Metadata supporting data files of the related manuscript: Clinicopathological and epidemiological significance of breast cancer subtype re-classification based on p53 immunohistochemical expression

This large-scale, case-only study, evaluated the clinical and epidemiological relevance of p53 protein expression in molecular subtypes of breast cancer. P53 immunohistochemical (IHC) status, IHC measures of hormone receptors and human epidermal growth factor receptor 2 (HER2) as well as histologic grade were used to define breast cancer subtypes in Chinese women with invasive breast cancer.

The TP53 gene is the most commonly mutated gene in human cancers and functions in many cellular pathways including cell cycle regulation, metabolism, angiogenesis, and DNA repair mechanisms. To date, no study has specifically examined the association between breast cancer risk factors and p53 expression in the context of breast cancer subtypes. In this study, p53 protein expression was used to further stratify women with luminal A-like breast cancer into subgroups with clinical and epidemiological relevance.

Patient consent: This project received ethical approval from the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS) Ethics Committee and was exempted from review by the Office of Human Research Protections at the National Institutes of Health, NIH (exempt number 11751), since it did not involve interaction with human subjects and/or use of individual’s personal identifying information. Informed consent was not required for the use of existing pathological materials with no reveal of identifiable patient information.

The aim of this study was to perform a large scale evaluation of the clinical and epidemiological significance of p53 protein expression, as a surrogate for TP53 mutation status, in molecular subtypes of breast cancer.

The present study is a hospital-based case-series comprised of women with histologically confirmed invasive breast cancer who were diagnosed and treated at the Cancer Hospital, Chinese Academy of Medical Sciences (CHCAMS), Beijing, China, between 2009 and 2016. A large sample of 7226 women had p53 IHC information available, as well as relevant tumor clinico-pathological characteristics and breast cancer risk factors. Data on histopathological characteristics including histologic grade, lymph node involvement, tumor size, estrogen receptor (ER), progesterone receptor (PR), HER2, Ki67, epidermal growth factor receptor (EGFR), and cytokeratin 5/6 (CK5/6) were obtained from pathology records. All IHC markers were stained using standard laboratory procedures.

Molecular subtypes were defined based on the St Gallen’s criteria by using IHC measures on ER, PR, and HER2 in conjunction with histologic grade. All the breast cancer subtypes were further refined using p53 expression.

For statistical analysis, frequency tables were used to assess the distribution of tumor clinico-pathological features (tumor size, lymph node involvement, and histologic grade) and epidemiological risk factors (age at menarche, parity, breastfeeding, body mass index (BMI) and family history of breast cancer in first-degree relatives). The chi-square test was used to assess differences in the distribution of categorical variables by p53 expression status and the nonparametric Kruskal-Wallis test for continuous variables. Odds ratios (ORs) and 95% confidence intervals (CIs) for the associations of p53 expression with breast cancer risk factors and tumor clinico-pathological features were estimated in age adjusted unconditional logistic regression models.

Please refer to the published article for more details on the methodology.

Dataset ChineseBCData_04022018.dta is in a .dta file format and supports tables 1-6 and supplementary tables 2 and 3 in the published article. Dataset Immunohistochemistry_details_CBC is in Excel file format and supports supplementary table 1 in the published article.

Table 3 shows subtype-specific logistic regression models describing the associations (adjusted odd ratios (ORs) and 95% confidence intervals (CIs)) between p53 IHC status and clinico-pathological factors within each major breast cancer subtype.

Table 4 shows subtype-specific logistic regression models describing the associations (adjusted odd ratios (ORs) and 95% confidence intervals (CIs)) between p53 expression and established breast cancer risk factors within each molecular subtype.

Table 5 shows multivariate polytomous logistic regression models describing the associations (adjusted odd ratios (ORs) and 95% confidence intervals (CIs)) between parity/breastfeeding and breast cancer subtypes, overall and following sub-classification of the luminal Alike subtype by p53 protein expression.

Table 6 shows multivariate logistic regression models describing the associations (adjusted odd ratios (ORs) and 95% confidence intervals (CIs)) between parity/breastfeeding status and multiple breast cancer clinico-pathological characteristics, including p53, clinico-pathological surrogate subtypes, CK5/6, and EGFR.

The following files are in openly accessible Microsoft Word format:

Supplementary table 2.docx shows subtype-specific logistic regression models describing the associations (odds ratios (OR) and 95% confidence intervals (CIs)) between breast cancer risk factors and phenotypes (p53+ vs p53-) of luminal A-like breast cancer stratified by age (i.e. ≤50 years and >50 years).

Supplementary table 3.docx shows the associations (adjusted odd ratios (ORs) and 95% confidence intervals (CIs) between breast cancer clinicopathological and risk factors and p53 protein expression in luminal A-like breast cancer defined using various (1%, 10%, 20%) cut-points for ER and PR expression.

The data files ChineseBCData_04022018.dta and Immunohistochemistry_details_CBC are available from the upon reasonable request from

The supplementary table files themselves are openly available from this data record.