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RESEARCH 

Breast Cancer Incidence and Mortality by 

Molecular Subtype: Statewide Age and 

Racial/Ethnic Disparities in New Jersey 
Aishwarya Kulkarni1,2,3, Antoinette M. Stroup, 1,2,3, Lisa E. Paddock, 1,2,3, Stephanie M. Hill, 2,3, Jesse J. 

Plascak,1,2, Adana A.M. Llanos1,2 

1 Department of Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA; 
2 Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA; 
3 New Jersey State Cancer Registry, State of New Jersey, Department of Health, Trenton, NJ, USA 

*Corresponding author email: Adana.Llanos@rutgers.edu 

 

ABSTRACT 
The objective of this study was to assess breast cancer incidence and mortality rates by molecular 

subtype for cases diagnosed in New Jersey. Data on all primary, histologically confirmed, invasive breast 

cancers diagnosed among women between January 1, 2008 and December 31, 2013 were retrieved from 

the New Jersey State Cancer Registry. Age-adjusted incidence rates were calculated for each subtype, 

by age and race/ethnicity. Logistic regression models, Cox proportional hazards models, and Kaplan 

Meier curves were used to describe the relative risks for breast cancer incidence, mortality, and survival, 

respectively. In this population-based sample of 32,770 breast cancer cases, non-Hispanic Blacks (NHBs) 

had the highest triple-negative breast cancer (TNBC) incidence rate (17.8 per 100,000, 95% CI 16.5-19.2) 

compared to other races/ethnicities. NHBs had also higher odds of TNBC (OR 2.1, 95% CI 1.95-2.36) and 

higher hazards of death when diagnosed with TNBC (HR 1.28, 95% CI 1.05-1.56), luminal A (HR 1.64, 

95% CI 1.41-1.91), or luminal B (HR 1.54, 95% CI 1.10-2.15) than non-Hispanic Whites (NHWs). Younger 

women (20-39 years) had higher odds of TNBC (OR 1.77, 95% CI 1.54-2.02) and luminal B (OR 1.56, 95% 

CI 1.35-1.80) compared to women 50-64 years; minority women had higher odds of non-luminal HER2-

expressing and lower odds of luminal A than NHWs. TNBC was associated with the poorest survival 

rates. These findings highlight a need for enhanced screening to promote earlier diagnosis and improve 

breast cancer outcomes, particularly in minorities and younger women, which will be essential for 

achieving health equity. 

KEYWORDS: breast cancer; surveillance; incidence; mortality; molecular subtype; diverse population; 

disparities 

 

Citation: Kulkarni A et al (2019) Breast Cancer Incidence and Mortality by Molecular Subtype: Statewide 

Age and Racial/Ethnic Disparities in New Jersey, Cancer Health Disparities 3:e1-

e17,doi:10.9777/chd.2019.1012.



 
 
 
 
 

 

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INTRODUCTION 

In the United States (US), breast cancer is the most 

commonly diagnosed cancer and the leading 

cause of cancer-related deaths among women of 

all age and racial/ethnic groups (ACS, 2017). In 

2017, approximately 252,710 new cases of invasive 

breast cancer were diagnosed and 40,610 breast 

cancer deaths occurred among US women (ACS, 

2017). Breast cancer was the most common cancer 

diagnosed among New Jersey women from 2010-

2014 (NJSCR, 2017). In 2014, New Jersey ranked 9th 

in the US for breast cancer incidence, with a rate 

higher than the US average (134.3 per 100,000 vs. 

123.9 per 100,000), and elevated age-adjusted 

rates among Whites (137.8 per 100,000), Blacks 

(120.8 per 100,000), and Hispanics (105.7 per 

100,000), in contrast to US average rates for these 

groups (124.8, 122.4, and 91.8 per 100,000, 

respectively) (CDC, 2017). The estimated breast 

cancer mortality rate was also slightly higher for 

New Jersey than the US average (21.5 per 100,000 

vs. 20.5 per 100,000), with higher rates among 

Whites and Blacks (20.9 and 30.6 per 100,000, 

respectively) when compared to US average rates 

for these groups (20.0 and 28.1 per 100,000, 

respectively) (CDC, 2017). 

Based on global gene expression patterns (Bastien 

et al., 2012; Network, 2012; Perou et al., 2000; 

Sorlie et al., 2001; Sweeney et al., 2014) and/or 

clinical approximation of immunohistochemistry 

(IHC) expression patterns of the estrogen receptor 

(ER), progesterone receptor (PR) and human 

epidermal growth factor receptor 2 (HER2) 

(Bhargava et al., 2009; Morrison et al., 2012; 

Tamimi et al., 2008), at least four breast cancer 

subtypes have been identified, including luminal A 

(ER+/PR+/HER2-), luminal B (ER+/PR+/HER2+), 

HER2-enriched (luminal, ER+/PR+; non-luminal, 

ER-/PR-) and triple-negative breast cancer (TNBC, 

ER-/PR-/HER2-), with differing distributions, risk 

factors, tumor behaviors and clinical outcomes 

(Carey et al., 2006; Clarke et al., 2012; Howlader et 

al., 2014; Kroenke et al., 2014; Sineshaw et al., 2014; 

Sweeney et al., 2014; Yang et al., 2011). Although 

gene expression profiles are the gold standard, 

data show that IHC expression patterns are 

concordant with gene expression profiles and have 

substantial clinical utility in subtype classification 

(Bastien et al., 2012; Carey et al., 2006). As a result, 

state and regional cancer registries began 

collecting HER2 data in 2010 (Thornton M).  

Distributions of breast cancer subtypes among 

racially and ethnically diverse populations such as 

those residing in New Jersey, are important data 

for understanding cancer disparities and ultimately 

achieving health equity, particularly in terms of 

disseminating optimal treatment (Albain et al., 

2009; Chlebowski et al., 2005; Dignam, 2001) 

within the state. We expanded on prior 

surveillance research by retrospectively collecting 

and validating two additional years of HER2 data 

(2008-2009) for invasive breast cancers diagnosed 

in New Jersey. Our objective was to assess age 

and racial/ethnic disparities in incidence and 

mortality by molecular subtype. We calculated 

age-adjusted incidence and mortality rates for 

each molecular subtype by age at diagnosis and 

race/ethnicity; and, compared New Jersey 

incidence and mortality rates to those of the 

general US population for diagnosis years 2010 to 

2013 (years for which ER, PR, and HER2 data were 

collected nationally). Finally, we estimated relative 

risks for breast cancer diagnosis and death by 

breast cancer subtype. 

 



 
 
 
 
 

 

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MATERIALS AND METHODS 

Study population and data collection 

Data for all primary, histologically confirmed, 

invasive breast cancers diagnosed among women 

of all races/ethnicities in New Jersey from January 

1, 2008 through December 31, 2013 were retrieved 

from existing records at New Jersey State Cancer 

Registry (NJSCR), which is a high-quality, 

population-based cancer incidence registry 

established in October 1978 serving the population 

of New Jersey (currently about 8.9 million residents 

(McCaig et al., 2002)). Women <20 years of age at 

breast cancer diagnosis, diagnosed with non-

invasive breast cancer, and non-residents of New 

Jersey diagnosed at an in-state medical facility 

were excluded from this study as the focus for the 

current analysis was adult women (age ≥20 years) 

diagnosed with invasive breast cancer and who 

reside in the state of New Jersey.  

