Whole transcriptome analysis reveal MammaPrint and BluePrint-associated gene expression patterns with early lymph node metastasis in early-stage breast cancer

Whole transcriptome analysis reveal MammaPrint and BluePrint-associated gene expression patterns with early lymph node metastasis in early-stage breast cancer | 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 Whole transcriptome analysis reveal MammaPrint and BluePrint-associated gene expression patterns with early lymph node metastasis in early-stage breast cancer Faisal Fa’ak, Josien Haan, Nicole Chmielewski-Stivers, Andrea Menicucci, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7347995/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Introduction: Early lymph node (LN) metastasis often precedes systemic metastasis and corresponds with significantly inferior survival for patients diagnosed with early-stage breast cancer (EBC). To understand the biological pathways involved in early LN metastasis, differential gene expression (DGE)analysis compared large tumors without evidence of LN metastasis (pT2-3pN0) to small tumors with LN metastasis (pT1pN+). Methods: This study included 2,349 patients with EBC who underwent MammaPrint and BluePrint testing as part of the FLEX (NCT03053193). DGE was performed between pT2-3pN0/pT1pN+ and across their MP/BP subtypes. Immune deconvolution was assessed using gene-signature-based methods, complemented by conventional tumor-infiltrating lymphocyte (TIL) analyses on a representative subset of patients. Results: Greater DGE was observed within the MammaPrint High Risk and BluePrint Luminal B subtypes compared to pathological stages. MammaPrint High Risk tumors saw 73 differentially expressed genes (DEGs), while 34 were found for Luminal B tumors. Gene set enrichment analysis (GSEA) of MammaPrint High Risk/Luminal B tumors showed upregulated proliferation pathways and downregulated epithelial-to-mesenchymal transition (EMT) and immune profiles in pT2-3pN0 vs. pT1pN+, respectively. Immune deconvolution analyses showed a higher abundance of T gamma delta cells and CD4+ Th1 cells and a lower abundance of T regulatory cells, M2 macrophages, and cancer-associated fibroblasts within pT2-3pN0 tumors. Conventional histological assessment revealed no significant differences in TILs. Conclusion: This study lays the groundwork for exploring mechanisms of LN metastasis in EBC and their relation to MammaPrint High Risk and Luminal B subtypes. These data support previous studies’ association of LN metastasis with EMT and immune dysregulation. Breast cancer locoregional metastasis nodal metastasis metastatic breast cancer genomic risk Figures Figure 1 Figure 2 Figure 3 Figure 4 INTRODUCTION Lymph node (LN) metastasis is a critical step in the metastatic progression of early-stage breast cancer (EBC). The notable 35% difference in 10-year survival between patients with and without LN metastasis underscores the importance of understanding the mechanisms associated with LN metastasis and identifying biomarkers to improve diagnostic and therapeutic approaches.[ 1 , 2 ] Clinical features, including tumor size, lymphovascular invasion, tumor grade, and patient age, may correlate with node-positive disease.[ 3 – 7 ] Tumor size modestly correlates with axillary metastases, but systemic metastases do not strictly follow local invasion. Small tumors often present with axillary LN metastases, while larger tumors may lack LN involvement. The tumor or host-specific factors underlying early LN metastasis, or conversely, the favorable factors responsible for the absence of metastases in locally extensive tumors, remain poorly understood. Biomarkers that more accurately and precisely predict LN metastasis can allow for more accurate patient risk stratification. Numerous gene pathways linked to LN metastasis have been explored. The overexpression of several oncogenes, including BCL2L1, AURKA, CDKN2A, MCL1, and MYC, have been associated with LN and distant metastasis. However, these are not precise enough to serve as predictive biomarkers.[ 8 – 12 ] Bao et al. identified a high-level gain of MCL1 and chromosome 8q amplification as key factors associated with tumor spread to lymph nodes. Moreover, gains in chromosomes 12q and 20q, along with losses in 1p and 9p, were predominantly observed in estrogen receptor (ER)-positive breast cancers with lymph node metastases. Elevated MYC copy numbers were also frequently reported in LN-positive breast cancer patients.[ 13 ] Additionally, gene pathways related to epithelial-mesenchymal transition (EMT) and immune function have been previously linked to LN metastasis. However, a transcriptomic signature specifically responsible for early LN metastases has not yet been identified.[ 8 , 14 ] The FLEX (NCT03053193) registry of breast cancer patients with stage I-III disease, consenting to MammaPrint (MP) risk-of-recurrence, BluePrint (BP) molecular subtyping, and whole transcriptome genomic studies, has provided insight into biological mechanisms associated with risk of distant metastasis [ 15 ] and treatment planning outcomes [ 16 ]. To provide more insight into the specific aspects of tumor biology that drive early LN metastasis in EBC within the FLEX registry, we conducted transcriptomic analyses comparing clinically characterized small tumors with axillary LN metastases (pathological [p] T1pN+) and larger tumors without such metastases (pT2-3pN0) across MP and BP molecular subtypes. Methods Patient Cohort and Genomic Assay This study analyzed a subset of patients from the FLEX trial (NCT03053193), a real-world, longitudinal, prospective registry enrolling stage I-III breast cancer patients. These patients underwent MP with or without BP testing, performed by Agendia (Irvine, CA, USA) as described previously,[17, 18] and consented to clinically annotated full transcriptome data collection. MP is a 70-gene signature that classifies tumors as having a High or Low Risk of distant recurrence.[18, 19] BP is an 80-gene molecular subtyping signature that further classifies MP Low Risk tumors as Luminal A-Type and High Risk tumors as Luminal B-Type, HER2-Type, or Basal-Type molecular subtypes.[18] Clinical Characteristic Analysis Descriptive statistics were used to summarize clinical characteristics and genomic results. Group differences were assessed using Pearson’s Chi-squared tests, Fisher’s exact test (for categorical variables), or Student’s t-test (for numerical variables). Statistical significance was defined by a two-sided p-value of p<0.05 for all tests. All statistical analyses were conducted using R (version 4.1.1). Gene Expression Analysis and Immune Deconvolution Gene expression quantile normalization and differential gene expression analysis (DGEA) were performed using the Limma R package in Bioconductor (version 3.42.2).[20] Primary comparison of large tumors without LN metastasis and small tumors with LN metastasis (pT2-3pN0 vs. pT1pN+) was performed and four control analyses: 1) large tumors without and with LN (pT2-3pN0 vs. pT2-3 pN+), 2) small tumors without and with LN (pT1pN0 vs. pT1pN+), 3) large tumors and small tumors without LN (pT2-3pN0 vs. pT1pN0), and 4) large tumors and small tumors with LN (pT2-3pN+ vs. pT1pN+). Comparisons were performed between all patient samples and within MP High and Low Risk cohorts. P-values were adjusted for multiple testing according to Benjamini-Hochberg (false discovery rate, FDR)[21]. DEGs with FDR-adjusted p-values 1.4-fold change were considered significant. Gene set enrichment analysis (GSEA) was performed using the fgsea R package in Bioconductor (version 1.12.0)[22] and 50 hallmark gene sets[23]. The gene ranking input for GSEA was based on the Limma output (adjusted p-value). Immune cell-type quantifications were defined by the gene-signature-based methods xCell [24] and Estimating the Proportion of Immune and Cancer cells (EPIC),[25] differences were determined by t-test. Tumor-Infiltrating Lymphocyte (TIL) Scoring For this analysis, TILs were quantified in a random subset of patients within stratified subsets of the four patient subgroups (n=300). Consistent with the main analysis, the primary cohorts for the TIL analysis were small tumors with early LN metastasis (pT1pN+) vs. larger tumors without LN metastasis (pT2-3pN0). Control cohorts were also included and comprised of small tumors without LN metastasis (pT1pN0) and larger tumors with LN metastasis (pT2-3pN+). TILs were quantified by a breast pathologist (JB) on Hematoxylin and Eosin (H&E) slides using previously described methods.