Abstract

   Increased trunk fat is associated with an elevated risk of breast
   cancer in normal-weight postmenopausal women. The main objective of
   this study was to determine whether levels of trunk fat are associated
   with changes in breast gene expression in normal-weight women.
   Non-tumorous breast tissue was collected from 32 normal BMI women who
   underwent mastectomy for breast cancer risk reduction or treatment.
   Body composition was measured by dual-energy x-ray absorptiometry. High
   levels of trunk fat were associated with a large number of
   differentially expressed genes and changes in multiple pathways and
   processes potentially linked to breast cancer pathogenesis. High levels
   of trunk fat were also associated with an elevated immune score and
   increased levels of leptin, CCL2, VEGF-C, IL6, and aromatase.
   Collectively, these results help to explain why high levels of trunk
   fat are associated with an increased risk of breast cancer in normal
   BMI women.

   Subject terms: Breast cancer, Cancer genomics

Introduction

   Obesity is a risk factor for numerous cancers, including postmenopausal
   hormone receptor-positive breast cancer^[40]1–[41]3. The link between
   excess body fat and malignancy has largely been established using
   anthropometric measurements such as body mass index (BMI), which takes
   into account weight and height but does not discriminate between
   muscle, bone, and fat. Some women deemed to be healthy because of a
   normal BMI have underlying cardiometabolic abnormalities and are
   commonly referred to as metabolically obese normal weight (MONW)^[42]4.
   Dual-energy x-ray absorptiometry (DXA) is used to obtain direct
   measures of body fat including trunk fat. In normal BMI women, high
   levels of body fat including trunk fat have been associated with
   enlarged breast adipocytes and breast white adipose tissue inflammation
   (B-WATi)^[43]5, as defined by the presence of crown-like structures
   (CLSs). B-WATi, in turn, has been associated with an elevated risk of
   breast cancer in women who have benign breast disease^[44]6. Given this
   background, we previously posited that normal BMI women with high
   levels of body fat would be at increased risk for postmenopausal breast
   cancer^[45]7. In a study of more than 3000 normal BMI women who had
   enrolled in the Women Health Initiative, we relied on DXA-derived
   measures of body fat and found that women in the top two quartiles of
   trunk fat had about a doubling in the risk of estrogen-dependent breast
   cancer compared to women in the lowest quartile of trunk fat^[46]8. The
   sample size was too small to determine whether the amount of trunk fat
   was also associated with altered risk of either
   HER-2/neu-overexpressing or triple-negative breast cancer. Circulating
   levels of C-reactive protein, insulin, leptin, and triglycerides were
   higher, whereas levels of sex hormone-binding globulin and high-density
   lipoprotein cholesterol were lower in those in the highest vs. lowest
   quartiles of trunk fat mass^[47]8. More recently, we found that high
   levels of blood C-reactive protein and testosterone or low blood levels
   of sex hormone-binding globulin were associated with an elevated risk
   of postmenopausal breast cancer among normal BMI women^[48]9.

   It has become increasingly clear that normal BMI as a category includes
   women with relatively high levels of body fat who are at increased risk
   for postmenopausal breast cancer and that abnormal levels of systemic
   factors may be contributory^[49]4,[50]10. Increased levels of
   aromatase, the rate-limiting enzyme for estrogen biosynthesis, have
   been reported in the breast tissue of normal BMI women with B-WATi and
   higher levels of trunk fat^[51]5,[52]7. Despite these recent advances,
   very little is known about the molecular changes that occur in the
   breast itself in normal BMI women with high vs. low levels of trunk
   fat. Accordingly, the main objective of the current study was to use
   RNA-sequencing (RNA-seq) to define the changes in the breast
   transcriptome that occur in normal-weight women who have high vs. low
   levels of trunk fat.

