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NO	YEAR	SUBJECT	JOURNAL NAME	LINK
34	2026	MOCDT: multi-cancer detection and tissue-of-origin classification via cfDNA multi-modal integration MOCDT: multi-cancer detection and tissue-of-origin classification via cfDNA multi-modal integration Abstract Circulating tumor DNA (ctDNA) contains rich molecular information that can be leveraged for both cancer detection and tissue-of-origin (TOO) prediction. However, effectively integrating multiple cfDNA-derived modalities, including methylation, copy number variation (CNV), and fragment size ratio (FSR), remains challenging. To address this issue, the authors developed MOCDT (Multi-Omics Cancer Detection and Tissue-of-Origin), a deep learning framework that integrates multi-modal cfDNA features through a clinically relevant two-stage workflow consisting of cancer detection followed by tissue-of-origin classification. Purpose The study aimed to: Develop a robust deep learning framework capable of integrating multiple cfDNA modalities, including methylation, CNV, and fragmentomics data. Improve the accuracy of blood-based multi-cancer detection. Accurately identify the tissue of origin for detected cancers. Overcome limitations of existing multi-omics approaches, such as modality imbalance, insufficient exploitation of inter-patient relationships, and inadequate learning of tissue-specific biological features. Results Study Cohort Total participants: 1,423 individuals Included healthy controls and patients with eight cancer types: Colorectal cancer Gastric cancer Liver cancer Pancreatic cancer Lung cancer Breast cancer Ovarian cancer Prostate cancer Cancer Detection Performance MOCDT achieved strong performance in distinguishing cancer patients fr\|om healthy individuals: Metric Performance Specificity 95.74% Sensitivity 96.22% Accuracy 96.09% The proposed framework outperformed several existing multi-omics integration methods, including MOGONET, MoGCN, and MO-GCAN. Tissue-of-Origin Classification Performance Metric Performance Top-1 Accuracy 75.20% Top-2 Accuracy 86.79% Top-3 Accuracy 91.06% These results indicate that the correct cancer type was included among the top three predicted tissue origins in over 91% of cases. Biological Interpretation Latent-space analyses demonstrated that MOCDT successfully learned tissue-specific molecular patterns rather than relying solely on generic cancer-associated signals. This finding supports the biological interpretability of the model and its ability to distinguish among different cancer origins. Conclusions MOCDT is an effective multi-modal deep learning framework that integrates methylation, CNV, and fragmentomics features fr\|om cfDNA for both cancer detection and tissue-of-origin classification. The model demonstrated: High sensitivity and specificity for cancer detection. Strong tissue-of-origin prediction performance. Effective learning of tissue-specific biological characteristics. A clinically applicable two-stage workflow suitable for multi-cancer early detection (MCED). Overall, the study suggests that integrating multiple cfDNA-derived signals through advanced artificial intelligence approaches can significantly enhance the performance of liquid biopsy-based cancer screening and tissue-of-origin prediction, supporting the future development of non-invasive multi-cancer early detection strategies. MOCDT: multi-cancer detection and tissue-of-origin classification via cfDNA multi-modal integration LINK VIEW	Bioinformatics	LINK _VIEW
33	2026	Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis Abstract This study developed a multimodal cfDNA-based multicancer detection assay combining whole-genome methylation sequencing with machine learning analysis. The model integrated methylation and fragmentomic features and demonstrated high performance across eight cancer types, achieving 93.2% sensitivity and 95% specificity. It also showed strong early-stage detection capability, with over 92% sensitivity for stage I and II cancers. The assay further achieved 85.7% accuracy in predicting tissue of origin, highlighting its potential for improving multicancer early detection and cancer screening. Introduction Cancer remains a leading cause of death worldwide, highlighting the urgent need for accurate and noninvasive early detection methods. Current screening approaches are often invasive, expensive and limited in detecting multiple cancer types, especially cancers without established screening guidelines. Liquid biopsy using cell-free DNA (cfDNA) has emerged as a promising strategy for multicancer early detection (MCED). Recent advances in sequencing technologies have enabled analysis of methylation, copy number variation (CNV) and fragmentomic features fr\|om circulating tumor DNA (ctDNA). However, existing MCED methods still face challenges in achieving high sensitivity for early-stage cancers due to the low abundance of ctDNA. To overcome these limitations, this study developed a multimodal MCED framework integrating four cfDNA features: average methylation fraction (AMF), CNV, fragment size ratio (FSR) and fragment size distribution (FSD). Using an ensemble machine learning model, the study evaluated detection performance across