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Received today — 7 October 2026 ⏭

A streamlined hybrid-capture and genome-wide multi-omic platform for highly sensitive ctDNA minimal residual disease monitoring

✇Omics in Hepatocellular
By: Tu Nguyen · Van-Anh Nguyen Hoang · Cang Huynh · Vy Nguyen · Son-Nam H Le · Quang Thinh Trac · Thien-Phuc Nguyen Hoang · Nhat-Anh Dao · Minh Tran Binh Nguyen · Nam Hb Tran · Duy Minh Phan · Hoa Giang · Hoai-Nghia Nguyen · Lan N Tu
5 October 2026 at 18:00

J Liq Biopsy. 2026 Sep 19;14:100496. doi: 10.1016/j.jlb.2026.100496. eCollection 2026 Dec.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) analysis has revolutionized minimal residual disease (MRD) monitoring, but conventional tumor-informed amplicon-based sequencing (AMP) is limited by the narrow variant capacity and diversity. Hybrid capture-based sequencing (HYB) is more versatile and enables both tumor-informed and tumor-naïve liquid biopsy profiling.

METHODS: We analytically validated the performance of our novel HYB workflow and VarSURE variant calling pipeline, using reference standards (n = 6), plasma samples of cancer patients (n = 75) and healthy donors (n = 90). Genome-wide (GW) non-mutation features including copy number alterations, fragmentomics, and end-motif signatures were also evaluated to enhance ctDNA-MRD detection. Clinical performance was directly compared against our legacy AMP method (K-TRACK, Gene Solutions), using pre-treatment blood samples across multiple cancers (n = 290) and longitudinal cohorts of colorectal cancer (CRC, n = 64), and hepatocellular carcinoma (HCC, n = 47).

RESULTS: Optimal parameters to maximize assay performance included single-stranded DNA ligation technology, cfDNA input ≥ 15 ng, post-UMI sequencing depth ≥ 2500X, and high number of tracked mutations. In the tumor-informed setting, the HYB workflow was modestly better than the AMP method in detection of pre-treatment ctDNA; addition of GW features was marginally beneficial except in lung cancer. Surveillance ctDNA determined by the HYB workflow had superior sensitivity to predict recurrence in both CRC (AMP: 90.0%, HYB: 100%) and HCC (AMP: 80.0%, HYB: 96.0%). In the tumor-naïve setting, the performance gap widened significantly, and the combined HYB and GW workflow showed the highest performance in baseline ctDNA detection across all cancers, and achieved sensitivity of 90.0% and 92.0% to detect recurrence in CRC and HCC respectively.

CONCLUSIONS: The new methodology offers a streamlined and scalable solution for both comprehensive liquid biopsy profiling and longitudinal MRD tracking in routine clinical practice.

PMID:42830887 | PMC:PMC13634064 | DOI:10.1016/j.jlb.2026.100496

A streamlined hybrid-capture and genome-wide multi-omic platform for highly sensitive ctDNA minimal residual disease monitoring

✇MRD
By: Tu Nguyen · Van-Anh Nguyen Hoang · Cang Huynh · Vy Nguyen · Son-Nam H Le · Quang Thinh Trac · Thien-Phuc Nguyen Hoang · Nhat-Anh Dao · Minh Tran Binh Nguyen · Nam Hb Tran · Duy Minh Phan · Hoa Giang · Hoai-Nghia Nguyen · Lan N Tu
5 October 2026 at 18:00

J Liq Biopsy. 2026 Sep 19;14:100496. doi: 10.1016/j.jlb.2026.100496. eCollection 2026 Dec.

ABSTRACT

BACKGROUND: Circulating tumor DNA (ctDNA) analysis has revolutionized minimal residual disease (MRD) monitoring, but conventional tumor-informed amplicon-based sequencing (AMP) is limited by the narrow variant capacity and diversity. Hybrid capture-based sequencing (HYB) is more versatile and enables both tumor-informed and tumor-naïve liquid biopsy profiling.

METHODS: We analytically validated the performance of our novel HYB workflow and VarSURE variant calling pipeline, using reference standards (n = 6), plasma samples of cancer patients (n = 75) and healthy donors (n = 90). Genome-wide (GW) non-mutation features including copy number alterations, fragmentomics, and end-motif signatures were also evaluated to enhance ctDNA-MRD detection. Clinical performance was directly compared against our legacy AMP method (K-TRACK, Gene Solutions), using pre-treatment blood samples across multiple cancers (n = 290) and longitudinal cohorts of colorectal cancer (CRC, n = 64), and hepatocellular carcinoma (HCC, n = 47).

