Exploring the Transcriptional Crosstalk Between Adipose Tissue and Locoregional Recurrence in Breast Cancer Using Independent Component Analysis

Exploring the Transcriptional Crosstalk Between Adipose Tissue and Locoregional Recurrence in Breast Cancer Using Independent Component Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Exploring the Transcriptional Crosstalk Between Adipose Tissue and Locoregional Recurrence in Breast Cancer Using Independent Component Analysis Marlous Arjaans, Bert van der Vegt, Renske Linstra, Arkajyoti Bhattacharya, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8544684/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 8 You are reading this latest preprint version Abstract Background Locoregional recurrence (LRR) poses a persistent clinical challenge in breast cancer, with emerging evidence implicating the tumor-associated adipose tissue in modulating recurrence risk. This study investigates shared transcriptional programs between adipose tissue and breast tumors and examines their association with disease-free survival (DFS), particularly in the context of reconstructive surgery where adipose tissue from different body compartments are commonly used. Methods We analyzed bulk gene expression data from 5,691 breast tumors and 978 human adipose tissue samples from different body compartments using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs). Gene set enrichment analysis (GSEA) and copy number alteration profiling were used for biological annotation. Associations between TCs and DFS were evaluated through univariate Cox regression. Key findings were validated using spatial transcriptomic and single-cell RNA sequencing datasets. Results Among the 411 TCs identified, 332 showed biological enrichment, and 35 were significantly associated with DFS. Four DFS-associated TCs (TC257, TC350, TC371, TC400) were enriched for adipogenesis-related genes and exhibited heightened activity in high-grade, triple-negative tumors and in patients with elevated BMI. Notably, TC350 was highly active in adipose tissue from common reconstructive donor sites (abdomen, omentum, subcutis) but not in native breast adipose tissue. Spatial transcriptomic and single-cell analyses confirmed the increased activity of these adipogenesis-related TCs in tumor regions and adipose cells. TC350 included FABP4 , a gene previously linked to poor prognosis in breast cancer and considered as a potential new therapeutic target. Conclusions Adipose tissue-derived transcriptional programs influence breast cancer prognosis and differ by tissue origin. These findings suggest that donor site selection for adipose tissue in reconstructive surgery may impact LRR risk through adipogenesis-associated mechanisms. Further research is warranted to elucidate the biological and clinical implications of adipose–tumor transcriptional interactions. Breast cancer adipose tissue locoregional recurrence breast reconstructive surgery Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 BACKGROUND Breast cancer remains the leading cause of cancer-related mortality among women in developing countries and ranks second in developed nations.( 1 ) Despite significant therapeutic progress, locoregional recurrences (LRR) persists as a major clinical challenge, with 5-year survival rates ranging from 40% to 65%.( 2 – 5 ) This highlights the urgent need to identify factors contributing to elevated LRR risk. Recent studies highlight the tumor microenvironment’s role in LRR, particularly the influence of adipose tissue. ( 6 – 8 ) Breast adipose tissue is predominately composed of mature adipocytes (~ 90%), with the remaining stromal-vascular fraction (SVF) contributing ~ 10%. ( 9 ) Once considered merely an energy reservoir, adipocytes can transition into cancer-associated adipocytes (CAAs) in the presence of tumor cells, promoting proliferation, migration, and invasiveness in preclinical models. ( 10 , 11 ) Additionally, genetic susceptibility factors such as breast cancer gene 1 (BRCA1) mutations may alter adipose-derived stem cells, driving more aggressive cancer behavior. ( 12 ) While direct clinical evidence linking adipose tissue to LRR is limited, findings from a phase III trial of nearly 5,000 patients suggest that obese individuals with hormone-receptor-positive disease have an increased risk of LRR between 3- and 8-years post-diagnosis, whereas obese patients with triple-negative breast cancer (TNBC) appear to have a lower risk during the first three years. ( 13 ) These observations underscore the complex interplay between obesity and LRR, emphasizing the need for a more nuanced understanding of adipose tissue’s role. With the increasing prevalence of breast reconstruction surgeries involving adipose tissue, understanding adipocytes’ impact on LRR is becoming even more crucial. Autologous reconstruction using adipofasciocutaneous flaps is the gold standard, with common donor sites including the abdomen, thigh, lower back, buttocks, and omentum. ( 14 – 17 ) However, lipofilling, which involves harvesting adipose tissue via liposuction for defect correction, has raised concerns regarding oncological safety. Specifically, harvested adipocytes stem cells may stimulate residual tumor cells and promote recurrence; however, the current literature—largely retrospective cohort studies—remains inconclusive. ( 18 , 19 ) The availability of bulk gene expression profiles from both breast cancer patients and adipose tissues offers a valuable opportunity to explore the role of adipose tissue in LRR risk. However, the bulk profiles represent an aggregate of tumor cells and various components of the tumor microenvironment, which can obscure subtle adipocyte-specific transcriptional signals pertinent to LRR. To address this limitation, we applied consensus-independent component analysis (c-ICA) to decompose bulk gene expression profiles into statistically independent transcriptional components (TCs), each reflecting distinct biological processes. ( 20 ) This method enables the identification of both dominant and more subtle TCs and allows for their activity to be quantified in individual samples. In this hypothesis-generating study, we investigated whether adipose tissue from different donor sites exhibits distinct transcriptional patterns and examined whether these patterns are associated with LRR in breast cancer patients. We compiled bulk gene expression profiles from non-metastatic breast tumor samples and human adipose tissue samples, used c-ICA and Gene Set Enrichment Analysis (GSEA) to identify adipose tissue-specific TCs, and subsequently assessed the association of these TCs with disease free survival (DFS). Finally, we evaluated the spatial co-localization of DFS-associated TCs in spatially resolved transcriptomic data. METHODS A detailed description is provided in the Additional Methods and in Fig. 1 an overview is presented. Data acquisition, preprocessing, and quality control Data acquisition, preprocessing, and quality control were performed as described previously. ( 20 – 22 ) Raw microarray bulk gene expression profiles of breast cancer samples and adipose tissues of non-cancer patients were obtained from the Gene Expression Omnibus (GEO). ( 22 ) Additionally, clinicopathological information and follow-up data was obtained. Only samples processed using the Affymetrix HG-U133 Plus 2.0 platform (GPL570) were included, while those derived from cell lines or animal models were excluded. Duplicate samples were identified and removed based on MD5 hashes and high Spearman correlation coefficients (R > 0.99). The robust multiarray algorithm was applied to preprocess and aggregate raw data, followed by principal component analysis for quality control, as previously described. ( 22 ) We additionally retrieved 11 spatially resolved transcriptomes from breast cancer patients through Zenodo and the 10x Genomics repository (see Additional methods). Moreover, 10,000 single-cell profiles from adipose tissue of a healthy participant were obtained from GEO (study ID GSE134355). Genes with no detectable expression in all samples were excluded. For both the spatially resolved and single-cell transcriptomes, mRNA expression levels were normalized by removing the first principal component derived from the sample-wise correlation matrix. Clinicopathological data collection Clinicopathological data extracted for the breast cancer profiles included sex, age, tumor histological subtype, tumor grade, tumor size, TNM stage, lymph node involvement, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status, BRCA1/2 mutation status, Ki67 proliferation index, menopausal status, body mass index (BMI), breast cancer subtype (both receptor based and intrinsic), treatment regimen, and DFS. Receptor status was included only when assessed according to the immunohistochemistry staining guidelines of the American Society of Clinical Oncology and College of American Pathologists. ( 23 , 24 , 25 ) BRCA1/2 mutation were further classified as either germline or somatic. DFS was defined as the interval from diagnosis until disease recurrence, death, or last follow up. For human adipose tissue profiles, we collected data on tissue’s anatomical origin, sex, age, BMI (categorized in accordance WITH World Health Organization definitions), and menopausal status. Missing data were designated as “Not available”. Consensus Independent Component Analysis (c-ICA) We applied c-ICA to the preprocessed gene expression data, decomposing the profiles into statistically independent transcriptional signatures, referred to as TCs. ( 26 , 27 ) Each TC represents a distinct biological process, and the weight assigned to each gene indicates the direction and magnitude of that process’s influence on the gene’s expression. In addition to identifying TCs, c-ICA also generated a mixing matrix, which provides the activity scores of each TC for individual samples. Biological characterization of transcriptional components We employed several approaches to characterize the identified TCs. First, we performed GSEA using 15 gene set collections from the Molecular Signatures Database (MsigDB) version 7.1 (see additional methods for details). ( 25 ) A TC was deemed significantly enriched for a given gene set if the absolute z-score exceeded 3. For visualization, we used the web-based tool ClustVis ( 28 ) to generate enrichment heatmaps, focusing on gene sets whose enrichment scores surpassed the Bonferroni-adjusted threshold for at least one TC. Second, we applied the Transcriptional Adaptation to Copy Number Alterations (TACNA) profiling technique ( 28 ) to identify TCs that capture the downstream effects of copy number alterations (CNAs) on gene expression levels. Univariate survival analysis We conducted univariate Cox proportional hazards regression on a subset of breast cancer patients for whom DFS data were available, aiming to evaluate the association between TC activity and DFS. To limit false positives resulting from multiple testing, we employed a permutation-based framework with a false discovery rate (FDR) threshold of 1% and a confidence level (CL) of 80%. Identification of significantly active TCs in individual gene expression profiles We calculated the activity score of each TC in a given gene expression profile by taking the dot product of the pseudo-inverse-derived TC vector with the corresponding expression levels in that profile. To determine the significance of this activity score, we used a permutation-based methodology: for each profile-TC pair, we performed 5,000 permutations of the gene weights within the TC and repeated activity score calculation, thus generating a null distribution of activity scores. If the Anderson-Darling test indicated that this null distribution deviated from normality, we applied a Johnson transformation— using one of three optimal families of distributions (S, SU, SL)—to both the null distribution and the observed activity score. Afterward, we standardized the transformed null distribution to have a mean of zero and a standard deviation of one, applying the same standardization to the observed activity score. We then fit symmetrical generalized Gaussian distributions to these null distributions to derive p-values for each profile-TC pair. A TC was deemed significantly active if its p-value was below 0.01. For each tissue compartment, we calculated the proportion of samples exhibiting significant activity for each TC, separately for positive and negative activity scores. In analyses involving spatially resolved and single-cell transcriptomes, we further used z-transformed p-values for data visualization. RESULTS Dataset characteristics of breast cancer and adipose tissue samples We compiled a comprehensive dataset of 6,083 breast cancer and 1,427 adipose tissue bulk gene expression profiles from GEO. Following preprocessing, duplicate removal, and quality control, the final dataset included 5,691 breast cancer samples and 978 adipose tissue samples, derived from 99 breast cancer