Identification of potentially actionable genetic variants in epithelial ovarian cancer: a retrospective cohort study.
Charlotte FieuwsJoni Van der MeulenKristiaan ProesmansEmiel A De JaeghereSiebe LoontiensJo Van DorpePhilippe TummersHannelore G DenysKoen Van de VijverKathleen B M ClaesPublished in: NPJ precision oncology (2024)
Ovarian cancer is the most lethal gynecologic malignancy, mainly due to late-stage diagnosis, frequent recurrences, and eventually therapy resistance. To identify potentially actionable genetic variants, sequencing data of 351 Belgian ovarian cancer patients were retrospectively captured from electronic health records. The cohort included 286 (81%) patients with high-grade serous ovarian cancer, 17 (5%) with low-grade serous ovarian cancer, and 48 (14%) with other histotypes. Firstly, an overview of the prevalence and spectrum of the BRCA1/2 variants highlighted germline variants in 4% (11/250) and somatic variants in 11% (37/348) of patients. Secondly, application of a multi-gene panel in 168 tumors revealed a total of 214 variants in 28 genes beyond BRCA1/2 with a median of 1 (IQR, 1-2) genetic variant per patient. The ten most often altered genes were (in descending order): TP53, BRCA1, PIK3CA, BRCA2, KRAS, ERBB2 (HER2), TERT promotor, RB1, PIK3R1 and PTEN. Of note, the genetic landscape vastly differed between the studied histotypes. Finally, using ESCAT the clinical evidence of utility for every genetic variant was scored. Only BRCA1/2 pathogenic variants were classified as tier-I. Nearly all patients (151/168; 90%) had an ESCAT tier-II variant, most frequently in TP53 (74%), PIK3CA (9%) and KRAS (7%). In conclusion, our findings imply that although only a small proportion of genetic variants currently have direct impact on ovarian cancer treatment decisions, other variants could help to identify novel (personalized) treatment options to address the poor prognosis of ovarian cancer, particularly in rare histotypes.
Keyphrases
- copy number
- high grade
- low grade
- genome wide
- poor prognosis
- electronic health record
- end stage renal disease
- newly diagnosed
- ejection fraction
- chronic kidney disease
- dna methylation
- mesenchymal stem cells
- risk factors
- machine learning
- gene expression
- bone marrow
- transcription factor
- patient reported outcomes
- breast cancer risk
- pi k akt
- solid state