Comparison of the Ability of Different Clinical Treatment Scores to Estimate Prognosis in High-Risk Early Breast Cancer Patients: A Hellenic Cooperative Oncology Group Study.

Background-Aim: Early breast cancer is a heterogeneous disease, and, therefore, prognostic tools have been developed to evaluate the risk for distant recurrence. In the present study, we sought to develop a risk for recurrence score (RRS) based on mRNA expression of three proliferation markers in high-risk early breast cancer patients and evaluate its ability to predict risk for relapse and death. In addition the Adjuvant! Online score (AOS) was also determined for each patient, providing a 10-year estimate of relapse and mortality risk. We then evaluated whether RRS or AOS might possibly improve the prognostic information of the clinical treatment score (CTS), a model derived from clinicopathological variables.
Methods: A total of 1,681 patients, enrolled in two prospective phase III trials, were treated with anthracycline-based adjuvant chemotherapy. Sufficient RNA was extracted from 875 samples followed by multiplex quantitative reverse transcription-polymerase chain reaction for assessing RACGAP1, TOP2A and Ki67 mRNA expression. The CTS, slightly modified to fit our cohort, integrated the prognostic information from age, nodal status, tumor size, histological grade and treatment. Patients were also classified to breast cancer subtypes defined by immunohistochemistry. Likelihood ratio (LR) tests and concordance indices were used to estimate the relative increase in the amount of information provided when either RRS or AOS is added to CTS.
Results: The optimal RRS, in terms of disease-free survival (DFS) and overall survival (OS), was based on the co-expression of two of the three evaluated genes (RACGAP1 and TOP2A). CTS was prognostic for DFS (p<0.001), while CTS, AOS and RRS were all prognostic for OS (p<0.001, p<0.001 and p = 0.036, respectively). The use of AOS in addition to CTS added prognostic information regarding DFS (LR-Δχ2 8.7, p = 0.003), however the use of RRS in addition to CTS did not. For estimating OS, the use of either AOS or RRS in addition to CTS added significant prognostic information. Specifically, the use of both CTS and AOS had significantly better prognostic value vs. CTS alone (LR-Δχ2 20.8, p<0.001), as well as the use of CTS and RRS vs. CTS alone (LR-Δχ2 4.8, p = 0.028). Additionally, more patients were scored as high-risk by AOS than CTS. According to immunohistochemical subtypes, prognosis was improved in the Luminal A (LR-Δχ2 7.2, p = 0.007) and Luminal B (LR-Δχ2 8.3, p = 0.004) subtypes, in HER2-negative patients (LR-Δχ2 23.4, p<0.001) and in patients with >3 positive nodes (LR-Δχ2 23.9, p<0.001) when AOS was added to CTS.
Conclusions: The current study has shown a clear benefit in predicting overall survival of high-risk early breast cancer patients when combining CTS with either AOS or RRS. The combination of CTS and AOS adds significant prognostic information compared to CTS alone for DFS, while the combination of CTS with either AOS or RRS has better prognostic value than CTS alone for OS. These findings could possibly add on the information needed for the best risk prediction strategy in high-risk early breast cancer patients in a rather simple and inexpensive way, especially in Luminal A and B subtypes, HER2-negative patients and those with >3 positive nodes.
Competing Interests: Zoi Alexopoulou is employed by Health Data Specialists Ltd. This does not alter our adherence to all the PLOS ONE policies on sharing data and materials. The rest of the authors have declared that no competing interests exist. There are no patents, products in development or marketed products to declare.