Background
Growing experimental evidence indicates that some fatty acids may influence breast cancer risk through a variety of immuno-inflammatory mechanisms. For example, marine-derived n-3 polyunsaturated fatty acids (PUFAs) have numerous anti-inflammatory effects [
1] which may reduce breast cancer risk [
2], while
trans fatty acids may increase breast cancer risk through proinflammatory mechanism [
3‐
6]. Endogenously synthesized fatty acids may also impact cancer risk, as most tumors are highly dependent on de novo fatty acid synthesis for cellular proliferation [
7,
8]. However, the epidemiologic evidence for an effect of circulating fatty acids on breast cancer risk remains inconclusive [
9‐
22]. Relatively few prior studies have assessed associations with erythrocyte membrane fatty acids [
17‐
22], which are indicative of both dietary fat intake over several months [
23] and endogenous fatty acid synthesis and transformation. Importantly, the effects of fatty acids on breast cancer risk may differ not only by type of fatty acid but also by breast tumor expression subtype, underscoring the importance of further characterizing associations by tumor subtype.
In a recent analysis within the Nurses’ Health Study II (NHSII), we found that some prediagnostic erythrocyte membrane fatty acids, including several
trans fatty acids, saturated fatty acids, dairy-derived fatty acids, and n-3 PUFAs, were associated with breast cancer risk among obese/overweight women, but not among women overall [
17]. These data suggest that specific fatty acids may have stronger effects on breast cancer risk in a state of chronic inflammation, such as in overweight/obesity. In fact, extensive research has illustrated a multifaceted role of specific fatty acids in immune regulation, T cell function, and inflammation [
1,
24‐
26], indicating that the relationships between fatty acids and breast cancer risk may also vary by tumor expression of immuno-inflammatory markers.
The immune cells in the tumor microenvironment play an important role in neoplastic evolution and can drive either antitumor or protumor activities [
27]. For example, in breast cancer, tumor infiltration by cytotoxic CD8+ T cells has been associated with improved disease-free and breast cancer-specific survival, particularly in triple-negative tumors [
28], while CD4+ T helper lymphocytes may have either tumor-promoting or tumor-inhibiting properties depending on their cytokine expression profiles and the tumor microenvironment [
29]. Although they are less well-studied, CD20+ B cells, which play an integral role in humoral immunity and shape the functions of other immune cells, and CD163+ cells, a marker of anti-inflammatory M2 macrophages [
30], may also have prognostic value in breast cancer [
31‐
34]. In addition, breast tumor tissue overexpression of cyclooxygenase-2 (COX-2), a key enzyme in fatty acid metabolism and prostaglandin production, has been associated with markers of poor prognosis [
35], while fatty acid synthase (FAS), a multi-enzyme complex that regulates de novo fatty acid synthesis, can provide proliferative and metastatic capacity to cancer cells and is also commonly overexpressed in breast cancer [
36]. Notably, experimental evidence indicates that the expression of each of these tumor markers may be influenced by fatty acid metabolism [
24‐
26,
36].
Thus, we hypothesized that the relationships between specific fatty acids and breast cancer risk may vary by tumor tissue expression subtypes and that the effects of anti-inflammatory fatty acids may only be observed in a subset of tumors with inflammatory expression profiles. Characterization of this potential heterogeneity by breast tumor expression subtype could provide mechanistic insight into the roles of fatty acids in breast cancer risk, particularly with regard to inflammation and the immune response. We therefore aimed to prospectively investigate the relationships between erythrocyte membrane fatty acid concentrations and subsequent breast cancer risk by tumor tissue expression of several immuno-inflammatory markers (CD4, CD8, CD20, CD163, COX-2) and FAS.
Results
Breast cancer cases and controls were similar with regard to most baseline demographic and epidemiologic characteristics (Table
1). However, compared to controls, cases were less likely to be parous, to have breastfed, and to be physically active. Cases were also more likely to have a history of biopsy-confirmed benign breast disease and a family history of breast cancer. The majority of cases were premenopausal both at baseline blood collection (75.7%) and at cancer diagnosis (64.7%). Median age at breast cancer diagnosis was 49.9 years (interquartile range [IQR] 46.1–52.5), and median time between blood collection and diagnosis was 3.4 years (IQR 1.6–5.9). Concentrations of fatty acids among cases and controls are shown in Additional file
1: Supplemental Table 1.