Because cancer registries did not routinely collect 

HER2 data for incident breast cancer diagnoses 

until January 1, 2010, HER2 data for diagnosis years 

2008 and 2009 were retrospectively collected and 

coded from pathology records for this study. HER2 

data for 2010 through 2013 were reviewed and 

validated from existing NJSCR records. If there was 

insufficient information to code HER2 status in 

existing records, data were obtained by contacting 

hospital cancer registrars and using in-house 

pathology reports. Electronic pathology reports 

were also reviewed to glean additional data. All 

coding was conducted within the NJSCR database 

(SEER*DMS).  

Classification of ER/PR/HER2 status 

An array of standard variables corresponding to 

Collaborative Stage Site-Specific Factors (SSFs) for 

breast cancer was used. ER status (SSF 1) and PR 

status (SSF 2), corresponding to the ER and PR 

assays, respectively, were coded as 

positive/elevated, negative/normal, borderline, or 

unknown (unknown includes test not done, 

borderline/undetermined, test ordered but results 

not entered in chart, or unknown for either ER or 

PR status). 

A series of eight (8) additional variables were used 

to code HER2 status (SSF 8 – SSF 15), and SSF 16 

was used to define breast cancer subtype 

(summary of ER/PR/HER2 status). For HER2 IHC 

screening, scores 0 and 1+ were coded as 

negative; score 2+ was coded as borderline; and 

score 3+ was coded as positive. Fluorescence in 

situ hybridization (FISH) was performed on IHC 

borderline cases. Without positive FISH 

information, tumors scored 2+ by IHC were coded 

as HER2 negative. FISH results were classified as a 

range of values: 0-120 was considered negative; 

120-180 was considered borderline; and values 

>180 were considered positive. These results were 

used to derive the HER2 summary result (SSF 15). 

The combination of ER, PR and HER2 (SSF 16) was 

used to classify breast cancer subtype. The final 

subtype classifications used were luminal A (ER+ 

and/or PR+/HER2-), luminal B (ER+ and/or 

PR+/HER2+), non-luminal HER2-expressing (ER-

/PR-/HER2+), TNBC (ER-/PR-/HER2-), and 

unknown. Although included in descriptive analysis 

(Table 1), cases with unknown breast cancer 

subtype were excluded from subsequent analyses. 

We reviewed SSFs 1, 2, and 8 through 16 and 

identified 1942 cases that were ineligible (due to 

unknown/borderline ER, PR, or HER2). We also 

identified 1442 unresolved cases requiring hospital 

follow-back (due to unknown or not applicable 

codes for ER, PR, and/or HER2) and for which we 

were unable to code SSF 16 (due to insufficient ER, 



 
 
 
 
 

 

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RESEARCH 

PR, and HER2 information). The final analytical sample included 32,770 women (Figure 1). 

 

Figure 1. Flow diagram describing the selection of the analytic cohort. 

Statistical analysis  

Sociodemographic and tumor characteristics were 

described using frequencies and proportions, and 

chi-square tests were used to compare the 

distributions of each variable. Custom incidence 

files for New Jersey data from 2008 to 2013 were 

imported into SEER*PREP v2.5.3 (NCI, 2017a) to 

create a SEER*STAT database for this study. 

SEER*Stat v8.3.2 (NCI, 2017b) was used to calculate 

the age-adjusted incidence rates for each breast 

cancer subtype. The population denominators 

used to generate rates were based on detailed 

county population estimates by age, sex and 

race/ethnicity available in the SEER*STAT database 

(NCI, 2017b). The 2000 US Standard Population 

was used for age-specific weights for direct age-

adjustment. Subtype-specific incidence rates were 

generated by age group (20-39, 40-49, 50-64, 

≥65), and race/ethnicity (non-Hispanic White 

[NHW], non-Hispanic Black [NHB], Asian Pacific 

Islander [API, non-Hispanic], Hispanic, 

Other/Unknown). Rates were estimated per 

100,000 population, and the Tiwari et al. (Tiwari et 

al., 2006) modification for 95% confidence intervals 

(CI) was used to quantify the associations between 

breast cancer subtype and age and race/ethnicity. 

We estimated odds ratios (ORs) for breast cancer 

risk by molecular subtype in New Jersey from 2008 

to 2013, overall and by age at diagnosis and 

race/ethnicity, using multivariable logistic 

regression models. Cox proportional hazards 

regression analysis was performed to estimate 

breast cancer-specific mortality hazard ratios 

(HRs). Tests for the assumption of proportional 

hazards were conducted by visual inspection of 

Schoenfeld residuals and no violations were found. 

The last date of follow-up for cases was December 

31, 2014. Models were adjusted for age at 

diagnosis, race/ethnicity, and tumor stage. The 



 
 
 
 
 

 

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ORs, HRs and corresponding 95% confidence 

intervals (Cis) were generated using SAS v9.4 (SAS 

Institute, Cary, NC). All statistical analyses were 

two-sided and P <0.05 was considered statistically 

significant. 

RESULTS 

Among 32,770 invasive breast cancer cases 

diagnosed in New Jersey women from 2008 to 

2013, 13.1% (n = 4,315) were of unknown subtype. 

For nearly all cases with unknown subtype (95%), 

the test result was borderline or uninterpretable; 

the test was not performed or it was unknown if 

the test was performed; or, the result was not 

documented and, therefore, not reported to 

NJSCR. Chi-square analyses revealed that women 

with unknown subtype were older (≥65), minority 

race/ethnicity (NHB, API, Hispanic), diagnosed as 

distant or unknown stage and deceased at last 

follow-up. Additionally, 13%, 20% and 3.6% had 

ER-, PR-, and HER2- breast cancer, respectively.  

The distribution of sociodemographic and tumor 

characteristics is shown in Table 1. Larger 

proportions of cases were diagnosed among 

women age 50-64 years (37.1%), ≥65 years 

(38.0%), and NHWs (73.0%). Almost one-third 

(32.8%) of breast cancers were diagnosed in the 

upper outer quadrant, most were histologically 

classified as ductal carcinoma (73.1%), diagnosed 

at localized (61.4%) or regional stage (29.9%), and 

were moderately (40.7%) or poorly differentiated 

(34.0%). Luminal A was the most common subtype 

(63.3%), followed by TNBC (10.2%), luminal B 

(8.9%) and non-luminal HER2-expressing (4.2%). 

Table 1. Selected sociodemographic and breast cancer clinicopathologic characteristics among incident breast cancer 

cases diagnosed in New Jersey, 2008-2013, N = 32,770. 