studies (73 unique GEO series) and 25 adipose tissue studies (19 unique GEO series) (See Figure 2, Additional Tables 1 and 2). Detailed clinicopathological information for both sample types is summarized in Tables 1 and 2. Adipose tissue samples originated from eight anatomical sites: breast (n=113), abdomen (n=616), omentum (n=41), pericard (n=26), parathyroid gland (n=9), visceral (n=66), subcutaneous (n= 105), and unknown origin (n=2). Samples from unknown sites were excluded from further analyses. Identification of 332 biologically enriched transcriptional components with c-ICA We applied c-ICA to uncover transcriptional signatures of biological processes, identifying 411 statistically independent TCs. Of these, 245 reflected the transcriptional effects of CNAs (Table 3), as determined by TACNA profiling. An additional 87 TCs were significantly enriched for at least one gene set from the 15 collections in MsigDB, bringing the total to 332 biologically enriched TCs (Table 4). To compare the 332 biologically enriched TCs with the remaining 79 non-enriched TCs (Additional Table 3), we employed Uniform Manifold Approximation and Projection (UMAP) to visualize their activity scores. As illustrated in Figure 3, the UMAP plot based on the enriched TCs exhibited markedly less clustering by study ID compared to the plot derived from the non-enriched TCs. This qualitative observation was corroborated by quantitative assessment using the sum of squares of error (SSE) for clustering by study ID (6,453.48 vs. 165.47). A permutation-based test confirmed that this difference was highly significant (p-value < 5.0x10 -5 ). These findings suggest that the 79 non-enriched TCs primarily represent non-biological batch effects. Accordingly, only the 332 biologically enriched TCs were used in subsequent analyses. Comprehensive information on TC composition and GSEA results is available in our public repository: http://transcriptional-landscape-breastcancer-adiposetissue.opendatainscience.net. Anatomical origin of adipose tissue associates with activity of biologically enriched TCs We investigated whether the activity of biologically enriched TCs varies among adipose tissue from different anatomical locations. After applying Bonferroni correction for multiple testing (p < 0.01), 331 of the 332 biologically enriched TCs displayed significant differences in activity scores across distinct anatomical sites (Additional Table 4). Furthermore, 292 of these 332 TCs showed significantly different activity scores when comparing adipose tissue and breast cancer samples (Additional Table 5). These finding indicate that the biological characteristics of adipose tissue vary considerably based on anatomical origin. Activity scores of 35 biologically enriched TCs are associated with disease free survival in breast cancer patients To investigate the relationship between TC activity scores and DFS, we performed univariate Cox proportional hazards regression analysis on a subset of 619 breast cancer patients with available DFS data. Of the 332 biologically enriched TCs, 35 demonstrated a significant association with DFS; these are referred to as DFS-associated TCs. Notably, 31 of these 35 DFS-associated TCs showed significantly different activity between adipose tissues and breast cancer tissues (Bonferroni-adjusted p-value < 0.01; Figure 4 and Additional Table 6). All 35 DFS-associated TCs showed significant enrichment for at least one gene set from the Gene Ontology Biological Processes collection, with a median top z-score of 2.828 (interquartile range of 1.901 to 3.133). Among these, TC37 showed the strongest association with DFS and was highly enriched for genes related to hypoxia, glycolysis and MTORC1 signaling. We identified four TCs (TC257, TC350, TC371, and TC400) enriched for adipogenesis-related genes (referred to as adipogenesis-related TCs); of these, TC350 was the most enriched. TC257, TC350, and TC400 were more active in cancer samples from patients with grade 3 disease, triple negative breast cancer, and elevated BMI. Conversely, TC257, TC350, TC371, and TC400 were less active in samples from patients with grade 1 or 2 disease, hormone-positive breast cancer, and low Ki67 score (figure 5). Other DFS-associated TCs were enriched for genes involved in diverse biological processes, including MYC targets and the mitotic spindle. These findings suggest that these TCs capture transcriptional effects from multiple biological processes that may influence the risk of LRR in breast cancer patients. Elevated activity of adipogenesis-related TCs in tumor regions of spatially resolved breast cancer transcriptomes and single cell transcriptomes from healthy adipose tissue To investigate the co-localized activity of the four adipogenesis-related TCs and determine their cell origin, we analyzed spatial transcriptomic profiles from 11 breast cancer samples and 10,000 single-cell profiles from the adipose tissue of a healthy individual. Analysis of DFS-associated TC activity in the spatial transcriptomes revealed high activity of these four adipogenesis-related TCs in distinct tumor regions (BC1-5; Figure 6). Likewise, when comparing all DFS-associated TCs in the single-cell data, these four adipogenesis-related TCs consistently showed higher activity than the other TCs (Additional Figure 1). Notably, they ranked among the top TCs exhibiting significant activity (p-value < 0.01) across the highest number of single-cell mRNA expression profiles (Additional Table 7). DISCUSSION Using bulk gene expression profiles from non-metastatic breast tumor samples and human adipose tissue, we identified biological enriched TCs, 35 of which were significantly associated with DFS. Detailed biological characterization of these TCs are available through our public portal. Among the TCs linked to DFS, four (TC257, TC350, TC371, and TC400) were strongly enrichment for genes involved in adipogenesis, exhibited high active in adipose tissue, and were associated with DFS in breast cancer. The role of adipogenesis in breast cancer was recently investigated using gene expression profiles from 5,098 patients, employing Gene Set Variant Analysis to predict adipogenesis activity based on the Hallmark adipogenesis gene set. ( 29 ) The study showed that heightened adipogenesis activity in TNBC correlates with an unfavorable immune microenvironment, characterized by elevated M2 macrophages infiltration and reduced CD8 + T cells infiltration. Furthermore, in TNBC patients, adipogenesis activity was associated with worse disease specific survival and overall survival, whereas no such correlation was observed in other breast cancer subtypes. ( 29 ) These findings align with our observation that the four adipogenesis-related TCs were active in patients with TNBC and high-grade disease but not in those with hormone-positive disease, low ki67 score, and intermediate or low-grade disease. However, while the above-mentioned study established a link between adipogenesis activity and an unfavorable immune microenvironment in TNBC, it did not further delve into the underlying transcriptional mechanisms driving this association. Using c-ICA, we were able to identify subtle but biologically relevant transcriptional components within the transcriptomes, allowing a more precise characterization of the transcriptional programs involved in adipogenesis. Moreover, our analysis incorporates adipose tissue from different compartments, providing additional insights into the activity of these TCs, which was not explored in previous studies. When examining the genes with the highest weight in these four adipogenesis-related TCs, only Fatty Acid Binding Protein 4 ( FABP4 ), found in TC350, was described in the gene set ‘adipogenesis’ in Hallmark. FABP4 is primarily expressed in adipocytes and macrophages, where it regulates metabolic and inflammatory pathways ( 30 , 31 ) Intracellular FABP4 in macrophages has been identified as a marker of pro-tumor, tumor associated macrophages (TAM). Additionally, elevated circulating FABP4 levels have been linked to breast cancer progression via increased activity of the IL-6/STAT3/ALDH1 pathway, thereby enhancing activity of ALDH1, a recognized stem cell marker in breast cancer. ( 31 – 36 ). Furthermore, higher circulating FABP4 levels have been associated with an increased risk of breast cancer ( 37 ) and elevated FABP4 expression is significantly correlated with shorter DFS and OS in TNBC patients compared to other subtypes ( 38 , 39 ) These findings point to FABP4 as a promising therapeutic target. Indeed, multiple studies have investigated inhibiting FABP4 with small molecule agents and specific antibodies in breast cancer cell lines and animal models. ( 40 – 43 ) We demonstrated that adipose tissue from different anatomical locations exhibits distinct transcriptional patterns. Earlier studies further indicated that subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) differ not only in embryogenic origin but also in metabolic characteristics. ( 44 – 46 ) These distinctions may be particularly relevant in reconstructive breast surgery, where adipose tissue from a donor site replaces or augments native breast tissue. We found that adipogenesis-related TC257, TC371, and TC400 generally showed low activity scores in both breast and donor sites; however, TC350 exhibited low activity in breast adipose tissue but high activity in adipose tissue from the abdomen, omentum, and subcutis. Moreover, TC350 was associated with worse DFS, suggesting that adipose tissue from these donor sites could potentially introduce new biological factors that increase LRR. An imaging study of 3,235 women with stage II or III breast cancer showed that increased SAT, but not VAT, correlated with worse overall mortality. ( 47 ) Similar findings have been reported in gastric cancer and hepatocellular carcinoma, where high SAT rather than VAT is a poor prognostic factor. ( 48 , 49 ) In contrast, increased VAT has been associated with higher incidence of colorectal cancer, whereas SAT was not. ( 50 , 51 ) Taken together, these observations bolster the hypothesis that adipose tissue from different anatomical locations may exert distinct effects on tumor cells. CONCLUSION Our transcriptional mapping the adipose tissue–breast cancer interfaces reveals distinct patterns associated with LRR, which vary in activity depending on the anatomical origin of the adipose tissues. These findings indicate that adipose tissue is not merely a passive bystander in breast cancer, particular in the context of reconstructive surgery using donor sites from outside the breast. Future studies should further elucidate how different types of adipose tissue influence tumor biology and clinical outcomes in reconstructive breast surgery. ABBREVIATIONS LRR locoregional recurrences SVF stromal-vascular fraction CAA cancer-associated adipocytes BRCA! Breast cancer gene 1 TNB triple-negative breast cancer c-ICA consensus-independent component analysis TC transcriptional component GSEA Gene Set Enrichment Analysis DFS disease free survival GEO Gene Expression Omnibus ER estrogen receptor PR progesterone receptor HER2 human epidermal growth factor receptor 2 BMI body mass index MsigDB Molecular Signatures Database TACNA Transcriptional Adaptation to Copy Number Alterations CAN copy number alterations FDR false discovery rate CL confidence level NA Not available FABP4 Fatty Acid Binding Protein 4 SAT subcutaneous adipose tissue VAT visceral adipose tissue Declarations Ethics approval and consent to participate Not applicable Consent for publication Not applicable Availability of data and materials The details of the datasets used are provided in the Additional Tables 1 and 2. Comprehensive information on TC composition and GSEA results is available in our public repository: https://transcriptional-landscape-breastcancer-adiposetissue.opendatainscience.net/. Competing interest The authors declare no competing interests Funding No additional funding was received for this study. Authors’ contributions Conceptualization MA, RF / data acquisition MA, RF, AB / data analysis and interpretation MA, AB, RF/ Identification of tumor regions in breast cancer patient samples BvdV, RL/ writing of the manuscript MA, AB,RF / all authors read and approved the final version of the manuscript. Acknowledgements The authors would like to thank Jie Ma for his work in data acquisition for this study. Authors’ information No additional authors’ information. References Society AC (2015) Global cancer facts & Figs. 3rd edition. Atlanta: American Cancer Society ed Zimmerman MP, Pharm BS, Stanton RM (2014) Recurrence of breast cancer years after the initial tumor. 