metabolic pathways, such as amino acid metabolism, energy metabolism, and lipid metabolism pathways. Other biomarkers associated with metabolic changes have been found, and they are related to changes in type II/type I alveolar epithelial cells (AECs I/II), changes in extracellular matrix (ECM), and inflammatory processes. Currently, IPF pathogenetic treatment remains unknown, and the mortality rates are increasing, and the patients are diagnosed at a late stage. Signaling pathways and metabolic dysfunction have a significant role in the disease occurrence, particularly the transforming growth factor-β (TGF-β) signaling pathway, which plays an essential role. TGF-β, Wnt, Hedgehog (Hh), and integrin signaling are the main drivers of fibrosis. These pathways activate the transformation of fibroblasts into myofibroblasts, extracellular matrix (ECM) deposition, and tissue remodeling fibrosis. Therapy targeting diverse signaling pathways to slow disease progression is crucial in the treatment of IPF. Two antifibrotic medications, including pirfenidone and nintedanib, are Food and Drug Administration (FDA)-approved for treatment. ccf-DNA could become a new biomarker for IPF diagnosis to detect the disease at the early stage, while FDA-approved therapies could help to prevent late conditions from forming and decrease mortality rates.

Emergence of Circulating Tumor DNA as a Precision Biomarker in Lung Cancer Radiation Oncology and Beyond

Circulating tumor DNA (ctDNA) is emerging as a transformative biomarker in the management of non-small cell lung cancer (NSCLC). This review focuses on its role in detecting minimal residual disease (MRD), predicting treatment response, and guiding therapeutic decision-making in radiation oncology and immunotherapy. Key studies demonstrate ctDNA's prognostic value, particularly in identifying relapse risk and refining patient stratification for curative-intent and consolidative treatments. Future research is essential to standardize ctDNA assays, optimize integration into clinical workflows, and expand its clinical utility. This biomarker holds substantial promise by enabling non-invasive, real-time monitoring and improving outcomes for patients with NSCLC and beyond.

Liquid Biopsy in HPV-Associated Head and Neck Cancer: A Comprehensive Review

Objectives: Head and neck squamous cell carcinoma (HNSCC) is the sixth most common cancer globally, with HPV-positive cases emerging as a distinct subtype with unique clinical and molecular characteristics. Current diagnostic methods, including tissue biopsy and imaging, face limitations in terms of invasiveness, static disease assessment, and difficulty in distinguishing recurrence from treatment-related changes. This review aimed to assess the potential of liquid biopsy as a minimally invasive tool for the diagnosis, treatment monitoring, and surveillance of HPV-associated HNSCC. Methods: This systematic review analyzed literature from PubMed/MEDLINE, Embase, and Web of Science, focusing on original research and reviews related to liquid biopsy applications in HPV-positive HNSCC. Included studies were evaluated based on the robustness of the study design, clinical relevance, and analytical performance of liquid biopsy technologies. Biomarker types, detection methods, and implementation strategies were assessed to identify advancements and challenges in this field. Results: Liquid biopsy technologies, including circulating HPV DNA, ctDNA, and extracellular vesicles, demonstrated high sensitivity (90-95%) and specificity (>98%) in detecting HPV-positive HNSCC. These methods enabled real-time monitoring of tumor dynamics, early detection of recurrence, and insights into treatment resistance. Longitudinal analysis revealed that biomarker clearance during treatment correlates strongly with patient outcomes. Conclusions: Liquid biopsy is a transformative diagnostic and monitoring tool for HPV-associated HNSCC, offering minimally invasive, real-time insights into tumor biology. While challenges remain in standardization and clinical implementation, ongoing research and technological innovations hold promise for integrating liquid biopsy into personalized cancer care, ultimately improving patient outcomes.

Transparency and Representation in Clinical Research Utilizing Artificial Intelligence in Oncology: A Scoping Review

INTRODUCTION

Artificial intelligence (AI) has significant potential to improve health outcomes in oncology. However, as AI utility increases, it is imperative to ensure that these models do not systematize racial and ethnic bias and further perpetuate disparities in health. This scoping review evaluates the transparency of demographic data reporting and diversity of participants included in published clinical studies utilizing AI in oncology.

METHODS

We utilized PubMed to search for peer-reviewed research articles published between 2016 and 2021 with the query type "("deep learning" or "machine learning" or "neural network" or "artificial intelligence") and ("neoplas$" or "cancer$" or "tumor$" or "tumour$")." We included clinical trials and original research studies and excluded reviews and meta-analyses. Oncology-related studies that described data sets used in training or validation of the AI models were eligible. Data regarding public reporting of patient demographics were collected, including age, sex at birth, and race. We used descriptive statistics to analyze these data across studies.

RESULTS

Out of 220 total studies, 118 were eligible and 47 (40%) had at least one described training or validation data set publicly available. 69 studies (58%) reported age data for patients included in training or validation sets, 60 studies (51%) reported sex, and six studies (5%) reported race. Of the studies that reported race, a range of 70.7%-93.4% of individuals were White. Only three studies reported racial demographic data with greater than two categories (i.e. "White" vs. "non-White" or "White" vs. "Black").

CONCLUSIONS

We found that a minority of studies (5%) analyzed reported racial and ethnic demographic data. Furthermore, studies that did report racial demographic data had few non-White patients. Increased transparency regarding reporting of demographics and greater representation in data sets is essential to ensure fair and unbiased clinical integration of AI in oncology.

A deep-learning model for quantifying circulating tumour DNA from the density distribution of DNA-fragment lengths

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.

Computational Modeling for Circulating Cell-Free DNA in Clinical Oncology

PURPOSE

Liquid biopsy, specifically circulating cell-free DNA (cfDNA), has emerged as a powerful tool for cancer early diagnosis, prognosis, and treatment monitoring over a wide range of cancer types. Computational modeling (CM) of cfDNA data is essential to harness its full potential for real-time, noninvasive insights into tumor biology, enhancing clinical decision making.

DESIGN

This work reviews CM-cfDNA methods applied to clinical oncology, emphasizing both machine learning (ML) techniques and mechanistic approaches. The latter integrate biological principles, enabling a deeper understanding of cfDNA dynamics and its relationship with tumor evolution.

RESULTS

Key findings highlight the effectiveness of CM-cfDNA approaches in improving diagnostic accuracy, identifying prognostic markers, and predicting therapeutic outcomes. ML models integrating cfDNA concentration, fragmentation patterns, and mutation detection achieve high sensitivity and specificity for early cancer detection. Mechanistic models describe cfDNA kinetics, linking them to tumor growth and response to treatment, for example, immune checkpoint inhibitors. Longitudinal data and advanced statistical constructs further refine these models for quantification of interindividual and intraindividual variability.

