Multi-omics integrated circulating cell-free DNA genomic signatures enhanced the diagnostic performance of early-stage lung cancer and postoperative minimal residual disease

Genomics and the early diagnosis of lung cancer

Lung cancer (LC) remains the leading cause of cancer-related mortality worldwide, with most cases diagnosed at advanced stages, resulting in poor survival rates. Early detection significantly improves outcomes, yet current screening methods, such as low-dose computed tomography (LDCT), are limited by high false-positive rates, radiation exposure, and restricted eligibility criteria. This review highlights the transformative potential of genomic and molecular technologies in advancing the early detection of LC. Key innovations include liquid biopsy tools, such as circulating tumor DNA (ctDNA) and cell-free DNA (cfDNA) analysis, which offer minimally invasive approaches to detect tumor-specific genetic and epigenetic alterations. Emerging biomarkers, including methylation signatures, cfDNA fragmentomics, and multi-omics profiles, demonstrate improved sensitivity and specificity in identifying early-stage tumors. Advanced platforms like next-generation sequencing (NGS) and machine-learning algorithms further enhance diagnostic accuracy. Integrated approaches that combine genomic data with LDCT imaging and artificial intelligence (AI) show promise in addressing current limitations by improving risk stratification and nodule characterization. The review also explores multi-cancer early detection assays and precision diagnostic strategies tailored for diverse at-risk populations. By leveraging these advancements, clinicians can achieve earlier diagnoses, reduce unnecessary procedures, and ultimately decrease LC mortality.

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.

Metallomic Classification of Pulmonary Nodules Using Blood by Deep-Learning-Boosted Synchrotron Radiation X-ray Fluorescence

Ambient air pollution is an important contributor to increasing cases of lung cancer, which is a malignant cancer with the highest mortality among all cancers. It primarily manifests in the form of pulmonary nodules, but not all will develop into lung cancer. Therefore, it is highly desired to distinguish between benign and malignant pulmonary nodules for the early prevention and treatment of lung cancer. Currently, histopathological examination is the gold standard for classifying pulmonary nodules, which is invasive, time-consuming, and labor-intensive. This study proposes a metallomics approach through synchrotron radiation X-ray fluorescence (SRXRF) with a simplified one-dimensional convolutional neural network (1DCNN) to distinguish pulmonary nodules by using serum samples. SRXRF spectra of serum samples were obtained and preliminarily analyzed using principal component analysis (PCA). Subsequently, machine learning algorithms (MLs) and 1DCNN were applied to develop classification models. Both MLs and 1DCNN based on full-channel spectra could distinguish patients with benign and malignant pulmonary nodules, but the highest accuracy rate of 96.7% was achieved when using 1DCNN. In addition, it was found that characteristic elements in serum from patients with malignant nodules were different from those in benign nodules, which can serve as the fingerprint metallome profile. The simplified model based on characteristic elements resulted in good performance of sensitivity and F1-score > 91.30%, G-mean, MCC and Kappa > 85.59%, and accuracy = 94.34%. In summary, metallomic classification of benign and malignant pulmonary nodules using serum samples can be achieved through 1DCNN-boosted SRXRF, which is easy to handle and much less invasive compared to histopathological examination.

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.

Integrated multiomics signatures to optimize the accurate diagnosis of lung cancer

Diagnosing lung cancer from indeterminate pulmonary nodules (IPLs) remains challenging. In this multi-institutional study involving 2032 participants with IPLs, we integrate the clinical, radiomic with circulating cell-free DNA fragmentomic features in 5-methylcytosine (5mC)-enriched regions to establish a multiomics model (clinic-RadmC) for predicting the malignancy risk of IPLs. The clinic-RadmC yields an area-under-the-curve (AUC) of 0.923 on the external test set, outperforming the single-omics models, and models that only combine clinical features with radiomic, or fragmentomic features in 5mC-enriched regions (p < 0.050 for all). The superiority of the clinic-RadmC maintains well even after adjusting for clinic-radiological variables. Furthermore, the clinic-RadmC-guided strategy could reduce the unnecessary invasive procedures for benign IPLs by 10.9% ~ 35%, and avoid the delayed treatment for lung cancer by 3.1% ~ 38.8%. In summary, our study indicates that the clinic-RadmC provides a more effective and noninvasive tool for optimizing lung cancer diagnoses, thus facilitating the precision interventions.

