Indications and timing for genetic testing in ovarian cancer - Miedziarek, Cezary; Markwitz, Maksymilian; Potograbski, Michał; Zaborowski, Mikołaj Piotr (Journal of Medical Science, 2025)
Modern management of ovarian cancer (OC) relies on molecular diagnostics, with genetic testing playing a central role in therapeutic decisions. High-grade serous ovarian cancer (HGSOC) is frequently associated with mutations in the BRCA1 and BRCA2 genes, as well as other alterations within the homologous recombination repair (HRR) pathway.  The identification of pathogenic variants is critical for selecting patients eligible for treatment with poly (ADP-ribose) polymerase inhibitors (PARPi), which significantly improve progression-free survival, especially in individuals with BRCA mutations and homologous recombination deficiency (HRD).Current guidelines recommend BRCA testing at diagnosis for all patients with HGSOC, followed by HRD testing. Various techniques are used to assess genetic alterations and HRD status. Commercial tests assess mutations in genes in HRR pathways, genomic instability, or HRR functional status to quantify HRD.Despite the availability of these assays, challenges remain regarding test standardisation, predictive accuracy, and cost-effectiveness. Moreover, emerging research highlights the potential for artificial intelligence (AI) to enhance molecular profiling, utilising whole-slide imaging (WSI) and deep learning to predict homologous recombination deficiency (HRD) and other tumour characteristics.The integration of molecular subtypes, as defined by The Cancer Genome Atlas (TCGA), into routine clinical practice holds promise for tailoring therapy beyond BRCA or homologous recombination deficiency (HRD) status. As the field advances, comprehensive genetic testing combined with AI-driven analytics may become the cornerstone of precision oncology in ovarian cancer.