Advancements in Computational Tools Enhance Cancer Risk Prediction for Family Genetic Variants

Recent research led by QIMR Berghofer has demonstrated that cutting-edge computational prediction tools significantly improve the accuracy of genetic testing for families affected by hereditary conditions that elevate cancer risk. Published in the American Journal of Human Genetics, these findings highlight how integrating innovative bioinformatics methods with existing models can enhance the classification of genetic variants, especially those with previously uncertain significance.

Genetic testing plays a crucial role in identifying inherited mutations linked to increased cancer susceptibility, guiding clinical management, and informing preventative strategies. However, the challenge lies in distinguishing benign variants from pathogenic ones, particularly as sequencing technologies uncover numerous novel gene variants still lacking clear classification.

The study focused on Li-Fraumeni Syndrome, a rare disorder caused by mutations in the TP53 gene, known as the 'guardian of the genome' for its tumor-suppressing functions. Faults in this gene markedly raise the likelihood of developing multiple cancers, with some estimates indicating up to a 95% risk by age 60.

By using advanced computational methods, including tools to analyze the structural impact of genetic variants on protein stability, researchers were able to better predict which alterations impair gene function. Combining these predictions with existing classification strategies resulted in more precise impact assessments, reducing uncertainty in genetic test results. This improvement enables clinicians to decide on appropriate screening and treatment plans more confidently—potentially filtering out unnecessary interventions or enabling early detection.

Nitsan Rotenberg, the first author, emphasized the clinical significance: "Refining variant classification provides clarity for patients and supports targeted screening for those with pathogenic mutations, which can lead to earlier diagnosis and more effective therapies."

Senior researcher Professor Amanda Spurdle highlighted the broader implications, noting that these computational tools could revolutionize genetic research and clinical practice across various hereditary cancers and other genetic disorders. The methods also complement ongoing studies, such as those exploring how mutations influence gene splicing and protein production, providing a comprehensive approach to understanding genetic contributions to cancer.

This research underscores the importance of continual technological innovation in genomics and personalized medicine, facilitating better risk assessment, informed decision-making, and improved patient outcomes.

For more details, refer to the publication: [DOI: 10.1016/j.ajhg.2025.01.012].

Source: https://medicalxpress.com/news/2025-04-analysis-cancer-families-genetic-variants.html