ABSTRACT
Ovarian cancer (OC) is an umbrella term for cancerous malignancies affecting the ovaries, yet treatment options for all subtypes are predominantly derived from high-grade serous ovarian cancer, the largest subgroup. The concept of "functional precision medicine" involves gaining personalized insights on therapy choice, based on direct exposure of patient tissues to drugs. This especially holds promise for rare subtypes like low-grade serous ovarian cancer (LGSOC). This study aims to establish an in vivo model for LGSOC using zebrafish embryos, comparing treatment responses previously observed in mouse PDX models, cell lines and 3D tumor models. To address this goal, a well-characterized patient-derived LGSOC cell line with the KRAS mutation c.35 G>T (p.(Gly12Val)) was used. Fluorescently labeled tumor cells were injected into the perivitelline space of 2 days' post-fertilization zebrafish embryos. At 1 day post-injection, xenografts were assessed for tumor size, followed by random allocation into treatment groups with trametinib, luminespib and trametinib + luminespib. Subsequently, xenografts were euthanized and analyzed for apoptosis and proliferation by confocal microscopy. Tumor cells formed compact tumor masses (n = 84) in vivo, with clear Ki67 staining, indicating proliferation. Zebrafish xenografts exhibited sensitivity to trametinib and luminespib, individually or combined, within a two-week period, establishing them as a rapid and complementary tool to existing in vitro and in vivo models for evaluating targeted therapies in LGSOC.
ABSTRACT
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.
ABSTRACT
Background: Nineteen genomic regions have been associated with high-grade serous ovarian cancer (HGSOC). We used data from the Ovarian Cancer Association Consortium (OCAC), Consortium of Investigators of Modifiers of BRCA1/BRCA2 (CIMBA), UK Biobank (UKBB), and FinnGen to identify novel HGSOC susceptibility loci and develop polygenic scores (PGS). Methods: We analyzed >22 million variants for 398,238 women. Associations were assessed separately by consortium and meta-analysed. OCAC and CIMBA data were used to develop PGS which were trained on FinnGen data and validated in UKBB and BioBank Japan. Results: Eight novel variants were associated with HGSOC risk. An interesting discovery biologically was finding that TP53 3'-UTR SNP rs78378222 was associated with HGSOC (per T allele relative risk (RR)=1.44, 95%CI:1.28-1.62, P=1.76×10-9). The optimal PGS included 64,518 variants and was associated with an odds ratio of 1.46 (95%CI:1.37-1.54) per standard deviation in the UKBB validation (AUROC curve=0.61, 95%CI:0.59-0.62). Conclusions: This study represents the largest GWAS for HGSOC to date. The results highlight that improvements in imputation reference panels and increased sample sizes can identify HGSOC associated variants that previously went undetected, resulting in improved PGS. The use of updated PGS in cancer risk prediction algorithms will then improve personalized risk prediction for HGSOC.
ABSTRACT
Hereditary Breast and Ovarian Cancer (HBOC) is a genetic condition associated with increased risk of cancers. The past decade has brought about significant changes to hereditary breast and ovarian cancer (HBOC) diagnostic testing with new treatments, testing methods and strategies, and evolving information on genetic associations. These best practice guidelines have been produced to assist clinical laboratories in effectively addressing the complexities of HBOC testing, while taking into account advancements since the last guidelines were published in 2007. These guidelines summarise cancer risk data from recent studies for the most commonly tested high and moderate risk HBOC genes for laboratories to refer to as a guide. Furthermore, recommendations are provided for somatic and germline testing services with regards to clinical referral, laboratory analyses, variant interpretation, and reporting. The guidelines present recommendations where 'must' is assigned to advocate that the recommendation is essential; and 'should' is assigned to advocate that the recommendation is highly advised but may not be universally applicable. Recommendations are presented in the form of shaded italicised statements throughout the document, and in the form of a table in supplementary materials (Table S4). Finally, for the purposes of encouraging standardisation and aiding implementation of recommendations, example report wording covering the essential points to be included is provided for the most common HBOC referral and reporting scenarios. These guidelines are aimed primarily at genomic scientists working in diagnostic testing laboratories.