Characteristics n (%) 

Sociodemographic characteristics   

Age at diagnosis (years)   

20-39 1,773 (5.4) 

40-49 6,344  (19.3) 

50-64 12,179  (37.1) 

≥65 12,474  (38.0) 

Race/ethnicity   

White, non-Hispanic 23,930  (73.0) 

Black, non-Hispanic 3,829  (11.7) 

Asian/Pacific Islander, non-Hispanic 1,826  (5.6) 

Hispanic (any race) 2,831  (8.6) 

Other/unknown 354 (1.1) 

Breast cancer clinicopathologic characteristics   

Primary site of breast cancer   

Nipple (areolar) 152 (0.5) 

Central (sub-areolar) 1,509  (4.6) 

Upper inner quadrant 3,513  (10.7) 

Lower inner quadrant 1,767  (5.4) 

Upper outer quadrant 10,760  (32.8) 

Lower outer quadrant 2,286  (7.0) 



 
 
 
 
 

 

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Axillary tail 158 (0.5) 

Overlapping lesion 6,532  (19.9) 

Breast, NOS 6,093  (18.6) 

Cancer sequence number   

One primary cancer diagnosed 29,373  (89.6) 

First of two or more primaries diagnosed 3,397 (10.3) 

Histology   

Adenocarcinoma 613 (1.9) 

Ductal carcinoma 23,948 (73.1) 

Lobular carcinoma 3,074 (9.4) 

Ductal and lobular carcinoma 1,942 (5.9) 

Mixed
a
 1,086 (3.3) 

Other/unknown
b
 2,107 (6.4) 

Tumor stage
c
   

Localized 20,117 (61.4) 

Regional 9,810 (29.9) 

Distant 2,039 (6.2) 

Unknown 804 (2.5) 

Tumor grade   

Well differentiated 4,883 (14.9) 

Moderately differentiated 13,364 (40.7) 

Poorly differentiated 11,140 (34.0) 

Missing/unknown 3,383 (10.3) 

ER status (SSF 1)   

Positive 25,750 (78.6) 

Negative 5,656 (17.3) 

Borderline 27 (0.1) 

Other/unknown 1,337 (4.1) 

PR status (SSF 2)   

Positive 22,518 (68.7) 

Negative 8,768  (26.8) 

Borderline 102 (0.3) 

Other/unknown 1,382 (4.2) 

HER2 IHC lab value (SSF 8)   

0 4,318 (13.1) 

1+ 5,750 (17.5) 

2+ 2,760 (8.4) 

3 2,019 (6.2) 

Unknown  17,923 (54.4)  

HER2 IHC interpretation (SSF 9)   

Positive 2,484  (7.7) 

Negative 11,875  (36.5) 

Borderline 2,414  (7.4) 



 
 
 
 
 

 

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Other/unknown 15,997 (48.4) 

HER2 FISH lab value (SSF 10)   

1.00-9.79 11,889 (36.3) 

9.80-9.87 127 (0.4) 

Other/unknown 20,754 (63.3) 

HER2 FISH interpretation (SSF 11)   

Positive 2,185 (6.7) 

Negative 12,153 (37.3) 

Borderline 410 (1.3) 

Other/unknown 18,022 (54.7) 

HER2 CISH lab value (SSF 12)   

1.00-9.79 32 (0.1) 

9.80-9.87 15 (0.1) 

Other/unknown 32,723 (99.8) 

HER2 CISH interpretation (SSF 13)   

Positive 30 (0.1) 

Negative 164 (0.5) 

Borderline 10 (0.1) 

Other/unknown 32,566 (99.4) 

HER2 result of other or unknown test (SSF 14)   

Positive 768 (2.4) 

Negative 5,351  (16.5) 

Borderline 166 (0.5) 

Other/unknown 26,485 (80.6) 

HER2 summary result (SSF 15)   

Positive 4,373  (13.5) 

Negative 24,268 (74.0) 

Borderline 630 (1.9) 

Other/unknown 3,499 (10.0) 

Breast cancer subtype (SSF 16)   

Luminal A (ER+ and/or PR+/HER2-) 20,775 (63.3) 

Luminal B (ER+ and/or PR+/HER+) 2,938 (8.9) 

Non-luminal HER2-expressing (ER-/PR-/HER2+) 1,396 (4.2) 

Triple-negative (ER-/PR-/HER2-) 3,346 (10.2) 

Unknown 4,315 (13.1) 

Vital status
d
   

Dead 5,734 (17.5) 

Alive 27,036 (82.5) 

NOTE: Percentages may not sum to 100 due to rounding. P values were generated using chi-square test. Abbreviations: CS SSF, Collaborative 

Stage Site-Specific Factor; ER, estrogen receptor; CISH, chromogenic in situ hybridization; FISH, fluorescence in situ hybridization; HER2, human 

epidermal growth factor 2; IHC, immunohistochemistry; NOS, not otherwise specified; PR, progesterone receptor 

a
 Mixed histology includes breast cancers classified as: duct and cribriform carcinoma, duct and mucinous carcinoma, duct and tubular carcinoma, 

or duct and colloid carcinoma. 



 
 
 
 
 

 

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b
 Other & unknown histology includes breast cancers classified as: neoplasm, tumor cells, carcinoma NOS, pleomorphic carcinoma, spindle cell 

carcinoma NOS, pseudosarcomatous carcinoma, small cell carcinoma NOS, papillary carcinoma, squamous cell carcinoma NOS, squamous cell 

carcinoma keratinizing NOS, basaloid carcinoma, adenocarcinoma, adenocarcinoma NOS, scirrhous adenocarcinoma, adenoid cystic carcinoma, 

cribriform carcinoma NOS, tubular adenocarcinoma, solid carcinoma NOS, neuroendocrine carcinoma, adenocarcinoma with mixed subtypes, 

papillary adenocarcinoma NOS, clear cell adenocarcinoma NOS, glycogen-rich carcinoma, mixed cell adenocarcinoma, apocrine adenocarcinoma, 

papillary serous cystadenocarcinoma, mucinous cystadenocarcinoma NOS, mucin producing adenocarcinoma, signet ring cell carcinoma, 

comedenocarcinoma NOS, secretory carcinoma of breast, intraductal papillary adenocarcinoma with invasion, intracystic carcinoma NOS, 

medullary carcinoma NOS, atypical medullary carcinoma, infiltrating ductular carcinoma, Infiltrating lobular mixed with other types of carcinoma, 

inflammatory carcinoma, Paget’s disease (mammary), Paget’s disease with intraductal carcinoma of breast, adenosquamous carcinoma, 

adenocarcinoma with spindle cell metaplasia, adenocarcinoma with neuroendocrine differentiation, metaplastic carcinoma NOS, sarcoma NOS, 

giant cell sarcoma, epithelioid sarcoma, malignant fibrous histiocytoma, liposarcoma NOS, myxoid liposarcoma, leiomyomatosis NOS, spindle cell 

rhabdomyosarcoma, stromal sarcoma NOS, carcinosarcoma NOS, malignant myoepithelioma, phyllodes tumor, hemangiosarcoma, or 

osteosarcoma NOS. 

c
 SEER summary stage. 

d
 Vital status as of December 31, 2014. 

Age-adjusted breast cancer incidence rates by 

subtype and age at diagnosis, race/ethnicity, and 

tumor stage are shown in Table 2. As expected, 

incidence rates for the luminal A subtype were 

highest among women ≥65 years (203.3, 95% CI 

198.9-207.8), compared to the other subtypes. 

