Clinical proteomics最新文献

{"title":"Evaluation of a proteomic signature coupled with the kidney failure risk equation in predicting end stage kidney disease in a chronic kidney disease cohort.","authors":"Carlos Raúl Ramírez Medina, Ibrahim Ali, Ivona Baricevic-Jones, Moin A Saleem, Anthony D Whetton, Philip A Kalra, Nophar Geifman","doi":"10.1186/s12014-024-09486-5","DOIUrl":"10.1186/s12014-024-09486-5","url":null,"abstract":"<p><strong>Background: </strong>The early identification of patients at high-risk for end-stage renal disease (ESRD) is essential for providing optimal care and implementing targeted prevention strategies. While the Kidney Failure Risk Equation (KFRE) offers a more accurate prediction of ESRD risk compared to static eGFR-based thresholds, it does not provide insights into the patient-specific biological mechanisms that drive ESRD. This study focused on evaluating the effectiveness of KFRE in a UK-based advanced chronic kidney disease (CKD) cohort and investigating whether the integration of a proteomic signature could enhance 5-year ESRD prediction.</p><p><strong>Methods: </strong>Using the Salford Kidney Study biobank, a UK-based prospective cohort of over 3000 non-dialysis CKD patients, 433 patients met our inclusion criteria: a minimum of four eGFR measurements over a two-year period and a linear eGFR trajectory. Plasma samples were obtained and analysed for novel proteomic signals using SWATH-Mass-Spectrometry. The 4-variable UK-calibrated KFRE was calculated for each patient based on their baseline clinical characteristics. Boruta machine learning algorithm was used for the selection of proteins most contributing to differentiation between patient groups. Logistic regression was employed for estimation of ESRD prediction by (1) proteomic features; (2) KFRE; and (3) proteomic features alongside KFRE.</p><p><strong>Results: </strong>SWATH maps with 943 quantified proteins were generated and investigated in tandem with available clinical data to identify potential progression biomarkers. We identified a set of proteins (SPTA1, MYL6 and C6) that, when used alongside the 4-variable UK-KFRE, improved the prediction of 5-year risk of ESRD (AUC = 0.75 vs AUC = 0.70). Functional enrichment analysis revealed Rho GTPases and regulation of the actin cytoskeleton pathways to be statistically significant, inferring their role in kidney function and the pathogenesis of renal disease.</p><p><strong>Conclusions: </strong>Proteins SPTA1, MYL6 and C6, when used alongside the 4-variable UK-KFRE achieve an improved performance when predicting a 5-year risk of ESRD. Specific pathways implicated in the pathogenesis of podocyte dysfunction were also identified, which could serve as potential therapeutic targets. The findings of our study carry implications for comprehending the involvement of the Rho family GTPases in the pathophysiology of kidney disease, advancing our understanding of the proteomic factors influencing susceptibility to renal damage.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"34"},"PeriodicalIF":3.8,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11102163/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140956523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A reduced proteomic signature in critically ill Covid-19 patients determined with plasma antibody micro-array and machine learning.","authors":"Maitray A Patel, Mark Daley, Logan R Van Nynatten, Marat Slessarev, Gediminas Cepinskas, Douglas D Fraser","doi":"10.1186/s12014-024-09488-3","DOIUrl":"10.1186/s12014-024-09488-3","url":null,"abstract":"<p><strong>Background: </strong>COVID-19 is a complex, multi-system disease with varying severity and symptoms. Identifying changes in critically ill COVID-19 patients' proteomes enables a better understanding of markers associated with susceptibility, symptoms, and treatment. We performed plasma antibody microarray and machine learning analyses to identify novel proteins of COVID-19.</p><p><strong>Methods: </strong>A case-control study comparing the concentration of 2000 plasma proteins in age- and sex-matched COVID-19 inpatients, non-COVID-19 sepsis controls, and healthy control subjects. Machine learning was used to identify a unique proteome signature in COVID-19 patients. Protein expression was correlated with clinically relevant variables and analyzed for temporal changes over hospitalization days 1, 3, 7, and 10. Expert-curated protein expression information was analyzed with Natural language processing (NLP) to determine organ- and cell-specific expression.