Research

Jump to: Review

16 pages, 2501 KiB

Open AccessArticle

Characterization of Hormone Receptor and HER2 Status in Breast Cancer Using Mass Spectrometry Imaging

by Juliana Pereira Lopes Gonçalves, Christine Bollwein, Aurelia Noske, Anne Jacob, Paul Jank, Sibylle Loibl, Valentina Nekljudova, Peter A. Fasching, Thomas Karn, Frederik Marmé, Volkmar Müller, Christian Schem, Bruno Valentin Sinn, Elmar Stickeler, Marion van Mackelenbergh, Wolfgang D. Schmitt, Carsten Denkert, Wilko Weichert and Kristina Schwamborn

Int. J. Mol. Sci. 2023, 24(3), 2860; https://doi.org/10.3390/ijms24032860 - 02 Feb 2023

Cited by 3 | Viewed by 2037

Abstract

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. [...] Read more.

Immunohistochemical evaluation of estrogen receptor, progesterone receptor, and human epidermal growth factor receptor-2 status stratify the different subtypes of breast cancer and define the treatment course. Triple-negative breast cancer (TNBC), which does not register receptor overexpression, is often associated with worse patient prognosis. Mass spectrometry imaging transcribes the molecular content of tissue specimens without requiring additional tags or preliminary analysis of the samples, being therefore an excellent methodology for an unbiased determination of tissue constituents, in particular tumor markers. In this study, the proteomic content of 1191 human breast cancer samples was characterized by mass spectrometry imaging and the epithelial regions were employed to train and test machine-learning models to characterize the individual receptor status and to classify TNBC. The classification models presented yielded high accuracies for estrogen and progesterone receptors and over 95% accuracy for classification of TNBC. Analysis of the molecular features revealed that vimentin overexpression is associated with TNBC, supported by immunohistochemistry validation, revealing a new potential target for diagnosis and treatment. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Graphical abstract

21 pages, 2033 KiB

Open AccessArticle

GC-MS Techniques Investigating Potential Biomarkers of Dying in the Last Weeks with Lung Cancer

by Elinor A. Chapman, James Baker, Prashant Aggarwal, David M. Hughes, Amara C. Nwosu, Mark T. Boyd, Catriona R. Mayland, Stephen Mason, John Ellershaw, Chris S. Probert and Séamus Coyle

Int. J. Mol. Sci. 2023, 24(2), 1591; https://doi.org/10.3390/ijms24021591 - 13 Jan 2023

Cited by 3 | Viewed by 3960

Abstract

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed [...] Read more.

Predicting when a patient with advanced cancer is dying is a challenge and currently no prognostic test is available. We hypothesised that a dying process from cancer is associated with metabolic changes and specifically with changes in volatile organic compounds (VOCs). We analysed urine from patients with lung cancer in the last weeks of life by headspace gas chromatography mass spectrometry. Urine was acidified or alkalinised before analysis. VOC changes in the last weeks of life were identified using univariate, multivariate and linear regression analysis; 12 VOCs increased (11 from the acid dataset, 2 from the alkali dataset) and 25 VOCs decreased (23 from the acid dataset and 3 from the alkali dataset). A Cox Lasso prediction model using 8 VOCs predicted dying with an AUC of 0.77, 0.78 and 0.85 at 30, 20 and 10 days and stratified patients into a low (median 10 days), medium (median 50 days) or high risk of survival. Our data supports the hypothesis there are specific metabolic changes associated with the dying. The VOCs identified are potential biomarkers of dying in lung cancer and could be used as a tool to provide additional prognostic information to inform expert clinician judgement and subsequent decision making. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

14 pages, 2704 KiB

Open AccessArticle

Contribution of Capillary Zone Electrophoresis Hyphenated with Drift Tube Ion Mobility Mass Spectrometry as a Complementary Tool to Microfluidic Reversed Phase Liquid Chromatography for Antigen Discovery

by Marie-Jia Gou, Murat Cem Kose, Jacques Crommen, Cindy Nix, Gael Cobraiville, Jo Caers and Marianne Fillet

Int. J. Mol. Sci. 2022, 23(21), 13350; https://doi.org/10.3390/ijms232113350 - 01 Nov 2022

Cited by 5 | Viewed by 1325

Abstract

The discovery of new antigens specific to multiple myeloma that could be targeted by novel immunotherapeutic approaches is currently of great interest. To this end, it is important to increase the number of proteins identified in the sample by combining different separation strategies. [...] Read more.

