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This file contains tables showing the association between BMI and each of ER and PR when adjusted for both risk factors and prognostic factors; associations between BMI and each of ER and PR when a consistent BMI reference group of normal weight is used; and risk stratification tables for the prediction of endometrial cancer-specific mortality using dichotomous tumor ER or PR status.

Background: Endometrial tumors arise from a hormonally responsive tissue. Defining subtypes by hormone receptor expression might better inform etiology and prediction of patient outcomes. We evaluated the potential role of tumor estrogen receptor (ER) and progesterone receptor (PR) expression to define endometrial cancer subtypes.Methods: We measured semi-continuous ER and PR protein expression in tissue specimens from 360 endometrial primary tumors from the Nurses' Health Study. To explore the impact of different definitions of marker positivity, we dichotomized ER and PR expression at different cut points in increments of 5% positive cells. Logistic regression was used to estimate associations between endometrial cancer risk factors, such as body mass index, with dichotomous ER or PR status. Reclassification statistics were used to assess whether adding dichotomous ER or PR status to standard prognostic factors of stage, grade, and histologic type would improve endometrial cancer-specific mortality prediction.Results: Compared with not being obese, obesity increased the odds of having an ER-positive tumor at cut points of 0% to 20% [maximum OR, 2.92; 95% confidence interval (CI), 1.34–6.33] as well as the odds of having a PR-positive tumor at cut points of 70% to 90% (maximum OR, 2.53; 95% CI, 1.36–4.68). Adding dichotomous tumor ER or PR status to the panel of standard predictors did not improve both model discrimination and calibration.Conclusions: Obesity may be associated with greater endometrial tumor expression of ER and PR. Adding either marker does not appear to improve mortality prediction beyond the standard predictors.Impact: Body mass index might explain some of the biological variation among endometrial tumors. Cancer Epidemiol Biomarkers Prev; 26(5); 727–35. ©2017 AACR.

This file provides detailed information on immunohistochemistry assay protocols as well as on the analytic handling of missing covariate data.

The Editor and Managing Editor of the August Wilson Journal met with four specialists from the University Library System (ULS) at the University of Pittsburgh and one University of Pittsburgh history professor via Zoom on January 14, 2021 to discuss the ULS’s recent acquisition of August Wilson’s archival materials.

Objective. Endometrial cancers have historically been classified by histomorphologic appearance, which is subject to interobserver disagreement. As molecular and biomarker testing has become increasingly available, the prognostic significance and accuracy of histomorphologic diagnoses have been questioned. To address these issues for a large, prospective cohort study, we provide the results of a centralized pathology review and biomarker analysis of all incidental endometrial carcinomas occurring between 1976 and 2012 in the Nurses’ Health Study. Methods. Routine histology of all ( n = 360 ) cases was reviewed for histomorphologic diagnosis. Cases were subsequently planted in a tissue microarray to explore expression of a variety of biomarkers (e.g., ER, PR, p53, PTEN, PAX2, AMACR, HNF1β, Napsin A, p16, PAX8, and GATA3). Results. Histologic subtypes included endometrioid (87.2%), serous (5.6%), carcinosarcoma (3.9%), clear cell (1.7%), and mixed type (1.7%). Biomarker results within histologic subtypes were consistent with existing literature: abnormal p53 was frequent in serous cases (74%), and HNF1β (67%), Napsin A (67%), and AMACR (83%) expression was frequent in clear cell carcinomas. Our dataset also allowed for examination of biomarker expression across non-preselected histologies. The results demonstrated that (1) HNF1β was not specific for clear cell carcinoma, (2) TP53 mutations occurred across many histologies, and (3) GATA3 was expressed across multiple histotypes, with 75% of positive cases demonstrating high-grade features. Conclusions. Our findings establish the subtypes of endometrial cancer occurring in the Nurses’ Health Study, corroborate the sensitivity of certain well-established biomarkers, and call into question previously identified associations between certain biomarkers (e.g., HNF1B) and particular histotypes.

