Breast Cancer Res Treat (2014) 145:753–763 DOI 10.1007/s10549-014-2976-9
EPIDEMIOLOGY
Association of race/ethnicity, socioeconomic status, and breast cancer subtypes in the National Cancer Data Base (2010–2011) Helmneh M. Sineshaw • Mia Gaudet • Elizabeth M. Ward • W. Dana Flanders • Carol Desantis • Chun Chieh Lin • Ahmedin Jemal
Received: 17 April 2014 / Accepted: 18 April 2014 / Published online: 3 May 2014 Ó Springer Science+Business Media New York 2014
Abstract To estimate the odds of breast cancer subtypes in minority populations versus non-Hispanic (NH) whites stratified by socioeconomic status (SES) [a composite of individual-level SES (insurance status) and area-level SES (median household income quartile from 2000 U.S. Census data)] using a large nationwide cancer database. We used the National Cancer Data Base to identify breast cancer cases diagnosed in 2010 and 2011, the only 2 years since U.S. cancer registries uniformly began collecting HER2 results. Breast cancer cases were classified into five subtypes based on hormone receptor (HR) and HER2 status: HR?/HER2-, HR?/HER2?, HR-/HER2? (HER2overexpressing), HR-/HER2- (TN), and unknown. A polytomous logistic regression was used to estimate odds ratios (ORs) comparing the odds of non-HR?/HER2-subtypes to HR?/HER2- for racial/ethnic groups controlling for and stratifying by SES, using a composite of insurance status and area-level income. Compared with NH whites, NH blacks and Hispanics were 84 % (OR = 1.84; 95 % CI 1.77–1.92) and 17 % (OR = 1.17; 95 % CI 1.11–1.24) more likely to have TN subtype versus HR?/HER2-, respectively. Asian/Pacific Islanders (API) had 1.45 times greater odds of being diagnosed with HER2Electronic supplementary material The online version of this article (doi:10.1007/s10549-014-2976-9) contains supplementary material, which is available to authorized users. H. M. Sineshaw (&) M. Gaudet E. M. Ward W. D. Flanders C. Desantis C. C. Lin A. Jemal American Cancer Society, Inc.,, 250 Williams Street NW, Atlanta, GA 30303 USA e-mail:
[email protected] W. D. Flanders Rollins School of Public Health, Emory University, 1518 Clifton Rd, Atlanta, GA 30322 USA
overexpressing subtype versus HR?/HER2- compared with NH whites (OR = 1.45; 95 % CI 1.31–1.61). We found similar ORs for race in high and low strata of SES. In a large nationwide hospital-based dataset, we found higher odds of having TN breast cancer in black women and of HER2-overexpressing in API compared with white women in every level of SES. Keywords negative
ER PR HER2 Breast cancer Triple-
Introduction Breast cancer is a heterogeneous cancer consisting of a number of subtypes that increasingly appear to have distinct risk factors, molecular characteristics, and response to treatment [1–6]. Five clinically relevant subtypes have been identified based on the combined tumor expression of receptors for estrogen (ER) and progesterone (PR), which are jointly referred as hormone receptor (HR) [7–9], and human epidermal growth factor receptor 2 (HER2) [2, 5, 10, 11]: HR?/HER2-, HR-/HER2-[triple-negative (TN)], HR?/HER2?, HR-/HER2? (HER2-overexpressing), and unknown [3–6, 10, 11]. HR?/HER2- subtype generally has the most favorable prognosis, whereas TN subtype has the least favorable prognosis [3, 5, 6, 9, 11–15]. Because of the importance of ER, PR, and HER2 expression in determining treatment options [1], the National Cancer Data Base (NCDB) began collecting information on ER and PR status since 2004 data and on HER2 status since 2010 [16]. A number of reports have indicated that non-Hispanic (NH) blacks and Hispanics were more likely to be diagnosed with TN and HER2-overexpressing breast cancer subtypes and Asian/Pacific Islanders (API) were more
123
754
likely to be diagnosed with HER2-overexpressing subtype, than NH whites [4, 7, 12–14, 17–25]. Previous studies also have reported higher odds of TN and HER2-overexpressing breast cancer subtypes in women with lowest socioeconomic status (SES) than in highest SES [12, 14]. In addition, some studies have suggested that the higher odds of these subtypes with unfavorable prognoses in minority racial/ ethnic groups could be explained by differences in SES, with lower SES showing higher odds for hormone receptornegative, TN, or HER2-overexpressing status [13, 17, 26– 28]. However, these studies were limited because they were based on institutional- or state-level cancer registries with only area-level SES data or without HER2 status and did not simultaneously stratify the analyses by race/ethnicity and SES. In this study, we estimate the odds of breast cancer subtypes in minority populations versus NH whites stratified by SES (a composite of individual-level SES [insurance status] and area-level SES [median household income quartile from 2000 U.S. Census data]) using a large nationwide cancer database. We also estimated odds ratios using individual-level (insurance status) SES.