eight major cancer types and assessed tissue-of-origin prediction accuracy. The findings demonstrate the potential of combining multiple cfDNA characteristics to improve noninvasive early cancer detection. Materials and methods This study analyzed plasma cfDNA samples fr\|om patients with eight cancer types—colorectal, gastric, liver, pancreatic, lung, breast, ovarian and prostate cancer—as well as healthy controls. Blood samples were collected before treatment, and cfDNA was extracted fr\|om plasma for analysis. Whole-genome methylation sequencing was performed using the IMBdx AlphaLiquid screening platform, followed by extensive NGS preprocessing and quality control. The study evaluated four major cfDNA features: average methylation fraction (AMF), copy number variation (CNV), fragment size distribution (FSD) and fragment size ratio (FSR). Cancer-specific methylation markers and genomic alterations were identified using statistical filtering and machine learning-based optimization. Single-feature models were first developed for each cfDNA characteristic using machine learning algorithms such as random forest, logistic regression and support vector machines. These models were then integrated into an ensemble framework combining methylation, genomic and fragmentomic signals along with demographic factors including age and sex. The final multimodal model was trained to detect cancer signals and predict tissue of origin while maintaining high specificity. Statistical analyses were performed using R and Python with rigorous validation procedures. Results The study analyzed 1,415 samples, including 1,034 cancer samples fr\|om eight cancer types and 381 healthy controls. More than half of the cancer cases were stage I–II, enabling robust evaluation of early cancer detection performance. Unsupervised clustering analyses using methylation, copy number variation (CNV), and fragmentomic features showed clear separation between healthy individuals and cancer patients, especially in advanced-stage disease. Distinct cancer-specific methylation signatures and fragmentomic patterns were identified, supporting the complementary value of integrating multiple cfDNA features. Among single-feature models, average methylation fraction (AMF) showed the highest overall sensitivity (85.3%), particularly for early-stage cancers. CNV, fragment size ratio (FSR), and fragment size distribution (FSD) also contributed meaningful diagnostic information across different cancer types. The multimodal ensemble model integrating AMF, CNV, FSR, and FSD achieved strong overall performance with 93.2% sensitivity and 95% specificity. Importantly, sensitivity remained high for early-stage cancers, reaching 92.3% for stage I and 92.2% for stage II disease. Sensitivity was especially high for colorectal, breast, and gastric cancers, while also demonstrating strong detection capability for difficult-to-screen cancers such as pancreatic, ovarian, and prostate cancer. For tissue-of-origin (TOO) prediction, the model achieved 72.9% top-1 accuracy and 85.7% top-2 accuracy across the eight cancer types. The ensemble framework successfully leveraged complementary methylation, genomic, and fragmentomic signals to improve both cancer detection and tissue classification performance. Discussion This study demonstrated that a multimodal cfDNA analysis framework integrating methylation (AMF), copy number variation (CNV), and fragmentomic features (FSR and FSD) can significantly improve multicancer early detection (MCED). The ensemble model achieved high sensitivity (93.2%) and specificity (95%), outperforming several existing cfDNA-based cancer screening approaches. The model showed particularly strong performance for early-stage cancers, highlighting the importance of combining complementary cfDNA features. AMF effectively detected early epigenetic alterations, while CNV and fragmentomic analyses contributed additional discriminatory power, especially in later-stage disease. The multimodal strategy also improved tissue-of-origin prediction accuracy. Despite these promising results, challenges remain for cancers with low ctDNA abundance, such as ovarian and prostate cancer, and for certain stage III cancers affected by variable ctDNA shedding. The authors also acknowledged limitations related to single-cohort validation and emphasized the need for large-scale prospective external validation studies. Overall, the findings support the potential clinical utility of multimodal cfDNA analysis as a scalable and noninvasive approach for improving early cancer detection and future cancer screening strategies. Data availability The raw data for this study were generated by IMBdx Inc. Data supporting the findings of this study are available fr\|om the corresponding author upon reasonable request. Data access may be subject to institutional and ethical regulations. Enhanced multicancer screening assay through whole-genome methylation sequencing-based multimodal cell-free DNA analysis LINK VIEW	Experimental & Molecular Medicine	LINK _VIEW