Results

Study population

   Subjects were recruited from April 2016 through August 2018 and
   underwent DXA scans prior to mastectomy^[53]5. RNA-seq data were
   obtained from non-tumorous breast tissue of 32 normal-weight women,
   which defined the study cohort. To determine the potential association
   between levels of trunk fat and breast gene expression, the 32 women
   were divided into two groups based on percent trunk fat and are
   referred to as high and low groups, respectively. A comparison was done
   of 16 normal-weight women with high levels of trunk fat (equal to or
   higher than the median of 29.45%) vs. 16 normal-weight women with low
   levels of trunk fat (less than the median of 29.45%). Women with high
   levels of trunk fat were older (P = 0.01), had a higher BMI
   (P = 0.003), were more likely to have B-WATi (P < 0.001) and had larger
   breast adipocytes (P < 0.001) (Table [54]1).

Table 1.

   Clinical features of normal weight women with high vs. low trunk fat.
   Variables All (n = 32) Trunk fat low (n = 16) Trunk fat high (n = 16) P
   value
   Age
    Median (IQR) 44.5 (39.5, 49.25) 40.5 (32, 47.25) 47 (44, 51.5) 0.011
   Race, n (%)
    White 21 (67.74%) 12 (75%) 9 (60%) 0.12
    Asian 6 (19.35%) 1 (6.25%) 5 (33.33%)
    Black or African American 2 (6.45%) 1 (6.25%) 1 (6.67%)
    Other 2 (6.45%) 2 (12.5%) 0 (0%)
    Unknown 1 (3.12%) 0 (0%) 1 (6.25%) 1
   BMI
    Median (IQR) 21.8 (20.1, 22.63) 20.5 (19.67, 21.93) 22.4 (21.75,
   23.42) 0.003
   Menopausal, n (%)
    Post 6 (20%) 1 (6.67%) 5 (33.33%)
    Pre 24 (80%) 14 (93.33%) 10 (66.67%) 0.169
    Unknown 2 (6.25%) 1 (6.25%) 1 (6.25%) 1
   CLS, n (%)
    No 22 (68.75%) 16 (100%) 6 (37.5%)
    Yes 10 (31.25%) 0 (0%) 10 (62.5%) <0.001
   Adipocyte diameter (µ)
    Median (IQR) 89.32 (79.48, 100.16) 79.1 (72.78, 87.24) 97.14 (89.86,
   106.15) <0.001
   Invasive, n (%)
    No 8 (25%) 5 (31.25%) 3 (18.75%)
    Yes 24 (75%) 11 (68.75%) 13 (81.25%) 0.685
   HTN, n (%)
    No 30 (93.75%) 16 (100%) 14 (87.5%)
    Yes 2 (6.25%) 0 (0%) 2 (12.5%) 0.484
   DM, n (%)
    No 32 (100%) 16 (100%) 16 (100%)
    Yes 0 (0%) 0 (0%) 0 (0%) 1
   Dyslipidemia, n (%)
    No 30 (93.75%) 16 (100%) 14 (87.5%)
    Yes 2 (6.25%) 0 (0%) 2 (12.5%) 0.484
   [55]Open in a new tab

   BMI body mass index, CLS crown-like structure, HTN hypertension, DM
   diabetes mellitus.

Excess body fat is associated with changes in the transcriptome in the breast

   Breast tissue was obtained from women who either did or did not have
   tumors. If a tumor was present, to minimize the possibility that the
   tumor would influence the transcriptome of non-tumorous tissue, we
   evaluated tissue from an uninvolved quadrant of the breast. Principal
   component analysis of tissue from women with or without tumors
   suggested a homogeneous distribution of cases (Supplementary Fig.
   [56]1); hence, the presence of a tumor did not have a significant
   effect on the transcriptome of tissue from an uninvolved quadrant of
   the breast. Next, we investigated the effects of high vs. low trunk fat
   on the transcriptome in the non-tumorous breast. A total of 226
   upregulated and 137 downregulated differentially expressed genes (DEGs)
   (P.adj <0.05, |Log[2]FoldChange| > 0.6) were found in high compared to
   low trunk fat women using DESeq2 (Fig. [57]1a and Supplementary Table
   [58]1). These effects are clearly seen in the heatmap and volcano plot
   shown in Fig. [59]1a and Fig. [60]1b (top panel), respectively. A
   similar analysis was carried out based on BMI. In this instance, high
   BMI was defined as equal to or higher than the median of 21.8 kg/m^2,
   whereas low BMI was defined as <21.8 kg/m^2. Interestingly, when
   comparing high vs. low BMI, there were no DEGs (Fig. [61]1b, bottom
   panel).

