Elsevier

The Lancet

Volume 394, Issue 10197, 10–16 August 2019, Pages 533-540
The Lancet

Series
Genomic medicine for undiagnosed diseases

https://doi.org/10.1016/S0140-6736(19)31274-7Get rights and content

Summary

One of the primary goals of genomic medicine is to improve diagnosis through identification of genomic conditions, which could improve clinical management, prevent complications, and promote health. We explore how genomic medicine is being used to obtain molecular diagnoses for patients with previously undiagnosed diseases in prenatal, paediatric, and adult clinical settings. We focus on the role of clinical genomic sequencing (exome and genome) in aiding patients with conditions that are undiagnosed even after extensive clinical evaluation and testing. In particular, we explore the impact of combining genomic and phenotypic data and integrating multiple data types to improve diagnoses for patients with undiagnosed diseases, and we discuss how these genomic sequencing diagnoses could change clinical management.

Introduction

An introduction to genomic medicine can be found in part 1 of this Series. In this paper, we focus on the role of genomic sequencing in obtaining diagnoses for patients with previously undiagnosed diseases, and the implications of these diagnoses for clinical management.

With the cost of DNA sequencing continuing to decrease,1 clinical exome and genome sequencing are being used across diverse clinical settings, with the goal of increasing diagnosis rates and improving clinical management. More information on the clinical utility of exome and genome sequencing for genomic medicine can be found in part 5 of this Series. Exome sequencing includes only the protein-coding regions (or exons) of the genome, whereas genome sequencing includes both protein-coding and non-protein-coding regions of the genome. In this paper, we use the term clinical genomic sequencing to refer to the clinical use of exome or genome DNA sequencing. We use the term diagnosis to refer to an aetiological molecular diagnosis, as a step beyond a descriptive diagnostic name for a condition with an unknown cause. Other key terms are defined in the panel.

Establishing a diagnosis for patients with complex phenotypes (or combinations of phenotypes) that have defied conventional medical evaluation is a rapidly emerging area of genomic medicine. Initial successes were reported from the National Institutes of Health Undiagnosed Diseases Program2 and more recently from the Undiagnosed Diseases Network;3 in turn, the global Undiagnosed Diseases Network International effort was established, which includes programmes in 16 countries.4 A common element of these programmes is the use of genomics as an important component of the diagnostic process. The International Rare Diseases Research Consortium has also recognised the importance of diagnosis in their global rare disease goals for 2017–27: “All patients coming to medical attention with a suspected rare disease will be diagnosed within one year if their disorder is known in the medical literature; all currently undiagnosable individuals will enter a globally coordinated diagnostic and research pipeline.”5 In this paper, we consider a patient to have an undiagnosed disease if they have received an appropriate, extensive clinical evaluation on the basis of their presenting signs and symptoms, yet remain without an aetiological diagnosis. Such individuals might also have received targeted genetic testing or low-resolution chromosomal copy number analyses (eg, chromosomal microarray) on the basis of their clinical presentation, or have a suspected diagnosis, or both, but no genomic-based diagnosis of disease has been made.

Many patients with undiagnosed diseases are eventually found to have rare diseases. In the USA, the Orphan Drug Act of 1983 and the Rare Disease Act of 2002 define rare diseases as conditions that affect fewer than 200 000 people in the country.6, 7 Although each rare disease affects few people, the large number of rare diseases (estimated to be around 7000) means that altogether they affect 25–30 million people in the USA, or approximately one in ten Americans.8 Based on patient surveys,9 reaching a diagnosis for patients with rare diseases takes an average of 7·6 years in the USA and 5·6 years in the UK. Patients can spend several years without a diagnosis, as reflected in paediatric applications to the National Institutes of Health Undiagnosed Diseases Program, which peak at two age ranges: 4–6 years and 16–18 years. Those applying at 4–6 years of age tend to have had congenital onset of disease, and those applying at 16–18 years tend to have had onset of symptoms at early school age.10 Living with an undiagnosed disease is a considerable burden for patients and their families. Patients visit an average of four primary care physicians and four specialists before reaching their diagnosis, repeat testing with an average of two to three misdiagnoses, have difficulty locating specialists, receive conflicting treatment guidance, and have difficulty coordinating care among clinicians.9 Once a diagnosis is found, adult patients with rare diseases report lower health-related quality of life compared with the general population and patients with common chronic diseases.11 Although some parents of undiagnosed children have been shown to be tolerant of uncertainty (remaining actively engaged in health care and having confidence in performing coping behaviours when faced with life challenges), 35–40% also experience anxiety and depression.12

Genomic sequencing has the potential to reduce the time to diagnosis for patients with undiagnosed diseases. At present, less than half of patients who present to medical genetics specialists and who are suspected of having a genetic disorder are diagnosed using traditional genetic diagnostic evaluations, comprehensive clinical evaluations, targeted genetic testing, and chromosomal copy number analyses.13 Therefore, approaches for improving diagnostic rates are still needed. In this paper, we will explore the potential of combining genomic and phenotypic data and integrating multiple data types to improve diagnoses in undiagnosed patients, and discuss how these genomic-sequencing diagnoses could change clinical management. The figure presents a vision for implementing genomic medicine for patients with undiagnosed diseases.

Section snippets

Combining genomic and phenotypic data

A considerable barrier to reaching a diagnosis in undiagnosed patients is the variable quality and quantity of phenotypic data that are available to the clinical sequencing laboratory that is searching for a causative genomic variant. Laboratories performing clinical genomic sequencing use phenotype data to interpret sequence variants they encounter, to determine pathogenicity and the prioritisation of variants in their clinical reports. However, many laboratories report receiving limited and

Improving diagnoses by integrating multiple data types

Genomic sequencing data can be integrated with additional data types (such as model organism data, metabolome data, and transcriptome data) to improve diagnoses (figure). In the first 20 months of the Undiagnosed Diseases Network study,3 combining clinical exome and genome sequencing data with functional information from Drosophila and zebrafish animal models led to diagnoses in eight of 132 patients. Metabolomics data contributed to diagnoses in three of 132 diagnoses.3 Clinical assessment by

Changing clinical management

The aim of collecting phenotypic information and clinical genomic sequencing is to diagnose patients and change clinical management. Diagnostic rates for clinical genomic sequencing vary considerably in different patient populations (table 2); however, multiple studies have shown overall diagnostic rates of 25–35% for paediatric and adult undiagnosed diseases.3, 14, 15, 16, 17, 19, 21, 22, 23, 24 In general, diagnostic rates tend to be higher in children and lower in adults. With use of

Conclusion

Genomic medicine can help undiagnosed patients and their families reach a diagnosis. Although adult patients have low diagnostic rates, structured longitudinal phenotyping might improve our understanding of the relationships between genomic variants and phenotypes across the lifespan. Improvements to structured phenotype collection methods are needed, and laboratories require access to global, up-to-date data on genes and phenotypes. Improved methods are also required to integrate additional

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