Computational image and molecular analysis reveal unique prognostic features of immune architecture in African Versus European American women with endometrial cancer
Year: 2025
Type: article
Study design: Observational Study
Abstract: Endometrial cancer (EC) disproportionately affects African American (AA) women in terms of progression and death. In our study, we sought to employ computerized image and bioinformatic analysis to tease out morphologic and molecular differences in EC between AA and European-American (EA) populations. We identified the differences in immune cell spatial patterns between AA and EA populations with markers of tumor biology, including histologic and molecular subtypes. The models performed best when they were trained and validated using data from the same population. Unsupervised clustering revealed a distinct association between immune cell features and known molecular subtypes of endometrial cancer that varied between AA and EA populations. Our genomic analysis revealed two distinct and novel gene sets with mutations associated with improved prognosis in AA and EA patients. Our study findings suggest the need for population-specific risk prediction models for women with endometrial cancer.
Source: npj Precision Oncology
Authors: Sepideh Azarianpour, Sirvan Khalighi, Arpit Aggarwal, Vidya Sankar Viswanathan, Pingfu Fu, Stefanie Avril, Anant Madabhushi
Language: English
Subfield: Obstetrics and Gynecology
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $4,090
Funders: U.S. Department of Defense, U.S. Department of Veterans Affairs, Eli Lilly and Company, AstraZeneca, National Institutes of Health, National Heart, Lung, and Blood Institute, National Cancer Institute, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources, DOD Prostate Cancer Research Program, DOD Peer Reviewed Cancer Research Program
Awards: G100871905, RadxTools for assessing tumor treatment response on imaging, SYSTEMS BIOLOGY APPROACH TO PREDICTING AND ASSESSING RESPONSE TO CHEMORADIATION FOR BRAIN TUMORS, G1119724336, G1266336578, G1313646905, G1471038061, G1478154455, G1505200503, G1596146291, G1680860089, G1817069843, G1828163000, G2228475719, G2291306530, BIOMEDICAL ENGINEERING RESEARCH FACILITIES, G2689512122, G2800195642, G2931214990, G3068466812, G3137332008, G338730684, G3564444776, G3716872827, G4119534169, HistoTools: A suite of digital pathology tools for quality control, annotation and dataset identification, FUSED BIOMARKER-BASED PREDICTION OF AGGRESSIVE FROM INDOLENT PROSTATE CANCER, G5758079931, G5850682332, NOVEL CT-DERIVED RADIOMIC BIOMARKERS FOR LUNG NODULE CHARACTERIZATION IN THE SCREENING POPULATION, G6123616905, G662883311, G7124118669, Computer-Assisted Histologic Evaluation of Cardiac Allograft Rejection, QUANTITATIVE VESSEL TORTUOSITY AS BIOMARKER FOR PREDICTING AND MONITORING RESPONSE TO IMMUNOTHERAPY FOR NON-SMALL CELL LUNG CANCER FROM ROUTINE CT SCANS, G7935030457, G8166743225, G854738250, G86714149, COMPUTATIONAL HISTOMORPHOMETRIC PREDICTOR OF PATHOLOGIC RESPONSE AND DISEASE RECURRENCE FOR TRIPLE-NEGATIVE BREAST CANCER, G9000000780, G9000016842, G9000021175, G9000023716, G9000064711, G9000153031, Novel Radiomics for Predicting Response to Immunotherapy for Lung Cancer, G942918344
OpenAlex ID: W4411532092