Methods We used data from the NCDB, which is a national hospitalbased cancer registry jointly sponsored by the American College of Surgeons and the American Cancer Society, and collects data on about 73 % of newly diagnosed breast cancer cases in the U.S. [29, 30]. We identified a total of 260,577 single primary or first primary invasive female breast cancer cases (International Classification of Diseases for Oncology, 3rd edition [ICD-O-3] histology codes 8000–8576, 8980–8981, and 9020/3), diagnosed in 2010–2011, aged 18 years or older, and ICD-O-3 site codes of C500–C506 or C508–C509, after excluding missing/ unknown values for diagnosis date. Variables were coded according to the Facility Oncology Registry Data Standards (FORDS) Manual revised for 2010 [31]. We categorized age at diagnosis into five groups (18–39, 40–49, 50–64, 65–74, and C75 years), race/ethnicity into six groups (non-Hispanic white, Hispanic, non-Hispanic black, Asian/Pacific Islander, non-Hispanic other, and missing/ unknown), and insurance status into six groups: uninsured, Medicaid, Medicare, private, government, and missing/ unknown. We categorized comorbidity into three groups (no comorbidity; score = 1; and score C2) based on the sum of weighted Charlson/Deyo score [32]. We created a composite SES variable using individual-level insurance status and area-level median household income quartile from 2000 U.S. Census data in the NCDB [31, 33]. We categorized individuals with private or Medicare with supplement insurance and in fourth median income quartile as high SES;
123
Breast Cancer Res Treat (2014) 145:753–763
those with Medicaid, Medicare with Medicaid supplement, or uninsured status, and/or in first median income quartile as low SES; those with unknown or missing insurance status and median income quartile as ‘‘unknown’’; and, those who had other types of insurance and/or in second or third median income quartile were categorized as moderate SES. When median income quartile or insurance status was missing/ unknown, the one with non-missing information was used to determine the value of the composite SES. Those with ‘‘unknown’’ SES (n = 403) were excluded. Stage was recoded into stage I, II, III, IV, and unknown/ missing using the American Joint Committee on Cancer’s (AJCC) AJCC Cancer Staging Manual (7th edition) [29]. We categorized tumor grade into grade I, II, III, and missing/unknown using AJCC’s collaborative stage sitespecific factor 7 and the Bloom–Richardson scoring method [34]. Tumor size was classified into three categories: \2, C2 to \5, and C5 cm. We used AJCC’s collaborative stage site-specific factors 1, 2, and 15 to recode ER, PR, and summary HER2 status, respectively, into positive, negative, and unknown. ER and PR positive groups include those with borderline results [35, 36], whereas those with borderline HER2 results were grouped as unknown [37]. We created a joint hormone receptor status using ER and PR status. Those with either ER or PR positive status were grouped as hormone receptor positive, and those with ER and PR negative status were grouped as hormone receptor negative. We then classified all breast cancers into five major subtypes: HR?/HER2-, HR?/HER2?, HR-/HER2? (HER2-overexpressing), HR-/HER2- (TN), and unclassified (unknown) [4, 9]. Statistical analysis was performed using SAS version 9.3 software (SAS Institute, Cary, NC, USA). We used descriptive analysis to examine the demographic and clinical characteristic distributions of breast cancer subtypes. A polytomous logistic regression model was utilized to calculate odds ratios (OR) and 95 % confidence intervals (CI) for various demographic and clinical characteristics to estimate the odds of having each of the subtypes compared with HR?/HER2-, after accounting for differences in diagnosis age, race/ethnicity, tumor grade, stage, comorbidity, US census region, and SES in the analysis model. We also conducted another polytomous logistic regression analysis stratified by SES, controlling for diagnosis age, race/ethnicity, tumor grade, stage, comorbidity, and US census region. Results were considered statistically significant when the two-sided P value was less than 0.05.
Results Of the 260,174 breast cancer cases in the NCDB, 64.4 % were HR?/HER2-, 9.2 % HR?/HER2?, 4.1 % HR-/
Breast Cancer Res Treat (2014) 145:753–763
HER2? (HER2-overexpressing), 11.7 % TN, and 10.8 % were unknown (Table 1). Younger patients (\40) had higher proportions of TN (19.7 %), HR?/HER2? (15.4 %), and HER2-overexpressing (5.9 %) breast cancer subtypes compared with 8.8, 6.8, and 2.6 %, respectively, in older patients (75?). Triple-negative breast cancer was more common in NH blacks (21.1 %) than other racial/ ethnic groups. TN (29.6 %), HR?/HER2? (13.8 %), and HER2-over expressing (9.0 %) were more common in grade III tumors than other grades. TN was more common in larger tumor size than smaller tumor size. The proportions of TN subtype were higher in uninsured (15.8 %) and Medicaid-insured (16.4 %) patients compared with patients with other insurance types. Patients with low SES had higher proportions of TN subtype than did patients with high or moderate SES (Table 1). Table 2 shows adjusted odds ratios (ORs) from polytomous logistic regression analysis using HR?/HER2- as the reference group. The odds of having TN breast cancer subtypes versus HR?/HER2- decreases with older age, with women aged 75? years showing a 43 % lower odds of being diagnosed with TN subtype than women aged \40 years (OR = 0.57; 95 % CI 0.53–0.61). NH blacks had 1.84 times greater odds of being diagnosed with TN subtype (OR = 1.84; 95 % CI 1.77–1.92) versus HR?/ HER2- compared with NH whites. Hispanics had 1.26 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.26; 95 % CI 1.16–1.37) versus HR?/HER2- compared with NH whites. Asian/Pacific Islanders had 1.45 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.45; 95 % CI 1.31–1.61) versus HR?/HER2- compared with NH whites. When we conducted sensitivity analyses excluding insurance status and SES from the polytomous regression model, odds ratios changed from 1.84 (95 % CI 1.77–1.92) to 1.91 (95 % CI 1.84–1.98) for TN in NH blacks compared with NH whites, and from 1.45 (95 % CI 1.31–1.61) to 1.46 (95 % CI 1.32–1.61) for HER2-overexpressing in API compared with NH whites, which did not make significant change in the results. Tumors with HER2-overexpressing and TN subtypes versus HR?/HER2- had substantially higher odds to be grade III than grade I ([OR = 18.05; 95 % CI 16.50–19.76], and [OR = 17.86; 95 % CI 16.97–18.79], respectively). Patients with low SES had 1.14 times greater odds of being diagnosed with TN subtype (OR = 1.14; 95 % CI 1.08–1.19) and 1.11 times greater odds of being diagnosed with HER2-overexpressing subtype (OR = 1.11; 95 % CI 1.03–1.19) versus HR?/HER2- compared with high SES. Table 3 illustrates adjusted ORs of breast cancer subtypes using HR?/HER2- as the reference outcome stratified by SES from polytomous logistic regression model for racial/ethnic groups with NH whites being the reference
755
group. NH blacks, compared with NH whites, were at greater odds of being diagnosed with TN than HR?/ HER2- in all SES strata, with odds ratio varying little across strata from 1.79 (1.68–1.91) in the low stratum to 1.91 (1.80–2.03) in moderate stratum. Asian/Pacific Islanders had higher odds of having HER2-overexpressing subtype versus HR?/HER2- than NH whites at each level of SES.