Transformer-based representation learning and multiple-instance learning for cancer diagnosis exclusively from raw sequencing fragments of bisulfite-treated plasma cell-free DNA

Early cancer diagnosis from bisulfite-treated cell-free DNA (cfDNA) fragments requires tedious data analytical procedures. Here, we present a deep-learning-based approach for early cancer interception and diagnosis (DECIDIA) that can achieve accurate cancer diagnosis exclusively from bisulfite-treated cfDNA sequencing fragments. DECIDIA relies on transformer-based representation learning of DNA fragments and weakly supervised multiple-instance learning for classification. We systematically evaluate the performance of DECIDIA for cancer diagnosis and cancer type prediction on a curated dataset of 5389 samples that consist of colorectal cancer (CRC; n = 1574), hepatocellular cell carcinoma (HCC; n = 1181), lung cancer (n = 654), and non-cancer control (n = 1980). DECIDIA achieved an area under the receiver operating curve (AUROC) of 0.980 (95% CI, 0.976-0.984) in 10-fold cross-validation settings on the CRC dataset by differentiating cancer patients from cancer-free controls, outperforming benchmarked methods that are based on methylation intensities. Noticeably, DECIDIA achieved an AUROC of 0.910 (95% CI, 0.896-0.924) on the externally independent HCC testing set in distinguishing HCC patients from cancer-free controls, although there was no HCC data used in model development. In the settings of cancer-type classification, we observed that DECIDIA achieved a micro-average AUROC of 0.963 (95% CI, 0.960-0.966) and an overall accuracy of 82.8% (95% CI, 81.8-83.9). In addition, we distilled four sequence signatures from the raw sequencing reads that exhibited differential patterns in cancer versus control and among different cancer types. Our approach represents a new paradigm towards eliminating the tedious data analytical procedures for liquid biopsy that uses bisulfite-treated cfDNA methylome.

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.

The clinical effect of thoracoscopic segmentectomy in the treatment of lung malignancies less than 2CM in diameter

OBJECTIVE

To investigate the clinical effect of thoracoscopic segmentectomy in the treatment of lung malignancies less than 2CM in diameter.

METHODS

In this retrospective study, a total of 103 patients with lung cancer who received outpatient or inpatient treatment from December 2020 to May 2022 were selected and divided into the lobectomy group (n = 48) and the segmentectomy group (n = 55) according to different surgical methods. The lobectomy group was treated with thoracoscopic lobectomy, while the segmentectomy group was treated with thoracoscopic segmentectomy. The prognostic effect, complications, blood gas level and respiratory function indexes of the two groups were observed and compared.

RESULTS

The general data of the two groups of patients, such as gender, age, course of disease, body mass index, lesion diameter, lesion site and pathological type, were analyzed by statistical software. There was no statistical significance in the operation time and the number of lymph node dissection between the two groups (P > 0.05), while the drainage volume and intraoperative blood loss in the segmentectomy group were lower than those in the lobectomy group, and the drainage time and hospital stay were shorter than those in the lobectomy group, with statistical significance (P < 0.05). Before treatment, there were no statistically significant differences in various lung function indexes between the two groups (P > 0.05). After treatment, the values of FVC, FEV1 and FEV1/FVC in each group had different amplitude changes, and the values of FVC, FEV1 and FEV1/FVC in the segmentectomy group were significantly higher than those in the lobectomy group, with statistical significance (P < 0.05). Thoracoscopic segmentectomy showed a lower incidence of respiratory complications (P = 0.042) and higher pulmonary air leak (P = 0.023) than thoracoscopic lobectomy. After propensity score-matched analysis, respiratory complications remained significantly higher in thoracoscopic segmentectomy (P = 0.017). However, the difference in the total complication rate between the two groups was not statistically significant (P > 0.05). There were no differences during the 2-year follow-up (median follow-up in months: 18.4; interquartile range, 14.8-21.3) in terms of overall survival (P = 0.49) and disease-free survival (P = 0.34) between groups (P > 0.05).