Fig. 1. Transcriptome profiles of 32 women with normal BMI.

   [62]Fig. 1
   [63]Open in a new tab

   a Heatmap demonstrating 226 upregulated and 137 downregulated
   differentially expressed genes (DEGs) from non-tumorous breast tissue
   of women with trunk fat high vs. trunk fat low. b Volcano plot
   illustrating the abundance of DEGs in (1) Trunk fat high vs. Trunk fat
   low and (2) BMI high vs. BMI low. Two vertical dashed lines correspond
   to −0.6 and 0.6, which are the Log[2]fold-change cutoffs for the DEGs.
   The horizontal dashed line corresponds to P adjusted value of 0.05,
   which is another cutoff for the DEG. c Biological pathway changes
   associated with increased trunk fat percent in normal BMI women. Gene
   ratio indicates the ratio of genes that are included within each
   pathway. q value is a p value that is adjusted for the false discovery
   rate. q < 0.05 is considered significant.

   Increased trunk fat is associated with older age, larger breast
   adipocytes, and B-WATi. Hence, we posited that there would be an
   overlap between the DEGs associated with excess trunk fat and these
   other parameters. First, we divided the patients into two age groups
   using the age of 45 years, the median age of the cohort. In an analysis
   of those 45 years of age and above vs. less than 45 years of age, there
   were only one upregulated and three downregulated DEGs (Supplementary
   Table [64]2). In a comparison of postmenopausal vs. premenopausal
   women, three upregulated DEGs and nine downregulated DEGs were found
   (Supplementary Table [65]3). In contrast to age and menopause status, a
   comparison of nontumorous breast tissue from CLS(+) vs. CLS(−) cases
   and for large vs. small breast adipocytes led to the identification of
   numerous DEGs. In the case of CLS(+) vs. CLS(−) cases, there were 45
   upregulated and 29 downregulated DEGs (Supplementary Table [66]4).
   Using the median value of adipocyte diameter (89.32 µ), the samples
   were divided into two groups: small adipocytes (<89.32 µ) and large
   adipocytes (> or =89.32 µ). 116 upregulated and 176 downregulated DEG
   were found (Supplementary Table [67]5). Next, we investigated whether
   there was an overlap between the DEGs for high vs. low trunk fat and
   the DEGs for BMI, menopause, CLS status, and adipocyte size. There was
   no overlap in upregulated or downregulated genes for trunk fat (%) vs.
   BMI or trunk fat (%) vs. menopausal status. By contrast, we did
   identify significant overlap between trunk fat (%) and upregulated
   (Supplementary Fig. [68]2a and Supplementary Tables [69]4 and [70]5)
   and downregulated genes (Supplementary Fig. [71]2b and Supplementary
   Tables [72]4 and [73]5) for CLS status and adipocyte size.

