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.