e16291 Background: Clinical management of hepatocellular carcinoma (HCC) is becoming increasingly complex, encompassing curative resection for early-stage disease and multimodal treatment strategies across disease stages. However, preoperative risk stratification and timely evaluation of treatment response remain challenging due to the lack of reliable blood-based tools. Tumor-agnostic circulating tumor DNA (ctDNA) methylation profiling may help address these unmet clinical needs by enabling real-time, noninvasive assessment of tumor biology and therapeutic efficacy. Methods: We prospectively enrolled 174 HCC patients undergoing curative hepatectomy and collected plasma samples within 24 hours prior to surgery (Ts). In parallel, an independent neoadjuvant therapy cohort of 27 HCC patients treated with immune checkpoint inhibitor-based systemic therapy was included, in which plasma samples were obtained at baseline before treatment initiation (Tb) and prior to surgery (Ts). All plasma samples were assayed using the GutSeer. A tumor-agnostic machine learning model was developed based on 342 HCC patients and 1,415 healthy subjects to generate a methylation-based cancer score (MCS) for prognostic stratification and pathological response assessment. Results: The hepatectomy cohort was followed for a median of 26.3 months with 47 recurrences. Preoperative Ts MCS was significantly associated with advanced tumor stage, larger tumor size, and vascular invasion, indicating its ability to reflect tumor burden and underlying biological aggressiveness. Based on Ts MCS, patients were stratified into low-risk (n = 45) and high-risk (n = 129) groups, yielding a hazard ratio (HR) of 9.36 (95%CI: 2.27–38.59; p < 0.001) and a negative predictive value of 95.6%. Multivariate analysis confirmed Ts MCS as an independent predictor of recurrence (HR = 5.09, 95%CI: 1.18-21.92; p < 0.05). In the neoadjuvant therapy cohort, MCS decreased significantly from Tb to Ts (p < 0.001). The change of MCS (Tb-Ts) was strongly negatively correlated with the proportion of residual viable tumor (R = -0.70, p < 0.001), indicating MCS changes closely mirror pathological response. Consistently, MCS change accurately discriminated major pathological response (MPR, ≤50% viable tumor cells) from non-MPR (AUC = 0.859), outperforming changes of alpha-fetoprotein (AUC = 0.731) and des-gamma-carboxy prothrombin (AUC = 0.686). Conclusions: This study demonstrates that tumor-agnostic ctDNA methylation profiling enables effective preoperative risk stratification in resectable HCC. Furthermore, dynamic changes in MCS provide a precise, noninvasive biomarker for assessing pathological response to systemic therapy. These findings highlight the broad clinical utility of methylation-based liquid biopsy approaches across surgical and systemic treatment settings in HCC. Clinical trial information: NCT06178809 .

PURPOSE:The purpose of this study was to develop a blood-based DNA methylation assay for noninvasive detection of gastric cancer (GC) across all stages, including early and premalignant lesions, and to evaluate whether preoperative GasAiQ positivity is associated with postoperative recurrence risk in patients with resectable GC. MATERIALS AND METHODS:We used reduced representation bisulfite sequencing to profile DNA methylation patterns and identify tumor-specific differentially methylated regions between patients with GC and controls. The 24 most discriminatory differentially methylated regions were validated in an independent set of tissues and plasma and then incorporated into a real-time methylation-specific PCR assay, GasAiQ. GasAiQ was evaluated in 1372 individuals, including GC patients and those with gastritis, atrophy, intestinal metaplasia, or intraepithelial neoplasia, across 3 cohorts: training (n = 518), validation (n = 519), and independent test (n = 335). Its association with recurrence risk was also assessed in 203 GC patients with resectable tumors for whom follow-up data were available after surgical resection. RESULTS:GasAiQ demonstrated high diagnostic accuracy across all cohorts. In the training cohort, an area under the curve (AUC) of 0.904 was achieved, with 83.8% sensitivity, 82.6% specificity, and 83.2% accuracy. The performance remained robust in the validation (AUC, 0.878; 83.9% sensitivity, 81.9% specificity, and 81.5% accuracy) and independent test (AUC, 0.891; 83.5% sensitivity, 86.3% specificity, and 85.3% accuracy) cohorts. The detection rates for early-stage GC (stage I) ranged from 70.2% to 75.0%, and GasAiQ identified 57.6% high-grade intraepithelial neoplasia. Notably, in a multivariate Cox regression model adjusting for key clinicopathological factors, preoperative GasAiQ positivity remained significantly associated with recurrence risk (hazard ratio, 7.5; 95% CI, 1.0-55.2; P = .047), supporting its potential prognostic value. CONCLUSIONS:GasAiQ is a noninvasive methylation assay that accurately detects GC, including early and premalignant stages, and predicts recurrence risk, offering significant potential for advanced screening, early diagnosis, and postoperative management.

