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

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

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

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Empowering LLMs for Structure-Based Drug Design via Exploration-Augmented Latent Inference

βœ‡cs.AI, q-bio.NC updates on arXiv.org
By: Xuanning Hu Β· Anchen Li Β· Qianli Xing Β· Jinglong Ji Β· Hao Tuo Β· Bo Yang
arXiv:2601.15333v2 Announce Type: replace-cross Abstract: Large Language Models (LLMs) possess strong representation and reasoning capabilities, but their application to structure-based drug design (SBDD) is limited by insufficient understanding of protein structures and unpredictable molecular generation. To address these challenges, we propose Exploration-Augmented Latent Inference for LLMs (ELILLM), a framework that reinterprets the LLM generation process as an encoding, latent space exploration, and decoding workflow. ELILLM explicitly explores portions of the design problem beyond the model's current knowledge while using a decoding module to handle familiar regions, generating chemically valid and synthetically reasonable molecules. In our implementation, Bayesian optimization guides the systematic exploration of latent embeddings, and a position-aware surrogate model efficiently predicts binding affinity distributions to inform the search. Knowledge-guided decoding further reduces randomness and effectively imposes chemical validity constraints. We demonstrate ELILLM on the CrossDocked2020 benchmark, showing strong controlled exploration and high binding affinity scores compared with seven baseline methods. These results demonstrate that ELILLM can effectively enhance LLMs capabilities for SBDD.
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Integrative spatial analysis reveals tumor heterogeneity and immune colony niche related to clinical outcomes in small cell lung cancer

βœ‡Omics In Lung
By: Haiquan Chen Β· Chaoqiang Deng Β· Jian Gao Β· Jun Wang Β· Fangqiu Fu Β· Yue Wang Β· Qiming Wang Β· Mou Zhang Β· Shiyue Zhang Β· Fanfan Fan Β· Kun Liu Β· Bo Yang Β· Qiming He Β· Qiang Zheng Β· Xuxia Shen Β· Jin Wang Β· Tao Hu Β· Changbin Zhu Β· Fei Yang Β· Yonghong He Β· Hong Hu Β· Jialei Wang Β· Yuan Li Β· Yang Zhang Β· Zhiwei Cao

Cancer Cell. 2025 Feb 14:S1535-6108(25)00030-3. doi: 10.1016/j.ccell.2025.01.012. Online ahead of print.

ABSTRACT

Recent advances have shed light on the molecular heterogeneity of small cell lung cancer (SCLC), yet the spatial organizations and cellular interactions in tumor immune microenvironment remain to be elucidated. Here, we employ co-detection by indexing (CODEX) and multi-omics profiling to delineate the spatial landscape for 165 SCLC patients, generating 267 high-dimensional images encompassing over 9.3 million cells. Integrating CODEX and genomic data reveals a multi-positive tumor cell neighborhood within ASCL1+ (SCLC-A) subtype, characterized by high SLFN11 expression and associated with poor prognosis. We further develop a cell colony detection algorithm (ColonyMap) and reveal a spatially assembled immune niche consisting of antitumoral macrophages, CD8+ T cells and natural killer T cells (MT2) which highly correlates with superior survival and predicts improving immunotherapy response in an independent cohort. This study serves as a valuable resource to study SCLC spatial heterogeneity and offers insights into potential patient stratification and personalized treatments.

PMID:39983726 | DOI:10.1016/j.ccell.2025.01.012

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