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Comparative Study
. 2014 Jul;8(5):1014-25.
doi: 10.1016/j.molonc.2014.03.022. Epub 2014 Apr 8.

Comparison of frequencies and prognostic effect of molecular subtypes between young and elderly breast cancer patients

Esther M de Kruijf  1 , Esther Bastiaannet  2 , Francesca Rubertá  1 , Anton J M de Craen  3 , Peter J K Kuppen  1 , Vincent T H B M Smit  4 , Cornelis J H van de Velde  1 , Gerrit Jan Liefers  5
Affiliations
Comparative Study

Comparison of frequencies and prognostic effect of molecular subtypes between young and elderly breast cancer patients

Esther M de Kruijf et al. Mol Oncol. 2014 Jul.

Abstract

Purpose: To compare the distribution and prognostic effect of the breast cancer molecular subtypes in young and elderly breast cancer patients.

Patients and methods: Our study population (n = 822) consisted of all early breast cancer patients primarily treated with surgery in our center between 1985 and 1996. A total of 142/822 fresh frozen tissues were available with good quality RNA and analyzed by gene expression microarray. Gene expression molecular subtypes were determined by correlation to the expression centroids of 534 "intrinsic" genes. Sections of a tissue micro array containing formalin-fixed paraffin-embedded tumor tissue of 714/822 patients were immunohistochemically (IHC) stained for Ki67, EGFR, CK5/6. Tumor expression of ER, PR, HER2 was previously determined. IHC molecular subtypes were defined based on expression of these markers: Luminal A: ER+ and/or PR+, HER2- and Ki67-; Luminal B: ER+ and/or PR+ and ki67+; ERBB2: ER-, PR- and HER2+; Basal-like: ER-, PR-, HER2- and EGFR+ and/or CK5/6+; Unclassified: ER-, PR-, HER2-, EGFR- and CK5/6-. IHC molecular subtypes were validated against gene expression defined molecular subtypes. Assessment of distribution and prognostic effect of molecular subtypes was stratified to age (<65 versus ≥65 years).

Results: Validation of molecular subtypes determined by IHC against gene expression revealed a substantial agreement in classification (Cohen's kappa coefficient 0.75). A statistically significant association (p = 0.02) was found between molecular subtypes and age, where Luminal tumors were more often found in elderly patients, while ERBB2, basal-like and unclassified subtypes were more often found in young patients. Molecular subtypes showed a prognostic association with outcome in young patients concerning relapse-free period (RFP) (p = 0.01) and relative survival (RS) (p < 0.001). No statistically significant prognostic effect was found for molecular subtypes in elderly patients (RFP p = 0.5; RS p = 0.1). Additional analyses showed that no molecular subtypes showed a statistically significant difference in outcome for elderly compare to young patients.

Conclusion: We have shown that molecular subtypes have a different distribution and prognostic effect in elderly compared to young breast cancer patients, emphasizing the fact that biomarkers may have different distributions and prognostic effects and therefore different implications in elderly compared to their younger counterparts. Our results support the premise that breast cancer clinical behavior is significantly affected by patient age. We suggest that competing risks of death in elderly patients, ER-driven differences and micro-environmental changes in biology are underlying these age-dependent variations in patient prognosis.

Keywords: Breast cancer; Elderly; Molecular subtypes; Prognostication.

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Figures

Figure 1
Figure 1
Molecular subtypes immunohistochemical stainings and distribution over age groups. A. Representative photographs of tissue microarray punches of human breast cancer specimens immunohistochemically stained for ER, PR, HER2, ki67, EGFR and CK5/6 and corresponding molecular subtypes. Bar represents 100 μm. B. Molecular subtypes according to age (<65 versus >65 years).
Figure 2
Figure 2
Diagram illustrating patient cohort and various stages of loss of cases due to unavailable tumor material, tumor core or tissue damage of TMA or inadequate mRNA quality as described in the Patients and methods and Results section.
Figure 3
Figure 3
Relapse‐free period according to molecular subtypes for all patients (A, D), for patients aged < 65 years (B, E) and for patients aged > 65 years (C, F), with no stratification for systemic treatment (A, B, C) and on selected patient population that did not receive any systemic treatment (D, E, F). Log‐rank P‐values are shown in each graph.
Figure 4
Figure 4
Relative survival according to molecular subtypes for all patients (A, D), for patients aged < 65 years (B, E) and for patients aged > 65 years (C, F), with no stratification for systemic treatment (A, B, C) and on selected patient population that did not receive any systemic treatment (D, E, F). Log‐rank P‐values are shown in each graph.
Figure 5
Figure 5
Breast cancer specific death per age category calculated by the percentage of observed death (O%) minus the expected death based on the general population according to age and time period (E%) divided by the total observed death (O%) per age category for the cohort breast cancer patients used in this study. Death cancer specific death calculation: (O%–E%/E%).
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