Discussion Using a large nationwide cancer database, we showed that the odds of being diagnosed with TN breast cancer subtype were greater in NH blacks than NH whites, in younger than older adults, and in low SES than high SES. We also showed that the odds of being diagnosed with HER2overexpressing breast cancer subtype were greater in Asian/Pacific Islanders and Hispanics than NH whites, in younger than older adults, and in high grade than low grade breast cancers. Notably, the higher odds of having TN breast cancer subtype in blacks and of HER2-overexpressing in Asian/Pacific Islanders compared with NH whites were similar at each level of SES. Previous studies suggested that the black–white differences in prevalence of hormone receptor negative or TN status could be explained by SES [26, 38–40]. However, the effect of race/ethnicity on the odds of having these breast cancer subtypes is still evident even after controlling for differences in SES [12, 14, 27]. For example, DeSantis et al. [27]. reported that adjusting for SES explained 18 % black–white difference in the odds of being diagnosed with hormone receptor-negative breast cancer, which was not statistically significant. A number of studies also reported that NH blacks had about twofold higher odds of being diagnosed with TN breast cancer than NH whites after controlling for SES, which is similar to the magnitude of the association in our study [12, 14, 41]. However, these studies used state-level cancer registry data with area-level SES did not show black–white odds ratio difference across SES strata, or showed results for breast cancer subtypes without including HER2 status. In our study, after controlling for SES and other factors, NH blacks had about 1.8 times higher odds of TN subtype than NH whites. Even in women with high SES, NH black women still had higher odds of being diagnosed with TN breast cancer compared with NH whites, implying a role for other uncontrolled factors such as genetic or non-genetic factors [42, 43]. In addition, race could be a proxy for other unmeasured factors [44]. We also tried to check the inter-play between race and SES in a sensitivity analysis creating a composite variable of race and SES and using NH white with high SES as a reference category, which did not show significant
123
123 13,598 (5.2) 45,292 (17.4) 101,222 (38.9) 56,437 (21.7) 43,625 (16.8)
50–64
65–74
75?
Insurance
Comorbidity
Stage
Grade
12,401 (4.8)
IV
6,472 (2.5)
9,946 (3.8)
2
Uninsured
216,440 (83.2) 33,788 (13.0)
0 1
4,073 (1.6)
29,651 (11.4)
III
Missing/unknown
82,022 (31.5)
38,684 (14.9)
Unknown 132,027 (50.7)
66,209 (25.4)
III
II
94,272 (36.2)
I
61,009 (23.4)
II
16,627 (6.4)
Missing/unknown
I
1,850 (0.7) 14,270 (5.5)
8,065 (3.1)
29,485 (11.3)
Other Hispanic
API
NH black
NH white
189,877 (73)
131,530 (50.6)
2011
Race/ethnicity
128,644 (49.4)
2010
3,530 (54.5)
6,314 (63.5)
138,817 (64.1) 22,365 (66.2)
1,978 (48.6)
5,950 (48.0)
16,780 (56.6)
49,867 (60.8)
92,921 (70.4)
19,361 (50.0)
26,027 (39.3)
71,257 (75.6)
50,851 (83.3)
10,907 (65.6)
1,060 (57.3) 8,299 (58.2)
5,121 (63.5)
15,253 (51.7)
126,856 (66.8)
87,380 (66.4)
80,116 (62.3)
30,299 (69.5)
39,148 (69.4)
64,081 (63.3)
27,332 (60.3)
6,636 (48.8)
N = 167,496 (64.4) N (row %)
N = 260,174 N (column %)
40–49
HR?/HER2-
Total
\40
Category
Diagnosis year
Age group
Variable
1,025 (15.8)
1,256 (12.6)
25,044 (11.6) 4,042 (12.0)
420 (10.3)
1,570 (12.7)
4,391 (14.8)
12,409 (15.1)
11,552 (8.7)
4,263 (11.0)
19,624 (29.6)
4,591 (4.9)
1,864 (3.1)
1,845 (11.1)
203 (11.0) 1,874 (13.1)
814 (10.1)
6,231 (21.1)
19,375 (10.2)
15,554 (11.8)
14,788 (11.5)
3,829 (8.8)
5,441 (9.6)
12,167 (12)
6,224 (13.7)
2,681 (19.7)
HR-/HER2(triple-negative) N = 30,342 (11.7) N (row %)
Table 1 Characteristics of breast cancer subtypes in the National Cancer Data Base (2010–2011)
746 (11.5)
819 (8.2)
20,274 (9.4) 2,821 (8.3)
336 (8.2)
1,610 (13.0)
3,448 (11.6)
8,305 (10.1)
10,215 (7.7)
3,542 (9.2)
9,139 (13.8)
8,597 (9.1)
2,636 (4.3)
1,513 (9.1)
192 (10.4) 1,510 (10.6)
855 (10.6)
2,948 (10.0)
16,896 (8.9)
12,126 (9.2)
11,788 (9.2)
2,952 (6.8)
4,129 (7.3)
9,595 (9.5)
5,143 (11.4)
2,095 (15.4)
N = 23,914 (9.2) N (row %)
HR?/HER2?