[26] TIL scores were evaluated as continuous measures. Results Baseline Patient Characteristics A total of 2349 patients were included in this study. The median age was 63, and 76% of the patients were postmenopausal. Among the patient cohort, 1974 patients had HR+ HER2- tumors, 90 were HER2+, 64 had TNBC, and 221 were unknown. For our primary comparison, 235 patients had large, LN-negative tumors (pT2-3pN0) and 355 patients had small, LN-positive tumors (pT1pN+). The control group comparisons comprised 1,384 patients with small, lymph node-negative tumors (pT1pN0) and 375 patients with large lymph node-positive tumors (pT2-3pN+). MP and BP characterized 53.6% of tumors as Luminal A-Type (n=1259), 33.5% as Luminal B-Type (n=787), 1.4% as HER2-Type (n=32), 5.2% as Basal-Type (n=122), and 6.3% as unknown (n=149). ( Table 1 ) Table 1. Clinical Characteristics Primary Comparison Control Groups Characteristic pT2-3pN0 pT1pN1+ pT2-3pN+ pT1pN0 P-value Patients (n) 235 355 375 1384 Age, median (SD) 61 (± 11) 59 (± 11) 60 (± 12) 62 (± 11) <0.001 Menopausal status Pre/peri- 44 (18.7%) 73 (20.6%) 97 (25.9%) 205 (14.8%) <0.001 Post- 184 (78.3%) 259 (73.0%) 253 (67.5%) 1103 (79.7%) Unknown 7 (3.0%) 23 (6.5%) 25 (6.7%) 76 (5.5%) Clinical subtype HR+HER2- 204 (86.8%) 297 (83.7%) 314 (83.7%) 1159 (83.7%) <0.001 HR+HER2+ 7 (3.0%) 7 (2.0%) 11 (2.9%) 50 (3.6%) HR-HER2+ 1 (0.4%) 5 (1.4%) 1 (0.3%) 8 (0.6%) TNBC 9 (3.8%) 6 (1.7%) 1 (0.3%) 48 (3.5%) Unknown 14 (6.0%) 40 (11.3%) 48 (12.8%) 119 (8.6%) Histological grade G1, Low 50 (21.3%) 104 (29.3%) 69 (18.4%) 479 (34.6%) <0.001 G2, Intermediate 111 (47.2%) 180 (50.7%) 223 (59.5%) 657 (47.5%) G3, High 61 (26.0%) 41 (11.5%) 55 (14.7%) 168 (12.1%) Unknown 13 (5.5%) 30 (8.5%) 28 (7.5%) 80 (5.8%) Tumor size T1 0 (0%) 355 (100%) 0 (0%) 1384 (100%) <0.001 T2 223 (94.9%) 0 (0%) 318 (84.8%) 0 (0%) T3 12 (5.1%) 0 (0%) 57 (15.2%) 0 (0%) Lymph node status, N N0 235 (100%) 0 (0%) 0 (0%) 1384 (100%) <0.001 N1 0 (0%) 326 (91.8%) 321 (85.6%) 0 (0%) N2 0 (0%) 24 (6.8%) 37 (9.9%) 0 (0%) N3 0 (0%) 5 (1.4%) 17 (4.5%) 0 (0%) Histology Invasive ductal carcinoma 176 (74.9%) 287 (80.8%) 273 (72.8%) 1151 (83.2%) <0.001 Invasive lobular carcinoma 52 (22.1%) 41 (11.5%) 82 (21.9%) 135 (9.8%) Mixed ductal and lobular features 2 (0.9%) 9 (2.5%) 6 (1.6%) 31 (2.2%) Other 5 (2.1%) 14 (3.9%) 13 (3.5%) 60 (4.3%) Unknown 0 (0%) 4 (1.1%) 1 (0.3%) 7 (0.5%) MammaPrint Low 111 (47.2%) 197 (55.5%) 202 (53.9%) 824 (59.5%) <0.001 High 124 (52.8%) 158 (44.5%) 173 (46.1%) 560 (40.5%) BluePrint Luminal A-Type 104 (44.3%) 173 (48.7%) 181 (48.3%) 801 (57.9%) <0.001 Luminal B-Type 94 (40.0%) 123 (34.6%) 142 (37.9%) 428 (30.9%) HER2-Type 1 (0.4%) 7 (2.0%) 3 (0.8%) 21 (1.5%) Basal-Type 25 (10.6%) 9 (2.5%) 6 (1.6%) 82 (5.9%) Unknown 11 (4.7%) 43 (12.1%) 43 (11.5%) 52 (3.8%) Data represented as n (%), unless otherwise specified. Differences in groups were assessed by using Pearson’s Chi-squared tests, Fisher’s exact test (for categorical variables),or Student’s t-test (for numerical variables). Statistical significance was defined as p<0.05. Abbreviations: n, number of participants; SD, standard deviation; p, pathological; HR, hormone receptor; HER2-, HER2-negative; TNBC, triple negative breast cancer. Differential Gene Expression Analysis Gene expression data was analyzed from 2349 breast tumor samples, each containing the expression levels of over 19,000 unique genes. We performed principal component analysis (PCA) on the gene expression data and found no clear patterns that distinguished the two groups (pT2-3pN0 vs. pT1pN+). However, the PCA of each sample's transcriptional profile showed most gene profile differences within (pT2-3pN0 vs. pT1pN+) MP High Risk (BP Luminal B, HER2, Basal) cohorts (n=1015). ( Figure S1 . A-C) On focused DGEA analysis of the MP High Risk cohort, 73 DEGs were identified as potentially contributing to LN metastasis in small tumors compared to large tumors (pT2-3pN0 vs. pT1pN+). ( Table 2, [Primary Comparison], Figure S2A-B ) Due to the small sample size in the HER2 and Basal molecular types, subsequent analyses were focused on the Luminal B subtype cohort with 34 DEGS identified, consisting of 18 upregulated and 16 downregulated DEGs when comparing pT2-3pN0 vs. pT1pN+. ( Table 3, [Primary Comparison], Figure 1 ) Table 2. Differentially Expressed Genes (DEGs) among MammaPrint High-Risk tumors High Risk Primary Comparison Secondary Comparisons Pathological Staging pT2-3pN0 vs pT1pN+ A. Within pT2-3 pN0 vs pN+ B. Within pT1 pN0 vs pN+ C. Within pN0 pT2-3 vs pT1 D. Within pN+ pT2-3 vs pT1 Upregulated genes AC139530.2, ATP5C1, JPT1, CBX2, BE2C,ZNF695,TPX2, CDC6, ESRP1, NDC80, SOX11, MEX3A, CCT5, ZNF469, MELK, UBAC2, CENPF, VEGFA, KIF4A, TOP2A, PIMREG, BIRC5,RASD2, PPP4R3CP, LAPTM4B, TPD52L1, CDCA7, XK, FBN3, USP48, KLHDC7B, SUSD4, DCX VGLL1, ZNF469, FOXC1, FBN3, EN1, USP48, MMP7, PROM1 CDK1, UBE2C, JPT1, BIRC5, MELK ZNF695, CENPF, CDCA7, BUB1, TPD52L1, PPP4R3CP, PRAME STMND1, CA12, POTEH, SLC39A6, ESR1, NKAIN1, POTEJ, POTEE, POTEC Downregulated genes SYNPO2, GLI1, JCHAIN, SRCIN1, CCL21, MFAP4, CLDN11, CCDC80, MMRN1, IGHA2, CILPCADM3, TNN, HSPB6, FOXA1, RPL10L, CX3CR1, UGT2B11, UGT2B, MUCL1, MYZAP, UGT2B28, TFF3, EFR3B, NOVA1, IGHV3-15, CHRDL1, CLCA2, FOSB, SCGB2A1, SCGB1D1, SCGB1D4, ADRB1, SCGB1D2, MUC6, IGHV3-49, IGKV1D-8, GFRA1, CEACAM6, ANKRD30A TFF3, MLPH, GFRA1, POTEJ, HELZ, FOXA1, POTEC, POTEE, AGR3, ZNF721, ESR1, ANXA9, POTED, POTEB, AR, SLC4A8, SCGB2A1, TFF1, POTEH, SLC44A4, FSIP1, PRR15, NR2F1, ANKRD30B, NAT1, ANKRD30A CCDC80, CADM3, HSPB6, MYZAP, GOLGA6L22, MFAP4, ADIPOQ, SLC2A4, SSTR2, EFR3B NEURL4, MUCL1, CHRDL1, DPCR1, MUC4 OCM2, CILP, CLEC3B, MAP3K9, RBP4, TRAPPC10, CASP10, AQP4, GOLGA6L4, G0S2, CD36, MMRN1, FUT7, CES1, FAM19A4, MUC3A, PLIN4, FABP4, BRIP1, PLIN1, JCHAIN, MUC5AC, PUS10, AQP7, ADH1B, MUC6, LPL, NRXN1, NTRK3, LEP, ADH1C, DUX4, SAA2, LTF, SCGB1D1, PIP, SCGB2A1, SCGB1D4, ANKRD30A, SCGB1D2, UGT2B7 HSPB6, C3orf85, FOSB, CCL21, HBB, HBG2, G0S2, BRIP1, HBD, PLIN1, UGT2B7, HBA2, HBA1, CHRDL1, MUC4, LEP, MMP9, TOM1, NRXN1 Differentially expressed genes (DEGs) upregulated and downregulated within MammaPrint High-Risk tumors among primary, pT2-3pN0 vs. pT1pN+, and secondary comparisons, A. pT2-3pN0 vs. pT2-3pN+, B. pT1pN0 vs. pT1pN+, C. pT2-3pN0 vs. pT1pN0, D. pT2-3pN+ vs. pT1pN+. P-values were adjusted for multiple testing to false discovery rate (FDR). Table 3. DEGs among BluePrint Luminal B-Type tumors Luminal B Primary Comparison Secondary Comparisons Pathological Staging pT2-3pN0 vs pT1pN+ A. Within pT2-3 pN0 vs pN+ B. Within pT1 pN0 vs pN+ C. Within pN0 pT2-3 vs pT1 D. Within pN+ pT2-3 vs pT1 Upregulated genes IDI1, CDC6, UBE2C , ESRP1, CA12, TOP2A, CBX2, CENPF, TPD52L1 , LAPTM4B, CKS2, GNAS, DHCR24, ACADSB, COA3, FAM234B, SUSD4, PPP4R3CP UBE2C, BIRC5, HIST1H1B, TPD52L1, AGR2, KCNJ3 STMND1, MNX1, POTEH, TPD52L1 Downregulated genes SYNPO2, JCHAIN , IGHV3-15, IGHA2, IGHV3-49 , MFAP4 , CCL21, IGHV3OR16-12, MYZAP , ADRB1 , HLF, UGT2B7, IGKV1D-8, IGKV1D-33, IGLV2-14, MMRN1 MYZAP, ADIPOQ, CADM3, MFAP4, EFR3B, RBP4, ATP8A1, HSPB6, SLC2A4, CES1, G0S2, LPL, GOLGA6L22, CILP, CHRDL1, PLIN1, JCHAIN, CD36, PUS10, PLIN4, SAA2, AQP7, LEP, FABP4, MUCL1, MUC4, ADRB1, ADH1B, LTF, IGHV3-49, MUC5AC, COL17A1 HBB, HSPB6, HBG2, HBA2, HBD, FOSB, HBA1, MMP9, BRIP1, G0S2, PLIN1, MUC4 Differentially expressed genes (DEGs) upregulated and downregulated within BluePrint Luminal B-Type tumors among primary, pT2-3pN0 vs. pT1pN+, and secondary comparisons, A. pT2-3pN0 vs. pT2-3pN+, B. pT1pN0 vs. pT1pN+, C. pT2-3pN0 vs. pT1pN0, D. pT2-3pN+ vs. pT1pN+. P-values were adjusted for multiple testing to false discovery rate (FDR). To specifically identify DEGs associated with early lymph node metastasis, we performed secondary DGEA comparisons, including control cohorts (pT2-3pN+ and pT1pN0). Distinct DEGs were not found when comparing large or small tumors with and without LN metastasis (pT2-3: pN0 vs. N+) (pT1: pN0 vs. pN+) within the Luminal B subgroup ( Table 3 [A-B], Figure 1B-C ). Within node-negative tumors, 6 genes were upregulated, and 32 genes were downregulated in larger tumors compared to smaller Luminal B-type tumors ( Table 3 [C], Figure 1D ). For node-positive tumors, 4 genes were upregulated, and 12 genes were downregulated in larger tumors compared to smaller Luminal B-type tumors ( Table 3 [D], Figure 1E ). Five genes (G0S2, HSPB6, MUC4, PLIN1, and TPD52L1) were seen in both comparisons, pN0: pT2-3 vs pT1 and pN+: pT2-3 vs pT1. Seven DEGs (ADRB1, IGHV3-49, JCHAIN, MFAP4, MYZAP, TPD52L1, and UBE2C) overlapped between our primary (pT2-3pN0 vs. pT1pN+) and secondary (pN0: pT2-3 vs. pT1 and pN+: pT2-3 vs. pT1) comparisons and are therefore suspected of having greater relevance in local tumor extension vs. locoregional metastasis. ( Table 3, Figure 1 ) Overall, we identified 16 upregulated and 11 downregulated DEGs unique in large tumors without LN metastasis compared to small tumors with LN metastasis (pT2-3pN0 vs. pT1pN+) after adjusting for DEG contributing to tumor growth within the Luminal B subtype cohort, highlighting their potential role in LN metastasis. Interestingly, no significant difference in gene expression was observed in the different groups upon looking at specific genes historically linked to LN metastasis, such as BCL2L1, AURKA, CDKN2A, MCL1, and MYC. Gene Set Enrichment Analysis We performed GSEA to identify pathways significantly enriched in the differentially expressed genes, influencing lymph node metastases in pT2-3pN0 vs pT1pN+. We found upregulation in several cancer proliferation-related pathways, such as the E2F targets, MYC targets V1/V2, and PI3K-AKT-MTOR signaling; in addition to the DNA repair and the Cell cycle regulators such as G2M checkpoint, and estrogen signaling pathways. GSEA also showed a downregulation in EMT and inflammatory responses. GSEA also demonstrated changes within the immune-related pathways, which may indicate the presence of active immune signaling, such as upregulated interferon responses and downregulated allograft