Lung cancer is the leading cause of cancer-related mortality worldwide, highlighting the urgent need for more accurate and minimally invasive diagnostic tools to improve early detection and patient outcomes. While low-dose computed tomography (LDCT) is effective for screening in high-risk individuals, its high false-positive rate necessitates more precise diagnostic strategies. Liquid biopsy, particularly ctDNA methylation analysis, represents a promising alternative for non-invasive classification of indeterminate pulmonary nodules (IPNs). This review highlights the progress and clinical potential of liquid biopsy technologies, including traditional proteins markers, cfDNA, exosomes, metabolomics, circulating tumor cells (CTCs) and platelets, in lung cancer diagnosis. We discuss the integration of ctDNA methylation analysis with traditional imaging and clinical data to enhance the early detection of IPNs, as well as potential solutions to address the challenges of low biomarker concentration and background noise. By advancing precision diagnostics, liquid biopsy technologies could transform lung cancer management, improve survival rates, and reduce the disease burden.

Early identification of high-risk individuals is crucial for optimizing cancer screening, particularly when considering expensive and invasive methods such as multi-omics technologies and endoscopic procedures. However, developing a robust, practical multi-cancer risk prediction model that integrates diverse, multi-scale data and with proper validation remains a significant challenge. We initialized the FuSion study by recruiting 42,666 participants from Taizhou, China, with a discovery cohort (n = 16,340) and an independent validation cohort (n = 26,308) after exclusion criteria. We integrated multi-scale data from 54 blood-derived biomarkers and 26 epidemiological exposures to develop a risk prediction model for five common cancers, including lung, esophageal, liver, gastric, and colorectal cancer. Employing five supervised machine learning approaches, we used a LASSO-based feature selection strategy to identify the most informative predictors. The model was trained and internally validated in the discovery cohort, externally applied in the validation cohort, and further evaluated through a prospective clinical follow-up to assess cancer events via clinical examinations. The final model comprising four key biomarkers along with age, sex, and smoking intensity, achieving an AUROC of 0.767 (95

Background: Identifying high-risk individuals is crucial for effective cancer screening. However, developing a practical risk prediction model with proper validation for multiple cancer types presents significant challenges. Methods: We initialized the FuSion cohort study by recruiting 42,666 participants from Taizhou, China, between 2011 and 2021. Among these participants, 16,340 were recruited from 2011 to 2014 and were designated as the discovery cohort, while 26,308 participants enrolled between 2018 and 2021 were utilized as the validation cohort. In the discovery phase, we developed a multi-cancer risk prediction model for five common cancers, including lung, esophageal, liver, gastric, and colorectal cancer, using a comprehensive variable selection framework based on five machine learning methods. The predictors were selected from 74 epidemiological risk factors and blood biomarkers. The participants from the validation cohort were classified into high-, intermediate-, and low-risk groups based on the established model. We followed up the different risk groups and conducted clinical medical examinations, such as CT scans and endoscopic examinations, to evaluate the model's effectiveness in predicting cancer risk. Findings: In the discovery phase, we developed a multi-cancer risk prediction model based on four biomarkers, AFP, CEA, CYFRA-211 and HBsAg, as well as age, sex, and smoking intensity. The model exhibited an AUROC of 0.767 (95%CI: 0.723-0.814) for five-year incidence prediction of multiple cancers, and the high-risk group exhibited a 15.19-fold (95%CI: 5.97-38.64) and 4.13-fold (95%CI: 2.67-6.39) increased risk compared to the low-risk population and intermediate-risk population, respectively. Among 17.19% of participants in the validation cohort that were identified as high risk, 50.41% of all new cancer cases were expected. In the face-to-face follow-up of 2,941 high-risk individuals, 9.64% were newly diagnosed with cancer or precancerous lesions. It was 5.02 times and 1.74 times as high as that in the low- and intermediate-risk group, respectively. In particular, the incidence of esophageal cancers in the high-risk group was 16.84 times as high as that in the low-risk group. Interpretation: This is the first population-based multi-cancer risk prediction study conducted on a large Chinese cohort. The effective risk stratification model developed in this study would facilitate targeted prevention strategies and enhance early screening efforts for high-risk populations, ultimately optimizing healthcare resources.

