P755: The impact of gene-specific guidelines on variant reassessment: Perspectives from a hereditary cancer clinic
Bibliographic record
Abstract
Introduction: The prevalence of variants of uncertain significance (VUS) leads to significant challenges for clinical management.Stringent classification criteria for variant interpretation set by the American College of Medical Genetics and Genomics (ACMG) rely on genetic population databases among other criteria.However, genetic population databases contain a disproportionately large amount of data from individuals of European ancestry compared to non-European ancestry.Our preliminary analysis aims (i) to manually interpret VUS identified in high-penetrance breast cancer genes (BRCA1, BRCA2, PALB2) among individuals of an underrepresented ancestry utilizing ACMG guidelines and (ii) to explore reclassification potential with hypothetical increased representation in population databases.Methods: We gathered a list of 1648 patients who underwent hereditary cancer germline testing at the UCSF Genomic Medicine Laboratory (GML) from January 2024-October 2024 and filtered to reveal 48 individuals with results that include a VUS in either BRCA1, BRCA2, or PALB2.Retrospective chart review was completed to obtain self-reported ancestry of the individual, indicating whether the patient has an underrepresented ancestry, only a represented (European) ancestry, or if they are of mixed ancestry.From this review, we further analyzed variants identified in individuals of non-European and mixed ancestries alike to examine a more diverse representation of the challenges in variant interpretation.Comparative analysis was performed on each distinct group.Population frequencies and computational data were obtained through gnomAD, PolyPhen-2, Mutation Taster, and CADD.Functional and segregation data were identified through literature search in PubMed, LitVar2, and Google Scholar.ACMG guideline criteria points were systematically applied for each variant without the use of automated tools revealing a classification.Hypothetical strong evidence for benign impact (BS1) was then applied to determine if this would alter classification.Results: Of the 48 individuals with a VUS in a high-penetrance breast cancer gene, 22 were individuals with an underrepresented ancestry (46%; 13 with underrepresented-only ancestry, and 9 with mixed ancestry).Independent evaluation of ACMG variant interpretation criterion for the 22 variants show 0 variants meet criteria for reclassification.Clearly defined criteria for classifying variants are categorized into strong, moderate, and supporting evidence of pathogenicity or benign impact.Population data analysis shows 1 variant meeting moderate evidence for pathogenicity (PM2) with no presence in population databases and the other 20 variants have a low allele frequency in gnomAD and do not meet PM2 due to the autosomal dominant association of the genes.Computational evidence suggesting impact on the gene or gene product was reviewed.This reflected 6 variants with multiple lines of evidence supporting no consequence (BP4), 7 variants with multiple evidence lines that suggest deleterious effects (PP3), and 9 variants with conflicting evidence.Functional studies were available for 2 variants, which support no damaging effect on the protein or gene function (BS3).Segregation data was not available for any of the studied variants.By considering hypothetical BS1 criteria, 9 (41%) of the 22 variants meet classification criteria for a downgrade to either benign or likely benign.Conclusion: This study highlights the obstacle of interpreting VUS and addresses barriers of inequitable population data for underrepresented ancestries.ACMG classification inherently relies on population data from overrepresented groups.An inability to apply population frequency classification criteria (PM2, BS1) due to the disparity of genetic information available for underrepresented ancestries reflects and calls for increased efforts in variant interpretation to ensure access to informative genetic test results across all populations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.046 | 0.104 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.022 | 0.020 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".