Elsevier

Vitamins and Hormones

Volume 107, 2018, Pages 515-531
Vitamins and Hormones

Chapter Eighteen - Characterization of the Ovarian Tumor Peptidome

https://doi.org/10.1016/bs.vh.2018.01.020Get rights and content

Abstract

Aberrant degradation of proteins is associated with many pathological states, including cancers. Mass spectrometric analysis of the tumor peptidome has the potential to provide biological insights on proteolytic processing in cancer. However, attempts to use the tumors peptidome information in cancer research have been fairly limited to date, largely due to the lack of effective approaches for robust peptidomics identification and quantification, and the prevalence of confounding factors and biases associated with sample handling and processing. To address this need, we have recently developed an effective and robust analytical platform as well as a novel informatics approach for comprehensive analyses of tissue peptidomes. The ability of this new peptidomics pipeline for high-throughput, comprehensive, and quantitative peptidomics analysis, as well as the suitability of clinical ovarian tumor samples with postexcision delay limited to less than 60 min before freezing for peptidomics analysis, has been demonstrated. These initial analyses set a stage for further determination of molecular details and functional significance of the peptidomic activities in ovarian cancer.

Introduction

By avoiding the digestion of proteins into peptides, as applied in conventional bottom-up proteomics, peptidomics can provide otherwise unobtainable insights on posttranslational modifications (PTMs) and endogenous proteolytic products. For example, our recent study of blood plasma peptidome revealed strikingly different peptidomics profiles between samples from breast cancer (BrCa) patients and healthy controls, despite the very similar bottom-up proteomic profiles, showing the potential of peptidomics to reveal endogenous proteolytic processing information to which conventional bottom-up proteomics is effectively blind (Shen, Tolic, et al., 2010). Recent studies in cell lines have also identified proteolytic products of intracellular and intercellular proteins that appear to be stable bioactive molecules with explicit roles in cellular signaling pathways, rather than simple transient protein degradation products (Cunha et al., 2008; Gelman, Sironi, Castro, Ferro, & Fricker, 2011). Therefore, peptidomics provides important insights into the activity of various endogenous proteases, as well as potential information on other biologically active peptides (Lopez-Otin & Overall, 2002; Villanueva et al., 2006). Broad- and large-scale peptidomics studies using advanced liquid chromatography-tandem mass spectrometry (LC-MS/MS) have been increasingly conducted to characterize endogenous peptides in different biological samples, including cell lines (Fricker, Gelman, Castro, Gozzo, & Ferro, 2012; Gelman et al., 2011), body fluids (Fiedler et al., 2007; Villanueva et al., 2006), as well as tissues (Xu et al., 2015), for biomarker screening or clinically related studies.

Interpreting the significance of the peptidomic components identified from clinical tissue samples in a biological context is also of biomedical interest, and particularly the ability to differentiate between specific regulated events that convey biological information and more general protein degradation. Identifying the natural proteases and their associated pathways in clinical tissue samples and exploring the biological significance of the protease/substrate interaction has the potential to provide novel insights into the mechanisms of molecular tumor pathology (Shahinian et al., 2013). To achieve this goal, it is desirable that the peptidomics pipeline provides comprehensive peptidome coverage and robust quantification, which is dependent on each step of the pipeline, including peptide extraction and/or enrichment/fractionation, separation, LC-MS data acquisition, as well as the subsequent informatics analysis (Tinoco & Saghatelian, 2011). Other issues related to peptidomics analysis of clinical samples, e.g., the ability to efficiently extract the full repertoire of peptides from complicated clinical tissue samples while maintaining the “original” status of the samples (e.g., avoiding potential confounding factors), also need to be considered. For example, postmortem stability is potentially problematic for peptidomic studies (Zhu & Desiderio, 1993), as are issues, associated with the handling of clinical samples, including degradation associated with postexcision delay, making it difficult to characterize the “true” peptidome of clinical tumor samples.