</p><p><strong>Results: </strong>Machine learning identified a 28-protein model that accurately differentiated COVID-19 patients from ICU non-COVID-19 patients (accuracy = 0.89, AUC = 1.00, F1 = 0.89) and healthy controls (accuracy = 0.89, AUC = 1.00, F1 = 0.88). An optimal nine-protein model (PF4V1, NUCB1, CrkL, SerpinD1, Fen1, GATA-4, ProSAAS, PARK7, and NET1) maintained high classification ability. Specific proteins correlated with hemoglobin, coagulation factors, hypertension, and high-flow nasal cannula intervention (P < 0.01). Time-course analysis of the 28 leading proteins demonstrated no significant temporal changes within the COVID-19 cohort. NLP analysis identified multi-system expression of the key proteins, with the digestive and nervous systems being the leading systems.</p><p><strong>Conclusions: </strong>The plasma proteome of critically ill COVID-19 patients was distinguishable from that of non-COVID-19 sepsis controls and healthy control subjects. The leading 28 proteins and their subset of 9 proteins yielded accurate classification models and are expressed in multiple organ systems. The identified COVID-19 proteomic signature helps elucidate COVID-19 pathophysiology and may guide future COVID-19 treatment development.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"33"},"PeriodicalIF":3.8,"publicationDate":"2024-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11100131/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140956522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mapping dynamic molecular changes in hippocampal subregions after traumatic brain injury through spatial proteomics.","authors":"Sudipa Maity, Yuanyu Huang, Mitchell D Kilgore, Abbigail N Thurmon, Lee O Vaasjo, Maria J Galazo, Xiaojiang Xu, Jing Cao, Xiaoying Wang, Bo Ning, Ning Liu, Jia Fan","doi":"10.1186/s12014-024-09485-6","DOIUrl":"10.1186/s12014-024-09485-6","url":null,"abstract":"<p><strong>Background: </strong>Traumatic brain injury (TBI) often results in diverse molecular responses, challenging traditional proteomic studies that measure average changes at tissue levels and fail to capture the complexity and heterogeneity of the affected tissues. Spatial proteomics offers a solution by providing insights into sub-region-specific alterations within tissues. This study focuses on the hippocampal sub-regions, analyzing proteomic expression profiles in mice at the acute (1 day) and subacute (7 days) phases of post-TBI to understand subregion-specific vulnerabilities and long-term consequences.</p><p><strong>Methods: </strong>Three mice brains were collected from each group, including Sham, 1-day post-TBI and 7-day post-TBI. Hippocampal subregions were extracted using Laser Microdissection (LMD) and subsequently analyzed by label-free quantitative proteomics.</p><p><strong>Results: </strong>The spatial analysis reveals region-specific protein abundance changes, highlighting the elevation of FN1, LGALS3BP, HP, and MUG-1 in the stratum moleculare (SM), suggesting potential immune cell enrichment post-TBI. Notably, established markers of chronic traumatic encephalopathy, IGHM and B2M, exhibit specific upregulation in the dentate gyrus bottom (DG2) independent of direct mechanical injury. Metabolic pathway analysis identifies disturbances in glucose and lipid metabolism, coupled with activated cholesterol synthesis pathways enriched in SM at 7-Day post-TBI and subsequently in deeper DG1 and DG2 suggesting a role in neurogenesis and the onset of recovery. Coordinated activation of neuroglia and microtubule dynamics in DG2 suggest recovery mechanisms in less affected regions. Cluster analysis revealed spatial variations post-TBI, indicative of dysregulated neuronal plasticity and neurogenesis and further predisposition to neurological disorders. TBI-induced protein upregulation (MUG-1, PZP, GFAP, TJP, STAT-1, and CD44) across hippocampal sub-regions indicates shared molecular responses and links to neurological disorders. Spatial variations were demonstrated by proteins dysregulated in both or either of the time-points exclusively in each subregion (ELAVL2, CLIC1 in PL, CD44 and MUG-1 in SM, and SHOC2, LGALS3 in DG).</p><p><strong>Conclusions: </strong>Utilizing advanced spatial proteomics techniques, the study unveils the dynamic molecular responses in distinct hippocampal subregions post-TBI. It uncovers region-specific vulnerabilities and dysregulated neuronal processes, and potential recovery-related pathways that contribute to our understanding of TBI's neurological consequences and provides valuable insights for biomarker discovery and therapeutic targets.