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Received β€” 2 April 2026 ⏭

Circulating Tumor DNA in Cholangiocarcinoma: A Precision Oncology Roadmap

βœ‡MRD
By: Haixing Wei Β· Jie Wang Β· Qing Wu Β· Mengbin Qin
26 March 2026 at 18:00

Cancer Manag Res. 2026 Feb 6;18:574678. doi: 10.2147/CMAR.S574678. eCollection 2026.

ABSTRACT

Cholangiocarcinoma (CCA) is a rare but aggressive malignancy with a rising global incidence and few therapeutic options for advanced disease. In recent decades, precision oncology for CCA has advanced rapidly, particularly through the development of targeted therapies for patients with actionable genetic alterations. These therapies have markedly prolonged survival and improved other clinical outcomes among patients with unresectable, advanced CCA. The implementation of precision oncology largely depends on detecting genetic mutations to guide patient selection and treatment, using tumor tissue biopsies or liquid biopsies, including circulating tumor DNA (ctDNA) from blood or bile. As a minimally invasive biomarker, ctDNA shows great promise for transforming the clinical management of CCA. This review provides a comprehensive overview of the roles of ctDNA in CCA, including early detection, prognostic stratification, minimal residual disease assessment, recurrence monitoring, therapeutic target identification, and treatment response evaluation. A synthesis of existing studies indicates that bile-derived ctDNA shows superior sensitivity compared with blood-based ctDNA in capturing the genetic profiles and heterogeneity of CCA. We also propose an integrative framework that illustrates how ctDNA profiling can inform diagnosis, treatment, and surveillance across the disease continuum. Because research on ctDNA in CCA remains in its infancy, we discuss current challenges and outline future directions for translating these findings into clinical practice. Collectively, the evidence positions ctDNA-particularly bile-derived ctDNA-as a dynamic tool for real-time genomic profiling, sensitive residual disease detection, and therapy monitoring. This integrative framework provides a roadmap for translating these capabilities into clinical practice, with the potential to enable earlier, more personalized interventions and improve outcomes for patients with CCA.

PMID:41883993 | PMC:PMC13012645 | DOI:10.2147/CMAR.S574678

Received β€” 25 February 2026 ⏭

Shuffle-R1: Efficient RL framework for Multimodal Large Language Models via Data-centric Dynamic Shuffle

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Linghao Zhu Β· Yiran Guan Β· Dingkang Liang Β· Jianzhong Ju Β· Zhenbo Luo Β· Bin Qin Β· Jian Luan Β· Yuliang Liu Β· Xiang Bai
24 February 2026 at 13:00
arXiv:2508.05612v5 Announce Type: replace-cross Abstract: Reinforcement learning (RL) has emerged as an effective post-training paradigm for enhancing the reasoning capabilities of multimodal large language model (MLLM). However, current RL pipelines often suffer from training inefficiencies caused by two underexplored issues: Advantage Collapsing, where most advantages in a batch concentrate near zero, and Rollout Silencing, where the proportion of rollouts contributing non-zero gradients diminishes over time. These issues lead to suboptimal gradient updates and hinder long-term learning efficiency. To address these issues, we propose Shuffle-R1, a simple yet principled framework that improves RL fine-tuning efficiency by dynamically restructuring trajectory sampling and batch composition. It introduces (1) Pairwise Trajectory Sampling, which selects high-contrast trajectories with large advantages to improve gradient signal quality, and (2) Advantage-based Trajectory Shuffle, which increases exposure of valuable rollouts through informed batch reshuffling. Experiments across multiple reasoning benchmarks show that our framework consistently outperforms strong RL baselines with minimal overhead. These results highlight the importance of data-centric adaptations for more efficient RL training in MLLM.
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