ReviewAdvanced science (Weinheim, Baden-Wurttemberg, Germany)2026
Integrating Spatial Proteogenomics in Cancer Research.
Review in Advanced science (Weinheim, Baden-Wurttemberg, Germany), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Deep learning in multi-omics integration for gastrointestinal cancer biomarker discovery.Frontiers in oncology · 2026Review
- Artificial intelligence in prostate biopsy: diagnostic applications, risk stratification, and precision oncology.Frontiers in oncology · 2026Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
14 authors.
Funding
Abstract
backgroundSpatial proteogenomics marks a paradigm shift in oncology by integrating molecular analysis with spatial information from both spatial proteomics and other data modalities (e.g., spatial transcriptomics), thereby unveiling tumor heterogeneity and dynamic changes in the microenvironment.
methodsWe systematically reviewed the evolution of spatial proteogenomics, from single-modality profiling to integration with transcriptomics and metabolomics, from the detection of abundant proteins to exploration of "dark proteome" with low abundance or stability, and from analytic software based on traditional machine learning algorithms to advanced artificial intelligence-driven analytical frameworks.
resultsKey advances of sub-fields of spatial proteogenomics include: RNA-protein co-localization: Spatial CITE-seq, enabling RNA-protein co-localization to reveal immune microenvironmental patterns and neoantigen distribution. Spatial Proteomics + Spatial Metabolomics: Matrix-assisted laser desorption/ionization imaging (MALDI), overcoming protein detection bottlenecks and capturing metabolic reprogramming. Deep visual proteomics (DVP): achieving unbiased spatial analysis via AI-guided microdissection. Spatial-aware multiplex dark proteome approaches: Examples are nanodroplet processing in one pot for trace samples (NanoPOTS) and proteoform imaging mass spectrometry (PiMS). Multimodal foundation AI models: Examples are KRONOS and HEIST, which integrate multiple data modalities and significantly improve diagnostic precision and therapeutic prediction. CONCLUSIONS AND FUTURE DIRECTIONS: Despite challenges of resolution, standardization, and data complexity, spatial proteomics is advancing rapidly. Together with frontier technologies such as quantum computing, live imaging, and organoid integration, it is driving breakthroughs in cancer diagnosis, personalized immunotherapy, and drug development.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.