rejection pathways ( Figure 2 ). These findings may suggest that dysregulated immune pathways could activate immune evasion mechanisms to escape immune surveillance and ultimately metastasize. Immune Cell Deconvolution Finally, we conducted xCell deconvolution analysis using the gene expression profiles. We found a lower abundance of gamma delta T cells, B plasma cells, CD4+ Th1 T cells, and a higher abundance of NK T cells, eosinophils, and Hematopoietic stem cells with minimally higher abundance of CD8+ T cells, CD8+ effector memory, and CD4+ central memory in small tumors with LN metastasis. The subset of immunosuppressive cells, such as T regulatory cells, M2 macrophages, and cancer-associated fibroblasts were higher in small tumors with LN metastasis. Adjusted p-values were significant for CD4+ Th1 T cells only, which were significantly lower in small tumors with LN metastasis. ( Figure 3A ) A different gene expression deconvolution analysis (EPIC) was performed to confirm our results. It showed similar trends, including a lower abundance of CD4+ T cells and a higher abundance of cancer-associated fibroblasts, endothelial cells, B cells, and CD8-positive T-cells in small tumors with LN metastases. ( Figure 3B ) Tumor-Infiltrating Lymphocyte Quantification Given transcriptomic differences in immune subset gene expression between smaller tumors with early LN metastasis as compared to large tumors without locoregional metastases, H&E TIL quantification was performed specifically comparing pT1pN+ vs. pT2-3pN0 tumors in a subset of study population (n=200). No difference in mean TIL density was identified between pT1pN+ vs. pT2-3pN0 tumors by conventional analyses (18.7% vs. 19.0%, p=0.93). To complete the analysis, we also evaluated for differences in TIL density between the primary patient subgroups and patients with small tumors without LN (pT1pN0, n=50) and larger tumors with locoregional lymph node metastasis (pT2-3pN+, n=50) and similarly did not identify any significant difference in mean TIL density. (Figure 4) Discussion Our analysis aims to identify genes associated with tumor growth and LN metastasis through differential gene expression and histopathologic analysis of patient subsets within a cohort of 2,349 patients diagnosed with EBC enrolled in FLEX. We classified patient samples based on the anatomical stage and MP/BP genomic risk groups. PCA revealed no significant differences in principal coordinate distribution by tumor size and nodal status. In contrast, MP/BP classification identified distinct differences in principal coordinate distribution patterns, suggesting that subclassification by MP/BP can more precisely identify tumor biological features than anatomic characteristics alone. Among patients with MP Low Risk, no DEGs were observed when comparing large tumors without LN metastasis and small tumors with LN metastasis, while the High Risk cohort exhibited significant differences, emphasizing the biological distinctions within MP High Risk BC subtypes. For our primary analysis within the High Risk, Luminal B cohort, which focused specifically on transcriptomic features of LN metastasis, by comparing tumoral gene expression between patients with larger breast tumors without lymph node metastasis (pT2-3pN0) and patients with small breast tumors with early LN metastasis (pT1pN+). In the primary analysis, we identified 34 DEGs between pT2-3pN0 vs pT1pN+. After adjusting for genes influencing tumor growth, we identified 16 upregulated genes: IDI1, CDC6, ESRP1, CA12, TOP2A, CBX2, CENPF, LAPTM4B, CKS2, GNAS, DHCR24, ACADSB, COA3, FAM234B, SUSD4, PPP4R3CP; along with 11 downregulated genes: SYNPO2, IGHV3-15, IGHA2, CCL21, IGHV3OR16-12, HLF, UGT2B7, IGKV1D-8, IGKV1D-33, IGLV2-14, MMRN1. The upregulated genes in (pT2-3pN0) are involved in cell cycle regulation and proliferation (CDC6, CENPF, TOP2A, CBX2, CKS2), steroid metabolism and hormone response (DHCR24, GNAS, CA12), RNA Splicing and Metastasis (ESRP1), lysosomal and cellular trafficking (LAPTM4B), metabolism (ACADSB, COA3), DNA repair and genomic Stability (IDI1), the rest (FAM234B, SUSD4, PPP4R3CP) are less well characterized in Breast Cancer. By contrast, genes involved in immune chemotaxis, including CCL21, activation of immune response (IGHV3-15, IGHA2, IGHV3OR16-12, IGKV1D-8, IGKV1D-33, IGLV2-14), gene regulation (HLF), hormone Metabolism (UGT2B7) as well as Cytoskeletal Dynamics (SYNPO2) were differentially expressed at higher level in pT1pN + tumors. Additionally, MMRN1, which was differentially expressed at a higher level in pT1pN + tumors, is a known endothelial signature that may be implicated in platelet/endothelial interactions in early metastasis. Our secondary comparison evaluated the other patient subsets and found 32 downregulated and 6 upregulated genes in large tumors versus small tumors without LN Metastasis. In the presence of LN metastasis, 12 genes were downregulated in large compared to small tumors, and 4 genes were upregulated. Among those, G0S2, HSPB6, PLIN1, and MUC4 genes were consistently downregulated, while TPD52L1 was consistently upregulated in large tumors, suggesting their role in tumor growth irrespective of LN metastasis. Notably, limited data has been reported to date regarding the impact of these genes on BC oncogenesis. For MUC4, which is a cell surface membrane-bound mucin protein, discordant data has been reported from different patient series with downregulation and overexpression associated with tumor oncogenesis.[ 27 , 28 ] Our results also showed seven genes, including 2 upregulated (UBE2C, TPD52L1) and 5 downregulated (JCHAIN, IGHV3-49, MFAP4, MYZAP, ADRB1) that overlapped between the primary comparison and secondary comparison, which indicates these genes may influence both tumor growth and metastasis. Of interest, several of the genes have been implicated in breast tumorigenesis, including UBE2C, which, when overexpressed, has been associated with lymphovascular invasion, lymph node metastasis, and poor prognosis.[ 29 ] A recent investigation demonstrated a link between CDK4/6 inhibitor's efficacy and the suppression of UBE2C expression.[ 30 ] One of the notable findings of our study is the limited differential gene expression observed between small tumors with and without LN metastasis (pT1pN0 vs. pT1pN+) and large tumors with and without LN metastasis (pT2-3pN0 vs. pT2-3pN+). Surprisingly, no DEGs emerged from this comparison despite the large sample size. These findings may be attributed to post-transcriptional and epigenetic changes, tumor heterogeneity, low transcript abundance, or the limited sensitivity of the method to capture subtle expression changes, as these analyses are based on a single timepoint and could warrant further confirmation using more sensitive techniques. Gene Set Enrichment Analysis (GSEA) provided insights into the pathways associated with differentially expressed genes. Upregulation in cancer proliferation-related pathways and downregulation in immune-related pathways in larger tumors without LN metastasis hinted at potential immune evasion mechanisms facilitating metastasis. Our immune deconvolution analysis further emphasized the role of the tumor microenvironment, revealing a higher abundance of immunosuppressive elements in small tumors with LN metastasis compared to large tumors within the MP High Risk and BP Luminal B cohort. These findings suggest the presence of an immunosuppressive microenvironment enabling LN metastasis in small tumors compared to large tumors. TIL quantification assessed the differences in the immune microenvironment between patient subgroups. Despite the findings on deconvolution analyses, within a representative subset of our patient population, conventional H&E TIL analysis did not demonstrate any difference between pT1pN + tumors and pT2-3pN0 tumors, underscoring the importance of moving beyond conventional TIL assessment when evaluating the tumor immune microenvironment. Conclusion These analyses unveil a complex interplay of gene expression patterns and immune responses in BC, providing insight into the biological mechanisms of early LN metastasis. In comparing small tumors with LN metastasis vs. large tumors without LN metastasis across MP/BP molecular subtypes, we observed differential expression of proliferation and chemotaxis-related genes and distinct immune deconvolution within the MP High Risk and Luminal B subset of tumors, suggestive of a more immunosuppressive microenvironment in tumors with early LN metastasis and genomically high risk disease. Declarations Competing Interests The authors have the following paid financial relationships with the funder:Receive research funding from funder: Nina D’Abreo and Douglas MarksEmployed and have stock ownership in funding company: Josien Haan, Nicole Chmielewski-Stivers, Andrea Menicucci, and William Audeh Funding: Agendia Author Contribution F.F. and D.M. contributed to study conception and design, data collection, analysis, and interpretation, and drafted the main manuscript. P.P.S., N.D., S.A., and Z.L. contributed to data collection. J.B., J.H., and A.M. contributed to data analysis and interpretation. N.C.S. contributed to drafting the main manuscript. All authors critically reviewed and approved the final version of the manuscript. Acknowledgement We would also like to thank the patients enrolled in FLEX, study site coordinators, research nurses, personnel, and the FLEX Investigators’ Group that made this study possible. 