RESULTS: Optimal parameters to maximize assay performance included single-stranded DNA ligation technology, cfDNA input ≥ 15 ng, post-UMI sequencing depth ≥ 2500X, and high number of tracked mutations. In the tumor-informed setting, the HYB workflow was modestly better than the AMP method in detection of pre-treatment ctDNA; addition of GW features was marginally beneficial except in lung cancer. Surveillance ctDNA determined by the HYB workflow had superior sensitivity to predict recurrence in both CRC (AMP: 90.0%, HYB: 100%) and HCC (AMP: 80.0%, HYB: 96.0%). In the tumor-naïve setting, the performance gap widened significantly, and the combined HYB and GW workflow showed the highest performance in baseline ctDNA detection across all cancers, and achieved sensitivity of 90.0% and 92.0% to detect recurrence in CRC and HCC respectively.

CONCLUSIONS: The new methodology offers a streamlined and scalable solution for both comprehensive liquid biopsy profiling and longitudinal MRD tracking in routine clinical practice.

PMID:42830887 | PMC:PMC13634064 | DOI:10.1016/j.jlb.2026.100496

Received — 5 March 2026 ⏭

LifeBench: A Benchmark for Long-Horizon Multi-Source Memory

✇cs.AI, q-bio.NC updates on arXiv.org
By: Zihao Cheng · Weixin Wang · Yu Zhao · Ziyang Ren · Jiaxuan Chen · Ruiyang Xu · Shuai Huang · Yang Chen · Guowei Li · Mengshi Wang · Yi Xie · Ren Zhu · Zeren Jiang · Keda Lu · Yihong Li · Xiaoliang Wang · Liwei Liu · Cam-Tu Nguyen
5 March 2026 at 13:00
arXiv:2603.03781v1 Announce Type: new Abstract: Long-term memory is fundamental for personalized agents capable of accumulating knowledge, reasoning over user experiences, and adapting across time. However, existing memory benchmarks primarily target declarative memory, specifically semantic and episodic types, where all information is explicitly presented in dialogues. In contrast, real-world actions are also governed by non-declarative memory, including habitual and procedural types, and need to be inferred from diverse digital traces. To bridge this gap, we introduce Lifebench, which features densely connected, long-horizon event simulation. It pushes AI agents beyond simple recall, requiring the integration of declarative and non-declarative memory reasoning across diverse and temporally extended contexts. Building such a benchmark presents two key challenges: ensuring data quality and scalability. We maintain data quality by employing real-world priors, including anonymized social surveys, map APIs, and holiday-integrated calendars, thus enforcing fidelity, diversity and behavioral rationality within the dataset. Towards scalability, we draw inspiration from cognitive science and structure events according to their partonomic hierarchy; enabling efficient parallel generation while maintaining global coherence. Performance results show that top-tier, state-of-the-art memory systems reach just 55.2\% accuracy, highlighting the inherent difficulty of long-horizon retrieval and multi-source integration within our proposed benchmark. The dataset and data synthesis code are available at https://github.com/1754955896/LifeBench.
Received — 17 February 2026 ⏭

AXE: An Agentic eXploit Engine for Confirming Zero-Day Vulnerability Reports

✇cs.AI, q-bio.NC updates on arXiv.org
By: Amirali Sajadi · Tu Nguyen · Kostadin Damevski · Preetha Chatterjee
17 February 2026 at 13:00
arXiv:2602.14345v1 Announce Type: cross Abstract: Vulnerability detection tools are widely adopted in software projects, yet they often overwhelm maintainers with false positives and non-actionable reports. Automated exploitation systems can help validate these reports; however, existing approaches typically operate in isolation from detection pipelines, failing to leverage readily available metadata such as vulnerability type and source-code location. In this paper, we investigate how reported security vulnerabilities can be assessed in a realistic grey-box exploitation setting that leverages minimal vulnerability metadata, specifically a CWE classification and a vulnerable code location. We introduce Agentic eXploit Engine (AXE), a multi-agent framework for Web application exploitation that maps lightweight detection metadata to concrete exploits through decoupled planning, code exploration, and dynamic execution feedback. Evaluated on the CVE-Bench dataset, AXE achieves a 30% exploitation success rate, a 3x improvement over state-of-the-art black-box baselines. Even in a single-agent configuration, grey-box metadata yields a 1.75x performance gain. Systematic error analysis shows that most failed attempts arise from specific reasoning gaps, including misinterpreted vulnerability semantics and unmet execution preconditions. For successful exploits, AXE produces actionable, reproducible proof-of-concept artifacts, demonstrating its utility in streamlining Web vulnerability triage and remediation. We further evaluate AXE's generalizability through a case study on a recent real-world vulnerability not included in CVE-Bench.
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