Spatial Proteomics Maps Protein Distribution Across the Human Body and Cancers, Opening New Avenues for Precision Oncology
核心洞察
A landmark Nature (搜索) study maps the spatial distribution of the proteome across the human body and cancers, revealing where proteins are expressed in healthy versus malignant tissue.
Understanding protein localization opens new doors for precision drug target identification, drug repurposing with tissue-specific relevance, and better prediction of on-target/off-target effects.
Vivek Subbiah (搜索), Chief of Early-Phase Drug Development at the Sarah Cannon Research Institute (搜索), called the implications "profound" for drug development, oncology, and biomarker discovery.
A newly published study in Nature (搜索) provides a comprehensive map of the spatial distribution of the proteome across the human body and in cancers, a resource that experts say could fundamentally reshape precision oncology and drug development. The paper, titled "Spatial distribution of the proteome in the human body and in cancers," was highlighted by Vivek Subbiah (搜索), Chief of Early-Phase Drug Development at the Sarah Cannon Research Institute (搜索), who described its implications as "profound."
The study was authored by a large international collaboration led by Liang Yue, Wenhao Jiang, Sainan Li, Meng Luo, Ning Fan, Xiaolu Zhan, Rui Sun, Honghan Cheng, Zhangzhi Xue, Tong Liu, Qianhe Zhou, Kexin Chen, Tian Lu, Fang Guo, Dongwei Li, Weigang Ge, Zongxiang Nie, Mengge Lyu, Jun A, Yingrui Wang, Yingdan Chen, Zhenhai Fu, Nan Xiang, Lu Li, Fengchao Yu, Guo Ci Teo, Alexey I. Nesvizhskii, Meng Wang, Michael P. Snyder, Ben C. Collins, Qi Xiao, Ruedi Aebersold, Fei Xu, Hui Yang, Sijia Zhang, Yi Han, Yi Zhu, Yong Ji, Yan Li, and Tiannan Guo.
Why Protein Location Matters
The central scientific insight of the work is that understanding where proteins are expressed — in healthy versus malignant tissue — provides information that goes beyond simply knowing which proteins are present. According to Subbiah, this spatial dimension "opens entirely new doors" for three key applications in drug development and oncology.
First, the map supports precision drug target identification, allowing researchers to select targets based on their tissue-specific expression patterns. Second, it enables drug repurposing with tissue-specific relevance, potentially identifying new indications for existing therapies based on where their targets are expressed. Third, it improves prediction of on-target and off-target effects, which is critical for anticipating both efficacy and toxicity before a drug enters clinical testing.
Subbiah recommended the paper as "worth a deep read if you work in drug development, oncology, or biomarker discovery."
Building on Foundational Proteome Atlases
The new spatial proteome map extends a lineage of foundational work in human proteome mapping. A seminal 2015 study by Uhlén and colleagues, published in Science, constructed the first comprehensive tissue-resolved map of the human proteome, integrating RNA sequencing with antibody-based spatial profiling. That work laid the foundation for understanding the molecular details of proteome variation in disease.
Subsequent efforts expanded this framework. A 2017 study by Uhlén and colleagues produced a pathology atlas of the human cancer transcriptome, while earlier antibody-based expression profiling work by Berglund and colleagues contributed gene-centric protein expression data. Together, these resources established the methodological and conceptual groundwork for the spatial proteome mapping now reported in Nature (搜索).
Implications for Precision Medicine
The clinical significance of spatially resolved proteomics lies in its potential to refine how therapeutic targets are chosen and how drug effects are predicted. By distinguishing protein expression in malignant tissue from that in healthy tissue, the approach addresses a persistent challenge in oncology: identifying targets that are selectively relevant to tumors while minimizing harm to normal tissues.
For the pharmaceutical R&D community, the resource offers a data-driven foundation for prioritizing targets, anticipating off-target liabilities, and identifying repurposing opportunities grounded in tissue-specific protein expression. As Subbiah's commentary underscores, the work is positioned to influence early-phase drug development, oncology research, and biomarker discovery alike.