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Received β€” 10 September 2026 ⏭

Targeting KRAS reprograms a Treg-dominant immunosuppressive microenvironment and sensitizes KRAS-mutant gastric adenocarcinoma to CTLA-4 immunotherapy

βœ‡(Multiomics OR Omics) AND (Lung OR gastric OR Hepatocellular)
By: Xiaohuan Tang Β· Peiyao Yang Β· Xiaoyong Guo Β· Shuhua Wei Β· Xiaojing Cheng Β· Mengyuan Li Β· Yigang Bao Β· Sachiyo Nomura Β· Yu Sun Β· Longtao Huangfu Β· Xiaofang Xing Β· Yongning Jia Β· Fei Shan Β· Xiangyu Gao Β· Chuanhui Han Β· Ziyu Li
9 September 2026 at 18:00

Sci China Life Sci. 2026 Sep 3. doi: 10.1007/s11427-026-3438-4. Online ahead of print.

ABSTRACT

Oncogenic KRAS mutations define a distinct molecular subset of gastric adenocarcinoma (GA), yet their impact on the tumor immune microenvironment remains incompletely understood. In this study, we established a genetically faithful and immunocompetent KRASG12D-driven mouse model of GA, together with matched organoids and cell lines, to investigate how oncogenic KRAS shapes tumor-immune interactions. KRAS-mutant tumors consistently developed an immunosuppressive microenvironment characterized by enrichment of regulatory T cells (Tregs), accompanied by reduced cytotoxic lymphocyte infiltration and intrinsic resistance to PD-1 blockade. Although pharmacologic targeting of KRAS effectively suppressed tumor growth and increased immune cell infiltration, functional immune analyses revealed persistent Treg-mediated immunosuppression that limited effective antitumor immunity. Mechanistically, TGF-Ξ² signaling was required to maintain Treg dominance and suppress effector T cell function in KRAS-driven tumors. Importantly, disruption of this suppressive axis through combined KRAS inhibition and CTLA-4 blockade attenuated TGF-Ξ² activity, impaired Treg function, and enhanced antitumor immune responses in vivo. Collectively, these findings identify oncogenic KRAS as a key regulator of TGF-Ξ²-dependent immune suppression in GA and provide mechanistic insight into immune evasion within this molecular subtype.

PMID:42714795 | DOI:10.1007/s11427-026-3438-4

Received β€” 17 March 2026 ⏭

Visual-ERM: Reward Modeling for Visual Equivalence

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Ziyu Liu Β· Shengyuan Ding Β· Xinyu Fang Β· Xuanlang Dai Β· Penghui Yang Β· Jianze Liang Β· Jiaqi Wang Β· Kai Chen Β· Dahua Lin Β· Yuhang Zang
16 March 2026 at 12:00
arXiv:2603.13224v1 Announce Type: cross Abstract: Vision-to-code tasks require models to reconstruct structured visual inputs, such as charts, tables, and SVGs, into executable or structured representations with high visual fidelity. While recent Large Vision Language Models (LVLMs) achieve strong results via supervised fine-tuning, reinforcement learning remains challenging due to misaligned reward signals. Existing rewards either rely on textual rules or coarse visual embedding similarity, both of which fail to capture fine-grained visual discrepancies and are vulnerable to reward hacking. We propose Visual Equivalence Reward Model (Visual-ERM), a multimodal generative reward model that provides fine-grained, interpretable, and task-agnostic feedback to evaluate vision-to-code quality directly in the rendered visual space. Integrated into RL, Visual-ERM improves Qwen3-VL-8B-Instruct by +8.4 on chart-to-code and yields consistent gains on table and SVG parsing (+2.7, +4.1 on average), and further strengthens test-time scaling via reflection and revision. We also introduce VisualCritic-RewardBench (VC-RewardBench), a benchmark for judging fine-grained image-to-image discrepancies on structured visual data, where Visual-ERM at 8B decisively outperforms Qwen3-VL-235B-Instruct and approaches leading closed-source models. Our results suggest that fine-grained visual reward supervision is both necessary and sufficient for vision-to-code RL, regardless of task specificity.
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