342 (5.3)
372 (3.7)
8,885 (4.1) 1,301 (3.9)
141 (3.5)
897 (7.2)
2,053 (6.9)
3,605 (4.4)
3,862 (2.9)
1,865 (4.8)
5,987 (9.0)
2,159 (2.3)
547 (0.9)
645 (3.9)
91 (4.9) 740 (5.2)
489 (6.1)
1,463 (5.0)
7,130 (3.8)
5,382 (4.1)
5,176 (4)
1,138 (2.6)
1,762 (3.1)
4,844 (4.8)
2,010 (4.4)
804 (5.9)
HR-/HER2? (HER2-overexpressing) N = 10,558 (4.1) N (row %)
829 (12.8)
1,185 (11.9)
23,420 (10.8) 3,259 (9.6)
1,198 (29.4)
2,374 (19.1)
2,979 (10.0)
7,836 (9.6)
13,477 (10.2)
9,653 (25.0)
5,432 (8.2)
7,668 (8.1)
5,111 (8.4)
1,717 (10.3)
304 (16.4) 1,847 (12.9)
786 (9.7)
3,590 (12.2)
19,620 (10.3)
11,088 (8.4)
16,776 (13)
5,407 (12.4)
5,957 (10.6)
10,535 (10.4)
4,583 (10.1)
1,382 (10.2)
N = 27,864 (10.7) N (row %)
Unknown
p \ 0.0001
p \ 0.0001
p \ 0.0001
p \ 0.0001
p \ 0.0001
p \ 0.0001
p \ 0.0001
p value
756 Breast Cancer Res Treat (2014) 145:753–763
96,557 (37.1) 43,940 (16.9)
South
West
Low
Moderate
High
-
54,812 (21.1)
116,025 (44.6)
89,337 (34.3)
5,819 (2.2)
24,111 (9.3)
C5 cm
Missing/unknown
85,686 (32.9)
144,558 (55.6)
2 to \5 cm
\2 cm
555 (0.2)
65,099 (25)
Missing/unknown
54,023 (20.8)
Midwest
4,962 (1.9)
138,360 (53.2)
Northeast
Unknown/missing
Private
509 (0.2)
91,386 (35.1)
Government
18,485 (7.1)
31,989 (58.4)
75,884 (65.4)
59,623 (66.7)
2,215 (38.1)
12,388 (51.4)
50,950 (59.5)
101,943 (70.5)
304 (54.8)
29,986 (68.2)
59,038 (61.1)
42,236 (64.9)
35,932 (66.5)
2,056 (41.4)
88,150 (63.7)
316 (62.1)
63,155 (69.1)
10,289 (55.7)
?
N = 167,496 (64.4) N (row %)
N = 260,174 N (column %)
Medicare
HR?/HER2-
Total
Medicaid
Category
7,998 (14.6)
13,168 (11.3)
9,176 (10.3)
623 (10.7)
4,079 (16.9)
13,340 (15.6)
12,300 (8.5)
62 (11.2)
4,543 (10.3)
12,290 (12.7)
7,645 (11.7)
5,802 (10.7)
399 (8.0)
16,898 (12.2)
72 (14.1)
8,911 (9.8)
3,037 (16.4)
HR-/HER2(triple-negative) N = 30,342 (11.7) N (row %)
5,552 (10.1)
10,311 (8.9)
8,051 (9.0)
597 (10.3)
2,568 (10.7)
9,211 (10.7)
11,538 (8)
63 (11.4)
4,015 (9.1)
8,882 (9.2)
5,860 (9.0)
5,094 (9.4)
308 (6.2)
13,981 (10.1)
62 (12.2)
6,674 (7.3)
2,143 (11.6)
N = 23,914 (9.2) N (row %)
HR?/HER2?
-
2,642 (4.8)
4,515 (3.9)
3,401 (3.8)
391 (6.7)
1,566 (6.5)
4,102 (4.8)
4,499 (3.1)
38 (6.8)
1,736 (4.0)
4,090 (4.2)
2,581 (4.0)
2,113 (3.9)
130 (2.6)
6,218 (4.5)
12 (2.4)
2,791 (3.1)
1,065 (5.8)
HR-/HER2? (HER2-overexpressing) N = 10,558 (4.1) N (row %)
6,631 (12.1)
12,147 (10.5)
9,086 (10.2)
1,993 (34.2)
3,510 (14.6)
8,083 (9.4)
14,278 (9.9)
88 (15.9)
3,660 (8.3)
12,257 (12.7)
6,777 (10.4)
5,082 (9.4)
2,069 (41.7)
13,113 (9.5)
47 (9.2)
9,855 (10.8)
1,951 (10.6)
N = 27,864 (10.7) N (row %)
Unknown
p \ 0.0001
p \ 0.0001
p \ 0.0001
p value
HR hormone receptor positive, HR hormone receptor negative, HER2 human epidermal growth factor receptor 2 positive, HER2 human epidermal growth factor receptor 2 negative, NH non-Hispanic, API Asian/Pacific Islander
?
SES
Tumor size
Region
Variable
Table 1 continued
Breast Cancer Res Treat (2014) 145:753–763 757
123
123
39,148
30,299
65–74
75?
1,060
8,299
10,907
NH other
Hispanic
Missing/unknown
26,027
19,361
III
Unknown
16,780
5,950
1,978
III
IV
Unknown
3,530
10,289
63,155
Uninsured
Medicaid
Medicare
88,150
6,314
C2
Insurance Private (ref)
22,365
1
0 (ref)
138,817
49,867
II
Comorbidity score
92,921
I(ref)
Stage
71,257
II
50,851
5,121
API
Grade I (ref)
15,253
NH black
NH white (ref)
126,856
64,081
50–64
Race
6,636
27,332
40–49
2,681
8,911
3,037
1,025
16,898
1,256
4,042
25,044
420
1,570
4,391
12,409
11,552
4,263
19,624
4,591
1,864
1,845
1,874
203
814
6,231
19,375
3,829
5,441
12,167
6,224
1.00 (0.96–1.05)
1.08 (1.02–1.15)
1.06 (0.98–1.16)
1.00
1.07 (1–10.15)
1.00 (0.96–1.04)
1.00
1.22 (1.09–1.37)
1.16 (1.09–1.24)
1.10 (1.06–1.15)
1.21 (1.18–1.25)
1.00
5.48 (5.17–5.81)
17.86 (16.97–18.79)
1.68 (1.59–1.77)
1.00
1.07 (1.01–1.13)
1.17 (1.11–1.24)
1.05 (0.90–1.24)
0.88 (0.81–0.96)
1.84 (1.77–1.92)
1.00
0.57 (0.53–0.61)
0.62 (0.58–0.66)
0.73 (0.69–0.77)
0.77 (0.72–0.81)
1.00
OR (95 % CI)
N
N
Reference
HR-/HER2(Triple-Negative) N = 30,342
HR?/HER2N = 167,496
\40 (ref)
Age group
Variable
6,674
2,143
746
13,981
819
2,821
20,274
336
1,610
3,448
8,305
10,215
3,542
9,139
8,597
2,636
1,513
1,510
192
855
2,948
16,896
2,952
4,129
9,595
5,143
2,095
N
0.95 (0.90–1.00)
1.04 (0.97–1.11)
1.02 (0.93–1.12)
1.00
0.97 (0.90–1.05)
0.92 (0.88–0.96)
1.00
1.29 (1.14–1.45)
1.74 (1.64–1.85)
1.27 (1.21–1.32)
1.12 (1.09–1.16)
1.00
3.05 (2.89–3.22)
5.85 (5.58–6.13)
2.20 (2.10–2.30)
1.00
1.02 (0.96–1.08)
1.11 (1.04–1.17)
1.17 (1.00–1.37)
1.07 (0.99–1.15)
1.13 (1.08–1.18)
1.00
0.45 (0.42–0.49)
0.49 (0.46–0.53)
0.62 (0.59–0.66)