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Obes (Silver Spring) 27(6):997–1004 von Hessen L, Roumet M, Maurer MH, Lange N, Reeves H, Dufour JF et al (2021) High subcutaneous adipose tissue density correlates negatively with survival in patients with hepatocellular carcinoma. Liver Int 41:828–836 Han J, Tang M, Lu C, Shen L, She J, Wu G (2021) Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with cachexia. Clin Nutr 40:5156–5161 Vazzana N, Riondino S, Toto V, Guadagni F, Roselli M, Davi G et al (2012) Obesity-driven inflammation and colorectal cancer. Curr Med Chem 19:5837–5853 Im JP, Kim D, Chung SJ, Jin EH, Han YM, Park MJ et al (2018) Visceral obesity as a risk factor for colorectal adenoma occurrence in surveillance colonoscopy. Gastrointest Endosc 88(1):119–127e4 Tables Table 3 is available in the supplementary files section Table 1. Patient characteristics of the breast tumor tissue samples Provided Samples (5,691) N (%) Inferred Samples (5,691) N (%) Sex Female 5,691 (100.00) Male 0 (0) Age in years 21-30 50 (0.88) 31-40 364 (6.40) 41-50 790 (13.88) 51-60 792 (13.92) 61-70 520 (9.14) 71-80 241 (4.23) 81-90 45 (0.79) NA 2,889 (50.76) Molecular subtype (IHC) Luminal A 47 (0.83) 1,529 (26.87) Luminal B 61 (1.07) 2,072 (36.41) HER2 enriched 72 (1.27) 572 (10.05) Basal like 397 (6.98) 1,518 (26.67) NA 5,114 (89.86) 0 (0.00) Molecular subtype (Intrinsic) Luminal A 185 (3.25) 1,959 (34.42) Luminal B 140 (2.46) 2,096 (36.83) HER2 enriched 99 (1.74) 340 (5.97) Basal like 225 (3.95) 1,189 (20.89) Normal like 39 (0.69) 107 (1.88) NA 5,003 (87.91) 0 (0.00) BRCA 1/2 mutation BRCA1 somatic mutation 34 (0.60) 656 (11.53) BRCA2 somatic mutation 6 (0.11) 6 (0.11) Germline mutation 15 (0.26) 56 (0.98) Wild type 139 (2.44) 4,973 (87.38) NA 5,497 (96.59) 0 (0.00) Tumor histological subtype DCIS 22 (0.39) IDC 1,043 (18.33) ILC 193 (3.39) Mixed 36 (0.63) Other 48 (0.84) NA 4349 (76.42) Tumor grade 1 262 (4.60) 398 (6.99) 2 818 (14.37) 1,957 (34.39) 3 1,236 (21.72) 3,336 (58.62) NA 3,375 (59.30) 0 (0.00) Tumor size (cm) size <= 2 408 (7.17) 2 < size 5 50 (0.88) NA 4,721(82.96) T - stage T1 548 (9.63) T2 1,103 (19.38) T3 177 (3.11) T4 59 (1.04) NA 3,804 (66.84) N - stage N0 713 (12.53) N1 482 (8.47) N2 196 (3.44) N3 146 (2.57) NA 4154 (72.99) M - stage M1 111 (1.95) 111 (1.95) M0 1,093 (19.21) 5,580 (98.05) NA 4487 (78.84) 0 (0.00) Lymph node involvement Positive 1,592 (27.97) Negative 1,336 (23.48) NA 2,763 (48.55) ER status Positive 1,849 (32.49) 3,537 (62.15) Negative 1,204 (21.16) 2,154 (37.85) NA 2,638 (46.35) 0 (0.00) PR status Positive 1,072 (18.84) 2,944 (51.73) Negative 1,097 (19.28) 2,747 (48.27) NA 3,522 (61.89) 0 (0.00) HER2 status Positive 652 (11.46) 1,289 (22.65) Negative 1,902 (33.42) 4,402 (77.35) NA 3,137 (55.12) 0 (0.00) BMI Underweight 4 (0.07) Normal weight 63 (1.11) Pre-obesity 86 (1.51) Obesity class I 47 (0.83) Obesity class II 17 (0.30) Obesity class III 7 (0.12) NA 5,467 (96.06) (Neo)Adjuvant therapy No treatment 142 (2.50) Hormone therapy 157 (2.76) Chemotherapy 697 (12.25) Anti-HER2 94 (1.65) Hormone therapy + Chemotherapy 6 (0.11) AntiHER2 + Chemotherapy 107 (1.88) Radiotherapy + Chemotherapy 28 (0.49) Radiotherapy + Hormone therapy 29 (0.51) Radiotherapy + Hormone therapy + Chemotherapy 19 (0.33) NA 4,412 (77.53) Disease Free Survival Events recurrence 120 (2.11) Events censored 279 (4.90) Median follow up time in months / range 73.37 / 0.00 - 222.29 NA 5,292 (92.99) Menopausal status Pre 209 (3.67) 797 (14.00) Post 474 (8.33) 4,894 (86.00) NA 5,008 (88.00) 0 (0.00) Ki67 score 20% 269 (4.73) 3,864 (67.90) NA 5,204 (91.44) 0 (0.00) NA = Not available Table 2. Patient characteristics of the adipose tissue samples Samples Provided (total = 978) N (%) Sex Female 508 (51.94) Male 249 (25.46) Not specified 66 (6.75) NA 155 (15.85) Age in years 0-10 8 (0.82) 11-20 2 (0.20) 21-30 4 (0.41) 31-40 7 (0.72) 41-50 23 (2.35) 51-60 60 (6.13) 61-70 5 (0.51) 71-80 1 (0.10) NA 868 (88.75) BMI Underweight 1(0.10) Normal weight 5 (0.51) Pre-obesity 49 (5.01) Obesity class I 20 (2.04) Obesity class II 17 (1.74) Obesity class III 30 (3.07) NA 856 (87.53) Menopausal status Pre 0 (0.00) Post 70 (7.16) NA 908 (92.84) Origin adipose tissue Abdomen 616 (62.99) Breast 113 (11.55) Subcutaneous tissue 105 (10.74) Visceral 66 (6.75) Omentum 41 (4.19) Pericardium 26 (2.66) Parathyroid gland 9 (0.92) NA 2 (0.20) NA = Not available Table 4. TCs not capturing effect of copy number alteration and enrichment score above Bonferroni corrected threshold. TC Maximum enrichment score TC 179 18,5713 TC 39 16,1594 TC 259 14,6489 TC 14 14,3722 TC 1 13,3054 TC 106 13,1567 TC 26 13,154 TC 272 12,6178 TC 95 12,0982 TC 48 11,7364 TC 137 11,7238 TC 235 11,6626 TC 15 11,3197 TC 65 10,9691 TC 13 10,8541 TC 301 10,6074 TC 4 10,4854 TC 89 10,3728 TC 220 10,372 TC 244 10,2134 TC 18 10,1743 TC 302 9,92192 TC 315 9,87039 TC 166 9,74199 TC 371 9,4633 TC 168 9,37375 TC 152 9,26517 TC 245 9,13608 TC 263 9,10884 TC 296 9,04494 TC 3 8,96657 TC 364 8,96597 TC 212 8,88753 TC 37 8,84051 TC 120 8,72606 TC 314 8,72341 TC 246 8,63747 TC 310 8,36778 TC 237 8,23424 TC 273 8,1167 TC 22 8,06705 TC 171 7,97131 TC 297 7,8838 TC 249 7,85138 TC 358 7,82819 TC 45 7,78023 TC 41 7,74855 TC 158 7,70934 TC 49 7,65509 TC 184 7,63996 TC 393 7,60179 TC 396 7,5131 TC 87 7,51125 TC 344 7,50429 TC 400 7,49823 TC 401 7,33484 TC 350 7,27542 TC 288 7,2202 TC 62 7,16419 TC 7 7,06408 TC 40 7,04966 TC 8 7,03639 TC 117 7,03014 TC 285 6,98395 TC 257 6,86179 TC 199 6,66557 TC 409 6,65945 TC 280 6,63155 TC 151 6,62098 TC 93 6,58075 TC 384 6,57088 TC 309 6,43255 TC 24 6,39584 TC 77 6,39545 TC 408 6,38709 TC 385 6,35949 TC 289 6,31232 TC 110 6,14364 TC 377 5,96976 TC 164 5,96233 TC 346 5,93033 TC 131 5,87961 TC 190 5,8592 TC 177 5,79046 TC 70 5,76551 TC 148 5,76171 TC 215 5,7187 Additional Declarations No competing interests reported. Supplementary Files Table3.xlsx Additionalfigure1.pdf Additional figure 1. Distribution of activity scores of DFS-associated TCs in single cell mRNA expression profiles from healthy adipose tissues. To get insight into the role of adipose tissues on the biology captured by these DFS-associated TCs, we used single-cell transcriptome data obtained from the Single Cell Atlas. Box and whisker plots illustrate the activity of DFS-associated TCs in these mRNA expression profiles. The lower end of the box and the upper end of the box indicate the 1st and 3rd quartile. The vertical line that splits the box in two indicates the median. The whiskers indicate 1.5 x interquartile range below and above the 1st and 3rd quartiles respectively. Individual points indicate outliers. DFS, disease-free survival; TCs, transcriptional components. Additionaltable1.docx Additional table 1. Overview of the studies containing the adipose tissue samples. Additionaltable2.docx Additional table 2. Overview of the studies containing the breast tumor samples. Additionaltable3.xlsx Additional table 3. Maximum enrichment score of TCs having an enrichment score below the Bonferroni corrected threshold. Additionaltable4.xlsx Additional table 4. Difference in activity scores of the biologically enriched TCs between the different adipose tissue compartments. Additionaltable5.xlsx Additional table 5. Difference in activity scores of biologically enriched TCs between breast cancer and adipose tissue samples. Additionaltable6.docx Additional table 6 A. Proportion of samples with significant high activity scores of DFS-associated TCs in adipose tissue compartments. B. Proportion of samples with significant low activity scores of DFS-associated TCs in adipose tissue compartments. Additionaltable7.xlsx Additional table 7. Number of single cell profiles with significant activity scores of DFS-associated TCs. Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 27 Mar, 2026 Reviewers agreed at journal 08 Feb, 2026 Reviewers agreed at journal 03 Feb, 2026 Reviewers agreed at journal 02 Feb, 2026 Reviewers invited by journal 02 Feb, 2026 Editor assigned by journal 08 Jan, 2026 Submission checks completed at journal 08 Jan, 2026 First submitted to journal 07 Jan, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8544684","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":584541382,"identity":"8f31f781-c5b4-4624-9fee-7e062f4057c4","order_by":0,"name":"Marlous Arjaans","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyElEQVRIiWNgGAWjYLACCRDB3sB4AETzEa+F5wADWAsbCVYlEKlFt4H52QeLGrs8+ZmPDxzm+cWQR1CL2QE24xkSx5KLDW6nJRzm7WMoJkILgzGDBBtz4gbpHIODM3sYEtsIa2H/zCDxrz5x/swzRGvhMWaQbDuc2HCDx+DAhx/EaDnMU8wg2Xc8ccOZtIQDHxskiNByvH0zs8S36sT57YcPPkj4Y5PYT0gLAzMQScA4jG0S+NQiAOMHOPMPcTpGwSgYBaNgZAEA6J5ActWbDaMAAAAASUVORK5CYII=","orcid":"","institution":"Onze Lieve Vrouwe Gasthuis","correspondingAuthor":true,"prefix":"","firstName":"Marlous","middleName":"","lastName":"Arjaans","suffix":""},{"id":584541383,"identity":"912e615a-3fea-415e-bb64-d040b35852a6","order_by":1,"name":"Bert van der Vegt","email":"","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Bert","middleName":"van der","lastName":"Vegt","suffix":""},{"id":584541384,"identity":"ef2a8651-f06b-41b0-9234-e990e381f1af","order_by":2,"name":"Renske Linstra","email":"","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Renske","middleName":"","lastName":"Linstra","suffix":""},{"id":584541385,"identity":"36eefc22-a61d-43b9-a590-8ffe5acf27a9","order_by":3,"name":"Arkajyoti Bhattacharya","email":"","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Arkajyoti","middleName":"","lastName":"Bhattacharya","suffix":""},{"id":584541386,"identity":"fea13f65-8116-43dc-9211-36259a0ec7a2","order_by":4,"name":"Rudolf S.N. Fehrmann","email":"","orcid":"","institution":"University Medical Center Groningen","correspondingAuthor":false,"prefix":"","firstName":"Rudolf","middleName":"S.N.","lastName":"Fehrmann","suffix":""}],"badges":[],"createdAt":"2026-01-07 19:08:29","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8544684/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8544684/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101874911,"identity":"5329c407-8df9-452d-9873-3c90469e151f","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":324060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eWorkflow indicating relations between the methods.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/b947dabc9b8151211de2143b.png"},{"id":101874918,"identity":"0b29f214-a71b-40f2-8d63-5d03ac38d817","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":213863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFlowchart of data acquisition.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/da3c64a0b902723109cd2b54.png"},{"id":101874922,"identity":"e810c24a-6ac1-432e-acdd-10dafbb8cd83","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":733951,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eComparative UMAP Distribution of Gene Expression Profiles Based on Biologically Enriched vs. Non-Enriched TCs.\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe figure features two panels, each representing a scatterplot of UMAP dimension weights for the samples. The left panel displays the distribution based on the activity scores of biologically enriched transcriptional components (TCs), while the right panel illustrates the distribution based on the activity scores of biologically non-enriched TCs. Biological enrichment is defined as having an enrichment score that surpasses the Bonferroni threshold for multiple testing correction. Each dot in the scatterplots is color-coded according to one of 92 unique series IDs (GSE ID) to demonstrate the distribution of samples from different studies.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/37c9abb336c23e20ca2ad2a3.png"},{"id":101881768,"identity":"36058777-a726-4d19-bd41-5b842fb8af97","added_by":"auto","created_at":"2026-02-04 15:16:19","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1687958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHeatmap Depicting Hallmark Gene Set Enrichment in DFS-Associated TCs and Activity Levels Across Adipose Tissue.\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eThe figure consists of three panels. The leftmost panel showcases GSEA results for the 35 TCs found to be associated with disease-free survival (DFS) in univariate analysis. Included are Hallmark gene sets that meet or exceed the Bonferroni threshold for multiple testing correction. Clustering of gene sets is performed using Pearson correlation and the Ward D2 method, with heatmap colors based on z-scores capped at an absolute value of ten. The middle panel illustrates the proportion of samples showing significantly high activity in each adipose tissue compartment, while the rightmost panel presents the proportion of samples with significantly low activity in these compartments. Proportions range from zero to one.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/24b7af81551fc2aa8963711b.png"},{"id":101881858,"identity":"32980321-3461-4a9b-8195-32e26a2bc695","added_by":"auto","created_at":"2026-02-04 15:17:14","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":216318,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eHeatmap Depicting activity scores of DFS-Associated TCs and association with clinic-pathological variables.