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Otto R, Detjen KM, Riemer P, Fattohi M, Grötzinger C, Rindi G, Wiedenmann B, Sers C, Leser U. Transcriptomic Deconvolution of Neuroendocrine Neoplasms Predicts Clinically Relevant Characteristics. Cancers (Basel) 2023; 15:cancers15030936. [PMID: 36765893 PMCID: PMC9913692 DOI: 10.3390/cancers15030936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 01/20/2023] [Accepted: 01/26/2023] [Indexed: 02/05/2023] Open
Abstract
Pancreatic neuroendocrine neoplasms (panNENs) are a rare yet diverse type of neoplasia whose precise clinical-pathological classification is frequently challenging. Since incorrect classifications can affect treatment decisions, additional tools which support the diagnosis, such as machine learning (ML) techniques, are critically needed but generally unavailable due to the scarcity of suitable ML training data for rare panNENs. Here, we demonstrate that a multi-step ML framework predicts clinically relevant panNEN characteristics while being exclusively trained on widely available data of a healthy origin. The approach classifies panNENs by deconvolving their transcriptomes into cell type proportions based on shared gene expression profiles with healthy pancreatic cell types. The deconvolution results were found to provide a prognostic value with respect to the prediction of the overall patient survival time, neoplastic grading, and carcinoma versus tumor subclassification. The performance with which a proliferation rate agnostic deconvolution ML model could predict the clinical characteristics was found to be comparable to that of a comparative baseline model trained on the proliferation rate-informed MKI67 levels. The approach is novel in that it complements established proliferation rate-oriented classification schemes whose results can be reproduced and further refined by differentiating between identically graded subgroups. By including non-endocrine cell types, the deconvolution approach furthermore provides an in silico quantification of panNEN dedifferentiation, optimizing it for challenging clinical classification tasks in more aggressive panNEN subtypes.
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Affiliation(s)
- Raik Otto
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
- Correspondence: ; Tel.: +49-030-2093-3086
| | - Katharina M. Detjen
- Department of Hepatology and Gastroenterology, Charité—Universitätsmedizin Berlin, Campus Virchow-Klinikum and Campus Charité Mitte, 13353 Berlin, Germany
| | - Pamela Riemer
- Laboratory of Molecular Tumor Pathology and Systems Biology, Institute of Pathology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Melanie Fattohi
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
| | - Carsten Grötzinger
- Department of Hepatology and Gastroenterology, Charité—Universitätsmedizin Berlin, Campus Virchow-Klinikum and Campus Charité Mitte, 13353 Berlin, Germany
| | - Guido Rindi
- Section of Anatomic Pathology, Department of Life Sciences and Public Health, Università Cattolica del Sacro Cuore, 00168 Roma, Italy
- Anatomic Pathology Unit, Department of Woman and Child Health and Public Health, Fondazione Policlinico Universitario A. Gemelli IRCCS, 00168 Roma, Italy
| | - Bertram Wiedenmann
- Department of Hepatology and Gastroenterology, Charité—Universitätsmedizin Berlin, Campus Virchow-Klinikum and Campus Charité Mitte, 13353 Berlin, Germany
| | - Christine Sers
- Laboratory of Molecular Tumor Pathology and Systems Biology, Institute of Pathology, Charité—Universitätsmedizin Berlin, 10117 Berlin, Germany
- German Cancer Consortium (DKTK), Partner Site Berlin and German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany
| | - Ulf Leser
- Knowledge Management in Bioinformatics, Institute for Computer Science, Humboldt-Universität zu Berlin, 10099 Berlin, Germany
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