TP53 Pathway Function, Estrogen Receptor Status, and Breast Cancer Risk Factors in the Carolina Breast Cancer Study

Abstract

Background: TP53 and estrogen receptor (ER) both play essential roles in breast cancer development and progression, with recent research revealing cross-talk between TP53 and ER signaling pathways. Although many studies have demonstrated heterogeneity of risk factor associations across ER subtypes, associations by TP53 status have been inconsistent. Methods: This case–case analysis included incident breast cancer cases (47% Black) from the Carolina Breast Cancer Study (1993–2013). Formalin-fixed paraffin-embedded tumor samples were classified for TP53 functional status (mutant-like/wild-type-like) using a validated RNA signature. For IHC-based TP53 status, mutant-like was classified as at least 10% positivity. We used two-stage polytomous logistic regression to evaluate risk factor heterogeneity due to RNA-based TP53 and/or ER, adjusting for each other and for PR, HER2, and grade. We then compared this with the results when using IHC-based TP53 classification. Results: The RNA-based classifier identified 55% of tumors as TP53 wild-type-like and 45% as mutant-like. Several hormone-related factors (oral contraceptive use, menopausal status, age at menopause, and pre- and postmenopausal body mass index) were associated with TP53 mutant-like status, whereas reproductive factors (age at first birth and parity) and smoking were associated with ER status. Multiparity was associated with both TP53 and ER. When classifying TP53 status using IHC methods, no associations were observed with TP53. Associations observed with RNA-based TP53 remained after accounting for basal-like subtype. Conclusions: This case–case study found breast cancer risk factors associated with RNA-based TP53 and ER. Impact: RNA-based TP53 and ER represent an emerging etiologic schema of interest in breast cancer prevention research.

Transcriptomic Analysis of Hub Genes Reveals Associated Inflammatory Pathways in Estrogen-Dependent Gynecological Diseases

“…ence through the ER signaling pathway, which may serve as a prognostic marker [90], ERα activity could repress p53 transcription and potentially lead to decreased apopto and increased tumor cell proliferation [91,92]. This further supports the results, as t TP53 gene was found to be downregulated in EM, OC, CC, and EC.…”

“…On the other hand, TP53 hotspot mutations may lead to mutated p53 function and were found to be common among high-grade serous ovarian carcinoma undergoing primary treatment [ 89 ]. The TP53 gene was also shown to have a strong hormonal influence through the ER signaling pathway, which may serve as a prognostic marker [ 90 ], as ERα activity could repress p53 transcription and potentially lead to decreased apoptosis and increased tumor cell proliferation [ 91 , 92 ]. This further supports the results, as the TP53 gene was found to be downregulated in EM, OC, CC, and EC.…”

Risk factors for breast cancer subtypes by race and ethnicity: A scoping review of the literature

“…For instance, studies have shown associations of individual risk factors with other molecular markers, such as histologic grade, KI67, and TP53 mutation status within luminal breast cancers. [51][52][53][54] Accordingly, a change in approach may be warranted to better characterize etiologic subtypes of breast cancer with epidemiologic and public health relevance.…”

RNA-Based Classification of Homologous Recombination Deficiency in Racially Diverse Patients with Breast Cancer

“…RNA-based expression of DNA repair genes is a convenient approach for tracking DNA repair pathway activity. Other RNA-based classifiers, such as those for TP53 mutant-like status ( 20, 21 ), exemplify that RNA-level data can indicate DNA alterations and complement somatic mutation data. For instance, RNA-based TP53 classifiers accurately identify TP53 DNA mutations and predict survival with hazard ratios that are similar to those from DNA-based assays ( 20 ).…”

“…Other RNA-based classifiers, such as those for TP53 mutant-like status ( 20, 21 ), exemplify that RNA-level data can indicate DNA alterations and complement somatic mutation data. For instance, RNA-based TP53 classifiers accurately identify TP53 DNA mutations and predict survival with hazard ratios that are similar to those from DNA-based assays ( 20 ). We hypothesized that relative expression levels of core mediators of error-free and error-prone genome maintenance pathways could be used to build RNA expression-based classifiers for DNA repair pathways.…”