CONCLUSIONS

For patients with lung cancer less than 2 cm in diameter, thoracoscopic segmentectomy can achieve good short-term efficacy, with rapid postoperative recovery and little impact on lung function, which may be helpful to improve patients' postoperative quality of life.

Values of circulating tumor DNA for non-small cell lung cancer patients receiving neoadjuvant therapy, progress and challenges: a narrative review

Background and Objective

The value of circulating tumor DNA (ctDNA) in neoadjuvant therapy (NAT) for lung cancer remains controversial. Therefore, we conducted a review to further investigate the role of ctDNA in non-small cell lung cancer (NSCLC) patients undergoing NAT for individualized management.

Methods

A search of online databases (PubMed, Embase, Web of Science, Science Direct, and Cochrane Library) was conducted to evaluate the value of ctDNA in predicting relapse, risk stratification, and efficacy of NAT in NSCLC. Only articles published in English within the last 25 years, between January 1st, 1998 and November 30th, 2023, were included. Additionally, the application of ctDNA in NSCLC is briefly reviewed.

Key Content and Findings

ctDNA is a non-invasive and dynamic method that plays an important role in future treatment guidance. Additionally, ctDNA successfully predicted the effect of neoadjuvant immunotherapy before surgery, and positive testing was strongly correlated with a lower major pathological response or complete pathological response rate. Sequential testing of ctDNA may serve as a secondary indicator to guide the adjustment of treatment programs. However, the application of this method has been limited by false negative results, a lack of objective indicators, and high costs. These issues must be addressed by researchers.

Conclusions

ctDNA has strong potential in NAT, based on positive preliminary studies. However, its widespread use is limited by the high cost of testing. Further research is needed to explore its value in risk stratification and treatment guidance in the future.

The systematic role of pancreatic cancer exosomes: distant communication, liquid biopsy and future therapy

Pancreatic cancer remains one of the most lethal diseases worldwide. Cancer-derived exosomes, benefiting from the protective role of the lipid membrane, exhibit remarkable stability in the circulatory system. These exosomes, released by tumor microenvironment, contain various biomolecules such as proteins, RNAs, and lipids that plays a pivotal role in mediating distant communication between the local pancreatic tumor and other organs or tissues. They facilitate the transfer of oncogenic factors to distant sites, contributing to the compromised body immune system, distant metastasis, diabetes, cachexia, and promoting a microenvironment conducive to tumor growth and metastasis in pancreatic cancer patients. Beyond their intrinsic roles, circulating exosomes in peripheral blood can be detected to facilitate accurate liquid biopsy. This approach offers a novel and promising method for the diagnosis and management of pancreatic cancer. Consequently, circulating exosomes are not only crucial mediators of systemic cell-cell communication during pancreatic cancer progression but also hold great potential as precise tools for pancreatic cancer management and treatment. Exosome-based liquid biopsy and therapy represent promising advancements in the diagnosis and treatment of pancreatic cancer. Exosomes can serve as drug delivery vehicles, enhancing the targeting and efficacy of anticancer treatments, modulating the immune system, and facilitating gene editing to suppress tumor growth. Ongoing research focuses on biomarker identification, drug delivery systems, and clinical trials to validate the safety and efficacy of exosome-based therapies, offering new possibilities for early diagnosis and precision treatment in pancreatic cancer. Leveraging the therapeutic potential of exosomes, including their ability to deliver targeted drugs and modulate immune responses, opens new avenues for innovative treatment strategies.

Tissue of origin detection for cancer tumor using low-depth cfDNA samples through combination of tumor-specific methylation atlas and genome-wide methylation density in graph convolutional neural networks

BACKGROUND

Cell free DNA (cfDNA)-based assays hold great potential in detecting early cancer signals yet determining the tissue-of-origin (TOO) for cancer signals remains a challenging task. Here, we investigated the contribution of a methylation atlas to TOO detection in low depth cfDNA samples.

