PXD038601-1

PXD038601 is an original dataset announced via ProteomeXchange.

Title	Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning
Description	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: Quantitative label-free mass spectrometry analysis was performed on a set of formalin-fixed, paraffin-embedded (FFPE) biopsies from renal transplant patients, comprising five biopsies with the diagnosis of T-cell-mediated rejection (TCMR), five biopsies for polyomavirus BK nephropathy (BKPyVN), and normal kidney control tissue (STA). 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 isobaric labeling 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 yields 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 quantitative label-free mass spectrometry analysis with a machine learning-derived diagnostic model. It costs less than 10 dollars per test.
HostingRepository	PRIDE
AnnounceDate	2023-02-01
AnnouncementXML	Submission_2023-02-01_04:28:00.899.xml
DigitalObjectIdentifier
ReviewLevel	Peer-reviewed dataset
DatasetOrigin	Original dataset
RepositorySupport	Unsupported dataset by repository
PrimarySubmitter	FeiFANG
SpeciesList	scientific name: Homo sapiens (Human); NCBI TaxID: 9606;
ModificationList	acetylated residue; iodoacetamide derivatized residue
Instrument	LTQ Orbitrap

Revision	Datetime	Status	ChangeLog Entry
0	2022-12-07 03:25:16	ID requested
⏵ 1	2023-02-01 04:28:01	announced
2	2023-11-14 08:59:35	announced	2023-11-14: Updated project metadata.

submitter keyword: machine learning, kidney transplantation, mass spectrometry, FFPE, diagnosis,biomarker, quantitative proteomics

KunhongXiao
contact affiliation	Center for Proteomics & Artificial Intelligence Center for Clinical Mass Spectrometry Allegheny Health Network Cancer Institute 100 S Jackson Ave., Pittsburgh, PA 15202
contact email	kevin.kh.xiao@gmail.com
lab head
FeiFANG
contact affiliation	Michigan State University
contact email	fiona.dicp@outlook.com
dataset submitter

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PRIDE project URI

• PRIDE
  1. PXD038601
     1. Label: PRIDE project
     2. Name: Diagnosis of T-cell-mediated kidney rejection by biopsy-based proteomic biomarkers and machine learning