Serum amino acids quantification by plasmonic colloidosome-coupled MALDI-TOF MS for triple-negative breast cancer diagnosis

Triple-negative breast cancer (TNBC) is characterized with high diffusion, metastasis and recurrence. The early treatment and early diagnosis of TNBC are highly important for the survival of TNBC patients. Nevertheless, traditional methods for TNBC diagnosis, i.e. imaging examinations and tissue biopsy, cannot achieve early diagnosis, are invasive and associated with the risk of arousing tumor spreading. Herein, we developed colloidosomes-coupled matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) to quantify free serum amino acids for the diagnosis of TNBC. Gold nanoparticles (AuNPs) were used to form water-in-oil colloidosomes and to encapsulate deproteinated serum for MALDI-TOF MS analysis. With small sample spot size (200-300 mu m) and densely packed AuNPs monolayer, the dried sample spot from colloidosomes can facilitate MALDI-TOF MS analysis of small molecule metabolites with high sensitivity, high reproducibility and good quantification performance. We used the method to quantify free amino acids in human serum. A cohort of 30 TNBC patients, 30 breast lump patients and 30 healthy controls were recruited for the study. It was found that the concentrations of free amino acids in TNBC patients were significantly lower than that of heathy controls, and the concentrations were also significantly different from that of breast lump patients. Based on the quantities of serum free amino acids, we have built a machine learning-based classification model to differentiate TNBC patients from the controls, including healthy controls and breast lump patients, and the sensitivity, specificity and accuracy were 95%, 100% and 97%, respectively. The assay consumes less than 1 mu L serum per analysis, and takes only minutes to analyze a sample. With the advantages of low cost, low sample consumption, high throughput in analysis and high accuracy in identification, the non-invasive liquid biopsy method is promising to be applied to clinical diagnosis of TNBC.

Automated summary

In this study, a non-invasive liquid biopsy method was developed for the diagnosis of TNBC by quantifying free serum amino acids using colloidosomes-coupled MALDI-TOF MS. A machine learning-based classification model was built based on the quantities of serum free amino acids to differentiate TNBC patients from the controls. The method showed high sensitivity, specificity, and accuracy. With the advantages of low cost, low sample consumption, high throughput in analysis, and high accuracy in identification, this method is promising for clinical diagnosis of TNBC.