METHODS

We constructed a tumor-specific methylation atlas (TSMA) using whole-genome bisulfite sequencing (WGBS) data from five types of tumor tissues (breast, colorectal, gastric, liver and lung cancer) and paired white blood cells (WBC). TSMA was used with a non-negative least square matrix factorization (NNLS) deconvolution algorithm to identify the abundance of tumor tissue types in a WGBS sample. We showed that TSMA worked well with tumor tissue but struggled with cfDNA samples due to the overwhelming amount of WBC-derived DNA. To construct a model for TOO, we adopted the multi-modal strategy and used as inputs the combination of deconvolution scores from TSMA with other features of cfDNA.

RESULTS

Our final model comprised of a graph convolutional neural network using deconvolution scores and genome-wide methylation density features, which achieved an accuracy of 69% in a held-out validation dataset of 239 low-depth cfDNA samples.

CONCLUSIONS

In conclusion, we have demonstrated that our TSMA in combination with other cfDNA features can improve TOO detection in low-depth cfDNA samples.

Development of new techniques and clinical applications of liquid biopsy in lung cancer management

Lung cancer is an exceedingly malignant tumor reported as having the highest morbidity and mortality of any cancer worldwide, thus posing a great threat to global health. Despite the growing demand for precision medicine, current methods for early clinical detection, treatment and prognosis monitoring in lung cancer are hampered by certain bottlenecks. Studies have found that during the formation and development of a tumor, molecular substances carrying tumor-related genetic information can be released into body fluids. Liquid biopsy (LB), a method for detecting these tumor-related markers in body fluids, maybe a way to make progress in these bottlenecks. In recent years, LB technology has undergone rapid advancements. Therefore, this review will provide information on technical updates to LB and its potential clinical applications, evaluate its effectiveness for specific applications, discuss the existing limitations of LB, and present a look forward to possible future clinical applications. Specifically, this paper will introduce technical updates from the prospectives of engineering breakthroughs in the detection of membrane-based LB biomarkers and other improvements in sequencing technology. Additionally, it will summarize the latest applications of liquid biopsy for the early detection, diagnosis, treatment, and prognosis of lung cancer. We will present the interconnectedness of clinical and laboratory issues and the interplay of technology and application in LB today.

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.

Beyond blood: Advancing the frontiers of liquid biopsy in oncology and personalized medicine

Liquid biopsy is emerging as a pivotal tool in precision oncology, offering a noninvasive and comprehensive approach to cancer diagnostics and management. By harnessing biofluids such as blood, urine, saliva, cerebrospinal fluid, and pleural effusions, this technique profiles key biomarkers including circulating tumor DNA, circulating tumor cells, microRNAs, and extracellular vesicles. This review discusses the extended scope of liquid biopsy, highlighting its indispensable role in enhancing patient outcomes through early detection, continuous monitoring, and tailored therapy. While the advantages are notable, we also address the challenges, emphasizing the necessity for precision, cost-effectiveness, and standardized methodologies in its broader application. The future trajectory of liquid biopsy is set to expand its reach in personalized medicine, fueled by technological advancements and collaborative research.

Unlocking the promise of liquid biopsies in precision oncology

Liquid biopsies have emerged as a promising and minimally invasive alternative to traditional tissue biopsies for detecting and monitoring cancer. Liquid biopsies offer a comprehensive analysis of cancer genetics and tumor burden by examining circulating cells and cell-derived analytes using a variety of assays, including conventional PCR methods and cutting-edge tools like long-read sequencing and nanotechnology. However, there are still some limitations and challenges that need to be overcome for their implementation in clinical routine, including the need for further research on their sensitivity and specificity, cost-effectiveness, standardization, and regulatory approval. Despite these challenges, liquid biopsies have the potential to become widely used tools in oncology. Here we provide an overview of the current state of liquid biopsies, highlighting recent advancements in the field and their potential benefits in clinical settings for cancer patients. The article further discusses the challenges that need to be addressed in order to facilitate their application worldwide. Prompt resolution of these challenges can be achieved by fostering international research collaborations and establishing standardized guidelines for liquid biopsy sample management and studies.