Interpreting the global transcriptome changes in terms of biological pathways

   Both BioCarta and KEGG databases were used to classify the DEGs into
   relevant biological pathways. Numerous pathways were enriched with
   upregulated DEGs but not downregulated DEGs (Fig. [74]1c). Among the
   pathways in the non-tumorous breast that were enriched in association
   with increased trunk fat were the complement pathway, inflammation
   pathway, cytokine-cytokine receptor interaction, NOD-like receptor
   signaling pathway and graft-vs.-host disease. Collectively, these
   findings suggested that there were significant immunological
   alterations associated with high vs. low levels of trunk fat. Based on
   the above findings, we next used Estimate R package^[75]11, a
   computational approach, to determine an immune score. A significant
   increase in the immune score was found in association with high levels
   of trunk fat (Trunk fat Low vs. Trunk fat High) (P = 0.002) but not
   high BMI (BMI Low vs. BMI High; P = 0.18) (Fig. [76]2a). In fact,
   percent trunk fat positively correlated with the immune score
   (rho = 0.53, P = 0.002; Fig. [77]2b, left hand panel). The same was not
   true for BMI (rho = 0.28, P = 0.12; Fig. [78]2b, right hand panel).
   Additional studies were carried out to examine the relationship between
   percent trunk fat and immune cell populations. xCell was used to
   estimate immune cell populations and suggested increased macrophages
   (M1 and M2), dendritic cells, and natural killer T cells in association
   with high compared with low trunk fat (Fig. [79]2c). The increase in
   dendritic cells and macrophages associated with high vs. low trunk fat
   was confirmed using immune cell markers defined by Danaher et
   al.^[80]12 (Supplementary Fig. [81]3). To validate the increase in
   macrophages, CD68 immunohistochemistry was carried out and revealed a
   positive correlation between percent trunk fat and density of CLS
   (rho = 0.67, P = 2.8e−05) (Fig. [82]2d, left-hand panel). The same was
   not true for BMI (rho = 0.13, P = 0.48) (Fig. [83]2d, right hand
   panel). Levels of CD68, a macrophage marker, also showed a strong
   positive correlation with the estimated population of macrophages
   (rho = 0.93, P = 2.9e−14) (Supplementary Fig. [84]4).

Fig. 2. Immune cell population changes in association with high vs. low trunk
fat in normal BMI individuals.

   [85]Fig. 2
   [86]Open in a new tab

   a Immune score was calculated to observe overall immune cell population
   change with trunk fat and BMI. The difference was more significant when
   trunk fat was used as a parameter. For both trunk fat and BMI, the
   median value was used to divide the samples into two groups
   (n = 16/group). **P < 0.01; ns not significant. b Correlation between
   immune score vs. trunk fat percentage (left) or BMI (right). c Heatmap
   illustrating increased immune cell population in trunk fat high group
   when compared to the trunk fat low group. The red dashed line
   corresponds to P value of 0.05. d Correlation between CLS density vs.
   trunk fat percentage (left) or BMI (right). CLS density equals
   #CLS/cm^2.

   Previously, the chemokine CCL2 has been implicated in the recruitment
   of blood monocytes into fat where the cells differentiate into
   macrophages contributing to the formation of CLS^[87]13,[88]14. Given
   the observed increase in macrophages in the breast tissue of
   normal-weight women with high vs. low trunk fat, we evaluated the
   relationship between levels of trunk fat (trunk fat percent) and CCL2
   expression. As shown in Fig. [89]3a, the percentage of trunk fat
   positively correlated with CCL2 expression (rho = 0.64, P = 9.4e−05).
   Notably, the level of CCL2 expression also positively correlated with
   CD68 expression (rho = 0.47, P = 0.007) and the density of CLS
   (rho = 0.37, P = 0.04) (Fig. [90]3b, c). In addition to CCL2,
   complement is believed to be important for leukocyte recruitment into
   adipose tissue. Activation of the C3A receptor (C3AR), a component of
   the complement pathway, attracts inflammatory cells including monocytes
   into adipose tissue contributing to CLS formation^[91]15. Consistent
   with the increased macrophage population found in the breast tissue of
   normal BMI women with high vs. low levels of trunk fat, levels of C3AR1
   positively correlated with trunk fat percentage (rho = 0.56, P = 0.001;
   Fig. [92]3d). Levels of C3AR1 expression also positively correlated
   with CD68 expression (rho = 0.79, P = 9.3e−07) and density of CLS
   (rho = 0.44, P = 0.01; Fig. [93]3e, f).

Fig. 3. Correlations between the levels of expression of genes that have a
role in white adipose inflammation, trunk fat%, and CLS density.

   [94]Fig. 3
   [95]Open in a new tab

   a Correlation between trunk fat percent and CCL2 expression level. b
   Correlation between levels of CCL2 and CD68. c Correlation between CCL2
   expression level and CLS density. CLS density equals #CLS/cm^2. d
   Correlation between trunk fat percent and C3AR1 expression level. e
   Correlation between levels of C3AR1 and CD68. f Correlation between
   C3AR1 expression level and CLS density. CLS density equals #CLS/cm^2.

