4 | DISCUSSION
Identifying patients is a critical but limiting step in the disease gene
discovery process for rare disorders.
Diagnostic laboratories provide
meaningful contributions to these efforts by screening large testing
cohorts for relevant phenotypes based on genotype matches, contacting
clinicians of appropriate cases, and issuing proactive reclassification
reports. Over the last 5 years, our laboratory has entered 246 candidate
entries into GeneMatcher of which 45.93% are now characterized. There
was no significant difference for the likelihood to be characterized
based on inheritance type, zygosity, or alteration type. However,
entries which met our candidate gene reporting criteria were
significantly more likely to be characterized, suggesting that whilede novo and biallelic alterations are easier to identify and
represent a large percentage of our GeneMatcher entries (56.91% and
33.74%, respectively), our process of evaluating existing evidence for
a gene-disease association is a strong predictor of disease association
regardless of mechanism.
By integrating standardized methods for evaluating the evidence
available for gene-disease associations into the clinical DES analysis
framework, diagnostic laboratories can be valuable partners in screening
genes for potential disease associations. All probands with informative
trios and negative results after characterized gene review undergo
additional candidate gene analysis at our laboratory. Analysts consider
the published literature and in-house DES data from previous cases. In
some instances, we amass sufficient evidence to characterize a
gene-disease association based on internal data. For example, we
identified six internal probands with de novo rare ZNF292alterations and similar neurodevelopmental phenotypes more than one year
before the manuscript describing the association was published. Our
laboratory submits data on genes with substantial evidence for a disease
association into GeneMatcher. This creates a more meaningful
contribution to data-sharing compared to automated ‘data dumping’
practices where every rare variant in an uncharacterized gene is
entered. Broad data deposits without evaluating the potential clinical
relevance can create analytical noise and superfluous communications
(Seaby, 2021). Follow-up communications to screen matches are laborious,
so contributors to GeneMatcher should have standardized methods to
appropriately vet candidate genes before submitting.
One advantage of DES is the ability to analyze the expanding catalog of
characterized genes over time. Gene characterization has a cascading
effect with the potential for multiple patients to receive a
reclassified diagnostic finding once a gene-disease relationship is
established. Here, we found GeneMatcher entries ultimately resulted in
480 probands receiving a reportable finding. This accounts for a
substantial portion (11.96%) of our total DES cohort with reported
characterized findings. Importantly, nearly half of the probands with
reported variants in these genes received a reclassification report.
Laboratories should have processes in place to curate and score new
evidence GDV in real-time, and retrospectively evaluate cases for
proactive reclassification. This is an ongoing effort and several cases
reported here are under review for clinical overlap and reporting
[Figure 2]. Without a process to re-evaluate previous cases, nearly
half of this cohort would not have received an updated report.
Establishing an accurate molecular diagnosis has profound impacts on
patients, especially for individuals with rare disorders. A diagnosis
can precipitate tailored clinical management, clearer recurrence risk
counseling, and in some cases, eligibility for clinical trials.
Further, because of the ability to reclassify previously reported DES
cases, diagnostic laboratories can quickly identify additional probands
and further power studies to clarify genotype-phenotype correlations and
phenotypic spectrum expansions. Often after an initial cohort describes
a new gene-disease relationship there is a follow-up publication of a
larger cohort to further delineate the newly characterized disorder. For
example, we co-authored Peng et al. (2017) which described the
association between biallelic FDXR alterations and auditory
neuropathy and optic atrophy. The following year, we collaborated with
Slone et al. (2018) on a follow-up study to include additional patient
reports and functional evidence. We participated in 24 follow-up cohort
studies as well as 14 publications describing a single case report or
other data in support of the association. Lastly, there were 5
publications we participated in that described a phenotypic expansion
for a gene to include major features not previously reported
[citations listed in supplement Table
2].
Diagnostic laboratories can serve as a
recruitment hub for researchers where more than one proband of interest
can be identified through a single source. We can screen the phenotype
of individuals with rare variants in a candidate gene of interest,
identify the probands with phenotypic overlap, and reach out to multiple
clinicians. In some cases, a candidate gene may not have met reporting
criteria. In these instances, we can provide reports documenting the
presence of an alteration, but not commenting on the clinical relevance
until sufficient information is amassed and reported that meets our
characterization status. Clinicians find these reports useful to
facilitate discussion with families about an alteration which was not
originally included on a DES report and the option to enroll in a study
further investigating a disease association. Laboratories can further
assist the research process by sharing remaining banked DNA (if
available and with the appropriate permissions) with researchers and
providing cascade testing to other family members for purposes of risk
prediction, recurrence risk estimates, and variant segregation within a
family.
While diagnostic laboratories can be an efficient tool for finding
matches given there is a warehouse of clinical data to screen, the level
of clinical data available is dependent on the information submitted at
the time of testing. Comprehensive clinic notes that summarize the
salient points of a proband’s phenotype should be submitted with all DES
testing (Seaby, 2020). A complete clinical description of a proband’s
presentation is not only important for variant classification, but also
for characterizing new disease genes. In reviewing previously reported
cases for collaborators, those with more specific and complete clinical
information are more evident as positive matches compared to vague
descriptions. Further, robust clinical data can help identify links
between a proband’s features and a gene with limited evidence-based on
animal models and other functional data.
There is still much work to be done, and many suspected gene-disease
associations with insufficient evidence for characterization exist. In
total, we have 70 genes that have been reported as a candidate, but
remained uncharacterized for more than 2 years, indicating that there
was sufficient experimental evidence at the time to associate the gene
with disease; however, robust patient data has not yet become available
despite the use of GeneMatcher. More than one novel candidate report was
issued in only nine of these genes, making these true “n-of-one” cases
in need of additional patient reports to corroborate the finding. For
example, in 2015, compound heterozygous predicted loss-of-function
alterations in the NMNAT2 gene were identified in a fetus with
multiple congenital brain anomalies, contractures, and absence of
muscle, which matched perfectly with a mouse model published years
earlier (Hicks, 2012). Follow-up in-house structural studies and
functional studies through a collaborator support the pathogenicity of
these alterations (Lukacs, 2019); however, this gene remains
uncharacterized as no additional patient reports have been published.
Another example of the importance of robust phenotypic data is theCHD3 gene, which was reported as a gene of interest in patients
with neurodevelopmental disorders in several large cohort studies
(O’Roark, 2012; Iossifov, 2014; DDD, 2016), but did not meet our
criteria for characterization until a case series with detailed
phenotypic data five years after the first published report (Snijders
Blok, 2017). Robust case series like this are needed to garnish enough
evidence to meet the strict criteria for gene-disease characterization.
However, these manuscripts are more likely to take longer to publish,
delaying the necessary evidence for characterization to be available.
This delay is most profound for gene-disease associations in extremely
rare cases that are less conducive to cohort studies and disorders with
clinical heterogeneity which require large cohorts to identify the
phenotypic commonalities. Collaborative efforts such as GeneMatcher are
needed to power these studies.
By involving diagnostic laboratories in research collaborations there is
also a direct clinical impact that results in diagnostic findings going
to patients faster. We previously found that clinically relevant
reclassification results were issued faster for publications on which we
were collaborators compared to genes for which we were not included on
the publication (internal data). Involvement of diagnostic laboratories
in data-sharing platforms like the Matchmaker Exchange may shorten the
time from publication to reclassification report issuance.