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OSppc: A web server for online survival analysis using proteome of pan-cancers. J Proteomics 2023; 273:104810. [PMID: 36587732 DOI: 10.1016/j.jprot.2022.104810] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 12/20/2022] [Accepted: 12/27/2022] [Indexed: 12/31/2022]
Abstract
Prognostic biomarker, as a feasible and objective indicator, is valuable in the assessment of cancer risk. With the development of high-throughput sequencing technology, the screening of prognostic biomarkers has become easy, but it is difficult to screen prognostic markers based on proteomic data. In this study we developed a tool named Online consensus Survival analysis web server based on Proteome of Pan-cancers, abbreviated as OSppc, to evaluate the prognostic values of protein biomarkers. >8000 cancer cases with proteomic data, transcriptomic data and clinical follow-up information were collected from TCGA and CPTAC. 14,038 proteins (including proteins and their phosphorylated forms) analyzed by reverse-phase protein arrays and mass spectrometry in 33 types of cancers were collected. In OSppc, three analysis modules are provided, including Survival Analysis, Differential Analysis and Correlation Analysis. Survival analysis module exhibits HR with 95% CI and KM curves with log-rank p value of protein and mRNA levels of input genes. Differential analysis module shows the box plots of protein expression levels in different tissues. Correlation analysis module provides scatter plot with pearson's and spearman's correlation coefficient of the protein and its corresponding mRNA. OSppc can be accessed at http://bioinfo.henu.edu.cn/Protein/OSppc.html. SIGNIFICANCE: OSppc can analyze the association between protein, mRNA and prognosis, the correlation between proteome data and gene expression profiles, the differential expression of proteome data between subgroups such as normal and cancer as well. OSppc is registration-free and very valuable to evaluate the prognostic potency of protein of interests. OSppc is very valuable for researchers and clinicians to screen, develop and validate potential protein prognostic biomarkers in pan-cancers, and offers the opportunities to investigate the clinical important functional genes and therapeutic targets of cancers.
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Jiang W, Jones JC, Shankavaram U, Sproull M, Camphausen K, Krauze AV. Analytical Considerations of Large-Scale Aptamer-Based Datasets for Translational Applications. Cancers (Basel) 2022; 14:2227. [PMID: 35565358 PMCID: PMC9105298 DOI: 10.3390/cancers14092227] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 04/15/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
Abstract
The development and advancement of aptamer technology has opened a new realm of possibilities for unlocking the biocomplexity available within proteomics. With ultra-high-throughput and multiplexing, alongside remarkable specificity and sensitivity, aptamers could represent a powerful tool in disease-specific research, such as supporting the discovery and validation of clinically relevant biomarkers. One of the fundamental challenges underlying past and current proteomic technology has been the difficulty of translating proteomic datasets into standards of practice. Aptamers provide the capacity to generate single panels that span over 7000 different proteins from a singular sample. However, as a recent technology, they also present unique challenges, as the field of translational aptamer-based proteomics still lacks a standardizing methodology for analyzing these large datasets and the novel considerations that must be made in response to the differentiation amongst current proteomic platforms and aptamers. We address these analytical considerations with respect to surveying initial data, deploying proper statistical methodologies to identify differential protein expressions, and applying datasets to discover multimarker and pathway-level findings. Additionally, we present aptamer datasets within the multi-omics landscape by exploring the intersectionality of aptamer-based proteomics amongst genomics, transcriptomics, and metabolomics, alongside pre-existing proteomic platforms. Understanding the broader applications of aptamer datasets will substantially enhance current efforts to generate translatable findings for the clinic.
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Affiliation(s)
- Will Jiang
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Jennifer C. Jones
- Translational Nanobiology Section, Laboratory of Pathology, NIH/NCI/CCR, Bethesda, MD 20892, USA;
| | - Uma Shankavaram
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Mary Sproull
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Kevin Camphausen
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
| | - Andra V. Krauze
- Radiation Oncology Branch, Center for Cancer Research, National Cancer Institute, NIH, 9000 Rockville Pike, Building 10, CRC, Bethesda, MD 20892, USA; (W.J.); (U.S.); (M.S.); (K.C.)