Additional molecular changes correlate with the amount of trunk fat

   Obesity has been associated with changes in gene expression in normal
   breast tissue that are believed to contribute to the pathogenesis of
   breast cancer^[96]16. In contrast, little is known about the expression
   of select genes linked to carcinogenesis in the breast tissue of
   normal-weight women with high vs. low trunk fat. Hence, we evaluated
   the relationship between trunk fat percentage and the expression of
   several genes that potentially play a role in the pathogenesis of
   breast cancer. Some studies have suggested that leptin contributes to
   the pathogenesis of breast cancer^[97]17. Moreover, blood levels of
   leptin are elevated in normal-weight women with high levels of trunk
   fat and B-WATi^[98]7,[99]8. Here we observed a strong correlation
   between trunk fat percentage and leptin expression (rho = 0.67,
   P = 2.5e−05; Fig. [100]4a). Excess body fat has been suggested to be
   associated with reduced oxygen levels in adipose tissue^[101]18. Tissue
   hypoxia is believed to stimulate the synthesis of reactive oxygen
   species which can, in turn, damage DNA potentially contributing to
   mutagenesis. Both NQO1 and HMOX1 are known to be induced by reactive
   oxygen species^[102]19. Interestingly, we observed a positive
   correlation between trunk fat percentage and the expression of both
   NQO1 (rho = 0.68, P = 2.2e−05) and HMOX1 (rho = 0.43, P = 0.01), which
   may reflect an increase in reactive oxygen species (Fig. [103]4b, c).
   In support of this potential mechanism, the reactive oxygen species
   pathway gene set from the Hallmark database and cellular response to
   reactive oxygen species from the Gene Ontology database were both
   elevated in association with high trunk fat (Fig. [104]4d). Obesity has
   also been reported to cause lymphatic dysfunction which could, in turn,
   impact the development and progression of breast cancer^[105]20. With
   this in mind, it is of considerable interest that levels of VEGF-C,
   which can promote the growth of lymphatic vessels, positively
   correlated with trunk fat percentage (rho = 0.54, P = 0.002; Fig.
   [106]4e). Interleukin (IL)-6, a pro-inflammatory cytokine, may also
   play a role in tumorigenesis. Systemic levels of IL-6 are elevated in
   association with obesity and B-WATi^[107]21. Here we demonstrate a
   positive correlation between trunk fat percentage and the expression of
   IL6 (rho = 0.51, P = 0.003; Fig. [108]4f). We also found evidence
   suggesting that levels of CYP19A1 which encodes aromatase, the
   rate-limiting enzyme for estrogen biosynthesis, were increased in
   association with high vs. low levels of trunk fat
   (log2FoldChange = 0.95; P = 0.01). A nearly significant positive
   correlation was found between trunk fat percentage and the expression
   of CYP19A1 (rho = 0.33, P = 0.07; Fig. [109]4g).

Fig. 4. Levels of trunk fat correlate with the expression of genes implicated
in the pathogenesis of breast cancer.

   [110]Fig. 4
   [111]Open in a new tab

   a Correlation between trunk fat percent vs. LEP expression level. b
   Correlation between trunk fat percent vs. NQO1 expression level. c
   Correlation between trunk fat percent vs. HMOX1 expression level. d
   Pathway enrichment scores of reactive oxygen species pathway from
   Hallmark and cellular response to reactive oxygen species pathway from
   Gene Ontology. **P < 0.01. e Correlation between trunk fat percent and
   VEGF-C expression level. f Correlation between trunk fat percent and
   IL6 expression level. g Correlation between trunk fat percent and
   CYP19A1 expression level.

Discussion

   Obesity is widely recognized to be a risk factor for numerous diseases,
   including solid and liquid tumors^[112]2. The fact that MONW was
   recently found to be associated with an increased risk of cancer
   suggests that the oncological consequences of excess body fat are
   greater than previously estimated in studies limited to the effects of
   obesity^[113]4,[114]8. Although the association between high levels of
   trunk fat and elevated risk of postmenopausal breast cancer in MONW is
   now established^[115]8, there is a limited understanding of the
   underlying mechanisms.