BACKGROUND:Gastrointestinal (GI) cancers are among the most prevalent and lethal malignancies worldwide. Early, non-invasive detection is essential for timely intervention and improved survival. To address this clinical need, we developed GutSeer, a blood-based assay combining DNA methylation and fragmentomics for multi-GI cancer detection. METHODS:Genome-wide methylome profiling identified 1,656 markers specific to five major GI cancers and their tissue origins. Based on these findings, we designed GutSeer, a targeted bisulfite sequencing panel, which was trained and validated using plasma samples from 1,057 cancer patients and 1,415 non-cancer controls. The locked model was blindly tested in an independent cohort of 846 participants, encompassing both inpatient and outpatient settings across five hospitals. RESULTS:In the validation cohort, GutSeer achieved an area under the curve (AUC) of 0.950 [95% Confidence Interval (CI): 0.937-0.962] for cancer detection, with 82.8% sensitivity (95% CI: 79.5-86.0) and 95.8% specificity (95% CI: 94.3-97.2). It detected 92.2% of colorectal, 75.5% of esophageal, 65.3% of gastric, 92.9% of liver, and 88.6% of pancreatic cancers. The independent test cohort included 198 early-stage cancers (stage I/II, 66.4%) and 63 advanced precancerous lesions. GutSeer maintained robust performance, with 81.5% sensitivity (95% CI: 77.1-85.9) for GI cancers and 94.4% specificity (95% CI: 92.4-96.5). It also demonstrated the ability to detect advanced precancerous lesions in the colorectum, esophagus, and stomach as a single, non-invasive blood test. CONCLUSIONS:By integrating DNA methylation and fragmentomics into a compact panel, GutSeer outperformed genome-wide sequencing in both accuracy and clinical applicability. Its high sensitivity for early-stage GI cancers and practicality as a non-invasive assay highlights its potential to revolutionize early cancer detection and improve patient outcomes. TRIAL REGISTRATION:ClinicalTrials.gov identifier: NCT05431621.

e16323 Background: Systemic therapy of hepatocellular carcinoma (HCC) faces several challenges, including tumor heterogeneity and drug resistance. Previous studies have indicated that circulating tumor DNA (ctDNA) may offer valuable insights for predicting the efficacy of systemic therapy in various cancers. Nevertheless, the majority of these studies are predominantly based on mutation detection. Recently, the ctDNA methylation detection has been used for real-time monitoring of tumor dynamic changes and assessment of treatment response. However, its applicability in HCC remains unclear. Methods: For HCC patients undergoing neoadjuvant therapy (immune checkpoint inhibitor [ICI] based), we collected paired plasma samples (n = 54) at baseline (Tb) and prior to surgery (Ts). Serial plasma samples (n = 28) were obtained from HCC patients receiving systemic therapy (ICI based) at baseline (C1) and 3 (C2), 6 (C3), and 9 (C4) weeks following treatment initiation. All samples were tested using the GutSeer panel and the cancer-specific methylation score (CSMS) were calculated. The ctDNA methylation response (CMR) was defined as the difference of CSMS between Tb and Ts greater than 0.01. The concordance between CMR and pathological response (major pathological response [MPR], ≤50% residual viable tumor) and radiological response (assessed by the modified Response Evaluation Criteria in Solid Tumors) was evaluated. Results: We enrolled 27 HCC patients undergoing neoadjuvant therapy. All patients completed at least two courses of ICI and the median treatment-to-surgery interval was 1.7 months. The difference of CSMS (Tb-Ts) was significantly negatively correlated with the proportion of residual viable tumor (R = -0.7, P < 0.001). Among them, 17 patients (63.0%) achieved CMR, of whom 15 (88.2%) were assessed as MPR. In contrast, only 2 (20%) MPR were observed among the remaining 10 patients without CMR. The area under the curve and accuracy of the predictive model based on CSMS were 85.9% and 85.2%, respectively. Additionally, we included 7 HCC patients receiving systemic therapy. Among them, 2 were assessed as partial response with one patient demonstrating a significant reduction in CSMS (~20% decrease from C1 to C4) in advance. As for the 5 patients classified as stable disease or progressive disease, 4 exhibited an early increase in CSMS, including one patient with a rise of ~80% (from C1 to C4). Conclusions: Our study found that ctDNA methylation detection is a promising indicator for monitoring tumor dynamic changes, enabling the prediction of pathological and radiological response in HCC patients during treatment. These discoveries pave the way for the development of effective efficacy surveillance strategies, thereby facilitating individualized management and enhancing the precision of medical interventions.