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The authors have the following paid financial relationships with the funder: Receive research funding from funder: Nina D’Abreo and Douglas Marks Employed and have stock ownership in funding company: Josien Haan, Nicole Chmielewski-Stivers, Andrea Menicucci, and William Audeh Supplementary Files SuppFigure1Principalcomponentanalyses.tif SuppFigure2HighRiskDEGvolcanoplot.tif Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Nov, 2025 Reviews received at journal 16 Nov, 2025 Reviewers agreed at journal 23 Oct, 2025 Reviewers agreed at journal 19 Oct, 2025 Reviews received at journal 19 Oct, 2025 Reviewers agreed at journal 04 Sep, 2025 Reviewers invited by journal 04 Sep, 2025 Editor assigned by journal 11 Aug, 2025 Submission checks completed at journal 11 Aug, 2025 First submitted to journal 11 Aug, 2025 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. 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15:11:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":134266,"visible":true,"origin":"","legend":"\u003cp\u003eGene set enrichment analysis (GSEA) on 50 hallmark gene sets for MammaPrint (MP) High Risk and BluePrint (BP) Luminal B-Type tumors.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7347995/v1/ed0833bea8b3bbbc09228d92.png"},{"id":91104690,"identity":"d394de68-c826-40b4-a055-a348206a08de","added_by":"auto","created_at":"2025-09-11 15:19:23","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":109341,"visible":true,"origin":"","legend":"\u003cp\u003eImmune cell-type quantification using xCell (A) and EPIC (B) immune deconvolution methods.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7347995/v1/e85c6cc93f4cd8bceea8cdb4.png"},{"id":91104026,"identity":"6d80a1cc-f4bc-4db5-bade-3e44343d4617","added_by":"auto","created_at":"2025-09-11 15:11:23","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":69493,"visible":true,"origin":"","legend":"\u003cp\u003eTILs distribution among the different cohorts (n=300).\u003c/p\u003e","description":"","filename":"44.png","url":"https://assets-eu.researchsquare.com/files/rs-7347995/v1/b672d9c8a09ba8fe04852abe.png"},{"id":91105797,"identity":"584f65cd-29f5-4b3c-9261-5b43b0a9f832","added_by":"auto","created_at":"2025-09-11 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15:19:24","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":20008568,"visible":true,"origin":"","legend":"","description":"","filename":"SuppFigure2HighRiskDEGvolcanoplot.tif","url":"https://assets-eu.researchsquare.com/files/rs-7347995/v1/97d3e893239b570f89a47399.tif"}],"financialInterests":"Competing interest reported. The authors have the following paid financial relationships with the funder:\nReceive research funding from funder: Nina D’Abreo and Douglas Marks\nEmployed and have stock ownership in funding company: Josien Haan, Nicole Chmielewski-Stivers, Andrea Menicucci, and William Audeh","formattedTitle":"Whole transcriptome analysis reveal MammaPrint and BluePrint-associated gene expression patterns with early lymph node metastasis in early-stage breast cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLymph node (LN) metastasis is a critical step in the metastatic progression of early-stage breast cancer (EBC). The notable 35% difference in 10-year survival between patients with and without LN metastasis underscores the importance of understanding the mechanisms associated with LN metastasis and identifying biomarkers to improve diagnostic and therapeutic approaches.[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eClinical features, including tumor size, lymphovascular invasion, tumor grade, and patient age, may correlate with node-positive disease.[\u003cspan additionalcitationids=\"CR4 CR5 CR6\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] Tumor size modestly correlates with axillary metastases, but systemic metastases do not strictly follow local invasion. Small tumors often present with axillary LN metastases, while larger tumors may lack LN involvement. The tumor or host-specific factors underlying early LN metastasis, or conversely, the favorable factors responsible for the absence of metastases in locally extensive tumors, remain poorly understood. Biomarkers that more accurately and precisely predict LN metastasis can allow for more accurate patient risk stratification.\u003c/p\u003e\u003cp\u003eNumerous gene pathways linked to LN metastasis have been explored. The overexpression of several oncogenes, including BCL2L1, AURKA, CDKN2A, MCL1, and MYC, have been associated with LN and distant metastasis. However, these are not precise enough to serve as predictive biomarkers.[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] Bao et al. identified a high-level gain of MCL1 and chromosome 8q amplification as key factors associated with tumor spread to lymph nodes. Moreover, gains in chromosomes 12q and 20q, along with losses in 1p and 9p, were predominantly observed in estrogen receptor (ER)-positive breast cancers with lymph node metastases. Elevated MYC copy numbers were also frequently reported in LN-positive breast cancer patients.[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] Additionally, gene pathways related to epithelial-mesenchymal transition (EMT) and immune function have been previously linked to LN metastasis. However, a transcriptomic signature specifically responsible for early LN metastases has not yet been identified.[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eThe FLEX (NCT03053193) registry of breast cancer patients with stage I-III disease, consenting to MammaPrint (MP) risk-of-recurrence, BluePrint (BP) molecular subtyping, and whole transcriptome genomic studies, has provided insight into biological mechanisms associated with risk of distant metastasis [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and treatment planning outcomes [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To provide more insight into the specific aspects of tumor biology that drive early LN metastasis in EBC within the FLEX registry, we conducted transcriptomic analyses comparing clinically characterized small tumors with axillary LN metastases (pathological [p] T1pN+) and larger tumors without such metastases (pT2-3pN0) across MP and BP molecular subtypes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003ePatient Cohort and Genomic Assay\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study analyzed a subset of patients from the FLEX trial (NCT03053193), a real-world, longitudinal, prospective registry enrolling stage I-III breast cancer patients. These patients underwent MP with or without BP testing, performed by Agendia (Irvine, CA, USA) as described previously,[17, 18] and consented to clinically annotated full transcriptome data collection. MP is a 70-gene signature that classifies tumors as having a High or Low Risk of distant recurrence.[18, 19] BP is an 80-gene molecular subtyping signature that further classifies MP Low Risk tumors as Luminal A-Type and High Risk tumors as Luminal B-Type, HER2-Type, or Basal-Type molecular subtypes.[18]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Characteristic Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize clinical characteristics and genomic results. Group differences were assessed using Pearson\u0026rsquo;s Chi-squared tests, Fisher\u0026rsquo;s exact test (for categorical variables), or Student\u0026rsquo;s t-test (for numerical variables). Statistical significance was defined by a two-sided p-value of p\u0026lt;0.05 for all tests. All statistical analyses were conducted using R (version 4.1.1). \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Expression Analysis and Immune Deconvolution\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression quantile normalization and differential gene expression analysis (DGEA) were performed using the Limma R package in Bioconductor (version 3.42.2).[20] Primary comparison of large tumors without LN metastasis and small tumors with LN metastasis (pT2-3pN0 vs. pT1pN+) was performed and four control analyses: 1) large tumors without and with LN (pT2-3pN0 vs. pT2-3 pN+), 2) small tumors without and with LN (pT1pN0 vs. pT1pN+), 3) large tumors and small tumors without LN (pT2-3pN0 vs. pT1pN0), and 4) large tumors and small tumors with LN (pT2-3pN+ vs. pT1pN+). Comparisons were performed between all patient samples and within MP High and Low Risk cohorts. P-values were adjusted for multiple testing according to Benjamini-Hochberg (false discovery rate, FDR)[21]. DEGs with FDR-adjusted p-values \u0026lt;0.05\u0026nbsp;and \u0026gt;1.4-fold change\u0026nbsp;were considered significant. Gene set enrichment analysis (GSEA) was performed using the fgsea R package in Bioconductor (version 1.12.0)[22] and 50 hallmark gene sets[23]. The gene ranking input for GSEA was based on the Limma output (adjusted p-value). Immune cell-type quantifications were defined by the gene-signature-based methods xCell\u0026nbsp;[24]\u0026nbsp;and Estimating the Proportion of Immune and Cancer cells (EPIC),[25]\u0026nbsp;differences were determined by t-test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor-Infiltrating Lymphocyte (TIL) Scoring\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFor this analysis, TILs were quantified in a random subset of patients within stratified subsets of the four patient subgroups (n=300). Consistent with the main analysis, the primary cohorts for the TIL analysis were small tumors with early LN metastasis (pT1pN+) vs. larger tumors without LN metastasis (pT2-3pN0). Control cohorts were also included and comprised of small tumors without LN metastasis (pT1pN0) and larger tumors with LN metastasis (pT2-3pN+). TILs were quantified by a breast pathologist (JB) on Hematoxylin and Eosin (H\u0026amp;E) slides using previously described methods.