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Cancers
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Proteomics in Cancer
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Proteomics in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Molecular Cancer Biology".

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 75690

Special Issue Editors

Dr. Udayan Guha

E-Mail Website
Guest Editor
1. Senior Director, Early Clinical Development, Hematology/Oncology and Cell Therapy, Bristol-Myers Squibb, 3401 Princeton Pike, Lawrenceville, NJ 08648, USA
2. Special Volunteer Investigator, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH), Bethesda, MD 20892, USA
Interests: quantitative mass spectrometry; proteogenomics; clinical trials
Dr. Xu Zhang

E-Mail
Co-Guest Editor
Director of Proteomics and Mass Spectrometry Facility, Thoracic and GI Malignancies Branch, Center for Cancer Research, National Cancer Institute (NCI), National Institutes of Health (NIH) Bethesda, MD 20892, USA
Interests: posttranslational modifications; quantitative mass spectrometry; proteogenomics; methods in mass spectrometry

Special Issue Information

Cancer is a disease typified by genomic and epigenetic variations that result in altered transcriptome, proteome, and metabolome. These alterations in turn cause perturbed signaling pathways, which manifest as the oncogenic phenotype. Next-generation sequencing (NGS) strategies have evolved rapidly and are the mainstay of personalized cancer medicine. However, the biological and phenotypic effects of the majority of somatic mutations and genomic alterations remain a mystery. The cancer proteome bridges the gap between cancer genotype and phenotype. The past decade has witnessed significant advances in identifying and characterizing the cancer proteome, leveraging major advances in data-dependent and data-independent mass spectrometry, targeted mass spectrometry using multiple-reaction monitoring (MRM), and microarray-based technologies, including reverse-phase protein arrays (RPPA).

This Special Issue will collect studies that characterize cancer phenotype and treatment effects using mass spectrometry, RPPA, single-cell proteomics, integrated proteogenomics, and chemical proteomics. Special emphasis will be given to validation studies using clinical specimens as well as to proteomic characterization in the context of prospective clinical studies.

Dr. Udayan Guha
Dr. Xu Zhang
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • mass spectrometry
  • Data-independent acquisition (DIA)
  • Multiple-reaction monitoring (MRM)
  • reverse-phase protein array (RPPA)
  • single-cell proteomics
  • biomarker

Published Papers (18 papers)

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25 pages, 4304 KiB  
Article
Proteomic Signatures of Diffuse and Intestinal Subtypes of Gastric Cancer
by Smrita Singh, Mohd Younis Bhat, Gajanan Sathe, Champaka Gopal, Jyoti Sharma, Anil K. Madugundu, Neha S. Joshi and Akhilesh Pandey
Cancers 2021, 13(23), 5930; https://doi.org/10.3390/cancers13235930 - 25 Nov 2021
Cited by 9 | Viewed by 3648
Abstract
Gastric cancer is a leading cause of death from cancer globally. Gastric cancer is classified into intestinal, diffuse and indeterminate subtypes based on histology according to the Laurén classification. The intestinal and diffuse subtypes, although different in histology, demographics and outcomes, are still [...] Read more.
Gastric cancer is a leading cause of death from cancer globally. Gastric cancer is classified into intestinal, diffuse and indeterminate subtypes based on histology according to the Laurén classification. The intestinal and diffuse subtypes, although different in histology, demographics and outcomes, are still treated in the same fashion. This study was designed to discover proteomic signatures of diffuse and intestinal subtypes. Mass spectrometry-based proteomics using tandem mass tags (TMT)-based multiplexed analysis was used to identify proteins in tumor tissues from patients with diffuse or intestinal gastric cancer with adjacent normal tissue control. A total of 7448 or 4846 proteins were identified from intestinal or diffuse subtype, respectively. This quantitative mass spectrometric analysis defined a proteomic signature of differential expression across the two subtypes, which included gremlin1 (GREM1), bcl-2-associated athanogene 2 (BAG2), olfactomedin 4 (OLFM4), thyroid hormone receptor interacting protein 6 (TRIP6) and melanoma-associated antigen 9 (MAGE-A9) proteins. Although GREM1, BAG2, OLFM4, TRIP6 and MAGE-A9 have all been previously implicated in tumor progression and metastasis, they have not been linked to intestinal or diffuse subtypes of gastric cancer. Using immunohistochemical labelling of a tissue microarray comprising of 124 cases of gastric cancer, we validated the proteomic signature obtained by mass spectrometry in the discovery cohort. Our findings should help investigate the pathogenesis of these gastric cancer subtypes and potentially lead to strategies for early diagnosis and treatment. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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16 pages, 1991 KiB  
Article
Cancer Tissue Classification Using Supervised Machine Learning Applied to MALDI Mass Spectrometry Imaging
by Paul Mittal, Mark R. Condina, Manuela Klingler-Hoffmann, Gurjeet Kaur, Martin K. Oehler, Oliver M. Sieber, Michelle Palmieri, Stefan Kommoss, Sara Brucker, Mark D. McDonnell and Peter Hoffmann
Cancers 2021, 13(21), 5388; https://doi.org/10.3390/cancers13215388 - 27 Oct 2021
Cited by 18 | Viewed by 2860
Abstract
Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI [...] Read more.
