Long-Term Breastfeeding is associated with Improved Pathological Response and Reduced Oncogenic Mutations in Triple Negative Breast Cancer

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Puchades-Olmos, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8140522/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 6 You are reading this latest preprint version Abstract Breastfeeding has been associated with a reduced risk of developing triple-negative breast cancer (TNBC), but its impact on tumor biology and treatment response remains unclear. We analyzed 43 patients with TNBC treated with taxane-based neoadjuvant chemotherapy (NAC), integrating detailed reproductive histories with clinicopathological features and targeted next-generation sequencing of pre- and post-treatment tumor samples. Long-term breastfeeding (≥ 6 months) was significantly associated with higher pathological complete response (pCR) rates (57% vs. 27%), as well as a lower mutational burden and reduced prevalence of alterations in KIT, NOTCH1, CDKN2A, and KDR. Non-responding tumors were enriched in KDR mutations suggesting a potential role in chemoresistance. NOTCH1 and CDKN2A mutations correlated with increased mutational burden, indicating enhanced genomic instability These findings suggest that extended breastfeeding may imprint lasting biological effects that shape mammary epithelial stability, immune surveillance, and subsequent chemosensitivity. Recent mechanistic evidence supports this concept, showing durable T-cell–mediated immunoprotection and epigenetic/metabolic programming induced by lactation. Although limited by sample size, our results provide the first clinical and genomic evidence linking breastfeeding duration with therapy response in TNBC and underscore the relevance of reproductive history in shaping tumor behavior and treatment outcomes. Biological sciences/Cancer Health sciences/Oncology triple-negative breast cancer NGS breastfeeding neoadjuvant chemotherapy mutational profiling KIT NOTCH1 CDKN2A therapeutic response Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Triple-negative breast cancer (TNBC), represents ~ 15–20% of breast tumors, is often high grade with earlier onset and poorer prognosis, and is enriched in carriers of germline BRCA1/2 variants ( 1 , 4 ). Unlike hormone receptor–positive or HER2-amplified breast cancers, TNBC lacks established targeted therapies. Cytotoxic chemotherapy, particularly in the neoadjuvant setting, remains the backbone of treatment, and achieving a pathological complete response (pCR) after neoadjuvant treatment constitutes the strongest surrogate marker of favorable prognosis ( 5 , 6 ). Nevertheless, a substantial proportion of patients fail to reach pCR and subsequently develop early recurrence, underscoring the need for reliable predictors of treatment response and resistance mechanisms ( 6 , 7 ). The biological heterogeneity of TNBC has prompted multiple molecular classification proposals ( 1 , 8 ). However, their clinical application remains limited due to technical complexity and lack of reproducibility. Recent molecular profiling studies have highlighted the high genetic heterogeneity of TNBC. More specifically, next-generation sequencing (NGS) has emerged as a powerful tool to comprehensively characterize the genomic landscape of TNBC using formalin-fixed paraffin-embedded (FFPE) tissue. NGS enables the detection of recurrent mutations in genes such as TP53, BRCA1/2, PIK3CA, and NOTCH1, which are involved in DNA repair, immune regulation, and proliferation signaling ( 9 – 11 ). These alterations may influence treatment resistance and disease progression, providing a rationale for longitudinal molecular monitoring. Furthermore, emerging evidence suggests that both intrinsic tumor biology and host-related factors may influence the development and progression of TNBC ( 12 ). Reproductive and lifestyle variables, such as age at menarche, parity, breastfeeding duration, and obesity, have been consistently linked to TNBC risk. However, their interaction with molecular profiles and treatment outcomes remains largely unexplored( 13 – 15 ). Beyond intrinsic tumor features, TNBC is increasingly recognized as being influenced by specific epidemiological and reproductive risk factors. Distinct from other breast cancer subtypes, TNBC risk has been associated with multiparity without prolonged breastfeeding, early age at first birth, African ancestry, and high breast density ( 13 , 14 ). Conversely, breastfeeding, especially beyond six months, has a protective effect, with epidemiological estimates indicating that up to 15% of TNBC cases in Black women and 12% in White women could be prevented by breastfeeding promotion ( 13 , 14 ). Although long-term breastfeeding has been established as a protective factor against TNBC incidence ( 12 – 15 ), no studies to date have evaluated its potential association with response to either adjuvant or neoadjuvant therapies in this subtype. These findings underscore the interaction between biological and social determinants of TNBC risk and highlight the importance of integrating lifestyle and reproductive history in prevention and risk stratification. Recent studies have investigated the mutational dynamics of TNBC under the pressure of neoadjuvant chemotherapy (NAC). Singh et al. ( 16 ) analyzed paired pre- and post-treatment samples from patients with residual disease after NAC and found a largely stable mutational burden, dominated by TP53 , PTEN , and ERBB2 variants, with a notable post-therapy enrichment of AR mutations. However, their study focused exclusively on non-PCR cases and did not explore the clinical or biological determinants of treatment response. In contrast, our study encompasses both complete and incomplete responders, integrating genomic, clinical, and host-related variables to identify predictive and protective factors associated with therapeutic response and disease recurrence. Particularly, we investigated the impact of breastfeeding duration, a lifestyle factor with growing epidemiological relevance, on genomic profiles, mutational burden, and pathological response. To our knowledge, this is the first study to link breastfeeding with genomic characteristics and treatment outcomes in TNBC, thereby expanding beyond the purely mutational scope addressed in previous post-NAC sequencing studies. Results 2.1 Clinical and Pathological Characteristics A total of 43 women with pathologically confirmed triple-negative breast cancer (TNBC) were included in the study (Table 1 ). The median age at diagnosis was 55 years (IQR 45–67; range 29–76), and most tumors were located in the right breast (58%). A family history of breast or ovarian cancer was reported in 23% of cases, and 60% of the patients were current or former smokers. The median age at menarche was 13 years (IQR 12–13.5). Regarding reproductive history, 35% of patients had never breastfed, 16% had breastfed for less than six months, and 49% had breastfed for six months or longer; this latter group was considered the long-term breastfeeding category for subsequent analyses. At presentation, most patients had clinical stage II disease (74%), with T2 tumors (65%) and node-positive disease (49%). Histologically, 93% were invasive ductal carcinomas and 72% were grade III tumors. Baseline immunohistochemistry showed high proliferative activity (Ki-67 ≥ 14% in 84%) and overexpression of p53 (≥ 10% in 60%). These findings were consistent with a biologically aggressive TNBC phenotype. All patients received taxane-based neoadjuvant chemotherapy (NAC), with or without anthracyclines, administered according to standard protocols in use between 2009 and 2019. No patient received immunotherapy. The median number of administered cycles was six (IQR 6–8). Dose reductions were required in 23% of patients (mean reduction 5.6%), and treatment delays occurred in 26%, typically short in duration. Treatment was generally well tolerated, with manageable toxicity. The most frequent adverse events were neutropenia (40%), mucositis (21%), dermatitis (16%), and vomiting (28%); thrombocytopenia and anemia each occurred in 7% of patients. Asthenia was reported in approximately half of the cohort. Supportive care and dose adjustments allowed completion of the planned chemotherapy in nearly all cases. Following NAC, 60% of patients underwent breast-conserving surgery and 40% mastectomy. Pathological examination of the surgical specimens revealed a median tumor eradication of 90% (IQR 49–100), and 18 patients (42%) achieved pathologic complete response (pCR), defined as absence of invasive carcinoma in both the breast and axillary nodes. The rate of pCR differed notably according to breastfeeding history: women who had breastfed for ≥ 6 months achieved a pCR rate of 57%, compared with 27% among those who had breastfed for a shorter duration or not at all (p = 0.036). Similarly, the median pathological response percentage was 100% in long-term breastfeeders versus 59% in the other group (p = 0.0361), suggesting enhanced chemosensitivity associated with extended breastfeeding. After treatment, 70% of patients were node-negative, and 68% achieved stage 0–I disease. After a median follow-up of 69.7 months (IQR 39.6–106.8), 22 patients (51%) experienced disease recurrence and 17 (40%) died. The median disease-free survival (DFS) was 58.4 months (IQR 22.7–97.5), and the mean overall survival (OS) was 75.3 months (SD 44.8). Although the study was not powered to detect survival differences by breastfeeding status, a favorable trend was observed among patients with longer breastfeeding duration, who showed longer DFS and OS compared with those with short or no breastfeeding. Table 1 Table 1 Clinicopathological and Treatment Characteristics of the TNBC Cohort (n = 43). Clinicopathological and treatment characteristics of 43 patients with triple-negative breast cancer treated with taxane-based neoadjuvant chemotherapy (NAC) with or without anthracyclines between 2009 and 2019. Data is presented as counts (percentages) or medians [IQR]. Percentages are based on available data. pCR = pathologic complete response; IQR = interquartile range. Variable Category / Statistic n (%) or Value Age at diagnosis (years) Median [IQR] (min–max) 55 [45–67] (29–76) Breast laterality Left / Right 18 (41.9) / 25 (58.1) Family history of breast/ovarian cancer Yes / No 10 (23.3) / 33 (76.7) Smoking history Smoker / Non-smoker 26 (60.5) / 17 (39.5) Age at menarche (years) Median [IQR] 13 [12–13.5] Breastfeeding duration None / <6 months / ≥6 months 15 (34.9) / 7 (16.3) / 21 (48.8) Clinical stage (pre-NAC) I / II / III / IV 4 (9.3) / 32 (74.4) / 5 (11.6) / 2 (4.7) T stage (pre-NAC) T1 / T2 / T3 / T4 6 (14.0) / 28 (65.1) / 7 (16.3) / 2 (4.7) N stage (pre-NAC) N0 / N1 22 (51.2) / 21 (48.8) Histologic type Invasive ductal / Other 40 (93.0) / 3 (7.0) Pathological grade (pre-NAC) Grade II / III 12 (27.9) / 31 (72.1) p53 expression (pre-NAC) < 10% / ≥10% 17 (40.5) / 25 (59.5) Ki-67 index (pre-NAC) < 14% / ≥14% 7 (16.3) / 36 (83.7) Neoadjuvant regimen Taxane ± anthracycline 43 (100) Cycles administered Median [IQR] (min–max) 6 [6–8] ( 3 – 10 ) Dose reduction Yes / No 10 (23.3) / 33 (76.7) Mean dose reduction (%) — 5.6 (± 9.3) Treatment delay Yes / No 11 (25.6) / 32 (74.4) Adverse events Neutropenia / Anemia / Mucositis / Dermatitis / Vomiting 17 (39.5) / 3 (7.0) / 9 (20.9) / 7 (16.3) / 12 (27.9) Surgical approach Breast-conserving / Mastectomy 26 (60.5) / 17 (39.5) Pathological response (%) Median [IQR] (min–max) 90 [49–100] (0–100) Pathologic complete response (pCR) Achieved / Not achieved 18 (41.9) / 25 (58.1) Clinical stage (post-NAC) 0 / I / II / III / IV 7 (16.3) / 22 (51.2) / 9 (20.9) / 4 (9.3) / 1 (2.3) Node status (post-NAC) N0 / N1–2 30 (69.8) / 13 (30.2) Recurrence Yes / No 22 (51.2) / 21 (48.8) Death Yes / No 17 (39.5) / 26 (60.5) Disease-free survival (months) Median [IQR] (min–max) 58.4 [22.7–97.5] (1.1–176.7) Overall survival (months) Median [IQR] (min–max) 69.7 [39.6–106.8] (3.5–176.7) 2.2 Tumor genomic profile and Mutational Landscape In the 40 pre-NAC tumor samples, a mean of 13.4 variants per sample were identified (range: 3–63). The most frequently altered genes were TP53 (95%), FLT3 (95%), CSF1R (92.5%), and ERBB4 (80%). Among the most prevalent variants, notable alterations included TP53 c.215C > G (p.Pro72Arg) (75%), a