European Network of Gynaecological Oncological Trial Groups’ requirements for trials between academic groups and industry partners – a new Model D for drug and medical device development

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FIGO staging of endometrial cancer: 2023

INTRODUCTION: Many advances in the understanding of the pathologic and molecular features of endometrial cancer have occurred since the FIGO staging was last updated in 2009. Substantially more outcome and biological behavior data are now available regarding the several histological types. Molecular and genetic findings have accelerated since the publication of The Cancer Genome Atlas (TCGA) data and provide improved clarity on the diverse biological nature of this collection of endometrial cancers and their differing prognostic outcomes. The goals of the new staging system are to better define these prognostic groups and create substages that indicate more appropriate surgical, radiation, and systemic therapies. METHODS: The FIGO Women\u27s Cancer Committee appointed a Subcommittee on Endometrial Cancer Staging in October 2021, represented by the authors. Since then, the committee members have met frequently and reviewed new and established evidence on the treatment, prognosis, and survival of endometrial cancer. Based on these data, opportunities for improvements in the categorization and stratification of these factors were identified in each of the four stages. Data and analyses from the molecular and histological classifications performed and published in the recently developed ESGO/ESTRO/ESP guidelines were used as a template for adding the new subclassifications to the proposed molecular and histological staging system. RESULTS: Based on the existing evidence, the substages were defined as follows: SUMMARY: The updated 2023 staging of endometrial cancer includes the various histological types, tumor patterns, and molecular classification to better reflect the improved understanding of the complex nature of the several types of endometrial carcinoma and their underlying biologic behavior. The changes incorporated in the 2023 staging system should provide a more evidence-based context for treatment recommendations and for the more refined future collection of outcome and survival data

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

Simple Summary: The heterogeneity of epithelial ovarian cancer and its associated molecular biological characteristics are continuously integrated in the development of therapy guidelines. In a next step, future therapy recommendations might also be able to focus on the patient's systemic status, not only the tumor's molecular pattern. Therefore, new methods to identify and validate host-related biomarkers need to be established. Using mass spectrometry, we developed and independently validated a blood-based proteomic classifier, stratifying epithelial ovarian cancer patients into good and poor survival groups. We also determined an age dependence of the prognostic performance of this classifier and its association with important biological processes. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response and could therefore be integrated into future processes of therapy planning. Abstract: Mass-spectrometry-based analyses have identified a variety of candidate protein biomarkers that might be crucial for epithelial ovarian cancer (EOC) development and therapy response. Comprehensive validation studies of the biological and clinical implications of proteomics are needed to advance them toward clinical use. Using the Deep MALDI method of mass spectrometry, we developed and independently validated (development cohort: n = 199, validation cohort: n = 135) a blood-based proteomic classifier, stratifying EOC patients into good and poor survival groups. We also determined an age dependency of the prognostic performance of this classifier, and our protein set enrichment analysis showed that the good and poor proteomic phenotypes were associated with, respectively, lower and higher levels of complement activation, inflammatory response, and acute phase reactants. This work highlights that, just like molecular markers of the tumor itself, the systemic condition of a patient (partly reflected in proteomic patterns) also influences survival and therapy response in a subset of ovarian cancer patients and could therefore be integrated into future processes of therapy planning

The presence of postmenopausal bleeding as prognostic parameter in patients with endometrial cancer: a retrospective multi-center study

Abstract Background To date, there is no consensus on the utility of screening procedures for the early detection of endometrial cancer. The value of transvaginal ultrasound for screening of asymptomatic endometrial cancer has been discussed controversially. This study was conducted to evaluate whether asymptomatic patients with endometrial cancer have a better prognosis than symptomatic patients with endometrial cancer diagnosed after postmenopausal bleeding. Methods In the present multi-center study, the effect of the presence of postmenopausal bleeding on prognosis was evaluated retrospectively in 605 patients with endometrial cancer using patients' files. 543 patients (133 patients were asymptomatic, 410 patients were symptomatic) with endometrioid endometrial cancer were enrolled in all further analysis. Student's t-test, Cox regression analysis and Kaplan-Meier analysis were used were appropriate. Results Presence/absence of a postmenopausal bleeding was not associated with tumor stage (p = 0.2) and age at diagnosis (p = 0.5). Asymptomatic patients with endometrial cancer had a significantly higher rate of well and moderate-differentiated tumors compared to symptomatic patients (p = 0.008). In univariable and multivariable survival analysis, tumor stage, tumor grade, and patients' age at diagnosis, but not presence/absence of a postmenopausal bleeding, were associated with disease free and overall survival. Conclusion Asymptomatic patients with endometrial cancer have a higher rate of well differentiated tumors compared to patients with a postmenopausal bleeding prior to diagnosis. The prognosis of both groups of patients was similar.</p

