Age as Prognostic Factor in Breast Cancer Review

Introduction

Chest cancer is the nigh common malignant illness and the 2nd cause of cancer-specific death among women in the US.¹ Patients diagnosed at a immature age, especially younger than 35 years, are relatively rare, accounting for only 1.viii% of total number of cases diagnosed among women each year.²

Whether immature age is an contained adverse prognostic factor in breast cancer remains controversial. There is a lot of show showing that immature breast cancer patients are more likely to present with more aggressive clinicopathological characteristics, including larger tumor size, more involved lymph nodes, higher grade, higher proliferation index, more than lymphovascular invasion, absenteeism of estrogen receptor (ER)/progesterone receptor (PR), and human epidermal growth factor receptor two (HER2) overexpression.^iii–12 Compared with older ones, the young breast cancer patients have inferior prognosis.^{4–6,10,xi,13–18} However, some studies have come to the opposite decision. Those authors advocated that young age was not an independent agin prognostic factor in breast cancer patients.^9,19–22 Gajdos et al⁹ compared the prognosis of 101 cases of women under historic period 36 and 631 cases of women aged 36 years and older with phase 0–III breast cancer, and establish that the cumulative five-year local and distant illness-gratuitous survival was significantly worse for patients younger than 36 years. However, after decision-making the covariates including tumor size, nodal interest, chemotherapy, and tamoxifen, age was no longer significantly associated with local or afar disease-free survival. Yet, these studies have some limitations. For example, these patients were diagnosed many years ago, just treatments have improved a lot during this time. Moreover, most of these studies did not contain HER2 status, which is an important independent prognostic factor. Finally, these studies were retrospective and poorly represented the situation of the existent globe.

Because whether young age is an independent adverse prognostic cistron in breast cancer remains uncertain, and information technology is of import for because tailored treatment programme, nosotros aimed to carry a population-based written report to compare the prognosis of young and older operable chest cancer patients utilizing Surveillance, Epidemiology, and Finish Results (SEER) database.

Materials and methods

Ethics statement

This is a database-based retrospective study, and hence informed consent and approval by institutional ethics committee are not required. Data-Employ Agreement for the SEER 1973-2014 Research Data File was completed.

Patients

We used SEER*Stat (version 8.3.iv) to download data from the SEER 18 registries inquiry database, which covers approximately 28% of the The states population. The SEER 18 database contains data from the SEER xiii registries (Atlanta, Connecticut, Detroit, Hawaii, Iowa, New Mexico, San Francisco-Oakland, Seattle-Puget Sound, Utah, Los Angeles, San Jose-Monterey, rural Georgia, and the Alaska Native Tumor Registry) and the registries of greater California, Kentucky, Louisiana, New Jersey, and greater Georgia. We generated a case listing co-ordinate to the following criteria: histologically confirmed breast cancer, invasive ductal cancer (ICD-O-3 8500), female, age at diagnosis less than 60 years quondam, diagnosed betwixt 2003 and 2014, chest cancer every bit the start and but malignancy, unilateral, histological grades I–Iv, T1mic-T3, N0–N3, M0, and received surgical treatment. We excluded patients whose tumors were not totally removed. Tumors with invasion of subcutaneous tissue and/or breast wall were not included in this study (T4). In total, 150,588 patients were eligible, including half-dozen,668 cases of younger than 35 years and 143,920 cases aged between 35 and 60 years.

Variables

The variables included age at diagnosis, race (White, Black, others, or unknown), laterality (left, right), chest-adapted AJCC sixth T (T1, T2, or T3), chest-adapted AJCC sixth N (N01, N1, N2, or N3), breast-adjusted AJCC 6th stage (I, IIA, IIB, IIIA, or IIIC), Grade (I, II, III, or Four), ER status (positive, negative, borderline, or unknown), PR condition (positive, negative, borderline, or unknown), and HER2 status (positive, negative, deadline, or unknown). In the multiple imputation, ER and PR borderline was classified every bit positive. Breast cancer-specific survival (BCSS) and overall survival (OS) were defined equally the time from diagnosis to decease due to breast cancer and any cause, respectively.

Statistical analysis

All statistical analyses were performed using R (version 3.ii.4, R Projection for Statistical Computing, Vienna, Austria) and SPSS 22 (IBM SPSS Statistics, Chicago, IL, Us). The clinicopathological characteristics of 2 groups were compared using chi-squared examination.