Among luminal B breast cancer cases, incidence 

was lowest among women 20-39 years (4.1, 95% 

CI 3.7-4.7) and highest among those 50-64 years 

(21.8, 95% CI 20.6-23.1). A similar pattern was 

observed for the non-luminal HER2-expressing 

subtype, with the lowest incidence among women 

20-39 years (1.9, 95% CI 1.6-2.3) and highest 

among those 50-64 years (11.3, 95% CI 10.4-12.2). 

Among TNBC cases, incidence rates increased with 

age (20-39 years: 4.8, 95% CI 4.3-5.3; 40-49 years: 

17.1, 95% CI 15.9-18.5; 50-64 years: 23.0, 95% CI 

21.8-24.3; and ≥65 years: 26.0, 95% CI 24.5-27.7). 



 
 
 
 
 

 

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Table 2. Age-adjusted breast cancer incidence rates (per 100,000) in New Jersey, by subtype and by age at diagnosis, race/ethnicity, and tumor stage, 2008-2013. 

Breast cancer subtype 
Age 

group 

20-39 years 40-49 years 50-64 years ≥65 years 

Count 
Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 

Luminal A
a
 

 
869 13.5 (12.7-14.5) 3,884 92.3 (89.4-95.3) 7,624 139.6 (136.4-142.7) 8,398 

203.3 (198.9-

207.8) 

Luminal B
b
  270 4.1 (3.7-4.7) 676 16.2 (15.0-17.5) 1,179 21.8 (20.6-23.1) 813 19.8 (18.5-21.3) 

Non-luminal HER2-

expressing
c
 

 
121 1.9 (1.6-2.3) 287 6.8 (6.1-7.7) 611 11.3 (10.4-12.2) 377 9.2 (8.2-10.1) 

Triple-negative
d
  310 4.8 (4.3-5.3) 711 17.1 (15.9-18.5) 1,250 23.0 (21.8-24.3) 1,075 26.0 (24.5-27.7) 

New Jersey Population  6,712,391 4,104,990 5,408,652 4,226,407 

Breast cancer subtype 
Race/ 

ethnicity 

20-39 years 40-49 years 50-64 years ≥65 years 

Count 
Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 
Count 

Incidence Rate 

(95% CI) 

Luminal A
a
  15,873 72.2 (71.0-73.3) 1,996 51.7 (49.5-54.1) 1,049 44.3 (41.5-47.1) 1,724 45.3 (43.1-47.5) 

Luminal B
b
  2,070 10.1 (9.7-10.6) 342 8.7 (7.8-9.7) 203 8.0 (6.9-9.2) 305 7.6 (6.8-8.6) 

Non-luminal HER2-

expressing
c
 

 
913 4.4 (4.1-4.7) 213 5.4 (4.7-6.2) 108 4.5 (3.6-5.4) 154 3.8 (3.2-4.5) 

Triple-negative
d
  2,151 10.4 (9.9-10.9) 693 17.8 (16.5-19.2) 162 6.6 (5.6-7.7) 333 8.3 (7.4-9.3) 

New Jersey Population  16,228,596 3,745,444 2,438,511 4,714,148 

NOTE: Risk estimates and 95% confidence intervals (CI) were generated using logistic regression; adjusted for age, race/ethnicity and stage at diagnosis. 

Counts by age group and race/ethnicity and subtype do not add up to 32,770 (which is the total number of breast cancer cases included in Table 1) due 

to missing data on race/ethnicity and/or breast cancer subtype among some cases. 

a Luminal A (ER+ and/or PR+/HER2-); b Luminal B (ER+ and/or PR+/HER2+); c Non-luminal HER2-expressing (ER-/PR-/HER2+); d Triple negative (ER-

/PR-/HER2-) 



 
 
 
 
 

 

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Breast cancer incidence and mortality risks by 

subtype and by age at diagnosis and 

race/ethnicity are shown in Table 3. Among all 

races/ethnicities, luminal A incidence rates were 

highest, ranging from 51.7 (95% CI 49.5-54.1) 

among NHBs to 72.2 (95% CI 71.0-73.3) per 

100,000 among NHWs. Incidence rates of TNBC 

among NHBs were nearly twice as high as those 

among other racial/ethnic groups, (17.8, 95% CI 

16.5-19.2). Incidence rates of the non-luminal 

HER2-expressing subtype were also higher among 

NHBs (5.4, 95% CI 4.7-6.2). 

NHBs (OR 2.15, 95% CI 1.95-2.36), Hispanics (OR 

1.19, 95% CI 1.05-1.35), and younger women (20-

39 years: OR 1.77, 95% CI 1.54-2.02 and 40-49 

years: OR 1.10, 95% CI 1.00-1.20) had higher odds 

of TNBC compared to NHWs and women aged 

50-64 years, respectively. Younger women also 

had higher odds of luminal B breast cancer (OR 

1.56, 95% CI 1.35-1.80). Women ≥65 years, 

however, had higher odds of the luminal A 

subtype (OR 1.22, 95% CI 1.16-1.29) and lower odds 

of all other subtypes. All minority women had 

higher odds of developing non-luminal HER2-

expressing breast cancer compared to NHW 

women (NHB: OR 1.31, 95% CI 1.12-1.53; API: OR 

1.55, 95% CI 1.27-1.90; Hispanic: OR 1.20, 95% CI 

1.00-1.44). APIs had an increased risk of the luminal 

B subtype (OR 1.24, 95% CI 1.06-1.44) compared to 

NHWs.  

When compared to women 50-64 years, those ≥65 

years had higher risk of breast cancer death 

regardless of subtype, but the risk of death was 

more than double among those with the non-

luminal HER2-expressing subtype (HR 2.21; 95% CI 

1.62-3.01). Women 40-49 years had lower risk of 

breast cancer death when diagnosed with luminal A 

and luminal B subtypes. NHBs had increased risk of 

breast cancer death for all subtypes except non-

luminal HER2-expressing subtype compared to 

NHW , with HRs ranging from 1.28 (95% 1.05-1.56) 

for TNBC to 1.64 (95% 1.41-1.91) for luminal A.

Table 3. Multivariable logistic regression and Cox proportional hazards regression models for breast cancer incidence 

and mortality risk, among incident breast cancer cases diagnosed in New Jersey, by molecular subtype and by age at 

diagnosis and race/ethnicity, 2008-2013. 