Table 1
Baseline characteristics of breast cancer cases and matched controls, Nurses’ Health Study II
Age at blood collection (years), median (IQR)2 | 45.8 (42.5–49.0) | 45.9 (42.6–49.2) |
Age at menarche (years), median (IQR) | 12.0 (12.0–13.0) | 13.0 (12.0–13.0) |
White race, %2 | 98.7 | 98.3 |
BMI at age 18 (kg/m2), median (IQR) | 20.2 (18.8–22.3) | 20.6 (19.1–22.3) |
BMI at blood collection (kg/m2), median (IQR) | 24.3 (21.7–27.9) | 24.3 (21.7–28.1) |
Weight change from age 18 to blood collection (kg), median (IQR) | 10.5 (4.5–18.2) | 9.5 (4.1–17.7) |
Fasting at blood collection (≥ 8 h since last meal), %2 | 71.9 | 74.0 |
Menopausal status at blood collection, %2 |
Premenopausal | 75.7 | 74.5 |
Postmenopausal | 12.3 | 14.5 |
Unknown | 11.9 | 11.1 |
Parous women, % | 79.6 | 82.6 |
Parity, median (IQR)3 | 2.0 (2.0–3.0) | 2.0 (2.0–3.0) |
Age at first birth (years), median (IQR)3 | 26.0 (23.0–29.0) | 25.0 (22.0–28.0) |
History of breastfeeding, %3 | 74.3 | 77.8 |
History of biopsy-confirmed benign breast disease, % | 20.9 | 16.2 |
Family history of breast cancer, % | 15.7 | 9.8 |
NSAIDs current regular use (≥ 2 times/week), % | 14.9 | 13.6 |
Physical activity (MET-hours/week), median (IQR) | 12.1 (7.1–22.2) | 14.0 (7.3–27.9) |
Alcohol consumption (grams/day), median (IQR) | 1.4 (0.0–6.2) | 1.0 (0.0–3.8) |
Total fat consumption (% energy intake), median (IQR) | 29.8 (25.5–34.3) | 30.6 (25.5–35.0) |
Total saturated fat consumption (% energy intake), median (IQR) | 10.4 (8.4–12.3) | 10.5 (8.6–12.6) |
Total monounsaturated fat consumption (% energy intake), median (IQR) | 11.6 (9.6–13.5) | 11.9 (10.1–13.6) |
Total polyunsaturated fat consumption (% energy intake), median (IQR) | 4.8 (4.2–5.5) | 4.6 (4.0–5.4) |
Immune marker expression levels and correlations are shown in Additional file
1: Supplemental Table 2. Median percent positivity varied across immune markers, with the lowest levels observed for CD20 (0.6% in stroma) and the highest levels observed for CD163 (13.2% in stroma). Within the same tumor cell compartment, correlations between immune markers were generally higher in epithelium than in stroma. The highest correlations were observed for CD8 with CD20 (Spearman rho = 0.70) and CD4 (rho = 0.69) within epithelium. Correlations between epithelial and stromal cells for the same marker ranged from 0.46 for CD163 to 0.79 for CD8. Agreement between automated and manual assessments varied across markers. Spearman correlations were 0.53 for CD4, 0.72 for CD8, 0.64 for CD20, and 0.11 for CD163 in stromal cells and 0.49 for FAS in epithelial cells. ICCs across cores from the same participant ranged from 0.38 for CD20 to 0.80 for COX-2.
Some tumor markers were associated with other tumor characteristics at diagnosis, notably FAS, which was overexpressed in ER+ tumors, PR+ tumors, and tumors of lower grade, COX-2, which was overexpressed in HER2− tumors, and CD4, which was overexpressed in tumors without nodal involvement (Table
2). All markers also tended to be more highly expressed in tumors that were smaller and lacked nodal involvement.