   In the current study, we have provided new insights into the molecular
   changes that occur in the breast tissue of normal BMI women with high
   vs. low levels of trunk fat. The fact that there were numerous DEGs is
   consistent with prior evidence that obesity is associated with
   significant changes in gene expression in the normal breast^[116]16. In
   contrast to the findings for high vs. low levels of trunk fat, we did
   not see a large difference in gene expression when relying on
   measurements of BMI. This finding underscores both the known
   limitations of BMI as a measurement of adiposity and the value of
   carrying out objective measures of body fat. Consistent with the
   previously established link between excess trunk fat in normal BMI
   women and B-WATi^[117]5, the immune score was elevated in the breast
   tissue of women with high compared with low trunk fat. The fact that
   both macrophages and dendritic cells were suggested to be increased in
   association with elevated trunk fat is consistent with the increased
   immune score. Future studies are warranted to determine whether the
   function of these immune cells is altered in association with excess
   trunk fat. It is possible, for example, that changes in both the number
   and function of these immune cells could contribute to the pathogenesis
   of breast cancer in MONW. Our results also provide insights into the
   mechanisms underlying the observed increase in macrophages in
   association with high trunk fat. The fact that we found elevated levels
   of both CCL2 and C3AR1 in association with high levels of trunk fat is
   likely to be important for explaining the link between MONW and B-WATi.
   In preclinical studies, both CCL2 and activation of the complement
   pathway have been shown to impact WATi. CCL2 is a chemokine produced by
   adipocytes that is important for the recruitment of monocytes from
   blood into fat leading to the formation of CLS^[118]13,[119]14. Levels
   of C3 complement are elevated in association with MONW^[120]22. C3 is
   upstream of the anaphylatoxin C3a, which binds to the C3a receptor, and
   thereby attracts inflammatory cells including monocytes into adipose
   tissue contributing to CLS formation^[121]15.

   Several other findings are worth commenting on because of their
   potential link to breast carcinogenesis. Some studies have suggested a
   role for leptin in explaining the link between obesity and increased
   risk of breast cancer^[122]17. Leptin is produced by adipocytes and is
   known to be elevated in the blood of normal BMI postmenopausal women
   with increased trunk fat^[123]8. The fact that we found leptin levels
   to be increased in the breast tissue of normal BMI women with high vs.
   low trunk fat is consistent with the increase in breast adipocyte size
   that was observed. Whether this local increase in leptin contributes to
   the elevated risk of breast cancer is uncertain but warrants further
   consideration. Excess body fat is associated with hypoxia in adipose
   tissue which can cause an increase in reactive oxygen species^[124]18.
   NQO1 and HMOX1 are Nrf-2-dependent genes that are induced by reactive
   oxygen species^[125]19, a cause of oxidative DNA damage. Here we
   demonstrated that levels of both NQO1 and HMOX1 were elevated in
   association in high vs. low trunk fat. Moreover, pathway analysis
   suggested an increase in reactive oxygen species in association with
   high trunk fat. Taken together, it is possible that excess trunk fat is
   associated with an increased risk of breast cancer because the chronic
   elevation of reactive oxygen species may increase the likelihood of
   mutagenesis. The observed elevation of IL6 in association with high vs.
   low trunk fat is consistent with a pro-inflammatory state and prior
   evidence from reduction mammoplasty specimens that obesity led to
   enrichment for a pathway involving IL6^[126]16. Local increases in IL6
   could impact the self-renewal of cancer stem cells or potentially the
   growth of breast cancer cells^[127]23. Elevated levels of CYP19A1 which
   encodes aromatase have previously been reported in association with
   excess body fat including in normal BMI women^[128]5,[129]7. This
   increase could help to explain the elevated risk of estrogen-dependent
   breast cancer in normal BMI postmenopausal women^[130]8^,[131]24.