[26] TIL scores were evaluated as continuous measures.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eBaseline Patient Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 2349 patients were included in this study. The median age was 63, and 76% of the patients were postmenopausal. Among the patient cohort, 1974 patients had HR+ HER2- tumors, 90 were HER2+, 64 had TNBC, and 221 were unknown. For our primary comparison, 235 patients had large, LN-negative tumors (pT2-3pN0) and 355 patients had small, LN-positive tumors (pT1pN+). The control group comparisons comprised 1,384 patients with small, lymph node-negative tumors (pT1pN0) and 375 patients with large lymph node-positive tumors (pT2-3pN+).\u0026nbsp;MP and BP characterized 53.6% of tumors as Luminal A-Type (n=1259), 33.5% as Luminal B-Type (n=787), 1.4% as HER2-Type (n=32), 5.2% as Basal-Type (n=122), and 6.3% as unknown (n=149).\u0026nbsp;(\u003cstrong\u003eTable 1\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"575\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 168px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Comparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 177px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl Groups\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\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 style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3pN0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT1pN1+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT1pN0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-value\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePatients (n)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e235\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e355\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge, median (SD)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e61 (\u0026plusmn; 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e59 (\u0026plusmn; 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e60 (\u0026plusmn; 12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e62 (\u0026plusmn; 11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMenopausal status\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePre/peri-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e44 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e73 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e97 (25.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e205 (14.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePost-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e184 (78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e259 (73.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e253 (67.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1103 (79.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e23 (6.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e25 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e76 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eClinical subtype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR+HER2-\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e204 (86.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e297 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e314 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1159 (83.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR+HER2+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e7 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e7 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e11 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e50 (3.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHR-HER2+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e8 (0.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTNBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e9 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e6 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e48 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e14 (6.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e40 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e48 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e119 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistological grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG1, Low\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e50 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e104 (29.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e69 (18.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e479 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG2, Intermediate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e111 (47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e180 (50.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e223 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e657 (47.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eG3, High\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e61 (26.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e41 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e55 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e168 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e13 (5.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e30 (8.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e28 (7.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e80 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTumor size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e355 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1384 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e223 (94.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e318 (84.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eT3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e12 (5.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e57 (15.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLymph node status, N\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN0\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e235 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1384 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e326 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e321 (85.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e24 (6.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e37 (9.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e5 (1.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e17 (4.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvasive ductal carcinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e176 (74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e287 (80.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e273 (72.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e1151 (83.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInvasive lobular carcinoma\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e52 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e41 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e82 (21.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e135 (9.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMixed ductal and lobular features\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e2 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e9 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e31 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e5 (2.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e14 (3.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e13 (3.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e60 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e4 (1.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e1 (0.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e7 (0.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMammaPrint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e111 (47.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e197 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e202 (53.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e824 (59.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e124 (52.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e158 (44.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e173 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e560 (40.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBluePrint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 87px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal A-Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e104 (44.