We have recently developed and applied a highly effective, label-free quantitative peptidomics pipeline to characterize the peptidomes in human ovarian cancer (OvCa) tumor samples. The ability of the platform to achieve highly reproducible measurements and comprehensive peptidomic coverage was demonstrated (Wu et al., 2015; Xu et al., 2015). Compared to the conventional LC-MS/MS-based pipeline, the use of a novel LC-MS-based informatics approach, informed quantification (IQ), provided not only significantly improved peptidome coverage but also dramatically reduced “missing values” across different sample analyses. In addition, analyses of tumor samples undergoing controlled postexcision delay of 5–60 min showed little or no effect of warm ischemia time on peptidomes from the OvCa tumors, an important observation supporting the utility of peptidomic approaches. In line with other observations, the proteasome was observed as the potential major contributor to human OvCa tumor peptidomes (Xu et al., 2015). These results demonstrate that robust quantitative peptidomics analysis can be effectively performed on clinical tumor samples using our peptidomics platform. We anticipate continued application of this platform to further determine molecular details and functional significance of the peptidomic activities in cancers.

Section snippets

Ovarian Tumor Samples

Ovarian tumor tissues (FIGO stage IIIC or IV) were collected from three patients with high-grade serous ovarian carcinoma under IRB-approved protocols. Prior to performing primary tumor resection and before any compromise to the vascular supply, a portion of ovarian tumor attached to the omentum was rapidly resected. The tumor specimen was immediately dissected into four contiguous and adjacent specimens strips each no larger than 10 × 3 × 3 mm, and placed into cryovials and frozen in liquid

Robust Characterization of the Tumor Peptidome

A total of 5756 distinct peptides ranging from 500 to ~ 6000 Da (Fig. 1A) were identified and quantified (label-free) from analyses of 12 ovarian cancer tumor tissue samples (from three patients, each with four time points). The number of identified peptides was highly consistent across samples (4952 ± 285; CV = 5.8%) (Fig. 1B), demonstrating the good, consistent peptidome coverage of the analytical platform. These peptides were mapped into 974 distinct precursor proteins (peptidome peptides, their

Conclusions

The study of low-molecular-weight bioreactive peptides, protein degradation products, and small intact proteins (i.e., the peptidome) is often criticized as being less sensitive, less reproducible, and as vulnerable to a lack of controls in sample collection and preparation (Fricker, Lim, Pan, & Che, 2006; Leichtle, Dufour, & Fiedler, 2013). Herein, we developed and evaluated a robust, efficient, and quantitative peptidomics platform and demonstrated its utility for in-depth characterization of

Acknowledgments

Portions of this work were supported by the Grant U24CA160019, from the National Cancer Institute Clinical Proteomic Tumor Analysis Consortium (CPTAC), National Institutes of Health Grant P41GM103493, and Department of Defense Interagency Agreement MIPR2DO89M2058. The experimental work described herein was performed in the Environmental Molecular Sciences Laboratory, a national scientific user facility sponsored by the DOE and located at Pacific Northwest National Laboratory, which is operated

References (36)

  • L.D. Fricker et al.

    Peptidomics: Identification and quantification of endogenous peptides in neuroendocrine tissues

    Mass Spectrometry Reviews

    (2006)
  • J.S. Gelman et al.

    Peptidomic analysis of human cell lines

    Journal of Proteome Research

    (2011)
  • C. Giglione et al.

    Protein N-terminal methionine excision

    Cellular and Molecular Life Sciences

    (2004)
  • M.H. Glickman et al.

    The ubiquitin-proteasome proteolytic pathway: Destruction for the sake of construction

    Physiological Reviews

    (2002)
  • J.D. Holman et al.

    Identifying proteomic LC-MS/MS data sets with Bumbershoot and IDPicker

    Current Protocols in Bioinformatics

    (2012)
  • N. Jaitly et al.

    Decon2LS: An open-source software package for automated processing and visualization of high resolution mass spectrometry data

    BMC Bioinformatics

    (2009)
  • B. Keil

    Specificity of proteolysis

    (1992)
  • S. Kim et al.

    MS-GF+ makes progress towards a universal database search tool for proteomics

    Nature Communications

    (2014)
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