The Kaplan–Meier method was performed to generate BCSS and Os survival curves, and the log-rank examination was used to compare the BCSS and OS of two groups. Univariate and multivariate Cox proportional adventure regression models were utilized to identify contained prognostic factors and calculate the HR and 95% CI. Propensity score matching method was performed to diminish the effect of unbalance of baseline clinicopathological characteristics between these ii groups. In order to reduce bias caused by the missing values, we used SPSS 22 to perform multiple imputation on the missing values, which generated five imported datasets. We then performed Cox proportional hazards models in 5 imported datasets and combined the results to produce merged 60 minutes.

We carried out subgroup assay stratified by lymph node status and ER status. In each subgroup, Kaplan–Meier method and log-rank exam were performed to compare the BCSS and Os of younger with older groups. Additionally, univariate and multivariate Cox proportional hazards models were used to calculate the 60 minutes and 95% CI after controlling for the potential confounder factors.

In society to explore the reasonable cutoff age to define "young age", we treated age as an ordered factor and categorized in each 2-year flow. Multivariate Cox proportional take chances model was performed to calculate the HR and 95% CI of each group compared with the reference group (age 59–60 years). The spline role of R packet was used to line smoothen the hazard plots.

All P-values were two-sided, and P<0.05 was considered as statistically significant.

Results

Patient characteristics

In full, 150,588 patients with early-phase invasive ductal breast cancer diagnosed from 2003 to 2014 were included in this study. Amongst them, vi,668 patients (4.4%) were younger than 35 years and 143,920 patients (95.half-dozen%) were between 35 and 60 years. Demographic and clinicopathological characteristics of this report are summarized in Table 1. The distribution of important clinicopathological characteristics was significantly different between the ii groups. Compared with the older group, the younger group presented with more than aggressive tumor characteristics, including larger tumor size (T3, x.ii% vs 4.5%, P<0.001), more lymph node metastasis (48.1% vs 35.iii%, P<0.001), higher grade (grades III and IV, 64.9% vs 43.one%, P<0.001), more than ER absenteeism (35.2% vs 22.9%, P<0.001) and PR absence (42.9% vs 31.iii%, P<0.001), and more HER2 overexpression (xi.i% vs 7.ix%, P<0.001). In add-on, the younger group had a college proportion of triple-negative (9.six% vs five.7%, P<0.001) and HER2-overexpression (ii.9% vs two.3%, P<0.001) subtypes.

Table 1 Patient characteristics of the study population Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth cistron receptor two.

Comparison of survival between the younger and older groups

Kaplan–Meier analysis was performed to compare the prognosis between the younger and older groups. As shown in Effigy ane, the younger group had significantly worse survival compared with the older group, in terms of BCSS (Hr, ane.923, 95% CI, 1.779–2.078, P<0.001) and Bone (HR, 1.684, 95% CI, 1.564–1.812, P<0.001). The five-year BCSS rates were 89.5% (95% CI, 88.six%–ninety.three%) in the younger group and 94.three% (95% CI, 94.i%–94.iv%) in the older group. Similarly, Os rates were 88.5% (95% CI, 87.half-dozen%–89.iv%) and 93.1% (95% CI, 92.nine%–93.2%) in the two groups, respectively. The 10-year BCSS and OS rates were 81.2% (95% CI, 79.8%–82.7%) and 79.5% (95% CI, 78.0%–81.0%) in the younger group, and ninety.0% (95% CI, 89.8%–90.3%) and 87.4% (95% CI, 87.1%–87.7%) in the older group, respectively.

Figure ane Kaplan–Meier curves of BCSS and OS based on age for all patients in the younger group (fifteen–35 years) vs older grouping (35–lx years). Abbreviations: BCSS, breast cancer-specific survival; OS, overall survival.

Univariate and multivariate Cox proportional gamble regression models were utilized to identify contained prognostic factors and calculate HR in early-stage breast cancer. In the univariate model, age, race, tumor size, lymph node condition, tumor grade, ER status, PR condition, and HER2 status were significantly associated with BCSS, equally summarized in Table ii. Afterward controlling the higher up factors, in the multivariate model, historic period, race, tumor size, lymph nodes, class, ER status, PR condition, and HER2 status remained significantly related to BCSS. Peculiarly, compared with the older group (between 35 and 60 years), the younger group (between xv and 35 years) had worse BCSS (Hr, 1.200, 95% CI, one.110–1.297, P<0.001). Equally presented in Table three, some factors including historic period, race, tumor size, lymph nodes, tumor course, ER status, PR status, and HER2 status were significantly related to OS in the univariate model. In the multivariate model, ER borderline condition and PR unknown status were no longer significantly associated with OS, while immature age was a pregnant worse prognostic predictor for OS (Hour, one.111, 95% CI, 1.032–1.196, P<0.001).