 
Luminal A

a
 Luminal B

b
 

Non-luminal HER2-

expressing
c
 

Triple-negative
d
 

Incidence OR (95% CI) OR (95% CI) OR (95% CI) OR (95% CI) 

Age at diagnosis (years) 

20-39 0.62 (0.56-0.68) 1.56 (1.35-1.80) 1.22 (0.10-1.50) 1.77 (1.54-2.02) 

40-49 0.95 (0.89-1.02) 1.10 (1.00-1.21) 0.87 (0.76-1.01) 1.10 (1.00-1.20) 

50-64 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 

≥65 1.22 (1.16-1.29) 0.67 (0.61-0.73) 0.61 (0.54-0.69) 0.84 (0.77-0.92) 

Race/ethnicity 

NHW 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 

NHB 0.59 (0.55-0.63) 0.93 (0.82-1.05) 1.31 (1.12-1.53) 2.15 (1.95-2.36) 

API 0.78 (0.71-0.86) 1.24 (1.06-1.44) 1.55 (1.27-1.90) 0.94 (0.80-1.11) 

Hispanic 0.76 (0.70-0.82) 1.05 (0.92-1.19) 1.20 (1.00-1.44) 1.19 (1.05-1.35) 

Mortality HR (95% CI) HR (95% CI) HR (95% CI) HR (95% CI) 



 
 
 
 
 

 

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Age at diagnosis (years) 

20-39 0.96 (0.74-1.25) 0.81 (0.50-1.30) 1.10 (0.66-1.84) 0.99 (0.74-1.34) 

40-49 0.80 (0.68-0.95) 0.65 (0.43-0.97) 0.75 (0.49-1.17) 1.03 (0.82-1.30) 

50-64 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 

≥65 1.51 (1.33-1.71) 1.69 (1.28-2.24) 2.21 (1.62-3.01) 1.25 (1.03-1.53) 

Race/ethnicity 

NHW 1.00 (ref) 1.00 (ref) 1.00 (ref) 1.00 (ref) 

NHB 1.64 (1.41-1.91) 1.54 (1.10-2.15) 1.34 (0.95-1.91) 1.28 (1.05-1.56) 

API 1.00 (0.76-1.31) 0.59 (0.28-1.25) 0.99 (0.56-1.74) 1.00 (0.69-1.46) 

Hispanic 0.94 (0.75-1.17) 1.06 (0.68-1.64) 0.81 (0.47-1.39) 1.14 (0.86-1.52) 

NOTE: Risk estimates and 95% confidence intervals (CI) were generated using logistic regression for 

incidence and Cox proportional hazards regression for breast cancer specific mortality; adjusted for age, 

race/ethnicity and stage at diagnosis. Bolded values represent statistical significance. 

a Luminal A (ER+ and/or PR+/HER2-); b Luminal B (ER+ and/or PR+/HER2+); c Non-luminal HER2-expressing 

(ER-/PR-/HER2+); d Triple negative (ER-/PR-/HER2-) 

As shown in the Kaplan-Meier survival curves 

(adjusted for age, race/ethnicity, and tumor stage), 

women diagnosed with TNBCs had the poorest 

breast cancer-specific survival, followed by those 

diagnosed with the non-luminal HER2-expressing 

subtype (P <0.0001; Figure 2). Analysis stratified by 

race/ethnicity (Figure 3) suggested that this was 

likely driven by TNBC diagnosed among NHBs (P 

<0.001) and APIs (P <0.05). 

 

Figure 2. Breast cancer survival by subtype, New Jersey, 2008-2013 



 
 
 
 
 

 

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Figure 3. Breast cancer survival by breast cancer subtype, stratified by race/ethnicity, A) Non-Hispanic 

Whites; B) Non-Hispanic Blacks; C) Asians/Pacific Islanders; and D) Hispanics (any race), in New Jersey, 

2008-2013. Note: models were adjusted for age at diagnosis and stage at diagnosis. 

DISCUSSION 

In this study, which is the largest population-based 

sample of breast cancer in New Jersey to date (N 

= 32,770), we demonstrated the feasibility of 

retrospectively coding HER2 data not previously 

recorded in NJSCR files for cases diagnosed in 

2008 and 2009. Analyses of these data showed 

that NHB women had the highest age-adjusted 

incidence rates of TNBCs (17.8 per 100,000) 

compared to all other racial/ethnic groups, which 

ranged from 6.6 (API) to 10.4 (NHW). NHBs also 

had a 28% higher risk of breast cancer death than 

their NHW counterparts, which is consistent with 

the literature (Clarke et al., 2012; DeSantis et al., 

2016; Howlader et al., 2014; Noone et al., 2016; 

Parise et al., 2009). In terms of incidence, young 

women (20-39 years) had higher risks of TNBC 

and luminal B breast cancers compared to women 

50-64 years, and Hispanic women had higher risks 

of non-luminal HER2-expressing and TNBC 

subtypes than NHWs. Our findings also showed 

TNBCs were associated with the poorest survival.  

Incidence of the non-luminal HER2-expressing 

subtype was highest among NHBs compared to 



 
 
 
 
 

 

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RESEARCH 

other racial/ethnic groups, while rates were lowest 

among Hispanics and similar between NHWs and 

APIs. The latter finding is in contrast to several 

studies, which have shown the highest rates for 

this subtype to be among APIs compared to 

NHWs (Clarke et al., 2012; Howlader et al., 2014; 

Parise et al., 2009; Sineshaw et al., 2014). We 

suspect that our findings are suggestive of 

differences in tumor biology and/or etiologic 

mechanisms of TNBCs and non-luminal HER2-

expressing breast cancers associated with 

racial/ethnic exposures, which are also related to 

poorer outcomes, as reported herein and 

elsewhere (Akinyemiju et al., 2015; Carey et al., 

2006; Leone et al., 2015; Li et al., 2017; Llanos et al., 

2015; Sorlie et al., 2001; Warner et al., 2015). 

Additionally, it is quite possible that our finding of 

similar rates of the non-luminal HER2-expressing 

subtype among APIs and NHWs could be 

reflective of differences in subgroups of APIs that 

reside in New Jersey (as compared to populations 

in other states), that may be underrepresented in 

prior breast cancer epidemiology studies. This 

warrants further analysis. Many studies have 

focused on the TNBC subtype due to its 

aggressive nature and limited treatment options, 

but it should be noted that non-luminal HER2-

expressing tumors are also associated with 

relatively poor survival, have similar penetrance 

among minority women, and exhibit features that 

are indicative of a more aggressive phenotype 

than the luminal A subtype. Poorer survival among 

TNBC and non-luminal HER2-expressing breast 

cancer cases may also relate to lack of timely and 

optimal/guideline-concordant treatment, 

particularly among racial/ethnic minorities and 

underserved populations (Bustami et al., 2014; 

Chen and Li, 2015; Daly and Olopade, 2015; 

Freedman et al., 2013; George et al., 2015; Hassett 

et al., 2016; Reeder-Hayes et al., 2014; Sheppard et 

al., 2015). A recent study suggested differences in 

response to treatment by race/ethnicity even when 

subtype was the same (Rauscher et al., 2017), 

warranting further analysis. Population-based 

studies with the ability to explore etiologic, risk 

factor, and prognostic differences by subtype are 

critically needed to better understand disparities 

and achieve health equity for breast cancer 

outcomes. 

As we consider the burden of breast cancer in 

New Jersey, another important finding was that 

approximately 30% and 6% of breast cancers were 

diagnosed at regional stage and distant stage, 

respectively. Given the high risk of mortality 

associated with later stage diagnosis reported here 

and elsewhere (Markossian and Hines, 2012; Tian 

et al., 2012; Tian et al., 2011), it is important to 

address this issue at the population level. In fact, 

data suggests a high-degree of spatial variation in 

late-stage breast cancer incidence in New Jersey 

(Roche et al., 2016). Future research to evaluate 

the geographic distribution of molecular subtypes 

is needed given the observed disparities in breast 

cancer incidence and mortality in New Jersey. 