Matrix assisted laser desorption/ionization mass spectrometry imaging (MALDI MSI) can determine the spatial distribution of analytes such as protein distributions in a tissue section according to their mass-to-charge ratio. Here, we explored the clinical potential of machine learning (ML) applied to MALDI MSI data for cancer diagnostic classification using tissue microarrays (TMAs) on 302 colorectal (CRC) and 257 endometrial cancer (EC)) patients. ML based on deep neural networks discriminated colorectal tumour from normal tissue with an overall accuracy of 98% in balanced cross-validation (98.2% sensitivity and 98.6% specificity). Moreover, our machine learning approach predicted the presence of lymph node metastasis (LNM) for primary tumours of EC with an accuracy of 80% (90% sensitivity and 69% specificity). Our results demonstrate the capability of MALDI MSI for complementing classic histopathological examination for cancer diagnostic applications. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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15 pages, 4500 KiB  
Article
Reinspection of a Clinical Proteomics Tumor Analysis Consortium (CPTAC) Dataset with Cloud Computing Reveals Abundant Post-Translational Modifications and Protein Sequence Variants
by Amol Prakash, Lorne Taylor, Manu Varkey, Nate Hoxie, Yassene Mohammed, Young Ah Goo, Scott Peterman, Abhay Moghekar, Yuting Yuan, Trevor Glaros, Joel R. Steele, Pouya Faridi, Shashwati Parihari, Sanjeeva Srivastava, Joseph J. Otto, Julius O. Nyalwidhe, O. John Semmes, Michael F. Moran, Anil Madugundu, Dong Gi Mun, Akhilesh Pandey, Keira E. Mahoney, Jeffrey Shabanowitz, Satya Saxena and Benjamin C. Orsburnadd Show full author list remove Hide full author list
Cancers 2021, 13(20), 5034; https://doi.org/10.3390/cancers13205034 - 9 Oct 2021
Cited by 7 | Viewed by 4087
Abstract
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number [...] Read more.
The Clinical Proteomic Tumor Analysis Consortium (CPTAC) has provided some of the most in-depth analyses of the phenotypes of human tumors ever constructed. Today, the majority of proteomic data analysis is still performed using software housed on desktop computers which limits the number of sequence variants and post-translational modifications that can be considered. The original CPTAC studies limited the search for PTMs to only samples that were chemically enriched for those modified peptides. Similarly, the only sequence variants considered were those with strong evidence at the exon or transcript level. In this multi-institutional collaborative reanalysis, we utilized unbiased protein databases containing millions of human sequence variants in conjunction with hundreds of common post-translational modifications. Using these tools, we identified tens of thousands of high-confidence PTMs and sequence variants. We identified 4132 phosphorylated peptides in nonenriched samples, 93% of which were confirmed in the samples which were chemically enriched for phosphopeptides. In addition, our results also cover 90% of the high-confidence variants reported by the original proteogenomics study, without the need for sample specific next-generation sequencing. Finally, we report fivefold more somatic and germline variants that have an independent evidence at the peptide level, including mutations in ERRB2 and BCAS1. In this reanalysis of CPTAC proteomic data with cloud computing, we present an openly available and searchable web resource of the highest-coverage proteomic profiling of human tumors described to date. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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23 pages, 4784 KiB  
Article
Alterations in HLA Class I-Presented Immunopeptidome and Class I-Interactome upon Osimertinib Resistance in EGFR Mutant Lung Adenocarcinoma
by Yue A. Qi, Tapan K. Maity, Shaojian Gao, Tao Gong, Meriam Bahta, Abhilash Venugopalan, Xu Zhang and Udayan Guha
Cancers 2021, 13(19), 4977; https://doi.org/10.3390/cancers13194977 - 4 Oct 2021
Cited by 5 | Viewed by 3416
Abstract
Immune checkpoint inhibitor (ICI) therapy has been a paradigm shift in the treatment of cancer. ICI therapy results in durable responses and survival benefit for a large number of tumor types. Osimertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) [...] Read more.