substitution affecting the splicing region; FLT3 c.1310-3T > C (95%), located in a splicing region; CSF1R c.35_36delCAinsTC (92.5%), situated in the 3'UTR; and three ERBB4 alterations, c.884-7delT (77.5%), c.884-8_884-7delTT (75%), and c.884-7dupT (72.5%), all proximal to splice sites. A high mutational burden (≥ 13 variants per sample) was significantly associated with mutations in NOTCH1, FGFR1, and CDKN2A (p ≤ 0.01). Following the administration of neoadjuvant chemotherapy, the 21 remaining tumor samples, due to the incomplete pathological response after treatment, were analyzed. The high mutation frequency in TP53, FLT3, CSF1R (95.24%), and ERBB4 (80.95%) was maintained. The mean number of variants per sample decreased to 11.4 (range: 1–57). The recurrent alterations observed pre-treatment, TP53 c.215C > G (85.71%), FLT3 c.1310-3T > C, CSF1R c.35_36delCAinsTC (95.24%), and the ERBB4 variants c.884-7delT, c.884-8_884-7delTT, and c.884-7dupT, persisted, being present in > 70% of cases. In the three metastatic samples analyzed, one brain and two lung metastases, a mean of 12 variants per sample was detected (range: 10–13), showing high concordance with the previous genomic profiles. The four most representative alterations, TP53 c.215C > G, FLT3 c.1310-3T > C, CSF1R c.35_36delCAinsTC, and the ERBB4 variants (c.884-7delT, c.884-8_884-7delTT, c.884-7dupT) were shared across 100% of the metastatic lesions. Although we could only sequence three metastasis, analysis across the three disease time points (pre-NAC, post-NAC, and metastases) revealed a set of “founder variants”, TP53 c.215C > G, FLT3 c.1310-3T > C, CSF1R c.35_36delCAinsTC, and the three ERBB4 variants, consistently retained, indicating a stable core genome resistant to cytotoxic pressure. No new driver variants of high frequency emerged in the post-NAC or metastatic setting, suggesting dominance of pre-existing clones over therapy-induced evolution. Figure 1 shows the genetic prevalence across tumor sample groups and the association between high mutational burden and specific significant gene alterations. 2.3 Clinicopathological and Molecular Variables Associated with Prognosis. Survival outcomes were mainly determined by post-neoadjuvant pathological response and residual disease burden, in line with established TNBC prognostic models. Patients who relapsed or died exhibited significantly lower pathological regression and higher residual nodal involvement and post-treatment stage (all p < 0.05). These findings are consistent with the well-validated prognostic value of pathological complete response (pCR) and Residual Cancer Burden (RCB), which are routinely used in clinical oncology to stratify risk and guide post-neoadjuvant management in TNBC. Among baseline factors, low p53 immunoexpression (< 10%) was more frequent in deceased patients (p = 0.046). As p53 IHC is not currently a standardized prognostic or therapeutic biomarker in TNBC, this association should be interpreted as exploratory. Overall, these results reaffirm that inadequate pathological response and residual tumor burden remain the strongest predictors of recurrence and death in TNBC, providing the clinical framework for evaluating novel prognostic factors such as breastfeeding history and molecular alterations. (More detailed in Supplementary Tables S1 and S2). 2.4 Clinicopathological and Molecular Variables Associated with Pathological Complete Response To better understand the clinicopathological and molecular determinants of therapeutic efficacy, patients were stratified according to their pathological response to neoadjuvant chemotherapy and disease recurrence during follow-up. This combined classification generated two subgroups: G0 (non-responders) and G1 (responders). Comparative analyses across these subgroups were then performed to assess differences in clinicopathological variables, total mutational burden, and gene-specific alterations, providing a comprehensive overview of the biological and clinical features associated with pathological complete response and recurrence risk in TNBC. 2.4.1 Prognostic Impact of KIT Mutations within Response Groups From all the gene mutations analyzed in our cohort, the only one showing a significant association when comparing subgroups defined by response to neoadjuvant therapy was KIT (c-KIT). We therefore examined its prognostic impact in greater detail. In the G0 subgroup, survival outcomes were comparable between KIT wild-type and mutated cases. Among patients with wild-type KIT (n = 17) and those harboring KIT mutations (n = 5), Kaplan–Meier curves overlapped, and the difference was not statistically significant ( p = 0.28, log-rank test). In contrast, in the G1 subgroup KIT mutational status clearly stratified DFS. Patients with KIT mutations (n = 5) experienced substantially earlier relapses compared with those with wild-type KIT (n = 13). Kaplan–Meier analysis demonstrated a significant separation of curves ( p = 0.037, log-rank test). Taken together, these results indicate that KIT mutations do not influence prognosis in patients who fail to respond to therapy. However, among responders, KIT mutations identify a subgroup at high risk of early recurrence despite initial treatment response (Fig. 2 ). 2.4.2 Breastfeeding and KDR Mutations as Predictors of Pathological Complete Response. Among other variables analyzed to see if there was any correlation with achieving a pathological complete response (pCR) following neoadjuvant therapy, breastfeeding history showed a significant association with response (Table S $ ). Patients who achieved a pCR had a higher frequency of long-term breastfeeding (66.7%) compared with non-responders (36.0%; p = 0.023 ). Surgical outcomes were also related: conservative surgery was more common among responders (77.8%) than non-responders (48.0%; p = 0.049 ). Importantly, achieving pCR translated into improved prognosis, as responders had significantly better survival outcomes compared with non-responders (77.8% vs. 48.0% alive at follow-up; p = 0.049 ). At the genomic level, KDR mutations, including the 1416A > T variant, were significantly enriched among non-responders. Overall, 72.7% of non-responders carried KDR alterations compared with only 33.3% of responders ( p = 0.013 ). (Fig. 3 ). Taken together, these results suggest that both clinical history and genomic background influence treatment response. Favorable factors such as prior breastfeeding and conservative surgery were enriched in responders, while non-responders were characterized by the presence of KDR mutations, which may represent a marker of resistance to neoadjuvant therapy. 2.4.3 Impact of Breastfeeding on Clinical Response and Tumor Molecular Features Given the well-established epidemiological evidence linking reproductive factors to TNBC risk, we evaluated its association with treatment response and genomic characteristics in our cohort. To determine whether breastfeeding history influences tumor behavior and treatment efficacy, patients were stratified into two groups according to breastfeeding duration: long-term breastfeeding (≥ 6 months) and short-term or no breastfeeding (< 6 months/none). A significant association was observed between breastfeeding duration and pathological complete response (pCR). Patients who had breastfed for ≥ 6 months achieved pCR in 12 of 21 cases (57.1%), compared with 6 of 22 (27.3%) in the short- or no-breastfeeding group (odds ratio = 3.56, 95% CI = 0.99–12.7; Pearson’s chi-square p = 0.047). These findings indicate that longer breastfeeding duration was associated with a markedly higher likelihood of achieving pCR following neoadjuvant chemotherapy in TNBC (Fig. 4 , Table 2 ). Table 2 . Table 2 Comparison of clinicopathological and molecular variables between patients with short or no breastfeeding (≤ 6 months) and those with prolonged breastfeeding (> 6 months) (n = 43). Continuous variables are shown as medians [IQR] or means ± SD, and categorical variables as frequencies (%). Statistical comparisons were performed using Wilcoxon rank-sum, Pearson’s χ², or Fisher’s exact tests, as appropriate. Prolonged breastfeeding was significantly associated with higher pathological response rates and a greater proportion of responders, while short or absent breastfeeding correlated with higher mutational burden (≥ 20 mutations) and increased prevalence of KIT, NOTCH1, CDKN2A, ERBB2, FGFR2, and IDH2 mutations. These findings support that long-term breastfeeding is linked to a biologically distinct TNBC profile characterized by lower genomic instability and enhanced chemosensitivity Variable Lower Breastfeeding (≤ 6 months / none) higher Breastfeeding (> 6 months) Test / Comparison p-value Pathological response Median 59 [0–100]Mean 52.8 ± 43.3 Median 100 [57.5–100]Mean 81.5 ± 28.4 Wilcoxon rank-sum 0.0361 Response status groups (cPR) Non-responders: 16 (72.7%)Responders: 6 (27.3%) Non-responders: 9 (42.9%)Responders: 12 (57.1%) Pearson’s χ² 0.0472 Total mutations < 10: 8 (42.1%)10–19: 6 (31.6%) ≥ 20: 5 (26.3%) < 10: 10 (47.6%)10–19: 11 (52.4%) ≥ 20: 0 (0%) Fisher’s exact 0.0180 KIT mutation Wild-type: 11 (57.9%)Mutated: 8 (42.1%) Wild-type: 19 (90.5%)Mutated: 2 (9.5%) Fisher’s exact 0.0281 NOTCH1 mutation Wild-type: 14 (73.7%)Mutated: 5 (26.3%) Wild-type: 21 (100%)Mutated: 0 (0%) Fisher’s exact 0.0177 CDKN2A mutation Wild-type: 15 (78.9%)Mutated: 4 (21.1%) Wild-type: 21 (100%)Mutated: 0 (0%) Fisher’s exact 0.0424 ERBB2 mutation Wild-type: 15 (78.9%)Mutated: 4 (21.1%) Wild-type: 21 (100%)Mutated: 0 (0%) Fisher’s exact 0.0424 FGFR2 mutation Wild-type: 15 (78.9%)Mutated: 4 (21.1%) Wild-type: 21 (100%)Mutated: 0 (0%) Fisher’s exact 0.0424 IDH2 mutation Wild-type: 15 (78.9%)Mutated: 4 (21.1%) Wild-type: 21 (100%)Mutated: 0 (0%) Fisher’s exact 0.0424 By contrast, no statistically significant differences were observed in recurrence rates, disease-free survival (DFS), or overall survival (OS) between breastfeeding categories, although trends suggested longer DFS among women with a history of long-term breastfeeding. At the molecular level, several somatic alterations were significantly enriched in patients with short-term or no breastfeeding. Both grouping strategies identified significantly lower frequencies of KIT, NOTCH1, and CDKN2A mutations in the ≥ 6 months group compared with the little/no group. In the binary analysis, mutations were present in 42.1% vs 9.5% for KIT ( p = 0.028), 26.3% vs 0% for NOTCH1 ( p = 0.018), and 15.8% vs 0% for CDKN2A ( p = 0.042), respectively (Fig. 3 ) The binary approach, by increasing power, additionally detected significant associations for ERBB2, FGFR2, and IDH2 mutations (all p = 0.042). These findings indicate that prolonged breastfeeding is linked to a lower prevalence of several driver alterations implicated in tumor progression and therapeutic resistance. Breastfeeding was also associated with the distribution of total mutations. In the categorical analysis (< 10, 10–19, ≥ 20), no tumors from the ≥ 6 months group (0/21) harbored ≥ 20 mutations, compared with 5/19 (26.3%) in the little/no group (Fig. 3 B) ( p = 0.018, Fisher’s exact test). Median total mutation counts did not differ between groups, underscoring that the signal arises from the enrichment of very high-burden tumors in women with little or no breastfeeding. Taken together, the clinical and molecular results converge toward a coherent model linking early-life reproductive exposures, such as long-term breastfeeding, with tumor genomic architecture and treatment sensitivity in TNBC. Patients who had breastfed for six months or longer not only demonstrated higher rates of pathological complete response to taxane-based chemotherapy but also harbored tumors with lower mutational burden and fewer alterations in key driver genes associated with resistance, including KIT , NOTCH1 , CDKN2A , and KDR . These findings suggest that the well-documented epidemiological protection conferred by breastfeeding against aggressive breast cancer subtypes may extend beyond incidence reduction to influence tumor biology, genomic stability, and therapeutic responsiveness once the disease develops. In this context, the integration of clinical, pathological, and genomic data highlights a potential biological imprinting effect of early-life exposures that could shape the molecular evolution and chemosensitivity of TNBC, a hypothesis that is