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e173 (48.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e181 (48.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e801 (57.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B-Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e94 (40.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e123 (34.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e142 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e428 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHER2-Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e1 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e7 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e3 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e21 (1.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBasal-Type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e25 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e9 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e6 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e82 (5.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 164px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnknown\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 87px;\"\u003e\n \u003cp\u003e11 (4.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 81px;\"\u003e\n \u003cp\u003e43 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 89px;\"\u003e\n \u003cp\u003e43 (11.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 88px;\"\u003e\n \u003cp\u003e52 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 66px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eData represented as n (%), unless otherwise specified. Differences in groups were assessed by using Pearson\u0026rsquo;s Chi-squared tests, Fisher\u0026rsquo;s exact test (for categorical variables),or Student\u0026rsquo;s t-test (for numerical variables). Statistical significance was defined as p\u0026lt;0.05. Abbreviations: n, number of participants; SD, standard deviation; p, pathological; HR, hormone receptor; HER2-, HER2-negative; TNBC, triple negative breast cancer.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Gene Expression Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene expression data was analyzed from 2349 breast tumor samples, each containing the expression levels of over 19,000 unique genes. We performed principal component analysis (PCA) on the gene expression data and found no clear patterns that distinguished the two groups (pT2-3pN0 vs. pT1pN+).\u0026nbsp;However, the PCA of each sample\u0026apos;s transcriptional profile showed most gene profile differences within (pT2-3pN0 vs. pT1pN+) MP High Risk (BP Luminal B, HER2, Basal) cohorts (n=1015). (\u003cstrong\u003eFigure S1\u003c/strong\u003e\u003cstrong\u003e. A-C)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOn focused DGEA analysis of the MP High Risk cohort, 73 DEGs were identified as potentially contributing to LN metastasis in small tumors compared to large tumors (pT2-3pN0 vs. pT1pN+). (\u003cstrong\u003eTable 2, [Primary Comparison], Figure S2A-B\u003c/strong\u003e) Due to the small sample size in the HER2 and Basal molecular types, subsequent analyses were focused on the Luminal B subtype cohort with 34 DEGS identified, consisting of 18 upregulated and 16 downregulated DEGs when comparing pT2-3pN0 vs. pT1pN+. (\u003cstrong\u003eTable 3, [Primary Comparison], Figure 1\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eDifferentially Expressed Genes (DEGs) among MammaPrint High-Risk\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003etumors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Risk\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Comparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary Comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological Staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3pN0 vs pT1pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA.\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eWithin pT2-3\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epN0 vs pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Within pT1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epN0 vs pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC. Within pN0\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3 vs pT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD. Within pN+\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3 vs pT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eAC139530.2, ATP5C1, JPT1, CBX2, BE2C,ZNF695,TPX2, CDC6, ESRP1, NDC80, SOX11, MEX3A, CCT5, ZNF469, MELK, UBAC2, CENPF, VEGFA, KIF4A, TOP2A, PIMREG, BIRC5,RASD2, PPP4R3CP, LAPTM4B, TPD52L1, CDCA7, XK, FBN3, USP48, KLHDC7B, SUSD4, DCX\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eVGLL1, ZNF469, FOXC1, FBN3, EN1, USP48, MMP7, PROM1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eCDK1, UBE2C, JPT1, BIRC5, MELK\u003c/p\u003e\n \u003cp\u003eZNF695, CENPF, CDCA7, BUB1, TPD52L1, PPP4R3CP, PRAME\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSTMND1, CA12, POTEH, SLC39A6, ESR1, NKAIN1, POTEJ, POTEE, POTEC\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownregulated genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003eSYNPO2, GLI1, JCHAIN, SRCIN1, CCL21, MFAP4, CLDN11, CCDC80, MMRN1, IGHA2, CILPCADM3, TNN, HSPB6, FOXA1, RPL10L, CX3CR1, UGT2B11, UGT2B, MUCL1, MYZAP, UGT2B28, TFF3, EFR3B, NOVA1, IGHV3-15, CHRDL1, CLCA2, FOSB, SCGB2A1, SCGB1D1, SCGB1D4, ADRB1, SCGB1D2, MUC6, IGHV3-49, IGKV1D-8, GFRA1, CEACAM6, ANKRD30A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003eTFF3, MLPH, GFRA1, POTEJ, HELZ, FOXA1, POTEC, POTEE, AGR3, ZNF721, ESR1, ANXA9, POTED, POTEB, AR, SLC4A8, SCGB2A1, TFF1, POTEH, SLC44A4, FSIP1, PRR15, NR2F1, ANKRD30B, NAT1, ANKRD30A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eCCDC80, CADM3, HSPB6, MYZAP, GOLGA6L22, MFAP4, ADIPOQ, SLC2A4, SSTR2, EFR3B\u003c/p\u003e\n \u003cp\u003eNEURL4, MUCL1, CHRDL1, DPCR1, MUC4\u003c/p\u003e\n \u003cp\u003eOCM2, CILP, CLEC3B, MAP3K9, RBP4, TRAPPC10, CASP10, AQP4, GOLGA6L4, G0S2, CD36, MMRN1, FUT7, CES1, FAM19A4, MUC3A, PLIN4, FABP4, BRIP1, PLIN1, JCHAIN, MUC5AC, PUS10, AQP7, ADH1B, MUC6, LPL, NRXN1, NTRK3, LEP, ADH1C, DUX4, SAA2, LTF, SCGB1D1, PIP, SCGB2A1, SCGB1D4, ANKRD30A, SCGB1D2, UGT2B7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHSPB6, C3orf85, FOSB, CCL21, HBB, HBG2, G0S2, BRIP1, HBD, PLIN1, UGT2B7, HBA2, HBA1, CHRDL1, MUC4, LEP, MMP9, TOM1, NRXN1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) upregulated and downregulated within MammaPrint High-Risk tumors among primary, pT2-3pN0 vs. pT1pN+, and secondary comparisons, A. pT2-3pN0 vs. pT2-3pN+, B. pT1pN0 vs. pT1pN+, C. pT2-3pN0 vs. pT1pN0, D. pT2-3pN+ vs. pT1pN+. P-values were adjusted for multiple testing to false discovery rate (FDR).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3. DEGs among BluePrint Luminal B-Type tumors\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLuminal B\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary Comparison\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 63px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSecondary Comparisons\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePathological Staging\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3pN0 vs pT1pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eA. Within pT2-3 pN0 vs pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eB. Within pT1 pN0 vs pN+\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eC. Within pN0 pT2-3 vs pT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eD. Within pN+\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003epT2-3 vs pT1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUpregulated genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eIDI1, CDC6,\u0026nbsp;\u003c/strong\u003eUBE2C\u003cstrong\u003e, ESRP1, CA12, TOP2A, CBX2, CENPF,\u0026nbsp;\u003c/strong\u003eTPD52L1\u003cstrong\u003e, LAPTM4B, CKS2, GNAS, DHCR24, ACADSB, COA3, FAM234B, SUSD4, PPP4R3CP\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eUBE2C, BIRC5, HIST1H1B, TPD52L1, AGR2, KCNJ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eSTMND1, MNX1, POTEH, TPD52L1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 16px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDownregulated genes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 20px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSYNPO2,\u0026nbsp;\u003c/strong\u003eJCHAIN\u003cstrong\u003e, IGHV3-15, IGHA2,\u0026nbsp;\u003c/strong\u003eIGHV3-49\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eMFAP4\u003cstrong\u003e, CCL21, IGHV3OR16-12,\u0026nbsp;\u003c/strong\u003eMYZAP\u003cstrong\u003e,\u0026nbsp;\u003c/strong\u003eADRB1\u003cstrong\u003e, HLF, UGT2B7, IGKV1D-8, IGKV1D-33, IGLV2-14, MMRN1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 14px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eMYZAP, ADIPOQ, CADM3, MFAP4, EFR3B, RBP4, ATP8A1, HSPB6, SLC2A4, CES1, G0S2, LPL, GOLGA6L22, CILP, CHRDL1, PLIN1, JCHAIN, CD36, PUS10, PLIN4, SAA2, AQP7, LEP, FABP4, MUCL1, MUC4, ADRB1, ADH1B, LTF, IGHV3-49, MUC5AC, COL17A1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 17px;\"\u003e\n \u003cp\u003eHBB, HSPB6, HBG2, HBA2, HBD, FOSB, HBA1, MMP9, BRIP1, G0S2, PLIN1, MUC4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) upregulated and downregulated within BluePrint Luminal B-Type tumors among primary, pT2-3pN0 vs.