Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning
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Annals of Biomedical Engineering ( 2020) https://doi.org/10.1007/s10439-020-02593-y
Review
Scalable Signature-Based Molecular Diagnostics Through On-chip Biomarker Profiling Coupled with Machine Learning JOHN MOLINSKI,1 AMOGHA TADIMETY,1 ALISON BURKLUND,1 and JOHN X. J. ZHANG 1,2 1
Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH 03755, USA; and 2Norris Cotton Cancer Center, Dartmouth Hitchcock Medical Center, Lebanon, NH, USA (Received 3 April 2020; accepted 11 August 2020) Associate Editor Tingrui Pan oversaw the review of this article.
Abstract—Molecular diagnostics have traditionally relied on discrete biological substances as diagnostic markers. In recent years however, advances in on-chip biomarker screening technologies and data analytics have enabled signaturebased diagnostics. Such diagnostics aim to utilize unique combinations of multiple biomarkers or diagnostic ‘fingerprints’ rather than discrete analyte measurements. This approach has shown to improve both diagnostic accuracy and diagnostic specificity. In this review, signature-based diagnostics enabled by microfluidic and micro-/nano- technologies will be reviewed with a focus on device design and data analysis pipelines and methodologies. With increasing amounts of data available from microfluidic biomarker screening, isolation, and detection platforms, advanced data handling and analytics approaches can be employed. Thus, current data analysis approaches including machine learning and recent advances with image processing, along with potential future directions will be explored. Lastly, the needs and gaps in current literature will be elucidated to inform future efforts towards development of molecular diagnostics and biomarker screening technologies. Keywords—Molecular profiling, Biomarker screening, Micro-/nano- technologies, Advanced data analytics.
INTRODUCTION Molecular diagnostics are critical tools for clinical decision-making in the treatment of both infectious and chronic disease.77 The choice of disease-specific biomarkers has long been fundamental to the design and development of diagnostic devices and assays. In contrast, signature-based diagnostics have emerged as Address correspondence to John X. J. Zhang, Thayer School of Engineering at Dartmouth, 14 Engineering Drive, Hanover, NH 03755, USA. Electronic mail: [email protected]
an attractive alternative to those based on traditional biomarkers. Signature-based diagnostics utilize biomarker signatures or multiple markers in combination, effectively creating a ‘diagnostic fingerprint’ for a disease state of interest. Patient-specific data resulting from a disease signature enables targeted and personalized medicine, streamlines the diagnostic pipeline, and has the potential to dramatically improve patient outcomes.8 Biomarker signatures are a panel of distinct yet often interrelated biomarkers (typically three or more) which represent a disease state of interest. Relationships between biomarkers within a signature can be simple (i.e. concentration of eac
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