Mass spectrometry based on Data Independent Acquisition (DIA) strategies has developed into powerful tools suitable for precision medicine applications. Assays performed in this setting provide confident protein identification and precise quantification on an absolute scale when combined with stable isotope recombinant protein standards. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1,800 blood plasma samples representing 15 different types of cancer. Using liquid biopsies, 253 proteins were absolutely quantified in multiplex. The targeted method resulted in a low intra-assay variability (CV 17.2%) and a total of 1,013 peptides were quantified across almost two thousand injections. The study identified several protein targets as potential biomarker panel for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of multiple markers, including the complement complex C1, JCHAIN and CD5L, resulted in an accurate predictive model with an AUC of 0.96 for the identification of multiple myeloma patients across a cohort of various cancer patients.

Here, we describe an application of approach in which 253 different isotope-labeled protein fragments are mixed in ratios representing the blood concentrations of the corresponding target proteins. The mixture is dried down into a salt pellet to decrease the assay volume and to facilitate storage. The samples are added onto dried mixture of isotope-labeled protein fragments followed by a standard proteomics protocol involving enzyme digestion and analysis using mass spectrometry to absolutely quantify target proteins. We show this approach is suitable for analysis of large cohort of 1800 samples with low CVs.

Human blood plasma, collected in EDTA-tubes.