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Received today β€” 7 October 2026 ⏭

Immune-related biomarkers in liquid biopsy for cancer: emerging tools for non-invasive precision oncology

βœ‡MRD
By: Lai Wen Β· Xinhui Wang Β· Jinming Liu Β· Xinxin Pan Β· Yixin Zhang Β· Shuang Li Β· Mengying Tong Β· Yuankuan Jiang Β· Hewen Guan
29 September 2026 at 18:00

Front Cell Dev Biol. 2026 Sep 14;14:1878092. doi: 10.3389/fcell.2026.1878092. eCollection 2026.

ABSTRACT

Liquid biopsy has emerged as a powerful non-invasive tool in precision oncology, providing real-time insights into tumor evolution, host immune responses, and dynamic changes in the tumor immune microenvironment. By enabling minimally invasive sampling, it can overcome several limitations of conventional tissue biopsy. This review summarizes the major biological sources and components of liquid biopsy, including circulating tumor DNA (ctDNA), circulating tumor cells (CTCs), exosomes, and circulating immune cells, and discusses their value as dynamic indicators of interactions during cancer immunotherapy. Particular attention is given to immune-related biomarkers associated with immune checkpoints, immunosuppressive mechanisms, and immune escape, including circulating immune cell populations, and inflammatory cytokine profiles. We further examine their potential applications in predicting treatment response, monitoring immune-related adverse events, assessing minimal residual disease, and detecting acquired resistance. In addition, recent technological advances that are accelerating the clinical translation of liquid biopsy are highlighted, including multi-omics integration, microfluidic platforms. These approaches have improved the sensitivity, accuracy, and multidimensional characterization of tumor- and immune-derived biomarkers. Nevertheless, biological heterogeneity, limited assay standardization, and the lack of large-scale prospective validation studies continue to restrict widespread clinical implementation. Overall, immune-related biomarkers detected through liquid biopsy offer considerable potential for the longitudinal monitoring of the tumor immune microenvironment and may improve non-invasive cancer diagnosis, therapeutic monitoring, and personalized immunotherapy in the era of precision oncology.

PMID:42807637 | PMC:PMC13617286 | DOI:10.3389/fcell.2026.1878092

Received β€” 5 March 2026 ⏭

FlexGuard: Continuous Risk Scoring for Strictness-Adaptive LLM Content Moderation

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Zhihao Ding Β· Jinming Li Β· Ze Lu Β· Jieming Shi
4 March 2026 at 13:00
arXiv:2602.23636v2 Announce Type: replace-cross Abstract: Ensuring the safety of LLM-generated content is essential for real-world deployment. Most existing guardrail models formulate moderation as a fixed binary classification task, implicitly assuming a fixed definition of harmfulness. In practice, enforcement strictness - how conservatively harmfulness is defined and enforced - varies across platforms and evolves over time, making binary moderators brittle under shifting requirements. We first introduce FlexBench, a strictness-adaptive LLM moderation benchmark that enables controlled evaluation under multiple strictness regimes. Experiments on FlexBench reveal substantial cross-strictness inconsistency in existing moderators: models that perform well under one regime can degrade substantially under others, limiting their practical usability. To address this, we propose FlexGuard, an LLM-based moderator that outputs a calibrated continuous risk score reflecting risk severity and supports strictness-specific decisions via thresholding. We train FlexGuard via risk-alignment optimization to improve score-severity consistency and provide practical threshold selection strategies to adapt to target strictness at deployment. Experiments on FlexBench and public benchmarks demonstrate that FlexGuard achieves higher moderation accuracy and substantially improved robustness under varying strictness. We release the source code and data to support reproducibility.
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