0.73 (0.69–0.77)
1.00
OR (95 % CI)
HR?/HER2? N = 23,914
804
2,791
1,065
342
6,218
372
1,301
8,885
141
897
2,053
3,605
3,862
1,865
5,987
2,159
547
645
740
91
489
1,463
7,130
1,138
1,762
4,844
2,010
N
0.94 (0.87–1.01)
1.04 (0.95–1.14)
0.92 (0.80–1.05)
1.00
0.95 (0.85–1.06)
0.94 (0.88–1.00)
1.00
1.26 (1.06–1.51)
2.10 (1.93–2.27)
1.68 (1.58–1.78)
1.12 (1.07–1.18)
1.00
7.52 (6.81–8.30)
18.05 (16.50–19.76)
2.62 (2.38–2.88)
1.00
1.02 (0.94–1.12)
1.26 (1.16–1.37)
1.34 (1.08–1.68)
1.45 (1.31–1.61)
1.17 (1.10–1.25)
1.00
0.57 (0.51–0.64)
0.69 (0.62–0.76)
0.96 (0.89–1.05)
0.83 (0.76–0.91)
1.00
OR (95 % CI)
HR-/HER2? (HER2-overexpressing) N = 10,558
Table 2 Adjusted odds ratios with 95 % confidence intervals from polytomous logistic regression analyses for characteristics of breast cancer subtypes
9,855
1,951
829
13,113
1,185
3,259
23,420
1,198
2,374
2,979
7,836
13,477
9,653
5,432
7,668
5,111
1,717
1,847
304
786
3,590
19,620
5,407
5,957
10,535
4,583
1,382
N
Unknown N = 27,864
0.98 (0.94–1.02)
0.95 (0.89–1.02)
0.99 (0.90–1.08)
1.00
1.04 (0.97–1.11)
0.88 (0.85–0.92)
1.00
2.66 (2.46–2.88)
1.61 (1.52–1.70)
0.94 (0.90–0.99)
0.94 (0.91–0.97)
1.00
4.21 (4.05–4.38)
2.06 (1.97–2.14)
1.07 (1.03–1.11)
1.00
0.95 (0.90–1.01)
1.27 (1.20–1.34)
1.43 (1.24–1.64)
1.07 (0.98–1.15)
1.20 (1.15–1.25)
1.00
1.12 (1.04–1.21)
0.98 (0.91–1.05)
0.97 (0.91–1.03)
0.95 (0.89–1.02)
1.00
OR (95 % CI)
758 Breast Cancer Res Treat (2014) 145:753–763
316
75,884
31,989
Moderate
Low
72
7,998
13,168
9,176
62
12,290 4,543
7,645
5,802
399
1.14 (1.08–1.19)
1.10 (1.07–1.14)
1.00
0.94 (0.70–1.26)
1.18 (1.14–1.23) 0.97 (0.93–1.01)
1.13 (1.08–1.17)
1.00
1.01 (0.90–1.13)
0.94 (0.71–1.24)
62 308
5,552
10,311
8,051
63
8,882 4,015
5,860
5,094
N 12 130
2,642
4,515
3,401
38
4,090 1,736
2,581
2,113
N
1.11 (1.03–1.19)
1.07 (1.02–1.12)
1.00
1.57 (1.10–2.24)
1.18 (1.11–1.24) 0.96 (0.90–1.03)
1.09 (1.03–1.16)
1.00
0.84 (0.70–1.01)
0.41 (0.23–0.74)
OR (95 % CI)
HR-/HER2? (HER2-overexpressing) N = 10,558
47
6,631
12,147
9,086
88
12,257 3,660
6,777
5,082
2,069
N
Unknown N = 27,864
1.21 (1.15–1.26)
1.03 (1.00–1.06)
1.00
1.68 (1.31–2.16)
1.51 (1.46–1.57) 1.02 (0.97–1.07)
1.36 (1.31–1.42)
1.00
4.44 (4.14–4.75)
0.79 (0.57–1.08)
OR (95 % CI)
HR? hormone receptor positive, HR- hormone receptor negative, HER2? human epidermal growth factor receptor 2 positive, HER2- human epidermal growth factor receptor 2 negative, OR odds ratio, CI confidence interval, NH non-Hispanic, API Asian/Pacific Islander, SES socioeconomic status
1.09 (1.03–1.14)
1.05 (1.02–1.09)
1.00
1.16 (0.88–1.54)
1.05 (1.01–1.10) 0.94 (0.90–0.99)
1.01 (0.97–1.05)
1.00
0.95 (0.84–1.07)
1.07 (0.81–1.42)
OR (95 % CI)
HR?/HER2? N = 23,914
Adjusted for diagnosis age, race, grade, stage, comorbidity, insurance status, census region, and SES
59,623
High
SES
304
59,038 29,986
South West
Missing/unknown
35,932
42,236
Midwest
2,056
OR (95 % CI)
N
N
Reference
HR-/HER2(Triple-Negative) N = 30,342
HR?/HER2N = 167,496
Northeast (ref)
Region
Unknown/missing
Government
Variable
Table 2 continued
Breast Cancer Res Treat (2014) 145:753–763 759
123
123
59,623
5,481
Hispanic
Missing/unknown
3,691
1,849
Hispanic
Missing/unknown
9,176
423
940
84
193
2,876
3,482
7,998
876
561
264 63
2,288
9,116
13,168
546
373
56
357
1,067
6,777
1.20 (1.06–1.35)
1.19 (1.09–1.30)
1.11 (0.86–1.44)
0.80 (0.68–0.95)
1.79 (1.68–1.91)
1.00
1.02 (0.94–1.11)
1.04 (0.94–1.15)
0.93 (0.80–1.07) 0.97 (0.73–1.29)
1.91 (1.80–2.03)
1.00
1.07 (0.97–1.19)
1.36 (1.20–1.55)
1.02 (0.76–1.39)
0.92 (0.81–1.04)
1.85 (1.70–2.02)
1.00
293
764
68
210
1,317
2,900
5,552
763
448
252 72
1,068
7,708
10,311
457
298
52
393
563
6,288
8,051
N
0.98 (0.86–1.12)
1.16 (1.06–1.27)
1.14 (0.87–1.49)
1.07 (0.91–1.25)
1.08 (1.00–1.16)
1.00
1.06 (0.97–1.15)
1.02 (0.92–1.14)
1.08 (0.94–1.24) 1.32 (1.02–1.71)
1.15 (1.07–1.24)
1.00
0.99 (0.89–1.10)
1.12 (0.99–1.28)
1.05 (0.78–1.41)
1.06 (0.95–1.19)
1.19 (1.08–1.31)
1.00
OR (95 % CI)
HR?/HER2? N = 23,914
138
388
3,411
139
704
1,239
2,642
315
218
141 28
490
3,323
4,515
192
134
29
209
269
2,568
3,401
N
1.11 (0.92–1.33)
1.38 (1.22–1.57)
1.32 (0.91–1.90)
1.60 (1.32–1.95)
1.27 (1.15–1.40)
1.00
1.01 (0.89–1.14)
1.12 (0.96–1.29)
1.40 (1.16–1.68) 1.18 (0.80–1.76)
1.09 (0.98–1.20)
1.00
1.01 (0.86-1.18)
1.24 (1.03–1.50)
1.42 (0.96–2.10)
1.42 (1.22–1.66)
1.24 (1.08–1.42)
1.00
OR (95 % CI)
HR-/HER2? (HER2-overexpressing) N = 10,558
384
861
70
206
1,704
3,406
6,631
807
618
249 92
1,262
9,119
12,147
526
368
142
331
624
7,095
9,086
N
Unknown N = 27,864
1.07 (0.94–1.20)
1.24 (1.13–1.35)
