Absolute Quantification of Pan-Cancer Plasma Proteomes Reveals Unique Signature in Multiple Myeloma

Simple Summary: A precise mass spectrometry-based method was utilized to study proteins in the blood samples of over a thousand cancer patients. By accurately identifying and measuring protein levels using mass spectrometry, we focused on multiple myeloma and found potential markers for diagnosing the disease. These markers, including the complement C1 complex, JCHAIN, and CD5L, were combined in a prediction model with high accuracy for identifying multiple myeloma patients. Our findings could significantly impact cancer research by improving diagnostic tools. Mass spectrometry based on data-independent acquisition (DIA) has developed into a powerful quantitative tool with a variety of implications, including precision medicine. Combined with stable isotope recombinant protein standards, this strategy provides confident protein identification and precise quantification on an absolute scale. Here, we describe a comprehensive targeted proteomics approach to profile a pan-cancer cohort consisting of 1800 blood plasma samples representing 15 different cancer types. We successfully performed an absolute quantification of 253 proteins in multiplex. The assay had low intra-assay variability with a coefficient of variation below 20% (CV = 17.2%) for a total of 1013 peptides quantified across almost two thousand injections. This study identified a potential biomarker panel of seven protein targets for the diagnosis of multiple myeloma patients using differential expression analysis and machine learning. The combination of markers, including the complement C1 complex, JCHAIN, and CD5L, resulted in a prediction model with an AUC of 0.96 for the identification of multiple myeloma patients across various cancer patients. All these proteins are known to interact with immunoglobulins.

Automated summary

This study utilized mass spectrometry to study proteins in the blood samples of cancer patients and identified potential markers for diagnosing multiple myeloma. By using machine learning and differential expression analysis, a high-accuracy diagnostic model was established, which has significant implications for diagnostic tools in cancer research.