Immune checkpoint inhibitor (ICI) therapy has been a paradigm shift in the treatment of cancer. ICI therapy results in durable responses and survival benefit for a large number of tumor types. Osimertinib, a third-generation epidermal growth factor receptor (EGFR) tyrosine kinase inhibitor (TKI) has shown great efficacy treating EGFR mutant lung cancers; however, all patients eventually develop resistance. ICI therapy has not benefitted EGFR mutant lung cancer. Herein, we employed stable isotope labeling by amino acids in cell culture (SILAC) quantitative mass spectrometry-based proteomics to investigate potential immune escape molecular mechanisms in osimertinib resistant EGFR mutant lung adenocarcinoma by interrogating the alterations in the human leukocyte antigen (HLA) Class I-presented immunopeptidome, Class I-interactome, and the whole cell proteome between isogenic osimertinib-sensitive and -resistant human lung adenocarcinoma cells. Our study demonstrates an overall reduction in HLA class I-presented immunopeptidome and downregulation of antigen presentation core complex (e.g., TAP1 and ERAP1/2) and immunoproteasome in osimertinib resistant lung adenocarcinoma cells. Several key components in autophagy pathway are differentially altered. S100 proteins and SLC3A2 may play critical roles in reduced antigen presentation. Our dataset also includes ~1000 novel HLA class I interaction partners and hundreds of Class I-presented immunopeptides in EGFR mutant lung adenocarcinoma. This large-scale unbiased proteomics study provides novel insights and potential mechanisms of immune evasion of EGFR mutant lung adenocarcinoma. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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20 pages, 4116 KiB  
Article
Defining the Tumor Microenvironment by Integration of Immunohistochemistry and Extracellular Matrix Targeted Imaging Mass Spectrometry
by Denys Rujchanarong, Julia Lefler, Janet E. Saunders, Sarah Pippin, Laura Spruill, Jennifer R. Bethard, Lauren E. Ball, Anand S. Mehta, Richard R. Drake, Michael C. Ostrowski and Peggi M. Angel
Cancers 2021, 13(17), 4419; https://doi.org/10.3390/cancers13174419 - 1 Sep 2021
Cited by 13 | Viewed by 3669
Abstract
Breast stroma plays a significant role in breast cancer risk and progression yet remains poorly understood. In breast stroma, collagen is the most abundantly expressed protein and its increased deposition and alignment contributes to progression and poor prognosis. Collagen post-translation modifications such as [...] Read more.
Breast stroma plays a significant role in breast cancer risk and progression yet remains poorly understood. In breast stroma, collagen is the most abundantly expressed protein and its increased deposition and alignment contributes to progression and poor prognosis. Collagen post-translation modifications such as hydroxylated-proline (HYP) control deposition and stromal organization. The clinical relevance of collagen HYP site modifications in cancer processes remains undefined due to technical issues accessing collagen from formalin-fixed, paraffin-embedded (FFPE) tissues. We previously developed a targeted approach for investigating collagen and other extracellular matrix proteins from FFPE tissue. Here, we hypothesized that immunohistochemistry staining for fibroblastic markers would not interfere with targeted detection of collagen stroma peptides and could reveal peptide regulation influenced by specific cell types. Our initial work demonstrated that stromal peptide peak intensities when using MALD-IMS following IHC staining (αSMA, FAP, P4HA3 and PTEN) were comparable to serial sections of nonstained tissue. Analysis of histology-directed IMS using PTEN on breast tissues and TMAs revealed heterogeneous PTEN staining patterns and suggestive roles in stromal protein regulation. This study sets the foundation for investigations of target cell types and their unique contribution to collagen regulation within extracellular matrix niches. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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18 pages, 27519 KiB  
Article
Quantitative Tyrosine Phosphoproteome Profiling of AXL Receptor Tyrosine Kinase Signaling Network
by Xinyan Wu, Li Wang, Nicole A. Pearson, Santosh Renuse, Ran Cheng, Ye Liang, Dong-Gi Mun, Anil K. Madugundu, Yaoyu Xu, Parkash S. Gill and Akhilesh Pandey
Cancers 2021, 13(16), 4234; https://doi.org/10.3390/cancers13164234 - 23 Aug 2021
Cited by 1 | Viewed by 3756
Abstract
Overexpression and amplification of AXL receptor tyrosine kinase (RTK) has been found in several hematologic and solid malignancies. Activation of AXL can enhance tumor-promoting processes such as cancer cell proliferation, migration, invasion and survival. Despite the important role of AXL in cancer development, [...] Read more.