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Hoff FW, Horton TM, Kornblau SM. Reverse phase protein arrays in acute leukemia: investigative and methodological challenges. Expert Rev Proteomics 2021; 18:1087-1097. [PMID: 34965151 DOI: 10.1080/14789450.2021.2020655] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
Abstract
INTRODUCTION Acute leukemia results from a series of mutational events that alter cell growth and proliferation. Mutations result in protein changes that orchestrate growth alterations characteristic of leukemia. Proteomics is a methodology appropriate for study of protein changes found in leukemia. The high-throughput reverse phase protein array (RPPA) technology is particularly well-suited for the assessment of protein changes in samples derived from clinical trials. AREAS COVERED This review discusses the technical, methodological, and analytical issues related to the successful development of acute leukemia RPPAs. EXPERT COMMENTARY To obtain representative protein sample lysates, samples should be prepared from freshly collected blood or bone marrow material. Variables such as sample shipment, transit time, and holding temperature only have minimal effects on protein expression. CellSave preservation tubes are preferred for cells collected after exposure to chemotherapy, and incorporation of standardized guidelines for antibody validation is recommended. A more systematic biological approach to analyze protein expression is desired, searching for recurrent patterns of protein expression that allow classification of patients into risk groups, or groups of patients that may be treated similarly. Comparing RPPA protein analysis between cell lines and primary samples shows that cell lines are not representative of patient proteomic patterns.
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Affiliation(s)
- Fieke W Hoff
- Department of Internal Medicine, The University of Texas Southwestern Medical Center, TX, USA
| | - Terzah M Horton
- Department of Pediatrics, Texas Children's Cancer Center, Baylor College of Medicine, Houston, TX, USA
| | - Steven M Kornblau
- Department of Leukemia, The University of Texas MD Anderson Cancer Center, Houston, TX, USA
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Ibrahim S, Lan C, Chabot C, Mitsa G, Buchanan M, Aguilar-Mahecha A, Elchebly M, Poetz O, Spatz A, Basik M, Batist G, Zahedi RP, Borchers CH. Precise Quantitation of PTEN by Immuno-MRM: A Tool To Resolve the Breast Cancer Biomarker Controversy. Anal Chem 2021; 93:10816-10824. [PMID: 34324311 DOI: 10.1021/acs.analchem.1c00975] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The tumor suppressor PTEN is the main negative regulator of PI3K/AKT/mTOR signaling and is commonly found downregulated in breast cancer (BC). Conflicting data from conventional immunoassays such as immunohistochemistry (IHC) has sparked controversy about PTEN's role as a prognostic and predictive biomarker in BC, which can be largely attributed to the lack of specificity, sensitivity, and interlaboratory standardization. Here, we present a fully standardized, highly sensitive, robust microflow immuno-MRM (iMRM) assay that enables precise quantitation of PTEN concentrations in cells and fresh frozen (FF) and formalin-fixed paraffin-embedded (FFPE) tissues, down to 0.1 fmol/10 μg of extracted protein, with high interday and intraday precision (CV 6.3%). PTEN protein levels in BC PDX samples that were determined by iMRM correlate well with semiquantitative IHC and WB data. iMRM, however, allowed the precise quantitation of PTEN-even in samples that were deemed to be PTEN negative by IHC or western blot (WB)-while requiring substantially less tumor tissue than WB. This is particularly relevant because the extent of PTEN downregulation in tumors has been shown to correlate with severity. Our standardized and robust workflow includes an 11 min microflow LC-MRM analysis on a triple-quadrupole MS and thus provides a much needed tool for the study of PTEN as a potential biomarker for BC.