Table 2
Associations between breast tumor markers and other tumor characteristics at diagnosis, Nurses’ Health Study II
ER status |
ER+ | 162 | 3.4 (1.5–9.6) | 5.5 (2.4–10.9) | 0.5 (0.1–1.7) | 13.4 (9.9–16.7) | 26.3 (13.6–43.9) | 86.3 (74.1–90.6) |
ER− | 41 | 3.5 (1.2–11.7) | 8.2 (1.7–16.3) | 0.3 (0.0–3.1) | 12.9 (10.8–17.6) | 25.2 (12.5–38.3) | 73.1 (39.3–88.3) |
P value3 | | 0.78 | 0.36 | 0.79 | 0.75 | 0.66 | 0.001 |
PR status |
PR+ | 144 | 3.4 (1.5–9.0) | 5.5 (2.3–10.9) | 0.4 (0.1–1.7) | 13.3 (9.9–16.7) | 26.5 (13.9–43.9) | 87.6 (76.1–91.1) |
PR− | 58 | 3.5 (1.5–12.4) | 5.9 (2.0–15.4) | 0.4 (0.1–2.9) | 13.3 (10.9–18.4) | 24.3 (12.3–43.3) | 72.3 (41.0–85.5) |
P value3 | | 0.35 | 0.50 | 0.81 | 0.29 | 0.51 | < 0.001 |
HER2 status |
HER2+ | 27 | 5.0 (2.1–7.9) | 6.2 (2.6–13.2) | 0.6 (0.1–1.8) | 13.5 (9.0–17.5) | 16.0 (10.6–28.0) | 85.4 (73.2–92.4) |
HER2− | 104 | 3.0 (1.5–10.1) | 7.0 (2.3–13.1) | 0.4 (0.1–1.8) | 13.3 (10.2–16.6) | 29.9 (17.9–43.9) | 84.1 (68.0–89.3) |
P value3 | | 0.55 | 0.94 | 0.88 | 0.94 | 0.05 | 0.44 |
Tumor grade |
Grade I | 41 | 3.3 (1.1–6.7) | 4.8 (1.9–8.7) | 0.6 (0.1–3.1) | 12.5 (9.4–16.1) | 28.2 (12.6–50.9) | 88.6 (80.4–94.8) |
Grade II | 71 | 2.4 (1.3–9.1) | 5.7 (2.0–13.1) | 0.4 (0.1–1.7) | 13.3 (9.9–16.7) | 25.4 (13.4–42.9) | 84.6 (68.7–90.2) |
Grade III | 58 | 3.0 (1.5–8.3) | 7.6 (2.7–15.4) | 0.2 (0.0–1.2) | 13.4 (10.8–16.5) | 29.9 (15.2–43.3) | 80.1 (63.6–88.2) |
P value3 | | 0.90 | 0.19 | 0.14 | 0.50 | 0.80 | 0.003 |
Tumor size |
< 2 cm | 126 | 3.6 (1.5–9.0) | 6.6 (2.2–12.3) | 0.4 (0.1–1.7) | 13.3 (10.2–16.7) | 29.2 (14.8–45.0) | 84.5 (70.6–89.3) |
≥ 2 cm | 50 | 2.1 (1.0–5.5) | 5.5 (1.7–10.9) | 0.2 (0.1–1.0) | 12.8 (10.4–16.5) | 24.1 (12.0–43.3) | 83.6 (69.3–91.1) |
P value3 | | 0.06 | 0.58 | 0.30 | 0.67 | 0.25 | 0.76 |
Nodal involvement |
No | 141 | 4.4 (1.6–9.9) | 6.8 (2.3–13.1) | 0.7 (0.1–2.1) | 13.3 (10.3–16.7) | 30.0 (15.0–45.5) | 85.4 (70.8–89.9) |
Yes | 58 | 2.1 (1.0–7.2) | 5.5 (2.2–10.8) | 0.2 (0.1–2.0) | 13.0 (10.1–16.7) | 22.1 (12.0–38.6) | 83.0 (64.7–90.0) |
P value3 | | 0.02 | 0.54 | 0.19 | 0.84 | 0.06 | 0.46 |
When we analyzed associations between fatty acid groups and subsequent breast cancer risk by tumor tissue expression subtype, no convincing evidence of heterogeneity was observed by CD4, CD20, CD163, or COX-2 expression (Table
3). However, some PUFAs differed by CD8 expression, including n-3 PUFAs which were inversely associated with CD8
low tumors (OR
T3 vs T1 = 0.45, 95% CI 0.23–0.87,
Ptrend = 0.02) but not clearly associated with CD8
high tumors (OR
T3 vs T1 = 1.19, 95% CI 0.62–2.26,
Ptrend = 0.62;
Phet = 0.04, degree of etiologic heterogeneity [ratio of OR
T3 vs T1 for CD8
high vs. CD8
low subtypes] = 2.64). This difference was largely driven by docosahexaenoic acid (DHA), which was inversely associated with CD8
low but not CD8
high tumors (Additional file
2: Supplemental Table 3). Statistical heterogeneity by CD8 expression levels was also observed for n-6 PUFAs, where there was a suggestive inverse association with CD8
high tumors (OR
T3 vs T1 = 0.61, 95% CI 0.32–1.14,
Ptrend = 0.11) but a suggestive positive association with CD8
low tumors (OR
T3 vs T1 = 1.63, 95% CI 0.87–3.04,
Ptrend = 0.12;
Phet = 0.02, degree of etiologic heterogeneity [ratio of OR
T3 vs T1 for CD8
high vs. CD8
low subtypes] = 0.37) (Table
3). Although it was not clear which individual n-6 PUFAs were driving this heterogeneity, linoleic acid showed the most similar pattern of results overall, and aolrenic acid was suggestively positively associated with CD8
low tumors only (Additional file
2: Supplemental Table 3). In addition, the ratio of total n-6/n-3 PUFAs was associated with increased risk of CD8
low but not CD8
high breast cancer.