32	2026	Preoperative Circulating Tumor DNA Detection and Risk Stratification in Esophageal Squamous Cell Carcinoma Abstract Preoperative circulating tumor DNA (ctDNA) detection may help identify patients with early-stage esophageal squamous cell carcinoma (ESCC) who harbor occult nodal metastasis or are at high risk of recurrence. In this cohort study of 74 patients with clinical stage I (T1b) or stage II (T2N0) ESCC who underwent upfront curative surgery without neoadjuvant therapy, tumor-informed ctDNA sequencing was performed on preoperative plasma samples. Preoperative ctDNA positivity was significantly associated with both pathologic nodal upstaging and worse survival outcomes (recurrence-free survival and overall survival). Incorporation of ctDNA status into guideline-based risk prediction models substantially improved the ability to predict occult lymph node metastasis, particularly in the T2N0 subgroup. The findings suggest that ctDNA may be a valuable preoperative biomarker for refining risk stratification and informing treatment decisions in early-stage ESCC. Purpose The purpose of this study was to determine whether preoperative detection of circulating tumor DNA (ctDNA) in patients with early-stage esophageal squamous cell carcinoma (clinical T1b or T2N0 ESCC) is associated with (1) pathologic nodal upstaging (occult lymph node metastasis discovered after surgery) and (2) postoperative recurrence and survival outcomes. The study also aimed to evaluate whether adding ctDNA status to existing guideline-based risk models could improve the prediction of occult nodal metastasis, particularly in patients with T2N0 disease where clinical decision-making regarding neoadjuvant therapy remains uncertain. Results - A total of 74 patients were included: 50 fr\|om Samsung Medical Center and 24 fr\|om Yonsei University Severance Hospital, all with clinical stage T1b or T2N0 ESCC who underwent surgery without neoadjuvant therapy.- Preoperative ctDNA was detected in 36 patients (48.6%) — 27 (54.0%) in the SMC cohort and 9 (37.5%) in the YUSH cohort. Detection was more frequent among patients with clinical T2N0 than T1b disease.- During a median follow-up of 37.7 months, ctDNA-positive patients had significantly worse recurrence-free survival (RFS) (HR, 4.15; P = .005) and overall survival (OS) (HR, 4.02; P = .006) compared with ctDNA-negative patients.- In the T2N0 subgroup specifically, ctDNA positivity showed very high positive predictive value for occult nodal metastasis, reaching 100% in the SMC cohort and 88.9% in the YUSH cohort.- In multivariable analysis, ctDNA positivity remained strongly associated with pathologic nodal metastasis (OR, ~19.98; P < .001), outperforming standard guideline-based risk factors (tumor size ≥ 3 cm, lymphovascular invasion, poor differentiation).- Incorporating ctDNA status into predictive models significantly improved the area under the ROC curve for predicting occult nodal metastasis (e.g., fr\|om 0.66 to 0.91 in the SMC cohort). Conclusions Preoperative ctDNA detection in patients with early-stage ESCC was significantly associated with occult nodal metastasis and poorer survival outcomes. Among patients with clinical T2N0 disease, ctDNA may serve as a complementary biomarker to current guideline-based risk criteria for refining preoperative risk stratification. Incorporating ctDNA status into clinical models could help clinicians identify high-risk patients who might benefit fr\|om neoadjuvant treatment escalation and support more personalized therapeutic strategies in early-stage ESCC. Prospective validation of ctDNA-guided treatment approaches is needed before clinical implementation. Preoperative Circulating Tumor DNA Detection and Risk Stratification in Esophageal Squamous Cell Carcinoma LINK VIEW	JAMA Surgery	LINK _VIEW
31	2026	Feasibility of tumor-informed circulating tumor DNA for detecting minimal residual disease in surgically resected biliary tract cancer Abstract We prospectively investigated the feasibility of tumor-informed circulating tumor DNA (ctDNA) for detecting minimal residual disease (MRD) in patients with surgically resected biliary tract cancer (BTC). Personalized ctDNA panels were designed based on whole-exome sequencing (WES) of the resected tumor tissue for each patient. Two serial blood samples (preoperative and within 6 weeks after surgery) were analyzed. A ctDNA test was considered positive if two or more patient-specific mutations were detected. Among 18 enrolled patients, 14 were evaluable due to inadequate target variants or surgical inoperability. Preoperative ctDNA positivity tended to be higher in advanced stages and node-positive disease. After a median follow-up of 17.4 months, progression-free survival (PFS) at 1 and 2 years was 78.6% and 58.2%, respectively. Changes in ctDNA status showed three patterns (negative-to-negative, positive-to-negative, positive-to-positive). There was a trend toward poorer PFS with positive ctDNA both before and after surgery. Although limited by the small sample size, results suggest a possible role for tumor-informed ctDNA in detecting MRD in resected BTC and warrant further research. Purpose The purpose of this study was to evaluate the feasibility and potential clinical utility of tumor-informed ctDNA for detecting minimal residual disease in patients undergoing surgical resection for biliary tract cancer. The authors aimed to determine whether personalized