e16245 Background: Minimal residual disease (MRD) is considered an essential factor leading to early relapse after radical surgery, which is challenging to be detected by conventional imaging. The majority of MRD studies in HCC are based on mutation detection. Simultaneously, in certain cancers such as lung and colorectal cancers, the use of methylation detection for MRD has been demonstrated to possess significant detection capabilities. In this study, we present a personalized methylation haplotype (MHP) method for MRD detection in HCC patients underwent curative resection. Methods: Tumor and paracancerous tissue samples were obtained from HCC patients undergoing surgical resections. WBC and plasma samples (T0) were collected before surgery, while plasma samples (T1) were collected in one month after surgery, and subsequent follow-up plasma samples were obtained every three months. All samples were tested using the GutSeer Panel. A personalized tumor-informed MRD detection approach, named TORNADO (Tumor-infORmed DNAm hAplotype DetectiOn), was then developed. Individual seed MHPs were pinpointed based on their presence in cancer tissue or plasma samples collected before surgery (T0 plasma), excluding those in para-cancer tissue and blood cells. The relapse risk score was determined by assessing the proportion of these MHPs detectable in follow-up evaluations. Results: We enrolled 39 early-stage HCC patients who have undergone surgical resections. All the patients have completed at least one year of regular clinical follow-up. Among them, 13 out of 39 patients (33.3%) were identified as MRD-positive based on predictions from T1. Of the MRD-positive patients, 6 experienced tumor relapse. In contrast, only 7.7% relapses were observed among the remaining 26 MRD-negative patients. When considering the T1 and T2 MRD cumulative risk score, 53.8% (21/39) of patients were classified as MRD-positive, and none of the MRD-negative patients experienced relapse. Regardless of whether based on T1 plasma alone or cumulative results, recurrence-free survival (RFS) was significantly correlated with MRD status. Kaplan-Meier analysis demonstrated that MRD-positive patients had significantly poorer RFS (p-value < 0.05). Cox regression analysis further revealed that MRD status was a significant variable for predicting recurrence-free survival (HR=16.00, 95% CI 2-130, P=0.009). Conclusions: The MRD status detected byTORNADOmethod emerged as a significant prognostic indicator for disease relapse in early-stage HCC patients undergoing curative resection, underscoring the clinical relevance of MHP-based MRD detection guided by tumor information. These discoveries pave the way for the development of effective postoperative cancer surveillance strategies specifically tailored for early-stage liver cancer.

Although gene/genome duplications in the early stage of vertebrates have been thought to provide major resources of raw genetic materials for evolutionary innovations, it is unclear whether they continuously contribute to the evolution of morphological complexity during the course of vertebrate evolution, such as the evolution from two heart chambers (fishes) to four heart chambers (mammals and birds). We addressed this issue by our heart RNA-Seq experiments combined with published data, using 13 vertebrates and one invertebrate (sea squirt, as an outgroup). Our evolutionary transcriptome analysis showed that number of ancient paralogous genes expressed in heart tends to increase with the increase of heart chamber number along the vertebrate phylogeny, in spite that most of them were duplicated at the time near to the origin of vertebrates or even more ancient. Moreover, those paralogs expressed in heart exert considerably different functions from heart-expressed singletons: the former are functionally enriched in cardiac muscle and muscle contraction-related categories, whereas the latter play more basic functions of energy generation like aerobic respiration. These findings together support the notion that recruiting anciently paralogous genes that are expressed in heart is associated with the increase of chamber number in vertebrate evolution.