</p>","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"21 1","pages":"32"},"PeriodicalIF":3.8,"publicationDate":"2024-05-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11089002/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140911880","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Kinome and phosphoproteome reprogramming underlies the aberrant immune responses in critically ill COVID-19 patients","authors":"Tomonori Kaneko, Sally Ezra, Rober Abdo, Courtney Voss, Shanshan Zhong, Xuguang Liu, Owen Hovey, Marat Slessarev, Logan Robert Van Nynatten, Mingliang Ye, Douglas D. Fraser, Shawn Shun‑Cheng Li","doi":"10.1186/s12014-024-09484-7","DOIUrl":"https://doi.org/10.1186/s12014-024-09484-7","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Correction: Clinical Proteomics (2024) 21:13&lt;/b&gt; &lt;b&gt;https://doi.org/10.1186/s12014-024-09457-w&lt;/b&gt;&lt;/p&gt;&lt;p&gt;Following publication of the original article [1], the authors identified an error in the author name of Douglas D. Fraser.&lt;/p&gt;&lt;p&gt;The incorrect author name is: Douglas Fraser.&lt;/p&gt;&lt;p&gt;The correct author name is: Douglas D. Fraser.&lt;/p&gt;&lt;p&gt;The author group has been updated above and the original article [1] has been corrected.&lt;/p&gt;&lt;ol data-track-component=\"outbound reference\"&gt;&lt;li data-counter=\"1.\"&gt;&lt;p&gt;Kaneko T, Ezra S, Abdo R, Voss C, Zhong S, Liu X, Hovey O, Slessarev M, Van Nynatten LR, Ye M, Fraser DD, Li SS-C. Kinome and phosphoproteome reprogramming underlies the aberrant immune responses in critically ill COVID-19 patients. Clin Proteom. 2024;21:13. https://doi.org/10.1186/s12014-024-09457-w.&lt;/p&gt;&lt;p&gt;Article CAS Google Scholar &lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;Download references&lt;svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"&gt;&lt;use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"&gt;&lt;/use&gt;&lt;/svg&gt;&lt;/p&gt;&lt;span&gt;Author notes&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Sally Ezra, Rober Abdo and Courtney Voss have contributed equally to this work.&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Department of Biochemistry, Western University, London, ON, N6A 5C1, Canada&lt;/p&gt;&lt;p&gt;Tomonori Kaneko, Sally Ezra, Courtney Voss, Shanshan Zhong, Xuguang Liu, Owen Hovey &amp; Shawn Shun‑Cheng Li&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Department of Pathology and Laboratory Medicine, Western University, London, Canada&lt;/p&gt;&lt;p&gt;Rober Abdo&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Departments of Medicine and Pediatrics, Western University, London, Canada&lt;/p&gt;&lt;p&gt;Marat Slessarev, Logan Robert Van Nynatten &amp; Douglas D. Fraser&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;CAS Key Laboratory of Separation Sciences for Analytical Chemistry, National Chromatographic R&amp;A Center, Dalian Institute of Chemical Physics, Chinese Academy of Sciences (CAS), Dalian, 116023, China&lt;/p&gt;&lt;p&gt;Mingliang Ye&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Lawson Health Research Institute, 750 Base Line Rd E, London, ON, N6C 2R5, Canada&lt;/p&gt;&lt;p&gt;Douglas D. Fraser&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Tomonori Kaneko&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Sally Ezra&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Rober Abdo&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Courtney Voss&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Shanshan Zhong&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Xuguang Liu&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"6 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140827837","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Proteomics study of primary and recurrent adamantinomatous craniopharyngiomas","authors":"Haidong Deng, Ting Lei, Siqi Liu, Wenzhe Hao, Mengqing Hu, Xin Xiang, Ling Ye, Dongting Chen, Yan Li, Fangjun Liu","doi":"10.1186/s12014-024-09479-4","DOIUrl":"https://doi.org/10.1186/s12014-024-09479-4","url":null,"abstract":"Adamantinomatous craniopharyngiomas (ACPs) are rare benign epithelial tumours with high recurrence and poor prognosis. Biological differences between recurrent and primary ACPs that may be associated with disease recurrence and treatment have yet to be evaluated at the proteomic level. In this study, we aimed to determine the proteomic profiles of paired recurrent and primary ACP, gain biological insight into ACP recurrence, and identify potential targets for ACP treatment. Patients with ACP (n = 15) or Rathke’s cleft cyst (RCC; n = 7) who underwent surgery at Sanbo Brain Hospital, Capital Medical University, Beijing, China and received pathological confirmation of ACP or RCC were enrolled in this study. We conducted a proteomic analysis to investigate the characteristics of primary ACP, paired recurrent ACP, and RCC. Western