Rucaparib in the landscape of PARP inhibition in ovarian cancer

Introduction: The landscape of poly (ADP-ribose) polymerase (PARP) inhibition in ovarian cancer is rapidly evolving and becoming increasingly complex. Ovarian cancer is leading therapeutic innovation by providing the proof of concept for DNA repair as a target. Three different PARP inhibitors have now received approvals in the US and Europe in different indications. Subtle but crucial differences can be found among the licensed indications for each PARP inhibitor in terms of histology, type of BRCA mutation (germline and/or somatic), number of prior lines of chemotherapy and whether the indication is in the treatment or maintenance settings.

Areas covered: We review the latest clinical data regarding the PARP inhibitor rucaparib in ovarian cancer, provide an update on the evolving landscape of PARP inhibition in ovarian cancer, and summarize avenues of ongoing and future research.

Expert opinion: All eligible patients should be offered a PARP inhibitor. SOLO1 trial results demonstrated an unprecedented benefit maintenance with PARP inhibitors in first line. Results from trials evaluating PARP inhibitors as maintenance in first line regardless of BRCA status and from trials evaluating combinatorial strategies are eagerly awaited.     

Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).