How can I interpret genomic test results to inform treatment decisions for my patients?

To interpret genomic test results effectively for informing treatment decisions, it is essential to integrate molecular findings with clinical context, focusing on actionable genetic alterations that influence prognosis and therapeutic options. Begin by identifying pathogenic or likely pathogenic variants in genes relevant to the patient's cancer type, such as homologous recombination repair (HRR) genes in metastatic prostate cancer or BRCA mutations in breast and ovarian cancers, as these can guide targeted therapies and eligibility for clinical trials ¹.

Use a structured reporting framework that clearly categorises variants by clinical significance—pathogenic, likely pathogenic, variants of uncertain significance, and benign—to prioritise treatment decisions and avoid overinterpretation ¹. Reports should include interpretation of variant impact on protein function, potential resistance mechanisms, and recommended targeted agents or treatment modifications based on current evidence (Casolino et al., 2024). This approach ensures that genomic data are actionable and clinically relevant.

Consider the tumour type and stage, as well as patient-specific factors, when integrating genomic results into treatment planning. For example, alterations in HRR genes may indicate sensitivity to PARP inhibitors in metastatic prostate cancer, as outlined in practical molecular testing pathways [1, Schostak et al., 2024]. Similarly, ESMO recommendations emphasise the importance of multidisciplinary review of genomic reports to contextualise findings within the broader clinical picture and to guide precision oncology decisions (van de Haar et al., 2024).

Finally, maintain awareness of evolving evidence and guidelines, as interpretation frameworks and actionable targets continue to expand. Regularly update knowledge bases and collaborate with molecular tumour boards or genetic specialists to optimise patient management (Casolino et al., 2024; van de Haar et al., 2024).