Big data and artificial intelligence in cancer research

The field of oncology has witnessed an extraordinary surge in the application of big data and artificial intelligence (AI). AI development has made multiscale and multimodal data fusion and analysis possible. A new era of extracting information from complex big data is rapidly evolving. However, challenges related to efficient data curation, in-depth analysis, and utilization remain. We provide a comprehensive overview of the current state of the art in big data and computational analysis, highlighting key applications, challenges, and future opportunities in cancer research. By sketching the current landscape, we seek to foster a deeper understanding and facilitate the advancement of big data utilization in oncology, call for interdisciplinary collaborations, ultimately contributing to improved patient outcomes and a profound understanding of cancer.

cfOmics: a cell-free multi-Omics database for diseases

Liquid biopsy has emerged as a promising non-invasive approach for detecting, monitoring diseases, and predicting their recurrence. However, the effective utilization of liquid biopsy data to identify reliable biomarkers for various cancers and other diseases requires further exploration. Here, we present cfOmics, a web-accessible database (https://cfomics.ncRNAlab.org/) that integrates comprehensive multi-omics liquid biopsy data, including cfDNA, cfRNA based on next-generation sequencing, and proteome, metabolome based on mass-spectrometry data. As the first multi-omics database in the field, cfOmics encompasses a total of 17 distinct data types and 13 specimen variations across 69 disease conditions, with a collection of 11345 samples. Moreover, cfOmics includes reported potential biomarkers for reference. To facilitate effective analysis and visualization of multi-omics data, cfOmics offers powerful functionalities to its users. These functionalities include browsing, profile visualization, the Integrative Genomic Viewer, and correlation analysis, all centered around genes, microbes, or end-motifs. The primary objective of cfOmics is to assist researchers in the field of liquid biopsy by providing comprehensive multi-omics data. This enables them to explore cell-free data and extract profound insights that can significantly impact disease diagnosis, treatment monitoring, and management.

Novel, high accuracy models for hepatocellular carcinoma prediction based on longitudinal data and cell-free DNA signatures

BACKGROUND & AIMS

Current hepatocellular carcinoma (HCC) risk scores do not reflect changes in HCC risk resulting from liver disease progression/regression over time. We aimed to develop and validate two novel prediction models using multivariate longitudinal data, with or without cell-free DNA (cfDNA) signatures.

METHODS

A total of 13,728 patients from two nationwide multicenter prospective observational cohorts, the majority of whom had chronic hepatitis B, were enrolled. aMAP score, as one of the most promising HCC prediction models, was evaluated for each patient. Low-pass whole-genome sequencing was used to derive multi-modal cfDNA fragmentomics features. A longitudinal discriminant analysis algorithm was used to model longitudinal profiles of patient biomarkers and estimate the risk of HCC development.

RESULTS

We developed and externally validated two novel HCC prediction models with a greater accuracy, termed aMAP-2 and aMAP-2 Plus scores. The aMAP-2 score, calculated with longitudinal data on the aMAP score and alpha-fetoprotein values during an up to 8-year follow-up, performed superbly in the training and external validation cohorts (AUC 0.83-0.84). The aMAP-2 score showed further improvement and accurately divided aMAP-defined high-risk patients into two groups with 5-year cumulative HCC incidences of 23.4% and 4.1%, respectively (p = 0.0065). The aMAP-2 Plus score, which incorporates cfDNA signatures (nucleosome, fragment and motif scores), optimized the prediction of HCC development, especially for patients with cirrhosis (AUC 0.85-0.89). Importantly, the stepwise approach (aMAP -> aMAP-2 -> aMAP-2 Plus) stratified patients with cirrhosis into two groups, comprising 90% and 10% of the cohort, with an annual HCC incidence of 0.8% and 12.5%, respectively (p <0.0001).

CONCLUSIONS

aMAP-2 and aMAP-2 Plus scores are highly accurate in predicting HCC. The stepwise application of aMAP scores provides an improved enrichment strategy, identifying patients at a high risk of HCC, which could effectively guide individualized HCC surveillance.

