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Liquid Biopsy in Non-Metastatic Prostate Cancer: Clinical Evidence and Future Directions

Cancers (Basel). 2026 Feb 28;18(5):800. doi: 10.3390/cancers18050800.

ABSTRACT

BACKGROUND AND OBJECTIVE: Liquid biopsy has transformed the management of advanced prostate cancer, yet its clinical role in non-metastatic disease remains uncertain. Conventional biomarkers such as PSA, imaging, and pathology have limited ability to capture minimal residual disease and biological aggressiveness. The objective of this review was to critically evaluate the current evidence on circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) in non-metastatic prostate cancer, focusing on feasibility, prognostic value, and potential clinical applications.

METHODS: A narrative review of PubMed-indexed original studies evaluating liquid biopsy in clinically localized or non-metastatic prostate cancer was performed. Eligible studies included patients treated with curative-intent local therapy or experiencing biochemical recurrence without radiologic metastases. Study designs were predominantly prospective or retrospective observational cohorts. Liquid biopsy analytes included CTCs and ctDNA assessed from peripheral blood plasma using EpCAM-based enrichment, targeted next-generation sequencing, whole-genome sequencing, or ultra-sensitive tumor-informed assays. Primary outcomes included detection rates, associations with clinicopathologic features, biochemical recurrence, metastasis-free survival, and overall survival. Key Findings and Limitations: Across 11 studies, CTC detection using EpCAM-based platforms was infrequent in localized disease and biochemical recurrence and showed limited prognostic value (10-11% in preoperative settings). In contrast, ctDNA was detectable in a minority of patients but consistently identified biologically aggressive disease and a higher risk of recurrence when present, particularly using tumor-informed ultra-sensitive assays. Limitations include low detection rates, heterogeneous methodologies, small sample sizes, and predominantly exploratory study designs.

CONCLUSIONS AND CLINICAL IMPLICATIONS: Currently, its most promising application is not broad screening, but as a selective, biology-driven tool for detecting minimal residual disease and refining risk assessment. CtDNA acts as a biological risk modifier, potentially guiding the escalation or de-escalation of adjuvant therapy. However, prospective biomarker-driven trials are required to validate these strategies before routine clinical implementation.

PMID:41827734 | PMC:PMC12984391 | DOI:10.3390/cancers18050800

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Harnessing pyroptosis in breast cancer therapy: immunological mechanisms and emerging biomaterial strategies

Cell Death Discovery, Published online: 12 March 2026; doi:10.1038/s41420-026-02996-1

Harnessing pyroptosis in breast cancer therapy: immunological mechanisms and emerging biomaterial strategies
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A large-scale coherent 4D imaging sensor

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10183-6

A 4D imaging architecture using a large-scale, coherent LiDAR focal plane array comprising more than 0.6 million photonic components and associated electronics integrated on-chip can obtain coherent images at high frame rates over useful distances.
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Multimodal electron microscopy of halide perovskite interfacial dynamics

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10238-8

A multimodal in situ electron microscopy approach enables direct visualization of structural and chemical evolution in a working halide perovskite light-emitting diode with nanometre precision.
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A sorghum pangenome reference improves global crop trait discovery

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10229-9

A pangenome reference for the phenotypically diverse crop sorghum aims to help accelerate future efforts to breed crops that are better adapted to changing environments.
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Author Correction: Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study

Nature Medicine, Published online: 11 March 2026; doi:10.1038/s41591-026-04288-8

Author Correction: Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study
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Nature Medicine, Published online: 11 March 2026; doi:10.1038/s41591-026-04241-9

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Gene conversion empowers natural selection in a clonal fish species

Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10180-9

Analysis of the asexually reproducing Amazon molly Poecilia formosa and its sexually reproducing progenitors Poecilia mexicana and Poecilia latipinna reveals that it maintains a divergent mutational landscape and has evaded functional mutational decay via gene conversion.
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Nature, Published online: 11 March 2026; doi:10.1038/s41586-026-10256-6

Myelopoiesis in response to a parasitic worm infection and the mechanism selective to this form of parasite are revealed.
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A Novel Multi-Agent Architecture to Reduce Hallucinations of Large Language Models in Multi-Step Structural Modeling

arXiv:2603.07728v1 Announce Type: new Abstract: Large language models (LLMs) such as GPT and Gemini have demonstrated remarkable capabilities in contextual understanding and reasoning. The strong performance of LLMs has sparked growing interest in leveraging them to automate tasks traditionally dependent on human expertise. Recently, LLMs have been integrated into intelligent agents capable of operating structural analysis software (e.g., OpenSees) to construct structural models and perform analyses. However, existing LLMs are limited in handling multi-step structural modeling due to frequent hallucinations and error accumulation during long-sequence operations. To this end, this study presents a novel multi-agent architecture to automate the structural modeling and analysis using OpenSeesPy. First, problem analysis and construction planning agents extract key parameters from user descriptions and formulate a stepwise modeling plan. Node and element agents then operate in parallel to assemble the frame geometry, followed by a load assignment agent. The resulting geometric and load information is translated into executable OpenSeesPy scripts by code translation agents. The proposed architecture is evaluated on a benchmark of 20 frame problems over ten repeated trials, achieving 100% accuracy in 18 cases and 90% in the remaining two. The architecture also significantly improves computational efficiency and demonstrates scalability to larger structural systems.
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Visualizing Coalition Formation: From Hedonic Games to Image Segmentation

arXiv:2603.07890v1 Announce Type: new Abstract: We propose image segmentation as a visual diagnostic testbed for coalition formation in hedonic games. Modeling pixels as agents on a graph, we study how a granularization parameter shapes equilibrium fragmentation and boundary structure. On the Weizmann single-object benchmark, we relate multi-coalition equilibria to binary protocols by measuring whether the converged coalitions overlap with a foreground ground-truth. We observe transitions from cohesive to fragmented yet recoverable equilibria, and finally to intrinsic failure under excessive fragmentation. Our core contribution links multi-agent systems with image segmentation by quantifying the impact of mechanism design parameters on equilibrium structures.
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