The Matchmaker Exchange API: Automating Patient Matching Through the Exchange of Structured Phenotypic and Genotypic Profiles

Abstract

Despite the increasing prevalence of clinical sequencing, the difficulty of identifying additional affected families is a key obstacle to solving many rare diseases. There may only be a handful of similar patients worldwide, and their data may be stored in diverse clinical and research databases. Computational methods are necessary to enable finding similar patients across the growing number of patient repositories and registries. We present the Matchmaker Exchange Application Programming Interface (MME API), a protocol and data format for exchanging phenotype and genotype profiles to enable matchmaking among patient databases, facilitate the identification of additional cohorts, and increase the rate with which rare diseases can be researched and diagnosed. We designed the API to be straightforward and flexible in order to simplify its adoption on a large number of data types and workflows. We also provide a public test data set, curated from the literature, to facilitate implementation of the API and development of new matching algorithms. The initial version of the API has been successfully implemented by three members of the Matchmaker Exchange and was immediately able to reproduce previously-identified matches and generate several new leads currently being validated. The API is available at https://github.com/ga4gh/mme-apis.

The Human Phenotype Ontology in 2017

“…The Matchmaker exchange (MME) platform provides a systematic approach to rare disease-gene discovery with a federated network of phenotype-genotype databases that enable data sharing and discovery of relevant data (52,53) over a secure API (54). The HPO is the standard vocabulary for communicating phenotype data.…”

Patient Similarity: Emerging Concepts in Systems and Precision Medicine

“…Various patient similarity algorithms have been deployed and have been found beneficial by improving clinical efficiency (Wang et al, 2015), enabling secure identification of similar patients and records sharing by clinicians and rare disease scientists (Buske et al, 2015a,b), predicting patients' prognosis or trajectory over time (Ebadollahi et al, 2010; Subirats et al, 2012; Wang et al, 2012; Gallego et al, 2015), providing clinical decision support (Daemen et al, 2009; Wang et al, 2011; Subirats et al, 2012; Sun et al, 2012; Gottlieb et al, 2013; Liu et al, 2013b; Gallego et al, 2015), tailoring individual treatments (Zhang et al, 2014), preventing unexpected adverse drug reactions (Hartge et al, 2006; Yang et al, 2014), flagging patients deserving more attention due to poor response to therapies (Zhang et al, 2014; Ozery-Flato et al, 2016), and pursuing comparative effectiveness studies (Wang et al, 2011), among other applications. In general, clinical guidelines often do not supply evidence on risks, secondary therapy effects, and long-term outcomes (Gallego et al, 2015).…”

“…Some promising examples are regarding mental and behavioral disorders (Roque et al, 2011), infectious diseases (Li et al, 2015), cancers (Wu et al, 2005; Teng et al, 2007; Chan et al, 2010, 2015; Klenk et al, 2010; Cho and Przytycka, 2013; Li et al, 2015; Wang, 2015; Bolouri et al, 2016; Wang et al, 2016), endocrine (Li et al, 2015; Wang, 2015), and metabolic diseases (Zhang et al, 2014; Ng et al, 2015). Others involve diseases of the nervous system (Lieberman et al, 2005; Carreiro et al, 2013; Cho and Przytycka, 2013; Qian et al, 2014; Buske et al, 2015a; Li et al, 2015; Bolouri et al, 2016; Wang et al, 2016), eyes (Buske et al, 2015a; Li et al, 2015), skin (Buske et al, 2015a; Li et al, 2015), heart (Wu et al, 2005; Tsymbal et al, 2007; Syed and Guttag, 2011; Buske et al, 2015a; Li et al, 2015; Panahiazar et al, 2015a,b; Wang, 2015; Björnson et al, 2016), liver (Chan et al, 2015), intestines (Buske et al, 2015a), musculoskeletal system (Buske et al, 2015a), congenital malformations (Buske et al, 2015a), and various other conditions or factors influencing health status (Gotz et al, 2012; Subirats et al, 2012; Ng et al, 2015). …”

Building the foundation for genomics in precision medicine

“…Launched in 2015, the Matchmaker Exchange is a centralized network for sharing case-level data within an international set of case-level repositories focused on gene discovery 42 . Although each database has its own data schema, the development of a common application programming interface 43 means that users can query genomic and phenotypic data across multiple systems. This has encouraged the member databases to move towards implementing a common set of fields to facilitate effective data exchange for gene discovery.…”