blotting was used to validate our proteomic results and assess the expression of key tumour-associated proteins in recurrent and primary ACPs. Flow cytometry was performed to evaluate the exhaustion of tumour-infiltrating lymphocytes (TILs) in primary and recurrent ACP tissue samples. Immunohistochemical staining for CD3 and PD-L1 was conducted to determine differences in T-cell infiltration and the expression of immunosuppressive molecules between paired primary and recurrent ACP samples. The bioinformatics analysis showed that proteins differentially expressed between recurrent and primary ACPs were significantly associated with extracellular matrix organisation and interleukin signalling. Cathepsin K, which was upregulated in recurrent ACP compared with that in primary ACP, may play a role in ACP recurrence. High infiltration of T cells and exhaustion of TILs were revealed by the flow cytometry analysis of ACP. This study provides a preliminary description of the proteomic differences between primary ACP, recurrent ACP, and RCC. Our findings serve as a resource for craniopharyngioma researchers and may ultimately expand existing knowledge of recurrent ACP and benefit clinical practice.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"55 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603276","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Plasma proteomic characterization of colorectal cancer patients with FOLFOX chemotherapy by integrated proteomics technology","authors":"Xi Wang, Keren Zhang, Wan He, Luobin Zhang, Biwei Gao, Ruijun Tian, Ruilian Xu","doi":"10.1186/s12014-024-09454-z","DOIUrl":"https://doi.org/10.1186/s12014-024-09454-z","url":null,"abstract":"Colorectal Cancer (CRC) is a prevalent form of cancer, and the effectiveness of the main postoperative chemotherapy treatment, FOLFOX, varies among patients. In this study, we aimed to identify potential biomarkers for predicting the prognosis of CRC patients treated with FOLFOX through plasma proteomic characterization. Using a fully integrated sample preparation technology SISPROT-based proteomics workflow, we achieved deep proteome coverage and trained a machine learning model from a discovery cohort of 90 CRC patients to differentiate FOLFOX-sensitive and FOLFOX-resistant patients. The model was then validated by targeted proteomics on an independent test cohort of 26 patients. We achieved deep proteome coverage of 831 protein groups in total and 536 protein groups in average for non-depleted plasma from CRC patients by using a Orbitrap Exploris 240 with moderate sensitivity. Our results revealed distinct molecular changes in FOLFOX-sensitive and FOLFOX-resistant patients. We confidently identified known prognostic biomarkers for colorectal cancer, such as S100A4, LGALS1, and FABP5. The classifier based on the biomarker panel demonstrated a promised AUC value of 0.908 with 93% accuracy. Additionally, we established a protein panel to predict FOLFOX effectiveness, and several proteins within the panel were validated using targeted proteomic methods. Our study sheds light on the pathways affected in CRC patients treated with FOLFOX chemotherapy and identifies potential biomarkers that could be valuable for prognosis prediction. Our findings showed the potential of mass spectrometry-based proteomics and machine learning as an unbiased and systematic approach for discovering biomarkers in CRC.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"3 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The relationship between serum astroglial and neuronal markers and AQP4 and MOG autoantibodies","authors":"Miyo K. Chatanaka, Lisa M. Avery, Maria D. Pasic, Shanthan Sithravadivel, Dalia Rotstein, Catherine Demos, Rachel Cohen, Taron Gorham, Mingyue Wang, Martin Stengelin, Anu Mathew, George Sigal, Jacob Wohlstadter, Ioannis Prassas, Eleftherios P. Diamandis","doi":"10.1186/s12014-024-09466-9","DOIUrl":"https://doi.org/10.1186/s12014-024-09466-9","url":null,"abstract":"Certain demyelinating disorders, such as neuromyelitis optica spectrum disorder (NMOSD) and myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) exhibit serum autoantibodies against aquaporin-4 (αAQP4) and myelin oligodendrocyte glycoprotein (αMOG). The variability of the autoantibody presentation warrants further research into subtyping each case. To elucidate the relationship between astroglial and neuronal protein concentrations in the peripheral circulation with occurrence of these autoantibodies, 86 serum samples were analyzed using immunoassays. The protein concentration of glial fibrillary acidic protein (GFAP), neurofilament light chain (NFL) and tau protein was measured in 3 groups of subcategories