A stepwise data interpretation process for renal amyloidosis typing by LMD-MS

Abstract

Abstract Backgrounds Systemic amyloidosis is classified according to the deposited amyloid fibril protein (AFP), which determines its best therapeutic scheme. The most common type of AFP found are immunoglobulin light chains. The laser microdissection combined with mass spectrometry (LMD-MS) technique is a promising approach for precise typing of amyloidosis, however, the major difficulty in interpreting the MS data is how to accurately identify the precipitated AFP from background. Objectives The objective of the present study is to establish a complete data interpretation procedure for LMD-MS based amyloidosis typing. Methods Formalin-fixed paraffin-embedded specimens from patients with renal amyloidosis and non-amyloid nephropathies (including diabetic nephropathy, fibrillary glomerulonephritis, IgA nephropathy, lupus nephritis, membranous nephropathy, and normal tissue adjacent to tumors) were analyzed by LMD-MS. Forty-two specimens were used to train the data interpretation procedure, which was validated by another 50 validation specimens. Area under receiver operating curve (AUROC) analysis of amyloid accompanying proteins (AAPs, including apolipoprotein A-IV, apolipoprotein E and serum amyloid P-component) for discriminating amyloidosis from non-amyloid nephropathies was performed. Results A stepwise data interpretation procedure that includes or excludes the types of amyloidosis group by group was established. The involvement of AFPs other than immunoglobulin was determined by P-score, as well as immunoglobulin light chain by variable of λ-κ, and immunoglobulin heavy chain by H-score. This achieved a total of 88% accuracy in 50 validation specimens. The AAPs showed significantly different expression levels between amyloidosis specimens and non-amyloid nephropathies. Each of the single AAP had a AUROC value more than 0.9 for diagnosis of amyloidosis from non-amyloid control, and the averaged level of the three AAPs showed the highest AUROC (0.966), which might be an alternative indicator for amyloidosis diagnosis. Conclusions The proteomic data interpretation procedure for LMD-MS based amyloidosis typing was established successfully that has a high practicability in clinical application.