Table 3
Multivariable-adjusted odds ratios (95% CI) for associations between tertiles of total erythrocyte fatty acid concentrations and subsequent breast cancer risk, stratified by tumor expression of immuno-inflammatory markers, Nurses’ Health Study II
| CD4low(n = 109)3 | CD4high(n = 110)3 | |
Saturated fatty acids | 1 (ref) | 0.39 (0.20–0.76) | 0.93 (0.51–1.71) | 0.80 | 1 (ref) | 0.50 (0.26–0.96) | 0.98 (0.55–1.75) | 0.77 | 0.94 |
Monounsaturated fatty acids | 1 (ref) | 0.93 (0.49–1.76) | 0.84 (0.43–1.67) | 0.63 | 1 (ref) | 0.75 (0.40–1.40) | 0.85 (0.45–1.60) | 0.63 | 0.80 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.72 (0.37–1.38) | 0.81 (0.43–1.53) | 0.55 | 1 (ref) | 0.77 (0.41–1.45) | 0.69 (0.37–1.30) | 0.26 | 0.61 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.57 (0.30–1.08) | 0.93 (0.50–1.74) | 0.80 | 1 (ref) | 0.80 (0.43–1.49) | 1.00 (0.55–1.82) | 1.00 | 0.84 |
Trans fatty acids | 1 (ref) | 0.98 (0.51–1.88) | 1.66 (0.85–3.23) | 0.12 | 1 (ref) | 1.50 (0.80–2.82) | 1.52 (0.78–2.94) | 0.25 | 0.75 |
| CD8low(n = 104)3 | CD8high(n = 105)3 | |
Saturated fatty acids | 1 (ref) | 0.43 (0.23–0.83) | 0.74 (0.40–1.34) | 0.53 | 1 (ref) | 0.44 (0.22–0.86) | 1.12 (0.61–2.05) | 0.42 | 0.34 |
Monounsaturated fatty acids | 1 (ref) | 0.88 (0.47–1.65) | 0.81 (0.42–1.58) | 0.55 | 1 (ref) | 0.78 (0.41–1.48) | 0.83 (0.43–1.61) | 0.60 | 0.72 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.49 (0.25–0.95) | 0.45 (0.23–0.87) | 0.02 | 1 (ref) | 1.14 (0.60–2.17) | 1.19 (0.62–2.26) | 0.62 | 0.04 |
n-6 polyunsaturated fatty acids | 1 (ref) | 1.19 (0.62–2.28) | 1.63 (0.87–3.04) | 0.12 | 1 (ref) | 0.57 (0.31–1.08) | 0.61 (0.32–1.14) | 0.11 | 0.02 |
Trans fatty acids | 1 (ref) | 1.23 (0.64–2.35) | 1.95 (1.00–3.82) | 0.05 | 1 (ref) | 1.37 (0.72–2.61) | 1.56 (0.79–3.08) | 0.21 | 0.51 |
| CD20low(n = 103)3 | CD20high(n = 104)3 | |
Saturated fatty acids | 1 (ref) | 0.61 (0.32–1.16) | 1.17 (0.64–2.16) | 0.42 | 1 (ref) | 0.25 (0.12–0.52) | 0.79 (0.43–1.45) | 0.83 | 0.72 |
Monounsaturated fatty acids | 1 (ref) | 1.05 (0.56–1.97) | 0.75 (0.38–1.49) | 0.39 | 1 (ref) | 0.73 (0.39–1.39) | 0.92 (0.47–1.77) | 0.78 | 0.55 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.74 (0.38–1.43) | 0.66 (0.34–1.29) | 0.24 | 1 (ref) | 0.75 (0.39–1.45) | 0.88 (0.47–1.67) | 0.74 | 0.76 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.71 (0.37–1.34) | 0.78 (0.42–1.46) | 0.44 | 1 (ref) | 0.74 (0.38–1.43) | 1.32 (0.72–2.44) | 0.36 | 0.32 |
Trans fatty acids | 1 (ref) | 0.99 (0.52–1.91) | 1.71 (0.87–3.37) | 0.11 | 1 (ref) | 1.73 (0.89–3.34) | 1.77 (0.89–3.53) | 0.13 | 0.73 |
| CD163low(n = 110)3 | CD163high(n = 111)3 | |
Saturated fatty acids | 1 (ref) | 0.35 (0.18–0.69) | 1.04 (0.58–1.88) | 0.55 | 1 (ref) | 0.57 (0.31–1.06) | 0.87 (0.49–1.56) | 0.85 | 0.66 |
