Lipid Profiling in Cancer Diagnosis with Hand-Held Ambient Mass Spectrometry Probes: Addressing the Late-Stage Performance Concerns

Untargeted lipid fingerprinting with hand-held ambient mass spectrometry (MS) probes without chromatographic separation has shown promise in the rapid characterization of cancers. As human cancers present significant molecular heterogeneities, careful molecular modeling and data validation strategies are required to minimize late-stage performance variations of these models across a large population. This review utilizes parallels from the pitfalls of conventional protein biomarkers in reaching bedside utility and provides recommendations for robust modeling as well as validation strategies that could enable the next logical steps in large scale assessment of the utility of ambient MS profiling for cancer diagnosis. Six recommendations are provided that range from careful initial determination of clinical added value to moving beyond just statistical associations to validate lipid involvements in disease processes mechanistically. Further guidelines for careful selection of suitable samples to capture expected and unexpected intragroup variance are provided and discussed in the context of demographic heterogeneities in the lipidome, further influenced by lifestyle factors, diet, and potential intersect with cancer lipid pathways probed in ambient mass spectrometry profiling studies.

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The New and the Old: Platform Cross-Validation of Immunoaffinity MASS Spectrometry versus ELISA for PromarkerD, a Predictive Test for Diabetic Kidney Disease

Proteomes ◽

10.3390/proteomes8040031 ◽

2020 ◽

Vol 8 (4) ◽

pp. 31

Author(s):

Scott Bringans ◽

Kirsten Peters ◽

Tammy Casey ◽

Jason Ito ◽

Richard Lipscombe

Keyword(s):

Mass Spectrometry ◽

Kidney Disease ◽

Large Scale ◽

Diabetic Kidney Disease ◽

Clinical Laboratory ◽

Predictive Test ◽

Protein Biomarkers ◽

Diabetic Kidney ◽

Technology Platforms ◽

Mass Spectrometry Assay

PromarkerD is a proteomics derived test for predicting diabetic kidney disease that measures the concentrations of three plasma protein biomarkers, APOA4, CD5L and IBP3. Antibodies against these proteins were developed and applied to a multiplexed immunoaffinity capture mass spectrometry assay. In parallel, and facilitating current clinical laboratory workflows, a standard ELISA was also developed to measure each protein. The performance characteristics of the two technology platforms were compared using a cohort of 100 samples, with PromarkerD test scores demonstrating a high correlation (R = 0.97). These technologies illustrate the potential for large scale, high throughput clinical applications of proteomics now and into the future.

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Mass Spectrometry-Based Proteomics: From Cancer Biology to Protein Biomarkers, Drug Targets, and Clinical Applications

American Society of Clinical Oncology Educational Book ◽

10.14694/edbook_am.2014.34.e504 ◽

2014 ◽

pp. e504-e510 ◽

Cited By ~ 22

Author(s):

Connie R. Jimenez ◽

Henk M. W. Verheul

Keyword(s):

Mass Spectrometry ◽

Cancer Biology ◽

Drug Targets ◽

Large Scale ◽

Specific Protein ◽

Protein Profiling ◽

Clinical Samples ◽

Post Translational Modification ◽

Protein Biomarkers ◽

Mass Spectrometry Technology

Proteomics is optimally suited to bridge the gap between genomic information on the one hand and biologic functions and disease phenotypes at the other, since it studies the expression and/or post-translational modification (especially phosphorylation) of proteins—the major cellular players bringing about cellular functions—at a global level in biologic specimens. Mass spectrometry technology and (bio)informatic tools have matured to the extent that they can provide high-throughput, comprehensive, and quantitative protein inventories of cells, tissues, and biofluids in clinical samples at low level. In this article, we focus on next-generation proteomics employing nanoliquid chromatography coupled to high-resolution tandem mass spectrometry for in-depth (phospho)protein profiling of tumor tissues and (proximal) biofluids, with a focus on studies employing clinical material. In addition, we highlight emerging proteogenomic approaches for the identification of tumor-specific protein variants, and targeted multiplex mass spectrometry strategies for large-scale biomarker validation. Below we provide a discussion of recent progress, some research highlights, and challenges that remain for clinical translation of proteomic discoveries.

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Biomarkers for predicting cancer in women with a suspicious mammogram.

Journal of Clinical Oncology ◽

10.1200/jco.2020.38.15_suppl.e15533 ◽

2020 ◽

Vol 38 (15_suppl) ◽

pp. e15533-e15533

Author(s):

Virginia A. Espina ◽

Ngoc Vuong ◽

Alessandra Luchini ◽

Claudius Mueller ◽

Denitra S Mack ◽

...