Overexpression and amplification of AXL receptor tyrosine kinase (RTK) has been found in several hematologic and solid malignancies. Activation of AXL can enhance tumor-promoting processes such as cancer cell proliferation, migration, invasion and survival. Despite the important role of AXL in cancer development, a deep and quantitative mapping of its temporal dynamic signaling transduction has not yet been reported. Here, we used a TMT labeling-based quantitative proteomics approach to characterize the temporal dynamics of the phosphotyrosine proteome induced by AXL activation. We identified >1100 phosphotyrosine sites and observed a widespread upregulation of tyrosine phosphorylation induced by GAS6 stimulation. We also detected several tyrosine sites whose phosphorylation levels were reduced upon AXL activation. Gene set enrichment-based pathway analysis indicated the activation of several cancer-promoting and cell migration/invasion-related signaling pathways, including RAS, EGFR, focal adhesion, VEGFR and cytoskeletal rearrangement pathways. We also observed a rapid induction of phosphorylation of protein tyrosine phosphatases, including PTPN11 and PTPRA, upon GAS6 stimulation. The novel molecules downstream of AXL identified in this study along with the detailed global quantitative map elucidating the temporal dynamics of AXL activation should not only help understand the oncogenic role of AXL, but also aid in developing therapeutic options to effectively target AXL. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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25 pages, 3276 KiB  
Article
Targeted Mass Spectrometry Enables Quantification of Novel Pharmacodynamic Biomarkers of ATM Kinase Inhibition
by Jeffrey R. Whiteaker, Tao Wang, Lei Zhao, Regine M. Schoenherr, Jacob J. Kennedy, Ulianna Voytovich, Richard G. Ivey, Dongqing Huang, Chenwei Lin, Simona Colantonio, Tessa W. Caceres, Rhonda R. Roberts, Joseph G. Knotts, Jan A. Kaczmarczyk, Josip Blonder, Joshua J. Reading, Christopher W. Richardson, Stephen M. Hewitt, Sandra S. Garcia-Buntley, William Bocik, Tara Hiltke, Henry Rodriguez, Elizabeth A. Harrington, J. Carl Barrett, Benedetta Lombardi, Paola Marco-Casanova, Andrew J. Pierce and Amanda G. Paulovichadd Show full author list remove Hide full author list
Cancers 2021, 13(15), 3843; https://doi.org/10.3390/cancers13153843 - 30 Jul 2021
Cited by 8 | Viewed by 3257
Abstract
The ATM serine/threonine kinase (HGNC: ATM) is involved in initiation of repair of DNA double-stranded breaks, and ATM inhibitors are currently being tested as anti-cancer agents in clinical trials, where pharmacodynamic (PD) assays are crucial to help guide dose and scheduling and support [...] Read more.
The ATM serine/threonine kinase (HGNC: ATM) is involved in initiation of repair of DNA double-stranded breaks, and ATM inhibitors are currently being tested as anti-cancer agents in clinical trials, where pharmacodynamic (PD) assays are crucial to help guide dose and scheduling and support mechanism of action studies. To identify and quantify PD biomarkers of ATM inhibition, we developed and analytically validated a 51-plex assay (DDR-2) quantifying protein expression and DNA damage-responsive phosphorylation. The median lower limit of quantification was 1.28 fmol, the linear range was over 3 orders of magnitude, the median inter-assay variability was 11% CV, and 86% of peptides were stable for storage prior to analysis. Use of the assay was demonstrated to quantify signaling following ionizing radiation-induced DNA damage in both immortalized lymphoblast cell lines and primary human peripheral blood mononuclear cells, identifying PD biomarkers for ATM inhibition to support preclinical and clinical studies. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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22 pages, 5043 KiB  
Article
Comprehensive Library Generation for Identification and Quantification of Endometrial Cancer Protein Biomarkers in Cervico-Vaginal Fluid
by Kelechi Njoku, Davide Chiasserini, Bethany Geary, Andrew Pierce, Eleanor R. Jones, Anthony D. Whetton and Emma J. Crosbie
Cancers 2021, 13(15), 3804; https://doi.org/10.3390/cancers13153804 - 28 Jul 2021
Cited by 9 | Viewed by 3008
Abstract
Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect [...] Read more.
Endometrial cancer is the most common gynaecological malignancy in high-income countries and its incidence is rising. Early detection, aided by highly sensitive and specific biomarkers, has the potential to improve outcomes as treatment can be provided when it is most likely to effect a cure. Sequential window acquisition of all theoretical mass spectra (SWATH-MS), an accurate and reproducible platform for analysing biological samples, offers a technological advance for biomarker discovery due to its reproducibility, sensitivity and potential for data re-interrogation. SWATH-MS requires a spectral library in order to identify and quantify peptides from multiplexed mass spectrometry data. Here we present a bespoke spectral library of 154,206 transitions identifying 19,394 peptides and 2425 proteins in the cervico-vaginal fluid of postmenopausal women with, or at risk of, endometrial cancer. We have combined these data with a library of over 6000 proteins generated based on mass spectrometric analysis of two endometrial cancer cell lines. This unique resource enables the study of protein biomarkers for endometrial cancer detection in cervico-vaginal fluid. Data are available via ProteomeXchange with unique identifier PXD025925. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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15 pages, 4133 KiB  
Article
Two Dimensional-Difference in Gel Electrophoresis (2D-DIGE) Proteomic Approach for the Identification of Biomarkers in Endometrial Cancer Serum
by Blendi Ura, Stefania Biffi, Lorenzo Monasta, Giorgio Arrigoni, Ilaria Battisti, Giovanni Di Lorenzo, Federico Romano, Michelangelo Aloisio, Fulvio Celsi, Riccardo Addobbati, Francesco Valle, Enrico Rampazzo, Marco Brucale, Andrea Ridolfi, Danilo Licastro and Giuseppe Ricci
Cancers 2021, 13(14), 3639; https://doi.org/10.3390/cancers13143639 - 20 Jul 2021
Cited by 13 | Viewed by 4503
Abstract
Endometrial cancer is the most common gynecologic malignancy arising from the endometrium. Identification of serum biomarkers could be beneficial for its early diagnosis. We have used 2D-Difference In Gel Electrophoresis (2D-DIGE) coupled with Mass Spectrometry (MS) procedures to investigate the serum proteome of [...] Read more.
