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Low-dose intestinal irradiation enhances the efficacy and prognosis of PD-1 blockade in metastatic non-small cell lung cancer

By: Baiyang Huang · Jiarui Zhao · Jingyu Zhu · Xingpeng Wang · Min Li · Jing Xu · Kaiyue Wang · Xiaohan Wang · Weiqing Wang · Cong Bo · Jinquan Yao · Menglin Bai · Bo Cheng · Jinming Yu · Guoxin Cai · Xue Meng

18 March 2026 at 18:00

Clin Cancer Res. 2026 Mar 18. doi: 10.1158/1078-0432.CCR-25-4153. Online ahead of print.

ABSTRACT

PURPOSE: Intestinal low-dose irradiation (ILDR) may enhance immunotherapy efficacy by modulating the gut microbiota and metabolism; however, its role in metastatic non-small cell lung cancer (mNSCLC), particularly in the first-line setting, remains unclear.

EXPERIMENTAL DESIGN: This multicenter retrospective and prospective study included mNSCLC patients receiving first- and second-line programmed cell death protein 1 (PD-1) inhibitors along with abdominopelvic radiotherapy between 2018 and 2025. Patients were stratified by the mean intestinal radiation dose into <1 Gy, 1-3 Gy, and >3 Gy groups and treatment outcomes were compared. The blood and fecal samples were subjected to multi-omics profiling.

RESULTS: g>309 patients were included in the retrospective analysis. Optimal efficacy was observed with a small intestinal mean radiation dose (SIMRD) of 1-3 Gy, showing longer progression-free survival (PFS, 10.2 months) and overall survival (OS, 22.8 months) (P < 0.01), which was consistent across subgroups. Compared with 1-3 Gy, SIMRD >3 Gy (Hazard ratio [HR] = 4.87, P < 0.001) and <1 Gy (HR = 1.85, P < 0.001) independently predicted worse OS. Prospective results confirmed the best disease control rate (P = 0.041) and PFS (P = 0.046) with SIMRD of 1-3 Gy. Responders were enriched in Bacillota, Clostridia, and indole derivatives, particularly indole-3-carboxylic acid. Moreover, the 1-3 Gy group exhibited increased circulating macrophage inflammatory protein-3α and reduced circulating α4β7+ regulatory T cells.

CONCLUSIONS: ILDR influences the efficacy of PD-1 blockade in patients with mNSCLC, particularly when SIMRD is maintained within the 1-3 Gy range, likely through modulation of the gut microbiota-metabolite-immune axis.

PMID:41849236 | DOI:10.1158/1078-0432.CCR-25-4153

CDRRM: Contrast-Driven Rubric Generation for Reliable and Interpretable Reward Modeling

cs.AI, q-bio.NC updates on arXiv.org

By: Dengcan Liu · Fengkai Yang · Xiaohan Wang · Shurui Yan · Jiajun Chai · Jiahao Li · Yikun Ban · Zhendong Mao · Wei Lin · Guojun Yin

10 March 2026 at 12:00

arXiv:2603.08035v1 Announce Type: new Abstract: Reward modeling is essential for aligning Large Language Models(LLMs) with human preferences, yet conventional reward models suffer from poor interpretability and heavy reliance on costly expert annotations. While recent rubric-based approaches enhance evaluation transparency, they lack systematic quality control, yielding noisy and redundant criteria, failing to mitigate persistent biases (e.g., verbosity, position) in LLM evaluators, and creating a scalability-reliability trade-off. To address these limitations, we propose CDRRM (Contrast-Driven Rubric Reward Model), a framework built on a novel Contrast-then-Synthesis paradigm for high-quality rubric generation and guided preference judgment. CDRRM first conducts multi-dimensional contrastive profiling on preference pairs to identify causal discriminative factors, then synthesizes these insights into compact, context-aware rubrics to guide preference judg- ments. Extensive experiments on three authoritative benchmarks (RewardBench, RMBench, RMB) demonstrate that CDRRM achieves state-of-the-art performance across diverse domains and effectively mitigates aforementioned evaluation biases. Notably, our approach delivers exceptional data efficiency: training the rubric generator on only 3k high-quality samples empowers a frozen pre-trained judge model to outperform fully fine-tuned baselines. This work offers a scalable, interpretable, and data-efficient path for reward modeling.