CONCLUSION

CM-cfDNA represents a pivotal advancement in precision oncology. It bridges the gap between extensive cfDNA data and actionable clinical insights, supporting its integration into routine cancer care. Future efforts should focus on standardizing protocols, validating models across populations, and exploring hybrid approaches combining ML with mechanistic modeling to improve biological understanding.

[Benefits and limitations of circulating tumor DNA in breast cancer]

The detection of circulating tumor DNA (ctDNA) has made significant advances in oncology in recent years. ctDNA offers a range of applications, including the identification of theranostic mutations, monitoring of tumor recurrence, and assessing treatment efficacy. In breast cancer, several ctDNA-based tests for detecting relapse during follow-up are currently under validation, with some already available in countries like the United States. In metastatic breast cancer, ctDNA levels and their dynamics during treatment have prognostic value. The PADA-1 trial demonstrated that a therapeutic adaptation based on the detection of a circulating subclone via circulating tumor DNA (ctDNA) was feasible and potentially beneficial for patients. This review will explore the methods for ctDNA detection and discuss the potential benefits of incorporating this technology into breast cancer monitoring and management across various clinical scenarios.

Circulating cell-free DNA methylation profiles as noninvasive multiple sclerosis biomarkers: A proof-of-concept study

In multiple sclerosis (MS), there is a critical need for non-invasive biomarkers to concurrently classify disease subtypes, evaluate disability severity, and predict long-term progression. In this proof-of-concept study, we performed low-coverage whole-genome bisulfite sequencing (WGBS) on 75 plasma cell-free DNA (cfDNA) samples and assessed the clinical utility of cfDNA methylation as a single assay for distinguishing MS patients from non-MS controls, identifying MS subtypes, estimating disability severity, and predicting disease trajectories. We identified thousands of differentially methylated CpGs and hundreds of differentially methylated regions (DMRs) that significantly distinguished MS from controls, separated MS subtypes, and stratified disability severity levels. These DMRs were highly enriched in immunologically and neurologically relevant regulatory elements (e.g., active promoters and enhancers) and contained motifs associated with neuronal function and T-cell differentiation. To distinguish MS subtypes and severity groups, we achieved area-under-the-curve (AUC) values ranging from 0.67 to 0.81 using DMRs and 0.70 to 0.82 using inferred tissue-of-origin patterns from cfDNA methylation, significantly outperforming benchmark neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) in the same cohort. Finally, a linear mixed-effects model identified "prognostic regions" where baseline cfDNA methylation levels were associated with disease progression and predicted future disability severity (AUC=0.81) within a 4-year evaluation window. As we plan to generate higher-depth WGBS data and validation in independent cohorts, the present findings suggest the potential clinical utility of circulating cfDNA methylation profiles as promising noninvasive biomarkers in MS diagnosis and prognosis.

Constructing a highly sensitive duplex immunoassay using AuNPs and AgNPs as nanolabels for investigating the epithelial-mesenchymal transition occurring on circulating tumor cells with lung cancer patients

Transformation of epithelial to mesenchymal (EMT) is an important event in the process of tumor initiation, invasion and metastasis. Circulating tumor cells (CTCs) are one kind of important markers in the field of liquid tumor biopsy, whose number and phenotype represent the occurrence and progression of tumors. Therefore, it is our interest to investigate the epithelial mesenchymal transition process occurring on the surface of CTCs. Herein in this work, two proteins of E-cadherin (E-cad) and N-cadherin (N-cad) were selected as representative proteins of EMT process. To achieve simultaneous analysis of E-cad and N-cad on the surface of rare CTCs, we designed a duplex and portable immunosensor using AuNPs and AgNPs as nanolabels to amplify the immunoreaction signals. The dual channel immunosensor not only exhibited good electrochemical responses for recombinant E-cad and N-cad as low as 0.1 ng/mL and 0.05 ng/mL, respectively, but also showed good linear correlations with different numbers of phenotypic CTCs (10-500 cells/10 μL). The above strategy was further employed to inspect the occurrence of EMT on CTCs surface, which displayed a high consistence with other molecular biological characterizations. Finally, this immunoassay was successfully applied to inspect the correlations of numbers, phenotype of CTCs, as well as E-cad and N-cad expressions on these CTCs in bloods of NSCLC patients with disease stage.

Epigenetic modifications of cfDNA in liquid biopsy for the cancer care continuum

This review provides a comprehensive overview of the latest advancements in the clinical utility of liquid biopsy, with a particular focus on epigenetic approaches aimed at overcoming challenges in cancer diagnosis and treatment. It begins by elucidating key epigenetic terms, including methylomics, fragmentomics, and nucleosomics. The review progresses to discuss methods for analyzing circulating cell-free DNA (cfDNA) and highlights recent studies showcasing the clinical relevance of epigenetic modifications in areas such as diagnosis, drug treatment response, minimal residual disease (MRD) detection, and prognosis prediction. While acknowledging hurdles like the complexity of interpreting epigenetic data and the absence of standardization, the review charts a path forward. It advocates for the integration of multi-omic data through machine learning algorithms to refine predictive models and stresses the importance of collaboration among clinicians, researchers, and data scientists. Such cooperative efforts are essential to fully leverage the potential of epigenetic features in clinical practice.

Cell-free epigenomes enhanced fragmentomics-based model for early detection of lung cancer

BACKGROUND

Lung cancer is a leading cause of cancer mortality, highlighting the need for innovative non-invasive early detection methods. Although cell-free DNA (cfDNA) analysis shows promise, its sensitivity in early-stage lung cancer patients remains a challenge. This study aimed to integrate insights from epigenetic modifications and fragmentomic features of cfDNA using machine learning to develop a more accurate lung cancer detection model.

METHODS

To address this issue, a multi-centre prospective cohort study was conducted, with participants harbouring suspicious malignant lung nodules and healthy volunteers recruited from two clinical centres. Plasma cfDNA was analysed for its epigenetic and fragmentomic profiles using chromatin immunoprecipitation sequencing, reduced representation bisulphite sequencing and low-pass whole-genome sequencing. Machine learning algorithms were then employed to integrate the multi-omics data, aiding in the development of a precise lung cancer detection model.

RESULTS

Cancer-related changes in cfDNA fragmentomics were significantly enriched in specific genes marked by cell-free epigenomes. A total of 609 genes were identified, and the corresponding cfDNA fragmentomic features were utilised to construct the ensemble model. This model achieved a sensitivity of 90.4% and a specificity of 83.1%, with an AUC of 0.94 in the independent validation set. Notably, the model demonstrated exceptional sensitivity for stage I lung cancer cases, achieving 95.1%. It also showed remarkable performance in detecting minimally invasive adenocarcinoma, with a sensitivity of 96.2%, highlighting its potential for early detection in clinical settings.

CONCLUSIONS

With feature selection guided by multiple epigenetic sequencing approaches, the cfDNA fragmentomics-based machine learning model demonstrated outstanding performance in the independent validation cohort. These findings highlight its potential as an effective non-invasive strategy for the early detection of lung cancer.