worth validating through larger patient cohorts and mechanistic studies. Discussion In this study, we analyzed clinical, pathological, and molecular factors associated with treatment response and outcomes in patients with TNBC treated with neoadjuvant chemotherapy. Our results provide new insight into how early-life reproductive exposures, particularly breastfeeding duration, may influence both the genomic landscape and therapeutic response of TNBC. By integrating detailed clinicopathological and mutational data from a well-defined cohort of 43 patients treated with taxane-based neoadjuvant chemotherapy, we found that women who had breastfed for at least six months achieved significantly higher pathological complete response rates and exhibited tumors with lower mutational burden and fewer alterations in key driver genes associated with chemoresistance. Although the cohort size was modest, the consistency between the clinical and genomic observations suggests that reproductive history may have a durable biological impact on tumor evolution and therapy sensitivity in TNBC. Previous epidemiological studies have consistently shown that breastfeeding reduces the risk of developing TNBC ( 12 – 15 ), and reproductive factors such as parity, breastfeeding, and age at menarche have been recognized as major determinants of TNBC incidence ( 12 , 13 , 17 ). Our results extend these observations by suggesting that breastfeeding may not only protect against the occurrence of TNBC but also modulate the likelihood of achieving pCR. The biological mechanisms underlying this association are not fully understood. Prolonged breastfeeding has been linked to durable remodeling of the breast tissue microenvironment, enrichment of beneficial microbial communities, and modulation of immune pathways, including T regulatory cell induction and anti-inflammatory cytokine production ( 18 , 19 ). Such changes could enhance chemotherapy-induced tumor clearance by fostering a less pro-inflammatory and more immunologically balanced tumor microenvironment. In contrast, abrupt weaning or absence of breastfeeding has been associated with pro-inflammatory profiles and dysbiotic microbial signatures, which may contribute to treatment resistance ( 15 , 20 , 21 ). These findings are novel, as no prior studies have examined the association between breastfeeding history and response to therapy in TNBC. The biological plausibility of this observation is supported by evidence that breastfeeding induces long-lasting remodeling of mammary tissue architecture, promotes immune tolerance, reduces chronic inflammation, and modulates the breast microbiota, all of which could contribute to improved tumor sensitivity to chemotherapy ( 22 , 23 ). An important aspect of this study was the analysis of tumor clonality between primary tumors, residual disease in non-pCR cases, and subsequent metastases. The persistence of clonal mutations in residual tumors after neoadjuvant therapy suggests that resistant subclones drive recurrence and metastatic spread ( 24 ). This observation has clinical relevance, as the molecular features of residual disease could inform post-neoadjuvant therapeutic strategies ( 25 ). Moreover, the identification of shared clonal mutations between primary and metastatic lesions highlights potential therapeutic targets that remain relevant throughout disease progression. The overall mutational burden in our cohort was variable, and although limited by sample size, patients with higher tumor mutational load tended to exhibit poorer survival, in agreement with previous studies linking elevated mutational burden with aggressive phenotypes and immune-evasive behavior in breast cancer ( 26 , 27 ). Beyond global genomic instability, specific gene alterations provided additional biological insights. NOTCH1 mutations were enriched in tumors with adverse clinicopathological features, consistent with the oncogenic role of aberrant Notch signaling in promoting epithelial-mesenchymal transition, stem-like properties, and chemoresistance in TNBC ( 28 , 29 ). Alterations in KIT, a receptor tyrosine kinase implicated in cell proliferation and survival, were observed predominantly in non-pCR tumors, suggesting a contribution to treatment resistance through sustained MAPK and PI3K pathway activation ( 30 ). Mutations in KDR (VEGFR2) were also detected, reinforcing the centrality of angiogenic dysregulation in TNBC biology, as KDR-driven signaling facilitates tumor vascularization and hypoxia-adapted growth ( 31 , 32 ). Finally, CDKN2A alterations, which lead to loss of p16 INK4A -mediated cell-cycle control, were associated with reduced disease-free survival, reflecting their known role in unchecked proliferation and genomic instability ( 33 ). Together, these observations underscore the heterogeneity of TNBC and support the notion that distinct mutational profiles, encompassing Notch signaling, receptor tyrosine kinases, angiogenic pathways, and cell-cycle regulation, may shape therapeutic response and disease progression. Recent high-impact research provides additional biological support for the associations observed in our study. In October 2025, Nature published evidence that pregnancy and breastfeeding induce the expansion of a population of long-lived tissue-resident T cells that persist in the breast for decades and contribute to immune surveillance against malignant transformation ( 34 ). These cells were enriched in post-lactational breast tissue and were shown to suppress tumor development, particularly in models of triple-negative breast cancer. This discovery offers a mechanistic framework that aligns with our findings: women who breastfed for ≥ 6 months developed tumors with lower mutational burden and achieved higher pathological response rates, features compatible with enhanced immunosurveillance and improved genomic stability conferred by these long-lasting immune populations. Complementary evidence from the October 2025 issue of Nature Metabolism further supports the concept that breastfeeding exerts durable biological programming ( 35 ). That study showed that infant-derived ketone bodies present during breastfeeding can trigger long-term metabolic and epigenetic reprogramming, influencing immune maturation and energy homeostasis well beyond infancy. Together, these findings reinforce the idea that breastfeeding is not only a short-term physiological process but also a form of early-life biological imprinting with lasting consequences. In the context of our results, these mechanisms provide a plausible link between breastfeeding duration, tumor genomic architecture, and chemotherapy sensitivity in TNBC, suggesting that reproductive exposures may shape breast tissue biology and tumor fate across the lifespan. Limitations and future directions Taken together, our findings integrate clinical and molecular perspectives to suggest factors that may refine prognostic assessment in TNBC. Although the sample size limits definitive conclusions, the results provide novel insights worthy of further exploration. Breastfeeding history emerges as a potentially valuable and easily ascertainable variable associated with treatment response, while molecular markers such as tumor clonality, mutational burden, and specific gene alterations (NOTCH1, KIT, KDR, CDKN2A) contribute complementary information on disease biology and resistance mechanisms. These observations warrant validation in larger, prospective studies integrating multi-omics approaches, including transcriptomic and microbiome profiling, to elucidate the biological links between reproductive history, tumor evolution, and therapy response. If confirmed, such knowledge could guide the development of predictive models and targeted strategies for TNBC, a disease still in urgent need of precision medicine approaches. Materials and Methods Study design and patient cohort This is a retrospective, observational, non-interventional, single-center case series with a descriptive and analytical approach. The study included patients diagnosed with TNBC at the Hospital de Dénia (Spain) who received taxane-based NAC, with or without anthracyclines, between January 2009 and June 2019. The diagnosis of TNBC was established by immunohistochemistry (IHC) for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), and confirmed by fluorescence in situ hybridization (FISH) according to the ASCO/CAP guidelines ( 36 ). Tumors were considered triple negative when both ER and PR expression were 0 or < 1%, and HER2 was not amplified by FISH. Only cases with available formalin-fixed paraffin-embedded (FFPE) tumor tissue containing ≥ 70% tumor cells were included. Clinical and demographic data were collected between July 2019 and December 2023 from the hospital’s electronic medical records (Cerner Millennium, Cerner Corporation, Kansas City, MO, USA), after completion of patient recruitment. Staging was performed according to the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) TNM classification ( 37 ). Eligibility criteria Inclusion criteria were: adult women with histologically confirmed invasive breast carcinoma meeting TNBC criteria (see below), treatment with taxane-based NAC (with or without anthracyclines) with curative intent, availability of pre-treatment tumor tissue (core biopsy), and comprehensive clinical data. When available, post-treatment surgical specimens were also analyzed for pathological response and, in a subset, for post-treatment genomic profiling. Exclusion criteria were: non-invasive carcinoma only, prior systemic therapy for the current tumor, metastatic disease at presentation precluding NAC with curative intent, or incomplete minimum clinical data. Ethical considerations The study protocol was approved by the Research Committee of the Dénia Health Department (Ref. PI00018, approval date: June 2, 2018) and conducted in accordance with the Declaration of Helsinki and international ethical standards for human research ( 38 ). Formal written informed consent was obtained from all living participants at study initiation. For deceased patients, the absence of explicit refusal to participate in research was verified. Patient autonomy, confidentiality, and privacy were respected throughout the study. Sample Processing. Formalin-fixed, paraffin-embedded (FFPE) tissue blocks from biopsies, surgical resections from 64 tumor samples of 43 patients were analyzed: 40 diagnostic pre-NAC biopsies, 21 post-NAC surgical specimens from patients without pCR, and 3 metastatic samples. All specimens were obtained from the Pathology Department’s. Histological sections of 8 µm thickness were prepared using a HistoCore AUTOCUT rotary microtome (Leica Biosystems, Wetzlar, Germany). Deparaffinization, extraction, and purification of tumor genomic DNA were performed using the GeneRead DNA FFPE Kit (QIAGEN, Hilden, Germany). The quantity of extracted DNA was measured using a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) with the dsDNA HS Assay Kit (Life Technologies, Carlsbad, CA, USA). DNA concentrations were adjusted to a range of 20–50 ng/µl with 1X TE buffer (Thermo Fisher Scientific, Waltham, MA, USA). Library Preparation and Sequencing Genetic libraries were prepared using the AmpliSeq Cancer HotSpot V2 panel (Illumina Inc., San Diego, CA, USA) capable of detecting somatic mutations with a variant allele frequency (VAF) < 5% in approximately 2800 specific regions and mutation hotspots described in COSMIC across 50 key tumor biology genes and treatment response genes, classified into oncogenes and tumor suppressor genes. PCR amplification was performed using an Eppendorf Mastercycler pro S (Eppendorf AG, Hamburg, Germany). Enzymatic digestions and specific adapter ligation to the 5’ and 3’ ends of the amplicons were carried out. Libraries were purified using AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA). The concentration of the libraries was verified by fluorometry, and their quality and size were assessed using capillary electrophoresis with the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Libraries were adjusted to a final concentration of 2 nM with 1X TE buffer. Denatured libraries with 0.2 N NaOH mixed with 200 mM Tris-HCl pH 7.0 (Sigma-Aldrich, St. Louis, MO, USA) were diluted to 20 pM with chilled HT1 buffer (Illumina Inc., San Diego, CA, USA). The final loading concentration on the MiSeq