Molecular features in young vs elderly breast cancer patients and the impacts on survival disparities by age at diagnosis

Abstract

Young and elderly breast cancer patients are more likely to have a poorer outcome than middle-aged patients. The intrinsic molecular features for this disparity are unclear. We obtained data from the Cancer Genome Atlas (TCGA) on May 15, 2017 to test the potential mediation effects of the molecular features on the association between age and prognosis with a four-step approach. The relative contributions of the molecular features (PAM50 subtype, risk stratification, DNAm age, and mutations in TP53, PIK3CA, MLL3, CDH1, GATA3, and MAP3K1) to age disparities in survival were estimated by Cox proportional hazard models with or without the features. Young patients were significantly more likely to have basal-like subtype, GATA3 mutations, and younger DNA methylation (DNAm) age than middle-aged patients (P < .05). Both the young and elderly patients had a significantly increased risk of breast cancer recurrence after adjusted by race, tumor size, and node status (Hazard ratio [HR] (95% confidence interval [CI]): 2.81 [1.44, 5.45], 2.37 [1.45, 3.89], respectively). This increased risk was weakened in the young patients after further adjustments in the molecular features, particularly basal-like subtype, GATA3 mutations, and DNAm age (HR [95%CI]: 1.87 [0.81, 4.32]), resulting in 33.5% decreased risk of recurrence. Meanwhile, the adjustments of the molecular features did not alter the recurrence risk for the elderly patients. Compared with middle-aged patients of breast cancer, poorer prognosis of elderly patients may be caused by aging, while poorer prognosis of young patients was probably mediated through intrinsic characteristics, such as basal-like subtype, GATA3 mutations, and DNAm age of the cancerous tissues.

Keywords: age; breast cancer; mediation; molecular features; prognosis.

Figure 1

Association between age and relapse‐free survival age was modeled as a continuous variable and fitted in a Cox proportional hazard model using cubic restricted splines with knots at the 5th, 35th, 65th, and 95th percentiles of age, hazard ratio adjusted for race, ER, pathologic stage, HER2; Gray represents the 95 percent confidence interval

Figure 2

Kaplan‐Meier plot for relapse‐free survival according to age at diagnosis of breast cancer

Figure 3

Comparisons of the absolute standardized mean differences (ASMD) by es.mean and ks.mean methods between the age group on the covariates before and after propensity score weighting. The covariates included race, tumor size, node status, PAM50 molecular subtype, risk stratification, DNAm age, and somatic mutations. The statistically significant difference is indicated by the solid circle. The decreases of ASMD after weighting indicates good covariate balance