While current guidelines provide a strong foundation for when and how to use genetic testing, clinical practice is increasingly shaped by innovations beyond those frameworks.1 Broad genomic profiling using high-content panels has demonstrated feasibility in decentralized settings, as shown in a multi-center implementation study that achieved a 93% sequencing success rate across 12 hospitals.2 Actionable variants were detected in 81% of patients, demonstrating the advantages of extended panels compared to limited ones.2
Despite promising findings, consistent implementation remains challenging. Variation in clinical interpretation, turnaround time, and access to expert input, such as molecular tumor boards, can lead to missed opportunities for matched therapy.2
Artificial intelligence is also beginning to reshape how clinicians approach molecular data.3 AI-supported platforms are being used to accelerate biomarker discovery, predict therapeutic response, and enable individualized treatment planning.3 These tools are also extending into radiomics and pathology, suggesting a future in which multi-modal data integration enhances early detection and treatment personalization.4
To ensure these advances translate into meaningful clinical gains, oncology providers must remain engaged in evidence review, data interpretation, and cross-disciplinary collaboration.
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