1.07 (0.82–1.40)
1.00 (0.86–1.18)
1.20 (1.12–1.28)
1.00
0.94 (0.86–1.01)
1.36 (1.24–1.50)
1.15 (1.00–1.32) 1.48 (1.167–1.88)
1.21 (1.13–1.29)
1.00
0.95 (0.87–1.05)
1.37 (1.21–1.55)
2.33 (1.89–2.88)
1.01 (0.89–1.14)
1.22 (1.11–1.34)
1.00
OR (95 % CI)
a
A composite variable created using insurance status and area-level median income quartile
Adjusted for diagnosis age, race, grade, stage, comorbidity, census region HR? hormone receptor positive, HR- hormone receptor negative, HER2? human epidermal growth factor receptor 2 positive, HER2- human epidermal growth factor receptor 2 negative, OR odds ratio, CI confidence interval, NH non-Hispanic, API Asian/Pacific Islander
352
1,122
API
NH other
6,698
18,277
NH black
NH white (ref)
31,989
2,860
API NH other
Low status
5,697
1,565 376
NH black
75,884
59,905
3,577
Missing/unknown
NH white (ref)
1,748
Hispanic
Moderate status
332
2,434
API
NH other
2,858
48,674
OR (95 % CI)
N
N
Reference
HR-/HER2(triple-negative) N = 30,342
HR?/HER2N = 167,496
NH black
NH white ref)
High status
SESa
Table 3 Adjusted odds ratios from polytomous logistic regression analyses showing race/ethnicity stratified by a composite socioeconomic status variable for breast cancer subtypes
760 Breast Cancer Res Treat (2014) 145:753–763
Breast Cancer Res Treat (2014) 145:753–763
change in our results (Supplementary Table 1). The factors that drive higher odds of TN breast cancer subtypes in black women in the U.S. still need more elaboration. In our study, we also found higher odds of HER2-overexpressing subtype in API than NH whites, which was consistent with previous reports [12, 13]. For instance, Parise et al. [12] showed that API had 1.41 times higher odds being diagnosed with HER2-overexpressing subtype than did NH whites after adjusting for SES. Our findings on the higher odds of HER2-overexpressing breast cancer subtypes in API women were similar in every level of SES. Lund et al. [41] reported that HER2-positivity did not vary by SES, although they did not examine racial differences across SES levels. The higher odds of HER2-overexpressing subtype in API could be due to genetic, sociocultural, or other risk factors, which require further studies [45, 46]. The higher odds of TN and HER2-overexpressing breast cancer subtypes in younger than older women are also in agreement with previous reports [3, 7, 12, 14, 47]. Anders et al. [47] found unique gene sets that distinguished breast cancers arising in younger (B45) but not in older (C65) women, suggesting that breast cancer in younger women is biologically distinct. The difference in subtype distributions of younger and older women could be due to differences in lifestyle, environmental, and other unidentified risk factors between these two groups. A particular strength of our study is the use of the NCDB, as its large number of breast cancer cases permitted simultaneous stratified analyses of molecular subtypes (including HER2 status) by race/ethnicity and SES for the first time. Furthermore, our results were robust to choice of SES measures or analytic methods. Specifically, we found similar results when we used individual-level SES (insurance) instead of a composite of individual- and area-level SES measure and multiple logistic regression instead of polytomous logistic regression analyses (Supplementary Tables 2, 3). We also conducted sensitivity analysis by excluding stage and grade from the model and then including them subsequently, which did not change our results significantly. One of the limitations of our study is that the NCDB is a national hospital-based cancer registries database, rather than a population-based cancer registry generalizable to the overall US population. However, the demographic and tumor characteristics of women diagnosed with breast cancer are found to be similar between the NCDB and the Surveillance, Epidemiology and End Results (SEER) program database, which is a population-based cancer registries database [48]. Second, information for HER2 status was missing for 10.5 % of breast cancer cases although there were no significant demographic or tumor characteristics differences between these cases and those with HER2 status information. In addition, we grouped borderline laboratory test results of ER and PR as ER? and
761
PR?, and borderline HER2 as unknown, which might have led to misclassification. Hospital differences in laboratory techniques for breast cancer markers, especially HER2 status, might also be an issue [49, 50].