Context Accurately distinguishing between benign thyroid nodules (BTNs) and papillary thyroid cancers (PTCs) with current conventional methods poses a significant challenge.Objective We identify DNA methylation markers of immune response-related genes for distinguishing BTNs and PTCs.Methods In this study, we analyzed a public reduced representative bisulfite sequencing dataset and revealed distinct methylation patterns associated with immune signals in PTCs and BTNs. Based on these findings, we developed a diagnostic classifier named the Methylation-based Immune Response Signature (MeIS), which was composed of 15 DNA methylation markers associated with immune response-related genes. We validated MeIS's performance in 2 independent cohorts: Z.S.'s retrospective cohort (50 PTC and 18 BTN surgery-leftover samples) and Z.S.'s preoperative cohort (31 PTC and 30 BTN fine-needle aspiration samples).Results The MeIS classifier demonstrated significant clinical promise, achieving areas under the curve of 0.96, 0.98, 0.89, and 0.90 in the training set, validation set, Z.S.'s retrospective cohort, and Z.S.'s preoperative cohort, respectively. For the cytologically indeterminate thyroid nodules, in Z.S.'s retrospective cohort, MeIS exhibited a sensitivity of 91% and a specificity of 82%; in Z.S.'s preoperative cohort, MeIS achieved a sensitivity of 84% and a specificity of 74%. Additionally, combining MeIS and BRAF V600E detection improved the detecting performance of cytologically indeterminate thyroid nodules, yielding sensitivities of 98% and 87%, and specificities of 82% and 74% in Z.S.'s retrospective cohort and Z.S.'s preoperative cohort, respectively.Conclusion The 15 markers we identified can be employed to improve the diagnostic of cytologically indeterminate thyroid nodules.

Background Thyroid nodule (TN) patients in China are subject to overdiagnosis and overtreatment. The implementation of existing technologies such as thyroid ultrasonography has indeed contributed to the improved diagnostic accuracy of TNs. However, a significant issue persists, where many patients undergo unnecessary biopsies, and patients with malignant thyroid nodules (MTNs) are advised to undergo surgery therapy.Methods This study included a total of 293 patients diagnosed with TNs. Differential methylation haplotype blocks (MHBs) in blood leukocytes between MTNs and benign thyroid nodules (BTNs) were detected using reduced representation bisulfite sequencing (RRBS). Subsequently, an artificial intelligence blood leukocyte DNA methylation (BLDM) model was designed to optimize the management and treatment of patients with TNs for more effective outcomes.Results The DNA methylation profiles of peripheral blood leukocytes exhibited distinctions between MTNs and BTNs. The BLDM model we developed for diagnosing TNs achieved an area under the curve (AUC) of 0.858 in the validation cohort and 0.863 in the independent test cohort. Its specificity reached 90.91% and 88.68% in the validation and independent test cohorts, respectively, outperforming the specificity of ultrasonography (43.64% in the validation cohort and 47.17% in the independent test cohort), albeit with a slightly lower sensitivity (83.33% in the validation cohort and 82.86% in the independent test cohort) compared to ultrasonography (97.62% in the validation cohort and 100.00% in the independent test cohort). The BLDM model could correctly identify 89.83% patients whose nodules were suspected malignant by ultrasonography but finally histological benign. In micronodules, the model displayed higher specificity (93.33% in the validation cohort and 92.00% in the independent test cohort) and accuracy (88.24% in the validation cohort and 87.50% in the independent test cohort) for diagnosing TNs. This performance surpassed the specificity and accuracy observed with ultrasonography. A TN diagnostic and treatment framework that prioritizes patients is provided, with fine-needle aspiration (FNA) biopsy performed only on patients with indications of MTNs in both BLDM and ultrasonography results, thus avoiding unnecessary biopsies.Conclusions This is the first study to demonstrate the potential of non-invasive blood leukocytes in diagnosing TNs, thereby making TN diagnosis and treatment more efficient in China.

Accurate differentiation between malignant and benign pulmonary nodules, especially those measuring 5–10 mm in diameter, continues to pose a significant diagnostic challenge. This study introduces a novel, precise approach by integrating circulating cell-free DNA (cfDNA) methylation patterns, protein profiling, and computed tomography (CT) imaging features to enhance the classification of pulmonary nodules. Blood samples were collected from 419 participants diagnosed with pulmonary nodules ranging from 5 to 30 mm in size, before any disease-altering procedures such as treatment or surgical intervention. High-throughput bisulfite sequencing was used to conduct DNA methylation profiling, while protein profiling was performed utilizing the Olink proximity extension assay. The dataset was divided into a training set and an independent test set. The training set included 162 matched cases of benign and malignant nodules, balanced for sex and age. In contrast, the test set consisted of 46 benign and 49 malignant nodules. By effectively integrating both molecular (DNA methylation and protein profiling) and CT imaging parameters, a sophisticated deep learning-based classifier was developed to accurately distinguish between benign and malignant pulmonary nodules. Our results demonstrate that the integrated model is both accurate and robust in distinguishing between benign and malignant pulmonary nodules. It achieved an AUC score 0.925 (sensitivity = 83.7 https://classic.clinicaltrials.gov/ct2/show/NCT05432128 .

