Shotgun proteomics of hereditary amyloid deposits generates all the information necessary to identify pathogenic mutant peptides and proteins. However, these mutant peptides are invisible to traditional database search strategies. We developed a two-pronged informatics workflow for detecting both known and novel amyloidogenic mutations from clinical proteomics data sets. We implemented the workflow in a CAP/CLIA certified clinical laboratory dedicated for proteomic subtyping of amyloid deposits extracted from formalin-fixed paraffin-embedded specimens. Performance of the workflow was characterized on a validation cohort of 49 hereditary amyloid samples, with confirmed mutations, and 85 controls. The sensitivity, specificity, positive predictive value, and negative predictive value of the known mutation detection workflow were determined to be 92%, 100%, 100%, and 96%, respectively. For novel mutation detection workflow, these performance parameters were 82%, 99%, 99%, and 90%, respectively. Validated workflow was applied to detect amyloidogenic mutations from a clinical cohort of 150 amyloid samples. The known mutation detection workflow detected rare frame shift mutations in apolipoprotein A1 and fibrinogen alpha amyloid deposits. The novel mutation detection workflow uncovered unanticipated mutations (W22G and C71Y) of the serum amyloid A4 protein present in patient amyloid deposits. In summary, clinical amyloid proteomics data sets contain mutant peptides of clinical significance that are recoverable with improved bioinformatics.