KEYPOINTS

Our study elucidated the regulatory relationships between epigenetic modifications and their effects on fragmentomic features. Identifying epigenetically regulated genes provided a critical foundation for developing the cfDNA fragmentomics-based machine learning model. The model demonstrated exceptional clinical performance, highlighting its substantial potential for translational application in clinical practice.

Minimally invasive biomarkers for triaging lung nodules-challenges and future perspectives

CT chest scans are commonly performed worldwide, either in routine clinical practice for a wide range of indications or as part of lung cancer screening programs. Many of these scans detect lung nodules, which are small, rounded opacities measuring 8-30 mm. While the concern about nodules is that they may represent early lung cancer, in screening programs, only 1% of such nodules turn out to be cancer. This leads to a series of complex decisions and, at times, unnecessary biopsies for nodules that are ultimately determined to be benign. Additionally, patients may be anxious about the status of detected lung nodules. The high rate of false positive lung nodule detections has driven advancements in biomarker-based research aimed at triaging lung nodules (benign versus malignant) to identify truly malignant nodules better. Biomarkers found in biofluids and breath hold promise owing to their minimally invasive sampling methods, ease of use, and cost-effectiveness. Although several biomarkers have demonstrated clinical utility, their sensitivity and specificity are still relatively low. Combining multiple biomarkers could enhance the characterisation of small pulmonary nodules by addressing the limitations of individual biomarkers. This approach may help reduce unnecessary invasive procedures and accelerate diagnosis in the future. This review offers a thorough overview of emerging minimally invasive biomarkers for triaging lung nodules, emphasising key challenges and proposing potential solutions for biomarker-based nodule differentiation. It focuses on diagnosis rather than screening, analysing research published primarily in the past five years with some exceptions. The incorporation of biomarkers into clinical practice will facilitate the early detection of malignant nodules, leading to timely interventions and improved outcomes. Further efforts are needed to increase the cost-effectiveness and practicality of many of these applications in clinical settings. However, the range of technologies is advancing rapidly, and they may soon be implemented in clinics in the near future.

Artificial intelligence and machine learning in cell-free-DNA-based diagnostics

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.

Global DNA methylomes reveal oncogenic-associated 5-hydroxylmethylated cytosine (5hmC) signatures in the cell-free DNA of cancer patients

Characterization of tumor epigenetic aberrations is integral to understanding the mechanisms of tumorigenesis and provide diagnostic, prognostic, and predictive information of high clinical relevance. Among the different tumor-associated epigenetic signatures, 5 methyl-cytosine (5mC) and 5-hydroxymethylcytosine (5hmC) are the two most well-characterized DNA methylation alterations linked to cancer pathogenesis. 5hmC has a tissue-specific distribution and its abundance is subjected to changes in tumor DNA, making it a promising biomarker. Detecting tumor-related DNA methylation alterations in tissues is highly invasive, while the analysis of the cell-free DNA (cfDNA) is poised to supplement, if not replace, surgical biopsies. Despite many studies attempted to identify new epigenetic targets for liquid biopsy assays, little is known about the regulatory roles of 5hmC, its impacts on the molecular phenotypes in tumors. Most importantly, whether the oncogenic-associated 5hmC signatures found in tumor tissues can be recapitulated in patients' cfDNA. In this study, we performed the unbiased and simultaneous detection of 5mC and 5hmC whole-genome DNA modifications at base-resolution from two distinct cancer cohorts, from patients with bladder cancer or B-Cell lymphoma, their corresponding normal tissues, and cfDNAs from plasma. We analyzed tissue-specific methylation patters and searched for signatures in gene coding and regulatory regions linked to cancerous states. We then looked for methylation signatures in patients' cfDNA to determine if they were consistent with the tumor-specific patterns. We determined the functional significance of 5hmC in tissue specific transcription and uncovered hundreds of tumor-associated 5hmC signatures. These tumor-associated 5hmC changes, particularly in genes and enhancers, were functionally significant in tumorigenesis pathways and correlated with tumor specific gene expression. To investigate if cfDNA is a faithful surrogate for tumor-associated 5hmC, we devised a targeted capture strategy to examine the alterations of 5hmC in cfDNA from patients with bladder cancer and lymphoma with sufficient sensitivity and specificity and confirmed that they recapitulated the patterns we observed in tumor tissues. Our results provide analytic validation of 5hmC as a cancer-specific biomarker. The methods described here for systematic characterization of 5hmC at functional elements open new avenues to discover epigenetic markers for non-invasive diagnosis, monitoring, and stratifying cancer.

Highly specific multiplex DNA methylation detection for liquid biopsy of colorectal cancer

BACKGROUND

Circulating tumor DNA (ctDNA) has emerged as a useful biomarker for cancer detection and prognosis. In this study, we developed a strategy for developing a highly specific multiplex qPCR assay to detect methylated ctDNA in the blood of colorectal cancer (CRC) patients and investigated the potential use for the detection and prognosis of CRC.

METHODS

Bisulfite conversion and amplicon sequencing were used to confirm potential CRC-specific DNA methylation markers. The selected DNA methylation candidates were validated by qMSP. The six best-performing markers were used to develop a new single-tube multiplex quantitative methylation-specific PCR assay (mqMSP). The mqMSP assay was applied to analyze plasma samples from 114 CRC patients, 47 patients with advanced adenoma, 45 patients with benign polyps, and 57 healthy controls. The clinical performance of the assay and associations with clinical outcomes were assessed.

RESULTS

Six DNA methylation biomarkers were confirmed to be specifically hypermethylated in CRC tumor tissues. The newly developed mqMSP assay detected CRC with extremely high specificity (specificity of 98.2 %, with sensitivity of 67.5 %). The detection rate of ctDNA was significantly correlated with tumor size and clinical stage, with ctDNA methylation levels in the blood markedly increased with larger tumor size, poor differentiation, and advanced stage. Moreover, high preoperative methylated ctDNA level was associated with worse recurrence-free survival and overall survival.

CONCLUSION

We provided a strategy for identification of multiple highly-specific DNA methylation markers for designing multiplex DNA methylation assays for liquid biopsies of CRC. The newly developed assay has potential for CRC early detection, and prognosis.

Early Detection of Ovarian Cancer Using Cell-Free DNA Fragmentomes and Protein Biomarkers

SIGNIFICANCE

There is an unmet need for effective ovarian cancer screening and diagnostic approaches that enable earlier-stage cancer detection and increased overall survival. We have developed a high-performing accessible approach that evaluates cfDNA fragmentomes and protein biomarkers to detect ovarian cancer.

