Browsing by Subject "Predictive modeling"

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    A triple hormone receptor ER, AR, and VDR signature is a robust prognosis predictor in breast cancer
    (Springer Nature, 2024-09-13) Omar, Mohamed; Harrell, J. Chuck; Tamimi, Rulla; Marchionni, Luigi; Erdogan, Cihat; Nakshatri, Harikrishna; Ince, Tan A.; Surgery, School of Medicine
    Background: Despite evidence indicating the dominance of cell-of-origin signatures in molecular tumor patterns, translating these genome-wide patterns into actionable insights has been challenging. This study introduces breast cancer cell-of-origin signatures that offer significant prognostic value across all breast cancer subtypes and various clinical cohorts, compared to previously developed genomic signatures. Methods: We previously reported that triple hormone receptor (THR) co-expression patterns of androgen (AR), estrogen (ER), and vitamin D (VDR) receptors are maintained at the protein level in human breast cancers. Here, we developed corresponding mRNA signatures (THR-50 and THR-70) based on these patterns to categorize breast tumors by their THR expression levels. The THR mRNA signatures were evaluated across 56 breast cancer datasets (5040 patients) using Kaplan-Meier survival analysis, Cox proportional hazard regression, and unsupervised clustering. Results: The THR signatures effectively predict both overall and progression-free survival across all evaluated datasets, independent of subtype, grade, or treatment status, suggesting improvement over existing prognostic signatures. Furthermore, they delineate three distinct ER-positive breast cancer subtypes with significant survival in differences-expanding on the conventional two subtypes. Additionally, coupling THR-70 with an immune signature identifies a predominantly ER-negative breast cancer subgroup with a highly favorable prognosis, comparable to ER-positive cases, as well as an ER-negative subgroup with notably poor outcome, characterized by a 15-fold shorter survival. Conclusions: The THR cell-of-origin signature introduces a novel dimension to breast cancer biology, potentially serving as a robust foundation for integrating additional prognostic biomarkers. These signatures offer utility as a prognostic index for stratifying existing breast cancer subtypes and for de novo classification of breast cancer cases. Moreover, THR signatures may also hold promise in predicting hormone treatment responses targeting AR and/or VDR.
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    Predictive modeling of indoor dust lead concentrations: Sources, risks, and benefits of intervention
    (Elsevier, 2023) Dietrich, Matthew; Barlow, Cynthia F.; Entwistle, Jane A.; Meza-Figueroa, Diana; Dong, Chenyin; Gunkel-Grillon, Peggy; Jabeen, Khadija; Bramwell, Lindsay; Shukle, John T.; Wood, Leah R.; Naidu, Ravi; Fry, Kara; Taylor, Mark Patrick; Filippelli, Gabriel M.; Earth and Environmental Sciences, School of Science
    Lead (Pb) contamination continues to contribute to world-wide morbidity in all countries, particularly low- and middle-income countries. Despite its continued widespread adverse effects on global populations, particularly children, accurate prediction of elevated household dust Pb and the potential implications of simple, low-cost household interventions at national and global scales have been lacking. A global dataset (∼40 countries, n = 1951) of community sourced household dust samples were used to predict whether indoor dust was elevated in Pb, expanding on recent work in the United States (U.S.). Binned housing age category alone was a significant (p < 0.01) predictor of elevated dust Pb, but only generated effective predictive accuracy for England and Australia (sensitivity of ∼80%), similar to previous results in the U.S. This likely reflects comparable Pb pollution legacies between these three countries, particularly with residential Pb paint. The heterogeneity associated with Pb pollution at a global scale complicates the predictive accuracy of our model, which is lower for countries outside England, the U.S., and Australia. This is likely due to differing environmental Pb regulations, sources, and the paucity of dust samples available outside of these three countries. In England, the U.S., and Australia, simple, low-cost household intervention strategies such as vacuuming and wet mopping could conservatively save 70 billion USD within a four-year period based on our model. Globally, up to 1.68 trillion USD could be saved with improved predictive modeling and primary intervention to reduce harmful exposure to Pb dust sources.