Monounsaturated fatty acids | 1 (ref) | 0.86 (0.46–1.61) | 0.85 (0.44–1.67) | 0.65 | 1 (ref) | 0.76 (0.41–1.40) | 0.82 (0.44–1.53) | 0.54 | 0.81 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.82 (0.43–1.58) | 1.02 (0.54–1.93) | 0.88 | 1 (ref) | 0.70 (0.37–1.30) | 0.58 (0.31–1.08) | 0.09 | 0.13 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.91 (0.49–1.70) | 0.98 (0.53–1.81) | 0.94 | 1 (ref) | 0.54 (0.29–1.02) | 0.94 (0.53–1.68) | 0.79 | 0.76 |
Trans fatty acids | 1 (ref) | 1.19 (0.63–2.23) | 1.51 (0.78–2.95) | 0.22 | 1 (ref) | 1.28 (0.68–2.41) | 1.92 (1.01–3.67) | 0.05 | 0.64 |
| Low CD4/CD8 ratio (n = 100)3 | High CD4/CD8 ratio (n = 100)3 | |
Saturated fatty acids | 1 (ref) | 0.29 (0.14–0.59) | 0.90 (0.49–1.64) | 0.85 | 1 (ref) | 0.66 (0.35–1.26) | 1.00 (0.54–1.86) | 0.83 | 0.88 |
Monounsaturated fatty acids | 1 (ref) | 0.91 (0.47–1.77) | 1.22 (0.62–2.40) | 0.52 | 1 (ref) | 0.67 (0.36–1.26) | 0.48 (0.24–0.96) | 0.04 | 0.008 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.88 (0.46–1.71) | 0.93 (0.48–1.80) | 0.86 | 1 (ref) | 0.69 (0.36–1.32) | 0.58 (0.30–1.12) | 0.11 | 0.20 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.62 (0.33–1.16) | 0.75 (0.40–1.43) | 0.36 | 1 (ref) | 0.97 (0.50–1.88) | 1.30 (0.69–2.46) | 0.40 | 0.11 |
Trans fatty acids | 1 (ref) | 1.54 (0.79–3.02) | 1.77 (0.87–3.59) | 0.13 | 1 (ref) | 1.25 (0.65–2.39) | 1.76 (0.90–3.44) | 0.10 | 0.62 |
| COX-2low(n = 103)3 | COX-2high(n = 104)3 | |
Saturated fatty acids | 1 (ref) | 0.47 (0.24–0.89) | 0.90 (0.50–1.65) | 0.99 | 1 (ref) | 0.38 (0.19–0.75) | 1.10 (0.61–1.98) | 0.39 | 0.34 |
Monounsaturated fatty acids | 1 (ref) | 0.84 (0.45–1.58) | 0.86 (0.45–1.65) | 0.65 | 1 (ref) | 0.81 (0.44–1.51) | 0.72 (0.37–1.42) | 0.35 | 0.73 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.46 (0.23–0.89) | 0.60 (0.31–1.13) | 0.14 | 1 (ref) | 1.08 (0.58–2.01) | 0.82 (0.43–1.56) | 0.53 | 0.76 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.94 (0.49–1.81) | 1.36 (0.74–2.52) | 0.31 | 1 (ref) | 0.59 (0.32–1.09) | 0.69 (0.37–1.29) | 0.23 | 0.06 |
Trans fatty acids | 1 (ref) | 1.32 (0.71–2.47) | 1.65 (0.83–3.26) | 0.15 | 1 (ref) | 1.39 (0.71–2.72) | 2.08 (1.06–4.07) | 0.03 | 0.24 |
There was also heterogeneity for total MUFAs, which did not appear to be associated with tumors with a low CD4/CD8 ratio (OR
T3 vs T1 = 1.22, 95% CI 0.62–2.40,
Ptrend = 0.52) but were inversely associated with tumors with a high CD4/CD8 ratio (OR
T3 vs T1 = 0.48, 95% CI 0.24–0.96,
Ptrend = 0.04;
Phet = 0.008, degree of etiologic heterogeneity [ratio of OR
T3 vs T1 for high vs. low CD4/CD8 ratio subtypes] = 0.39) (Table
3). Among the individual MUFAs, this same pattern was observed for oleic acid and palmitoleic acid (Additional file
2: Supplemental Table 3).