Keyword(s):

Breast Cancer ◽

Mass Spectrometry ◽

Cancer Diagnosis ◽

Protein Trafficking ◽

Breast Imaging ◽

Breast Cancer Diagnosis ◽

Screening Mammography ◽

Protein Biomarkers ◽

Abnormal Mammogram ◽

Mammographic Abnormality

e15533 Background: Biomarker identification for early breast cancer diagnosis is confounded by comparing healthy control patients to patients undergoing surgical procedures and stress of a potential cancer diagnosis. We implemented a clinical research protocol that combines biomarker harvesting and identification with Breast Imaging-Reporting and Data System (BIRADS) results, within a cohort of women with a suspicious mammogram who donated samples prior to biopsy. The primary goals were to discover candidate novel plasma markers for stage I breast cancer versus benign lesions, and validate the markers by mass spectrometry and immunohistochemistry. Methods: 150 women found on screening mammography to have a BIRADS IV or V mammographic abnormality were enrolled in the IRB approved study, with one year follow-up. After informed consent, serum, plasma, and saliva specimens were obtained and frozen. The patient underwent image guided biopsy of the mammographic abnormality. Hydrogel nanoparticles were used to harvest and concentrate low abundance protein biomarkers from plasma. Proteins were identified by mass spectrometry. The BIRADS score and biopsy outcome were blinded to the laboratory researchers. Results: 37/150 women (median age 64, 73% ER+, 70% PR+) were diagnosed with biopsy-proven breast cancer. 15/37 had a family history of breast cancer. Within the context of stress of an abnormal mammogram and invasive biopsy, we identified 5478 plasma peptides. A model to predict endpoints that discriminate cancer vs no cancer was developed using cross-validation and lasso shrinkage method. The best fit multi-analyte ROC/AUC model of peptide spectral matches revealed 10 candidate peptides, including PLAA, TRAPPC9, PROS1, DDX41, ANKRD63, EGFLAM (AUC = 0.81), that discriminated cancer versus no cancer. The functional mechanisms of these proteins are calcium metabolism, GPI anchor biosynthesis, neural-immune crosstalk, DNA repair, and ubiquitin-mediated protein trafficking. Conclusions: Molecular profiling of blood can potentially complement imaging to improve diagnostic specificity in the setting of a suspicious mammogram. This unique trial design, enhanced by nanotechnology protein harvesting, identified potential novel cancer biomarkers in the presence of a suspicious mammogram. A confirmation trial is underway.

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Breast cancer diagnosis based on lipid profiling by probe electrospray ionization mass spectrometry

British Journal of Surgery ◽

10.1002/bjs.11613 ◽

2020 ◽

Vol 107 (6) ◽

pp. 632-635 ◽

Cited By ~ 2

Author(s):

T. Iwano ◽

K. Yoshimura ◽

S. Inoue ◽

T. Odate ◽

K. Ogata ◽

...

Keyword(s):

Breast Cancer ◽

Mass Spectrometry ◽

Electrospray Ionization ◽

Cancer Diagnosis ◽

Electrospray Ionization Mass Spectrometry ◽

Breast Cancer Diagnosis ◽

Ionization Mass Spectrometry ◽

Ionization Mass ◽

Lipid Profiling ◽

Probe Electrospray Ionization

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Ambient mass spectrometry for extractionless analyses of plants: Holy Grail, useful tool or hoax?

Planta Medica ◽

10.1055/s-0032-1320279 ◽

2012 ◽

Vol 78 (11) ◽

Author(s):

TA van Beek ◽

Y Shen ◽

T Verweij ◽

A Villela ◽

F Claassen

Keyword(s):

Mass Spectrometry ◽

Ambient Mass Spectrometry ◽

Holy Grail

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Applications of ambient mass spectrometry of natural products in the fields of food safety, phytochemistry and forensics

Planta Medica ◽

10.1055/s-0036-1596141 ◽

2016 ◽

Vol 81 (S 01) ◽

pp. S1-S381

Author(s):

TA van Beek ◽

W Duvivier ◽

Y Shen ◽

B Chen ◽

MWF Nielen

Keyword(s):

Mass Spectrometry ◽

Natural Products ◽

Food Safety ◽

Ambient Mass Spectrometry

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Algorithm of combining chromatography mass spectrometry-untargeted profiling and multivariate analysis for identification of marker-substances in samples of complex composition

Industrial laboratory Diagnostics of materials ◽

10.26896/1028-6861-2020-86-7-12-19 ◽

2020 ◽

Vol 86 (7) ◽

pp. 12-19

Author(s):

I. V. Plyushchenko ◽

D. G. Shakhmatov ◽

I. A. Rodin

Keyword(s):

Mass Spectrometry ◽

Multivariate Analysis ◽

Large Scale ◽

Complex Composition ◽

Unified Protocol ◽

Chromatography Mass Spectrometry ◽

Marker Substances ◽

Selection Testing ◽

Untargeted Profiling

A viral development of statistical data processing, computing capabilities, chromatography-mass spectrometry, and omics technologies (technologies based on the achievements of genomics, transcriptomics, proteomics, metabolomics) in recent decades has not led to formation of a unified protocol for untargeted profiling. Systematic errors reduce the reproducibility and reliability of the obtained results, and at the same time hinder consolidation and analysis of data gained in large-scale multi-day experiments. We propose an algorithm for conducting omics profiling to identify potential markers in the samples of complex composition and present the case study of urine samples obtained from different clinical groups of patients. Profiling was carried out by the method of liquid chromatography mass spectrometry. The markers were selected using methods of multivariate analysis including machine learning and feature selection. Testing of the approach was performed using an independent dataset by clustering and projection on principal components.

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