Inferring Negative Molecular Biomarker Data at Scale

Patients who represent the negative biomarker population, those tested for a biomarker but found to be negative, are a critical component of the growing molecular data repository. Many next-generation sequencing (NGS)-based tumor sequencing panels test hundreds of genes, but most laboratories do not provide explicit negative results on test reports nor in their structured data. However, the need for a complete picture of the testing landscape is significant. Syapse has created an internal ingestion and data transformation pipeline that uses the power of natural language processing (NLP), terminology management, and internal rulesets to semantically align data and infer negative results not explicitly stated.

Patients within the learning health network with a cancer diagnosis and at least one NGS-based molecular report were included. To obtain this critical negative result data, laboratory gene panel information was extracted and transformed using NLP techniques into a semistructured format for analysis. A normalization ontology was created in tandem. With this approach, we were able to successfully leverage positive biomarker data to derive negative data and create a comprehensive data set for molecular testing paradigms.

The application of this process resulted in a drastic improvement in data completeness and clarity, especially when compared with other similar data sets.

The ability to accurately determine positivity and testing rates among patient populations is imperative. With only positive results, it is impossible to draw conclusions about the entire tested population or the characteristics of the subgroup who are negative for the biomarker in question. We leverage these values to perform quality checks on ingested data, and end users can easily monitor their adherence to testing recommendations.