739 Background: Gastrointestinal (GI) cancers account for over one-third of cancer-related deaths. We have previously developed GutSeer, a non-invasive assay utilizing cell-free DNA (cfDNA) methylation and fragmentation signatures, to cost-effectively detect and localize five major GI cancers, including colorectal (CC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC). Methods: GutSeer was initially developed and validated in a large retrospective cohort of 1844 plasma samples, demonstrating excellent performance with specificity, sensitivity, and tissue of origin (TOO) accuracy at 96.7%, 86.2%, and 82%, respectively. In this study, we conducted a blind, multi-center clinical test for further validation. Participants were recruited from two centers, Beijing (BJ) and Shanghai (SH). Cohort BJ included 42 participants (20 healthy, 22 cancer), and Cohort SH comprised 403 participants (105 healthy, 123 benign, 175 cancer). Over half of the cancer samples in Cohort SH were in early-stage disease (TNM stages I and II). Results: In Cohort BJ, GutSeer showed 100% specificity, 68.2% sensitivity, and 86.7% TOO accuracy. Individual cancer type sensitivities within Cohort BJ were 100% for CC, 57.1% for EC, 75% for GC, 40% for PC, and 100% for LC. In Cohort SH, GutSeer exhibited 94.6% specificity for healthy samples and 86.2% for benign conditions. For cancer samples in Cohort SH, the overall sensitivity achieved 78.9%, with specific sensitivities of 92.3% for CC, 81.0% for EC, 78.4% for GC, 52.9% for PC, and 86.4% for LC. Moreover, GutSeer also efficiently detected early-stage cancers, with stage-wise sensitivities ranging from 64.7% (stage I) to 96.3% (stage IV). TOO accuracy in Cohort SH was 79.0%, with individual cancer types achieving accuracies from 71.7% to 94.7% (72.2% for CC, 71.7% for EC/GC, 94.7% for LC, and 77.8% for PC). Conclusions: GutSeer demonstrates promising potential as a cost-effective, non-invasive test for early detection and localization of major GI cancers. Its consistently high performance in both retrospective and multi-center blind clinical tests implies its potential to revolutionize the screening and diagnosis paradigm for GI cancers.

In eukaryotes, cytosine methylation is a primary heritable epigenetic modification of the genome that regulates many cellular processes. In invertebrate, methylated cytosine generally located on specific genomic elements (e.g., gene bodies and silenced repetitive elements) to show a "mosaic" pattern. While in jawed vertebrate (teleost and tetrapod), highly methylated cytosine located genome-wide but only absence at regulatory regions (e.g., promoter and enhancer). Many studies imply that the evolution of DNA methylation reprogramming may have helped the transition from invertebrates to jawed vertebrates, but the detail remains largely elusive. In this study, we used the whole-genome bisulfite-sequencing technology to investigate the genome-wide methylation in three tissues (heart, muscle, and sperm) from the sea lamprey, an extant agnathan (jawless) vertebrate. Strikingly, we found that the methylation level of the sea lamprey is very similar to that in sea urchin (a deuterostome) and sea squirt (a chordate) invertebrates. In sum, the global pattern in sea lamprey is intermediate methylation level (around 30%), that is higher than methylation level in the genomes of pre-bilaterians and protostomes (1%-10%), but lower than methylation level appeared in jawed vertebrates (around 70%, teleost and tetrapod). We anticipate that, in addition to genetic dynamics such as genome duplications, epigenetic dynamics such as global methylation reprograming was also orchestrated toward the emergence and evolution of vertebrates.