   This study has both strengths and limitations. A major strength is the
   use of RNA-seq to describe global changes in the breast transcriptome
   in association with high vs. low trunk fat in normal BMI women. This
   strategy has provided significant new mechanistic insights into the
   link between MONW and elevated risk of breast cancer. The fact that we
   had objective DXA-derived measurements of trunk fat and didn’t rely on
   BMI was a major strength of the study. At the same time, there were
   limitations including both the small sample size and the number of
   postmenopausal women who were enrolled. In the future, it would be very
   useful to both expand the sample size and to compare the effects of
   high vs. low trunk fat on gene expression in postmenopausal vs.
   premenopausal women. The nontumorous tissue that we evaluated came from
   patients either at elevated risk for breast cancer or with breast
   cancer. Although in cancer patients, we utilized tissue from an
   uninvolved quadrant of the breast, it is possible that the results
   could be different in normal women. Hence, in the future, it would be
   worthwhile to carry out a validation study using normal breast tissue
   from a cohort of cancer-free patients. Our findings are also limited
   because we are not able to delineate individual contribution to the
   variation in gene expression from all variables of interest including
   trunk fat, age, BMI, CLS status, and adipocyte size due to the limited
   sample size and the correlations between these variables. Our separate
   analyses provide evidence of the existence of trunk fat-related gene
   expression signatures. Nonetheless, such findings should be validated
   in larger studies. Finally, bulk RNA-seq allowed us to identify
   important differences between normal BMI women with high vs. low trunk
   fat, but we were unable to determine whether the observed differences
   in gene expression reflect changes in transcription, cellular
   composition of the tissue or both. The current findings provide a
   strong rationale for future studies utilizing single-cell RNA-seq.

   In summary, we demonstrate that there are significant changes in gene
   expression in the breast tissue of normal BMI women with high vs. low
   levels of trunk fat that reflect alterations in the complement pathway,
   cytokines and inflammatory response, cytokine–cytokine receptor
   interaction, and reactive oxygen species. Changes in immune cells and
   the expression of numerous genes linked to the pathogenesis of cancer
   were found that may help to explain why MONW is associated with an
   increased risk of postmenopausal breast cancer.

Methods

Study population and clinical data collection

   The subjects in this study of normal BMI women represent a subgroup of
   a previously reported cohort study that included measurements of body
   composition, B-WATi, and breast adipocyte diameter in women of all
   sizes^[132]5. In the previous study, the breast transcriptome was not
   evaluated. Informed consent was provided by women who were scheduled to
   undergo mastectomy for breast cancer treatment or risk reduction at
   Memorial Sloan Kettering Cancer Center (MSKCC). Institutional Review
   Board approval was obtained from MSKCC (IRB Protocol Number 15-235) and
   Weill Cornell Medicine (IRB Protocol Number 1511016737) (New York, NY)
   to conduct this study. Trunk fat was measured by DXA using a Lunar
   Prodigy multiple detector fan-beam densitometer (GE Healthcare) before
   surgery. Following calibration, single-beam, whole-body scanning was
   performed in the supine position. Weight and height were recorded
   before surgery and used to calculate BMI. A standard definition was
   used to define normal BMI (18.5–<25 kg/m^2). Clinicopathological data
   (age, menopause status, race) were extracted from the electronic
   medical record. Menopause status was categorized as either
   postmenopausal or premenopausal based on National Comprehensive Cancer
   Network criteria^[133]25.

Biospecimen acquisition

   Breast WAT specimens were obtained prospectively under a standard
   tissue acquisition protocol. In specimens that contained tumors, tissue
   from an uninvolved quadrant of the breast was used for research
   purposes. For each subject, paraffin blocks were prepared from breast
   WAT not involved by a tumor on the day of surgery. Frozen samples were
   stored in the presence RNAlater (Ambion).