ctDNA analysis could identify MRD and correlate with disease progression following curative surgery. Results Study Population and ctDNA Panels - 18 BTC patients were initially enrolled, but 1 patient was excluded due to inoperability and 3 patients were excluded due to too few tumor variants for panel design. Ultimately, 14 patients were analyzed. ctDNA Detection Before and After Surgery - Preoperative ctDNA was positive in 10 out of 14 (71.4%) patients. - Postoperative ctDNA was positive in 6 out of 12 (50.0%) patients with available samples. - The observed changes in ctDNA status were: √ Negative → Negative: 3 patients (25.0%) √ Positive → Negative: 3 patients (25.0%) √ Positive → Positive: 6 patients (50.0%) Correlation With Clinical Features and Outcomes - There was a trend toward higher preoperative ctDNA positivity in patients with advanced stage (III-IV) and node-positive disease, though not reaching statistical significance. - Among patients negative for ctDNA preoperatively, no progression was observed. - Five out of 10 preoperative ctDNA positive patients experienced disease progression. - Postoperatively, progression occurred in 3 out of 6 ctDNA positive patients versus 1 out of 6 ctDNA negative patients. Progression-Free Survival (PFS) √ After a median follow-up of 17.4 months, the 1-year PFS was 78.6% and the 2-year PFS was 58.2%. √ There was an apparent trend toward worse PFS in patients with positive ctDNA before or after surgery, though sample size limited statistical power. Conclusions This prospective study suggests that tumor-informed ctDNA analysis is feasible in surgically resected biliary tract cancer and has potential utility for detecting minimal residual disease. Positive ctDNA status both before and after surgery tended to correlate with poorer clinical outcomes, indicating that ctDNA may serve as a biomarker for residual disease and early progression. Given the small cohort size, these findings require validation in larger studies before clinical implementation. Feasibility of tumor-informed circulating tumor DNA for detecting minimal residual disease in surgically resected biliary tract cancer LINK VIEW	PLOS One	LINK _VIEW
30	2026	Clinical utility of pleural effusion supernatant cell-free DNA genotyping in previously treated patients with advanced non-small-cell lung cancer and disease progression: a multicenter retrospective study Abstract This multicenter retrospective study evaluated the clinical utility of cell-free DNA (cfDNA) fr\|om pleural effusion supernatant in detecting actionable genomic alterations in advanced non-small-cell lung cancer (NSCLC) patients with disease progression after prior therapy. cfDNA next-generation sequencing (NGS) increased the detection of driver mutations compared with standard molecular testing. Specific genomic changes such as MET copy number gain (CNG) and MYC CNG were linked to poorer outcomes, demonstrating that pleural cfDNA genotyping is a valuable tool for guiding personalized treatment strategies in NSCLC. Purpose The study aimed to assess whether pleural effusion supernatant cfDNA sequencing can improve the detection of genomic alterations in patients with advanced NSCLC who have experienced disease progression after prior treatments, and to determine the clinical relevance of these findings in informing subsequent therapy decisions. Materials and Methods -A multicenter retrospective cohort of 95 advanced NSCLC patients with disease progression after at least one line of therapy was analyzed. -Pleural effusion supernatant samples were collected and subjected to cfDNA next-generation sequencing (NGS). -The results were compared to standard molecular testing to determine the frequency of actionable mutations detected. -Associations between specific genomic alterations (e.g., EGFR mutations, MET CNG, MYC CNG) and clinical outcomes such as progression-free survival (PFS) were evaluated. Results -Initial standard molecular testing identified driver mutations in 73.7% of patients, whereas pleural effusion cfDNA NGS increased the detection rate to 85.3%. -Acquired EGFR T790M mutations were detected in a significant proportion of patients after progression on earlier EGFR tyrosine kinase inhibitors, allowing effective subsequent therapy with osimertinib. -MET CNG in patients progressing after first-line osimertinib was associated with a significantly shorter median PFS (12.0 vs. 23.0 months). -In patients progressing after second-line osimertinib, MYC CNG was linked to significantly poorer outcomes. -cfDNA fr\|om pleural effusion revealed driver mutations even when tissue or plasma tests were negative. Conclusions cfDNA obtained fr\|om pleural effusion supernatant is a feasible and informative biomarker source for genomic profiling in NSCLC patients with disease progression. This approach increases the detection of actionable mutations and provides critical insights for personalized treatment, particularly in cases where tissue or plasma genotyping may be inadequate. Clinical utility of pleural effusion supernatant cell-free DNA genotyping in previously treated patients with advanced non-small-cell lung cancer and disease progression: a multicenter retrospective study LINK VIEW	ESMO Open	LINK _VIEW