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Affiliation(s)
- Sahar Ibrahim
- Division of Experimental Medicine, McGill University, Montréal, Quebec H4A 3J1 Canada.,Clinical Pathology Department, Menoufia University, Shebeen, El Kom 32511, Egypt.,Segal Cancer Proteomics Centre, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - Cathy Lan
- Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - Catherine Chabot
- Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - Georgia Mitsa
- Division of Experimental Medicine, McGill University, Montréal, Quebec H4A 3J1 Canada.,Segal Cancer Proteomics Centre, McGill University, Montréal, Quebec H3T 1E2, Canada
| | | | | | - Mounib Elchebly
- Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - Oliver Poetz
- University of Tuebingen, Reutlingen 72770, Germany.,SIGNATOPE GmbH, Reutlingen 72770, Germany
| | - Alan Spatz
- Division of Experimental Medicine, McGill University, Montréal, Quebec H4A 3J1 Canada.,Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada.,Department of Pathology, McGill University, Montréal, Quebec H3A 2B4, Canada.,OPTILAB-McGill University Health Centre, Montréal, Quebec H4A 3J1, Canada
| | - Mark Basik
- Division of Experimental Medicine, McGill University, Montréal, Quebec H4A 3J1 Canada.,Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - Gerald Batist
- Segal Cancer Centre, McGill University, Montréal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Quebec H3T 1E2, Canada
| | - René P Zahedi
- Segal Cancer Proteomics Centre, McGill University, Montréal, Quebec H3T 1E2, Canada.,Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
| | - Christoph H Borchers
- Segal Cancer Proteomics Centre, McGill University, Montréal, Quebec H3T 1E2, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Quebec H3T 1E2, Canada.,Center for Computational and Data-Intensive Science and Engineering, Skolkovo Institute of Science and Technology, Moscow 121205, Russia
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Byron A, Bernhardt S, Ouine B, Cartier A, Macleod KG, Carragher NO, Sibut V, Korf U, Serrels B, de Koning L. Integrative analysis of multi-platform reverse-phase protein array data for the pharmacodynamic assessment of response to targeted therapies. Sci Rep 2020; 10:21985. [PMID: 33319783 PMCID: PMC7738515 DOI: 10.1038/s41598-020-77335-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Accepted: 03/11/2020] [Indexed: 12/30/2022] Open
Abstract
Reverse-phase protein array (RPPA) technology uses panels of high-specificity antibodies to measure proteins and protein post-translational modifications in cells and tissues. The approach offers sensitive and precise quantification of large numbers of samples and has thus found applications in the analysis of clinical and pre-clinical samples. For effective integration into drug development and clinical practice, robust assays with consistent results are essential. Leveraging a collaborative RPPA model, we set out to assess the variability between three different RPPA platforms using distinct instrument set-ups and workflows. Employing multiple RPPA-based approaches operated across distinct laboratories, we characterised a range of human breast cancer cells and their protein-level responses to two clinically relevant cancer drugs. We integrated multi-platform RPPA data and used unsupervised learning to identify protein expression and phosphorylation signatures that were not dependent on RPPA platform and analysis workflow. Our findings indicate that proteomic analyses of cancer cell lines using different RPPA platforms can identify concordant profiles of response to pharmacological inhibition, including when using different antibodies to measure the same target antigens. These results highlight the robustness and the reproducibility of RPPA technology and its capacity to identify protein markers of disease or response to therapy.
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Affiliation(s)
- Adam Byron
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK.
| | - Stephan Bernhardt
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
- Pfizer Pharma GmbH, Berlin, Germany
| | - Bérèngere Ouine
- Department of Translational Research, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
| | - Aurélie Cartier
- Department of Translational Research, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France
- Sederma, Le Perray-en-Yvelines, France
| | - Kenneth G Macleod
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
| | - Neil O Carragher
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
| | - Vonick Sibut
- U900 INSERM, Institut Curie, PSL Research University, Paris, France
- U1236 INSERM, Faculté de Médecine, Université de Rennes 1, Rennes, France
| | - Ulrike Korf
- Division of Molecular Genome Analysis, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Bryan Serrels
- Cancer Research UK Edinburgh Centre, Institute of Genetics and Molecular Medicine, University of Edinburgh, Crewe Road South, Edinburgh, EH4 2XR, UK
- NanoString Technologies, Inc., Seattle, WA, USA
| | - Leanne de Koning
- Department of Translational Research, Institut Curie, PSL Research University, 26 rue d'Ulm, 75005, Paris, France.