Detection and measurement of B-WATi

   The absence or presence of B-WATi was determined by histologic
   assessment as described before^[134]26,[135]27. Briefly, B-WATi was
   detected by the presence of CLS, which represents a dead or dying
   adipocyte surrounded by CD68-positive macrophages^[136]28. Five
   formalin-fixed paraffin-embedded (FFPE) blocks were prepared from each
   mastectomy specimen and one section per FFPE block (approximately 2 cm
   in diameter, 5 µm thick) was generated such that five sections were
   stained for CD68, a macrophage marker (mouse monoclonal KP1 antibody;
   Dako). Light microscopy was used to examine immunostained tissue
   sections to detect the presence or absence of CLS and record the number
   of CLS per slide. Digital photographs were generated and the WAT area
   was measured with the Image J Software (NIH, Bethesda, MD). The number
   of CLS per square centimeter of WAT (#CLS/cm^2) also referred to as CLS
   density was calculated to determine the severity of B-WATi.

Adipocyte measurement

   Two hematoxylin & eosin-stained sections per case were prepared from
   FFPE breast tissue in order to measure adipocyte diameters as
   previously described^[137]5. Adipocyte diameter is expressed in
   microns.

RNA isolation and sequencing

   Qiagen’s RNeasy minikit was used to isolate total RNA from non-tumorous
   breast tissue. Then, using the Illumina TrueSeq Stranded Total RNA
   Library preparation protocol with rRNA depletion, the libraries were
   constructed. RNA-seq was carried out with paired-end 51 bp using the
   Illumina HiSeq4000 platform (Weill Cornell Medicine). Raw sequenced
   reads were pseudoaligned to the human reference genome (UCSC hg19)
   using Kallisto^[138]29. Then, the transcript abundance was quantified
   to obtain raw counts.

DEG analysis

   Raw counts acquired from Kallisto were used to characterize DEGs and
   DESeq2 R package was used^[139]30. The genes with low expression values
   were filtered out (baseMean ≤ 15) first. Then, genes with adjusted P
   value < 0.05 and |Log[2]FoldChange| > 0.6 were defined as DEGs. Volcano
   plots were generated to visually display the most biologically
   significant genes using statistical significance vs. fold-change. Two
   vertical dashed lines correspond to −0.6 and 0.6, which represent one
   of the cutoffs for the DEG. The horizontal dashed line corresponds to P
   adjusted value of 0.05, which is another cutoff for the DEG.

Pathway enrichment analysis

   To determine the molecular function of DEGs in various biological
   processes, KEGG^[140]31,[141]32 and BioCarta^[142]31,[143]33 gene sets
   from the Molecular signature database were used.

Immune cell population analyses

   To determine whether the overall immune cell population in breast WAT
   changed in association with percent trunk fat, Estimate R package was
   used to generate an immune score^[144]11. Then, specific immune
   cell-type changes were determined using a webtool, xCell
   ([145]https://xcell.ucsf.edu/)^[146]34. To confirm findings from xCell,
   immune cell expression markers from the study of Danaher et al. were
   used^[147]12.

Pathway score analysis

   To determine whether a given pathway is coordinately upregulated or
   downregulated in a sample set, single-sample GSEA (ssGSEA) was used.
   ssGSEA calculates an enrichment score for each gene set and the score
   represents the activity level of a biological process. The analysis was
   carried out on the public server
   ([148]https://www.genepattern.org/modules/docs/ssGSEAProjection/4).
   Gene sets from Hallmark and Gene Ontology databases were
   used^[149]31,[150]35–[151]37.

Statistics

   Patient characteristics were summarized in terms of counts and
   proportions for categorical variables and median and interquartile
   range for continuous variables. Fisher’s exact test was used to examine
   the difference in the distribution of a categorical variable between
   two independent groups. Wilcoxon rank-sum test was used to examine the
   difference of a continuous variable between two independent groups.
   Correlation between two continuous variables was examined using
   Spearman’s method. For all analyses, statistical significance was set
   at two-tailed P < 0.05. All statistical analyses were conducted using
   the R software (R Foundation for Statistical Computing, Vienna,
   Austria).

Reporting summary

   Further information on research design is available in the [152]Nature
   Research Reporting Summary linked to this article.

Supplementary information

   [153]Supplementary Information^ (4.1MB, pdf)
   [154]Reporting Summary^ (72KB, pdf)

Acknowledgements