Associations between total
trans fatty acids and breast cancer risk varied by FAS expression levels (Table
4). Total
trans fatty acids were not associated with FAS
low tumors (OR
T3 vs T1 = 0.99, 95% CI 0.52–1.92,
Ptrend = 0.97), whereas they were positively associated with FAS
high tumors (OR
T3 vs T1 = 2.94, 95% CI 1.46–5.91,
Ptrend = 0.002;
Phet = 0.01, degree of etiologic heterogeneity [ratio of OR
T3 vs T1 for FAS
high vs. FAS
low subtypes] = 2.97). This finding was mainly driven by 18:1
trans fatty acids, and a similar association was also observed with all industrial
trans fatty acids (Additional file
2: Supplemental Table 3).
Table 4
Multivariable-adjusted odds ratio (95% CI) for associations between tertiles of total erythrocyte fatty acid concentrations and subsequent breast cancer risk, stratified by tumor expression of fatty acid synthase (FAS), Nurses’ Health Study II
Saturated fatty acids | 1 (ref) | 0.46 (0.24–0.88) | 0.90 (0.49–1.64) | 0.97 | 1 (ref) | 0.46 (0.24–0.89) | 1.11 (0.62–1.99) | 0.44 | 0.67 |
Monounsaturated fatty acids | 1 (ref) | 0.84 (0.45–1.60) | 0.99 (0.51–1.89) | 0.99 | 1 (ref) | 0.75 (0.40–1.39) | 0.61 (0.31–1.17) | 0.14 | 0.19 |
n-3 polyunsaturated fatty acids | 1 (ref) | 0.99 (0.52–1.87) | 0.88 (0.47–1.65) | 0.68 | 1 (ref) | 0.63 (0.33–1.20) | 0.65 (0.34–1.24) | 0.21 | 0.59 |
n-6 polyunsaturated fatty acids | 1 (ref) | 0.69 (0.37–1.28) | 0.82 (0.44–1.52) | 0.51 | 1 (ref) | 0.65 (0.34–1.24) | 1.09 (0.60–1.99) | 0.72 | 0.22 |
trans fatty acids | 1 (ref) | 1.15 (0.62–2.15) | 0.99 (0.52–1.92) | 0.97 | 1 (ref) | 1.66 (0.85–3.24) | 2.94 (1.46–5.91) | 0.002 | 0.01 |
For most fatty acid groups, there was no evidence of a non-linear relationship with subtype-specific breast cancer risk. However, total saturated and monounsaturated fatty acids showed potential evidence of non-linearity across multiple subtypes (P values for non-linearity ≤ 0.25). Nevertheless, qualitative comparisons of the spline graphs did not provide strong evidence of heterogeneity by tumor subtype beyond that which was identified in the primary analyses.
Heterogeneity findings differed in epithelial cells vs. stromal cells for several fatty acids (Additional file
2: Supplemental Table 3). For example, the heterogeneity by CD8 expression observed for total n-3 and n-6 PUFAs was not observed in epithelial cells. Some heterogeneity findings for COX-2 also varied when comparing the two commercial antibodies.
In sensitivity analyses, the 235 breast cancer cases included in this analysis differed from the 344 cases without tumor tissue who were otherwise eligible for inclusion with regard to year of diagnosis, tumor invasiveness and size, HER2 enrichment, menopausal status at diagnosis, and some breast cancer risk factors (Additional file
1: Supplemental Table 4). However, after using inverse probability weights to account for potential selection bias, the pattern of results was not substantially altered, though statistical power was lower (Additional file
1: Supplemental Table 5). The pattern of heterogeneity findings was also similar after restricting the analyses to women who were premenopausal at blood collection (Additional file
1: Supplemental Table 6). In addition, tumor marker expression levels were not strongly correlated with BMI at blood collection (all Spearman correlations ≤ 0.10, except for COX-2 [rho = 0.14] and CD8 [rho = 0.13]). Finally, for breast cancer associations with the strongest evidence of heterogeneity by tumor subtype, plots of continuous fatty acid concentrations and tumor marker expression levels are shown in Additional file
1: Supplemental Fig. 1.