Multimodal cell-free DNA whole-genome TAPS is sensitive and reveals specific cancer signals

The analysis of circulating tumour DNA (ctDNA) through minimally invasive liquid biopsies is promising for early multi-cancer detection and monitoring minimal residual disease. Most existing methods focus on targeted deep sequencing, but few integrate multiple data modalities. Here, we develop a methodology for ctDNA detection using deep (80x) whole-genome TET-Assisted Pyridine Borane Sequencing (TAPS), a less destructive approach than bisulphite sequencing, which permits the simultaneous analysis of genomic and methylomic data. We conduct a diagnostic accuracy study across multiple cancer types in symptomatic patients, achieving 94.9% sensitivity and 88.8% specificity. Matched tumour biopsies are used for validation, not for guiding the analysis, imitating an early detection scenario. Furthermore, in silico validation demonstrates strong discrimination (86% AUC) at ctDNA fractions as low as 0.7%. Additionally, we successfully track tumour burden and ctDNA shedding from precancerous lesions post-treatment without requiring matched tumour biopsies. This pipeline is ready for further clinical evaluation to extend cancer screening and improve patient triage and monitoring.

Multi-Cancer Early Detection Tests: State of the Art and Implications for Radiologists

Multi-cancer early detection (MCED) tests are already being marketed as noninvasive, convenient opportunities to test for multiple cancer types with a single blood sample. The technology varies-involving detection of circulating tumor DNA, fragments of DNA, RNA, or proteins unique to each targeted cancer. The priorities and tradeoffs of reaching diagnostic resolution in the setting of possible false positives and negatives remain under active study. Given the well-established role of imaging in lesion detection and characterization for most cancers, radiologists have an essential role to play in selecting diagnostic pathways, determining the validity of test results, resolving false-positive MCED test results, and evaluating tradeoffs for clinical policy. Appropriate access to and use of imaging tests will also factor into clinical guidelines. Thus, all clinicians potentially involved with MCED tests for cancer screening will need to weigh the benefits and harms of MCED testing, including consideration of how the tests will be used alongside or in place of other screening options, how diagnostic confirmation tests should be selected, and what the implications are for policy and reimbursement decisions. Further, patients will need regular support to make informed decisions about screening using MCED tests in the context of their personal cancer risks, health-related values, and access to care.

Screening Criteria Evaluation for Expansion in Pulmonary Neoplasias (SCREEN) II

BACKGROUND

There is a need to expand eligibility criteria for lung cancer screening beyond age and smoking history. In this study, we sought to assess whether light-or-never-smokers and heavy smokers differ in molecular and immunologic markers based on conventional lung cancer screening criteria.

METHODS

We conducted a retrospective review of lung cancer cases from 2005 to 2018 at a tertiary Canadian institution. We used multivariable logistic regression to compare the rate of molecular mutations (KRAS, EGFR, BRAF, PIK3CA, ALK, and PD-L1 [< 1%, 1%-49%, ≥ 50%]) and survival between light-or-never-smokers and heavy smokers.

RESULTS

We included 1156 patients with lung cancer. Overall, 46.4% (National Lung Screening Trial [NLST], n = 536) and 63.3% (Nederlands-Leuvens Long-kanker Screenings Onderzoek [NELSON], n = 732) of the patients were heavy smokers. Using NELSON criteria, screen-ineligible light-or-never-smokers were more frequently from areas at high risk for radon exposure (n = 175 [41.3%]) than screen-eligible heavy smokers (n = 285 [38.9%]). Light-or-never-smokers were more likely to be EGFR-positive in both NLST (odds ratio [OR] 0.79, 95% confidence interval [CI] 0.21-1.37; p = 0.008] and NELSON (OR 0.79, 95% CI 0.28-1.31; p = 0.002) models. Female light-or-never-smokers were more likely than male light-or-never-smokers to be EGFR-positive in NELSON (OR 0.59, 95% CI 0.06-1.12; p = 0.03] but not NLST (OR 0.51, 95% CI 0.02-1.05; p = 0.06) models. Light-or-never-smokers were more often PIK3CA-positive using NLST (OR 1.33, 95% CI 0.54-2.13; p = 0.001) and NELSON (OR 1.19, 95% CI 0.49-1.90; p = 0.001) models. Light-or-never-smokers in the NELSON model were at higher risk of death.

CONCLUSION

Screen-ineligible light-or-never-smokers had a higher rate of EGFR-and PIK3CA-positive lung cancers than screen-eligible heavy smokers when defined using trial-based lung cancer screening eligibility criteria. Molecular profiling, particularly where targeted therapy is available, should be considered in future studies establishing criteria for lung cancer screening.

CONTEXTE

Il faut élargir les critères d'admissibilité au dépistage du cancer du poumon au-delà de l'âge et des antécédents tabagiques. Dans cette étude, nous avons voulu vérifier s'il y a des différences entre les personnes dont le tabagisme est léger, voire nul (groupe 1) et celles qui fument beaucoup (groupe 2) au plan des marqueurs moléculaires et immunologiques selon les critères classiques de dépistage du cancer du poumon. MÉTHODES: Nous avons procédé à une revue rétrospective des cas de cancer du poumon de 2005 à 2018 dans un établissement de soins tertiaires canadien. Nous avons utilisé la régression logistique multivariée pour comparer les taux de mutations moléculaires (KRAS, EGFR, BRAF, PIK3CA, ALK et PD-L1 [< 1 %, 1 %-49 %, ≥ 50 %]) et la survie entre les 2 groupes. RÉSULTATS: Nous avons inclus 1156 cas de cancer du poumon. En tout, 46,4 % (étude NLST [National Lung Screening Trial], n = 536) et 63,3 % (étude NELSON [ Nederlands-Leuvens Longkanker Screenings Onderzoek], n = 732) des malades étaient de gros fumeurs. À partir des critères de l'étude NELSON, le groupe 1, non admissible au dépistage, venait de secteurs à risque élevé d'exposition au radon (n = 175 [41,3 %]) comparativement au groupe 2, admissible au dépistage (n = 285 [38,9 %]). Le groupe 1 était plus susceptible d'être EGFR-positif, tant selon le modèle NLST (rapport des cotes [RC] 0,79, intervalle de confiance [IC] de 95 % 0,21-1,37; p = 0,008), que le modèle NELSON (RC 0,79, IC de 95 % 0,28-1,31; p = 0,002). Dans le groupe 1, les femmes étaient plus susceptibles que les hommes d'être EGFR-positives selon le modèle NELSON (RC 0,59, IC de 95 % 0,06-1,12; p = 0,03), mais non selon le modèle NLST (RC 0,51, IC de 95 % 0,02-1,05; p = 0,06). Le groupe 1 avait plus tendance à être PIK3CA-positif selon les modèles NLST (RC 1,33, IC de 95 % 0,54-2,13; p = 0,001) et NELSON (RC 1,19, IC de 95 % 0,49-1,90; p = 0,001). Selon le modèle NELSON, le groupe 1 était exposé à un risque de mortalité plus élevé.