Table ii Univariate and multivariate Cox proportional run a risk model of BCSS Notes: Multivariate analysis includes historic period, race, tumor size, lymph nodes, grade, ER condition, PR status, and HER2 status. The P-values are derived from Wald exam. Abbreviations: BCSS, breast cancer-specific survival; ER, estrogen receptor; PR, progesterone receptor; HER2, homo epidermal growth factor receptor ii.

Table 3 Univariate and multivariate Cox proportional gamble model of OS Notes: Multivariate analysis includes age, race, tumor size, lymph nodes, class, ER status, PR status, and HER2 status. The P-values are derived from Wald test. Abbreviations: Os, overall survival; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth cistron receptor two.

Propensity score matching method was utilized to give more convincing evidence for the survival deviation between the younger and older groups. As shown in Table S1, the distribution of important clinicopathological characteristics of matched patients was not significantly different between the ii groups. Kaplan–Meier assay was performed to compare the survival between the younger and older groups. Equally shown in Effigy 2, the younger group had significantly worse survival compared with the older group, both in terms of BCSS (Hour, 1.196, 95% CI, 1.072–1.335, P=0.001) and OS (60 minutes, 1.119, 95% CI, ane.009–i.240, P=0.032).

Figure 2 Kaplan–Meier curves of BCSS and Bone of 1:1 matched groups based on age for all patients in the younger grouping (15–35 years) vs older group (35–60 years). Abbreviations: BCSS, chest cancer-specific survival; OS, overall survival.

Results of multivariate Cox proportional risk regression analysis of multiple imputed datasets were consistent with those presented in Tables 2 and 3. As presented in Table S2, the younger patients had junior BCSS (60 minutes, i.214, 95% CI, ane.120–1.316, P<0.001) and OS (Hour, 1.123, 95% CI, 1.041–one.211, P=0.003) compared with the older ones.

Subgroup analysis of BCSS and Os

Since lymph node status and ER status are important prognostic factors in breast cancer, and the distribution of these ii factors was significantly different between the younger and older groups, we conducted subgroup analyses of survival by stratifying lymph node status and ER condition. As shown in Figures 3 and four, when stratified by lymph node status, the younger group had worse BCSS compared with the older group in N0 (P=0.031) and N1 subgroups (P<0.001), and worse Os in N1 subgroup (P=0.009).

Figure 3 Subgroup analyses of BCSS based on lymph node condition (N0, N1, N2, N3). The HR was calculated by multivariate Cox proportional take chances model adapted for age, race, tumor size, grade, ER status, PR status, and HER2 status (Wald examination). Abbreviations: BCSS, chest cancer-specific survival; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2.

Figure 4 Subgroup analyses of OS based on lymph node status (N0, N1, N2, N3). The HR was calculated by multivariate Cox proportional hazard model adapted for age, race, tumor size, class, ER status, PR status, and HER2 status (Wald test). Abbreviations: OS, overall survival; ER, estrogen receptor; PR, progesterone receptor; HER2, man epidermal growth factor receptor two.

As presented in Figures v and half-dozen, in the ER-positive subgroup, the younger grouping showed worse BCSS (Hour, 1.354, 95% CI, 1.208–1.518, P≤0.001) and OS (HR, one.187, 95% CI, 1.065–1.323, P=0.002) compared with the older group. Withal, in the ER-negative subgroups, in that location was no significant difference in BCSS and Os between the 2 groups (HR, ane.085, 95% CI, 0.970–1.214, P=0.152 and HR, i.044, 95% CI, 0.939–1.161, P=0.427, respectively).

Effigy 5 Subgroup analyses of BCSS and Os based on ER status (positive). The 60 minutes was calculated past multivariate Cox proportional take chances model adjusted for age, race, tumor size, lymph nodes, class, PR status, and HER2 status (Wald test). Abbreviations: BCSS, breast cancer-specific survival; Bone, overall survival; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor 2.

Figure 6 Subgroup analyses of BCSS and OS based on ER status (negative). The HR was calculated by multivariate Cox proportional hazard model adapted for historic period, race, tumor size, lymph nodes, grade, PR status, and HER2 condition (Wald test). Abbreviations: BCSS, breast cancer-specific survival; OS, overall survival; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor two.

Explore the reasonable cutoff historic period to ascertain "young historic period"

To explore a reasonable age to define young age breast cancer, we grouped patients past age of two years, and so multivariate Cox proportional hazard regression model was utilized to compare the prognosis of patients in dissimilar groups with controls.