Reagent Kit v2 (Illumina Inc., San Diego, CA, USA) was 8–12 pM. 600 µl of the denatured libraries were transferred to the loading well of the prepared cartridge. The flow cell was washed with grade 1 water, and the glass surface and channels were cleaned with 70% ethanol-moistened lens wipes before being placed in the MiSeq sequencer (Illumina Inc., San Diego, CA, USA). Following functional checks, sequencing by synthesis was initiated on the MiSeq system, monitored in real-time via Sequencing Analysis Viewer (SAV) and remotely with BaseSpace (Illumina Inc., San Diego, CA, USA). Data Analysis FASTQ files were generated using MiSeq Reporter (Illumina Inc., San Diego, CA, USA). Reanalysis and alignment to the human reference genome (hg19) were performed using Local Run Manager v3 (LRM) (Illumina Inc., San Diego, CA, USA) to generate VCF files. Filtering, analysis, interpretation, annotation, and screening of genetic variants were carried out using BaseSpace Variant Interpreter (BVI) (Illumina Inc., San Diego, CA, USA). Filters were applied to restrict coverage depths to ≥ 100 reads and a VAF of ≥ 2.5%. Sequencing results focused on variant types such as base substitutions, short insertions or deletions (SNP, MNP, INDEL), and their translational consequences on proteins. Potential pathogenicity was evaluated using in silico algorithms (PolyPhen and SIFT) along with other variant annotation tools (ClinVar, RefSeq, COSMIC, My Cancer Genome). Variants were classified as pathogenic, likely pathogenic, and of uncertain significance or benign. All variants included are described in Supplementary Table S4 Statistical analysis. All clinical, pathological, and molecular data were collected and curated into a study dataset prior to analysis. Descriptive and inferential statistical analyses were performed using RStudio software (version 2024.12.0.467) ( 39 ). Categorical variables were summarized as absolute frequencies and percentages, and their associations were assessed with Pearson’s chi-square test or, when expected cell counts were < 5, with the nonparametric Fisher’s exact test. Numerical variables were presented as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on distribution. Between-group comparisons were performed using two-sample t tests when normality assumptions were met or with the Wilcoxon rank-sum test as a nonparametric alternative. For survival outcomes, Kaplan–Meier (KM) estimators were used to calculate and visualize survival probabilities over time. Differences between survival curves were compared using the log-rank test. In addition, Cox proportional hazards regression models were applied to estimate hazard ratios (HR) and 95% confidence intervals (CI) for disease-free and overall survival. Survival analyses were implemented using the Survminer and Survival packages in R ( 40 ) Given the relatively small sample size (n = 43), a post hoc power analysis was conducted using G*Power (version 3.1) ( 41 ) to evaluate whether the study was sufficiently powered to detect significant differences. With a two-tailed α of 0.05 and assuming a high effect size, the achieved statistical power was 0.81. All tests were two-sided, and p-values < 0.05 were considered statistically significant. Abbreviations TNBC Triple negative breast cancer NAC Neoadjuvant chemotherapy OS Overall survival pCR Pathological complete response IHC Immunohistochemistry ER Estrogen receptor PR Progesterone receptor HER2 Human epidermal growth factor receptor-2. Declarations Author Contributions: Conceptualization: J.C. Methodology: J.C., J.R.B.M., R.B.R, Clinical data acquisition: J.S.R., C.A.P.O., J.B.L., J.M.G.B. Pathology review: C.A.P.O., J.B.L., J.M.G.B. Genomic sequencing and data generation: J.C., J.R.B., L.A., R.B.R. Bioinformatics and statistical analysis: C.H.O., J.P., A.F. Data curation: L.S., A.D. Visualization: J.C., J.S.R. Funding acquisition: J.C., J.M.G.B. Supervision: J.C. Writing – original draft: J.C., J.R.B.M. Writing – review & editing: All authors. All authors approved the manuscript and accepted responsibility for its contents. Funding: This work was supported by the ECMOR Research Chair (English Cathedra of Modern Oncology Research) established at CEU Cardenal Herrera University in collaboration with Hospital de Dénia and Charity Cancer Care Jávea, and by the UCH-CEU Research Program for Consolidated Research Groups. The funding institutions had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Acknowledgments: The authors wish to express their sincere gratitude to the Cancer Care Jávea Association for their invaluable support, both through fundraising efforts and through their continuous commitment to patient care and advocacy. Their contribution made this research possible. We also extend our special thanks to Dr. Ignacio Pérez-Roger for his dedication to the establishment of the ECMOR Research Chair at CEU Cardenal Herrera University (CEU-UCH) and for his dedicated support for this project. Conflicts of Interest: The authors declare no conflicts of interest. Data Availability : The datasets generated and analyzed during the current study are from the corresponding and first author upon request. References Lehmann, B. D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. J. Clin. Invest. 121 (7), 2750–2767 (2011). Anders, C. K. & Carey, L. A. Biology, metastatic patterns, and treatment of patients with triple-negative breast cancer. Clin. Breast Cancer . 9 (Suppl 2), S73–81 (2009). Sukumar, J., Gast, K., Quiroga, D., Lustberg, M. B. & Williams, N. Triple-negative breast cancer: emerging therapeutic modalities and novel combination therapies. Cancer J. 27 (1), 59–70 (2021). Valcárcel, B., Domínguez, A., Fuster, D., González-Flores, E. & Rodríguez-Lescure, Á. Triple-negative breast cancer and BRCA mutations: diagnostic and therapeutic implications. J. Clin. Med. 12 (3), 654 (2023). Cortazar, P. et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384 (9938), 164–172 (2014). Liedtke, C. et al. Response to neoadjuvant therapy and long-term survival in patients with triple-negative breast cancer. J. Clin. Oncol. 26 (8), 1275–1281 (2008). von Minckwitz, G. et al. 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Parity and lactation induce T cell mediated breast cancer protection. Nature https://doi.org/10.1038/s41586-025-09713-5 (2025). Wang, J. & Jia, D. Infant ketones set the thermogenic tone. Nat. Metab. 7 , 1963–1964. https://doi.org/10.1038/s42255-025-01357-z (2025). Wolff, A. C. et al. Human epidermal growth factor receptor 2 testing in breast cancer: ASCO/CAP guideline update. J. Clin. Oncol. 36 (20), 2105–2122 (2018). Edge, S. B. & Compton, C. C. The American Joint Committee on Cancer: the 7th edition of the AJCC Cancer Staging Manual and the future of TNM. Ann. Surg. Oncol. 17 (6), 1471–1474 (2010). World Medical Association. Declaration of Helsinki: ethical principles for medical research involving human subjects. JAMA 310 (20), 2191–2194 (2013). Posit Team. RStudio: Integrated Development Environment for R. Posit Software, PBC & Boston, M. A. (2024). Available from: http://www.posit.co/ Kassambara, A., Kosinski, M., Biecek, P. & survminer Drawing survival curves using ggplot2. R package version 0.5.0. (2024). Available from: https://rpkgs.datanovia.com/survminer/index.html Faul, F., Erdfelder, E., Buchner, A. & Lang, A. G. Statistical power analyses using G*Power 3.1: tests for correlation and regression analyses. Behav. Res. Methods . 41 , 1149–1160 (2009). Additional Declarations No competing interests reported. Supplementary Files Supplementarytables.zip Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 18 Mar, 2026 Reviewers invited by journal 25 Nov, 2025 Editor invited by journal 20 Nov, 2025 Editor assigned by journal 18 Nov, 2025 Submission checks completed at journal 18 Nov, 2025 First submitted to journal 17 Nov, 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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(A) \u003c/strong\u003eBar plot showing the frequency of somatic mutations identified in pre-neoadjuvant (Pre-NAC, \u003cem\u003en\u003c/em\u003e = 40), post-neoadjuvant (Post-NAC, \u003cem\u003en\u003c/em\u003e = 21), and metastatic (\u003cem\u003en\u003c/em\u003e = 3) triple-negative breast cancer samples. The most frequently altered genes included TP53, FLT3, CSF1R, and ERBB4, followed by recurrent alterations in KDR, FGFR1, PIK3CA, APC, and KIT. The distribution highlights consistent high-frequency mutations across stages, with some genes showing differential prevalence after treatment or in metastatic samples, suggesting therapy-related clonal dynamics. \u003cstrong\u003e(B-E) \u003c/strong\u003eAssociation between high mutational burden and specific gene alterations. Violin plots showing the distribution of total somatic variants in tumors stratified by mutation status for NOTCH1 \u003cstrong\u003e(B)\u003c/strong\u003e, FGFR1 \u003cstrong\u003e(C)\u003c/strong\u003e, ERBB2 \u003cstrong\u003e(D), \u003c/strong\u003eand CDKN2A \u003cstrong\u003e(E)\u003c/strong\u003e. Tumors harboring mutations in these genes exhibited significantly higher mutational burden (≥13 variants per sample) compared with wild-type counterparts (\u003cem\u003ep\u003c/em\u003e ≤ 0.01), suggesting these alterations are enriched in highly mutated triple-negative breast cancers.\u003c/p\u003e","description":"","filename":"FIGURASDEF1.png","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/10f79d1c773959471342a340.png"},{"id":97125367,"identity":"ffba82c8-8f1d-4e29-ab08-0bdab5eb9ab0","added_by":"auto","created_at":"2025-12-01 08:17:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":52574,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease-Free Survival According to KIT Mutation Status in G0 and G1 Subgroups \u003c/strong\u003eKaplan–Meier curves illustrate disease-free survival (DFS) stratified by \u003cstrong\u003eKIT\u003c/strong\u003e mutational status in the G0 and G1 molecular subgroups. In the \u003cstrong\u003eG0 subgroup\u003c/strong\u003e, survival outcomes were similar between \u003cstrong\u003eKIT\u003c/strong\u003e wild-type (n = 17) and \u003cstrong\u003eKIT\u003c/strong\u003e-mutated (n = 5) cases, with overlapping curves and no significant difference (log-rank p = 0.28). In contrast, in the \u003cstrong\u003eG1 subgroup\u003c/strong\u003e, \u003cstrong\u003eKIT\u003c/strong\u003e mutations (n = 5) were associated with markedly shorter DFS compared with wild-type tumors (n = 13), showing a clear separation of survival curves (log-rank p = 0.037). These results indicate that the prognostic impact of \u003cstrong\u003eKIT\u003c/strong\u003ealterations may depend on the molecular context defined by the G0/G1 classification, with mutations conferring adverse outcomes specifically within the G1 subgroup\u003c/p\u003e","description":"","filename":"FIGURASDEF2.png","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/2e3eb9132b0cb663f3f0e9de.png"},{"id":97142889,"identity":"5f8c5eca-4cee-42ad-ba4b-4496695cc0f9","added_by":"auto","created_at":"2025-12-01 10:08:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":35159,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of breastfeeding history and KDR mutations with pathological complete response (pCR). Bars represent the proportion of patients among non-responders (pCR = 0, blue) and responders (pCR = 1, orange). Breastfeeding (long-term) was more frequent in responders (12/18, 66.7%) compared with non-responders (9/25, 36.0%; \u003cem\u003ep\u003c/em\u003e = 0.023). In contrast, KDR mutations were significantly enriched in non-responders (16/22, 72.7%) compared with responders (6/18, 33.3%; \u003cem\u003ep\u003c/em\u003e = 0.013).\u003c/p\u003e","description":"","filename":"FIGURASDEF3.png","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/0d32a1364dd8f85565500eac.png"},{"id":97142345,"identity":"31c858ca-5edf-4c20-adb0-be3d214f1862","added_by":"auto","created_at":"2025-12-01 10:07:33","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":73589,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003ePathological complete response (pCR) and driver mutations by breastfeeding category. Comparison between patients with long-term breastfeeding (≥6 months) and those with short-term or no breastfeeding (\u0026lt;6 months). Bars represent the frequency of pCR and the prevalence of recurrent somatic mutations (KIT, NOTCH1, CDKN2A, ERBB2, FGFR2, IDH2) across groups. Long-term breastfeeding was associated with a higher pCR rate (57.1% vs. 27.3%, \u003cem\u003ep = 0.0472\u003c/em\u003e) and with the absence of mutations that were enriched in the short-term / no breastfeeding group (KIT 42.1% vs. 9.5%, \u003cem\u003ep = 0.028\u003c/em\u003e; NOTCH1 26.3% vs. 0%, \u003cem\u003ep = 0.018\u003c/em\u003e; CDKN2A 15.8% vs. 0%, \u003cem\u003ep = 0.042\u003c/em\u003e; ERBB2 21.1% vs. 0%, \u003cem\u003ep = 0.042\u003c/em\u003e).