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Received today β€” 7 October 2026 ⏭

Spatial, single-nucleus and pathological profiling of the invasive front in early hepatocellular carcinoma for characterizing specific leading-edge cell niche and improving recurrence modeling

βœ‡Omics in Hepatocellular
By: Xicheng Wang Β· Xiaolan Mu Β· Yong Fu Β· Wei Dong Β· Wenfeng Lu Β· Xiuhua Li Β· Yanxiang Song Β· Changcheng Liu Β· Baoju Hou Β· Haibin Zhang Β· Xiong Chen Β· Yongchao Cai Β· Zhiying He
29 September 2026 at 18:00

Int J Biol Sci. 2026 Sep 10;22(14):8090-8118. doi: 10.7150/ijbs.137262. eCollection 2026.

ABSTRACT

The tumor leading edge (TLE) is a critical region where tumor cells interact with the microenvironment to drive invasion and metastasis; however, its cellular architecture in early hepatocellular carcinoma (HCC) remains poorly understood. Here, we integrated single-nucleus RNA-seq (snRNA-seq), spatial transcriptomics, and computational pathology to investigate TLE in early HCC. We annotated 35 cell subpopulations and identified STMN1-high tumor cells as a key malignant subset enriched at the invasive front, interacting with Treg, plasma B, LAMP3⁺ dendritic cells and SPP1⁺ macrophages. Spatial analysis revealed three co-localized cell pairs-(SPP1⁺ macrophages co-localized with Tip-like and inflammatory endothelial cells), (LAMP3⁺ DCs co-localized with naive T cells), and (plasma B cells co-localized with cancer-associated fibroblasts)-forming a leading-edge tumor microenvironment (L-TME) niche associated with early relapse. We developed an L-TME-related machine-learning benchmark framework incorporating 71 imaging features (65 deep-learning + 6 pathological) based on the snRNA-seq, spatial transcriptomics and pathomics. The pathology model achieved robust performance (mean C-index=0.77) and successfully predicted the recurrence of early HCC (log-rank p < 0.05) in TCGA (n=147) and an independent in-house cohort (n=123). This study delineates the TLE cellular ecosystem of early HCC, defines a spatially coordinated immunosuppressive L-TME niche, and provides a clinically applicable predictive tool for postoperative recurrence. Integrating multi-omics with computational pathology deepens our understanding of early HCC metastasis and offers insights into improved prognostication and therapeutic strategies.

PMID:42807944 | PMC:PMC13618224 | DOI:10.7150/ijbs.137262

Received β€” 5 March 2026 ⏭

CMI-RewardBench: Evaluating Music Reward Models with Compositional Multimodal Instruction

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Yinghao Ma Β· Haiwen Xia Β· Hewei Gao Β· Weixiong Chen Β· Yuxin Ye Β· Yuchen Yang Β· Sungkyun Chang Β· Mingshuo Ding Β· Yizhi Li Β· Ruibin Yuan Β· Simon Dixon Β· Emmanouil Benetos
5 March 2026 at 13:00
arXiv:2603.00610v2 Announce Type: replace-cross Abstract: While music generation models have evolved to handle complex multimodal inputs mixing text, lyrics, and reference audio, evaluation mechanisms have lagged behind. In this paper, we bridge this critical gap by establishing a comprehensive ecosystem for music reward modeling under Compositional Multimodal Instruction (CMI), where the generated music may be conditioned on text descriptions, lyrics, and audio prompts. We first introduce CMI-Pref-Pseudo, a large-scale preference dataset comprising 110k pseudo-labeled samples, and CMI-Pref, a high-quality, human-annotated corpus tailored for fine-grained alignment tasks. To unify the evaluation landscape, we propose CMI-RewardBench, a unified benchmark that evaluates music reward models on heterogeneous samples across musicality, text-music alignment, and compositional instruction alignment. Leveraging these resources, we develop CMI reward models (CMI-RMs), a parameter-efficient reward model family capable of processing heterogeneous inputs. We evaluate their correlation with human judgments scores on musicality and alignment on CMI-Pref along with previous datasets. Further experiments demonstrate that CMI-RM not only correlates strongly with human judgments, but also enables effective inference-time scaling via top-k filtering. The necessary training data, benchmarks, and reward models are publicly available.
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