Utility of NIST Whole-Genome Reference Materials for the Technical Validation of a Multigene Next-Generation Sequencing Test

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

The inequity of targeted cystic fibrosis reproductive carrier screening tests in Australia

OBJECTIVE

European and Australian guidelines for cystic fibrosis (CF) reproductive carrier screening recommend testing a small number of high frequency CF causing variants, rather than comprehensive CFTR sequencing. The study objective was to determine variant detection rates of commercially available targeted reproductive carrier screening tests in Australia.

METHODS

Next-generation DNA sequencing of the CFTR gene was performed on 2552 individuals from a whole population sample to identify CF causing variants. The variant detection rates of two commercially available Australian reproductive carrier screening tests, which target 50 or 175 CF causing variants, in this population were calculated. The ethnicity of individuals was determined using principal component analysis.

RESULTS

Variant detection rates of the tests for 50 and 175 CF causing variants were 88.2% and 90.8%, respectively. No CF causing variants in individuals of East Asian ethnicity (n = 3) were detected by either test, while >86.6% (n = 69) of CF causing variants in Europeans would be identified by either test.

CONCLUSIONS

Reproductive carrier screening tests for a targeted set of high frequency CF variants are unable to detect approximately 10% of CF variants in a multiethnic Australian population, and individuals of East Asian ethnicity are disproportionally affected by this test limitation.

Racially equitable diagnosis of cystic fibrosis using next-generation DNA sequencing: a case report

Background

Cystic Fibrosis (CF) is one of the most prevalent autosomal recessive inherited disease in Caucasians. Rates of CF were thought to be negligible in non-Caucasians but growing epidemiological evidence shows CF is more common in Indian, African, Hispanic, Asian, and other ethnic groups than previously thought. Almost all second-tier molecular diagnostic tools currently used to confirm the diagnosis of CF consist of panels of the most common CF-causing DNA variants in Caucasians. However non-Caucasian individuals with CF often have a different spectrum of pathogenic variants than Caucasians, limiting the clinical utility of existing molecular diagnostic panels in this group. As a consequence of racially inequitable CF testing frameworks, non-Caucasians with CF encounter greater delays in diagnosis and associated harms than Caucasians. An unbiased approach of detecting CF-causing DNA variants using full gene sequencing could potentially address racial inequality in current CF testing.

Case presentation

We present the case of a female baby from rural India who had a borderline first-tier newborn screening result for CF. Instead of choosing a targeted CF panel for second-tier testing, we used next-generation DNA sequencing to comprehensively analyze the cystic fibrosis transmembrane conductance regulator gene as an unbiased approach for molecular confirmation of CF. Sequencing identified two pathogenic variants that cause CF. One variant (c.1521_1523delCTT) is the most common cause of CF, while the other variant (c.870-1G > C) is absent from all population allele databases and has not been found in the Indian population previously. The rare variant would not have been detected by all currently available targeted CF panels used for second- or third-tier molecular CF testing.

Conclusions

Our use of full gene sequencing as a second-tier CF test in a non-Caucasian patient avoided the problems of missed diagnosis from using Caucasian-biased targeted CF panels currently recommended for second-tier testing. Full gene sequencing should be considered as the standard methodology of second-tier CF testing to enable equal opportunity for CF diagnosis in non-Caucasians.

Control Charting Genomic Data

BACKGROUND

Control charting is routine in the quality assurance of traditional clinical laboratory testing. Genomic tests are not typically managed by control charting. We examined control charting to monitor the performance of a clinical next-generation sequencing (NGS) assay.

METHODS

We retrospectively examined 3 years of control material (NA12878) data from clinical genomic epilepsy testing. Levey-Jennings plots were used to visualize changes in control material depth of sequencing coverage in genomic regions of an epilepsy genomic panel. Changes in depth of coverage were correlated with changes in the manufactured lot of capture probe reagent. Depth of coverage was also correlated between quality control material and clinical samples.

RESULTS

Fifty-seven sequencing runs of NA12878 were analyzed for 1811 genomic regions targeting 108 genes. Manufactured probe lot changes were associated with significant changes in the average coverage of 537 genomic regions and the lowest coverage of 173 regions (using a critical cut-off of P < 5.52 x 10-6). Genomic regions with the highest sensitivity to lot-to-lot variation by average sequencing depth of coverage were not the same regions with the highest sensitivity by lowest sequencing depth of coverage. Levey-Jennings plots displayed differences in genomic depth of coverage across capture probe reagent lot changes. There was moderate correlation between the changes in depth of sequencing across lot changes for control material and clinical cases (r2 = 0.45).

CONCLUSIONS

Genomic control charting can be used routinely by clinical laboratories to monitor assay performance and ensure the quality of testing.