\u003cstrong\u003e (B) \u003c/strong\u003eBar plot showing the distribution of patients by total number of somatic mutations and breastfeeding history. Patients were categorized into three mutational burden groups (\u003cstrong\u003e≤10\u003c/strong\u003e, \u003cstrong\u003e10–19\u003c/strong\u003e, and \u003cstrong\u003e≥20 mutations\u003c/strong\u003e) and two breastfeeding groups (\u003cstrong\u003e≤6 months/none\u003c/strong\u003evs. \u003cstrong\u003e\u0026gt;6 months\u003c/strong\u003e). All patients with \u003cstrong\u003ehigh mutational burden (≥20 mutations)\u003c/strong\u003e were exclusively found in the \u003cstrong\u003eshort/no breastfeeding\u003c/strong\u003egroup (26.3%), whereas none were observed among women with prolonged breastfeeding. Conversely, patients with \u003cstrong\u003e≤10 mutations\u003c/strong\u003e were more common in the \u003cstrong\u003e\u0026gt;6 months\u003c/strong\u003e group (69% vs. 53%). A significant association was observed between breastfeeding duration and total mutational burden (χ² = 7.22, \u003cem\u003ep\u003c/em\u003e = 0.03, V\u0026lt;sub\u0026gt;Cramer\u0026lt;/sub\u0026gt; = 0.36), indicating that \u003cstrong\u003eprolonged breastfeeding\u003c/strong\u003e is linked to \u003cstrong\u003elower genomic instability\u003c/strong\u003e in triple-negative breast cancer.\u003c/p\u003e","description":"","filename":"FIGURASDEF4.png","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/11552e571040cc2e918803fc.png"},{"id":97145408,"identity":"fa579595-c36a-4fc5-bac4-634cf2406442","added_by":"auto","created_at":"2025-12-01 10:13:52","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1896204,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/458b2c61-d528-42a0-9d1b-0bd6bca4a912.pdf"},{"id":97141789,"identity":"b2173eb6-dd5a-4334-ab5b-bbb4bb7c7a30","added_by":"auto","created_at":"2025-12-01 10:07:00","extension":"zip","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":147767,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytables.zip","url":"https://assets-eu.researchsquare.com/files/rs-8140522/v1/21e352a16ef3b1c47ba0d8c1.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Long-Term Breastfeeding is associated with Improved Pathological Response and Reduced Oncogenic Mutations in Triple Negative Breast Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTriple-negative breast cancer (TNBC), represents ~ 15–20% of breast tumors, is often high grade with earlier onset and poorer prognosis, and is enriched in carriers of germline BRCA1/2 variants (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Unlike hormone receptor–positive or HER2-amplified breast cancers, TNBC lacks established targeted therapies. Cytotoxic chemotherapy, particularly in the neoadjuvant setting, remains the backbone of treatment, and achieving a pathological complete response (pCR) after neoadjuvant treatment constitutes the strongest surrogate marker of favorable prognosis (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). Nevertheless, a substantial proportion of patients fail to reach pCR and subsequently develop early recurrence, underscoring the need for reliable predictors of treatment response and resistance mechanisms (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe biological heterogeneity of TNBC has prompted multiple molecular classification proposals (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). However, their clinical application remains limited due to technical complexity and lack of reproducibility. Recent molecular profiling studies have highlighted the high genetic heterogeneity of TNBC. More specifically, next-generation sequencing (NGS) has emerged as a powerful tool to comprehensively characterize the genomic landscape of TNBC using formalin-fixed paraffin-embedded (FFPE) tissue. NGS enables the detection of recurrent mutations in genes such as TP53, BRCA1/2, PIK3CA, and NOTCH1, which are involved in DNA repair, immune regulation, and proliferation signaling (\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e–\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). These alterations may influence treatment resistance and disease progression, providing a rationale for longitudinal molecular monitoring. Furthermore, emerging evidence suggests that both intrinsic tumor biology and host-related factors may influence the development and progression of TNBC (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Reproductive and lifestyle variables, such as age at menarche, parity, breastfeeding duration, and obesity, have been consistently linked to TNBC risk. However, their interaction with molecular profiles and treatment outcomes remains largely unexplored(\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eBeyond intrinsic tumor features, TNBC is increasingly recognized as being influenced by specific epidemiological and reproductive risk factors. Distinct from other breast cancer subtypes, TNBC risk has been associated with multiparity without prolonged breastfeeding, early age at first birth, African ancestry, and high breast density (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Conversely, breastfeeding, especially beyond six months, has a protective effect, with epidemiological estimates indicating that up to 15% of TNBC cases in Black women and 12% in White women could be prevented by breastfeeding promotion (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Although long-term breastfeeding has been established as a protective factor against TNBC incidence (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e–\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), no studies to date have evaluated its potential association with response to either adjuvant or neoadjuvant therapies in this subtype. These findings underscore the interaction between biological and social determinants of TNBC risk and highlight the importance of integrating lifestyle and reproductive history in prevention and risk stratification.\u003c/p\u003e\u003cp\u003eRecent studies have investigated the mutational dynamics of TNBC under the pressure of neoadjuvant chemotherapy (NAC). Singh \u003cem\u003eet al.\u003c/em\u003e (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e) analyzed paired pre- and post-treatment samples from patients with residual disease after NAC and found a largely stable mutational burden, dominated by \u003cem\u003eTP53\u003c/em\u003e, \u003cem\u003ePTEN\u003c/em\u003e, and \u003cem\u003eERBB2\u003c/em\u003e variants, with a notable post-therapy enrichment of \u003cem\u003eAR\u003c/em\u003e mutations. However, their study focused exclusively on non-PCR cases and did not explore the clinical or biological determinants of treatment response. In contrast, our study encompasses both complete and incomplete responders, integrating genomic, clinical, and host-related variables to identify predictive and protective factors associated with therapeutic response and disease recurrence. Particularly, we investigated the impact of breastfeeding duration, a lifestyle factor with growing epidemiological relevance, on genomic profiles, mutational burden, and pathological response. To our knowledge, this is the first study to link breastfeeding with genomic characteristics and treatment outcomes in TNBC, thereby expanding beyond the purely mutational scope addressed in previous post-NAC sequencing studies.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cb\u003e2.1 Clinical and Pathological Characteristics\u003c/b\u003e\u003c/p\u003e\u003cp\u003eA total of 43 women with pathologically confirmed triple-negative breast cancer (TNBC) were included in the study (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age at diagnosis was 55 years (IQR 45–67; range 29–76), and most tumors were located in the right breast (58%). A family history of breast or ovarian cancer was reported in 23% of cases, and 60% of the patients were current or former smokers. The median age at menarche was 13 years (IQR 12–13.5). Regarding reproductive history, 35% of patients had never breastfed, 16% had breastfed for less than six months, and 49% had breastfed for six months or longer; this latter group was considered the long-term breastfeeding category for subsequent analyses.\u003c/p\u003e\u003cp\u003eAt presentation, most patients had clinical stage II disease (74%), with T2 tumors (65%) and node-positive disease (49%). Histologically, 93% were invasive ductal carcinomas and 72% were grade III tumors. Baseline immunohistochemistry showed high proliferative activity (Ki-67 ≥ 14% in 84%) and overexpression of p53 (≥ 10% in 60%). These findings were consistent with a biologically aggressive TNBC phenotype.\u003c/p\u003e\u003cp\u003eAll patients received taxane-based neoadjuvant chemotherapy (NAC), with or without anthracyclines, administered according to standard protocols in use between 2009 and 2019. No patient received immunotherapy. The median number of administered cycles was six (IQR 6–8). Dose reductions were required in 23% of patients (mean reduction 5.6%), and treatment delays occurred in 26%, typically short in duration. Treatment was generally well tolerated, with manageable toxicity. The most frequent adverse events were neutropenia (40%), mucositis (21%), dermatitis (16%), and vomiting (28%); thrombocytopenia and anemia each occurred in 7% of patients. Asthenia was reported in approximately half of the cohort. Supportive care and dose adjustments allowed completion of the planned chemotherapy in nearly all cases. Following NAC, 60% of patients underwent breast-conserving surgery and 40% mastectomy.\u003c/p\u003e\u003cp\u003ePathological examination of the surgical specimens revealed a median tumor eradication of 90% (IQR 49–100), and 18 patients (42%) achieved pathologic complete response (pCR), defined as absence of invasive carcinoma in both the breast and axillary nodes. The rate of pCR differed notably according to breastfeeding history: women who had breastfed for ≥ 6 months achieved a pCR rate of 57%, compared with 27% among those who had breastfed for a shorter duration or not at all (p = 0.036). Similarly, the median pathological response percentage was 100% in long-term breastfeeders versus 59% in the other group (p = 0.0361), suggesting enhanced chemosensitivity associated with extended breastfeeding. After treatment, 70% of patients were node-negative, and 68% achieved stage 0–I disease.\u003c/p\u003e\u003cp\u003eAfter a median follow-up of 69.7 months (IQR 39.6–106.8), 22 patients (51%) experienced disease recurrence and 17 (40%) died. The median disease-free survival (DFS) was 58.4 months (IQR 22.7–97.5), and the mean overall survival (OS) was 75.3 months (SD 44.8). Although the study was not powered to detect survival differences by breastfeeding status, a favorable trend was observed among patients with longer breastfeeding duration, who showed longer DFS and OS compared with those with short or no breastfeeding.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cb\u003eClinicopathological and Treatment Characteristics of the TNBC Cohort (n = 43).\u003c/b\u003e \u003cem\u003eClinicopathological and treatment characteristics of 43 patients with triple-negative breast cancer treated with taxane-based neoadjuvant chemotherapy (NAC) with or without anthracyclines between 2009 and 2019. Data is presented as counts (percentages) or medians [IQR]. Percentages are based on available data. pCR = pathologic complete response; IQR = interquartile range.\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory / Statistic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003en (%) or Value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at diagnosis (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR] (min–max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e55 [45–67] (29–76)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBreast laterality\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLeft / Right\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (41.9) / 25 (58.