\u003c/em\u003e\u003cstrong\u003e A. \u003c/strong\u003eThe figure consists of two panels. The top panel showcases the different categories of clinico-pathological variables (BMI, ki67, HER2 status, PR status, ER status, lymph node involvement, tumor grade, BRCA1 mutation status) per sample. The bottom panel showcases activity scores of the 35 TCs found to be associated with disease-free survival (DFS) in univariate analysis. Clustering of samples was performed using Euclidean distance and the complete method, with heatmap colors based on activity scores. \u003cstrong\u003eB. \u003c/strong\u003eThe heatmap showcases the association of activity scores of the 35 TCs found to be associated with disease-free survival (DFS) in univariate analysis with different categories of clinico-pathological variables (BMI, ki67, HER2 status, PR status, ER status, lymph node involvement, tumor grade, BRCA1 mutation status). For categorical variables, -log\u003csub\u003e10\u003c/sub\u003e(p-value)*sign(difference between medians of two grouprs) from Mann-whitney U test was utilized and if the variable is numeric, -log\u003csub\u003e10\u003c/sub\u003e(p-value)*sign(estimate) from correlation test was utilized. Clustering of association score was performed using Euclidean distance and the complete method, with heatmap colors based on association scores.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/2d720a744f310334aae69511.png"},{"id":101881120,"identity":"7791cf18-cabf-4f55-a74e-14f0e4e1ce14","added_by":"auto","created_at":"2026-02-04 15:10:00","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":2549505,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSpatial transcriptomic profiles in breast cancer samples\u003c/em\u003e. To pinpoint the areas of significant TC activity in spatial transcriptomic profiles, we employed a permutation-based approach. We ran 5,000 permutations for each TC-profile combination, yielding a p-value that indicates the extent to which the TC's activity in the corresponding profile differs from what would be expected by chance (the null distribution). We then transformed these p-values into z-scores and represented them using a heatmap for 11 samples and four of the TCs related to adipogenesis. The following individual spatial transcriptomic breast cancer samples were used: BC1 (Block 738811QB Section 1. Tumor Grade II), BC2(T2N0M0, ER positive, PR negative, Her2 positive, Tumor Grade III), BC3 (Invasive Ductal Carcinoma breast tissue, ER positive, PR negative, Her2 positive), BC4 (Section 2 of BC3), BC5 (Invasive Lobular Carcinoma breast tissue, ER positive, PR positive, HER2 negative), CID4290 (BC ER positive), CID4535 (BC ER postive), 1142243F (BC TNBC), 1160920F (BC TNBC), CID4465 (BC TNBC) and CID44971 (BC TNBC).\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/a25f701b612e8a905d43adf3.png"},{"id":101882674,"identity":"e7bd4400-e63c-4cae-b9be-b38564ffffb3","added_by":"auto","created_at":"2026-02-04 15:24:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7517935,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/8f9d8333-81d0-455f-9a7c-bc73041fef68.pdf"},{"id":101881825,"identity":"7addf6d9-beeb-46cb-9c3f-d33732f7f5f4","added_by":"auto","created_at":"2026-02-04 15:16:55","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15029,"visible":true,"origin":"","legend":"","description":"","filename":"Table3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/e92d6db74a8b81cb22c29a19.xlsx"},{"id":101881591,"identity":"efeeafe3-980a-4873-9a72-ec66a9f8eaea","added_by":"auto","created_at":"2026-02-04 15:13:32","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":286399,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional figure 1.\u003c/strong\u003e Distribution of activity scores of DFS-associated TCs in single cell mRNA expression profiles from healthy adipose tissues.\u003c/p\u003e\n\u003cp\u003eTo get insight into the role of adipose tissues on the biology captured by these DFS-associated TCs, we used single-cell transcriptome data obtained from the Single Cell Atlas. Box and whisker plots illustrate the activity of DFS-associated TCs in these mRNA expression profiles. The lower end of the box and the upper end of the box indicate the 1st and 3rd quartile. The vertical line that splits the box in two indicates the median. The whiskers indicate 1.5 x interquartile range below and above the 1st and 3rd quartiles respectively. Individual points indicate outliers. DFS, disease-free survival; TCs, transcriptional components.\u003c/p\u003e","description":"","filename":"Additionalfigure1.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/d4831b203c28dcb20ec58def.pdf"},{"id":101874913,"identity":"1e361c4b-95c9-4264-8b73-951908ee3a53","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":18655,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 1. \u003c/strong\u003eOverview of the studies containing the adipose tissue samples.\u003c/p\u003e","description":"","filename":"Additionaltable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/d82b8ab4d0fec64f20bbdc90.docx"},{"id":101874915,"identity":"0488da24-5112-41e2-9559-1e5b842ef993","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":31725,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 2. \u003c/strong\u003eOverview of the studies containing the breast tumor samples.\u003c/p\u003e","description":"","filename":"Additionaltable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/f38c17a43ab681fb2a2e7d54.docx"},{"id":101874924,"identity":"2e590767-5910-4e2d-9fe9-38b2afd0d92a","added_by":"auto","created_at":"2026-02-04 14:05:26","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":12009,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 3. \u003c/strong\u003eMaximum enrichment score of TCs having an enrichment score below the Bonferroni corrected threshold.\u003c/p\u003e","description":"","filename":"Additionaltable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/da5df25e33592089ee725636.xlsx"},{"id":101881859,"identity":"349418b7-97da-472d-84ce-3445470ab97e","added_by":"auto","created_at":"2026-02-04 15:17:14","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":21356,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 4.\u003c/strong\u003e\u003cem\u003e \u003c/em\u003eDifference in activity scores of the biologically enriched TCs between the different adipose tissue compartments.\u003c/p\u003e","description":"","filename":"Additionaltable4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/f417c163bbabecc2bfb9a6e1.xlsx"},{"id":101881781,"identity":"23159d1c-06b4-4d81-85bb-2e9108def755","added_by":"auto","created_at":"2026-02-04 15:16:24","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":25230,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 5.\u003c/strong\u003e Difference in activity scores of biologically enriched TCs between breast cancer and adipose tissue samples.\u003c/p\u003e","description":"","filename":"Additionaltable5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/f3d66f0633c4dbc3ebc15ae7.xlsx"},{"id":101874919,"identity":"c5417ac0-8b5c-4a0f-89da-083618a25ec8","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":28993,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eA.\u003c/strong\u003e Proportion of samples with significant high activity scores of DFS-associated TCs in adipose tissue compartments.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eB.\u003c/strong\u003e Proportion of samples with significant low activity scores of DFS-associated TCs in adipose tissue compartments.\u003c/p\u003e","description":"","filename":"Additionaltable6.docx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/3d8c45184dfab78581f9570c.docx"},{"id":101874923,"identity":"d4c5a961-3d94-4183-9993-358180d58fc6","added_by":"auto","created_at":"2026-02-04 14:05:25","extension":"xlsx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":11242,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAdditional table 7.\u003c/strong\u003e Number of single cell profiles with significant activity scores of DFS-associated TCs.\u003c/p\u003e","description":"","filename":"Additionaltable7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8544684/v1/d101f4154e80b47b6a9cf6ab.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eExploring the Transcriptional Crosstalk Between Adipose Tissue and Locoregional Recurrence in Breast Cancer Using Independent Component Analysis\u003c/p\u003e","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003eBreast cancer remains the leading cause of cancer-related mortality among women in developing countries and ranks second in developed nations.(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) Despite significant therapeutic progress, locoregional recurrences (LRR) persists as a major clinical challenge, with 5-year survival rates ranging from 40% to 65%.(\u003cspan additionalcitationids=\"CR3 CR4\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) This highlights the urgent need to identify factors contributing to elevated LRR risk.\u003c/p\u003e \u003cp\u003eRecent studies highlight the tumor microenvironment\u0026rsquo;s role in LRR, particularly the influence of adipose tissue. (\u003cspan additionalcitationids=\"CR7\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e) Breast adipose tissue is predominately composed of mature adipocytes (~\u0026thinsp;90%), with the remaining stromal-vascular fraction (SVF) contributing\u0026thinsp;~\u0026thinsp;10%. (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) Once considered merely an energy reservoir, adipocytes can transition into cancer-associated adipocytes (CAAs) in the presence of tumor cells, promoting proliferation, migration, and invasiveness in preclinical models. (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e) Additionally, genetic susceptibility factors such as breast cancer gene 1 (BRCA1) mutations may alter adipose-derived stem cells, driving more aggressive cancer behavior. (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e) While direct clinical evidence linking adipose tissue to LRR is limited, findings from a phase III trial of nearly 5,000 patients suggest that obese individuals with hormone-receptor-positive disease have an increased risk of LRR between 3- and 8-years post-diagnosis, whereas obese patients with triple-negative breast cancer (TNBC) appear to have a lower risk during the first three years. (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e) These observations underscore the complex interplay between obesity and LRR, emphasizing the need for a more nuanced understanding of adipose tissue\u0026rsquo;s role.\u003c/p\u003e \u003cp\u003eWith the increasing prevalence of breast reconstruction surgeries involving adipose tissue, understanding adipocytes\u0026rsquo; impact on LRR is becoming even more crucial. Autologous reconstruction using adipofasciocutaneous flaps is the gold standard, with common donor sites including the abdomen, thigh, lower back, buttocks, and omentum. (\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e) However, lipofilling, which involves harvesting adipose tissue via liposuction for defect correction, has raised concerns regarding oncological safety. Specifically, harvested adipocytes stem cells may stimulate residual tumor cells and promote recurrence; however, the current literature\u0026mdash;largely retrospective cohort studies\u0026mdash;remains inconclusive. (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eThe availability of bulk gene expression profiles from both breast cancer patients and adipose tissues offers a valuable opportunity to explore the role of adipose tissue in LRR risk. However, the bulk profiles represent an aggregate of tumor cells and various components of the tumor microenvironment, which can obscure subtle adipocyte-specific transcriptional signals pertinent to LRR. To address this limitation, we applied consensus-independent component analysis (c-ICA) to decompose bulk gene expression profiles into statistically independent transcriptional components (TCs), each reflecting distinct biological processes. (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e) This method enables the identification of both dominant and more subtle TCs and allows for their activity to be quantified in individual samples.\u003c/p\u003e \u003cp\u003eIn this hypothesis-generating study, we investigated whether adipose tissue from different donor sites exhibits distinct transcriptional patterns and examined whether these patterns are associated with LRR in breast cancer patients. We compiled bulk gene expression profiles from non-metastatic breast tumor samples and human adipose tissue samples, used c-ICA and Gene Set Enrichment Analysis (GSEA) to identify adipose tissue-specific TCs, and subsequently assessed the association of these TCs with disease free survival (DFS). Finally, we evaluated the spatial co-localization of DFS-associated TCs in spatially resolved transcriptomic data.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cp\u003eA detailed description is provided in the Additional Methods and in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e an overview is presented.