CONCLUSION

Les personnes dont le tabagisme est léger voire nul qui ne sont pas admissibles au dépistage ont présenté un taux plus élevé de cancer du poumon EGFRet PIK3CA-positifs comparativement aux gros fumeurs, lorsqu'on appliquait les critères d'admissibilité au dépistage du cancer du poumon des 2 essais cités. Il faudrait envisager un profilage moléculaire lors des prochaines études qui porteront sur les critères d'admissibilité au dépistage du cancer du poumon, surtout lorsqu'il existe des modalités thérapeutiques ciblées.

Cell-free and extrachromosomal DNA profiling of small cell lung cancer

Small cell lung cancer (SCLC) is highly aggressive with poor prognosis. Despite a relative prevalence of circulating tumour DNA (ctDNA) in SCLC, liquid biopsies are not currently implemented, unlike non-SCLC where cell-free DNA (cfDNA) mutation profiling in the blood has utility for guiding targeted therapies and assessing minimal residual disease. cfDNA methylation profiling is highly sensitive for SCLC detection and holds promise for disease monitoring and molecular subtyping; cfDNA fragmentation profiling has also demonstrated clinical potential. Extrachromosomal DNA (ecDNA), that is often observed in SCLC, promotes tumour heterogeneity and chemotherapy resistance and can be detected in blood. We discuss how these cfDNA profiling modalities can be harnessed to expand the clinical applications of liquid biopsy in SCLC.

Integrating multi-omics features enables non-invasive early diagnosis and treatment response prediction of diffuse large B-cell lymphoma

BACKGROUND

Multi-omics features of cell-free DNA (cfDNA) can effectively improve the performance of non-invasive early diagnosis and prognosis of cancer. However, multimodal characterization of cfDNA remains technically challenging.

METHODS

We developed a comprehensive multi-omics solution (COMOS) to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA. The COMOS was tested on 214 plasma samples of diffuse large B-cell lymphoma (DLBCL) and matched healthy controls.

RESULTS

For early diagnosis, COMOS improved the area under the curve (AUC) value to .993 compared with the individual omics model, with a sensitivity of 95% at 98% specificity. Detection sensitivity achieved 91% at 99% specificity in early-stage patients, while the AUC values of the individual omics model were 0.942, 0.968, 0.989, 0.935, 0.921, 0.781 and 0.917, respectively, with lower sensitivity and specificity. In the treatment response cohort, COMOS yielded a superior sensitivity of 88% at 86% specificity (AUC, 0.903). COMOS has achieved excellent performance in early diagnosis and treatment response prediction.

CONCLUSIONS

Our study provides an effectively improved approach with high accuracy for the diagnosis and prognosis of DLBCL, showing great potential for future clinical application.

KEY POINTS

A comprehensive multi-omics solution to specifically obtain an extensive fragmentomics landscape, presented by breakpoint characteristics of nucleosomes, CpG islands, DNase clusters and enhancers, besides typical methylation, copy number alteration of cfDNA. Integrated model of cfDNA multi-omics could be used for non-invasive early diagnosis of DLBCL. Integrated model of cfDNA multi-omics could effectively evaluate the efficacy of R-CHOP before DLBCL treatment.

Interfacial cfDNA Enrichment and Amplification with On-Chip Thermoplasmonics for Highly Sensitive Cancerous Liquid Biopsy

Tumor-derived cell-free DNA (cfDNA) has been exploited as an effective liquid biopsy biomarker for early cancer diagnosis. However, the fragmented and low-abundance nature in circulating blood pose challenges for highly sensitive cfDNA quantification. Herein, a multifunctional plasmonic biosensor termed Interfacial cfDNA Enrichment, Amplification and Sensing with on-chip Thermoplasmonics (INEAST) is developed for cfDNA-based liquid biopsy and lung cancer diagnosis. The INEAST biosensor achieved in situ thermoregulation and label-free cfDNA biosensing by simultaneously harnessing interfacial thermoplasmonics and localized surface plasmon resonance. Typical cfDNA biomarkers, including epidermal growth factor receptor (EGFR), tumor protein 53 (TP53), phosphatase and tensin homologue deleted on chromosome 10 (PTEN), and cyclin-dependent kinase inhibitor (CDKN2A), are quantified with detection limits down to femtomolar-level. Through further validation using blood samples from lung cancer patients, the proposed INEAST bioassays demonstrated superior reliability for lung cancer screening, particularly when combined with clinically available tumor-protein metrics. This study demonstrated that the INEAST biosensor enables rapid and sensitive cfDNA quantification, yielding a promising and compatible liquid biopsy for early-stage lung cancer diagnosis.

Evaluation of cfDNA fragmentation characteristics in plasma for the diagnosis of lung cancer: A prospective cohort study

Lung cancer is one of the most prevalent cancers worldwide, yet only approximately 16% of patients are diagnosed in early stage, highlighting the urgent need for novel, highly accurate detection models. In our study, patients with suspected lung cancer or lung disease, as identified through radiographic imaging, along with healthy individuals, were consecutively recruited from Beijing Chest Hospital. Circulating free DNA (cfDNA) was extracted from plasma samples, and low-depth whole-genome sequencing was performed to identify fragmentomic features for model construction. A total of 265 participants were prospectively enrolled, comprising 124 lung cancer patients and 141 noncancer individuals. The model we developed was based on four cfDNA fragmentation characteristics, including 20-bp breakpoint nucleotides motif, fragmentation size coverage, fragmentation size distribution, and copy number variation. In an independent test cohort, the model achieved an area under the curve (AUC) of 0.861 (95% CI: 0.781-0.942) and demonstrated a sensitivity of 70% (95% CI: 53.5%-83.4%) at a specificity of 89.4% (95% CI: 76.9%-96.5%). Notably, the model was also effective in detecting early-stage cancer, with an AUC of 0.808 (95% CI: 0.69-0.925). In summary, our lung cancer detection model shows strong screening capabilities by leveraging four cfDNA fragmentation characteristics, exhibiting robust performance in early cancer diagnosis.

Protocol for analyzing plasma cell-free DNA fragment end motifs from ultra-low-pass whole-genome sequencing

Circulating cell-free DNA (cfDNA) fragment end motif profiles are a promising biomarker in precision oncology. Here, we present a protocol for analyzing plasma cfDNA fragment end motifs from ultra-low-pass whole-genome sequencing (WGS) data. We detail a pipeline composed of sequential bash scripts for processing post-alignment BAM files. Subsequently, we outline the procedure for downstream analysis and visualization of 4-mer as well as other n-mer cfDNA end motifs in R. For complete details on the use and execution of this protocol, please refer to Liu et al.1.

