José Manuel Martínez

Evaluation of Somatic Mutations in Urine Samples as a Noninvasive Method for the Detection and Molecular Classification of Endometrial Cancer

Abstract Purpose: Current diagnostic methods for endometrial cancer lack specificity, leading to many women undergoing invasive procedures. The aim of this study was to evaluate somatic mutations in urine to accurately discriminate patients with endometrial cancer from controls. Experimental Design: Overall, 72 samples were analyzed using next-generation sequencing (NGS) with molecular identifiers targeting 47 genes. We evaluated urine supernatant samples from women with endometrial cancer (n = 19) and age-matched controls (n = 20). Cell pellets from urine and plasma samples from seven cases were sequenced; further, we also evaluated paired tumor samples from all cases. Finally, immunohistochemical markers for molecular profiling were evaluated in all tumor samples. Results: Overall, we were able to identify mutations in DNA from urine supernatant samples in 100% of endometrial cancers. In contrast, only one control (5%) showed variants at a variant allele frequency (VAF) ≥ 2% in the urine supernatant samples. The molecular classification obtained by using tumor samples and urine samples showed good agreement. Analyses in paired samples revealed a higher number of mutations and VAF in urine supernatants than in urine cell pellets and blood samples. Conclusions: Evaluation of somatic mutations using urine samples may offer a user-friendly and reliable tool for endometrial cancer detection and molecular classification. The diagnostic performance for endometrial cancer detection was very high, and cases could be molecularly classified using these noninvasive and self-collected samples. Additional multicenter evaluations using larger sample sizes are needed to validate the results and understand the potential of urine samples for the early detection and prognosis of endometrial cancer.

Predicting Ovarian-Cancer Burden in Catalonia by 2030: An Age–Period–Cohort Modelling

Ovarian cancer is the most lethal gynaecological cancer in very-high-human-development-index regions. Ovarian cancer incidence and mortality rates are estimated to globally rise by 2035, although incidence and mortality rates depend on the region and prevalence of the associated risk factors. The aim of this study is to assess changes in incidence and mortality of ovarian cancer in Catalonia by 2030. Bayesian autoregressive age–period–cohort models were used to predict the burden of OC incidence and mortality rates for the 2015–2030 period. Incidence and mortality rates of ovarian cancer are expected to decline in Catalonia by 2030 in women ≥ 45 years of age. A decrease in ovarian-cancer risk was observed with increasing year of birth, with a rebound in women born in the 1980s. A decrease in mortality was observed for the period of diagnosis and period of death. Nevertheless, ovarian-cancer mortality remains higher among older women compared to other age groups. Our study summarizes the most plausible scenario for ovarian-cancer changes in terms of incidence and mortality in Catalonia by 2030, which may be of interest from a public health perspective for policy implementation.