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData acquisition, preprocessing, and quality control\u003c/h2\u003e \u003cp\u003eData acquisition, preprocessing, and quality control were performed as described previously. (\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Raw microarray bulk gene expression profiles of breast cancer samples and adipose tissues of non-cancer patients were obtained from the Gene Expression Omnibus (GEO). (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e) Additionally, clinicopathological information and follow-up data was obtained. Only samples processed using the Affymetrix HG-U133 Plus 2.0 platform (GPL570) were included, while those derived from cell lines or animal models were excluded. Duplicate samples were identified and removed based on MD5 hashes and high Spearman correlation coefficients (R\u0026thinsp;\u0026gt;\u0026thinsp;0.99). The robust multiarray algorithm was applied to preprocess and aggregate raw data, followed by principal component analysis for quality control, as previously described. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWe additionally retrieved 11 spatially resolved transcriptomes from breast cancer patients through Zenodo and the 10x Genomics repository (see Additional methods). Moreover, 10,000 single-cell profiles from adipose tissue of a healthy participant were obtained from GEO (study ID GSE134355). Genes with no detectable expression in all samples were excluded. For both the spatially resolved and single-cell transcriptomes, mRNA expression levels were normalized by removing the first principal component derived from the sample-wise correlation matrix.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eClinicopathological data collection\u003c/h3\u003e\n\u003cp\u003eClinicopathological data extracted for the breast cancer profiles included sex, age, tumor histological subtype, tumor grade, tumor size, TNM stage, lymph node involvement, estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status, BRCA1/2 mutation status, Ki67 proliferation index, menopausal status, body mass index (BMI), breast cancer subtype (both receptor based and intrinsic), treatment regimen, and DFS. Receptor status was included only when assessed according to the immunohistochemistry staining guidelines of the American Society of Clinical Oncology and College of American Pathologists. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) BRCA1/2 mutation were further classified as either germline or somatic. DFS was defined as the interval from diagnosis until disease recurrence, death, or last follow up.\u003c/p\u003e \u003cp\u003eFor human adipose tissue profiles, we collected data on tissue\u0026rsquo;s anatomical origin, sex, age, BMI (categorized in accordance WITH World Health Organization definitions), and menopausal status. Missing data were designated as \u0026ldquo;Not available\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eConsensus Independent Component Analysis (c-ICA)\u003c/h3\u003e\n\u003cp\u003eWe applied c-ICA to the preprocessed gene expression data, decomposing the profiles into statistically independent transcriptional signatures, referred to as TCs. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e) Each TC represents a distinct biological process, and the weight assigned to each gene indicates the direction and magnitude of that process\u0026rsquo;s influence on the gene\u0026rsquo;s expression. In addition to identifying TCs, c-ICA also generated a mixing matrix, which provides the activity scores of each TC for individual samples.\u003c/p\u003e\n\u003ch3\u003eBiological characterization of transcriptional components\u003c/h3\u003e\n\u003cp\u003eWe employed several approaches to characterize the identified TCs. First, we performed GSEA using 15 gene set collections from the Molecular Signatures Database (MsigDB) version 7.1 (see additional methods for details). (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) A TC was deemed significantly enriched for a given gene set if the absolute z-score exceeded 3. For visualization, we used the web-based tool ClustVis (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) to generate enrichment heatmaps, focusing on gene sets whose enrichment scores surpassed the Bonferroni-adjusted threshold for at least one TC. Second, we applied the Transcriptional Adaptation to Copy Number Alterations (TACNA) profiling technique (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e) to identify TCs that capture the downstream effects of copy number alterations (CNAs) on gene expression levels.\u003c/p\u003e\n\u003ch3\u003eUnivariate survival analysis\u003c/h3\u003e\n\u003cp\u003eWe conducted univariate Cox proportional hazards regression on a subset of breast cancer patients for whom DFS data were available, aiming to evaluate the association between TC activity and DFS. To limit false positives resulting from multiple testing, we employed a permutation-based framework with a false discovery rate (FDR) threshold of 1% and a confidence level (CL) of 80%.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of significantly active TCs in individual gene expression profiles\u003c/h2\u003e \u003cp\u003eWe calculated the activity score of each TC in a given gene expression profile by taking the dot product of the pseudo-inverse-derived TC vector with the corresponding expression levels in that profile. To determine the significance of this activity score, we used a permutation-based methodology: for each profile-TC pair, we performed 5,000 permutations of the gene weights within the TC and repeated activity score calculation, thus generating a null distribution of activity scores. If the Anderson-Darling test indicated that this null distribution deviated from normality, we applied a Johnson transformation\u0026mdash; using one of three optimal families of distributions (S, SU, SL)\u0026mdash;to both the null distribution and the observed activity score. Afterward, we standardized the transformed null distribution to have a mean of zero and a standard deviation of one, applying the same standardization to the observed activity score. We then fit symmetrical generalized Gaussian distributions to these null distributions to derive p-values for each profile-TC pair. A TC was deemed significantly active if its p-value was below 0.01. For each tissue compartment, we calculated the proportion of samples exhibiting significant activity for each TC, separately for positive and negative activity scores. In analyses involving spatially resolved and single-cell transcriptomes, we further used z-transformed p-values for data visualization.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cp\u003e\u003cstrong\u003eDataset characteristics of breast cancer and adipose tissue samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe compiled a comprehensive dataset of 6,083 breast cancer and 1,427 adipose tissue bulk gene expression profiles from GEO. Following preprocessing, duplicate removal, and quality control, the final dataset included 5,691 breast cancer samples and 978 adipose tissue samples, derived from 99 breast cancer studies (73 unique GEO series) and 25 adipose tissue studies (19 unique GEO series) (See Figure 2, Additional Tables 1 and 2). Detailed clinicopathological information for both sample types is summarized in Tables 1 and 2. Adipose tissue samples originated from eight anatomical sites: breast (n=113), abdomen (n=616), omentum (n=41), pericard (n=26), parathyroid gland (n=9), visceral (n=66), subcutaneous (n= 105), and unknown origin (n=2). Samples from unknown sites were excluded from further analyses.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of 332 biologically enriched transcriptional components with c-ICA\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe applied c-ICA to uncover transcriptional signatures of biological processes, identifying 411 statistically independent TCs. Of these, 245 reflected the transcriptional effects of CNAs (Table 3), as determined by TACNA profiling. An additional 87 TCs were significantly enriched for at least one gene set from the 15 collections in MsigDB, bringing the total to 332 biologically enriched TCs (Table 4).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;To compare the 332 biologically enriched TCs with the remaining 79 non-enriched TCs (Additional Table 3), we employed Uniform Manifold Approximation and Projection (UMAP) to visualize their activity scores. As illustrated in Figure 3, the UMAP plot based on the enriched TCs exhibited markedly less clustering by study ID compared to the plot derived from the non-enriched TCs. This qualitative observation was corroborated by quantitative assessment using the sum of squares of error (SSE) for clustering by study ID (6,453.48 vs. 165.47). A permutation-based test confirmed that this difference was highly significant (p-value \u0026lt; 5.0x10\u003csup\u003e-5\u003c/sup\u003e). These findings suggest that the 79 non-enriched TCs primarily represent non-biological batch effects. Accordingly, only the 332 biologically enriched TCs were used in subsequent analyses. Comprehensive information on TC composition and GSEA results is available in our public repository: http://transcriptional-landscape-breastcancer-adiposetissue.opendatainscience.net.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnatomical origin of adipose tissue associates with activity of biologically enriched TCs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe investigated whether the activity of biologically enriched TCs varies among adipose tissue from different anatomical locations. After applying Bonferroni correction for multiple testing (p \u0026lt; 0.01), 331 of the 332 biologically enriched TCs displayed significant differences in activity scores across distinct anatomical sites (Additional Table 4). Furthermore, 292 of these 332 TCs showed significantly different activity scores when comparing adipose tissue and breast cancer samples (Additional Table 5). These finding indicate that the biological characteristics of adipose tissue vary considerably based on anatomical origin.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eActivity scores of 35 biologically enriched TCs are associated with disease free survival in breast cancer patients\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the relationship between TC activity scores and DFS, we performed univariate Cox proportional hazards regression analysis on a subset of 619 breast cancer patients with available DFS data. Of the 332 biologically enriched TCs, 35 demonstrated a significant association with DFS; these are referred to as DFS-associated TCs. Notably, 31 of these 35 DFS-associated TCs showed significantly different activity between adipose tissues and breast cancer tissues (Bonferroni-adjusted p-value \u0026lt; 0.01; Figure 4 and Additional Table 6).\u003c/p\u003e\n\u003cp\u003eAll 35 DFS-associated TCs showed significant enrichment for at least one gene set from the Gene Ontology Biological Processes collection, with a median top z-score of 2.828 (interquartile range of 1.901 to 3.133). Among these, TC37 showed the strongest association with DFS and was highly enriched for genes related to hypoxia, glycolysis and MTORC1 signaling. We identified four TCs (TC257, TC350, TC371, and TC400) enriched for adipogenesis-related genes (referred to as adipogenesis-related TCs); of these, TC350 was the most enriched. TC257, TC350, and TC400 were more active in cancer samples from patients with grade 3 disease, triple negative breast cancer, and elevated BMI. Conversely, TC257, TC350, TC371, and TC400 were less active in samples from patients with grade 1 or 2 disease, hormone-positive breast cancer, and low Ki67 score (figure 5).\u003c/p\u003e\n\u003cp\u003eOther DFS-associated TCs were enriched for genes involved in diverse biological processes, including MYC targets and the mitotic spindle. These findings suggest that these TCs capture transcriptional effects from multiple biological processes that may influence the risk of LRR in breast cancer patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eElevated activity of adipogenesis-related TCs in tumor regions of spatially resolved breast cancer transcriptomes and single cell transcriptomes from healthy adipose tissue\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the co-localized activity of the four adipogenesis-related TCs and determine their cell origin, we analyzed spatial transcriptomic profiles from 11 breast cancer samples and 10,000 single-cell profiles from the adipose tissue of a healthy individual. Analysis of DFS-associated TC activity in the spatial transcriptomes revealed high activity of these four adipogenesis-related TCs in distinct tumor regions (BC1-5; Figure 6). Likewise, when comparing all DFS-associated TCs in the single-cell data, these four adipogenesis-related TCs consistently showed higher activity than the other TCs (Additional Figure 1). Notably, they ranked among the top TCs exhibiting significant activity (p-value \u0026lt; 0.01) across the highest number of single-cell mRNA expression profiles (Additional Table 7).