Endometrial cancer is the most common gynecologic malignancy arising from the endometrium. Identification of serum biomarkers could be beneficial for its early diagnosis. We have used 2D-Difference In Gel Electrophoresis (2D-DIGE) coupled with Mass Spectrometry (MS) procedures to investigate the serum proteome of 15 patients with endometrial cancer and 15 non-cancer subjects. We have identified 16 proteins with diagnostic potential, considering only spots with a fold change in %V ≥ 1.5 or ≤0.6 in intensity, which were statistically significant (p < 0.05). Western blotting data analysis confirmed the upregulation of CLU, ITIH4, SERPINC1, and C1RL in endometrial and exosome cancer sera compared to those of control subjects. The application of the logistic regression constructed based on the abundance of these four proteins separated the controls from the cancers with excellent levels of sensitivity and specificity. After a validation phase, our findings support the potential of using the proposed algorithm as a diagnostic tool in the clinical stage. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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16 pages, 3997 KiB  
Article
Tyrosine Phosphoproteomics of Patient-Derived Xenografts Reveals Ephrin Type-B Receptor 4 Tyrosine Kinase as a Therapeutic Target in Pancreatic Cancer
by Santosh Renuse, Vijay S. Madamsetty, Dong-Gi Mun, Anil K. Madugundu, Smrita Singh, Savita Udainiya, Kiran K. Mangalaparthi, Min-Sik Kim, Ren Liu, S. Ram Kumar, Valery Krasnoperov, Mark Truty, Rondell P. Graham, Parkash S. Gill, Debabrata Mukhopadhyay and Akhilesh Pandey
Cancers 2021, 13(14), 3404; https://doi.org/10.3390/cancers13143404 - 7 Jul 2021
Cited by 2 | Viewed by 2799
Abstract
Pancreatic ductal adenocarcinoma is a recalcitrant tumor with minimal response to conventional chemotherapeutic approaches. Oncogenic signaling by activated tyrosine kinases has been implicated in cancers resulting in activation of diverse effector signaling pathways. Thus, the discovery of aberrantly activated tyrosine kinases is of [...] Read more.
Pancreatic ductal adenocarcinoma is a recalcitrant tumor with minimal response to conventional chemotherapeutic approaches. Oncogenic signaling by activated tyrosine kinases has been implicated in cancers resulting in activation of diverse effector signaling pathways. Thus, the discovery of aberrantly activated tyrosine kinases is of great interest in developing novel therapeutic strategies in the treatment and management of pancreatic cancer. Patient-derived tumor xenografts (PDXs) in mice serve as potentially valuable preclinical models as they maintain the histological and molecular heterogeneity of the original human tumor. Here, we employed high-resolution mass spectrometry combined with immunoaffinity purification using anti-phosphotyrosine antibodies to profile tyrosine phosphoproteome across 13 pancreatic ductal adenocarcinoma PDX models. This analysis resulted in the identification of 1199 tyrosine-phosphorylated sites mapping to 704 proteins. The mass spectrometric analysis revealed widespread and heterogeneous activation of both receptor and non-receptor tyrosine kinases. Preclinical studies confirmed ephrin type-B receptor 4 (EphB4) as a potential therapeutic target based on the efficacy of human serum albumin-conjugated soluble EphB4 in mice bearing orthotopic xenografts. Immunohistochemistry-based validation using tissue microarrays from 346 patients with PDAC showed significant expression of EphB4 in >70% of patients. In summary, we present a comprehensive landscape of tyrosine phosphoproteome with EphB4 as a promising therapeutic target in pancreatic ductal adenocarcinoma. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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14 pages, 3719 KiB  
Article
Imaging Mass Spectrometry-Based Proteomic Analysis to Differentiate Melanocytic Nevi and Malignant Melanoma
by Rita Casadonte, Mark Kriegsmann, Katharina Kriegsmann, Isabella Hauk, Rolf R. Meliß, Cornelia S. L. Müller and Jörg Kriegsmann
Cancers 2021, 13(13), 3197; https://doi.org/10.3390/cancers13133197 - 26 Jun 2021
Cited by 13 | Viewed by 2271
Abstract
The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, [...] Read more.