The pathologic diagnosis of neoplasia requires localization and classification of lesional tissue, a process that depends on the recognition of an abnormal spatial distribution of neoplastic elements relative to admixed normal background tissue. In endometrial intraepithelial neoplasia (EIN), a pre-cancer usually managed by hysterectomy, a clonally mutated proliferation of cytologically altered glands ('neoplastic-EIN') aggregates in clusters that also contain background non-neoplastic glands ('background-NL'). Here, we used image analysis to classify individual glands within endometrial tissue fragments as neoplastic-EIN or background-NL, and we used the distribution of predictions to localize foci diagnostic of EIN. Nuclear coordinates were automatically assigned and were used as vertices to generate Delaunay triangulations for each gland. Graph statistical variables were used to develop random forest algorithms to classify glands as neoplastic-EIN or background-NL. Individual glands in an independent validation set were scored by a 'ground truth' biomarker (PAX2 immunohistochemistry). We found that exclusion of small glands led to improvement in classification accuracy. Using an inclusion threshold of 200 nuclei per gland, our final model classification accuracy was 77.5% in the validation set, with a positive predictive value of 0.81. We leveraged this high positive predictive value in a point cloud overlay display to assist end-user identification of EIN foci. This study demonstrates that graph theory approaches applied to small-scale anatomic elements in the endometrium allow biologic classification by machine learning, and that spatial superimposition over large-scale tissue expanses can have practical diagnostic utility. We expect this augmented diagnostic approach to be generalizable to commonly encountered problems in other organ systems. © 2020 The Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Developing a system of molecular subtyping for endometrial tumors might improve insight into disease etiology and clinical prediction of patient outcomes. High body mass index (BMI) has been implicated in development of endometrial cancer through hormonal pathways and might influence tumor expression of biomarkers involved in BMI-sensitive pathways. We evaluated whether endometrial tumor expression of 7 markers from BMI-sensitive pathways of insulin resistance could effectively characterize molecular subtypes: Adiponectin Receptor 1, Adiponectin Receptor 2, Leptin Receptor, Insulin Receptor (beta subunit), Insulin Receptor Substrate 1, Insulin-Like Growth Factor 1 Receptor, and Insulin-Like Growth Factor 2 Receptor. Using endometrial carcinoma tissue specimens from a case-only prospective sample of 360 women from the Nurses’ Health Study, we scored categorical immunohistochemical measurements of protein expression for each marker. Logistic regression was used to estimate associations between endometrial cancer risk factors, especially BMI, and tumor marker expression. Proportional hazards modeling was performed to estimate associations between marker expression and time to all-cause mortality as well as time to endometrial cancer-specific mortality. No association was observed between BMI and tumor expression of any marker. No marker was associated with time to either all-cause mortality or endometrial cancer-specific mortality in models with or without standard clinical predictors of patient mortality (tumor stage, grade, and histologic type). It did not appear that any of the markers evaluated here could be used effectively to define molecular subtypes of endometrial cancer.

Background: Endometrial tumors arise from a hormonally responsive tissue. Defining subtypes by hormone receptor expression might better inform etiology and prediction of patient outcomes. We evaluated the potential role of tumor estrogen receptor (ER) and progesterone receptor (PR) expression to define endometrial cancer subtypes. Methods: We measured semi-continuous ER and PR protein expression in tissue specimens from 360 endometrial primary tumors from the Nurses' Health Study. To explore the impact of different definitions of marker positivity, we dichotomized ER and PR expression at different cut points in increments of 5% positive cells. Logistic regression was used to estimate associations between endometrial cancer risk factors, such as body mass index, with dichotomous ER or PR status. Reclassification statistics were used to assess whether adding dichotomous ER or PR status to standard prognostic factors of stage, grade, and histologic type would improve endometrial cancer-specific mortality prediction. Results: Compared with not being obese, obesity increased the odds of having an ER-positive tumor at cut points of 0% to 20% [maximum OR, 2.92; 95% confidence interval (CI), 1.34–6.33] as well as the odds of having a PR-positive tumor at cut points of 70% to 90% (maximum OR, 2.53; 95% CI, 1.36–4.68). Adding dichotomous tumor ER or PR status to the panel of standard predictors did not improve both model discrimination and calibration. Conclusions: Obesity may be associated with greater endometrial tumor expression of ER and PR. Adding either marker does not appear to improve mortality prediction beyond the standard predictors. Impact: Body mass index might explain some of the biological variation among endometrial tumors. Cancer Epidemiol Biomarkers Prev; 26(5); 727–35. ©2017 AACR.