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eUsing bulk gene expression profiles from non-metastatic breast tumor samples and human adipose tissue, we identified biological enriched TCs, 35 of which were significantly associated with DFS. Detailed biological characterization of these TCs are available through our public portal.\u003c/p\u003e \u003cp\u003eAmong the TCs linked to DFS, four (TC257, TC350, TC371, and TC400) were strongly enrichment for genes involved in adipogenesis, exhibited high active in adipose tissue, and were associated with DFS in breast cancer. The role of adipogenesis in breast cancer was recently investigated using gene expression profiles from 5,098 patients, employing Gene Set Variant Analysis to predict adipogenesis activity based on the Hallmark adipogenesis gene set. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) The study showed that heightened adipogenesis activity in TNBC correlates with an unfavorable immune microenvironment, characterized by elevated M2 macrophages infiltration and reduced CD8\u0026thinsp;+\u0026thinsp;T cells infiltration. Furthermore, in TNBC patients, adipogenesis activity was associated with worse disease specific survival and overall survival, whereas no such correlation was observed in other breast cancer subtypes. (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e) These findings align with our observation that the four adipogenesis-related TCs were active in patients with TNBC and high-grade disease but not in those with hormone-positive disease, low ki67 score, and intermediate or low-grade disease. However, while the above-mentioned study established a link between adipogenesis activity and an unfavorable immune microenvironment in TNBC, it did not further delve into the underlying transcriptional mechanisms driving this association. Using c-ICA, we were able to identify subtle but biologically relevant transcriptional components within the transcriptomes, allowing a more precise characterization of the transcriptional programs involved in adipogenesis. Moreover, our analysis incorporates adipose tissue from different compartments, providing additional insights into the activity of these TCs, which was not explored in previous studies.\u003c/p\u003e \u003cp\u003eWhen examining the genes with the highest weight in these four adipogenesis-related TCs, only Fatty Acid Binding Protein 4 (\u003cem\u003eFABP4\u003c/em\u003e), found in TC350, was described in the gene set \u0026lsquo;adipogenesis\u0026rsquo; in Hallmark. \u003cem\u003eFABP4\u003c/em\u003e is primarily expressed in adipocytes and macrophages, where it regulates metabolic and inflammatory pathways (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) Intracellular FABP4 in macrophages has been identified as a marker of pro-tumor, tumor associated macrophages (TAM). Additionally, elevated circulating FABP4 levels have been linked to breast cancer progression via increased activity of the IL-6/STAT3/ALDH1 pathway, thereby enhancing activity of ALDH1, a recognized stem cell marker in breast cancer. (\u003cspan additionalcitationids=\"CR32 CR33 CR34 CR35\" citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Furthermore, higher circulating FABP4 levels have been associated with an increased risk of breast cancer (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e) and elevated FABP4 expression is significantly correlated with shorter DFS and OS in TNBC patients compared to other subtypes (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e) These findings point to FABP4 as a promising therapeutic target. Indeed, multiple studies have investigated inhibiting FABP4 with small molecule agents and specific antibodies in breast cancer cell lines and animal models. (\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e)\u003c/p\u003e \u003cp\u003eWe demonstrated that adipose tissue from different anatomical locations exhibits distinct transcriptional patterns. Earlier studies further indicated that subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) differ not only in embryogenic origin but also in metabolic characteristics. (\u003cspan additionalcitationids=\"CR45\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e) These distinctions may be particularly relevant in reconstructive breast surgery, where adipose tissue from a donor site replaces or augments native breast tissue. We found that adipogenesis-related TC257, TC371, and TC400 generally showed low activity scores in both breast and donor sites; however, TC350 exhibited low activity in breast adipose tissue but high activity in adipose tissue from the abdomen, omentum, and subcutis. Moreover, TC350 was associated with worse DFS, suggesting that adipose tissue from these donor sites could potentially introduce new biological factors that increase LRR. An imaging study of 3,235 women with stage II or III breast cancer showed that increased SAT, but not VAT, correlated with worse overall mortality. (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) Similar findings have been reported in gastric cancer and hepatocellular carcinoma, where high SAT rather than VAT is a poor prognostic factor. (\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e) In contrast, increased VAT has been associated with higher incidence of colorectal cancer, whereas SAT was not. (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e) Taken together, these observations bolster the hypothesis that adipose tissue from different anatomical locations may exert distinct effects on tumor cells.\u003c/p\u003e"},{"header":"CONCLUSION","content":"\u003cp\u003eOur transcriptional mapping the adipose tissue\u0026ndash;breast cancer interfaces reveals distinct patterns associated with LRR, which vary in activity depending on the anatomical origin of the adipose tissues. These findings indicate that adipose tissue is not merely a passive bystander in breast cancer, particular in the context of reconstructive surgery using donor sites from outside the breast. Future studies should further elucidate how different types of adipose tissue influence tumor biology and clinical outcomes in reconstructive breast surgery.\u003c/p\u003e"},{"header":"ABBREVIATIONS","content":"\u003cp\u003eLRR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;locoregional recurrences\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSVF\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;stromal-vascular fraction\u003c/p\u003e\n\u003cp\u003eCAA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;cancer-associated adipocytes\u003c/p\u003e\n\u003cp\u003eBRCA!\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Breast cancer gene 1\u003c/p\u003e\n\u003cp\u003eTNB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;triple-negative breast cancer\u003c/p\u003e\n\u003cp\u003ec-ICA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;consensus-independent component analysis\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;transcriptional component\u003c/p\u003e\n\u003cp\u003eGSEA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Set Enrichment Analysis\u003c/p\u003e\n\u003cp\u003eDFS\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;disease free survival\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGEO\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Gene Expression Omnibus\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eER\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;estrogen receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;progesterone receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHER2\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;human epidermal growth factor receptor 2\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eBMI\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;body mass index\u003c/p\u003e\n\u003cp\u003eMsigDB\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Molecular Signatures Database\u003c/p\u003e\n\u003cp\u003eTACNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Transcriptional Adaptation to Copy Number Alterations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCAN\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;copy number alterations\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFDR\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;false discovery rate\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCL\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;confidence level\u003c/p\u003e\n\u003cp\u003eNA\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;Not available\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFABP4\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/em\u003eFatty Acid Binding Protein 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;subcutaneous adipose tissue\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eVAT\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;visceral adipose tissue\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe details of the datasets used are provided in the Additional Tables 1 and 2. Comprehensive information on TC composition and GSEA results is available in our public repository:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ehttps://transcriptional-landscape-breastcancer-adiposetissue.opendatainscience.net/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional funding was received for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization MA, RF / data acquisition MA, RF, AB / data analysis and interpretation MA, AB, RF/ Identification of tumor regions in breast cancer patient samples BvdV, RL/ writing of the manuscript MA, AB,RF / all authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank Jie Ma for his work in data acquisition for this study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo additional authors\u0026rsquo; information.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSociety AC (2015) Global cancer facts \u0026amp; Figs. 3rd edition. 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Nat Metab 2:97\u0026ndash;109\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHwang I, Kim JB (2019) Two faces of White Adipose tissue with heterogeneous adipogenic progenitors. Diabetes Metab J 43:752\u0026ndash;762\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradshaw PT, Cespedes Feliciano EM, Prado CM, Alexeeff S, Albers KB, Chen WY et al (2019) Adipose Tissue Distribution and Survival Among Women with Nonmetastatic Breast Cancer. Obes (Silver Spring) 27(6):997\u0026ndash;1004\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evon Hessen L, Roumet M, Maurer MH, Lange N, Reeves H, Dufour JF et al (2021) High subcutaneous adipose tissue density correlates negatively with survival in patients with hepatocellular carcinoma. Liver Int 41:828\u0026ndash;836\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan J, Tang M, Lu C, Shen L, She J, Wu G (2021) Subcutaneous, but not visceral, adipose tissue as a marker for prognosis in gastric cancer patients with cachexia. Clin Nutr 40:5156\u0026ndash;5161\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVazzana N, Riondino S, Toto V, Guadagni F, Roselli M, Davi G et al (2012) Obesity-driven inflammation and colorectal cancer. Curr Med Chem 19:5837\u0026ndash;5853\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIm JP, Kim D, Chung SJ, Jin EH, Han YM, Park MJ et al (2018) Visceral obesity as a risk factor for colorectal adenoma occurrence in surveillance colonoscopy. Gastrointest Endosc 88(1):119\u0026ndash;127e4\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 3 is available in the supplementary files section\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 1.\u0026nbsp;\u003c/strong\u003ePatient characteristics of the breast tumor tissue samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"593\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eProvided Samples \u0026nbsp;(5,691)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInferred Samples (5,691)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,691 (100.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21-30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e50 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31-40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e364 (6.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e790 (13.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51-60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e792 (13.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61-70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e520 (9.14)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71-80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e241 (4.