The discrimination of malignant melanoma from benign nevi may be difficult in some cases. For this reason, immunohistological and molecular techniques are included in the differential diagnostic toolbox for these lesions. These methods are time consuming when applied subsequently and, in some cases, no definitive diagnosis can be made. We studied both lesions by imaging mass spectrometry (IMS) in a large cohort (n = 203) to determine a different proteomic profile between cutaneous melanomas and melanocytic nevi. Sample preparation and instrument setting were tested to obtain optimal results in term of data quality and reproducibility. A proteomic signature was found by linear discriminant analysis to discern malignant melanoma from benign nevus (n = 113) with an overall accuracy of >98%. The prediction model was tested in an independent set (n = 90) reaching an overall accuracy of 93% in classifying melanoma from nevi. Statistical analysis of the IMS data revealed mass-to-charge ratio (m/z) peaks which varied significantly (Area under the receiver operating characteristic curve > 0.7) between the two tissue types. To our knowledge, this is the largest IMS study of cutaneous melanoma and nevi performed up to now. Our findings clearly show that discrimination of melanocytic nevi from melanoma is possible by IMS. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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18 pages, 1945 KiB  
Article
Discovery and Evaluation of Protein Biomarkers as a Signature of Wellness in Late-Stage Cancer Patients in Early Phase Clinical Trials
by Bethany Geary, Erin Peat, Sarah Dransfield, Natalie Cook, Fiona Thistlethwaite, Donna Graham, Louise Carter, Andrew Hughes, Matthew G. Krebs and Anthony D. Whetton
Cancers 2021, 13(10), 2443; https://doi.org/10.3390/cancers13102443 - 18 May 2021
Cited by 4 | Viewed by 2351
Abstract
TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient’s ability to perform a variety of activities. However, the quality of present [...] Read more.
TARGET (tumour characterisation to guide experimental targeted therapy) is a cancer precision medicine programme focused on molecular characterisation of patients entering early phase clinical trials. Performance status (PS) measures a patient’s ability to perform a variety of activities. However, the quality of present algorithms to assess PS is limited and based on qualitative clinician assessment. Plasma samples from patients enrolled into TARGET were analysed using the mass spectrometry (MS) technique: sequential window acquisition of all theoretical fragment ion spectra (SWATH)-MS. SWATH-MS was used on a discovery cohort of 55 patients to differentiate patients into either a good or poor prognosis by creation of a Wellness Score (WS) that showed stronger prediction of overall survival (p = 0.000551) compared to PS (p = 0.001). WS was then tested against a validation cohort of 77 patients showing significant (p = 0.000451) prediction of overall survival. WS in both sets had receiver operating characteristic curve area under the curve (AUC) values of 0.76 (p = 0.002) and 0.67 (p = 0.011): AUC of PS was 0.70 (p = 0.117) and 0.55 (p = 0.548). These signatures can now be evaluated further in larger patient populations to assess their utility in a clinical setting. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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13 pages, 1480 KiB  
Article
Empirical Evaluation of the Use of Computational HLA Binding as an Early Filter to the Mass Spectrometry-Based Epitope Discovery Workflow
by Rachid Bouzid, Monique T. A. de Beijer, Robbie J. Luijten, Karel Bezstarosti, Amy L. Kessler, Marco J. Bruno, Maikel P. Peppelenbosch, Jeroen A. A. Demmers and Sonja I. Buschow
Cancers 2021, 13(10), 2307; https://doi.org/10.3390/cancers13102307 - 12 May 2021
Cited by 2 | Viewed by 2538
Abstract
Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made [...] Read more.
