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      Proteomic profiling improves prognostic risk stratification of the Sarculator nomogram in soft tissue sarcomas of the extremities and trunk wall

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          Abstract

          Background

          High‐risk soft tissue sarcomas of the extremities and trunk wall (eSTS), as defined by the Sarculator nomogram, are more likely to benefit from (neo)adjuvant anthracycline‐based therapy compared to low/intermediate‐risk patients. The biology underpinning these differential treatment outcomes remain unknown.

          Methods

          We analysed proteomic profiles and clinical outcomes of 123 eSTS patients. A Cox model for overall survival including the Sarculator was fitted to individual data to define four risk groups. A DNA replication protein signature‐Sarcoma Proteomic Module 6 (SPM6) was evaluated for association with clinicopathological factors and risk groups. SPM6 was added as a covariate together with Sarculator in a multivariable Cox model to assess improvement in prognostic risk stratification.

          Results

          DNA replication and cell cycle proteins were upregulated in high‐risk versus very low‐risk patients. Evaluation of the functional effects of CRISPR‐Cas9 gene knockdown of proteins enriched in high‐risk patients using the cancer cell line encyclopaedia database identified candidate drug targets. SPM6 was significantly associated with tumour malignancy grade ( p = 1.6e‐06), histology ( p = 1.4e‐05) and risk groups ( p = 2.6e‐06). Cox model analysis showed that SPM6 substantially contributed to a better calibration of the Sarculator nomogram (Index of Prediction Accuracy = 0.109 for Sarculator alone versus 0.165 for Sarculator + SPM6).

          Conclusions

          Risk stratification of patient with STS is defined by distinct biological pathways across a range of cancer hallmarks. Incorporation of SPM6 protein signature improves prognostic risk stratification of the Sarculator nomogram. This study highlights the utility of integrating protein signatures for the development of next‐generation nomograms.

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          Most cited references58

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            clusterProfiler: an R package for comparing biological themes among gene clusters.

            Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
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              The Molecular Signatures Database (MSigDB) hallmark gene set collection.

              The Molecular Signatures Database (MSigDB) is one of the most widely used and comprehensive databases of gene sets for performing gene set enrichment analysis. Since its creation, MSigDB has grown beyond its roots in metabolic disease and cancer to include >10,000 gene sets. These better represent a wider range of biological processes and diseases, but the utility of the database is reduced by increased redundancy across, and heterogeneity within, gene sets. To address this challenge, here we use a combination of automated approaches and expert curation to develop a collection of "hallmark" gene sets as part of MSigDB. Each hallmark in this collection consists of a "refined" gene set, derived from multiple "founder" sets, that conveys a specific biological state or process and displays coherent expression. The hallmarks effectively summarize most of the relevant information of the original founder sets and, by reducing both variation and redundancy, provide more refined and concise inputs for gene set enrichment analysis.
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                Author and article information

                Contributors
                paul.huang@icr.ac.uk
                Journal
                Cancer Med
                Cancer Med
                10.1002/(ISSN)2045-7634
                CAM4
                Cancer Medicine
                John Wiley and Sons Inc. (Hoboken )
                2045-7634
                23 July 2024
                July 2024
                : 13
                : 14 ( doiID: 10.1002/cam4.v13.14 )
                : e70026
                Affiliations
                [ 1 ] Division of Molecular Pathology The Institute of Cancer Research London UK
                [ 2 ] Unit of Biostatistics for Clinical Research Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 3 ] Precision Sarcoma Research Group German Cancer Research Center (DKFZ) and National Center for Tumor Diseases Heidelberg Germany
                [ 4 ] Department of Surgery Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                [ 5 ] The Royal Marsden NHS Foundation Trust London UK
                [ 6 ] Division of Clinical Studies The Institute of Cancer Research London UK
                [ 7 ] Molecular Pharmacology, Department of Experimental Oncology Fondazione IRCCS Istituto Nazionale dei Tumori Milan Italy
                Author notes
                [*] [* ] Correspondence

                Paul H. Huang, Institute of Cancer Research, 15 Cotswold Road, Sutton SM2 5NG, UK.

                Email: paul.huang@ 123456icr.ac.uk

                Author information
                https://orcid.org/0000-0002-1319-5148
                https://orcid.org/0000-0002-3392-4002
                https://orcid.org/0000-0003-3972-5087
                Article
                CAM470026 CAM4-2023-10-4956.R1
                10.1002/cam4.70026
                11263812
                39041188
                6167605f-682c-46eb-af10-a5c0cc7fbaea
                © 2024 The Author(s). Cancer Medicine published by John Wiley & Sons Ltd.

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 05 June 2024
                : 11 October 2023
                : 07 July 2024
                Page count
                Figures: 4, Tables: 1, Pages: 12, Words: 6900
                Funding
                Funded by: Cancer Research UK , doi 10.13039/501100000289;
                Award ID: C56167/A29363
                Funded by: Fondazione Regionale per la Ricerca Biomedica , doi 10.13039/501100022720;
                Award ID: 1751036
                Funded by: Fondazione AIRC per la Ricerca sul Cancro
                Award ID: 28546
                Funded by: Fundación Científica Asociación Española Contra el Cáncer , doi 10.13039/501100002704;
                Award ID: Foundation AECC‐GEACC19007MA
                Categories
                Research Article
                Research Article
                Custom metadata
                2.0
                July 2024
                Converter:WILEY_ML3GV2_TO_JATSPMC version:6.4.5 mode:remove_FC converted:23.07.2024

                Oncology & Radiotherapy
                biomarkers,nomogram,proteomics,risk stratification,soft tissue sarcoma
                Oncology & Radiotherapy
                biomarkers, nomogram, proteomics, risk stratification, soft tissue sarcoma

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