of suspected NMOSD: αAQP4 positive (n = 20), αMOG positive (n = 32) and αMOG/αAQP4 seronegative (n = 34). Kruskal-Wallis analysis, univariate predictor analysis, and multivariate logistic regression with ROC curves were performed. GFAP and NFL concentrations were significantly elevated in the αAQP4 positive group (p = 0.003; p = 0.042, respectively), and tau was elevated in the αMOG/αAQP4 seronegative group (p < 0.001). A logistic regression model to classify serostatus was able to separate αAQP4 seropositivity using GFAP + tau, and αMOG seropositivity using tau. The areas under the ROC curves (AUCs) were 0.77 and 0.72, respectively. Finally, a combined seropositivity versus negative status logistic regression model was generated, with AUC = 0.80. The 3 markers can univariately and multivariately classify with moderate accuracy the samples with seropositivity and seronegativity for αAQP4 and αMOG.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"54 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140595945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Simultaneous targeted and discovery-driven clinical proteotyping using hybrid-PRM/DIA","authors":"Sandra Goetze, Audrey van Drogen, Jonas B. Albinus, Kyle L. Fort, Tejas Gandhi, Damiano Robbiani, Véronique Laforte, Lukas Reiter, Mitchell P. Levesque, Yue Xuan, Bernd Wollscheid","doi":"10.1186/s12014-024-09478-5","DOIUrl":"https://doi.org/10.1186/s12014-024-09478-5","url":null,"abstract":"Clinical samples are irreplaceable, and their transformation into searchable and reusable digital biobanks is critical for conducting statistically empowered retrospective and integrative research studies. Currently, mainly data-independent acquisition strategies are employed to digitize clinical sample cohorts comprehensively. However, the sensitivity of DIA is limited, which is why selected marker candidates are often additionally measured targeted by parallel reaction monitoring. Here, we applied the recently co-developed hybrid-PRM/DIA technology as a new intelligent data acquisition strategy that allows for the comprehensive digitization of rare clinical samples at the proteotype level. Hybrid-PRM/DIA enables enhanced measurement sensitivity for a specific set of analytes of current clinical interest by the intelligent triggering of multiplexed parallel reaction monitoring (MSxPRM) in combination with the discovery-driven digitization of the clinical biospecimen using DIA. Heavy-labeled reference peptides were utilized as triggers for MSxPRM and monitoring of endogenous peptides. We first evaluated hybrid-PRM/DIA in a clinical context on a pool of 185 selected proteotypic peptides for tumor-associated antigens derived from 64 annotated human protein groups. We demonstrated improved reproducibility and sensitivity for the detection of endogenous peptides, even at lower concentrations near the detection limit. Up to 179 MSxPRM scans were shown not to affect the overall DIA performance. Next, we applied hybrid-PRM/DIA for the integrated digitization of biobanked melanoma samples using a set of 30 AQUA peptides against 28 biomarker candidates with relevance in molecular tumor board evaluations of melanoma patients. Within the DIA-detected approximately 6500 protein groups, the selected marker candidates such as UFO, CDK4, NF1, and PMEL could be monitored consistently and quantitatively using MSxPRM scans, providing additional confidence for supporting future clinical decision-making. Combining PRM and DIA measurements provides a new strategy for the sensitive and reproducible detection of protein markers from patients currently being discussed in molecular tumor boards in combination with the opportunity to discover new biomarker candidates.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"62 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140603271","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry","authors":"Carlos R. Ramírez Medina, Ibrahim Ali, Ivona Baricevic-Jones, Aghogho Odudu, Moin A. Saleem, Anthony D. Whetton, Philip A. Kalra, Nophar Geifman","doi":"10.1186/s12014-024-09471-y","DOIUrl":"https://doi.org/10.1186/s12014-024-09471-y","url":null,"abstract":"&lt;p&gt;&lt;b&gt;Correction to: Clinical Proteomics (2023) 20:19&lt;/b&gt;&lt;/p&gt;&lt;p&gt;&lt;b&gt;https://doi.org/10.1186/s12014-023-09405-0&lt;/b&gt;&lt;/p&gt;&lt;p&gt;In this article the affiliation details for authors Ivona Baricevic-Jones and Philip A Kalra were incorrectly given as “School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford GU2 7XH, UK” but should have been “Salford Royal Hospital, Northern Care Alliance Foundation Trust, Salford, UK”.&lt;/p&gt;&lt;p&gt;The original article has been corrected.