The discovery of new antigens specific to multiple myeloma that could be targeted by novel immunotherapeutic approaches is currently of great interest. To this end, it is important to increase the number of proteins identified in the sample by combining different separation strategies. A capillary zone electrophoresis (CZE) method, coupled with drift tube ion mobility (DTIMS) and quadrupole time-of-flight mass spectrometry (QTOF), was developed for antigen discovery using the human myeloma cell line LP-1. This method was first optimized to obtain a maximum number of identifications. Then, its performance in terms of uniqueness of identifications was compared to data acquired by a microfluidic reverse phase liquid chromatography (RPLC) method. The orthogonality of these two approaches and the physicochemical properties of the entities identified by CZE and RPLC were evaluated. In addition, the contribution of DTIMS to CZE was investigated in terms of orthogonality as well as the ability to provide unique information. In conclusion, we believe that the combination of CZE-DTIMS-QTOF and microfluidic RPLC provides unique information in the context of antigen discovery. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

18 pages, 2137 KiB

Open AccessArticle

Quantitative Proteomics of Medium-Sized Extracellular Vesicle-Enriched Plasma of Lacunar Infarction for the Discovery of Prognostic Biomarkers

by Arnab Datta, Christopher Chen, Yong-Gui Gao and Siu Kwan Sze

Int. J. Mol. Sci. 2022, 23(19), 11670; https://doi.org/10.3390/ijms231911670 - 01 Oct 2022

Cited by 2 | Viewed by 2144

Abstract

Lacunar infarction (LACI), a subtype of acute ischemic stroke, has poor mid- to long-term prognosis due to recurrent vascular events or incident dementia which is difficult to predict using existing clinical data. Herein, we aim to discover blood-based biomarkers for LACI as a [...] Read more.

Lacunar infarction (LACI), a subtype of acute ischemic stroke, has poor mid- to long-term prognosis due to recurrent vascular events or incident dementia which is difficult to predict using existing clinical data. Herein, we aim to discover blood-based biomarkers for LACI as a complementary prognostic tool. Convalescent plasma was collected from forty-five patients following a non-disabling LACI along with seventeen matched control subjects. The patients were followed up prospectively for up to five years to record an occurrence of adverse outcome and grouped accordingly (i.e., LACI-no adverse outcome, LACI-recurrent vascular event, and LACI-cognitive decline without any recurrence of vascular events). Medium-sized extracellular vesicles (MEVs), isolated from the pooled plasma of four groups, were analyzed by stable isotope labeling and 2D-LC-MS/MS. Out of 573 (FDR < 1%) quantified proteins, 146 showed significant changes in at least one LACI group when compared to matched healthy control. A systems analysis revealed that major elements (~85%) of the MEV proteome are different from the proteome of small-sized extracellular vesicles obtained from the same pooled plasma. The altered MEV proteins in LACI patients are mostly reduced in abundance. The majority of the shortlisted MEV proteins are not linked to commonly studied biological processes such as coagulation, fibrinolysis, or inflammation. Instead, they are linked to oxygen-glucose deprivation, endo-lysosomal trafficking, glucose transport, and iron homeostasis. The dataset is provided as a web-based data resource to facilitate meta-analysis, data integration, and targeted large-scale validation. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

16 pages, 2878 KiB

Open AccessArticle

Plasma Proteomics Enable Differentiation of Lung Adenocarcinoma from Chronic Obstructive Pulmonary Disease (COPD)

by Thilo Bracht, Daniel Kleefisch, Karin Schork, Kathrin E. Witzke, Weiqiang Chen, Malte Bayer, Jan Hovanec, Georg Johnen, Swetlana Meier, Yon-Dschun Ko, Thomas Behrens, Thomas Brüning, Jana Fassunke, Reinhard Buettner, Julian Uszkoreit, Michael Adamzik, Martin Eisenacher and Barbara Sitek

Int. J. Mol. Sci. 2022, 23(19), 11242; https://doi.org/10.3390/ijms231911242 - 24 Sep 2022

Cited by 2 | Viewed by 1901

Abstract

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, [...] Read more.