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e81-90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e45 (0.79)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e2,889 (50.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtype (IHC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e47 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,529 (26.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e61 (1.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e2,072 (36.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 enriched\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e72 (1.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e572 (10.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal like\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e397 (6.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,518 (26.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,114 (89.86)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular subtype (Intrinsic)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e185 (3.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,959 (34.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e140 (2.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e2,096 (36.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 enriched\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e99 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e340 (5.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal like\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e225 (3.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,189 (20.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal like\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e39 (0.69)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e107 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,003 (87.91)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRCA 1/2 mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRCA1 somatic mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e34 (0.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e656 (11.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBRCA2 somatic mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e6 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e6 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGermline mutation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e15 (0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e56 (0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWild type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e139 (2.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e4,973 (87.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,497 (96.59)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor histological subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDCIS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e22 (0.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,043 (18.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eILC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e193 (3.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMixed\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e36 (0.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e48 (0.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4349 (76.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e262 (4.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e398 (6.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e818 (14.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,957 (34.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,236 (21.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e3,336 (58.62)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e3,375 (59.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size (cm)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003esize \u0026lt;= 2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e408 (7.17)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2 \u0026lt; size \u0026lt;= 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e512 (9.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003esize \u0026gt; 5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e50 (0.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4,721(82.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT - stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e548 (9.63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,103 (19.38)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e177 (3.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e59 (1.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e3,804 (66.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN - stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e713 (12.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e482 (8.47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e196 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e146 (2.57)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4154 (72.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM - stage\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e111 (1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e111 (1.95)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eM0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,093 (19.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e5,580 (98.05)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4487 (78.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node involvement\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,592 (27.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,336 (23.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e2,763 (48.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eER status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,849 (32.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e3,537 (62.15)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,204 (21.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e2,154 (37.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e2,638 (46.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePR status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,072 (18.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e2,944 (51.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,097 (19.28)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e2,747 (48.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e3,522 (61.89)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2 status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePositive\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e652 (11.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,289 (22.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNegative\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e1,902 (33.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e4,402 (77.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e3,137 (55.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderweight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4 (0.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e63 (1.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e86 (1.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e47 (0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e17 (0.30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e7 (0.12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,467 (96.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e(Neo)Adjuvant therapy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo treatment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e142 (2.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormone therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e157 (2.76)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e697 (12.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnti-HER2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e94 (1.65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHormone therapy +\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eChemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e6 (0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAntiHER2 + Chemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e107 (1.88)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy + Chemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e28 (0.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy + Hormone therapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e29 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiotherapy + Hormone therapy + Chemotherapy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e19 (0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e4,412 (77.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease Free Survival\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents recurrence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e120 (2.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEvents censored\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e279 (4.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMedian follow up time in months /\u003cem\u003e\u0026nbsp;\u003c/em\u003erange\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e73.37 \u0026nbsp;/ 0.00 - 222.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,292 (92.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e209 (3.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e797 (14.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e474 (8.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e4,894 (86.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,008 (88.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eKi67 score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;= 20%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e218 (3.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e1,827 (32.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt; 20%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e269 (4.73)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e3,864 (67.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 34.4013%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 33.7268%;\"\u003e\n \u003cp\u003e5,204 (91.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 31.8718%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA = Not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003ePatient characteristics of\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ethe adipose tissue samples\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"376\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSamples Provided (total = 978)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFemale\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e508 (51.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMale\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e249 (25.46)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNot specified\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e66 (6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e155 (15.85)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge in years\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0-10\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e8 (0.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11-20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e2 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e21-30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e4 (0.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e31-40\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e7 (0.72)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e41-50\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e23 (2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e51-60\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e60 (6.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e61-70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e5 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e71-80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e1 (0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e868 (88.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnderweight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e1(0.10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal weight\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e5 (0.51)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre-obesity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e49 (5.01)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class I\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e20 (2.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e17 (1.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eObesity class III\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e30 (3.07)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e856 (87.53)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e0 (0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e70 (7.