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Related Concept Videos

Proteomics01:33

Proteomics

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A proteome is the entire set of proteins that a cell type produces. We can study proteomes using the knowledge of genomes because genes code for mRNAs, and the mRNAs encode proteins. Although mRNA analysis is a step in the right direction, not all mRNAs are translated into proteins.
Proteomics is the study of proteomes' function. It involves the large-scale systematic study of the proteome to denote the protein complement expressed by a genome. Scientist Mark Wilkins coined the term...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
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Updated: Nov 4, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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Models that combine transcriptomic with spatial protein information exceed the predictive value for either single

Ioannis A Vathiotis1,2, Zhi Yang3, Jason Reeves3

  • 1Department of Pathology, Yale School of Medicine, New Haven, CT, USA.

NPJ Precision Oncology
|May 29, 2021
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Summary
This summary is machine-generated.

Combining spatial protein data with mRNA expression improves prediction of immunotherapy response in melanoma patients. This novel approach offers a more accurate companion diagnostic tool for cancer treatment selection.

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Area of Science:

  • Oncology
  • Immunotherapy
  • Biomarker Discovery

Background:

  • Immunotherapy has transformed cancer treatment, but patient benefit is limited, necessitating companion diagnostics.
  • Current companion diagnostics like PD-L1 IHC and MSI PCR lack comprehensive predictive power.
  • Integrating spatial and quantitative data may enhance predictive accuracy for immunotherapy response.

Purpose of the Study:

  • To develop and validate a combined spatial protein and mRNA expression profiling approach for predicting immunotherapy outcomes.
  • To assess the predictive performance of this integrated diagnostic compared to individual modalities.

Main Methods:

  • Utilized NanoString GeoMx® Digital Spatial Profiler for spatially resolved protein analysis.
  • Employed NanoString nCounter® PanCancer IO 360™ panel for bulk mRNA gene expression profiling.
  • Integrated both data types to build predictive models in immunotherapy-treated melanoma patients.

Main Results:

  • The combined analysis of spatial protein and mRNA expression achieved a high predictive accuracy (AUC 0.97).
  • This integrated approach outperformed models based on either protein or mRNA data alone.
  • Demonstrated proof of concept for a multi-omic companion diagnostic strategy.

Conclusions:

  • Combining spatially resolved protein and transcriptomic data offers superior prediction of clinical outcomes in cancer immunotherapy.
  • This integrated approach represents a promising advancement for developing more effective companion diagnostic tests.
  • Future development could lead to personalized treatment strategies based on multi-modal biomarker analysis.