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Endocrine therapy is an effective treatment for low-grade serous ovarian cancer. However, the role of estrogen and progesterone receptors as biomarkers for high-grade serous ovarian cancer (HGSOC) is yet to be elucidated because not all estrogen and progesterone receptor-positive tumors benefit from anti-estrogen therapy. The degree of expression is presumed to play a vital role; however, that role is not well-defined in ovarian cancer. We aimed to determine the role of estrogen and progesterone receptor expression in primary and paired relapsed HGSOC. In this study, primary and matched relapsed tumor samples were collected from 80 patients with International Federation of Gynecology and Obstetrics Stage II–IV HGSOC. Tissue microarray was conducted and immunohistochemistry for estrogen and progesterone receptor expression was performed. Two independent pathologists performed the tissue microarray analysis with the Immunoreactive Score and Allred Total score. In the paired analysis, no significant difference in estrogen receptor expression was observed. However, progesterone receptor expression was significantly lower in patients with recurrent platinum-sensitive HGSOC. We conclude that anti-estrogen therapy targeting estrogen receptor positive HGSOC could be administered in primary and relapsed settings. The use of endocrine maintenance with an aromatase inhibitor in patients with estrogen receptor positive HGSOC needs to be further evaluated and validated in a randomized controlled trial. [ABSTRACT FROM AUTHOR]

Identifying prognostic biomarkers and risk stratification for COVID-19 patients is a challenging necessity. One of the core survival factors is patient age. However, chronological age is often severely biased due to dormant conditions and existing comorbidities. In this retrospective cohort study, we analyzed the data from 5315 COVID-19 patients (1689 lethal cases) admitted to 11 public hospitals in New York City from 1 March 2020 to 1 December. We calculated patients' pace of aging with BloodAge—a deep learning aging clock trained on clinical blood tests. We further constructed survival models to explore the prognostic value of biological age compared to that of chronological age. A COVID-19 score was developed to support a practical patient stratification in a clinical setting. Lethal COVID-19 cases had higher predicted age, compared to non-lethal cases (Δ = 0.8–1.6 years). Increased pace of aging was a significant risk factor of COVID-related mortality (hazard ratio = 1.026 per year, 95% CI = 1.001–1.052). According to our logistic regression model, the pace of aging had a greater impact (adjusted odds ratio = 1.09 ± 0.00, per year) than chronological age (1.04 ± 0.00, per year) on the lethal infection outcome. Our results show that a biological age measure, derived from routine clinical blood tests, adds predictive power to COVID-19 survival models. [ABSTRACT FROM AUTHOR]