Line smoothing of 60 minutes plots was performed to show the tendency that Hour increased when age decreased. As demonstrated in Effigy 7, in terms of both BCSS and OS, in patients younger than 35 years, 60 minutes increased significantly with decreasing historic period. Yet, in patients aged between 35 and threescore years, 60 minutes was not associated with the change of age.

Figure 7 Cubic spline curve of the human relationship between age and Hour (BCSS and Bone). Age was treated every bit a dummy variable categorized in 2-yr strata. Cox proportional hazard model (Wald test) was utilized to summate the 60 minutes of each historic period group compared with the grouping (≥58 and <lx) (reference).

Discussion

Breast cancer in immature patients is idea to exist a special subgroup, and immature historic period is an important factor for personalizing the treatment of breast cancer.²³ There is no definite definition of "immature age". Although historic period is a continuum and whatever cutoff is arbitrary, many studies chose 35^{4,6,9,15,17,18,24–26} or 40^10,27,28 as threshold to distinguish immature and old age. Han et al^v suggested that the adventure of expiry in patients younger than 35 years increased dramatically, whereas patients aged between 35 and 40 years had like risk of death in comparing with older premenopausal patients. In our written report, in patients younger than 35 years, the risk of death increased significantly with decreasing age, whereas there was no such trend in patients aged between 35 and threescore years. In this example, it is reasonable to ascertain younger than 35 years equally "young age" in this study. Meanwhile, we chose patients aged between 35 and 60 years equally the control group, since patients older than threescore years are considered postmenopausal as per the National Comprehensive Cancer Network (NCCN) guidelines.

Many studies suggest that breast cancer in young patients is associated with more advanced stage, such as college T and Due north grade. Meanwhile, tumors in young patients e'er present with more aggressive tumor biology, including higher histological course, higher proliferation index, more lymphovascular invasion, more triple negative, and HER2-enriched subtypes.^3–12 The trend of advanced stage may exist attributed to the lack of effective screening in mammography because of the dumbo breast tissue in young women. Additionally, the incidence of breast cancer in immature women is relatively low, and hence clinicians are more inclined to treat the masses in breasts of young women as beneficial. The aggressive tumor biology too causes more lymph nodes involvement in young patients. Keegan et al¹² analyzed 5,606 breast cancer patients anile betwixt fifteen and 39 years, and plant that young patients had higher proportions of ER+/HER2+, triple-negative, and ER−/HER2+ subtypes. Anders et al^xi compared the microarray data from 200 cases of young and 211 cases of older breast cancer patients, and suggested that young patients illustrated lower ER and PR mRNA expression, and higher HER2 and EGFR expression. In our written report, the results are consistent with the previous studies that more aggressive clinicopathological characteristics were observed in younger chest cancer patients. However, the underlying mechanism of this phenomenon remains uncertain. Further studies are needed to elucidate the molecular biologic feature of chest cancer arising in young women, and new treatment approaches may be adult.

