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23rd Human Proteome Organization World Congress

Opportunities and challenges in clinical proteomics: from tissue discovery to plasma validation

Professor

Keynote speaker

Keynote lecture

PD Dr.

Prof. Dr.

Absolute quantification of the synaptic blood marker β-synuclein by targeted mass spectrometry ranks synaptic degeneration after amyloid deposition in autosomal dominant Alzheimer's disease

Clinical Proteomics

Introduction: Synaptic degeneration is a major hallmark of Alzheimer´s disease (AD) and the pathological correlate of memory impairment. Monitoring of synaptic changes in AD patients is therefore of importance for diagnosis, prognosis and assessing disease progression but also to track beneficial effects on synaptic loss in clinical trials. The protein β-Synuclein is a promising and easily accessible blood marker to track synaptic degeneration in AD but changes in preclinical AD are unclear. The Dominantly Inherited Alzheimer Network (DIAN) observational study is a worldwide multicenter initiative to longitudinally study dominantly inherited AD and provides the unique opportunity to study blood β-synuclein levels in very early (asymptomatic) AD and assess the timing and relation of β-synuclein changes to other pathological alterations.Methods: We used immunoprecipitation and multiple reaction monitoring mass spectrometry (IP-MS) with an Eksigent MicroLC200 and a QTRAP6500 for the absolute quantification of β-synuclein in serum samples of 178 participants of the DIAN study including 69 cognitively unimpaired mutation non-carriers (NC), 78 cognitively unimpaired AD mutation carriers (asymptomatic AD, aMC) and 31 symptomatic MC (sMC). Changes of β-synuclein serum levels were compared and correlated with cerebrospinal fluid (CSF) biomarkers (Aβ42/40, tTau, pTau variants), amyloid deposition ([11C]-Pittsburgh Compound B - positron emission tomography (PiB-PET)), brain atrophy (magnetic resonance imaging (MRI)) and metabolism ([18F]-fluorodeoxyglucose - PET (FDG-PET)), axonal degeneration (serum NfL) and cognitve impairment (MMSE, Clinical Dementia Rating sum-of-boxes (CDR-SB), DIAN cognitive composite). Longitudinal trajectories were estimated using the expected years to symptom onset (EYO).Results: Blood β-synuclein levels were already higher in asymptomatic autosomal dominant AD (ADAD) mutation carriers (median 7.29pg/mL, interquartile range (IQR) 5.89-8.59) compared with non-carriers (5.25pg/mL, IQR 4.63-6.66) and highest in sMC (14.4pg/mL, IQR 11.7-18.4). Longitudinal trajectories based on the EYO and correlation analyses indicated that β-synuclein levels start to rise after amyloid deposition preceding axonal degeneration, brain atrophy and hypometabolism and cognitive decline. β-synuclein levels were associated with cognitive impairment and gradually increased with declining cognition. Thus, our data rank synaptic degeneration as one of the earliest events in the ADAD pathophysiological cascade as it is close to the start of amyloid deposition and it supports a retrograde degeneration mechanism starting in the synapse.Conclusion: Our study supports the use of blood β-synuclein to track synaptic changes in preclinical AD and as a surrogate marker for cognitive impairment which might be used in early diagnosis and to support patient selection and monitoring of treatment effects in clinical trials of putative AD-modifying therapies.

Oral presentation

Prof.

Identification of novel diagnostic protein biomarkers in human plasma for breast cancer by targeted protein assay