The Role of ctDNA in the Management of Non-Small-Cell Lung Cancer in the AI and NGS Era

Liquid biopsy (LB) involves the analysis of circulating tumour-derived DNA (ctDNA), providing a minimally invasive method for gathering both quantitative and qualitative information. Genomic analysis of ctDNA through next-generation sequencing (NGS) enables comprehensive genetic profiling of tumours, including non-driver alterations that offer prognostic insights. LB can be applied in both early-stage disease settings, for the diagnosis and monitoring of minimal residual disease (MRD), and advanced disease settings, for monitoring treatment response and understanding the mechanisms behind disease progression and tumour heterogeneity. Currently, LB has limited use in clinical practice, primarily due to its significant costs, limited diagnostic yield, and uncertain prognostic role. The application of artificial intelligence (AI) in the medical field is a promising approach to processing extensive information and applying it to individual cases to enhance therapeutic decision-making and refine risk assessment.

LBFextract: Unveiling transcription factor dynamics from liquid biopsy data

Motivation

The analysis of circulating cell-free DNA (cfDNA) holds immense promise as a non-invasive diagnostic tool across various human conditions. However, extracting biological insights from cfDNA fragments entails navigating complex and diverse bioinformatics methods, encompassing not only DNA sequence variation, but also epigenetic characteristics like nucleosome footprints, fragment length, and methylation patterns.

Results

We introduce Liquid Biopsy Feature extract (LBFextract), a comprehensive package designed to streamline feature extraction from cfDNA sequencing data, with the aim of enhancing the reproducibility and comparability of liquid biopsy studies. LBFextract facilitates the integration of preprocessing and postprocessing steps through alignment fragment tags and a hook mechanism. It incorporates various methods, including coverage-based and fragment length-based approaches, alongside two novel feature extraction methods: an entropy-based method to infer TF activity from fragmentomics data and a technique to amplify signals from nucleosome dyads. Additionally, it implements a method to extract condition-specific differentially active TFs based on these features for biomarker discovery. We demonstrate the use of LBFextract for the subtype classification of advanced prostate cancer patients using coverage signals at transcription factor binding sites from cfDNA. We show that LBFextract can generate robust and interpretable features that can discriminate between different clinical groups. LBFextract is a versatile and user-friendly package that can facilitate the analysis and interpretation of liquid biopsy data.

Data and Code Availability and Implementation

LBFextract is freely accessible at https://github.com/Isy89/LBF. It is implemented in Python and compatible with Linux and Mac operating systems. Code and data to reproduce these analyses have been uploaded to 10.5281/zenodo.10964406.

An Investigation into Cell-Free DNA in Different Common Cancers

Diagnosis is the most important step in different diseases, especially in cancers and blood malignancies. There are different methods in order to better diagnose of cancer, but many of them are invasive and also, some of them are not useful for immediate diagnosis. Cell-free DNA (cfDNA) or liquid biopsy easily accessible in peripheral blood is one of the non-invasive prognostic biomarkers in various areas of cancer management. In fact, amounts of cfDNA in serum or plasma can be used for diagnosis. In this review, we have considered some cancers such as hepatocellular carcinoma, lung cancer, breast cancer, and hematologic malignancies to compare the various methods of cfDNA diagnosis.

Recent advances in the use of liquid biopsy for the diagnosis and treatment of lung cancer

INTRODUCTION

In the era of precision medicine, liquid biopsy rapidly emerges as an integrative diagnostic tool to successfully stratify solid tumor patients in accordance with molecular fingerprinting. As the matter of fact, a plethora of analytes may be isolated from liquid biosources supporting the potential application of liquid biopsy in several clinical scenarios. Despite this promising role, liquid biopsy is drastically affected by low abundance of analytes in biological matrix requiring highly sensitive technologies, trained personnel, and optimized diagnostic procedures to successfully administrate this revolutionary diagnostic tool in clinical practice.

AREAS COVERED

This review aims to investigate the recent advancements in technical approaches available to manage liquid biopsy samples, particularly focusing on their application in LC diagnosis and treatment.

EXPERT OPINION

The rapidly evolving scenario of liquid biopsy-based approaches is revolutionizing clinical administration of lung cancer patients. Of note, the integration of genomic, epigenomic, and transcriptomic markers lays the basis for 'comprehensive' molecular fingerprinting of lung cancer patients. Here, the next-generation technologies are fundamental in molecular profiling in diagnostic routine biofluids.

Aberrant fragmentomic features of circulating cell-free mitochondrial DNA as novel biomarkers for multi-cancer detection

Fragmentomic features of circulating cell free mitochondrial DNA (ccf-mtDNA) including fragmentation profile, 5' end base preference and motif diversity are poorly understood. Here, we generated ccf-mtDNA sequencing data of 1607 plasma samples using capture-based next generation sequencing. We firstly found that fragmentomic features of ccf-mtDNA were remarkably different from those of circulating cell free nuclear DNA. Furthermore, region-specific fragmentomic features of ccf-mtDNA were observed, which was associated with protein binding, base composition and special structure of mitochondrial DNA. When comparing to non-cancer controls, six types of cancer patients exhibited aberrant fragmentomic features. Then, cancer detection models were built based on the fragmentomic features. Both internal and external validation cohorts demonstrated the excellent capacity of our model in distinguishing cancer patients from non-cancer control, with all area under curve higher than 0.9322. The overall accuracy of tissue-of-origin was 89.24% and 87.92% for six cancer types in two validation cohort, respectively. Altogether, our study comprehensively describes cancer-specific fragmentomic features of ccf-mtDNA and provides a proof-of-principle for the ccf-mtDNA fragmentomics-based multi-cancer detection and tissue-of-origin classification.

Integrated SERS-Microfluidic Sensor Based on Nano-Micro Hierarchical Cactus-like Array Substrates for the Early Diagnosis of Prostate Cancer

The detection and analysis of cancer cell exosomes with high sensitivity and precision are pivotal for the early diagnosis and treatment strategies of prostate cancer. To this end, a microfluidic chip, equipped with a cactus-like array substrate (CAS) based on surface-enhanced Raman spectroscopy (SERS) was designed and fabricated for the detection of exosome concentrations in Lymph Node Carcinoma of the Prostate (LNCaP). Double layers of polystyrene (PS) microspheres were self-assembled onto a polyethylene terephthalate (PET) film to form an ordered cactus-like nanoarray for detection and analysis. By combining EpCAM aptamer-labeled SERS nanoprobes and a CD63 aptamer-labeled CAS, a 'sandwich' structure was formed and applied to the microfluidic chips, further enhancing the Raman scattering signal of Raman reporter molecules. The results indicate that the integrated microfluidic sensor exhibits a good linear response within the detection concentration range of 105 particles μL-1 to 1 particle μL-1. The detection limit of exosomes in cancer cells can reach 1 particle μL-1. Therefore, we believed that the CAS integrated microfluidic sensor offers a superior solution for the early diagnosis and therapeutic intervention of prostate cancer.