Gene expression of PMP22 is an independent prognostic factor for disease-free and overall survival in breast cancer patients

<p>Abstract</p> <p>Background</p> <p>Gene expression of peripheral myelin protein 22 (<it>PMP22</it>) and the epithelial membrane proteins (<it>EMPs</it>) was found to be differentially expressed in invasive and non-invasive breast cell lines in a previous study. We want to evaluate the prognostic impact of the expression of these genes on breast cancer.</p> <p>Methods</p> <p>In a retrospective multicenter study, gene expression of <it>PMP22 </it>and the <it>EMPs </it>was measured in 249 primary breast tumors by real-time PCR. Results were statistically analyzed together with clinical data.</p> <p>Results</p> <p>In univariable Cox regression analyses PMP22 and the EMPs were not associated with disease-free survival or tumor-related mortality. However, multivariable Cox regression revealed that patients with higher than median <it>PMP22 </it>gene expression have a 3.47 times higher risk to die of cancer compared to patients with equal values on clinical covariables but lower <it>PMP22 </it>expression. They also have a 1.77 times higher risk to relapse than those with lower <it>PMP22 </it>expression. The proportion of explained variation in overall survival due to <it>PMP22 </it>gene expression was 6.5% and thus PMP22 contributes equally to prognosis of overall survival as nodal status and estrogen receptor status. Cross validation demonstrates that 5-years survival rates can be refined by incorporating <it>PMP22 </it>into the prediction model.</p> <p>Conclusions</p> <p><it>PMP22 </it>gene expression is a novel independent prognostic factor for disease-free survival and overall survival for breast cancer patients. Including it into a model with established prognostic factors will increase the accuracy of prognosis.</p

Clinical research in ovarian cancer: consensus recommendations from the Gynecologic Cancer InterGroup

The Gynecologic Cancer InterGroup (GCIG) sixth Ovarian Cancer Conference on Clinical Research was held virtually in October, 2021, following published consensus guidelines. The goal of the consensus meeting was to achieve harmonisation on the design elements of upcoming trials in ovarian cancer, to select important questions for future study, and to identify unmet needs. All 33 GCIG member groups participated in the development, refinement, and adoption of 20 statements within four topic groups on clinical research in ovarian cancer including first line treatment, recurrent disease, disease subgroups, and future trials. Unanimous consensus was obtained for 14 of 20 statements, with greater than 90% concordance in the remaining six statements. The high acceptance rate following active deliberation among the GCIG groups confirmed that a consensus process could be applied in a virtual setting. Together with detailed categorisation of unmet needs, these consensus statements will promote the harmonisation of international clinical research in ovarian cancer

Global estimates of mortality associated with long-term exposure to outdoor fine particulate matter.

Exposure to ambient fine particulate matter (PM2.5) is a major global health concern. Quantitative estimates of attributable mortality are based on disease-specific hazard ratio models that incorporate risk information from multiple PM2.5 sources (outdoor and indoor air pollution from use of solid fuels and secondhand and active smoking), requiring assumptions about equivalent exposure and toxicity. We relax these contentious assumptions by constructing a PM2.5-mortality hazard ratio function based only on cohort studies of outdoor air pollution that covers the global exposure range. We modeled the shape of the association between PM2.5 and nonaccidental mortality using data from 41 cohorts from 16 countries-the Global Exposure Mortality Model (GEMM). We then constructed GEMMs for five specific causes of death examined by the global burden of disease (GBD). The GEMM predicts 8.9 million [95% confidence interval (CI): 7.5-10.3] deaths in 2015, a figure 30% larger than that predicted by the sum of deaths among the five specific causes (6.9; 95% CI: 4.9-8.5) and 120% larger than the risk function used in the GBD (4.0; 95% CI: 3.3-4.8). Differences between the GEMM and GBD risk functions are larger for a 20% reduction in concentrations, with the GEMM predicting 220% higher excess deaths. These results suggest that PM2.5 exposure may be related to additional causes of death than the five considered by the GBD and that incorporation of risk information from other, nonoutdoor, particle sources leads to underestimation of disease burden, especially at higher concentrations