Conclusions In a large nationwide hospital-based dataset in the U.S., we found that NH black women had nearly twofold higher odds of being diagnosed with triple-negative breast cancer subtype than did their white counterparts, regardless of their socioeconomic group. Similarly, the higher odds of presenting with HER2-overexpressing breast cancer in Asian/Pacific Islander women compared with white women were observed also at every level of SES. Further studies are needed to identify factors that contribute to the higher odds of TN or HER2-overexpressing breast cancer subtypes in minority women. Disclosure interest.
The authors declare that they have no conflict of
References 1. Carlson RW, Allred DC, Anderson BO, Burstein HJ, Carter WB et al (2009) Breast cancer. Clinical practice guidelines in oncology. J Natl Compr Cancer Netw 7:122–192 2. Sotiriou C, Neo S-Y, McShane LM, Korn EL, Long PM et al (2003) Breast cancer classification and prognosis based on gene expression profiles from a population-based study. Proc Natl Acad Sci USA 100:10393–10398 3. Onitilo AA, Engel JM, Greenlee RT, Mukesh BN (2009) Breast cancer subtypes based onER/PR and Her2 expression: comparison of clinicopathologic features and survival. Clin Med Res 7:4–13 4. Carey LA, Perou CM, Livasy CA, Dressler LG, Cowan D et al (2006) Race, breast cancer subtypes, and survival in the Carolina Breast Cancer Study. JAMA 295:2492–2502 5. Haque R, Ahmed SA, Inzhakova G, Shi J, Avila C et al (2012) Impact of breast cancer subtypes and treatment on survival: an analysis spanning two decades. Cancer Epidemiol Biomark Prev 21:1848–1855 6. O’Brien KM, Cole SR, Tse C-K, Perou CM, Carey LA et al (2010) Intrinsic breast tumor subtypes, race, and long-term survival in the Carolina Breast Cancer Study. Clin Cancer Res 16:6100–6110 7. Joslyn SA (2002) Hormone receptors in breast cancer: racial differences in distribution and survival. Breast Cancer Res Treat 73:45–59 8. Anders CK, Deal AM, Miller CR, Khorram C, Meng H et al (2011) The prognostic contribution of clinical breast cancer subtype, age, and race among patients with breast cancer brain metastases. Cancer 117:1602–1611 9. Puig-Vives M, Sanchez MJ, Sanchez-Cantalejo J, Torrella-Ramos A, Martos C et al (2013) Distribution and prognosis of molecular breast cancer subtypes defined by immunohistochemical biomarkers in a Spanish population-based study. Gynecol Oncol 130:609–614
123
762 10. O’Brien KM, Cole SR, Engel LS, Bensen JT, Poole CL et al (2013) Breast cancer subtypes and previously established genetic risk factors: a Bayesian approach. Cancer Epidemiol Biomark Prev 23(1):84–97 11. Sorlie T, Perou CM, Tibshirani R, Aas T, Geisler S et al (2001) Gene expression patterns of breast carcinomas distinguish tumor subclasses with clinical implications. Proc Natl Acad Sci USA 98:10869–10874 12. Parise CA, Bauer KR, Brown MM, Caggiano V (2009) Breast cancer subtypes as defined by the estrogen receptor (ER), progesterone receptor (PR), and the human epidermal growth factor receptor 2 (HER2) among women with invasive breast cancer in California, 1999–2004. Breast J 15:593–602 13. Brown M, Tsodikov A, Bauer KR, Parise CA, Caggiano V (2008) The role of human epidermal growth factor receptor 2 in the survival of women with estrogen and progesterone receptornegative, invasive breast cancer: the California Cancer Registry, 1999–2004. Cancer 112:737–747 14. Bauer KR, Brown M, Cress RD, Parise CA, Caggiano V (2007) Descriptive analysis of estrogen receptor (ER)-negative, progesterone receptor (PR)-negative, and HER2-negative invasive breast cancer, the so-called triple-negative phenotype. Cancer 109:1721–1728 15. Cheang MC, Voduc D, Bajdik C, Leung S, McKinney S et al (2008) Basal-like breast cancer defined by five biomarkers has superior prognostic value than triple-negative phenotype. Clin Cancer Res 14:1368–1376 16. American College of Surgeons (2013) All Edits in the NCDB Metafile for 2012 Submissions Using Layout 12.1: NCDB_22_v121_12012011.rmf. http://www.facsorg/cancer/ ncdb/edits_22_v121pdf. Accessed on 13 Nov 2013 17. Simon MS, Severson RK (1997) Racial differences in breast cancer survival: the interaction of socioeconomic status and tumor biology. Am J Obstet Gynecol 176:S233–S239 18. Clarke CA, Keegan TH, Yang J, Press DJ, Kurian AW et al (2012) Age-specific incidence of breast cancer subtypes: understanding the black–white crossover. J Natl Cancer Inst 104:1094–1101 19. Ferguson NL, Bell J, Heidel R, Lee S, Vanmeter S et al (2013) Prognostic value of breast cancer subtypes, Ki-67 proliferation index, age, and pathologic tumor characteristics on breast cancer survival in Caucasian women. Breast J 19:22–30 20. Morris GJ, Naidu S, Topham AK, Guiles F, Xu Y et al (2007) Differences in breast carcinoma characteristics in newly diagnosed African-American and Caucasian patients. Cancer 110:876–884 21. Telli ML, Chang ET, Kurian AW, Keegan TH, McClure LA et al (2011) Asian ethnicity and breast cancer subtypes: a study from the California Cancer Registry. Breast Cancer Res Treat 127:471–478 22. Setiawan VW, Monroe KR, Wilkens LR, Kolonel LN, Pike MC et al (2009) Breast cancer risk factors defined by estrogen and progesterone receptor status: the multiethnic cohort study. Am J Epidemiol 169:1251–1259 23. Kwan ML, Kushi LH, Weltzien E, Maring B, Kutner SE et al (2009) Epidemiology of breast cancer subtypes in two prospective cohort studies of breast cancer survivors. Breast Cancer Res 11:R31 24. Lund MJB, Butler EN, Bumpers HL, Okoli J, Rizzo M et al (2008) High prevalence of triple-negative tumors in an urban cancer center. Cancer 113:608–615 25. Millikan RC, Newman B, Tse C-K, Moorman PG, Conway K et al (2008) Epidemiology of basal-like breast cancer. Breast Cancer Res Treat 109:123–139 26. Gordon NH (2003) Socioeconomic factors and breast cancer in black and white Americans. Cancer Metastasis Rev 22:55–65