\u0026nbsp;pT1pN+, and secondary comparisons, A. pT2-3pN0 vs.\u0026nbsp;pT2-3pN+, B. pT1pN0 vs.\u0026nbsp;pT1pN+, C. pT2-3pN0 vs.\u0026nbsp;pT1pN0, D. pT2-3pN+ vs.\u0026nbsp;pT1pN+. P-values were adjusted for multiple testing to false discovery rate (FDR).\u003c/p\u003e\n\u003cp\u003eTo specifically identify DEGs associated with early lymph node metastasis, we performed secondary DGEA comparisons, including control cohorts (pT2-3pN+ and pT1pN0). Distinct DEGs were not found when comparing large or small tumors with and without LN metastasis (pT2-3: pN0 vs. N+) (pT1: pN0 vs. pN+) within the Luminal B subgroup (\u003cstrong\u003eTable 3 [A-B], Figure 1B-C\u003c/strong\u003e). Within node-negative tumors, 6 genes were upregulated, and 32 genes were downregulated in larger tumors compared to smaller Luminal B-type tumors (\u003cstrong\u003eTable 3 [C], Figure 1D\u003c/strong\u003e). For node-positive tumors, 4 genes were upregulated, and 12 genes were downregulated in larger tumors compared to smaller Luminal B-type tumors (\u003cstrong\u003eTable 3 [D], Figure 1E\u003c/strong\u003e). Five genes (G0S2, HSPB6, MUC4, PLIN1, and TPD52L1) were seen in both comparisons, pN0: pT2-3 vs pT1 and pN+: pT2-3 vs pT1.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eSeven DEGs (ADRB1, IGHV3-49, JCHAIN, MFAP4, MYZAP, TPD52L1, and UBE2C) overlapped between our primary (pT2-3pN0 vs. pT1pN+) and secondary (pN0: pT2-3 vs. pT1 and pN+: pT2-3 vs. pT1) comparisons and are therefore suspected of having greater relevance in local tumor extension vs. locoregional metastasis. (\u003cstrong\u003eTable 3, Figure 1\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, we identified 16 upregulated and 11 downregulated DEGs unique in large tumors without LN metastasis compared to small tumors with LN metastasis (pT2-3pN0 vs. pT1pN+) after adjusting for DEG contributing to tumor growth within the Luminal B subtype cohort, highlighting their potential role in LN metastasis. Interestingly, no significant difference in gene expression was observed in the different groups upon looking at specific genes historically linked to LN metastasis, such as BCL2L1, AURKA, CDKN2A, MCL1, and MYC.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGene Set Enrichment Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed GSEA to identify pathways significantly enriched in the differentially expressed genes, influencing lymph node metastases in\u0026nbsp;pT2-3pN0 vs pT1pN+. We found upregulation in several cancer proliferation-related pathways, such as the E2F targets, MYC targets V1/V2, and PI3K-AKT-MTOR signaling; in addition to the DNA repair and the Cell cycle regulators such as G2M checkpoint, and estrogen signaling pathways. GSEA also showed a downregulation in EMT and inflammatory responses. GSEA also demonstrated changes within the immune-related pathways, which may indicate the presence of active immune signaling, such as upregulated interferon responses and downregulated allograft rejection pathways (\u003cstrong\u003eFigure 2\u003c/strong\u003e). These findings may suggest that dysregulated immune pathways could activate immune evasion mechanisms to escape immune surveillance and ultimately metastasize.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune Cell Deconvolution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFinally, we conducted xCell deconvolution analysis using the gene expression profiles. We found a lower abundance of gamma delta T cells, B plasma cells, CD4+ Th1 T cells, and a higher abundance of \u0026nbsp;NK T cells, eosinophils, and Hematopoietic stem cells with minimally higher abundance of CD8+ T cells, CD8+ effector memory, and CD4+ central memory in small tumors with LN metastasis. The subset of immunosuppressive cells, such as T regulatory cells, M2 macrophages, and cancer-associated fibroblasts were higher in small tumors with LN metastasis. Adjusted p-values were significant for CD4+ Th1 T cells only, which were significantly lower in small tumors with LN metastasis. (\u003cstrong\u003eFigure 3A\u003c/strong\u003e) A different gene expression deconvolution analysis (EPIC) was performed to confirm our results. It showed similar trends, including a lower abundance of CD4+ T cells and a higher abundance of cancer-associated fibroblasts, endothelial cells, B cells, and CD8-positive T-cells in small tumors with LN metastases. (\u003cstrong\u003eFigure 3B\u003c/strong\u003e)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor-Infiltrating Lymphocyte Quantification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiven transcriptomic differences in immune subset gene expression between smaller tumors with early LN metastasis as compared to large tumors without locoregional metastases, H\u0026amp;E TIL quantification was performed specifically comparing pT1pN+ vs. pT2-3pN0 tumors in a subset of study population (n=200). No difference in mean TIL density was identified between pT1pN+ vs. pT2-3pN0 tumors by conventional analyses (18.7% vs. 19.0%, p=0.93). To complete the analysis, we also evaluated for differences in TIL density between the primary patient subgroups and patients with small tumors without LN (pT1pN0, n=50) and larger tumors with locoregional lymph node metastasis (pT2-3pN+, n=50) and similarly did not identify any significant difference in mean TIL density. \u003cstrong\u003e(Figure 4)\u003c/strong\u003e\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur analysis aims to identify genes associated with tumor growth and LN metastasis through differential gene expression and histopathologic analysis of patient subsets within a cohort of 2,349 patients diagnosed with EBC enrolled in FLEX.\u003c/p\u003e\u003cp\u003eWe classified patient samples based on the anatomical stage and MP/BP genomic risk groups. PCA revealed no significant differences in principal coordinate distribution by tumor size and nodal status. In contrast, MP/BP classification identified distinct differences in principal coordinate distribution patterns, suggesting that subclassification by MP/BP can more precisely identify tumor biological features than anatomic characteristics alone. Among patients with MP Low Risk, no DEGs were observed when comparing large tumors without LN metastasis and small tumors with LN metastasis, while the High Risk cohort exhibited significant differences, emphasizing the biological distinctions within MP High Risk BC subtypes.\u003c/p\u003e\u003cp\u003eFor our primary analysis within the High Risk, Luminal B cohort, which focused specifically on transcriptomic features of LN metastasis, by comparing tumoral gene expression between patients with larger breast tumors without lymph node metastasis (pT2-3pN0) and patients with small breast tumors with early LN metastasis (pT1pN+). In the primary analysis, we identified 34 DEGs between pT2-3pN0 vs pT1pN+. After adjusting for genes influencing tumor growth, we identified 16 upregulated genes: IDI1, CDC6, ESRP1, CA12, TOP2A, CBX2, CENPF, LAPTM4B, CKS2, GNAS, DHCR24, ACADSB, COA3, FAM234B, SUSD4, PPP4R3CP; along with 11 downregulated genes: SYNPO2, IGHV3-15, IGHA2, CCL21, IGHV3OR16-12, HLF, UGT2B7, IGKV1D-8, IGKV1D-33, IGLV2-14, MMRN1.\u003c/p\u003e\u003cp\u003eThe upregulated genes in (pT2-3pN0) are involved in cell cycle regulation and proliferation (CDC6, CENPF, TOP2A, CBX2, CKS2), steroid metabolism and hormone response (DHCR24, GNAS, CA12), RNA Splicing and Metastasis (ESRP1), lysosomal and cellular trafficking (LAPTM4B), metabolism (ACADSB, COA3), DNA repair and genomic Stability (IDI1), the rest (FAM234B, SUSD4, PPP4R3CP) are less well characterized in Breast Cancer. By contrast, genes involved in immune chemotaxis, including CCL21, activation of immune response (IGHV3-15, IGHA2, IGHV3OR16-12, IGKV1D-8, IGKV1D-33, IGLV2-14), gene regulation (HLF), hormone Metabolism (UGT2B7) as well as Cytoskeletal Dynamics (SYNPO2) were differentially expressed at higher level in pT1pN\u0026thinsp;+\u0026thinsp;tumors. Additionally, MMRN1, which was differentially expressed at a higher level in pT1pN\u0026thinsp;+\u0026thinsp;tumors, is a known endothelial signature that may be implicated in platelet/endothelial interactions in early metastasis.\u003c/p\u003e\u003cp\u003eOur secondary comparison evaluated the other patient subsets and found 32 downregulated and 6 upregulated genes in large tumors versus small tumors without LN Metastasis. In the presence of LN metastasis, 12 genes were downregulated in large compared to small tumors, and 4 genes were upregulated. Among those, G0S2, HSPB6, PLIN1, and MUC4 genes were consistently downregulated, while TPD52L1 was consistently upregulated in large tumors, suggesting their role in tumor growth irrespective of LN metastasis. Notably, limited data has been reported to date regarding the impact of these genes on BC oncogenesis. For MUC4, which is a cell surface membrane-bound mucin protein, discordant data has been reported from different patient series with downregulation and overexpression associated with tumor oncogenesis.