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0The paper introduces SecAct, a novel computational framework designed to infer the signaling activities of over 1,100 human secreted proteins. Traditional methods for quantifying these proteins are limited by narrow scope or non-human data, but this new model utilizes spatial transcriptomics from 1,258 tumor samples to map intercellular communication. By analyzing the spatial correlation between proteins and their target genes, SecAct accurately predicts treatment outcomes and biological responses across spatial, single-cell, and bulk datasets. The research validates the tool's effectiveness through clinical trials and proteomic data, outperforming existing models in accuracy and coverage. Furthermore, the authors demonstrate the framework's practical utility by identifying LY86 as a previously unknown antitumor regulator that enhances immunotherapy success. The study concludes that this platform provides a comprehensive resource for exploring cell-cell communication and discovering new therapeutic targets in oncology.
References:
Ru B, Gong L, Yang E, et al. Inference of secreted protein signaling activities in intercellular communication[J]. Nature methods, 2026: 1-11.