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFamily history of breast/ovarian cancer\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes / No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (23.3) / 33 (76.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSmoker / Non-smoker\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (60.5) / 17 (39.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge at menarche (years)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e13 [12–13.5]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBreastfeeding duration\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNone / \u0026lt;6 months / ≥6 months\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e15 (34.9) / 7 (16.3) / 21 (48.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical stage (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eI / II / III / IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e4 (9.3) / 32 (74.4) / 5 (11.6) / 2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eT stage (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT1 / T2 / T3 / T4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 (14.0) / 28 (65.1) / 7 (16.3) / 2 (4.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eN stage (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0 / N1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (51.2) / 21 (48.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistologic type\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eInvasive ductal / Other\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e40 (93.0) / 3 (7.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological grade (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eGrade II / III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e12 (27.9) / 31 (72.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ep53 expression (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; 10% / ≥10%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (40.5) / 25 (59.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKi-67 index (pre-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; 14% / ≥14%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (16.3) / 36 (83.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNeoadjuvant regimen\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eTaxane ± anthracycline\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e43 (100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCycles administered\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR] (min–max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e6 [6–8] (\u003cspan additionalcitationids=\"CR4 CR5 CR6 CR7 CR8 CR9\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e–\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDose reduction\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes / No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e10 (23.3) / 33 (76.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMean dose reduction (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e—\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e5.6 (± 9.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTreatment delay\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes / No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e11 (25.6) / 32 (74.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAdverse events\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNeutropenia / Anemia / Mucositis / Dermatitis / Vomiting\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (39.5) / 3 (7.0) / 9 (20.9) / 7 (16.3) / 12 (27.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSurgical approach\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eBreast-conserving / Mastectomy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26 (60.5) / 17 (39.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological response (%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR] (min–max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e90 [49–100] (0–100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathologic complete response (pCR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAchieved / Not achieved\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e18 (41.9) / 25 (58.1)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eClinical stage (post-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 / I / II / III / IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e7 (16.3) / 22 (51.2) / 9 (20.9) / 4 (9.3) / 1 (2.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNode status (post-NAC)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN0 / N1–2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30 (69.8) / 13 (30.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eRecurrence\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes / No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e22 (51.2) / 21 (48.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDeath\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes / No\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e17 (39.5) / 26 (60.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eDisease-free survival (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR] (min–max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e58.4 [22.7–97.5] (1.1–176.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eOverall survival (months)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian [IQR] (min–max)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e69.7 [39.6–106.8] (3.5–176.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003e\u003cb\u003e2.2 Tumor genomic profile and Mutational Landscape\u003c/b\u003e\u003c/p\u003e\u003cp\u003eIn the 40 pre-NAC tumor samples, a mean of 13.4 variants per sample were identified (range: 3–63). The most frequently altered genes were TP53 (95%), FLT3 (95%), CSF1R (92.5%), and ERBB4 (80%). Among the most prevalent variants, notable alterations included TP53 c.215C \u0026gt; G (p.Pro72Arg) (75%), a substitution affecting the splicing region; FLT3 c.1310-3T \u0026gt; C (95%), located in a splicing region; CSF1R c.35_36delCAinsTC (92.5%), situated in the 3'UTR; and three ERBB4 alterations, c.884-7delT (77.5%), c.884-8_884-7delTT (75%), and c.884-7dupT (72.5%), all proximal to splice sites. A high mutational burden (≥ 13 variants per sample) was significantly associated with mutations in NOTCH1, FGFR1, and CDKN2A (p ≤ 0.01).\u003c/p\u003e\u003cp\u003eFollowing the administration of neoadjuvant chemotherapy, the 21 remaining tumor samples, due to the incomplete pathological response after treatment, were analyzed. The high mutation frequency in TP53, FLT3, CSF1R (95.24%), and ERBB4 (80.95%) was maintained. The mean number of variants per sample decreased to 11.4 (range: 1–57). The recurrent alterations observed pre-treatment, TP53 c.215C \u0026gt; G (85.71%), FLT3 c.1310-3T \u0026gt; C, CSF1R c.35_36delCAinsTC (95.24%), and the ERBB4 variants c.884-7delT, c.884-8_884-7delTT, and c.884-7dupT, persisted, being present in \u0026gt; 70% of cases.\u003c/p\u003e\u003cp\u003eIn the three metastatic samples analyzed, one brain and two lung metastases, a mean of 12 variants per sample was detected (range: 10–13), showing high concordance with the previous genomic profiles. The four most representative alterations, TP53 c.215C \u0026gt; G, FLT3 c.1310-3T \u0026gt; C, CSF1R c.35_36delCAinsTC, and the ERBB4 variants (c.884-7delT, c.884-8_884-7delTT, c.884-7dupT) were shared across 100% of the metastatic lesions. Although we could only sequence three metastasis, analysis across the three disease time points (pre-NAC, post-NAC, and metastases) revealed a set of “founder variants”, TP53 c.215C \u0026gt; G, FLT3 c.1310-3T \u0026gt; C, CSF1R c.35_36delCAinsTC, and the three ERBB4 variants, consistently retained, indicating a stable core genome resistant to cytotoxic pressure. No new driver variants of high frequency emerged in the post-NAC or metastatic setting, suggesting dominance of pre-existing clones over therapy-induced evolution. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the genetic prevalence across tumor sample groups and the association between high mutational burden and specific significant gene alterations.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.3 Clinicopathological and Molecular Variables Associated with Prognosis.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eSurvival outcomes were mainly determined by post-neoadjuvant pathological response and residual disease burden, in line with established TNBC prognostic models. Patients who relapsed or died exhibited significantly lower pathological regression and higher residual nodal involvement and post-treatment stage (all p \u0026lt; 0.05). These findings are consistent with the well-validated prognostic value of pathological complete response (pCR) and Residual Cancer Burden (RCB), which are routinely used in clinical oncology to stratify risk and guide post-neoadjuvant management in TNBC. Among baseline factors, low p53 immunoexpression (\u0026lt; 10%) was more frequent in deceased patients (p = 0.046). As p53 IHC is not currently a standardized prognostic or therapeutic biomarker in TNBC, this association should be interpreted as exploratory. Overall, these results reaffirm that inadequate pathological response and residual tumor burden remain the strongest predictors of recurrence and death in TNBC, providing the clinical framework for evaluating novel prognostic factors such as breastfeeding history and molecular alterations. (More detailed in Supplementary Tables S1 and S2).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4 Clinicopathological and Molecular Variables Associated with Pathological Complete Response\u003c/b\u003e\u003c/p\u003e\u003cp\u003eTo better understand the clinicopathological and molecular determinants of therapeutic efficacy, patients were stratified according to their pathological response to neoadjuvant chemotherapy and disease recurrence during follow-up. This combined classification generated two subgroups: G0 (non-responders) and G1 (responders). Comparative analyses across these subgroups were then performed to assess differences in clinicopathological variables, total mutational burden, and gene-specific alterations, providing a comprehensive overview of the biological and clinical features associated with pathological complete response and recurrence risk in TNBC.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4.1 Prognostic Impact of KIT Mutations within Response Groups\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFrom all the gene mutations analyzed in our cohort, the only one showing a significant association when comparing subgroups defined by response to neoadjuvant therapy was KIT (c-KIT). We therefore examined its prognostic impact in greater detail.\u003c/p\u003e\u003cp\u003eIn the G0 subgroup, survival outcomes were comparable between KIT wild-type and mutated cases. Among patients with wild-type KIT (n = 17) and those harboring KIT mutations (n = 5), Kaplan–Meier curves overlapped, and the difference was not statistically significant (\u003cem\u003ep\u003c/em\u003e = 0.28, log-rank test).\u003c/p\u003e\u003cp\u003eIn contrast, in the G1 subgroup KIT mutational status clearly stratified DFS. Patients with KIT mutations (n = 5) experienced substantially earlier relapses compared with those with wild-type KIT (n = 13). Kaplan–Meier analysis demonstrated a significant separation of curves (\u003cem\u003ep\u003c/em\u003e = 0.037, log-rank test).\u003c/p\u003e\u003cp\u003eTaken together, these results indicate that KIT mutations do not influence prognosis in patients who fail to respond to therapy. However, among responders, KIT mutations identify a subgroup at high risk of early recurrence despite initial treatment response (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4.2 Breastfeeding and KDR Mutations as Predictors of Pathological Complete Response.