Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Despite many years of studies, ovarian cancer remains one of the top ten cancers worldwide. Its high mortality rate is mainly due to lack of sufficient diagnostic methods. For this reason, our research focused on the identification of blood markers whose appearance would precede the clinical manifestation of the disease. ITRAQ-tagging (isobaric Tags for Relative and Absolute Quantification) coupled with mass spectrometry technology was applied. Three groups of samples derived from patients with: ovarian cancer, benign ovarian tumor, and healthy controls, were examined. Mass spectrometry analysis allowed for highlighting the dysregulation of several proteins associated with ovarian cancer. Further validation of the obtained results indicated that five proteins (Serotransferrin, Amyloid A1, Hemopexin, C-reactive protein, Albumin) were differentially expressed in ovarian cancer group. Interestingly, the addition of Albumin, Serotransferrin, and Amyloid A1 to CA125 (cancer antigen 125) and HE4 (human epididymis protein4) improved the diagnostic performance of the model discriminating between benign and malignant tumors. Identified proteins shed light on the molecular signaling pathways that are associated with ovarian cancer development and should be further investigated in future studies. Our findings indicate five proteins with a strong potential to use in a multimarker test for screening and detection of ovarian cancer.

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Evaluation of Collagen Alterations in Early Precursor Lesions of High Grade Serous Ovarian Cancer by Second Harmonic Generation Microscopy and Mass Spectrometry

Cancers ◽

10.3390/cancers13112794 ◽

2021 ◽

Vol 13 (11) ◽

pp. 2794

Author(s):

Kristal L. Gant ◽

Alexander N. Jambor ◽

Zihui Li ◽

Eric C. Rentchler ◽

Paul Weisman ◽

...

Keyword(s):

Mass Spectrometry ◽

Ovarian Cancer ◽

Second Harmonic Generation ◽

Harmonic Generation ◽

Collagen Fiber ◽

Second Harmonic ◽

Mass Spectrometry Analysis ◽

High Grade ◽

Serous Ovarian Cancer ◽

Fiber Morphology

Background: The collagen architecture in high grade serous ovarian cancer (HGSOC) is highly remodeled compared to the normal ovary and the fallopian tubes (FT). We previously used Second Harmonic Generation (SHG) microscopy and machine learning to classify the changes in collagen fiber morphology occurring in serous tubal intraepithelial carcinoma (STIC) lesions that are concurrent with HGSOC. We now extend these studies to examine collagen remodeling in pure p53 signatures, STICs and normal regions in tissues that have no concurrent HGSOC. This is an important distinction as high-grade disease can result in distant collagen changes through a field effect mechanism. Methods: We trained a linear discriminant model based on SHG texture and image features as a classifier to discriminate the tissue groups. We additionally performed mass spectrometry analysis of normal and HGSOC tissues to associate the differential expression of collagen isoforms with collagen fiber morphology alterations. Results: We quantified the differences in the collagen architecture between normal tissue and the precursors with good classification accuracy. Through proteomic analysis, we identified the downregulation of single α-chains including those for Col I and III, where these results are consistent with our previous SHG-based supramolecular analyses. Conclusion: This work provides new insights into ECM remodeling in early ovarian cancer and suggests the combined use of SHG microscopy and mass spectrometry as a new diagnostic/prognostic approach.

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Dickkopf-related protein 3 is a novel biomarker for chronic GVHD after allogeneic hematopoietic cell transplantation

Blood Advances ◽

10.1182/bloodadvances.2020001485 ◽

2020 ◽

Vol 4 (11) ◽

pp. 2409-2417 ◽

Cited By ~ 1

Author(s):

Yoshihiro Inamoto ◽

Paul J. Martin ◽

Stephanie J. Lee ◽

Amin A. Momin ◽

Laura Tabellini ◽

...

Keyword(s):