Immunopeptidomics is used to identify novel epitopes for (therapeutic) vaccination strategies in cancer and infectious disease. Various false discovery rates (FDRs) are applied in the field when converting liquid chromatography-tandem mass spectrometry (LC-MS/MS) spectra to peptides. Subsequently, large efforts have recently been made to rescue peptides of lower confidence. However, it remains unclear what the overall relation is between the FDR threshold and the percentage of obtained HLA-binders. We here directly evaluated the effect of varying FDR thresholds on the resulting immunopeptidomes of HLA-eluates from human cancer cell lines and primary hepatocyte isolates using HLA-binding algorithms. Additional peptides obtained using less stringent FDR-thresholds, although generally derived from poorer spectra, still contained a high amount of HLA-binders and confirmed recently developed tools that tap into this pool of otherwise ignored peptides. Most of these peptides were identified with improved confidence when cell input was increased, supporting the validity and potential of these identifications. Altogether, our data suggest that increasing the FDR threshold for peptide identification in conjunction with data filtering by HLA-binding prediction, is a valid and highly potent method to more efficient exhaustion of immunopeptidome datasets for epitope discovery and reveals the extent of peptides to be rescued by recently developed algorithms. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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20 pages, 6303 KiB  
Article
SWATH-MS Based Proteomic Profiling of Prostate Cancer Cells Reveals Adaptive Molecular Mechanisms in Response to Anti-Androgen Therapy
by Chamikara Liyanage, Adil Malik, Pevindu Abeysinghe, Judith Clements and Jyotsna Batra
Cancers 2021, 13(4), 715; https://doi.org/10.3390/cancers13040715 - 9 Feb 2021
Cited by 9 | Viewed by 4384
Abstract
Prostate cancer (PCa) is the second most common cancer affecting men worldwide. PCa shows a broad-spectrum heterogeneity in its biological and clinical behavior. Although androgen targeted therapy (ATT) has been the mainstay therapy for advanced PCa, it inevitably leads to treatment resistance and [...] Read more.
Prostate cancer (PCa) is the second most common cancer affecting men worldwide. PCa shows a broad-spectrum heterogeneity in its biological and clinical behavior. Although androgen targeted therapy (ATT) has been the mainstay therapy for advanced PCa, it inevitably leads to treatment resistance and progression to castration resistant PCa (CRPC). Thus, greater understanding of the molecular basis of treatment resistance and CRPC progression is needed to improve treatments for this lethal phenotype. The current study interrogated both proteomics and transcriptomic alterations stimulated in AR antagonist/anti-androgen (Bicalutamide and Enzalutamide) treated androgen-dependent cell model (LNCaP) in comparison with androgen-independent/castration-resistant cell model (C4-2B). The analysis highlighted the activation of MYC and PSF/SFPQ oncogenic upstream regulators in response to the anti-androgen treatment. Moreover, the study revealed anti-androgen induced genes/proteins related to transcription/translation regulation, energy metabolism, cell communication and signaling cascades promoting tumor growth and proliferation. In addition, these molecules were found dysregulated in PCa clinical proteomic and transcriptomic datasets, suggesting their potential involvement in PCa progression. In conclusion, our study provides key molecular signatures and associated pathways that might contribute to CRPC progression despite treatment with anti-androgens. Such molecular signatures could be potential therapeutic targets to improve the efficacy of existing therapies and/or predictive/prognostic value in CRPC for treatment response. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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21 pages, 21466 KiB  
Article
Large-Scale Proteomic Analysis of Follicular Lymphoma Reveals Extensive Remodeling of Cell Adhesion Pathway and Identifies Hub Proteins Related to the Lymphomagenesis
by Kamila Duś-Szachniewicz, Grzegorz Rymkiewicz, Anil Kumar Agrawal, Paweł Kołodziej and Jacek R. Wiśniewski
Cancers 2021, 13(4), 630; https://doi.org/10.3390/cancers13040630 - 5 Feb 2021
Cited by 5 | Viewed by 2413
Abstract
Follicular lymphoma (FL) represents the major subtype of indolent B-cell non-Hodgkin lymphomas (B-NHLs) and results from the malignant transformation of mature B-cells in lymphoid organs. Although gene expression and genomic studies have identified multiple disease driving gene aberrations, only a few proteomic studies [...] Read more.
Follicular lymphoma (FL) represents the major subtype of indolent B-cell non-Hodgkin lymphomas (B-NHLs) and results from the malignant transformation of mature B-cells in lymphoid organs. Although gene expression and genomic studies have identified multiple disease driving gene aberrations, only a few proteomic studies focused on the protein level. The present work aimed to examine the proteomic profiles of follicular lymphoma vs. normal B-cells obtained by fine-needle aspiration biopsy (FNAB) to gain deep insight into the most perturbed pathway of FL. The cells of interest were purified by magnetic-activated cell sorting (MACS). High-throughput proteomic profiling was performed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) and allowed to identify of 6724 proteins in at least 75% of each group of samples. The ‘Total Protein Approach’ (TPA) was applied to the absolute quantification of proteins in this study. We identified 1186 differentially abundant proteins (DAPs) between FL and control samples, causing an extensive remodeling of several molecular pathways, including the B-cell receptor signaling pathway, cellular adhesion molecules, and PPAR pathway. Additionally, the construction of protein–protein interactions networks (PPINs) and identification of hub proteins allowed us to indicate the key player proteins for FL pathology. Finally, ICAM1, CD9, and CD79B protein expression was validated in an independent cohort by flow cytometry (FCM), and the results were consistent with the mass spectrometry (MS) data. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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Review

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31 pages, 1741 KiB  
Review
Proteomics, Personalized Medicine and Cancer
by Miao Su, Zhe Zhang, Li Zhou, Chao Han, Canhua Huang and Edouard C. Nice
Cancers 2021, 13(11), 2512; https://doi.org/10.3390/cancers13112512 - 21 May 2021
Cited by 19 | Viewed by 7655
Abstract
As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including [...] Read more.
