DISCUSSION
The interpretation of genomic variation data in clinical settings has
been one of the biggest challenges in achieving a successful use of next
generation sequencing in medical practice. Given the relevance of this
process for current diagnosis of genetic diseases, variant
interpretation is one of the most important topics in current
bioinformatics. To develop this model, we combined different machine
learning techniques and scores from widely used tools, as an effort to
try to improve the accuracy of classification of Variants with Uncertain
Significance (VUS). Our models showed improved accuracy compared to
current solutions analyzing data from a large set of variants previously
classified as VUS and distributed over different consequence types..
VUS raises concerns for both patients and for clinicians working on
genetic diagnosis. From the patient perspective, genetic testing can
yield or confirm a diagnosis, inform the probability of developing a
disease for the patient or relatives, and, if the variant is actionable,
offer a possibility of treatment. Thus, the uncertainty created by VUS
on genetic testing can lead to a variety of emotional responses in
patients. Some of the most reported answers to a VUS result are stress
and distress, both on patients and on their relatives. Additionally, VUS
results are more difficult to understand as patients tend to have a more
deterministic view of genetics, and in many scenarios tend to
misinterpret VUS results as more similar to a negative result (Clift, et
al., 2019). Moreover, due to the variability of patient medical
and psychosocial contexts, there is no consensus on clinical best
practices to handle VUS results. Some propose to withhold them from
patients, so clinicians and labs have to deal with them on a
case-by-case basis focusing on pre and post diagnosis counseling to
minimize potential harm (Hoffman-Andrews, 2017).
From the clinician point of view, a VUS result raises concerns on how to
counsel the affected patient and their family, and how it might change
the clinical management. The ACMG guidelines state that a VUS should not
be used as part of clinical decision making. Therefore, it is advised
that, whenever feasible, the clinician should pursue additional efforts
to classify the variant. Additional monitoring and tracing of the
patient might be needed if the variant is reclassified (Hoffman-Andrews,
2017). In any case, these efforts involve important time and monetary
investments: they can be directed at the patient and family level (i.e.
testing for the variant on parents and other relatives) or, if the
laboratory has research facilities, functional studies to validate the
variant consequence. Other approaches include the work by Sun, et al.
(2020), which aims to tackle this problem by proactively creating
comprehensive maps of cell-based assays for the missense variants of
specific genes, or the work by Walsh, et al. (2019), which compares
variant frequency between patient cohorts and reference population
cohorts. However, so far these approaches are available for a number of
selected genes and diseases. Thus, in resource limited settings VUS
prioritization is a paramount need and our tools can help us select VUS
with the highest probability of being pathogenic with a high accuracy.
As demonstrated by Liu, Wu, Li, & Boerwinle, et al. (2016), combining
information of several predictive scores increases the predictive
accuracy of missense variant classification. Here, we show that
combining the information of high accuracy conservation-based variant
deleteriousness tools like CADD, SIFT, and Eigen (Ionita-Laza, McCallum,
Xu, & Buxbaum, 2016) yields improved accuracy across a variety of
variant types including missense , splice , intron ,intergenic , and synonymous . However, synonymous variants
obtained the less accurate results with both our models and CADD (Figure
3). Recent work suggests that synonymous variants might be more
deleterious than would be predicted from current clinical significance
annotations (Zeng & Bromberg, 2020). We believe that additional
research might be needed to ascertain the true pathogenic potential of
these class of variants.