Abstract PO5-09-02: Breast Tissue Proteomic Profile of Breast Cancer in Premenopausal Women and Association with Mammographic Breast Density
Authors: Shaili Tapiavala, Minsoo Son, Graham Colditz, Ah Young Goo, Adetunji Toriola
Journal: Cancer Research (2024)

Abstract Introduction: Breast cancer incidence is rising in premenopausal, hence, there is a critical need to understand factors underlying premenopausal breast cancer development in order to guide targeted prevention. Mammographic breast density is a strong risk factor for, as well as an intermediate phenotype for premenopausal breast cancer. Yet, the molecular mechanisms underlying the associations of dense breasts with breast cancer are not well understood. Our objectives in this study are to perform proteomic analysis in breast tissues to (i) identify proteins that are associated with breast cancer development in premenopausal women; (ii) determine which of these proteins are also associated with dense breasts. Methods: We performed proteomic analysis on tumor and adjacent normal tissues from 50 premenopausal women with breast cancer who had breast tissue samples archived at the St. Louis Breast Tumor Registry. Samples were analyzed on Orbitrap Eclipse Tribrid coupled with Vanquish Neo LC system (Thermo Fisher Scientific). Database search was performed using MaxQuant and MS1 LFQ intensities were used for further data analysis. We performed multiple imputation using Bayesian Principal Component Analysis and hierarchical clustering analysis using ‘ConsensusClusterPlus’ package in R. Pathway enrichment analysis was performed using Reactome Pathway database. 10 times repeated 5-fold cross validation using Logistic Regression (LR) was performed for model development. Top 10 proteins were selected by variable importance in logistic regression, and recursive feature elimination was performed for feature selection. We corrected for multiple testing by setting the false discovery rate, FDR, p-value &amp;lt; 0.05. Results: After imputing for missing values, we identified 3,571 proteins that were expressed in tumor and normal breast tissues. We selected 1,640 proteins for further analysis using median absolute deviation analysis. In multivariable-adjusted analysis, 451 proteins were up-regulated, and 180 proteins were down-regulated in tumor tissues compared to adjacent normal tissues (FDR &amp;lt; 0.05). 53 of the 451 proteins that were upregulated in tumor tissues were also upregulated in dense breasts. Some of the top proteins that were upregulated in both tumor tissues and dense breasts were AIMP2, ALDH18A1, BZW1, CKAP5, COPG1, ERGIC1, GSPT1, ILF2, IPO7, LRBA, and PSMD12. Mammographic breast density protein clusters were highly correlated with hormone receptor positivity clusters and were enriched in ESR-mediated signaling, IGF1R signaling, NOTCH4, PTEN regulation, and NFkB pathway. Discussion: Using untargeted proteomics, we identified proteomic signatures of breast cancer development in premenopausal women. We further identified protein biomarkers and pathways that are shared between breast tumors and dense breast tissues in premenopausal women. Our novel findings highlight mammographic breast density as a strong intermediate phenotype that can be targeted in breast cancer prevention in premenopausal women. Larger studies are needed to validate our findings. Citation Format: Shaili Tapiavala, Minsoo Son, Graham Colditz, Ah Young Goo, Adetunji Toriola. Breast Tissue Proteomic Profile of Breast Cancer in Premenopausal Women and Association with Mammographic Breast Density [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO5-09-02.