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e908 (92.84)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 100%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOrigin adipose tissue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAbdomen\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e616 (62.99)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e113 (11.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubcutaneous tissue\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e105 (10.74)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVisceral\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e66 (6.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOmentum\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e41 (4.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePericardium\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e26 (2.66)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParathyroid gland\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e9 (0.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 42.2872%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 57.7128%;\"\u003e\n \u003cp\u003e2 (0.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNA = Not available\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003eTCs not capturing effect of copy number alteration and enrichment score above Bonferroni corrected threshold.\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eMaximum enrichment score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e18,5713\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e16,1594\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 259\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e14,6489\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e14,3722\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e13,3054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e13,1567\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e13,154\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e12,6178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e12,0982\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e11,7364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e11,7238\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e11,6626\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e11,3197\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,9691\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,8541\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 301\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,6074\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,4854\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,3728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,2134\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e10,1743\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 302\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,92192\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 315\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,87039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,74199\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 371\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,4633\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 168\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,37375\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 152\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,26517\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 245\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,13608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,10884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e9,04494\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,96657\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 364\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,96597\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 212\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,88753\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,84051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,72606\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 314\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,72341\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 246\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,63747\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 310\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,36778\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 237\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,23424\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 273\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,1167\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e8,06705\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 171\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,97131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,8838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 249\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,85138\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,82819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,78023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,74855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 158\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,70934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,65509\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 184\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,63996\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 393\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,60179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,5131\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,51125\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,50429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,49823\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 401\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,33484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 350\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,27542\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 288\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,2202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,16419\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,06408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,04966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,03639\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e7,03014\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,98395\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 257\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,86179\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 199\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,66557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 409\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,65945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,63155\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,62098\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,58075\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,57088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,43255\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,39584\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,39545\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,38709\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,35949\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 289\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,31232\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 110\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e6,14364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 377\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,96976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 164\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,96233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,93033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,87961\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,8592\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 177\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,79046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,76551\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,76171\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 50%;\"\u003e\n \u003cp\u003eTC 215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50%;\"\u003e\n \u003cp\u003e5,7187\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, adipose tissue, locoregional recurrence, breast reconstructive surgery","lastPublishedDoi":"10.21203/rs.3.rs-8544684/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8544684/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLocoregional recurrence (LRR) poses a persistent clinical challenge in breast cancer, with emerging evidence implicating the tumor-associated adipose tissue in modulating recurrence risk. This study investigates shared transcriptional programs between adipose tissue and breast tumors and examines their association with disease-free survival (DFS), particularly in the context of reconstructive surgery where adipose tissue from different body compartments are commonly used.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analyzed bulk gene expression data from 5,691 breast tumors and 978 human adipose tissue samples from different body compartments using consensus-independent component analysis (c-ICA) to identify transcriptional components (TCs). Gene set enrichment analysis (GSEA) and copy number alteration profiling were used for biological annotation. Associations between TCs and DFS were evaluated through univariate Cox regression. Key findings were validated using spatial transcriptomic and single-cell RNA sequencing datasets.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 411 TCs identified, 332 showed biological enrichment, and 35 were significantly associated with DFS. Four DFS-associated TCs (TC257, TC350, TC371, TC400) were enriched for adipogenesis-related genes and exhibited heightened activity in high-grade, triple-negative tumors and in patients with elevated BMI. Notably, TC350 was highly active in adipose tissue from common reconstructive donor sites (abdomen, omentum, subcutis) but not in native breast adipose tissue. Spatial transcriptomic and single-cell analyses confirmed the increased activity of these adipogenesis-related TCs in tumor regions and adipose cells. TC350 included \u003cem\u003eFABP4\u003c/em\u003e, a gene previously linked to poor prognosis in breast cancer and considered as a potential new therapeutic target.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAdipose tissue-derived transcriptional programs influence breast cancer prognosis and differ by tissue origin. These findings suggest that donor site selection for adipose tissue in reconstructive surgery may impact LRR risk through adipogenesis-associated mechanisms. Further research is warranted to elucidate the biological and clinical implications of adipose\u0026ndash;tumor transcriptional interactions.\u003c/p\u003e","manuscriptTitle":"Exploring the Transcriptional Crosstalk Between Adipose Tissue and Locoregional Recurrence in Breast Cancer Using Independent Component Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-04 14:05:17","doi":"10.21203/rs.3.rs-8544684/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-03-27T18:02:23+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315457073133271247949404416416367448827","date":"2026-02-08T18:44:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"18481473296391682761992189146005693067","date":"2026-02-03T23:44:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"213537670877096079366347088227637718088","date":"2026-02-02T14:34:26+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-02T10:13:59+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-01-08T12:42:28+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-01-08T12:40:58+00:00","index":"","fulltext":""},{"type":"submitted","content":"Functional \u0026 Integrative Genomics","date":"2026-01-07T18:55:10+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"functional-and-integrative-genomics","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"fige","sideBox":"Learn more about [Functional \u0026 Integrative Genomics](http://link.springer.com/journal/10142)","snPcode":"10142","submissionUrl":"https://submission.nature.com/new-submission/10142/3","title":"Functional \u0026 Integrative Genomics","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"327f069b-12fe-4f98-b40b-7e81e95089cd","owner":[],"postedDate":"February 4th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-04T14:05:19+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-04 14:05:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8544684","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8544684","identity":"rs-8544684","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}