There were some limitations of this study that 

should be considered. First, our use of hormone 

receptor expression by IHC rather than gene 

expression for classifying breast cancer subtypes 

was a limitation, although one could argue that 

gene expression has its limitations as well. Studies 

have shown good concordance between IHC and 

gene expression for classifying the major subtypes 

(Bastien et al., 2012; Carey et al., 2006), supporting 

utility of IHC, and its use in the SEER program. 

Another limitation was that >10% of the cases had 

unknown breast cancer subtype, due to 

incomplete reporting of hormone receptor data or 



 
 
 
 
 

 

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RESEARCH 

inconsistencies in reporting, particularly for HER2, 

as a result of variations in reporting sources (e.g., 

physician’s private offices vs. larger 

hospitals/medical facilities). Additionally, we were 

unable to account for known (and suspected) 

breast cancer risk factors (e.g. age at menarche, 

age at menopause, menopausal status, family 

history of breast cancer, parity, BMI) (Althuis et al., 

2004; Ambrosone et al., 2015; Bethea et al., 2016; 

Hwang et al., 2005; Krieger, 2008; Rosenberg et 

al., 2016), which could have strengthened our 

analysis. Nonetheless, the distribution of breast 

cancer subtypes reported herein were consistent 

with other studies (Bastien et al., 2012; Howlader et 

al., 2014; Morrison et al., 2012; Network, 2012; 

Perou et al., 2000; Sorlie et al., 2001; Sweeney et 

al., 2014). Bias that may have resulted from missing 

data is also a concern, particularly for missing or 

misclassified race/ethnicity. However, this is 

minimal due to the stringent data quality 

standards promulgated by the North American 

Association of Central Cancer Registries 

(NAACCR). Prior studies have assessed the use of 

standard registry data and have demonstrated 

sufficient reliability of race and ethnicity variables 

(Clegg et al., 2007; Knowlton et al., 2014; Patel et 

al., 2005). The exclusion of 13% of New Jersey 

breast cancer cases due to unknown ER/PR/HER2 

status is another source of bias. As our analysis 

revealed, women with unknown subtype were 

older (≥65), minority race/ethnicity, diagnosed at 

distant stage or have unknown stage, and 

deceased at the time of last follow-up. The 

exclusion of these women would have likely biased 

our results toward the null, thereby 

underestimating incidence rates, risks of non-

luminal HER2-expression and TNBC subtypes, and 

risks of breast cancer death. There were also 

important strengths of this study, including a large, 

population-based sample of racially and ethnically 

diverse women with data on ER, PR and HER2 

status. In fact, this is the largest dataset currently 

available with breast cancer subtypes in New 

Jersey.  

Findings reported herein highlight a need for 

enhanced screening among some subgroups of 

women to promote earlier diagnosis, and improve 

breast cancer outcomes. Understanding the 

mechanisms leading to the development of each 

breast cancer subtype is essential and will play a 

major role in improving prognosis and addressing 

breast cancer outcomes disparities; and, may 

contribute to improved treatment options that will 

hopefully reduce the observed breast cancer 

mortality and survival differences by molecular 

subtype. 

Acknowledgements 

We sincerely thank Jie Li, MPH, Gerald Harris, PhD, 

Annette Werts, Rudmilla Chowdhury, CTR, and 

Adrian Botchway, CTR at the New Jersey State 

Cancer Registry for their contributions to this work. 

This study was supported by the National Cancer 

Institute (Cancer Center Support Grant Number 

P30 CA072720) through a New Investigator Award 

and K01 CA193527 awarded to A.A.M. Llanos. 

Cancer Epidemiology Services, including the New 

Jersey State Cancer Registry, receives financial 

support from the: Surveillance, Epidemiology, and 

End Results Program of the National Cancer 

Institute, National Institutes of Health, under 

contract HHSN 261201300021I and control No. 

N01-PC-2013-00021; National Program of Cancer 

Registries, Centers for Disease Control and 

Prevention, under cooperative agreement 5U58/ 

DP003931; the State of New Jersey and Rutgers 

Cancer Institute of New Jersey. 



 
 
 
 
 

 

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RESEARCH 

Conflict of interest 
The authors have no conflicts of interest to 

declare. 

Authors’ contributions 
Conceptualization: AK AMS AAML. Development 

of methodology: AK AMS LEP AAML. Acquisition 

of data: AK SMH. Analysis and interpretation of 

data: AK AMS LEP JJP AAML. Writing, review, 

and/or revision of the manuscript: AK AMS LEP 

SMH JJP AAML. Administrative, technical, or 

material support: AMS AAML. 

 

REFERENCES 

 ACS (2017). American Cancer Society. Breast Cancer Facts & 

Figures 2017-2018 (Atlanta, GA: American Cancer Society, 

Inc.). 

Akinyemiju, T., Moore, J.X., and Altekruse, S.F. (2015). Breast 

cancer survival in African-American women by hormone 

receptor subtypes. Breast cancer research and treatment 

153, 211-218. 

Albain, K.S., Unger, J.M., Crowley, J.J., Coltman, C.A., Jr., and 

Hershman, D.L. (2009). Racial disparities in cancer survival 

among randomized clinical trials patients of the 

Southwest Oncology Group. Journal of the National 

Cancer Institute 101, 984-992. 

Althuis, M.D., Fergenbaum, J.H., Garcia-Closas, M., Brinton, 

L.A., Madigan, M.P., and Sherman, M.E. (2004). Etiology 

of hormone receptor-defined breast cancer: a systematic 

review of the literature. Cancer epidemiology, biomarkers 

& prevention : a publication of the American Association 

for Cancer Research, cosponsored by the American 

Society of Preventive Oncology 13, 1558-1568. 

Ambrosone, C.B., Zirpoli, G., Hong, C.C., Yao, S., Troester, 

M.A., Bandera, E.V., Schedin, P., Bethea, T.N., Borges, V., 

Park, S.Y., et al. (2015). Important Role of Menarche in 

Development of Estrogen Receptor-Negative Breast 

Cancer in African American Women. Journal of the 

National Cancer Institute 107. 

Bastien, R.R., Rodriguez-Lescure, A., Ebbert, M.T., Prat, A., 

Munarriz, B., Rowe, L., Miller, P., Ruiz-Borrego, M., 

Anderson, D., Lyons, B., et al. (2012). PAM50 breast 

cancer subtyping by RT-qPCR and concordance with 

standard clinical molecular markers. BMC medical 

genomics 5, 44. 

Bethea, T.N., Rosenberg, L., Castro-Webb, N., Lunetta, K.L., 

Sucheston-Campbell, L.E., Ruiz-Narvaez, E.A., Charlot, 

M., Park, S.Y., Bandera, E.V., Troester, M.A., et al. (2016). 

Family History of Cancer in Relation to Breast Cancer 

Subtypes in African American Women. Cancer 

epidemiology, biomarkers & prevention : a publication of 

the American Association for Cancer Research, 

cosponsored by the American Society of Preventive 

Oncology 25, 366-373. 

Bhargava, R., Striebel, J., Beriwal, S., Flickinger, J.C., Onisko, A., 

Ahrendt, G., and Dabbs, D.J. (2009). Prevalence, 

morphologic features and proliferation indices of breast 

carcinoma molecular classes using immunohistochemical 

surrogate markers. International journal of clinical and 

experimental pathology 2, 444-455. 

