Newborn screening (NBS) using tandem mass spectrometry (MS/MS) has transformed our ability to identify and provide early, lifesaving treatment to infants with inborn errors of metabolism. While MS/MS screening identifies most affected babies, it is accompanied by frequent false-positive results that require collecting blood and urine samples for additional confirmatory testing. While DNA sequencing has become an important part of confirmatory testing, newborn dried blood spots (DBS) yield only small and highly variable DNA amounts. There is an urgent need for a more efficient second-tier NBS approach for confirming all screen-positive cases directly from the DBS cards collected at birth. This is especially critical for infants at risk for metabolic disease in their first weeks of life. The overall objective of this proposal is to combine novel DNA sequencing and mass spectrometry technology to diagnose inborn metabolic disorders from DBS, and to demonstrate the clinical feasibility of this approach for second-tier screening. To achieve this objective, the following specific aims will be pursued: (1) Develop multiplex gene sequencing (RUSPseq) and 10X linked-read sequencing for rapid genetic diagnosis without the need for additional parental testing; (2) Develop mass spectrometry (Q-TOF/LC-MS) and Random Forest (RF) machine learning to identify novel metabolic markers, which will be integrated in a novel second-tier screening panel to separate true and false-positive cases; and (3) Demonstrate clinical and translational feasibility of this approach to more rapidly identify both true and false-positive cases. We will work with the public NBS program and NBSTRN?s Pilot Research and Implementation workgroup to translate this combined approach into second-tier NBS. These outcomes will have significant impact by reducing diagnostic delays and uncertainties, and by reducing iterative testing rounds and the cost associated with them, thereby reducing the burden on the healthcare system as well as patients and their families.
We will establish novel genetic and metabolomic technology for newborn screening (NBS) from dried blood spots, and apply machine learning to reduce false-positive screens and delayed diagnosis of true-positive cases. Early detection and confirmation of inborn errors of metabolism that can present in the first weeks of life is critical, specifically when screening in diverse multiethnic populations where we have less understanding of the biochemical genetic and clinical phenotypes. We will work with local hospitals and the NBS program to establish clinical and translational validity to this new project, which will contribute to maintaining parental trust in public NBS.