&lt;/p&gt;&lt;ol data-track-component=\"outbound reference\"&gt;&lt;li data-counter=\"1.\"&gt;&lt;p&gt;Ram?rez Medina C.R., Ali I., Baricevic-Jones I, et al. Proteomic signature associated with chronic kidney disease (CKD) progression identified by data-independent acquisition mass spectrometry. Clin Proteom. 2023;20:19. https://doi.org/10.1186/s12014-023-09405-0&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;p&gt;Download references&lt;svg aria-hidden=\"true\" focusable=\"false\" height=\"16\" role=\"img\" width=\"16\"&gt;&lt;use xlink:href=\"#icon-eds-i-download-medium\" xmlns:xlink=\"http://www.w3.org/1999/xlink\"&gt;&lt;/use&gt;&lt;/svg&gt;&lt;/p&gt;&lt;span&gt;Author notes&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Philip A. Kalra and Nophar Geifman have equal senior authorship.&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;h3&gt;Authors and Affiliations&lt;/h3&gt;&lt;ol&gt;&lt;li&gt;&lt;p&gt;Stoller Biomarker Discovery Centre, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester, UK&lt;/p&gt;&lt;p&gt;Carlos R. Ramírez Medina, Ivona Baricevic-Jones &amp; Anthony D. Whetton&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Salford Royal Hospital, Northern Care Alliance NHS Foundation Trust, Salford, UK&lt;/p&gt;&lt;p&gt;Ibrahim Ali, Ivona Baricevic-Jones &amp; Philip A. Kalra&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Division of Cardiovascular Sciences, The University of Manchester, Manchester, UK&lt;/p&gt;&lt;p&gt;Aghogho Odudu&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;Bristol Renal and Children’s Renal Unit, Bristol Medical School, University of Bristol, Bristol, UK&lt;/p&gt;&lt;p&gt;Moin A. Saleem&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;School of Veterinary Medicine, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK&lt;/p&gt;&lt;p&gt;Anthony D. Whetton &amp; Nophar Geifman&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;p&gt;School of Health Sciences, Faculty of Health and Medical Sciences, University of Surrey, Guildford, GU2 7XH, UK&lt;/p&gt;&lt;p&gt;Nophar Geifman&lt;/p&gt;&lt;/li&gt;&lt;/ol&gt;&lt;span&gt;Authors&lt;/span&gt;&lt;ol&gt;&lt;li&gt;&lt;span&gt;Carlos R. Ramírez Medina&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Ibrahim Ali&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Ivona Baricevic-Jones&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Aghogho Odudu&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Moin A. Saleem&lt;/span&gt;View author publications&lt;p&gt;You can also search for this author in &lt;span&gt;PubMed&lt;span&gt; &lt;/span&gt;Google Scholar&lt;/span&gt;&lt;/p&gt;&lt;/li&gt;&lt;li&gt;&lt;span&gt;Anthony D. Whetton&lt;/sp","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"44 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140313660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantitative proteomic analysis of HER2 protein expression in PDAC tumors","authors":"Jamie Randall, Allison L. Hunt, Aratara Nutcharoen, Laura Johnston, Safae Chouraichi, Hongkun Wang, Arthur Winer, Raymond Wadlow, Jasmine Huynh, Justin Davis, Brian Corgiat, Nicholas W. Bateman, John F. Deeken, Emanuel F. Petricoin, Thomas P. Conrads, Timothy L. Cannon","doi":"10.1186/s12014-024-09476-7","DOIUrl":"https://doi.org/10.1186/s12014-024-09476-7","url":null,"abstract":"Metastatic pancreatic adenocarcinoma (PDAC) is the third leading cause of cancer-related death in the United States, with a 5-year survival rate of only 11%, necessitating identification of novel treatment paradigms. Tumor tissue specimens from patients with PDAC, breast cancer, and other solid tumor malignancies were collected and tumor cells were enriched using laser microdissection (LMD). Reverse phase protein array (RPPA) analysis was performed on enriched tumor cell lysates to quantify a 32-protein/phosphoprotein biomarker panel comprising known anticancer drug targets and/or cancer-related total and phosphorylated proteins, including HER2Total, HER2Y1248, and HER3Y1289. RPPA analysis revealed significant levels of HER2Total in PDAC patients at abundances comparable to HER2-positive (IHC 3+) and HER2-low (IHC 1+ /2+ , FISH−) breast cancer tissues, for which HER2 screening is routinely performed. These data support a critical unmet need for routine clinical evaluation of HER2 expression in PDAC patients and examination of the utility of HER2-directed antibody–drug conjugates in these patients.","PeriodicalId":10468,"journal":{"name":"Clinical proteomics","volume":"87 1","pages":""},"PeriodicalIF":3.8,"publicationDate":"2024-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140166454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}