Background: Early diagnosis of hepatocellular carcinoma (HCC) can significantly improve patient survival. We aimed to develop a blood-based assay to aid in the diagnosis, detection and prognostic evaluation of HCC. Methods: A three-phase multicentre study was conducted to screen, optimise and validate HCC-specific differentially methylated regions (DMRs) using next-generation sequencing and quantitative methylation-specific PCR (qMSP). Results: Genome-wide methylation profiling was conducted to identify DMRs distinguishing HCC tumours from peritumoural tissues and healthy plasmas. The twenty most effective DMRs were verified and incorporated into a multilocus qMSP assay (HepaAiQ). The HepaAiQ model was trained to separate 293 HCC patients (Barcelona Clinic Liver Cancer (BCLC) stage 0/A, 224) from 266 controls including chronic hepatitis B (CHB) or liver cirrhosis (LC) (CHB/LC, 96), benign hepatic lesions (BHL, 23), and healthy controls (HC, 147). The model achieved an area under the curve (AUC) of 0.944 with a sensitivity of 86.0% in HCC and a specificity of 92.1% in controls. Blind validation of the HepaAiQ model in a cohort of 523 participants resulted in an AUC of 0.940 with a sensitivity of 84.4% in 205 HCC cases (BCLC stage 0/A, 167) and a specificity of 90.3% in 318 controls (CHB/LC, 100; BHL, 102; HC, 116). When evaluated in an independent test set, the HepaAiQ model exhibited a sensitivity of 70.8% in 65 HCC patients at BCLC stage 0/A and a specificity of 89.5% in 124 patients with CHB/LC. Moreover, HepaAiQ model was assessed in paired pre- and postoperative plasma samples from 103 HCC patients and correlated with 2-year patient outcomes. Patients with high postoperative HepaAiQ score showed a higher recurrence risk (Hazard ratio, 3.33, p < .001). Conclusions: HepaAiQ, a noninvasive qMSP assay, was developed to accurately measure HCC-specific DMRs and shows great potential for the diagnosis, detection and prognosis of HCC, benefiting at-risk populations.

e16338 Background: Many cancers are symptoms free in early clinical stages, resulting in nearly half of cancer patients diagnosed in advanced-stages when therapeutic options are limited. Early cancer detection is key to improve clinical outcomes. We developed PanSeer7, a multi-cancer detection assay based on targeted bisulfite sequencing of circulating cell-free DNA (cfDNA) and evaluated its technical performances of reproducibility and sensitivity. Methods: The panel of PanSeer7 consists of 2447 markers which were either differentially methylated between healthy and cancer samples, or distinctively methylated in a specific cancer. We assessed PanSeer7’s reproducibility by using it to sequence technical replicates of cfDNA samples. For its limit of detection (LOD), we prepared samples mimicking cancer plasma DNA by diluting fragmented cancer cell line DNA, which represent 7 common cancer types, into GM12878 control at ratios of 1/10,000 to 1/100. We further tested PanSeer7’s ability to identify tissue of origin (TOO) by analyzing DNA samples from formalin-fixed paraffin-embedded (FFPE) tissues and healthy plasma. Results: We analyzed PanSeer7’s reproducibility by sequencing 40 replicates of synthetic healthy cfDNA samples on four independent batches and with different inputs (from 2ng to 20ng). Results show that at a minimum of 10ng input, PanSeer7 produced highly consistent methylation levels among replicates. As to cancer signal detection, we found that PanSeer7’s technical LOD was 1/10,000 for lung cancer cell line H1650, liver cancer line HepG2, gastric cancer line HGC27, esophageal cancer line KYSE150 and colorectal cancer line SW480; it was slightly lower as 5/10,000 for pancreatic cancer line PANC1 and breast cancer line MDA-MB-231. To evaluate PanSeer7’s accuracy of TOO identification, we sequenced 38 healthy plasma and 121 FFPE tissues (17 of liver cancer, 13 of pancreatic cancer, 21 of gastric cancer, 18 of esophageal cancer, 16 of colorectal cancer, 14 of lung cancer, and 22 of breast cancer). Clustering analysis showed that they were segregated according to their TOO based on methylation levels. We also generated 3500 sets of simulated data by mixing the reads of cancer tissue into those of healthy plasma at ratios of 1/10,000 to 1/100, and trained TOO-predicting models with train data sets. At a ratio of 5/10,000, the model predicted TOO of test data sets with an accuracy of over 95%. Conclusions: PanSeer7 required as low as 10ng input DNA for high reproducibility. Its technical LOD in detecting cancer signal was no lower than 5/10,000 for all 7 cancer cell lines tested, and has an in silico TOO detection LOD of 5/10,000. Thus, PanSeer7 had excellent performances in both cancer signal detection and TOO identification, showing promise to be clinically applied for non-invasive multi-cancer detection after future optimization and validation.

