Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning - Fang, Fei; Liu, Peng; Song, Lei; Wagner, Patrick; Bartlett, David; Ma, Liane; Li, Xue; Rahimian, M. Amin; Tseng, George; Randhawa, Parmjeet; Xiao, Kunhong (Frontiers in Immunology, 2023)
Background: Biopsy-based diagnosis is essential for maintaining renal allograft longevity by ensuring prompt treatment for graft complications. Although histologic assessment remains the gold standard, it carries significant limitations such as subjective interpretation, suboptimal reproducibility, and imprecise quantitation of disease burden. It is hoped that molecular diagnostics could enhance the efficiency, accuracy, and reproducibility of traditional histologic methods. Methods: Label-free quantitative proteome profiling was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal allograft injury patients, with ten biopsies for each selected representing T-cell-mediated rejection (TCMR) and normal kidney control tissue (STA), and five biopsies for polyomavirus BK nephropathy (BKPyVN). Using the differential protein expression result as a classifier, three different machine learning algorithms were tested to build a molecular diagnostic model for TCMR. Results: The label-free proteomics method yielded 800-1350 proteins that could be quantified with high confidence per sample by single-shot measurements. Among these candidate proteins, 329 and 467 proteins were defined as differentially expressed proteins (DEPs) for TCMR in comparison with STA and BKPyVN, respectively. Comparing the FFPE quantitative proteomics data set obtained in this study using label-free method with a data set we previously reported using tandem mass tag (TMT) technology, a classifier pool comprised of features from DEPs commonly quantified in both data sets, was generated for TCMR prediction. Leave-one-out cross-validation result demonstrated that the random forest (RF)-based model achieved the best predictive power. In a follow-up blind test using an independent sample set, the RF-based model yield 80% accuracy for TCMR and 100% for STA. When applying the established RF-based model to two public transcriptome datasets, 78.1%-82.9% sensitivity and 58.7%-64.4% specificity was achieved respectively. Conclusions: This proof-of-principle study demonstrates the clinical feasibility of proteomics profiling for FFPE biopsies using an accurate, efficient, and cost-effective platform integrated of label-free quantitative proteomics with a machine learning-based diagnostic model. It costs less than 10 dollars per test.