Mass Spectrometry ◽

Hematopoietic Cell Transplantation ◽

Biomarker Discovery ◽

Interleukin 1 ◽

Chronic Gvhd ◽

Linear Regression Analysis ◽

Wnt Signaling Pathway ◽

Mass Spectrometry Analysis ◽

Newly Diagnosed ◽

Related Protein

Abstract To identify plasma biomarkers associated with fibrotic mechanisms of chronic graft-versus-host disease (GVHD), we used multiplex mass spectrometry with pooled samples for biomarker discovery in comparing proteomic profiles between patients with newly diagnosed sclerotic chronic GVHD (n = 21), those with newly diagnosed nonsclerotic chronic GVHD (n = 33), and those without chronic GVHD (n = 20). Immunoassay was used to measure protein concentrations of individual discovery samples and 186 independent verification samples. The discovery mass spectrometry analysis identified 2 candidate proteins with at least 1.5-fold difference in sclerotic GVHD: Dickkopf-related protein 3 (DKK3) and interleukin-1 receptor accessory protein (IL1RAP). Analysis of individual discovery samples by immunoassay showed that DKK3, a modulator of the Wnt signaling pathway, was a biomarker for both sclerotic and nonsclerotic chronic GVHD. Verification analysis of 186 patients confirmed that elevated plasma DKK3 concentrations were associated with chronic GVHD, regardless of the presence or absence of sclerosis, and that the area under the receiver operating characteristic curve was 0.85 for association of DKK3 concentrations with chronic GVHD. Multiple linear regression analysis showed that chronic GVHD with or without steroid treatment and patient age were independently associated with DKK3 concentrations. Patients with high DKK3 concentrations had a higher nonrelapse mortality than those with low concentrations. The lower IL1RAP concentrations in patients with sclerotic GVHD compared with other conditions in the discovery cohort were not confirmed in the verification cohort. DKK3 is a novel biomarker for chronic GVHD. Further studies are needed to determine the biological functions of DKK3 in the pathogenesis of chronic GVHD.

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Ovarian cancer in the proteomics era

International Journal of Gynecological Cancer ◽

10.1111/j.1525-1438.2007.01096.x ◽

2008 ◽

Vol 18 (Suppl 1) ◽

pp. 1-6 ◽

Cited By ~ 13

Author(s):

C. M. Annunziata ◽

N. Azad ◽

A. S. Dhamoon ◽

G. Whiteley ◽

E. C. Kohn

Keyword(s):

Mass Spectrometry ◽

Ovarian Cancer ◽

New Technologies ◽

Molecular Signaling ◽

Cell Of Origin ◽

Proteomic Profiling ◽

Diagnostic Challenge ◽

High Throughput Analysis ◽

Tissue Samples ◽

Protein Array Analysis

Ovarian cancer presents a diagnostic challenge because of its subtle clinical presentation and elusive cell of origin. Two new technologies of proteomics have advanced the dissection of the underlying molecular signaling events and the proteomic characterization of ovarian cancer: mass spectrometry and protein array analysis. Mass spectrometry can provide a snapshot of a proteome in time and space, with sensitivity and resolution that may allow identification of the elusive “needle in the haystack” heralding ovarian cancer. Proteomic profiling of tumor tissue samples can survey molecular targets during treatment and quantify changes using reverse phase protein arrays generated from tumor samples captured by microdissection, lysed and spotted in serial dilutions for high-throughput analysis. This approach can be applied to identify the optimal biological dose of a targeted agent and to validate target to outcome link. The evolution of proteomic technologies has the capacity to advance rapidly our understanding of ovarian cancer at a molecular level and thus elucidate new directions for the treatment of this disease

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Mass spectrometry analysis of the diversity of Aβ peptides: difficulties and future perspectives for AD biomarker discovery

Expert Review of Proteomics ◽

10.1080/14789450.2018.1525296 ◽

2018 ◽

Vol 15 (10) ◽

pp. 773-775 ◽

Cited By ~ 4

Author(s):

Natalia V. Zakharova ◽

Anna E. Bugrova ◽

Alexey S. Kononikhin ◽

Maria I. Indeykina ◽

Igor A. Popov ◽

...

Keyword(s):

Mass Spectrometry ◽

Biomarker Discovery ◽

Mass Spectrometry Analysis ◽

Future Perspectives ◽

Aβ Peptides ◽

Spectrometry Analysis

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Accounting for isotopic clustering in Fourier transform mass spectrometry data analysis for clinical diagnostic studies

Statistical Applications in Genetics and Molecular Biology ◽

10.1515/sagmb-2016-0005 ◽

2016 ◽

Vol 15 (5) ◽

Cited By ~ 4

Author(s):

Alexia Kakourou ◽

Werner Vach ◽

Simone Nicolardi ◽

Yuri van der Burgt ◽

Bart Mertens

Keyword(s):

Mass Spectrometry ◽

High Throughput ◽

Biomarker Discovery ◽

Protein Profiling ◽

Mass Spectrometry Data ◽

Clinical Proteomics ◽

Intensity Level ◽

Data Set ◽

Fticr Mass Spectrometry ◽

Mass Spectrometry Technology

AbstractMass spectrometry based clinical proteomics has emerged as a powerful tool for high-throughput protein profiling and biomarker discovery. Recent improvements in mass spectrometry technology have boosted the potential of proteomic studies in biomedical research. However, the complexity of the proteomic expression introduces new statistical challenges in summarizing and analyzing the acquired data. Statistical methods for optimally processing proteomic data are currently a growing field of research. In this paper we present simple, yet appropriate methods to preprocess, summarize and analyze high-throughput MALDI-FTICR mass spectrometry data, collected in a case-control fashion, while dealing with the statistical challenges that accompany such data. The known statistical properties of the isotopic distribution of the peptide molecules are used to preprocess the spectra and translate the proteomic expression into a condensed data set. Information on either the intensity level or the shape of the identified isotopic clusters is used to derive summary measures on which diagnostic rules for disease status allocation will be based. Results indicate that both the shape of the identified isotopic clusters and the overall intensity level carry information on the class outcome and can be used to predict the presence or absence of the disease.