Background Cell-free DNA (cfDNA) is being explored as biomarker for non-invasive diagnosis of cancer. We aimed to establish a cfDNA-based DNA methylation marker panel to differentially diagnose papillary thyroid carcinoma (PTC) from benign thyroid nodule (BTN).Methods 220 PTC-and 188 BTN patients were enrolled. Methylation markers of PTC were identified from patients' tissue and plasma by reduced representation bisulfite sequencing and methylation haplotype analyses. They were combined with PTC markers from literatures and were tested on additional PTC and BTN samples to verify PTC-detecting ability using targeted methylation sequencing. Top markers were developed into ThyMet and were tested in 113 PTC and 88 BTN cases to train and validate a PTC-plasma classifier. Integration of ThyMet and thyroid ultrasonography was explored to improve accuracy.Findings From 859 potential PTC plasma-discriminating markers that include 81 markers identified by us, the top 98 most PTC plasma-discriminating markers were selected for ThyMet. A 6-marker ThyMet classifier for PTC plasma was trained. In validation it achieved an Area Under the Curve (AUC) of 0.828, similar to thyroid ultrasonography (0.833) but at higher specificity (0.722 and 0.625 for ThyMet and ultrasonography, respectively). A combinatorial classifier by them, ThyMet-US, improved AUC to 0.923 (sensitivity = 0.957, specificity = 0.708).Interpretation The ThyMet classifier improved the specificity of differentiating PTC from BTN over ultrasonography. The combinatorial ThyMet-US classifier may be effective in preoperative diagnosis of PTC.Funding This work was supported by the grants from National Natural Science Foundation of China (82072956 and 81772850). Copyright (c) 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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

4169 Background: Five major gastrointestinal (GI) cancers - colorectal (CRC), gastric (GC), liver (LC), esophageal (EC), and pancreatic cancer (PC) - are responsible for hundreds of thousands of mortalities annually worldwide. Unfortunately, there is a lack of cost-effective, blood-based screening method for their early detection. To address this issue, we aimed to develop GutSeer, a noninvasive, targeted methylation sequencing-based test by leveraging methylation and fragmentomic signatures carried by cell-free DNA (cfDNA). Methods: The panel of GutSeer consists of 1656 target regions which were either differentially methylated between healthy and cancer samples, or distinctively methylated in a specific GI cancer. Cancer and healthy participants were recruited and randomly divided into a training and a validation cohort. Their plasma DNA samples were analyzed to generate DNA methylation and fragmentomic features. These multi-dimensional features were integrated to build ensemble stacked machine learning models to differentiate cancer against healthy, and to determine the tissue-of-origin (TOO) of the cancer. Results: A total of 1844 cases (787 healthy, 342 LC, 239 GC, 209 EC, 180 CRC, and 87 PC cases) were recruited for this study. A cancer- vs-healthy model achieved an AUC of 0.94 and 0.95 (sensitivity of 77.7% and 77.1% under the specificity around 96%) using either methylation or fragmentomic features only, respectively. Combining both methylation and fragmentomic features further improved performances, achieving an AUC of 0.96 (sensitivity = 86.2% at a specificity of 96.7%). For individual type of cancer, GutSeer has a sensitivity of 93.3% for CRC, 81.1% for EC, 70.3% for GC, 96.5% for LC, and 86.4% for PC. An independent test using 629 benign cases as controls achieved a specificity of 87.1%. A separate TOO model was built using all features and achieved an overall accuracy of 82% for all cancer cases (66.7% for CRC, 87.0% for GC and EC combined, 89.0% for LC, and 63.2% for PC). Same as the cancer detection model, using multi-dimensional features in TOO prediction yielded higher accuracy than when models using only methylation or fragmentomics features (accuracy = 75.6% or 75.4%, respectively). When compared with whole-genome sequencing (WGS) based approaches, GutSeer showed a comparable performance in cancer detection but a higher accuracy in TOO identification, further confirming its effectiveness for detection of GI cancers. Conclusions: GutSeer, a non-invasive test integrating multi-dimensional features, was demonstrated to detect and localize the 5 main types of GI cancer with high accuracy. Our results further showed that a reasonably sized panel can perform comparably or even better than WGS-based methods in cancer detection and TOO localization, indicating GutSeer may be a low-cost solution for blood-based early screening for GI cancers.