Chronic obstructive pulmonary disease (COPD) is a major risk factor for the development of lung adenocarcinoma (AC). AC often develops on underlying COPD; thus, the differentiation of both entities by biomarker is challenging. Although survival of AC patients strongly depends on early diagnosis, a biomarker panel for AC detection and differentiation from COPD is still missing. Plasma samples from 176 patients with AC with or without underlying COPD, COPD patients, and hospital controls were analyzed using mass-spectrometry-based proteomics. We performed univariate statistics and additionally evaluated machine learning algorithms regarding the differentiation of AC vs. COPD and AC with COPD vs. COPD. Univariate statistics revealed significantly regulated proteins that were significantly regulated between the patient groups. Furthermore, random forest classification yielded the best performance for differentiation of AC vs. COPD (area under the curve (AUC) 0.935) and AC with COPD vs. COPD (AUC 0.916). The most influential proteins were identified by permutation feature importance and compared to those identified by univariate testing. We demonstrate the great potential of machine learning for differentiation of highly similar disease entities and present a panel of biomarker candidates that should be considered for the development of a future biomarker panel. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

13 pages, 1617 KiB

Open AccessArticle

Potential of Single Pulse and Multiplexed Drift-Tube Ion Mobility Spectrometry Coupled to Micropillar Array Column for Proteomics Studies

by Cindy Nix, Gael Cobraiville, Marie-Jia Gou and Marianne Fillet

Int. J. Mol. Sci. 2022, 23(14), 7497; https://doi.org/10.3390/ijms23147497 - 06 Jul 2022

Cited by 3 | Viewed by 1553

Abstract

Proteomics is one of the most significant methodologies to better understand the molecular pathways involved in diseases and to improve their diagnosis, treatment and follow-up. The investigation of the proteome of complex organisms is challenging from an analytical point of view, because of [...] Read more.

Proteomics is one of the most significant methodologies to better understand the molecular pathways involved in diseases and to improve their diagnosis, treatment and follow-up. The investigation of the proteome of complex organisms is challenging from an analytical point of view, because of the large number of proteins present in a wide range of concentrations. In this study, nanofluidic chromatography, using a micropillar array column, was coupled to drift-tube ion mobility and time-of-flight mass spectrometry to identify as many proteins as possible in a protein digest standard of HeLa cells. Several chromatographic parameters were optimized. The high interest of drift-tube ion mobility to increase the number of identifications and to separate isobaric coeluting peptides was demonstrated. Multiplexed drift-tube ion mobility spectrometry was also investigated, to increase the sensitivity in proteomics studies. This innovative proteomics platform will be useful for analyzing patient samples to better understand unresolved disorders. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

19 pages, 1861 KiB

Open AccessArticle

Metabolic Alteration Analysis of Steroid Hormones in Niemann–Pick Disease Type C Model Cell Using Liquid Chromatography/Tandem Mass Spectrometry

by Ai Abe, Masamitsu Maekawa, Toshihiro Sato, Yu Sato, Masaki Kumondai, Hayato Takahashi, Masafumi Kikuchi, Katsumi Higaki, Jiro Ogura and Nariyasu Mano

Int. J. Mol. Sci. 2022, 23(8), 4459; https://doi.org/10.3390/ijms23084459 - 18 Apr 2022

Cited by 4 | Viewed by 2499

Abstract

Niemann–Pick disease type C (NPC) is an autosomal recessive disease caused by a functional deficiency of cholesterol-transporting proteins in lysosomes, and exhibits various clinical symptoms. Since mitochondrial dysfunction in NPC has recently been reported, cholesterol catabolism to steroid hormones may consequently be impaired. [...] Read more.