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Comparison of Risk of Ovarian Malignancy Algorithm and cancer antigen 125 to discriminate between benign ovarian tumor and early‑stage ovarian cancer according to imaging tumor subtypes

Oncology Letters ◽

10.3892/ol.2020.11629 ◽

2020 ◽

Vol 20 (1) ◽

pp. 931-938

Author(s):

Young‑Jae Lee ◽

Yong‑Man Kim ◽

Ji‑Sik Kang ◽

So‑Hyun Nam ◽

Dae‑Yeon Kim ◽

...

Keyword(s):

Ovarian Cancer ◽

Ovarian Tumor ◽

Early Stage ◽

Ovarian Malignancy ◽

Cancer Antigen 125 ◽

Cancer Antigen ◽

Tumor Subtypes ◽

Benign Ovarian Tumor ◽

Early Stage Ovarian Cancer

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ABC algorithm as feature selection for biomarker discovery in mass spectrometry analysis

2012 4th Conference on Data Mining and Optimization (DMO) ◽

10.1109/dmo.2012.6329800 ◽

2012 ◽

Cited By ~ 3

Author(s):

M. Y. SyarifahAdilah ◽

Rosni Abdullah ◽

Ibrahim Venkat

Keyword(s):

Mass Spectrometry ◽

Feature Selection ◽

Biomarker Discovery ◽

Mass Spectrometry Analysis ◽

Abc Algorithm ◽

Spectrometry Analysis ◽

Selection For

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Mass spectrometry analysis of mouse hematopoietic stem cells and their progenitors reveals differential expression within and between proteome and transcriptome throughout adult and aged hematopoiesis

10.1101/836692 ◽

2019 ◽

Author(s):

Balyn W. Zaro ◽

Joseph J. Noh ◽

Victoria L. Mascetti ◽

Janos Demeter ◽

Benson M. George ◽

...

Keyword(s):

Mass Spectrometry ◽

Stem Cells ◽

Protein Expression ◽

Hematopoietic Stem Cells ◽

Differential Expression ◽

Cell Types ◽

Mass Spectrometry Analysis ◽

Hematopoietic Stem ◽

Spectrometry Analysis ◽

Mass Spectrometry Technology

SummaryHematopoietic stem cells (HSCs) are responsible for the generation of blood and immune cells throughout life. They have the unique ability to self-renew and generate more HSCs or differentiate into a progenitor cell in response to cell-intrinsic and -extrinsic stimuli. The balance of HSC fate commitment is critical for a healthy blood supply. Imbalances during hematopoiesis, which are frequent in aging, can result in hematological malignancies and pre-malignancies as well as increase risk of atherosclerosis. Given the importance of HSCs and their progenitors, they have been extensively characterized in genomic and transcriptomic studies. However, an understanding of protein expression within the HSC compartment and more broadly throughout hematopoiesis remains poorly understood, and it has been widely reported that the correlation between mRNA and proteins is more complicated than previously thought. Previous mouse mass spectrometry studies have focused either specifically on stem and the first early progenitor or broadly across mixed populations of stem and progenitor cells, which do not allow for cell-type specific protein resolution across stages of differentiation. Mass cytometry has been employed to characterize transcription factor expression in human HSCs and progenitors but does not apply an unbiased discovery approach. New mass spectrometry technology now allows for deep proteomic coverage with no more than 200 ng of sample input. We report here a proteomics resource characterizing protein expression in mouse adult and aged HSCs, multipotent progenitors and oligopotent progenitors, 12 cell types in total. We validated differential expression by flow cytometry analysis and immunofluorescence staining. Additionally, we investigated the relationship between mRNA and protein levels of individual genes in HSCs compared to progenitors through RNA sequencing studies and identified two proteins that appear to be uniquely regulated in the HSC compartment, Cpin1 and Adnp. In summary, this resource provides proteomic coverage of adult and aged hematopoietic stem cells and their progenitors and reveals changes in protein abundance between cell types, with potential future implications in understanding mechanisms for stem-cell maintenance, niche interactions and fate determination.

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