Currently, whether young breast cancer patients have worse prognosis in comparison with older patients remains controversial. Some researchers suggested that there was no difference in prognosis between young and older patients.^{21,26,29–31} In some studies, young patients even presented with better prognosis.^32,33 All the same, there are more evidences that young chest cancer patients take worse prognosis compared with older ones. Since young breast cancer patients have more than aggressive clinicopathological characteristics, some researchers suggested that the poor prognosis of young patients was attributed to these adverse factors.^{9,12,19,20,22,34} Anders et al²² compared the microarray data of 140 young breast cancer (≤45 years old) and 252 older breast cancer (≥65 years old), and constitute that at that place was greater proportion of aggressive intrinsic subtypes in the young breast cancer group. However, age lone did not provide an additional biologic complication above breast cancer subtype and grade. In add-on, handling decisions should be driven past subtype and course, not past historic period. Withal, a lot of studies reported that young age remained an independent adverse prognostic factor after controlling the adverse clinicopathological factors.^{four–six,10,13–18,27} Cancello et al⁴ suggested that subsequently controlling the multiple factors, including tumor diameter, nodal involvement, ER and PR expression, Ki-67 labeling alphabetize, HER2 overexpression, vascular invasion, form, histotype, and molecular subtypes, breast cancer patients younger than 35 years erstwhile (315 cases, eleven%) presented a significantly increased take a chance of recurrence and death (60 minutes=ane.65, 95% CI=1.3–2.i and Hr=1.78, 95% CI=ane.12–two.85, respectively) compared with older patients (2,655 cases, 89%). These alien results may exist due to the following factors: one) the inconsistent definition of "young age" and different control groups; 2) the minor number of patients involved; 3) dissimilar therapy strategies; 4) lack of HER2 condition, which is an of import prognostic factor; and 5) heterogeneity of patients in terms of races. In this study, nosotros conducted a population-based assay that included 6,668 cases of breast cancer patients younger than 35 years and 143,920 patients aged between 35 and sixty from the SEER 18 database. This large cohort represented a large proportion of the population in the US and could provide more comprehensive results for the real-earth situation. Furthermore, all patients were diagnosed with breast cancer in the latest 12 years, which reflected the results of state-of-the-art therapy strategies, and HER2 condition was recorded in many cases. Additionally, we included patients younger than sixty years as the control group, the difference of comorbidity which could influence therapy strategies betwixt these 2 groups was not significant, and co-ordinate to the NCCN guidelines, the patients of both groups were premenopausal. Nosotros carried out the Kaplan–Meier assay and establish that compared with the older ones, the young patients presented with inferior prognosis in terms of both BCSS and OS. Additionally, later on controlling the potential confounders, including tumor size, lymph node status, tumor grade, ER/PR, and HER2 status, young age remained an contained agin prognostic factor in terms of both BCSS and Bone. However, the underlying machinery remains unclear, and farther studies are needed to uncover the underlying key pathways and molecules, based on which new prognostic molecular biomarkers and therapy strategies may exist developed. Additionally, since young historic period is an independent adverse prognostic factor, immature breast cancer patients may receive more intensive handling.

In the subgroups defined by unlike clinicopathological factors, immature age may influence prognosis in different ways. Some studies revealed that young historic period could influence prognosis negatively in lymph node negative breast cancer,^31,35 while others reported that immature age was not related to prognosis.^15,17 In the present study, subgroup analysis was performed based on lymph node status, and patients younger than 35 years presented with junior prognosis in N0 (BCSS) and N1 (both BCSS and Bone) subgroups. The inconsistency of these studies may be attributed to the unlike definition of young age, unlike criteria of molecular subtype, and indigenous disparity. When stratified past ER status, the impact of young age on prognosis was different. Some studies^5,6,17 suggested that in the ER-positive group, immature patients presented with poorer prognosis than the older ones, while in the ER-negative group, at that place was no meaning difference in prognosis between these two groups. On the other hand, Cancello et al⁴ reported that the immature patients presented with inferior prognosis in the luminal B, HER2-enriched, and triple-negative subtypes compared with the older ones. In the present report, young patients were significantly related to increased risk of BCSS and Bone just in the ER-positive subgroup, and there was no survival difference in the ER-negative subgroups. The underlying mechanism may be that young patients always present with higher level of estrogen, which can stimulate the growth of ER-positive tumors. This implies that young patients may need more than intensive endocrine therapy.

Our study has some limitations. Firstly, the handling data, which is important for determining the prognosis, is not available in the public-accessed SEER database. In our study, we included patients aged from 35 to 60 years as the control grouping; there may be few differences in adjuvant therapies between these 2 groups.²⁷ Secondly, the HER2 status was non available until 2010; this value was divers as unknown in the data before 2010. Lastly, since some of import clinicopathological factors such as comorbidities, Ki-67, lymphovascular invasion, receipt of chemotherapy, and endocrine therapy were not available, this study may have unknown bias.

In decision, the immature patients with operable breast cancer presented with more than aggressive clinicopathological factors than the older patients. Furthermore, young age was an independent adverse prognostic factor in operable chest cancer. In the clinical exercise, young patients may need more intensive treatment. Further research is required to elucidate the underlying mechanism and to discover new biomarkers and therapy strategies.

Acknowledgments

We give thanks all the colleagues of the College of Life Sciences and Section of Breast Surgery for their support.

Disclosure

The authors report no conflicts of interest in this piece of work.

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Supplementary materials

Table S1 Baseline characteristics of the younger and older patients in a 1:1 matched group Abbreviations: ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth factor receptor ii.

Tabular array S2 Imputed multivariate Cox proportional take a chance model of BCSS and OS Notes: Multivariate analysis includes historic period, race, tumor size, lymph nodes, grade, ER condition, PR status, and HER2 status. The P-values are derived from Wald exam. Abbreviations: BCSS, breast cancer-specific survival; OS, overall survival; ER, estrogen receptor; PR, progesterone receptor; HER2, human epidermal growth cistron receptor 2.

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