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Received — 5 March 2026 ⏭ Favourites

Exploration of multi-omics liquid biopsy approaches for multi-cancer early detection: The PROMISE study

✇(Multiomics OR Omics) AND (Pancreatic)
By: Jianchun Duan · Qiang Gao · Zhijie Wang · Jiachen Xu · Yuzi Zhang · Yanan Wang · Xi Yang · Lei Zhang · Yu Xu · Qiaoxia Zhou · Bo Yang · Xingyu Yang · Guoqiang Wang · Jing Zhao · Xuefei Wang · Di Ge · Lijie Tan · Wenju Chang · Jianmin Xu · Pinghong Zhou · Mengjiang He · Yuanlin Song · Jie Hu · Wenhui Lou · Wenchuan Wu · Tingyan Shi · Rongyu Zang · Yuan Ji · Sunfang Jiang · Youpei Lin · Liangqing Dong · Shangli Cai · Jia Fan · Jie Wang
25 February 2026 at 19:00

Innovation (Camb). 2025 Aug 6;7(1):101076. doi: 10.1016/j.xinn.2025.101076. eCollection 2026 Jan 5.

ABSTRACT

Although circulating cell-free DNA (cfDNA) methylation has emerged as the mainstream approach in multi-cancer detection blood tests (MCDBTs), the potential of integrating proteins and mutations, to enhance its performance remains unclear. The PROMISE study (NCT04972201) was conducted to investigate the feasibility of a multi-omics integration strategy in MCDBTs across nine types of cancers in head and neck (excluding nasopharynx), esophagus, lung, stomach, liver, biliary tract, pancreas, colorectum, and ovary. Blood samples were prospectively collected from 1,706 participants (840 non-cancer; 866 cancer) and then randomly divided into training and validation sets. The complementarity between various omics were investigated, and specific omics features were carefully selected for further multimodal model construction. The methylation-based classifier outperformed both the mutation-based and protein-based classifiers. As 95.0% of cancer cases detected by the mutation-based classifier were simultaneously identified by the methylation-based classifier, while 14.0% of the protein-positive samples were missed, protein markers may provide complementary value to the methylation-based classifier. Compared with the methylation-based classifier, the multimodal classifier combining methylation and protein features exhibited an improved sensitivity of 75.1% (95% confidence interval [CI], 69.3%-80.3%) at the same specificity of 98.8% with the accuracy of top predicted origin (TPO1) of 73.1% (95% CI, 66.2%-79.2%). Notably, the TPO1 accuracy reached 100% in liver and ovarian cancers with negative results of the methylation-based classifier. Collectively, these data suggest that the integration of protein markers in the multimodal classifier can offer additional benefits to the methylation-based classifier, particularly in identifying liver and ovarian cancers.

PMID:41737326 | PMC:PMC12925926 | DOI:10.1016/j.xinn.2025.101076

Received — 27 January 2026 ⏭ Favourites

From Specialist to Generalist: Unlocking SAM's Learning Potential on Unlabeled Medical Images

✇cs.AI, q-bio.NC updates on arXiv.org
By: Vi Vu · Thanh-Huy Nguyen · Tien-Thinh Nguyen · Ba-Thinh Lam · Hoang-Thien Nguyen · Tianyang Wang · Xingjian Li · Min Xu
27 January 2026 at 13:00
arXiv:2601.17934v1 Announce Type: cross Abstract: Foundation models like the Segment Anything Model (SAM) show strong generalization, yet adapting them to medical images remains difficult due to domain shift, scarce labels, and the inability of Parameter-Efficient Fine-Tuning (PEFT) to exploit unlabeled data. While conventional models like U-Net excel in semi-supervised medical learning, their potential to assist a PEFT SAM has been largely overlooked. We introduce SC-SAM, a specialist-generalist framework where U-Net provides point-based prompts and pseudo-labels to guide SAM's adaptation, while SAM serves as a powerful generalist supervisor to regularize U-Net. This reciprocal guidance forms a bidirectional co-training loop that allows both models to effectively exploit the unlabeled data. Across prostate MRI and polyp segmentation benchmarks, our method achieves state-of-the-art results, outperforming other existing semi-supervised SAM variants and even medical foundation models like MedSAM, highlighting the value of specialist-generalist cooperation for label-efficient medical image segmentation. Our code is available at https://github.com/vnlvi2k3/SC-SAM.
Received — 14 January 2026 ⏭ Favourites

Imaging-anchored Multiomics in Cardiovascular Disease: Integrating Cardiac Imaging, Bulk, Single-cell, and Spatial Transcriptomics

✇cs.AI, q-bio.NC updates on arXiv.org
By: Minh H. N. Le · Tuan Vinh · Thanh-Huy Nguyen · Tao Li · Bao Quang Gia Le · Han H. Huynh · Monika Raj · Carl Yang · Min Xu · Nguyen Quoc Khanh Le
14 January 2026 at 13:00
arXiv:2601.07871v1 Announce Type: cross Abstract: Cardiovascular disease arises from interactions between inherited risk, molecular programmes, and tissue-scale remodelling that are observed clinically through imaging. Health systems now routinely generate large volumes of cardiac MRI, CT and echocardiography together with bulk, single-cell and spatial transcriptomics, yet these data are still analysed in separate pipelines. This review examines joint representations that link cardiac imaging phenotypes to transcriptomic and spatially resolved molecular states. An imaging-anchored perspective is adopted in which echocardiography, cardiac MRI and CT define a spatial phenotype of the heart, and bulk, single-cell and spatial transcriptomics provide cell-type- and location-specific molecular context. The biological and technical characteristics of these modalities are first summarised, and representation-learning strategies for each are outlined. Multimodal fusion approaches are reviewed, with emphasis on handling missing data, limited sample size, and batch effects. Finally, integrative pipelines for radiogenomics, spatial molecular alignment, and image-based prediction of gene expression are discussed, together with common failure modes, practical considerations, and open challenges. Spatial multiomics of human myocardium and atherosclerotic plaque, single-cell and spatial foundation models, and multimodal medical foundation models are collectively bringing imaging-anchored multiomics closer to large-scale cardiovascular translation.
Received — 26 June 2025 ⏭ Favourites

Rescuing dendritic cell interstitial motility sustains antitumour immunity

✇Nature - Issue - nature.com science feeds
By: Haichao Tang · Zongfang Wei · Bei Zheng · Yumeng Cai · Peihan Wu · Lulu Wu · Xiaohe Ma · Yanqin Chen · Si Su · Jinmin Xu · Yu Qiao · Ying Zhang · Juju Miao · Zijing Yu · Yaodong Zhao · Zhen Xia · Rongjing Zhou · Jian Liu · Jufeng Guo · Zhaoyuan Liu · Qi Xie · Florent Ginhoux · Luming Zhao · Xu Li · Bing Xia · Huanwen Wu · Yongdeng Zhang · Ting Zhou
25 June 2025 at 08:00

Nature, Published online: 25 June 2025; doi:10.1038/s41586-025-09202-9

Disruption of dendritic cell (DC) interstitial motility in the tumour microenvironment promotes immune evasion, and enhancement of DC interstitial motility offers a route for DC-centric immunotherapy.
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