In Addington, Thomas. "Bibliography for August Wilson Journal: Spring 2019." August Wilson Journal doi:https://doi.org/10.5195/awj/2019.28, author "Thomas Addington" was changed to "Thom C. Addington.”

In Ridley, Leticia. "Stage Review of Gem of the Ocean." August Wilson Journal doi:https://doi.org/10.5195.awj.2019.22, author Leticia Ridley’s credentials were updated from master’s candidate to doctoral candidate.

American Century Cycle This note relates to the Journal's terminology involving the cycle of ten plays that August Wilson wrote commonly known as the "Pittsburgh Cycle" or the "Century Cycle." At the 2018 August Wilson Society Colloquium held at the August Wilson Center in Pittsburgh, Pennsylvania, Constanza Romero, August's widow and coordinator of the August Wilson Estate, said to the group that her preference, going forward, is that the phrase "August Wilson's American Century Cycle" be used to refer to those ten plays. Being Pittsburghers, David and I you both love the term, "Pittsburgh Cycle," but we also understand that it is not entirely accurate as Ma Rainey's Black Bottom is set in Chicago. Therefore, all references to Wilson's cycle in the August Wilson Journal will be standardized as the "August Wilson American Century Cycle," "August Wilson's American Century Cycle," or shortened to "American Century Cycle."

A welcome to the inaugural edition of the August Wilson Journal, written by the editor.

Endometrial carcinoma (EC), a malignancy that arises from the epithelial lining of the uterus, is heterogeneous at histologic and molecular levels. Risk factors and outcomes also differ by type. Though prior studies characterized the genomic landscape of endometrial carcinoma, few integrated histologic, clinical, and prospectively collected epidemiologic data into the analysis. We collected formalin-fixed paraffin embedded tumor tissue from women enrolled in the Nurses’ Health Study who were diagnosed with EC between 1976 and 2012. We targeted 50 cancer related genes for high-throughput sequencing to identify genetic variants in 37 ECs and correlate findings with immunohistochemical, histologic, and epidemiologic data. Case selection was designed to include maximal power to discover genetic changes associated with p53 immunohistochemical status and clinical stage of disease at diagnosis. Mutations most frequently occurred in TP53 (57%), PTEN (46%), and PIK3CA (38%). TP53 mutations were seen in 83% of ECs that immunostained positive for mutant p53, with the most frequent TP53 mutations occurring in R248. Well-differentiated endometrioid tumors had elevated frequencies of PTEN and PIK3CA mutations compared to less differentiated tumors (p < 0.05). The mutation profiles of these samples are consistent with previous studies, supporting the viability of archival paraffin-embedded tissue in mutation detection. This study’s interdisciplinary approach to tumor characterization may help inform future development of personalized models for EC. Citation Format: Maxine Chen, Marta Crous-Bou, Michael J. Downing, Evan L. Busch, Kimberley Glass, Jennifer Prescott, George L. Mutter, Immaculata De Vivo. Mutation analysis of endometrial cancer in a population-based study by targeted next-generation sequencing [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1291. doi:10.1158/1538-7445.AM2017-1291

One problem with many introductory level turbomachinery courses is a lack of easy to use design and visualization tools. Oftentimes students will become too focused on the underlying math and never develop a good understanding of the physical systems they are working with. To combat this problem, a series of GUI based design and visualization codes have been created. The codes are intended primarily for educational purposes, but in many cases they are robust enough for actual design use. All of the new codes have been designed to complement and to a small extent connect to the existing T-Axi suite of codes. This paper will focus on two new freely available codes: T-Axi Blade and T-Axi Disk. T-Axi Blade was created to help visualize the key design features of a single rotor, with special emphasis on vector triangles, and the ability to design compressor, turbine, axial and centrifugal rotors with a universal approach. T-Axi Blade can also output files for use with T-Axi Disk, a code to aid in the design of a lightweight disk to support the blade row. This code allows the user to design a disk interactively with instantaneous feedback in the form of weights, stresses, and a series of 2D and 3D visualizations. Taken together these codes offer a simple introduction to multidisciplinary engineering. In this paper the structure of these codes and the numerical models are discussed. Ideas are presented of how these codes can be used as a classroom tool and as an actual design tool. An example analysis of the third stage GE EEE HPC axial rotor is presented to demonstrate the features of these codes.Copyright © 2009 by ASME