As of 2020 the human genome and proteome are both at >90% completion based on high stringency analyses. This has been largely achieved by major technological advances over the last 20 years and has enlarged our understanding of human health and disease, including cancer, and is supporting the current trend towards personalized/precision medicine. This is due to improved screening, novel therapeutic approaches and an increased understanding of underlying cancer biology. However, cancer is a complex, heterogeneous disease modulated by genetic, molecular, cellular, tissue, population, environmental and socioeconomic factors, which evolve with time. In spite of recent advances in treatment that have resulted in improved patient outcomes, prognosis is still poor for many patients with certain cancers (e.g., mesothelioma, pancreatic and brain cancer) with a high death rate associated with late diagnosis. In this review we overview key hallmarks of cancer (e.g., autophagy, the role of redox signaling), current unmet clinical needs, the requirement for sensitive and specific biomarkers for early detection, surveillance, prognosis and drug monitoring, the role of the microbiome and the goals of personalized/precision medicine, discussing how emerging omics technologies can further inform on these areas. Exemplars from recent onco-proteogenomic-related publications will be given. Finally, we will address future perspectives, not only from the standpoint of perceived advances in treatment, but also from the hurdles that have to be overcome. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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15 pages, 1102 KiB  
Review
Proteomic Studies on the Management of High-Grade Serous Ovarian Cancer Patients: A Mini-Review
by Melissa Bradbury, Eva Borràs, Assumpció Pérez-Benavente, Antonio Gil-Moreno, Anna Santamaria and Eduard Sabidó
Cancers 2021, 13(9), 2067; https://doi.org/10.3390/cancers13092067 - 25 Apr 2021
Cited by 7 | Viewed by 4742
Abstract
High-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the [...] Read more.
High-grade serous ovarian cancer (HGSC) remains the most common and deadly subtype of ovarian cancer. It is characterized by its late diagnosis and frequent relapse despite standardized treatment with cytoreductive surgery and platinum-based chemotherapy. The past decade has seen significant advances in the clinical management and molecular understanding of HGSC following the publication of the Cancer Genome Atlas (TCGA) researchers and the introduction of targeted therapies with anti-angiogenic drugs and poly(ADP-ribose) polymerase inhibitors in specific subgroups of patients. We provide a comprehensive review of HGSC, focusing on the most important molecular advances aimed at providing a better understanding of the disease and its response to treatment. We emphasize the role that proteomic technologies are now playing in these two aspects of the disease, through the identification of proteins and their post-translational modifications in ovarian cancer tumors. Finally, we highlight how the integration of proteomics with genomics, exemplified by the work performed by the Clinical Proteomic Tumor Analysis Consortium (CPTAC), can guide the development of new biomarkers and therapeutic targets. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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40 pages, 2341 KiB  
Review
Current Methods of Post-Translational Modification Analysis and Their Applications in Blood Cancers
by Katie Dunphy, Paul Dowling, Despina Bazou and Peter O’Gorman
Cancers 2021, 13(8), 1930; https://doi.org/10.3390/cancers13081930 - 16 Apr 2021
Cited by 24 | Viewed by 7481
Abstract
Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability [...] Read more.
Post-translational modifications (PTMs) add a layer of complexity to the proteome through the addition of biochemical moieties to specific residues of proteins, altering their structure, function and/or localization. Mass spectrometry (MS)-based techniques are at the forefront of PTM analysis due to their ability to detect large numbers of modified proteins with a high level of sensitivity and specificity. The low stoichiometry of modified peptides means fractionation and enrichment techniques are often performed prior to MS to improve detection yields. Immuno-based techniques remain popular, with improvements in the quality of commercially available modification-specific antibodies facilitating the detection of modified proteins with high affinity. PTM-focused studies on blood cancers have provided information on altered cellular processes, including cell signaling, apoptosis and transcriptional regulation, that contribute to the malignant phenotype. Furthermore, the mechanism of action of many blood cancer therapies, such as kinase inhibitors, involves inhibiting or modulating protein modifications. Continued optimization of protocols and techniques for PTM analysis in blood cancer will undoubtedly lead to novel insights into mechanisms of malignant transformation, proliferation, and survival, in addition to the identification of novel biomarkers and therapeutic targets. This review discusses techniques used for PTM analysis and their applications in blood cancer research. Full article
(This article belongs to the Special Issue Proteomics in Cancer)
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