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Updated: Nov 25, 2025
Predictive Immune Modeling of Solid Tumors
Published on: February 25, 2020
Predictability of 21-Gene Recurrence Score Assay by Using Pathological and Immunohistochemical Parameters in Breast
Abdulmohsen Alkushi1,2, Ahmad Omair3, Haitham Arabi1
1Department of Pathology, King Abdulaziz Medical City of National Guard, Riyadh, Saudi Arabia.
Background:
Oncotype Dx is used to predict the long-term recurrence risk in patients with estrogen receptor (ER)-positive and human epidermal growth factor receptor 2 (HER2)-negative invasive breast cancer (BC). This study aimed at establishing a correlation between clinicopathological parameters and recurrence score (RS), subsequently improving predictability and ultimately justifying the use of the multigene assay.
Materials And Methods:
A retrospective analysis of the pathology and clinical data of 114 female patients with BC who had Oncotype Dx testing between 2012 and 2019. The pathological parameters included are tumor cell type, tumor grade, pathological stage, and mitotic index (MI). The expression of ER, progesterone receptor (PR), HER2, and Ki67 was assessed by immunohistochemistry. A univariate and multivariate linear regression analysis was performed to assess the correlation between these parameters and the RS.
Results:
In univariate analysis, age (˂40 years), higher tumor grade, and low PR expression were significantly associated with higher RS (P = .02; ˂.001; and ˂.001, respectively). Both MI and Ki67 were also strongly correlated with an increase in the RS with a P value of .01 (Spearman correlation 0.34 and 0.33). In multivariate linear regression analysis, age, MI, and Ki67 lost their significance, but both higher grade and PR remained significantly associated with a higher RS along with the tumor stage (P ˂ .001; ˂.001; and .04, respectively).
Conclusions:
Tumor grade and PR immunohistochemical expression are the main predictors of RS in our study population. Other clinicopathological features were not significant predictors of change in RS in multivariate analysis.
