Data-Independent Acquisition Mass Spectrometry in Tumor Classification and Cancer Biomarker Research.

Area of Science:

• Proteomics and Bioinformatics
• Cancer Molecular Biology
• Biomarker Discovery

Background:

• Current cancer classification lacks detailed molecular and phenotypic insights.
• Proteins are key molecular effectors, serving as crucial cancer biomarkers and therapeutic targets.
• Omics technologies generate complex data for molecular tumor classification.

Purpose of the Study:

• To review the methodological advancements of data-independent acquisition mass spectrometry (DIA-MS) in cancer proteomics.
• To highlight DIA-MS's advantages over traditional data-dependent acquisition (DDA) for molecular tumor profiling.
• To discuss the role of DIA-MS in identifying biomarkers and therapeutic targets for improved cancer treatment.

Main Methods:

• Review of recent studies employing DIA-MS for cancer proteome analysis.
• Comparison of DIA-MS with DDA-MS in terms of data quality and biological insights.
• Emphasis on the development and utility of tissue-specific spectral libraries.

Main Results:

• DIA-MS has successfully recapitulated molecular classifications for colorectal and breast cancers.
• DIA-MS has improved molecular classification for prostate and other solid tumors.
• Validated diagnostic, prognostic, and predictive biomarkers and therapy targets have been identified using DIA-MS.

Conclusions:

• DIA-MS is a powerful tool for deep characterization of tissue proteomes and advancing cancer classification.
• Integration of DIA-MS with single-cell and spatial proteomics, alongside clinical data, offers future research directions.
• Functional and clinical validation of DIA-MS findings is essential for personalized targeted cancer therapy.