Novel Strategies for Lung Cancer Interventional Diagnostics

Lung cancer is a major global health issue, with 2.21 million cases and 1.80 million deaths reported in 2020. It is the leading cause of cancer death worldwide. Most lung cancers have been linked to tobacco use, with changes in cigarette composition over the years contributing to shifts in cancer types and tumor locations within the lungs. Additionally, there is a growing incidence of lung cancer among never-smokers, particularly in East Asia, which is expected to increase the global burden of the disease. The classification of non-small cell lung cancer (NSCLC) into distinct subtypes is crucial for treatment efficacy and patient safety, especially as different subtypes respond differently to chemotherapy. For instance, certain chemotherapeutic agents are more effective for adenocarcinoma than for squamous carcinoma, which has led to the exclusion of squamous carcinoma from treatments like Bevacizumab due to safety concerns. This necessitates accurate histological diagnosis, which requires sufficient tissue samples from biopsies. However, acquiring adequate tissue is challenging due to the complex nature of lung tumors, patient comorbidities, and potential complications from biopsy procedures, such as bleeding, pneumothorax, and the purported risk of local recurrence. The need for improved diagnostic techniques has led to the development of advanced technologies like electromagnetic navigation bronchoscopy (ENB), radial endobronchial ultrasound (rEBUS), and robotic bronchoscopy. ENB and rEBUS have enhanced the accuracy and safety of lung biopsies, particularly for peripheral lesions, but both have limitations, such as the dependency on the presence of a bronchus sign. Robotic bronchoscopy, which builds on ENB, offers greater maneuverability and stability, improving diagnostic yields. Additionally, new imaging adjuncts, such as Cone Beam Computed Tomography (CBCT) and augmented fluoroscopy, further enhance the precision of these procedures by providing real-time, high-resolution imaging. These advancements are crucial as lung cancer is increasingly being detected at earlier stages due to screening programs, which require minimally invasive, accurate diagnostic methods to improve patient outcomes. This review aims to provide a comprehensive overview of the current challenges in lung cancer diagnostics and the innovative technological advancements in this rapidly evolving field, which represents an increasingly exciting career path for aspiring pulmonologists.

Deep generative AI models analyzing circulating orphan non-coding RNAs enable detection of early-stage lung cancer

Liquid biopsies have the potential to revolutionize cancer care through non-invasive early detection of tumors. Developing a robust liquid biopsy test requires collecting high-dimensional data from a large number of blood samples across heterogeneous groups of patients. We propose that the generative capability of variational auto-encoders enables learning a robust and generalizable signature of blood-based biomarkers. In this study, we analyze orphan non-coding RNAs (oncRNAs) from serum samples of 1050 individuals diagnosed with non-small cell lung cancer (NSCLC) at various stages, as well as sex-, age-, and BMI-matched controls. We demonstrate that our multi-task generative AI model, Orion, surpasses commonly used methods in both overall performance and generalizability to held-out datasets. Orion achieves an overall sensitivity of 94% (95% CI: 87%-98%) at 87% (95% CI: 81%-93%) specificity for cancer detection across all stages, outperforming the sensitivity of other methods on held-out validation datasets by more than  ~ 30%.

Evaluation and integration of cell-free DNA signatures for detection of lung cancer

Cell-free DNA (cfDNA) analysis has shown potential in detecting early-stage lung cancer based on non-genetic features. To distinguish patients with lung cancer from healthy individuals, peripheral blood were collected from 926 lung cancer patients and 611 healthy individuals followed by cfDNA extraction. Low-pass whole genome sequencing and targeted methylation sequencing were conducted and various features of cfDNA were evaluated. With our customized algorithm using the most optimal features, the ensemble stacked model was constructed, called ESim-seq (Early Screening tech with Integrated Model). In the independent validation cohort, the ESim-seq model achieved an area under the curve (AUC) of 0.948 (95 % CI: 0.915-0.981), with a sensitivity of 79.3 % (95 % CI: 71.5-87.0 %) across all stages at a specificity of 96.0 % (95 % CI: 90.6-100.0 %). Specifically, the sensitivity of the ESim-seq model was 76.5 % (95 % CI: 67.3-85.8 %) in stage I patients, 100 % (95 % CI: 100.0-100.0 %) in stage II patients, 100 % (95 % CI: 100.0-100.0 %) in stage III patients and 87.5 % (95 % CI: 64.6%-100.0 %) in stage IV patients in the independent validation cohort. Besides, we constructed LCSC model (Lung Cancer Subtype multiple Classification), which was able to accurately distinguish patients with small cell lung cancer from those with non-small cell lung cancer, achieving an AUC of 0.961 (95 % CI: 0.949-0.957). The present study has established a framework for assessing cfDNA features and demonstrated the benefits of integrating multiple features for early detection of lung cancer.

Cell-free DNA assay for malignancy classification of high-risk lung nodules

OBJECTIVE

Although low-dose computed tomography has been proven effective to reduce lung cancer-specific mortality, a considerable proportion of surgically resected high-risk lung nodules were still confirmed pathologically benign. There is an unmet need of a novel method for malignancy classification in lung nodules.

METHODS

We recruited 307 patients with high-risk lung nodules who underwent curative surgery, and 247 and 60 cases were pathologically confirmed malignant and benign lung lesions, respectively. Plasma samples from each patient were collected before surgery and performed low-depth (5×) whole-genome sequencing. We extracted cell-free DNA characteristics and determined radiomic features. We built models to classify the malignancy using our data and further validated models with 2 independent lung nodule cohorts.

RESULTS

Our models using one type of profile were able to distinguish lung cancer and benign lung nodules at an area under the curve metrics of 0.69 to 0.91 in the study cohort. Integrating all the 5 base models using cell-free DNA profiles, the cell-free DNA-based ensemble model achieved an area under the curve of 0.95 (95% CI, 0.92-0.97) in the study cohort and 0.98 (95% CI, 0.96-1.00) in the validation cohort. At a specificity of 95.0%, the sensitivity reached 80.0% in the study cohort. With the same threshold, the specificity and sensitivity had similar performances in both validation cohorts. Furthermore, the performance of area under the curve reached 0.97 in both the study and validation cohorts when considering the radiomic profile.

CONCLUSIONS

The cell-free DNA profiles-based method is an efficient noninvasive tool to distinguish malignancies and high-risk but pathologically benign lung nodules.

