Dataset Information

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

ABSTRACT: The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases. The main clinical and hematochemical data associated with disease outcome were grouped with serological data to form a dataset for the supervised machine learning techniques. We identified nine proteins (i.e., CD200R1, MCP1, MCP3, IL6, LTBP2, MATN3, TRANCE, α2-MRAP, and KIT) that contributed to the correct classification of COVID-19 disease severity when combined with relative neutrophil and lymphocyte counts. By analyzing PEA, clinical and hematochemical data with statistical methods that were able to handle many variables in the presence of a relatively small sample size, we identified nine potential serum biomarkers of a "severe" outcome. Most of these were confirmed by literature data. Importantly, we found three biomarkers associated with central nervous system pathologies and protective factors, which were downregulated in the most severe cases.

SUBMITTER: Beltrami AP

PROVIDER: S-EPMC9409308 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature

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Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

Beltrami Antonio Paolo AP De Martino Maria M Dalla Emiliano E Malfatti Matilde Clarissa MC Caponnetto Federica F Codrich Marta M Stefanizzi Daniele D Fabris Martina M Sozio Emanuela E D'Aurizio Federica F Pucillo Carlo E M CEM Sechi Leonardo A LA Tascini Carlo C Curcio Francesco F Foresti Gian Luca GL Piciarelli Claudio C De Nardin Axel A Tell Gianluca G Isola Miriam M

International journal of molecular sciences 20220815 16

The persistence of long-term coronavirus-induced disease 2019 (COVID-19) sequelae demands better insights into its natural history. Therefore, it is crucial to discover the biomarkers of disease outcome to improve clinical practice. In this study, 160 COVID-19 patients were enrolled, of whom 80 had a "non-severe" and 80 had a "severe" outcome. Sera were analyzed by proximity extension assay (PEA) to assess 274 unique proteins associated with inflammation, cardiometabolic, and neurologic diseases ...[more]

PMID: 36012423

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Project description:BackgroundGenomic data is essential for clinical decision-making in precision oncology. Bioinformatic algorithms are widely used to analyze next-generation sequencing (NGS) data, but they face two major challenges. First, these pipelines are highly complex, involving multiple steps and the integration of various tools. Second, they generate features that are human-interpretable but often result in information loss by focusing only on predefined genetic properties. This limitation restricts the full potential of NGS data in biomarker extraction and slows the discovery of new biomarkers in precision oncology.MethodsWe propose an end-to-end deep learning (DL) approach for analyzing NGS data. Specifically, we developed a multiple instance learning DL framework that integrates somatic mutation sequences to predict two compound biomarkers: microsatellite instability (MSI) and homologous recombination deficiency (HRD). To achieve this, we utilized data from 3,184 cancer patients obtained from two public databases: The Cancer Genome Atlas (TCGA) and the Clinical Proteome Tumor Analysis Consortium (CPTAC).ResultsOur proposed deep learning method demonstrated high accuracy in identifying clinically relevant biomarkers. For predicting MSI status, the model achieved an accuracy of 0.98, a sensitivity of 0.95, and a specificity of 1.00 on an external validation cohort. For predicting HRD status, the model achieved an accuracy of 0.80, a sensitivity of 0.75, and a specificity of 0.86. Furthermore, the deep learning approach significantly outperformed traditional machine learning methods in both tasks (MSI accuracy, p-value = 5.11×10-18; HRD accuracy, p-value = 1.07×10-10). Using explainability techniques, we demonstrated that the model's predictions are based on biologically meaningful features, aligning with key DNA damage repair mutation signatures.ConclusionWe demonstrate that deep learning can identify patterns in unfiltered somatic mutations without the need for manual feature extraction. This approach enhances the detection of actionable targets and paves the way for developing NGS-based biomarkers using minimally processed data.

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Dataset Information

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

Publications

Combining Deep Phenotyping of Serum Proteomics and Clinical Data via Machine Learning for COVID-19 Biomarker Discovery.

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