Discussion
We prospectively evaluated the relationship between circulating fatty acids and breast cancer risk in what is, to our knowledge, the first study to leverage tumor tissue to assess heterogeneity by expression levels of immuno-inflammatory markers and FAS. Although most erythrocyte membrane fatty acids did not appear to have differential effects on breast cancer risk by tumor tissue expression subtypes, there was evidence of effect heterogeneity by CD8 and FAS expression. Results suggested a possible protective effect of n-3 PUFAs on CD8low but not CD8high breast tumors, while there was evidence of a potential protective effect of n-6 PUFAs on CD8high tumors only. In addition, trans fatty acids appeared to increase the risk of FAShigh but not FASlow breast cancers. These findings provide insight into potential immuno-modulatory mechanisms of n-3 and n-6 PUFAs as well as potential FAS-mediated mechanisms of trans fatty acids in breast carcinogenesis.
In spite of mounting experimental evidence supporting an anti-inflammatory, anti-carcinogenic effect of n-3 PUFAs [
1], the epidemiologic evidence for a role of n-3 PUFAs in breast cancer risk reduction remains inconclusive. For example, while findings from a meta-analysis of 16 prospective cohort studies indicated an inverse association between marine n-3 PUFA intake and breast cancer risk (relative risk for highest vs. lowest intake 0.86, 95% CI 0.78–0.94) and this association was consistent in studies measuring fatty acid intake using biomarkers [
55], secondary analyses of a recent randomized control trial showed less convincing evidence for a reduction in breast cancer risk following marine n-3 PUFA supplementation (hazard ratio for intervention vs. placebo 0.90, 95% CI 0.70–1.16) [
56].
In the present study, n-3 PUFAs were associated with lower risk of breast cancer only among cases with low levels of CD8 stromal cell infiltration, and this association was driven primarily by DHA. n-3 PUFAs are hypothesized to have a multifaceted role in immune regulation and inflammation, including modulation of T cell proliferation [
1]. Preclinical research suggests that DHA and other n-3 PUFAs may reduce breast cancer risk by decreasing proinflammatory eicosanoids, generating bioactive lipid mediators involved in inflammation resolution, reducing cytokine production, modulating oncogenic protein signaling via disruption of plasma lipid membranes, and increasing apoptosis [
1,
57,
58]. Our epidemiologic findings provide greater credence to the hypothesis that n-3 PUFAs may reduce breast cancer risk through these immuno-modulatory mechanisms because cytotoxic CD8 T cells have antitumorigenic properties [
27], and tumors that arise in an immune microenvironment with a dearth of CD8 cells may therefore derive greater benefit from the anti-inflammatory, immuno-modulatory effects of n-3 PUFAs. These data also suggest that potential anti-carcinogenic effects of n-3 PUFAs may have been obscured by breast tumor heterogeneity in some prior studies. Finally, given that low levels of CD8 cells in tumor tissue have been associated with poor breast cancer prognosis [
28], our findings also support the hypothesis that interventions aimed at increasing n-3 PUFA/DHA intake may have the potential to reduce the risk of developing more aggressive breast tumors.
As with n-3 PUFAs, the role of n-6 PUFAs in breast carcinogenesis remains controversial. While some prior studies of specific circulating n-6 PUFAs suggest inverse associations with breast cancer risk [
13,
16,
21,
22], others suggest positive associations [
9,
18,
19,
21]. In the present study, n-6 PUFAs were suggestively inversely associated with breast tumors with high CD8 expression but suggestively positively associated with tumors with low CD8 expression. This finding may have been driven in part by heterogeneous effects of individual n-6 PUFAs, with linoleic acid and aolrenic acid showing different patterns of association. However, an underlying biologic explanation for this differential association remains unclear. In addition, a mechanistic explanation for the differential association between MUFAs and breast cancer risk by CD4/CD8 ratio remains to be elucidated.
Trans fats have also been hypothesized to influence breast cancer risk; however, the epidemiologic evidence remains insufficient and suggestive positive [
9,
11‐
13,
17] and inverse [
9,
11] associations have been reported in previous studies of individual and total circulating
trans fatty acids. Some [
3‐
6] though not all [
59‐
62] controlled dietary intervention studies suggest that
trans fat intake may result in higher levels of circulating proinflammatory markers. Preclinical studies also suggest that
trans fatty acids could promote de novo fatty acid synthesis [
63,
64].