Introduction: Breast cancer (BC) is the most common cancer among women worldwide and ranks as the second leading cause of cancer-related deaths. Fortunately, the prognosis of BC is good when detected at an early stage, however, sensitive diagnostic tools for early detection of BC are vital for improving survival rates. This study aims to identify protein biomarkers with diagnostic and prognostic value in human plasma to improve early BC diagnosis.Methods: An absolute quantification of 131 proteins was performed on 279 well-defined Icelandic biobank-based plasma samples from 135 BC patients and 144 healthy controls by UPLC-MRM-MS assay. Among the BC patients, 45 were BRCA2 mutation carriers. Absolute quantification of the proteins were conducted using a PeptiQuantTM protein human kit containing synthetic light peptide and matching heavy peptide as internal standard for each protein. Prior to analysis the plasma samples were proteolytically cleaved with trypsin, internal standards were added to the peptide mixture and concentrated by solid-phase extraction using liquid handling robot. Data analyses were conducted using Skyline Quantitative Analysis software for pre-processing of the data. SIMCA Pro-17, R studio, and Python were used for statistical analysis, multivariate data analysis, and machine learning. Predictive models including random forest, support vector machine, and logistic regression were developed and validated through cross-validation using the SciKit-Learn library.Results: The targeted proteomic assay was successfully implemented for absolute quantification of 131 proteins in human plasma samples with precision and accuracy for calibration standards and quality controls within 20% relative standard deviation. Out of the 131 proteins, 99 proteins were quantifiable in the Icelandic bio-bank plasma samples, surpassing the lower limit of quantification. The samples were analyzed in eight batches, each containing 25 cases and 25 controls, showing minimal batch-to-batch variability. The results reveal that protein signatures of the patients with a BRCA2 germline mutation are different from patients with sporadic BC. This difference was found to be significant for few proteins in comparison to control groups as well as between the patient groups. Notably, predictive models could distinguish between patients with sporadic BC from BC patients with BRCA2 mutation with AUC &gt; 0.8, offering valuable insights for targeted screening for BC. Furthermore, seven proteins showed the potential to discriminate BC patients with the luminal B subtype from controls.Conclusions: Targeted proteomics using UPLC-MRM-MS shows potential for identifying and quantifying protein biomarkers in human plasma for diagnosis of BC in BRCA2 mutation carriers and for the luminal B subtype.

Prof

Preoperative diagnosis of thyroid nodules using a multi-omics test

BackgroundApproximately 15-30% of thyroid nodules display indeterminate cytology on fine needle aspiration (FNA) biopsy, creating a clinical diagnostic dilemma in determining their benign or malignant nature. Multiple gene-panel tests have been developed; however, these tests also possess certain limitations, such as heterogeneity. Protein samples are less susceptible to spontaneous degradation in clinical specimens. We hypothesized that the introduction of proteomics molecular testing may help tackle this clinical challenge.MethodsWe conducted a prospective, noninterventional, multicenter study. All patient's FNA samples were classified using the Bethesda System for Reporting Thyroid Cytopathology. Results of a histopathological review served as the reference standard. Proteins are extracted and digested from FNA samples and peptide samples mixed with synthesized stable isotope labeled peptides (called AQUA peptides) were analyzed using multiple reaction monitoring (MRM) mass spectrometry (MS) on a Sciex 4500 MD instrument over an 8-min LC gradient. MS data were analyzed using OpenMS. BRAF V600E mutation status was determined by ARMS-PCR Kit. A machine learning approach was employed, utilizing the Sklearn package to construct a random forest model.ResultsA total of 1954 FNA samples from 1952 patients collected from 15 medical centers from January 2020 to September 2022 were used for feature selection, model training and validation. Based on previous studies, 211 proteins were selected as candidate diagnostic biomarkers. After multiple rounds of protein biomarker screening, including machine learning model screening, evaluation of AQUA peptides stability and linearity, and MS signal screening, three proteins were ultimately selected as the final features of the classifier model (ThyroProt). The ThyroProt incorporated the absolute quantitative data of three proteins, along with age, sex, and BRAF V600E mutation information.Independent test set is composed of 290 FNA samples collected from 283 patients from five medical centers, spanning from October 2022 to March 2023. Classifier test on independent set 1 showed an area under the curve (AUC) of 0.94, an accuracy of 89.7% (95% confidence interval [CI]: 89.6-89.7), sensitivity of 85.9% (95% CI: 80.7-91.0), specificity of 95.6% (95% CI: 91.8-99.4), positive predictive value (PPV) of 96.8% (95% CI: 94.1-99.6) and negative predictive value (NPV) of 81.2% (95% CI: 74.6-87.8) for thyroid malignancy. Test on 45 FNA samples reported as Bethesda III or IV showed an AUC of 0.87 and an accuracy of 88.9% (95% CI: 88.5-89.3), sensitivity, specificity, PPV, and NPV of 86.7% (95% CI: 69.5-100), 90.0% (95% CI: 79.3-100), 81.2% (95% CI: 62.1-100), and 93.1% (95% CI: 83.9-100), respectively.ConclusionsThis study demonstrates the feasibility and practicality of using targeted mass spectrometry to detect multiple proteins and machine learning for clinical diagnostics in real-world scenarios.

Plenary hall

Session

Clinical proteomics: from discovery to validation

Session
Clinical proteomics: from discovery to validation

Appointment

Chairs

Program

SessionClinical proteomics: from discovery to validation

Appointment

Chairs

Program

Session
Clinical proteomics: from discovery to validation