123
Breast Cancer Res Treat (2014) 145:753–763 27. DeSantis C, Jemal A, Ward E (2010) Disparities in breast cancer prognostic factors by race, insurance status, and education. Cancer Causes Control 21:1445–1450 28. Andaya AA, Enewold L, Horner M-J, Jatoi I, Shriver CD et al (2012) Socioeconomic disparities and breast cancer hormone receptor status. Cancer Causes Control 23:951–958 29. American College of Surgeons (2013) National Cancer Data Base. http://www.facsorg/cancer/ncdb/. Accessed 29 Oct 2013 30. Lerro CC, Robbins AS, Phillips JL, Stewart AK (2013) Comparison of cases captured in the national cancer data base with those in population-based central cancer registries. Ann of Surg Oncol 20:1759–1765 31. Facility Oncology Registry Data Standards Manual (2013) http:// www.facsorg/cancer/coc/fords/FORDS_for_2010d_ 05012010pdf. Accessed 29 Oct 2013 32. Deyo RA, Cherkin DC, Ciol MA (1992) Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases. J Clin Epidemiol 45:613–619 33. Yost K, Perkins C, Cohen R, Morris C, Wright W (2001) Socioeconomic status and breast cancer incidence in California for different race/ethnic groups. Cancer Causes Control 12:703–711 34. American College of Surgeons (2013) Collaborative stage. Sitespecific factor 7. http://web2.facs.org/cstage/breast/Breast_saa. html. Accessed 30 Oct 2013 35. Hahn KM, Bondy ML, Selvan M, Lund MJ, Liff JM et al (2007) Factors associated with advanced disease stage at diagnosis in a population-based study of patients with newly diagnosed breast cancer. Am J of Epidemiol 166:1035–1044 36. Hammond MEH, Hayes DF, Dowsett M, Allred DC, Hagerty KL et al (2010) American Society of Clinical Oncology/College of American Pathologists guideline recommendations for immunohistochemical testing of estrogen and progesterone receptors in breast cancer (unabridged version). Arch Path Lab Med 134:e48– e72 37. Wolff AC, Hammond MEH, Schwartz JN, Hagerty KL, Allred DC et al (2007) American Society of Clinical Oncology/College of American Pathologists guideline recommendations for human epidermal growth factor receptor 2 testing in breast cancer. Arch Pathol Lab Med 131:18–43 38. Brawley OW (2010) Toward a better understanding of race and cancer. Clin Cancer Res 16:5920–5922 39. Taylor A, Cheng K (2003) Social deprivation and breast cancer. J Public Health 25:228–233 40. Dunn BK, Agurs-Collins T, Browne D, Lubet R, Johnson KA (2010) Health disparities in breast cancer: biology meets socioeconomic status. Breast Cancer Res Treat 121:281–292 41. Lund MJ, Butler EN, Hair BY, Ward KC, Andrews JH et al (2010) Age/race differences in HER2 testing and in incidence rates for breast cancer triple subtypes. Cancer 116:2549–2559 42. Palmer JR, Ruiz-Narvaez EA, Rotimi CN, Cupples LA, Cozier YC et al (2013) Genetic susceptibility loci for subtypes of breast cancer in an African American population. Cancer Epidemiol Biomark Prev 22:127–134 43. Kagawa-Singer M (2001) From genes to social science. Cancer 91:226–232 44. Kagawa-Singer M, Valdez Dadia A, Yu MC, Surbone A (2010) Cancer, culture, and health disparities: time to chart a new course? Cancer J Clin 60:12–39 45. Keegan TH, Gomez SL, Clarke CA, Chan JK, Glaser SL (2007) Recent trends in breast cancer incidence among 6 Asian groups in the Greater Bay Area of Northern California. Int J Cancer 120:1324–1329 46. Gomez SL, Quach T, Horn-Ross PL, Pham JT, Cockburn M et al (2010) Hidden breast cancer disparities in Asian women: disaggregating incidence rates by ethnicity and migrant status. Am J Public Health 100:S125
Breast Cancer Res Treat (2014) 145:753–763 47. Anders CK, Hsu DS, Broadwater G, Acharya CR, Foekens JA et al (2008) Young age at diagnosis correlates with worse prognosis and defines a subset of breast cancers with shared patterns of gene expression. J Clin Oncol 26:3324–3330 48. Fedewa SA, Ward EM, Stewart AK, Edge SB (2010) Delays in adjuvant chemotherapy treatment among patients with breast cancer are more likely in African American and Hispanic populations: a national cohort study 2004–2006. J Clin Oncol 28:4135–4141
763 49. Dowsett M, Hanna WM, Kockx M, Penault-Llorca F, Ru¨schoff J et al (2007) Standardization of HER2 testing: results of an international proficiency-testing ring study. Mod Path 20:584–591 50. Reichman ME, Altekruse S, Li CI, Chen VW, Deapen D et al (2010) Feasibility study for collection of HER2 data by National Cancer Institute (NCI) Surveillance, Epidemiology, and End Results (SEER) Program central cancer registries. Cancer Epidemiol Biomark Prev 19:144–147
123