An international worldwide retrospective cohort observational study comparing primary cytoreductive surgery with neoadjuvant chemotherapy and interval cytoreductive surgery in patients with carcinoma of the ovary, fallopian tubes, and peritoneum (SUROVA trial).

BACKGROUND: Currently, a lively debate exists within the scientific community regarding the most suitable procedure for treating stages IIIB-IVB carcinoma of the ovary, fallopian tubes, and peritoneum. The options under most consideration are primary cytoreductive surgery or neoadjuvant chemotherapy followed by interval cytoreductive surgery. PRIMARY OBJECTIVE: To compare overall survival at 5 years in patients who underwent primary cytoreductive surgery versus neoadjuvant chemotherapy and interval cytoreductive surgery for stage IIIB-IVB ovarian cancer STUDY HYPOTHESIS: The treatment with primary cytoreductive surgery results in superior patient survival compared with neoadjuvant chemotherapy followed by interval cytoreductive surgery. TRIAL DESIGN: This is a multicenter, retrospective cohort observational study. Data will be collected from patients undergoing surgery in hospitals worldwide. Two arms will be compared: primary cytoreductive surgery and neoadjuvant chemotherapy followed by interval cytoreductive surgery. MAJOR INCLUSION/EXCLUSION CRITERIA: Patients must have suspected or histologically confirmed International Federation of Gynecology and Obstetrics (FIGO) stages IIIB-IVB ovarian, peritoneal, or fallopian tube cancers. They must have undergone primary surgery or first course of neoadjuvant chemotherapy between January 1, 2018 and December 31, 2019. Based on all available information before the surgery (primary or interval), the patient must have been considered completely resectable. PRIMARY ENDPOINT: Overall survival at 5 years from the first treatment (chemotherapy in the case of neoadjuvant chemotherapy and cytoreduction in the case of primary cytoreductive surgery). SAMPLE SIZE: An estimated total of 5000 patients will be enrolled in the study. ESTIMATED DATES FOR COMPLETING ACCRUAL AND PRESENTING RESULTS: March 2025 TRIAL REGISTRATION: NCT06223763.

Impact of population structure in the estimation of recent historical effective population size by the software GONE.

BACKGROUND: Effective population size (Ne) is a crucial parameter in conservation genetics and animal breeding. A recent method, implemented by the software GONE, has been shown to be rather accurate in estimating recent historical changes in Ne from a single sample of individuals. However, GONE estimations assume that the population being studied has remained isolated for a period of time, that is, without migration or confluence of other populations. If this occurs, the estimates of Ne can be heavily biased. In this paper, we evaluate the impact of migration and admixture on the estimates of historical Ne provided by GONE through a series of computer simulations considering several scenarios: (a) the mixture of two or more ancestral populations; (b) subpopulations that continuously exchange individuals through migration; (c) populations receiving migrants from a large source; and (d) populations with balanced systems of chromosomal inversions, which also generate genetic structure. RESULTS: Our results indicate that the estimates of historical Ne provided by GONE may be substantially biased when there has been a recent mixture of populations that were previously separated for a long period of time. Similarly, biases may occur when the rate of continued migration between populations is low, or when chromosomal inversions are present at high frequencies. However, some biases due to population structuring can be eliminated by conducting population structure analyses and restricting the estimation to the differentiated groups. In addition, disregarding the genomic regions that are involved in inversions can also remove biases in the estimates of Ne. CONCLUSIONS: Different kinds of deviations from isolation and panmixia of the populations can generate biases in the recent historical estimates of Ne. Therefore, estimation of past demography could benefit from performing population structure analyses beforehand, by mitigating the impact of these biases on historical Ne estimates.