Niemann–Pick disease type C (NPC) is an autosomal recessive disease caused by a functional deficiency of cholesterol-transporting proteins in lysosomes, and exhibits various clinical symptoms. Since mitochondrial dysfunction in NPC has recently been reported, cholesterol catabolism to steroid hormones may consequently be impaired. In this study, we developed a comprehensive steroid hormone analysis method using liquid chromatography/tandem mass spectrometry (LC–MS/MS) and applied it to analyze changes in steroid hormone concentrations in NPC model cells. We investigated the analytical conditions for simultaneous LC–MS/MS analysis, which could be readily separated from each other and showed good reproducibility. The NPC phenotype was verified as an NPC model with mitochondrial abnormalities using filipin staining and organelle morphology observations. Steroid hormones in the cell suspension and cell culture medium were also analyzed. Steroid hormone analysis indicated that the levels of six steroid hormones were significantly decreased in the NPC model cell and culture medium compared to those in the wild-type cell and culture medium. These results indicate that some steroid hormones change during NPC pathophysiology and this change is accompanied by mitochondrial abnormalities. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

Review

Jump to: Research

19 pages, 756 KiB

Open AccessReview

Diagnosis by Volatile Organic Compounds in Exhaled Breath from Patients with Gastric and Colorectal Cancers

by Jinwook Chung, Salima Akter, Sunhee Han, Yoonhwa Shin, Tae Gyu Choi, Insug Kang and Sung Soo Kim

Int. J. Mol. Sci. 2023, 24(1), 129; https://doi.org/10.3390/ijms24010129 - 21 Dec 2022

Cited by 11 | Viewed by 2835

Abstract

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been [...] Read more.

One in three cancer deaths worldwide are caused by gastric and colorectal cancer malignancies. Although the incidence and fatality rates differ significantly from country to country, the rates of these cancers in East Asian nations such as South Korea and Japan have been increasing each year. Above all, the biggest danger of this disease is how challenging it is to recognize in its early stages. Moreover, most patients with these cancers do not present with any disease symptoms before receiving a definitive diagnosis. Currently, volatile organic compounds (VOCs) are being used for the early prediction of several other diseases, and research has been carried out on these applications. Exhaled VOCs from patients possess remarkable potential as novel biomarkers, and their analysis could be transformative in the prevention and early diagnosis of colon and stomach cancers. VOCs have been spotlighted in recent studies due to their ease of use. Diagnosis on the basis of patient VOC analysis takes less time than methods using gas chromatography, and results in the literature demonstrate that it is possible to determine whether a patient has certain diseases by using organic compounds in their breath as indicators. This study describes how VOCs can be used to precisely detect cancers; as more data are accumulated, the accuracy of this method will increase, and it can be applied in more fields. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

21 pages, 967 KiB

Open AccessReview

Mass Spectrometric-Based Proteomics for Biomarker Discovery in Osteosarcoma: Current Status and Future Direction

by Nutnicha Sirikaew, Dumnoensun Pruksakorn, Parunya Chaiyawat and Somchai Chutipongtanate

Int. J. Mol. Sci. 2022, 23(17), 9741; https://doi.org/10.3390/ijms23179741 - 28 Aug 2022

Cited by 5 | Viewed by 2413

Abstract

Due to a lack of novel therapies and biomarkers, the clinical outcomes of osteosarcoma patients have not significantly improved for decades. The advancement of mass spectrometry (MS), peptide quantification, and downstream pathway analysis enables the investigation of protein profiles across a wide range [...] Read more.

Due to a lack of novel therapies and biomarkers, the clinical outcomes of osteosarcoma patients have not significantly improved for decades. The advancement of mass spectrometry (MS), peptide quantification, and downstream pathway analysis enables the investigation of protein profiles across a wide range of input materials, from cell culture to long-term archived clinical specimens. This can provide insight into osteosarcoma biology and identify candidate biomarkers for diagnosis, prognosis, and stratification of chemotherapy response. In this review, we provide an overview of proteomics studies of osteosarcoma, indicate potential biomarkers that might be promising therapeutic targets, and discuss the challenges and opportunities of mass spectrometric-based proteomics in future osteosarcoma research. Full article

(This article belongs to the Special Issue Mass Spectrometry Techniques for Biomarker Discovery)

► Show Figures

Figure 1

Journal Menu

Journal Browser

Mass Spectrometry Techniques for Biomarker Discovery

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Published Papers (9 papers)

Research

Review

Further Information

Guidelines

MDPI Initiatives

Follow MDPI