Plasma Cell-Free DNA Concentration and Fragmentomes Predict Neoadjuvant Chemotherapy Response in Cervical Cancer Patients

Cervical cancer remains one of the most lethal gynecological malignancies. However, biomarkers for more precise patient care are an unmet need. Herein, the concentration of 285 plasma cell-free DNA (cfDNA) samples are analyzed from 84 cervical patients and the clinical significance of cfDNA fragmentomic characteristics across the neoadjuvant chemotherapy (NACT) treatment. Patients with poor NACT response exhibit a significantly greater escalation in cfDNA levels following the initial cycle of treatment, in comparison to patients with a favorable response. Distinctive end motif profiles and promoter coverages of cfDNA in initial plasma are observed between patients with differing NACT responses. Notably, the DNASE1L3 analysis further demonstrates the intrinsic association between cfDNA characteristics and chemotherapy resistance. The cfDNA and motif ratios show a good discriminative capacity for predicting non-responders from responders (area under the curve (AUC) > 0.8). In addition, transcriptional start sites (TSS) coverages around promoters discern the alteration of biological processes associated with chemotherapy resistance and reflect the potential value in predicting chemotherapy response. These findings in predictive biomarkers may optimize treatment selection, minimize unnecessary treatment, and assist in establishing personalized treatment strategies for cervical cancer patients.

Circulating tumor DNA methylation detection as biomarker and its application in tumor liquid biopsy: advances and challenges

Circulating tumor DNA (ctDNA) methylation, an innovative liquid biopsy biomarker, has emerged as a promising tool in early cancer diagnosis, monitoring, and prognosis prediction. As a noninvasive approach, liquid biopsy overcomes the limitations of traditional tissue biopsy. Among various biomarkers, ctDNA methylation has garnered significant attention due to its high specificity and early detection capability across diverse cancer types. Despite its immense potential, the clinical application of ctDNA methylation faces substantial challenges pertaining to sensitivity, specificity, and standardization. In this review, we begin by introducing the basic biology and common detection techniques of ctDNA methylation. We then explore recent advancements and the challenges faced in the clinical application of ctDNA methylation in liquid biopsies. This includes progress in early screening and diagnosis, identification of clinical molecular subtypes, monitoring of recurrence and minimal residual disease (MRD), prediction of treatment response and prognosis, assessment of tumor burden, and determination of tissue origin. Finally, we discuss the future perspectives and challenges of ctDNA methylation detection in clinical applications. This comprehensive overview underscores the vital role of ctDNA methylation in enhancing cancer diagnostic accuracy, personalizing treatments, and effectively monitoring disease progression, providing valuable insights for future research and clinical practice.

Blood microRNA testing in participants with suspicious low-dose CT findings: follow-up of the BioMILD lung cancer screening trial

Background

The proper management of suspicious radiologic findings is crucial to optimize the effectiveness of low-dose computed tomography (LDCT) lung cancer screening trials. In the BioMILD study, we evaluated the utility of combining a plasma 24-microRNA signature classifier (MSC) and LDCT to define the individual risk and personalize screening strategies. Here we aim to assess the utility of repeated MSC testing during annual screening rounds in 1024 participants with suspicious LDCT findings.

Methods

The primary outcome was two-year lung cancer incidence in relation to MSC test results, reported as relative risk (RR) with 95% confidence interval (CI). Lung cancer incidence and mortality were estimated using extended Cox models for time-dependent covariates, yielding the respective hazard ratios (HR). Clinicaltrials.gov ID: NCT02247453.

Findings

With a median follow-up of 8.5 years, the full study set included 1403 indeterminate LDCT (CTind) and 584 positive LDCT (CT+) results. A lung cancer RR increase in MSC+ compared to MSC- participants was observed in both the CTind (RR: 2.5; 95% CI: 1.4-4.32) and CT+ (RR: 2.6; 95% CI: 1.81-3.74) groups and was maintained when considering stage I or resectable tumors only. A 98% negative predictive value in CTind/MSC- and a 30% positive predictive value in CT+/MSC+ lesions were recorded. At seven years' follow-up, MSC+ participants had a cumulative HR of 4.4 (95% CI: 3.0-6.4) for lung cancer incidence and of 8.1 (95% CI: 2.7-24.5) for lung cancer mortality.

Interpretation

Our study shows that MSC can be reliably performed during LDCT screening rounds to increase the accuracy of lung cancer risk and mortality prediction and supports its clinical utility in the management of LDCT findings of uncertain malignancy.

Funding

Italian Association for Cancer Research; Italian Ministry of Health; Horizon2020; National Cancer Institute (NCI); Gensignia LifeScience.

Analysis and identification of mRNAsi‑related expression signatures via RNA sequencing in lung cancer

High stemness index scores are associated with poor survival in patients with lung cancer. Studies on the mRNA expression-based stemness index (mRNAsi) are typically conducted using tumor tissues; however, mRNAsi-related expression signatures based on cell-free RNA (cfRNA) are yet to be comprehensively investigated. The present study aimed to elucidate the gene expression profiles of tumor stemness in lung cancer tissues and corresponding cfRNAs in blood, and to assess their links with immune infiltration. Tumor tissue, paracancerous tissue, peripheral blood and lymph node samples were collected from patients with stage I-III non-small cell lung cancer and RNA sequencing was performed. The TCGAbiolinks package was used to calculate the mRNAsi for each of these four types of sample. Weighted gene co-expression network analysis and differentially expressed gene analyses were performed to investigate mRNAsi-related genes, and pathway enrichment analysis was performed using the Kyoto Encyclopedia of Genes and Genomes (KEGG) orthology-based annotation system. In addition, the STAR-Fusion tool was used to detect fusion variants, and CIBERSORT was used to analyze the correlations of stemness signatures in tissues and blood with immune cell infiltration. The mRNAsi values in peripheral blood and lymph nodes were found to be higher than those in cancer tissues. 'Hematopoietic cell lineage' was the only KEGG pathway enriched in mRNAsi-related genes in both lung cancer tissues and peripheral blood. In addition, the protein tyrosine phosphatase receptor type C associated protein gene was the only gene commonly associated with the mRNAsi in these two types of sample. The expression of mRNAsi-related genes was increased in the dendritic and Treg cells in tumor tissues, but was elevated in Treg and CD8 cells in the blood. In conclusion, cfRNAs in the blood exhibit unique stemness signatures that have potential for use in the diagnosis of lung cancer.

Biomarkers in high grade serous ovarian cancer

High-grade serous ovarian cancer (HGSC) is the most common subtype of ovarian cancer. HGSC patients typically present with advanced disease, which is often resistant to chemotherapy and recurs despite initial responses to therapy, resulting in the poor prognosis associated with this disease. There is a need to utilise biomarkers to manage the various aspects of HGSC patient care. In this review we discuss the current state of biomarkers in HGSC, focusing on the various available immunohistochemical (IHC) and blood-based biomarkers, which have been examined for their diagnostic, prognostic and theranostic potential in HGSC. These include various routine clinical IHC biomarkers such as p53, WT1, keratins, PAX8, Ki67 and p16 and clinical blood-borne markers and algorithms such as CA125, HE4, ROMA, RMI, ROCA, and others. We also discuss various components of the liquid biopsy as well as a number of novel IHC biomarkers and non-routine blood-borne biomarkers, which have been examined in various ovarian cancer studies. We also discuss the future of ovarian cancer biomarker research and highlight some of the challenges currently facing the field.