In this analysis, total
trans fatty acids were positively associated with FAS
high but not FAS
low tumors, and this difference was predominantly driven by 18:1 and industrial
trans fats. These results suggest that the effects of
trans fatty acids on breast cancer risk may be mediated through FAS expression and that
trans fats may increase the risk of a FAS
high breast cancer subtype. This hypothesis is supported by mechanistic studies in mice, where a diet high in
trans fat produced a 2- to 3-fold increase in mRNA of FAS [
63,
64] and a 2-fold increase in mRNA of sterol regulatory element binding protein (SREBP)-1 [
64], a transcription factor that upregulates fatty acid synthesis [
65]. Breast tumor overexpression of FAS has also long been recognized as an indicator of poor clinical prognosis [
66], lending greater public health importance to the hypothesis that
trans fatty acids may increase the risk of breast cancers with high FAS expression.
In the present study, tumor marker expression levels were not strongly correlated with BMI at blood collection, which contrasts with the hypothesis that tumor markers indicating proinflammatory environments might be more highly expressed in obese/overweight individuals. Nevertheless, some of our heterogeneity findings by tumor tissue expression levels were similar to previously observed patterns of heterogeneity by BMI [
17].
Several exposures have been differentially associated with breast cancer risk by tumor ER, PR, and HER2 status in prior studies [
67]. Thus, the associations we observed between fatty acids and breast cancer subtypes may further vary by other tumor characteristics. However, we lacked sufficient statistical power to assess differences in associations by these characteristics. Prior analyses have also illustrated that breast tumor expression of immuno-inflammatory markers and fatty acid synthase vary across established breast tumor subtypes [
68‐
71]. In the present study, tumor expression levels of FAS, COX-2, and CD4 were associated with several other tumor characteristics. Notably, FAS was overexpressed in ER+, PR+, and low-grade tumors. Although the proliferative and metastatic capacity of FAS is well-recognized [
36], prior studies have similarly identified FAS overexpression in hormone receptor-positive and low-grade breast tumors [
68,
69], suggesting that it may be important to consider other tumor characteristics when assessing the role of FAS in breast carcinogenesis.
Our study has several strengths, including its prospective design and unique characterization of tumors according to immuno-inflammatory markers and FAS, which allowed us to identify specific breast cancer subtypes that may be susceptible to intervention. We also comprehensively evaluated a large number of fatty acids measured in erythrocyte membranes, which incorporate both diet-derived and endogenous sources of fatty acids and capture a longer exposure window than other blood-based measurements [
23]. With detailed information on breast cancer risk factors, we were able to adjust for many potential confounders.
There are also several important limitations to our study. First, our study was limited by a modest sample size which precluded additional stratification by other breast tumor subtypes. Second, we lacked tumor tissue data on approximately 60% of breast cancer cases potentially eligible for this study, which could introduce selection bias. However, results were similar after accounting for potential selection bias using inverse probability weighs. Third, the tumor microenvironment changes over time and in response to cancer therapies. Although our analysis of formalin-fixed paraffin-embedded tissue did not allow us to capture this dynamic process, we expect that most women during this time period were not undergoing treatment prior to primary breast tumor resection. Fourth, we relied on measurements from three tumor cores, which might not be representative of the entire tumor, and we measured fatty acids from a single blood sample. However, a single fatty acid measure was fairly reproducible among postmenopausal women in NHS [
72], and correcting for measurement error using ICCs from a reproducibility study did not substantially alter the results in our prior analysis [
17]. Fifth, we assessed multiple associations, which increases the risk of spurious findings as a result of multiple comparisons. Although our heterogeneity findings would not meet traditional thresholds for statistical significance after stringent correction for multiple comparisons, our analyses were guided by strong, biologically driven a priori hypotheses. Finally, the women included in this study were predominantly white and premenopausal, potentially limiting the generalizability of our findings.
Acknowledgements
We would like to thank the participants and staff of the Nurses’ Health Study II for their valuable contributions as well as the following state cancer registries for their help: AL, AZ, AR, CA, CO, CT, DE, FL, GA, ID, IL, IN, IA, KY, LA, ME, MD, MA, MI, NE, NH, NJ, NY, NC, ND, OH, OK, OR, PA, RI, SC, TN, TX, VA, WA, and WY. The authors assume full responsibility for analyses and interpretation of these data. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
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