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAmong other variables analyzed to see if there was any correlation with achieving a pathological complete response (pCR) following neoadjuvant therapy, breastfeeding history showed a significant association with response (Table S\u003cspan\u003e$\u003c/span\u003e). Patients who achieved a pCR had a higher frequency of long-term breastfeeding (66.7%) compared with non-responders (36.0%; \u003cem\u003ep = 0.023\u003c/em\u003e). Surgical outcomes were also related: conservative surgery was more common among responders (77.8%) than non-responders (48.0%; \u003cem\u003ep = 0.049\u003c/em\u003e). Importantly, achieving pCR translated into improved prognosis, as responders had significantly better survival outcomes compared with non-responders (77.8% vs. 48.0% alive at follow-up; \u003cem\u003ep = 0.049\u003c/em\u003e). At the genomic level, KDR mutations, including the 1416A \u0026gt; T variant, were significantly enriched among non-responders. Overall, 72.7% of non-responders carried KDR alterations compared with only 33.3% of responders (\u003cem\u003ep = 0.013\u003c/em\u003e). (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Taken together, these results suggest that both clinical history and genomic background influence treatment response. Favorable factors such as prior breastfeeding and conservative surgery were enriched in responders, while non-responders were characterized by the presence of KDR mutations, which may represent a marker of resistance to neoadjuvant therapy.\u003c/p\u003e\u003cp\u003e\u003cb\u003e2.4.3 Impact of Breastfeeding on Clinical Response and Tumor Molecular Features\u003c/b\u003e\u003c/p\u003e\u003cp\u003eGiven the well-established epidemiological evidence linking reproductive factors to TNBC risk, we evaluated its association with treatment response and genomic characteristics in our cohort. To determine whether breastfeeding history influences tumor behavior and treatment efficacy, patients were stratified into two groups according to breastfeeding duration: long-term breastfeeding (≥ 6 months) and short-term or no breastfeeding (\u0026lt; 6 months/none). A significant association was observed between breastfeeding duration and pathological complete response (pCR). Patients who had breastfed for ≥ 6 months achieved pCR in 12 of 21 cases (57.1%), compared with 6 of 22 (27.3%) in the short- or no-breastfeeding group (odds ratio = 3.56, 95% CI = 0.99–12.7; Pearson’s chi-square \u003cem\u003ep\u003c/em\u003e = 0.047). These findings indicate that longer breastfeeding duration was associated with a markedly higher likelihood of achieving pCR following neoadjuvant chemotherapy in TNBC (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003e\u003cem\u003eComparison of clinicopathological and molecular variables between patients with short or no breastfeeding (≤ 6 months) and those with prolonged breastfeeding (\u0026gt; 6 months) (n = 43). Continuous variables are shown as medians [IQR] or means ± SD, and categorical variables as frequencies (%). Statistical comparisons were performed using Wilcoxon rank-sum, Pearson’s χ², or Fisher’s exact tests, as appropriate. Prolonged breastfeeding was significantly associated with higher pathological response rates and a greater proportion of responders, while short or absent breastfeeding correlated with higher mutational burden (≥ 20 mutations) and increased prevalence of KIT, NOTCH1, CDKN2A, ERBB2, FGFR2, and IDH2 mutations. These findings support that long-term breastfeeding is linked to a biologically distinct TNBC profile characterized by lower genomic instability and enhanced chemosensitivity\u003c/em\u003e\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eLower Breastfeeding (≤ 6 months / none)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003ehigher Breastfeeding (\u0026gt; 6 months)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTest / Comparison\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ep-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePathological response\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMedian 59 [0–100]Mean 52.8 ± 43.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMedian 100 [57.5–100]Mean 81.5 ± 28.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eWilcoxon rank-sum\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0361\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eResponse status groups (cPR)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eNon-responders: 16 (72.7%)Responders: 6 (27.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNon-responders: 9 (42.9%)Responders: 12 (57.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePearson’s χ²\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0472\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eTotal mutations\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt; 10: 8 (42.1%)10–19: 6 (31.6%) ≥ 20: \u003cb\u003e5 (26.3%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026lt; 10: 10 (47.6%)10–19: 11 (52.4%) ≥ 20: \u003cb\u003e0 (0%)\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0180\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eKIT mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 11 (57.9%)Mutated: 8 (42.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 19 (90.5%)Mutated: 2 (9.5%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0281\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eNOTCH1 mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 14 (73.7%)Mutated: 5 (26.3%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 21 (100%)Mutated: 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0177\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eCDKN2A mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 15 (78.9%)Mutated: 4 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 21 (100%)Mutated: 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0424\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eERBB2 mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 15 (78.9%)Mutated: 4 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 21 (100%)Mutated: 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0424\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFGFR2 mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 15 (78.9%)Mutated: 4 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 21 (100%)Mutated: 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0424\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eIDH2 mutation\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eWild-type: 15 (78.9%)Mutated: 4 (21.1%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eWild-type: 21 (100%)Mutated: 0 (0%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eFisher’s exact\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u003cb\u003e0.0424\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e\u003cp\u003eBy contrast, no statistically significant differences were observed in recurrence rates, disease-free survival (DFS), or overall survival (OS) between breastfeeding categories, although trends suggested longer DFS among women with a history of long-term breastfeeding.\u003c/p\u003e\u003cp\u003eAt the molecular level, several somatic alterations were significantly enriched in patients with short-term or no breastfeeding. Both grouping strategies identified significantly lower frequencies of KIT, NOTCH1, and CDKN2A mutations in the ≥ 6 months group compared with the little/no group. In the binary analysis, mutations were present in 42.1% vs 9.5% for KIT (\u003cem\u003ep\u003c/em\u003e = 0.028), 26.3% vs 0% for NOTCH1 (\u003cem\u003ep\u003c/em\u003e = 0.018), and 15.8% vs 0% for CDKN2A (\u003cem\u003ep\u003c/em\u003e = 0.042), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) The binary approach, by increasing power, additionally detected significant associations for ERBB2, FGFR2, and IDH2 mutations (all \u003cem\u003ep\u003c/em\u003e = 0.042). These findings indicate that prolonged breastfeeding is linked to a lower prevalence of several driver alterations implicated in tumor progression and therapeutic resistance. Breastfeeding was also associated with the distribution of total mutations. In the categorical analysis (\u0026lt; 10, 10–19, ≥ 20), no tumors from the ≥ 6 months group (0/21) harbored ≥ 20 mutations, compared with 5/19 (26.3%) in the little/no group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB) (\u003cem\u003ep\u003c/em\u003e = 0.018, Fisher’s exact test). Median total mutation counts did not differ between groups, underscoring that the signal arises from the enrichment of very high-burden tumors in women with little or no breastfeeding.\u003c/p\u003e\u003cp\u003eTaken together, the clinical and molecular results converge toward a coherent model linking early-life reproductive exposures, such as long-term breastfeeding, with tumor genomic architecture and treatment sensitivity in TNBC.\u003c/p\u003e\u003cp\u003ePatients who had breastfed for six months or longer not only demonstrated higher rates of pathological complete response to taxane-based chemotherapy but also harbored tumors with lower mutational burden and fewer alterations in key driver genes associated with resistance, including \u003cem\u003eKIT\u003c/em\u003e, \u003cem\u003eNOTCH1\u003c/em\u003e, \u003cem\u003eCDKN2A\u003c/em\u003e, and \u003cem\u003eKDR\u003c/em\u003e.\u003c/p\u003e\u003cp\u003eThese findings suggest that the well-documented epidemiological protection conferred by breastfeeding against aggressive breast cancer subtypes may extend beyond incidence reduction to influence tumor biology, genomic stability, and therapeutic responsiveness once the disease develops. In this context, the integration of clinical, pathological, and genomic data highlights a potential biological imprinting effect of early-life exposures that could shape the molecular evolution and chemosensitivity of TNBC, a hypothesis that is worth validating through larger patient cohorts and mechanistic studies.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we analyzed clinical, pathological, and molecular factors associated with treatment response and outcomes in patients with TNBC treated with neoadjuvant chemotherapy. Our results provide new insight into how early-life reproductive exposures, particularly breastfeeding duration, may influence both the genomic landscape and therapeutic response of TNBC. By integrating detailed clinicopathological and mutational data from a well-defined cohort of 43 patients treated with taxane-based neoadjuvant chemotherapy, we found that women who had breastfed for at least six months achieved significantly higher pathological complete response rates and exhibited tumors with lower mutational burden and fewer alterations in key driver genes associated with chemoresistance. Although the cohort size was modest, the consistency between the clinical and genomic observations suggests that reproductive history may have a durable biological impact on tumor evolution and therapy sensitivity in TNBC. Previous epidemiological studies have consistently shown that breastfeeding reduces the risk of developing TNBC (\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e), and reproductive factors such as parity, breastfeeding, and age at menarche have been recognized as major determinants of TNBC incidence (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Our results extend these observations by suggesting that breastfeeding may not only protect against the occurrence of TNBC but also modulate the likelihood of achieving pCR. The biological mechanisms underlying this association are not fully understood. Prolonged breastfeeding has been linked to durable remodeling of the breast tissue microenvironment, enrichment of beneficial microbial communities, and modulation of immune pathways, including T regulatory cell induction and anti-inflammatory cytokine production (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Such changes could enhance chemotherapy-induced tumor clearance by fostering a less pro-inflammatory and more immunologically balanced tumor microenvironment. In contrast, abrupt weaning or absence of breastfeeding has been associated with pro-inflammatory profiles and dysbiotic microbial signatures, which may contribute to treatment resistance (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). These findings are novel, as no prior studies have examined the association between breastfeeding history and response to therapy in TNBC. The biological plausibility of this observation is supported by evidence that breastfeeding induces long-lasting remodeling of mammary tissue architecture, promotes immune tolerance, reduces chronic inflammation, and modulates the breast microbiota, all of which could contribute to improved tumor sensitivity to chemotherapy (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eAn important aspect of this study was the analysis of tumor clonality between primary tumors, residual disease in non-pCR cases, and subsequent metastases. The persistence of clonal mutations in residual tumors after neoadjuvant therapy suggests that resistant subclones drive recurrence and metastatic spread (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This observation has clinical relevance, as the molecular features of residual disease could inform post-neoadjuvant therapeutic strategies (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Moreover, the identification of shared clonal mutations between primary and metastatic lesions highlights potential therapeutic targets that remain relevant throughout disease progression.