Bustami, R.T., Shulkin, D.B., O'Donnell, N., and Whitman, E.D. 

(2014). Variations in time to receiving first surgical 

treatment for breast cancer as a function of racial/ethnic 

background: a cohort study. JRSM Open 5, 

2042533313515863. 

Cancer Genome Atlas Newtork (2012). Comprehensive 

molecular portraits of human breast tumours. Nature 

490, 61-70. 

Carey, L.A., Perou, C.M., Livasy, C.A., Dressler, L.G., Cowan, D., 

Conway, K., Karaca, G., Troester, M.A., Tse, C.K., 

Edmiston, S., et al. (2006). Race, breast cancer subtypes, 

and survival in the Carolina Breast Cancer Study. Jama 

295, 2492-2502. 

CDC (2017). U.S. Cancer Statistics Working Group. United 

States Cancer Statistics: 1999–2014 Incidence and 

Mortality Web-based Report (Atlanta: Department of 

Health and Human Services, Centers for Disease Control 

and Prevention, and National Cancer Institute). 

Chen, L., and Li, C.I. (2015). Racial disparities in breast cancer 

diagnosis and treatment by hormone receptor and HER2 

status. Cancer epidemiology, biomarkers & prevention : a 

publication of the American Association for Cancer 

Research, cosponsored by the American Society of 

Preventive Oncology 24, 1666-1672. 

Chlebowski, R.T., Chen, Z., Anderson, G.L., Rohan, T., Aragaki, 

A., Lane, D., Dolan, N.C., Paskett, E.D., McTiernan, A., 

Hubbell, F.A., et al. (2005). Ethnicity and breast cancer: 

factors influencing differences in incidence and outcome. 

Journal of the National Cancer Institute 97, 439-448. 

Clarke, C.A., Keegan, T.H., Yang, J., Press, D.J., Kurian, A.W., 

Patel, A.H., and Lacey, J.V., Jr. (2012). Age-specific 

incidence of breast cancer subtypes: understanding the 

black-white crossover. Journal of the National Cancer 

Institute 104, 1094-1101. 

Clegg, L.X., Reichman, M.E., Hankey, B.F., Miller, B.A., Lin, Y.D., 

Johnson, N.J., Schwartz, S.M., Bernstein, L., Chen, V.W., 

Goodman, M.T., et al. (2007). Quality of race, Hispanic 

ethnicity, and immigrant status in population-based 

cancer registry data: implications for health disparity 

studies. Cancer Causes Control 18, 177-187. 

Daly, B., and Olopade, O.I. (2015). A perfect storm: How tumor 

biology, genomics, and health care delivery patterns 

collide to create a racial survival disparity in breast cancer 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e16                                              Cancer Health Disparities 

RESEARCH 

and proposed interventions for change. CA Cancer J Clin 

65, 221-238. 

DeSantis, C.E., Fedewa, S.A., Goding Sauer, A., Kramer, J.L., 

Smith, R.A., and Jemal, A. (2016). Breast cancer statistics, 

2015: Convergence of incidence rates between black and 

white women. CA Cancer J Clin 66, 31-42. 

Dignam, J.J. (2001). Efficacy of systemic adjuvant therapy for 

breast cancer in African-American and Caucasian 

women. Journal of the National Cancer Institute 

Monographs, 36-43. 

Freedman, R.A., He, Y., Winer, E.P., and Keating, N.L. (2013). 

Racial/Ethnic differences in receipt of timely adjuvant 

therapy for older women with breast cancer: are delays 

influenced by the hospitals where patients obtain surgical 

care? Health Serv Res 48, 1669-1683. 

George, P., Chandwani, S., Gabel, M., Ambrosone, C.B., 

Rhoads, G., Bandera, E.V., and Demissie, K. (2015). 

Diagnosis and surgical delays in African American and 

white women with early-stage breast cancer. J Womens 

Health (Larchmt) 24, 209-217. 

Hassett, M.J., Schymura, M.J., Chen, K., Boscoe, F.P., Gesten, 

F.C., and Schrag, D. (2016). Variation in breast cancer 

care quality in New York and California based on 

race/ethnicity and Medicaid enrollment. Cancer 122, 420-

431. 

Howlader, N., Altekruse, S.F., Li, C.I., Chen, V.W., Clarke, C.A., 

Ries, L.A., and Cronin, K.A. (2014). US incidence of breast 

cancer subtypes defined by joint hormone receptor and 

HER2 status. Journal of the National Cancer Institute 106. 

Hwang, E.S., Chew, T., Shiboski, S., Farren, G., Benz, C.C., and 

Wrensch, M. (2005). Risk factors for estrogen receptor-

positive breast cancer. Archives of surgery 140, 58-62. 

Knowlton, R., Gershman, S., Solis, A., and Das, B. (2014). An 

assessment of the reliability of race, Hispanic ethnicity, 

birthplace, and tobacco history data in the Massachusetts 

cancer registry, 2005-2009. J Registry Manag 41, 5. 

Krieger, N. (2008). Hormone therapy and the rise and perhaps 

fall of US breast cancer incidence rates: critical reflections. 

International journal of epidemiology 37, 627-637. 

Kroenke, C.H., Sweeney, C., Kwan, M.L., Quesenberry, C.P., 

Weltzien, E.K., Habel, L.A., Castillo, A., Bernard, P.S., 

Factor, R.E., Kushi, L.H., et al. (2014). Race and breast 

cancer survival by intrinsic subtype based on PAM50 

gene expression. Breast cancer research and treatment 

144, 689-699. 

Leone, J.P., Leone, J., Zwenger, A.O., Iturbe, J., Vallejo, C.T., 

and Leone, B.A. (2015). Prognostic significance of tumor 

subtypes in male breast cancer: a population-based 

study. Breast cancer research and treatment 152, 601-

609. 

Li, X., Yang, J., Peng, L., Sahin, A.A., Huo, L., Ward, K.C., 

O'Regan, R., Torres, M.A., and Meisel, J.L. (2017). Triple-

negative breast cancer has worse overall survival and 

cause-specific survival than non-triple-negative breast 

cancer. Breast cancer research and treatment 161, 279-

287. 

Llanos, A.A., Chandwani, S., Bandera, E.V., Hirshfield, K.M., Lin, 

Y., Ambrosone, C.B., and Demissie, K. (2015). Associations 

between sociodemographic and clinicopathological 

factors and breast cancer subtypes in a population-based 

study. Cancer Causes Control 26, 1737-1750. 

Markossian, T.W., and Hines, R.B. (2012). Disparities in late 

stage diagnosis, treatment, and breast cancer-related 

death by race, age, and rural residence among women in 

Georgia. Women Health 52, 317-335. 

McCaig, C., Perks, C.M., and Holly, J.M. (2002). Intrinsic actions 

of IGFBP-3 and IGFBP-5 on Hs578T breast cancer 

epithelial cells: inhibition or accentuation of attachment 

and survival is dependent upon the presence of 

fibronectin. Journal of cell science 115, 4293-4303. 