IMPACT AND IMPLICATIONS

In this multicenter nationwide cohort study, we developed and externally validated two novel hepatocellular carcinoma (HCC) risk prediction models (called aMAP-2 and aMAP-2 Plus scores), using longitudinal discriminant analysis algorithm and longitudinal data (i.e., aMAP and alpha-fetoprotein) with or without the addition of cell-free DNA signatures, based on 13,728 patients from 61 centers across mainland China. Our findings demonstrated that the performance of aMAP-2 and aMAP-2 Plus scores was markedly better than the original aMAP score, and any other existing HCC risk scores across all subsets, especially for patients with cirrhosis. More importantly, the stepwise application of aMAP scores (aMAP -> aMAP-2 -> aMAP-2 Plus) provides an improved enrichment strategy, identifying patients at high risk of HCC, which could effectively guide individualized HCC surveillance.

Circulating cell-free DNA sequencing for early detection of lung cancer

INTRODUCTION

Lung cancer is a leading cause of death in patients with cancer. Early diagnosis is crucial to improve the prognosis of patients with lung cancer. Plasma circulating cell-free DNA (cfDNA) contains comprehensive genetic and epigenetic information from tissues throughout the body, suggesting that early detection of lung cancer can be done non-invasively, conveniently, and cost-effectively using high-sensitivity techniques such as sequencing.

AREAS COVERED

In this review, we summarize the latest technological innovations, coupled with next-generation sequencing (NGS), regarding genomic alterations, methylation, and fragmentomic features of cfDNA for the early detection of lung cancer, as well as their clinical advances. Additionally, we discuss the suitability of study designs for diagnostic accuracy evaluation for different target populations and clinical questions.

EXPERT OPINION

Currently, cfDNA-based early screening and diagnosis of lung cancer faces many challenges, such as unsatisfactory performance, lack of quality control standards, and poor repeatability. However, the progress of several large prospective studies employing epigenetic features has shown promising predictive performance, which has inspired cfDNA sequencing for future clinical applications. Furthermore, the development of multi-omics markers for lung cancer, including genome-wide methylation and fragmentomics, is expected to play an increasingly important role in the future.

Current and emerging applications of liquid biopsy in pan-cancer

Cancer morbidity and mortality are growing rapidly worldwide and it is urgent to develop a convenient and effective method that can identify cancer patients at an early stage and predict treatment outcomes. As a minimally invasive and reproducible tool, liquid biopsy (LB) offers the opportunity to detect, analyze and monitor cancer in any body fluids including blood, complementing the limitations of tissue biopsy. In liquid biopsy, circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are the two most common biomarkers, displaying great potential in the clinical application of pan-cancer. In this review, we expound the samples, targets, and newest techniques in liquid biopsy and summarize current clinical applications in several specific cancers. Besides, we put forward a bright prospect for further exploring the emerging application of liquid biopsy in the field of pan-cancer precision medicine.

Liquid Biopsy in Lung Cancer: Biomarkers for the Management of Recurrence and Metastasis

Liquid biopsies have emerged as a promising tool for the detection of metastases as well as local and regional recurrence in lung cancer. Liquid biopsy tests involve analyzing a patient's blood, urine, or other body fluids for the detection of biomarkers, including circulating tumor cells or tumor-derived DNA/RNA that have been shed into the bloodstream. Studies have shown that liquid biopsies can detect lung cancer metastases with high accuracy and sensitivity, even before they are visible on imaging scans. Such tests are valuable for early intervention and personalized treatment, aiming to improve patient outcomes. Liquid biopsies are also minimally invasive compared to traditional tissue biopsies, which require the removal of a sample of the tumor for further analysis. This makes liquid biopsies a more convenient and less risky option for patients, particularly those who are not good candidates for invasive procedures due to other medical conditions. While liquid biopsies for lung cancer metastases and relapse are still being developed and validated, they hold great promise for improving the detection and treatment of this deadly disease. Herein, we summarize available and novel approaches to liquid biopsy tests for lung cancer metastases and recurrence detection and describe their applications in clinical practice.