Challenges and approaches to calibrating patient phenotype as evidence for cancer gene variant classification under ACMG/AMP guidelines

• Published in: Human molecular genetics 2024-04, Vol.33 (8), p.724-732
• Main Authors: Fortuno, Cristina, Michailidou, Kyriaki, Parsons, Michael, Dolinsky, Jill S, Pesaran, Tina, Yussuf, Amal, Mester, Jessica L, Hruska, Kathleen S, Hiraki, Susan, O’Connor, Robert, Chan, Raymond C, Kim, Serra, Tavtigian, Sean V, Goldgar, David, James, Paul A, Spurdle, Amanda B
• Format: Article
• Language: English
• Subjects: Genes, Neoplasm; Genetic Testing - methods; Genetic Variation - genetics; Genome, Human; Humans; Neoplasms - genetics; Phenotype
• ISSN: 0964-6906; 1460-2083; 1460-2083
• DOI: 10.1093/hmg/ddae009

Abstract Since first publication of the American College of Medical Genetics and Genomics/Association for Medical Pathology (ACMG/AMP) variant classification guidelines, additional recommendations for application of certain criteria have been released (https://clinicalgenome.org/docs/), to improve their application in the diagnostic setting. However, none have addressed use of the PS4 and PP4 criteria, capturing patient presentation as evidence towards pathogenicity. Application of PS4 can be done through traditional case–control studies, or “proband counting” within or across clinical testing cohorts. Review of the existing PS4 and PP4 specifications for Hereditary Cancer Gene Variant Curation Expert Panels revealed substantial differences in the approach to defining specifications. Using BRCA1, BRCA2 and TP53 as exemplar genes, we calibrated different methods proposed for applying the “PS4 proband counting” criterion. For each approach, we considered limitations, non-independence with other ACMG/AMP criteria, broader applicability, and variability in results for different datasets. Our findings highlight inherent overlap of proband-counting methods with ACMG/AMP frequency codes, and the importance of calibration to derive dataset-specific code weights that can account for potential between-dataset differences in ascertainment and other factors. Our work emphasizes the advantages and generalizability of logistic regression analysis over simple proband-counting approaches to empirically determine the relative predictive capacity and weight of various personal clinical features in the context of multigene panel testing, for improved variant interpretation. We also provide a general protocol, including instructions for data formatting and a web-server for analysis of personal history parameters, to facilitate dataset-specific calibration analyses required to use such data for germline variant classification.