Morrison, D.H., Rahardja, D., King, E., Peng, Y., and Sarode, 

V.R. (2012). Tumour biomarker expression relative to age 

and molecular subtypes of invasive breast cancer. British 

journal of cancer 107, 382-387. 

NCI (2017a). Surveillance Research Program, National Cancer 

Institute. Surveillance, Epidemiology, and End Results 

Program. SEER*Prep Software version 2.5.3. 

NCI (2017b). Surveillance Research Program, National Cancer 

Institute. Surveillance, Epidemiology, and End Results 

Program. SEER*Stat Software version 8.3.2. 

NJSCR (2017). New Jersey State Cancer Registry Annual 

Report. Cancer Incidence and Mortality in New Jersey, 

2010-2014. (Trenton, New Jersey: Cancer Epidemiology 

Services, New Jersey Department of Health). 

Noone, A.M., Cronin, K.A., Altekruse, S.F., Howlader, N., Lewis, 

D.R., Petkov, V.I., and Penberthy, L. (2016). Cancer 

incidence and survival trends by subtype using data from 

the Surveillance Epidemiology and End Results Program, 

1992-2013. Cancer epidemiology, biomarkers & 

prevention : a publication of the American Association 

for Cancer Research, cosponsored by the American 

Society of Preventive Oncology. 

Parise, C.A., Bauer, K.R., Brown, M.M., and 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. 

Patel, D.A., Knowles, A., Schwartz, A.G., and Schwartz, K. 

(2005). Evaluation of African-American and white racial 

classification in a surveillance, epidemiology, and end 

results cancer registry. Ethn Dis 15, 713-719. 

Perou, C.M., Sorlie, T., Eisen, M.B., van de Rijn, M., Jeffrey, S.S., 

Rees, C.A., Pollack, J.R., Ross, D.T., Johnsen, H., Akslen, 

L.A., et al. (2000). Molecular portraits of human breast 

tumours. Nature 406, 747-752. 

Rauscher, G.H., Silva, A., Pauls, H., Frasor, J., Bonini, M.G., and 

Hoskins, K. (2017). Racial disparity in survival from 

estrogen and progesterone receptor-positive breast 



 
 
 
 
 

 

www.companyofscientists.com/index.php/chd                   e17                                              Cancer Health Disparities 

RESEARCH 

cancer: implications for reducing breast cancer mortality 

disparities. Breast cancer research and treatment. 

Reeder-Hayes, K.E., Meyer, A.M., Dusetzina, S.B., Liu, H., and 

Wheeler, S.B. (2014). Racial disparities in initiation of 

adjuvant endocrine therapy of early breast cancer. Breast 

cancer research and treatment 145, 743-751. 

Roche, L.M., Niu, X., Stroup, A.M., and Henry, K.A. (2016). 

Disparities in Female Breast Cancer Stage at Diagnosis in 

New Jersey: A Spatial-Temporal Analysis. J Public Health 

Manag Pract. 

Rosenberg, L., Bethea, T.N., Viscidi, E., Hong, C.C., Troester, 

M.A., Bandera, E.V., Haiman, C.A., Kolonel, L.N., Olshan, 

A.F., Ambrosone, C.B., et al. (2016). Postmenopausal 

Female Hormone Use and Estrogen Receptor-Positive 

and -Negative Breast Cancer in African American 

Women. Journal of the National Cancer Institute 108. 

Sheppard, V.B., Oppong, B.A., Hampton, R., Snead, F., Horton, 

S., Hirpa, F., Brathwaite, E.J., Makambi, K., Onyewu, S., 

Boisvert, M., et al. (2015). Disparities in breast cancer 

surgery delay: the lingering effect of race. Ann Surg 

Oncol 22, 2902-2911. 

Sineshaw, H.M., Gaudet, M., Ward, E.M., Flanders, W.D., 

Desantis, C., Lin, C.C., and Jemal, A. (2014). Association of 

race/ethnicity, socioeconomic status, and breast cancer 

subtypes in the National Cancer Data Base (2010-2011). 

Breast cancer research and treatment 145, 753-763. 

Sorlie, T., Perou, C.M., Tibshirani, R., Aas, T., Geisler, S., 

Johnsen, H., Hastie, T., Eisen, M.B., van de Rijn, M., 

Jeffrey, S.S., et al. (2001). Gene expression patterns of 

breast carcinomas distinguish tumor subclasses with 

clinical implications. Proceedings of the National 

Academy of Sciences of the United States of America 98, 

10869-10874. 

Sweeney, C., Bernard, P.S., Factor, R.E., Kwan, M.L., Habel, 

L.A., Quesenberry, C.P., Jr., Shakespear, K., Weltzien, E.K., 

Stijleman, I.J., Davis, C.A., et al. (2014). Intrinsic subtypes 

from PAM50 gene expression assay in a population-

based breast cancer cohort: differences by age, race, and 

tumor characteristics. Cancer epidemiology, biomarkers 

& prevention : a publication of the American Association 

for Cancer Research, cosponsored by the American 

Society of Preventive Oncology 23, 714-724. 

Tamimi, R.M., Baer, H.J., Marotti, J., Galan, M., Galaburda, L., 

Fu, Y., Deitz, A.C., Connolly, J.L., Schnitt, S.J., Colditz, G.A., 

et al. (2008). Comparison of molecular phenotypes of 

ductal carcinoma in situ and invasive breast cancer. 

Breast cancer research : BCR 10, R67. 

Thornton M, e. Standards for Cancer Registries Volume II: 

Data Standards and Data Dictionary, Record Layout 

Version 12.2 (Springfield, IL: North American Association 

of Central Cancer Registries). 

Tian, N., Goovaerts, P., Zhan, F.B., Chow, T.E., and Wilson, J.G. 

(2012). Identifying risk factors for disparities in breast 

cancer mortality among African-American and Hispanic 

women. Womens Health Issues 22, e267-276. 

Tian, N., Wilson, J.G., and Zhan, F.B. (2011). Spatial association 

of racial/ethnic disparities between late-stage diagnosis 

and mortality for female breast cancer: where to 

intervene? Int J Health Geogr 10, 24. 

Tiwari, R.C., Clegg, L.X., and Zou, Z. (2006). Efficient interval 

estimation for age-adjusted cancer rates. Stat Methods 

Med Res 15, 547-569. 

Warner, E.T., Tamimi, R.M., Hughes, M.E., Ottesen, R.A., 

Wong, Y.N., Edge, S.B., Theriault, R.L., Blayney, D.W., 

Niland, J.C., Winer, E.P., et al. (2015). Racial and Ethnic 

Differences in Breast Cancer Survival: Mediating Effect of 

Tumor Characteristics and Sociodemographic and 

Treatment Factors. Journal of clinical oncology : official 

journal of the American Society of Clinical Oncology 33, 

2254-2261. 

Yang, X.R., Chang-Claude, J., Goode, E.L., Couch, F.J., 

Nevanlinna, H., Milne, R.L., Gaudet, M., Schmidt, M.K., 

Broeks, A., Cox, A., et al. (2011). Associations of breast 

cancer risk factors with tumor subtypes: a pooled analysis 

from the Breast Cancer Association Consortium studies. 

Journal of the National Cancer Institute 103, 250-263. 

 

 

 

 

 

 


