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

Cancer treatment is far from optimal also because current classification systems do not reflect the complex molecular status of the tumor and its phenotype in sufficient detail. To construct molecular tumor classifiers, omics tools provide complex molecular data reflecting many aspects from genotype to phenotype. However, the true molecular effectors in the cells are proteins which often serve as potent cancer biomarkers and therapy targets. This review summarizes the method aspects that allowed the data-independent acquisition (DIA) mass spectrometry (MS) to outperform the traditional, data-dependent acquisition (DDA) approach in recent years. DIA-MS studies have already recapitulated molecular classification of colorectal and breast cancer, provided data improving molecular classification of prostate and other cancers, and led to validated diagnostic, prognostic, predictive biomarkers and therapy targets for common solid tumors. Tissue-specific spectral libraries are important for a deep characterization of tissue proteomes. Further perspectives of current cancer proteomics lie in the fields of single-cell and spatial proteomics and their integration with clinical data. The importance of functional and clinical validation is highlighted to allow stratified and/or personalized targeted therapy.