Maximizing NGS lab efficiency in the analysis and interpretation of genomic data
Dr Eva Serra
Pre-registered Clinical Scientist & Product Manager at Congenica
Maximizing NGS lab efficiency in the analysis and interpretation of next-generation sequencing (NGS) data is essential to be able to deliver fast turnarounds of results and value to patients at scale.
Recent findings have uncovered the true pain points and time and cost implications of NGS analysis for diagnostic labs. These findings, recently published in Genetics in Medicine (PMID: 31358947) aimed at determining the true and complete costs of routine clinical grade sequencing in the context of a UK NHS accredited laboratory processing around 400 samples per year (pediatric and adult trio cases with a broad disease spectrum).
The clinical analysis included a clinical bioinformatics analysis (looking at known disease genes) and then a research, gene-agnostic bioinformatics analysis, in those cases that could not be solved with the first approach.
The authors collected cost data for all steps in the sequencing pathway (including interpretation, staff time and staff salary). Their results highlighted that although sequencing is the most expensive step of the process (accounting for 79% of the costs), clinical analysis is the most time-consuming step and is more costly than expected and a major backlog to be addressed in order to maximize NGS lab efficiency.
In terms of concrete times and costs, the study uncovered that the true costs of clinical grade sequencing is estimated at ~$9000 per trio, or ~$3000 per genome, well above the anticipated “$1000” genome. Importantly, their results showed that clinical analysis can take up 11hrs in just simple rare disease cases, with complex cases taking up to 16 hours when the gene-agnostic approach is applied. Interestingly, 70% of the clinical analysis costs is staff time – a reflection of the intensive human-led, often manual, process that is clinical interpretation of patient NGS data.
How to maximize NGS lab efficiency
The implications of these findings for patients and lab staff is that the time and cost of genomic data analysis leads to low case throughput, increased backlog and difficulty to adhere to turnaround times, long waiting times for patients, impact on productivity for staff and subsequently, difficulty for NGS labs to scale-up in a time where we are seeing increased demand for genetic services.
Congenica’s clinical decision support platform is an end-to-end solution for rapid analysis of genomic data from sequencer to report. The software significantly increases workflow efficiency, diagnostic yield and confidence by streamlining and automating secondary and tertiary analyses, reducing interpretation costs by up to 95% and analyzing data more than 20 times faster.
Clinical examples of NGS lab efficiency gains
The following three real-world cases exemplify specific features in the Congenica platform that are empowering healthcare professionals and diagnostic labs to maximize efficiency, diagnostic yield and throughput.
Case one: Interpreting SNVs and CNVs simultaneously
One example of how workflow efficiency is increased is by using a single pipeline to call SNVs and CNVs simultaneously, without the need to run sequential pipelines, and performing interpretation of both variant types in a single integrated solution right from the start.
In a recent clinical case, Congenica was used to help rapidly diagnose a 10 year old boy (singleton) suffering from disproportionate short stature, vertebral abnormalities and fusion of the tarsal bones (bones in the foot). This clinical presentation was suggestive of a specific skeletal disorder. Our customer exome sequenced the patient and leveraged Congenica to uncover the genetic basis of the disease, leading to the successful diagnosis of pathogenic SNV and CNV compound heterozygous variants in the FLNB gene.
- Removes the need to run a 2-stage bioinformatics pipeline for sequential calling of various variant types
- Reduces analysis waiting times and makes CNVs available from the start
- Expedites diagnosis and increases staff productivity
This real-life clinical example illustrated the benefits of a pipeline that simultaneously identifies SNVs and CNVs and a solution to perform clinical interpretation of both variant types in a single integrated platform:
Case two: Instantly prioritize variants based on phenotype
A second example showcases the value of instantly prioritizing variants based on phenotype with Exomiser. Exomiser is the Congenica variant prioritization algorithm that takes into account patient phenotypes in the form of HPO terms (Human Phenotype Ontology) to rank variants and genes based on their phenotypic relevance.
In this case a 4 year old boy with consanguineous parents were exome sequenced and analyzed as a trio in Congenica. The patient was suffering from pontocerebellar hypoplasia, which indicates progressive atrophy of various parts of the brain such as the cerebellum. Our customer used Exomiser prioritization scores to pinpoint a pathogenic homozygous variant in TSEN54, which led to a diagnosis.
- Immediately focus on the most highly relevant disease variants, avoiding unnecessary review of non-causal mutations
- Improve speed and ease of use, even in complex cases such as those where a gene-agnostic approach is applied, saving staff time
- Gain a quantitative measure of phenotypic relevance, which increases confidence in diagnosis.
Being able to instantly prioritize variants based on phenotype enables Congenica customers to:
Case three: Leveraging existing knowledge to focus interpretation
A third example showcases the value of leveraging existing knowledge, in the form of knowledgebases or curated variant lists, to focus interpretation and uncover unexpected diagnostic variants that would otherwise be missed.
The clinical example here is of a couple that have had 5 pregnancies, 3 of which were affected with a lethal skeletal dysplasia between 2010 and 2018. Postmortem examination of the 1st fetus confirmed a very severe abnormality of the skeleton including extremely short and bent long bones and abnormal skull development.
The genetic cause of the skeletal dysplasia was unknown during this period and previous investigations were unsuccessful at pinpointing the underlying cause. Our customer collected DNA from the 3rd fetus and performed exome sequencing and clinical interpretation using Congenica.
With the use of a custom curated variant list, our customer identified two non-coding exon variants in the untranslated RMRP gene, which encodes a noncoding RNA component. Both of these variants were previously published in compound heterozygosity in several skeletal dysplasia patients and the variants were proven to be regulatory enhancers, leading to abnormal ribosomal processing.
This example highlights the value of leveraging existing knowledge to perform clinical interpretation:
- Instantly detect known variants that would otherwise escape traditional exome-base filters
- Accelerate interpretation by querying specific variants
- Increase diagnostic yield in cases that would otherwise be negative, as exemplified here by this extremely rare case where a non-coding variant was the cause of three affected pregnancies.
Learn more about optimizing NGS lab efficiency
Watch our ‘Real-world examples of efficiency gains in the analysis and interpretation of genomic data’ webinar recording.
In this webinar, Dr Serra discusses the challenges faced in the analysis of NGS data and provided clinical examples where workflow efficiency is optimized using Congenica.
Request your personalized demo and see how Congenica could increase the efficiency of your NGS data analysis