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<p>Liquid biopsy has emerged as a transformative development in oncology, enabling the minimally invasive detection and monitoring of cancer through the analysis of tumor-derived material in blood. Moving beyond single-variable analysis, multifeature sequencing-based liquid biopsy (MSLB) integrates diverse classes of data from a single blood sample to provide deep multifactorial insight into tumor biology. In this review, MSLB is defined as the extraction of multiple biological signals from a single sequencing dataset and is put in the context of other layers of multimodal diagnostics. We focus on how recent advances in patient-, and potentially microbe-derived, cell-free nucleic acid analysis expand the biological information that can be extracted from a single blood sample. MSLB enables this by allowing the concurrent assessment of, for example, DNA methylation, copy number, fragmentation, and, in exploratory workflows, microbe-associated signals. This provides a broader view of tumor, immune, and microenvironment states. When combined with emerging bioinformatic and machine-learning frameworks, these complementary signals may improve early detection, disease monitoring, and treatment selection. Addressing challenges in standardization, validation, and regulatory alignment will be essential to determine how MSLB can be integrated into routine oncologic practice.</p>

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Multifeature sequencing-based liquid biopsy for cancer diagnosis and monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月1日

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  • Mariano A. Molina,
  • Martina De Simoni,
  • Norbert Moldovan,
  • Florent Mouliere,
  • Daniel W. Hagey

摘要

Liquid biopsy has emerged as a transformative development in oncology, enabling the minimally invasive detection and monitoring of cancer through the analysis of tumor-derived material in blood. Moving beyond single-variable analysis, multifeature sequencing-based liquid biopsy (MSLB) integrates diverse classes of data from a single blood sample to provide deep multifactorial insight into tumor biology. In this review, MSLB is defined as the extraction of multiple biological signals from a single sequencing dataset and is put in the context of other layers of multimodal diagnostics. We focus on how recent advances in patient-, and potentially microbe-derived, cell-free nucleic acid analysis expand the biological information that can be extracted from a single blood sample. MSLB enables this by allowing the concurrent assessment of, for example, DNA methylation, copy number, fragmentation, and, in exploratory workflows, microbe-associated signals. This provides a broader view of tumor, immune, and microenvironment states. When combined with emerging bioinformatic and machine-learning frameworks, these complementary signals may improve early detection, disease monitoring, and treatment selection. Addressing challenges in standardization, validation, and regulatory alignment will be essential to determine how MSLB can be integrated into routine oncologic practice.