Germline pathogenic variants in TP53 are associated with Li-Fraumeni syndrome, a cancer predisposition disorder inherited in an autosomal dominant pattern associated with a high risk of malignancy, including early-onset breast cancers, sarcomas, adrenocortical carcinomas, and brain tumors. Intense cancer surveillance for individuals with TP53 germline pathogenic variants is associated with reduced cancer-related mortality. Accurate and consistent classification of germline variants across clinical and research laboratories is important to ensure appropriate cancer surveillance recommendations. Here, we describe the work performed by the Clinical Genome Resource TP53 Variant Curation Expert Panel (ClinGen TP53 VCEP) focused on specifying the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG/AMP) guidelines for germline variant classification to the TP53 gene. Specifications were developed for 20 ACMG/AMP criteria, while nine were deemed not applicable. The original strength level for the 10 criteria was also adjusted due to current evidence. Use of TP53-specific guidelines and sharing of clinical data among experts and clinical laboratories led to a decrease in variants of uncertain significance from 28% to 12% compared with the original guidelines. The ClinGen TP53 VCEP recommends the use of these TP53-specific ACMG/AMP guidelines as the standard strategy for TP53 germline variant classification.

Background

Germline pathogenic variants in CDH1 are associated with increased risk of diffuse gastric cancer and lobular breast cancer. Risk reduction strategies include consideration of prophylactic surgery, thereby making accurate interpretation of germline CDH1 variants critical for physicians deciding on these procedures. The Clinical Genome Resource (ClinGen) CDH1 Variant Curation Expert Panel (VCEP) developed specifications for CDH1 variant curation with a goal to resolve variants of uncertain significance (VUS) and with ClinVar conflicting interpretations and continues to update these specifications.

Methods

CDH1 variant classification specifications were modified based on updated genetic testing clinical criteria, new recommendations from ClinGen and expert knowledge from ongoing CDH1 variant curations. The CDH1 VCEP reviewed 273 variants using updated CDH1 specifications and incorporated published and unpublished data provided by diagnostic laboratories.

Results

Updated CDH1-specific interpretation guidelines include 11 major modifications since the initial specifications from 2018. Using the refined guidelines, 97% (36 of 37) of variants with ClinVar conflicting interpretations were resolved to benign, likely benign, likely pathogenic or pathogenic, and 35% (15 of 43) of VUS were resolved to benign or likely benign. Overall, 88% (239 of 273) of curated variants had non-VUS classifications. To date, variants classified as pathogenic are either nonsense, frameshift, splicing, or affecting the translation initiation codon, and the only missense variants classified as pathogenic or likely pathogenic have been shown to affect splicing.

Conclusions

The development and evolution of CDH1-specific criteria by the expert panel resulted in decreased uncertain and conflicting interpretations of variants in this clinically actionable gene, which can ultimately lead to more effective clinical management recommendations.

The use of genome-scale sequencing allows for identification of genetic findings beyond the original indication for testing (secondary findings). The ClinGen Actionability Working Groups (AWG) protocol for evidence synthesis and semi-quantitative metric scoring evaluates four domains of clinical actionability for potential secondary findings: severity and likelihood of the outcome, and effectiveness and nature of the intervention. As of February 2018, the AWG has scored 127 genes associated with 78 disorders (up-to-date topics/scores are available at www.clinicalgenome.org). Scores across these disorders were assessed to compare genes/disorders recommended for return as secondary findings by the American College of Medical Genetics and Genomics (ACMG) with those not currently recommended. Disorders recommended by the ACMG scored higher on outcome-related domains (severity and likelihood), but not on intervention-related domains (effectiveness and nature of the intervention). Current practices indicate that return of secondary findings will expand beyond those currently recommended by the ACMG. The ClinGen AWG evidence reports and summary scores are not intended as classifications of actionability, rather they provide a resource to aid decision makers as they determine best practices regarding secondary findings. The ClinGen AWG is working with the ACMG Secondary Findings Committee to update future iterations of their secondary findings list.

BACKGROUND: Identification of clinically significant genetic alterations involved in human disease has been dramatically accelerated by developments in next-generation sequencing technologies. However, the infrastructure and accessible comprehensive curation tools necessary for analyzing an individual patient genome and interpreting genetic variants to inform healthcare management have been lacking. RESULTS: Here we present the ClinGen Variant Curation Interface (VCI), a global open-source variant classification platform for supporting the application of evidence criteria and classification of variants based on the ACMG/AMP variant classification guidelines. The VCI is among a suite of tools developed by the NIH-funded Clinical Genome Resource (ClinGen) Consortium and supports an FDA-recognized human variant curation process. Essential to this is the ability to enable collaboration and peer review across ClinGen Expert Panels supporting users in comprehensively identifying, annotating, and sharing relevant evidence while making variant pathogenicity assertions. To facilitate evidence-based improvements in human variant classification, the VCI is publicly available to the genomics community. Navigation workflows support users providing guidance to comprehensively apply the ACMG/AMP evidence criteria and document provenance for asserting variant classifications. CONCLUSIONS: The VCI offers a central platform for clinical variant classification that fills a gap in the learning healthcare system, facilitates widespread adoption of standards for clinical curation, and is available at https://curation.clinicalgenome.org.

Recommendations for laboratories to report incidental findings from genomic tests have stimulated interest in such results. In order to investigate the criteria and processes for assigning the pathogenicity of specific variants and to estimate the frequency of such incidental findings in patients of European and African ancestry, we classified potentially actionable pathogenic single-nucleotide variants (SNVs) in all 4300 European- and 2203 African-ancestry participants sequenced by the NHLBI Exome Sequencing Project (ESP). We considered 112 gene-disease pairs selected by an expert panel as associated with medically actionable genetic disorders that may be undiagnosed in adults. The resulting classifications were compared to classifications from other clinical and research genetic testing laboratories, as well as with in silico pathogenicity scores. Among European-ancestry participants, 30 of 4300 (0.7%) had a pathogenic SNV and six (0.1%) had a disruptive variant that was expected to be pathogenic, whereas 52 (1.2%) had likely pathogenic SNVs. For African-ancestry participants, six of 2203 (0.3%) had a pathogenic SNV and six (0.3%) had an expected pathogenic disruptive variant, whereas 13 (0.6%) had likely pathogenic SNVs. Genomic Evolutionary Rate Profiling mammalian conservation score and the Combined Annotation Dependent Depletion summary score of conservation, substitution, regulation, and other evidence were compared across pathogenicity assignments and appear to have utility in variant classification. This work provides a refined estimate of the burden of adult onset, medically actionable incidental findings expected from exome sequencing, highlights challenges in variant classification, and demonstrates the need for a better curated variant interpretation knowledge base.