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Horton TM, Hoff FW, van Dijk A, Jenkins GN, Morrison D, Bhatla T, Hogan L, Romanos-Sirakis E, Meyer J, Carroll WL, Qiu Y, Wang T, Mo Q, Kornblau SM. The effects of sample handling on proteomics assessed by reverse phase protein arrays (RPPA): Functional proteomic profiling in leukemia. J Proteomics 2020; 233:104046. [PMID: 33212251 DOI: 10.1016/j.jprot.2020.104046] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 10/27/2020] [Accepted: 11/10/2020] [Indexed: 10/23/2022]
Abstract
Reverse phase protein arrays (RPPA) can assess protein expression and activation states in large numbers of samples (n > 1000) and evidence suggests feasibility in the setting of multi-institution clinical trials. Despite evidence in solid tumors, little is known about protein stability in leukemia. Proteins collected from leukemia cells in blood and bone marrow biopsies must be sufficiently stable for analysis. Using 58 leukemia samples, we initially assessed protein/phospho-protein integrity for the following preanalytical variables: 1) shipping vs local processing, 2) temperature (4 °C vs ambient temperature), 3) collection tube type (heparin vs Cell Save (CS) preservation tubes), 4) treatment effect (pre- vs post-chemotherapy) and 5) transit time. Next, we assessed 1515 samples from the Children's Oncology Group Phase 3 AML clinical trial (AAML1031, NCT01371981) for the effects of transit time and tube type. Protein expression from shipped blood samples was stable if processed in ≤72 h. While protein expression in pre-chemotherapy samples was stable in both heparin and CS tubes, post-chemotherapy samples were stable in only CS tubes. RPPA protein extremes is a successful quality control measure to identify and exclude poor quality samples. These data demonstrate that a majority of shipped proteins can be accurately assessed using RPPA. SIGNIFICANCE: RPPA can assess protein abundance and activation states in large numbers of samples using small amounts of material, making this method ideal for use in multi-institution clinical trials. However, there is little known about the effect of preanalytical handling variables on protein stability and the integrity of protein concentrations after sample collection and shipping. In this study, we used RPPA to assess preanalytical variables that could potentially affect protein concentrations. We found that the preanalytical variables of shipping, transit time, and temperature had minimal effects on RPPA protein concentration distributions in peripheral blood and bone marrow, demonstrating that these preanalytical variables could be successfully managed in a multi-site clinical trial setting.
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Affiliation(s)
- Terzah M Horton
- Department of Pediatrics, Texas Children's Cancer Center/Baylor College of Medicine, 1102 Bates, Suite 750, Houston, TX, United States.
| | - Fieke W Hoff
- Department of Pediatric Oncology/Hematology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Anneke van Dijk
- Department of Pediatric Oncology/Hematology, Beatrix Children's Hospital, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Gaye N Jenkins
- Department of Pediatrics, Texas Children's Cancer Center/Baylor College of Medicine, 1102 Bates, Suite 750, Houston, TX, United States
| | - Debra Morrison
- The Feinstein Institute for Medical Research, 350 Community Dr., Manhasset, NY, United States
| | - Teena Bhatla
- Children's Hospital of New Jersey at Newark, Beth Israel Medical Center, NJ, United States
| | - Laura Hogan
- Department of Pediatrics, Stony Brook Children's HSCT11-061, Stony Brook, NY, United States
| | - Eleny Romanos-Sirakis
- Department of Pediatric Hematology/Oncology, Staten Island University Northwell Health, 475 Seaview Ave., Staten Island, NY, United States
| | - Julia Meyer
- University of California San Francisco, San Francisco, CA, United States.
| | - William L Carroll
- New York University/Langone Medical Center, 160 E. 32nd St., New York, NY, United States
| | - Yihua Qiu
- Departments of Leukemia and Stem Cell Transplantation and Cellular Therapy, University of Texas, M.D. Anderson Cancer Center, Houston, TX, United States
| | - Tao Wang
- Department of Biostatistics, Dan L. Duncan Cancer Center, Baylor College of Medicine, Houston, TX, United States
| | - Qianxing Mo
- Department of Biostatistics and Bioinformatics, Moffitt Cancer Center, 12902 USF Magnolia Drive, Tampa, FL 33612, United States
| | - Steven M Kornblau
- Departments of Leukemia and Stem Cell Transplantation and Cellular Therapy, University of Texas, M.D. Anderson Cancer Center, Houston, TX, United States
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