[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOur results also showed seven genes, including 2 upregulated (UBE2C, TPD52L1) and 5 downregulated (JCHAIN, IGHV3-49, MFAP4, MYZAP, ADRB1) that overlapped between the primary comparison and secondary comparison, which indicates these genes may influence both tumor growth and metastasis. Of interest, several of the genes have been implicated in breast tumorigenesis, including UBE2C, which, when overexpressed, has been associated with lymphovascular invasion, lymph node metastasis, and poor prognosis.[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] A recent investigation demonstrated a link between CDK4/6 inhibitor's efficacy and the suppression of UBE2C expression.[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\u003cp\u003eOne of the notable findings of our study is the limited differential gene expression observed between small tumors with and without LN metastasis (pT1pN0 vs. pT1pN+) and large tumors with and without LN metastasis (pT2-3pN0 vs. pT2-3pN+). Surprisingly, no DEGs emerged from this comparison despite the large sample size. These findings may be attributed to post-transcriptional and epigenetic changes, tumor heterogeneity, low transcript abundance, or the limited sensitivity of the method to capture subtle expression changes, as these analyses are based on a single timepoint and could warrant further confirmation using more sensitive techniques.\u003c/p\u003e\u003cp\u003eGene Set Enrichment Analysis (GSEA) provided insights into the pathways associated with differentially expressed genes. Upregulation in cancer proliferation-related pathways and downregulation in immune-related pathways in larger tumors without LN metastasis hinted at potential immune evasion mechanisms facilitating metastasis.\u003c/p\u003e\u003cp\u003eOur immune deconvolution analysis further emphasized the role of the tumor microenvironment, revealing a higher abundance of immunosuppressive elements in small tumors with LN metastasis compared to large tumors within the MP High Risk and BP Luminal B cohort. These findings suggest the presence of an immunosuppressive microenvironment enabling LN metastasis in small tumors compared to large tumors.\u003c/p\u003e\u003cp\u003eTIL quantification assessed the differences in the immune microenvironment between patient subgroups. Despite the findings on deconvolution analyses, within a representative subset of our patient population, conventional H\u0026amp;E TIL analysis did not demonstrate any difference between pT1pN\u0026thinsp;+\u0026thinsp;tumors and pT2-3pN0 tumors, underscoring the importance of moving beyond conventional TIL assessment when evaluating the tumor immune microenvironment.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThese analyses unveil a complex interplay of gene expression patterns and immune responses in BC, providing insight into the biological mechanisms of early LN metastasis. In comparing small tumors with LN metastasis vs. large tumors without LN metastasis across MP/BP molecular subtypes, we observed differential expression of proliferation and chemotaxis-related genes and distinct immune deconvolution within the MP High Risk and Luminal B subset of tumors, suggestive of a more immunosuppressive microenvironment in tumors with early LN metastasis and genomically high risk disease.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eThe authors have the following paid financial relationships with the funder:Receive research funding from funder: Nina D\u0026rsquo;Abreo and Douglas MarksEmployed and have stock ownership in funding company: Josien Haan, Nicole Chmielewski-Stivers, Andrea Menicucci, and William Audeh\u003c/p\u003e\n\u003ch2\u003eFunding:\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eAgendia\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eF.F. and D.M. contributed to study conception and design, data collection, analysis, and interpretation, and drafted the main manuscript. P.P.S., N.D., S.A., and Z.L. contributed to data collection. J.B., J.H., and A.M. contributed to data analysis and interpretation. N.C.S. contributed to drafting the main manuscript. All authors critically reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgement\u003c/h2\u003e\n\u003cp\u003eWe would also like to thank the patients enrolled in FLEX, study site coordinators, research nurses, personnel, and the FLEX Investigators\u0026rsquo; Group that made this study possible.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eData are available upon request from the corresponding author but may require data transfer agreements. No personalized health information will be shared.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eRibnikar D, Cardoso F (2016) Tailoring Chemotherapy in Early-Stage Breast Cancer: Based on Tumor Biology or Tumor Burden? 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Cancer Res 83:P2-26\u0026ndash;14. https://doi.org/10.1158/1538-7445.SABCS22-P2-26-14\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Breast cancer, locoregional metastasis, nodal metastasis, metastatic breast cancer, genomic risk","lastPublishedDoi":"10.21203/rs.3.rs-7347995/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7347995/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eIntroduction:\u003c/strong\u003e Early lymph node (LN) metastasis often precedes systemic metastasis and corresponds with significantly inferior survival for patients diagnosed with early-stage breast cancer (EBC). To understand the biological pathways involved in early LN metastasis, differential gene expression (DGE)analysis compared large tumors without evidence of LN metastasis (pT2-3pN0) to small tumors with LN metastasis (pT1pN+).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e This study included 2,349 patients with EBC who underwent MammaPrint and BluePrint testing as part of the FLEX (NCT03053193). DGE was performed between pT2-3pN0/pT1pN+ and across their MP/BP subtypes. Immune deconvolution was assessed using gene-signature-based methods, complemented by conventional tumor-infiltrating lymphocyte (TIL) analyses on a representative subset of patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Greater DGE was observed within the MammaPrint High Risk and BluePrint Luminal B subtypes compared to pathological stages. MammaPrint High Risk tumors saw 73 differentially expressed genes (DEGs), while 34 were found for Luminal B tumors. Gene set enrichment analysis (GSEA) of MammaPrint High Risk/Luminal B tumors showed upregulated proliferation pathways and downregulated epithelial-to-mesenchymal transition (EMT) and immune profiles in pT2-3pN0 vs. pT1pN+, respectively. Immune deconvolution analyses showed a higher abundance of T gamma delta cells and CD4+ Th1 cells and a lower abundance of T regulatory cells, M2 macrophages, and cancer-associated fibroblasts within pT2-3pN0 tumors. Conventional histological assessment revealed no significant differences in TILs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e This study lays the groundwork for exploring mechanisms of LN metastasis in EBC and their relation to MammaPrint High Risk and Luminal B subtypes. These data support previous studies’ association of LN metastasis with EMT and immune dysregulation.\u003c/p\u003e","manuscriptTitle":"Whole transcriptome analysis reveal MammaPrint and BluePrint-associated gene expression patterns with early lymph node metastasis in early-stage breast cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-11 15:11:18","doi":"10.21203/rs.3.rs-7347995/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-11-17T19:24:58+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-11-16T14:25:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"8841883160041001001638724245886995959","date":"2025-10-24T01:44:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"238184313563359955340385779227454129688","date":"2025-10-19T20:19:46+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-10-19T08:08:05+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315457073133271247949404416416367448827","date":"2025-09-04T14:20:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-09-04T14:14:58+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-12T03:03:48+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-12T03:03:11+00:00","index":"","fulltext":""},{"type":"submitted","content":"Breast Cancer Research and Treatment","date":"2025-08-11T15:30:54+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"breast-cancer-research-and-treatment","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"brea","sideBox":"Learn more about [Breast Cancer Research and Treatment](https://www.springer.com/journal/10549)","snPcode":"10549","submissionUrl":"https://submission.nature.com/new-submission/10549/3","title":"Breast Cancer Research and Treatment","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"374981da-948d-4452-9981-feed1a1b7782","owner":[],"postedDate":"September 11th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-11T00:38:31+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-11 15:11:18","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7347995","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7347995","identity":"rs-7347995","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}