\u003c/p\u003e\u003cp\u003eThe overall mutational burden in our cohort was variable, and although limited by sample size, patients with higher tumor mutational load tended to exhibit poorer survival, in agreement with previous studies linking elevated mutational burden with aggressive phenotypes and immune-evasive behavior in breast cancer (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Beyond global genomic instability, specific gene alterations provided additional biological insights. NOTCH1 mutations were enriched in tumors with adverse clinicopathological features, consistent with the oncogenic role of aberrant Notch signaling in promoting epithelial-mesenchymal transition, stem-like properties, and chemoresistance in TNBC (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Alterations in KIT, a receptor tyrosine kinase implicated in cell proliferation and survival, were observed predominantly in non-pCR tumors, suggesting a contribution to treatment resistance through sustained MAPK and PI3K pathway activation (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Mutations in KDR (VEGFR2) were also detected, reinforcing the centrality of angiogenic dysregulation in TNBC biology, as KDR-driven signaling facilitates tumor vascularization and hypoxia-adapted growth (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Finally, CDKN2A alterations, which lead to loss of p16\u003csup\u003eINK4A\u003c/sup\u003e-mediated cell-cycle control, were associated with reduced disease-free survival, reflecting their known role in unchecked proliferation and genomic instability (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). Together, these observations underscore the heterogeneity of TNBC and support the notion that distinct mutational profiles, encompassing Notch signaling, receptor tyrosine kinases, angiogenic pathways, and cell-cycle regulation, may shape therapeutic response and disease progression.\u003c/p\u003e\u003cp\u003eRecent high-impact research provides additional biological support for the associations observed in our study. In October 2025, \u003cem\u003eNature\u003c/em\u003e published evidence that pregnancy and breastfeeding induce the expansion of a population of long-lived tissue-resident T cells that persist in the breast for decades and contribute to immune surveillance against malignant transformation (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). These cells were enriched in post-lactational breast tissue and were shown to suppress tumor development, particularly in models of triple-negative breast cancer. This discovery offers a mechanistic framework that aligns with our findings: women who breastfed for \u0026ge;\u0026thinsp;6 months developed tumors with lower mutational burden and achieved higher pathological response rates, features compatible with enhanced immunosurveillance and improved genomic stability conferred by these long-lasting immune populations. Complementary evidence from the October 2025 issue of \u003cem\u003eNature Metabolism\u003c/em\u003e further supports the concept that breastfeeding exerts durable biological programming (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). That study showed that infant-derived ketone bodies present during breastfeeding can trigger long-term metabolic and epigenetic reprogramming, influencing immune maturation and energy homeostasis well beyond infancy. Together, these findings reinforce the idea that breastfeeding is not only a short-term physiological process but also a form of early-life biological imprinting with lasting consequences. In the context of our results, these mechanisms provide a plausible link between breastfeeding duration, tumor genomic architecture, and chemotherapy sensitivity in TNBC, suggesting that reproductive exposures may shape breast tissue biology and tumor fate across the lifespan.\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eLimitations and future directions\u003c/h2\u003e\u003cp\u003eTaken together, our findings integrate clinical and molecular perspectives to suggest factors that may refine prognostic assessment in TNBC. Although the sample size limits definitive conclusions, the results provide novel insights worthy of further exploration. Breastfeeding history emerges as a potentially valuable and easily ascertainable variable associated with treatment response, while molecular markers such as tumor clonality, mutational burden, and specific gene alterations (NOTCH1, KIT, KDR, CDKN2A) contribute complementary information on disease biology and resistance mechanisms. These observations warrant validation in larger, prospective studies integrating multi-omics approaches, including transcriptomic and microbiome profiling, to elucidate the biological links between reproductive history, tumor evolution, and therapy response. If confirmed, such knowledge could guide the development of predictive models and targeted strategies for TNBC, a disease still in urgent need of precision medicine approaches.\u003c/p\u003e\u003c/div\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003eStudy design and patient cohort\u003c/h2\u003e\u003cp\u003eThis is a retrospective, observational, non-interventional, single-center case series with a descriptive and analytical approach. The study included patients diagnosed with TNBC at the Hospital de D\u0026eacute;nia (Spain) who received taxane-based NAC, with or without anthracyclines, between January 2009 and June 2019. The diagnosis of TNBC was established by immunohistochemistry (IHC) for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2), and confirmed by fluorescence in situ hybridization (FISH) according to the ASCO/CAP guidelines (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Tumors were considered triple negative when both ER and PR expression were 0 or \u0026lt;\u0026thinsp;1%, and HER2 was not amplified by FISH. Only cases with available formalin-fixed paraffin-embedded (FFPE) tumor tissue containing\u0026thinsp;\u0026ge;\u0026thinsp;70% tumor cells were included. Clinical and demographic data were collected between July 2019 and December 2023 from the hospital\u0026rsquo;s electronic medical records (Cerner Millennium, Cerner Corporation, Kansas City, MO, USA), after completion of patient recruitment. Staging was performed according to the 7th and 8th editions of the American Joint Committee on Cancer (AJCC) TNM classification (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEligibility criteria\u003c/h3\u003e\n\u003cp\u003eInclusion criteria were: adult women with histologically confirmed invasive breast carcinoma meeting TNBC criteria (see below), treatment with taxane-based NAC (with or without anthracyclines) with curative intent, availability of pre-treatment tumor tissue (core biopsy), and comprehensive clinical data. When available, post-treatment surgical specimens were also analyzed for pathological response and, in a subset, for post-treatment genomic profiling. Exclusion criteria were: non-invasive carcinoma only, prior systemic therapy for the current tumor, metastatic disease at presentation precluding NAC with curative intent, or incomplete minimum clinical data.\u003c/p\u003e\n\u003ch3\u003eEthical considerations\u003c/h3\u003e\n\u003cp\u003eThe study protocol was approved by the Research Committee of the D\u0026eacute;nia Health Department (Ref. PI00018, approval date: June 2, 2018) and conducted in accordance with the Declaration of Helsinki and international ethical standards for human research (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Formal written informed consent was obtained from all living participants at study initiation. For deceased patients, the absence of explicit refusal to participate in research was verified. Patient autonomy, confidentiality, and privacy were respected throughout the study.\u003c/p\u003e\u003cp\u003e\u003cb\u003eSample Processing.\u003c/b\u003e\u003c/p\u003e\u003cp\u003eFormalin-fixed, paraffin-embedded (FFPE) tissue blocks from biopsies, surgical resections from 64 tumor samples of 43 patients were analyzed: 40 diagnostic pre-NAC biopsies, 21 post-NAC surgical specimens from patients without pCR, and 3 metastatic samples. All specimens were obtained from the Pathology Department\u0026rsquo;s. Histological sections of 8 \u0026micro;m thickness were prepared using a HistoCore AUTOCUT rotary microtome (Leica Biosystems, Wetzlar, Germany). Deparaffinization, extraction, and purification of tumor genomic DNA were performed using the GeneRead DNA FFPE Kit (QIAGEN, Hilden, Germany). The quantity of extracted DNA was measured using a Qubit 3.0 fluorometer (Thermo Fisher Scientific, Waltham, MA, USA) with the dsDNA HS Assay Kit (Life Technologies, Carlsbad, CA, USA). DNA concentrations were adjusted to a range of 20\u0026ndash;50 ng/\u0026micro;l with 1X TE buffer (Thermo Fisher Scientific, Waltham, MA, USA).\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLibrary Preparation and Sequencing\u003c/h2\u003e\u003cp\u003eGenetic libraries were prepared using the AmpliSeq Cancer HotSpot V2 panel (Illumina Inc., San Diego, CA, USA) capable of detecting somatic mutations with a variant allele frequency (VAF)\u0026thinsp;\u0026lt;\u0026thinsp;5% in approximately 2800 specific regions and mutation hotspots described in COSMIC across 50 key tumor biology genes and treatment response genes, classified into oncogenes and tumor suppressor genes. PCR amplification was performed using an Eppendorf Mastercycler pro S (Eppendorf AG, Hamburg, Germany). Enzymatic digestions and specific adapter ligation to the 5\u0026rsquo; and 3\u0026rsquo; ends of the amplicons were carried out. Libraries were purified using AMPure XP magnetic beads (Beckman Coulter, Brea, CA, USA). The concentration of the libraries was verified by fluorometry, and their quality and size were assessed using capillary electrophoresis with the Bioanalyzer 2100 system (Agilent Technologies, Santa Clara, CA, USA). Libraries were adjusted to a final concentration of 2 nM with 1X TE buffer.\u003c/p\u003e\u003cp\u003eDenatured libraries with 0.2 N NaOH mixed with 200 mM Tris-HCl pH 7.0 (Sigma-Aldrich, St. Louis, MO, USA) were diluted to 20 pM with chilled HT1 buffer (Illumina Inc., San Diego, CA, USA). The final loading concentration on the MiSeq Reagent Kit v2 (Illumina Inc., San Diego, CA, USA) was 8\u0026ndash;12 pM. 600 \u0026micro;l of the denatured libraries were transferred to the loading well of the prepared cartridge. The flow cell was washed with grade 1 water, and the glass surface and channels were cleaned with 70% ethanol-moistened lens wipes before being placed in the MiSeq sequencer (Illumina Inc., San Diego, CA, USA). Following functional checks, sequencing by synthesis was initiated on the MiSeq system, monitored in real-time via Sequencing Analysis Viewer (SAV) and remotely with BaseSpace (Illumina Inc., San Diego, CA, USA).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eFASTQ files were generated using MiSeq Reporter (Illumina Inc., San Diego, CA, USA). Reanalysis and alignment to the human reference genome (hg19) were performed using Local Run Manager v3 (LRM) (Illumina Inc., San Diego, CA, USA) to generate VCF files. Filtering, analysis, interpretation, annotation, and screening of genetic variants were carried out using BaseSpace Variant Interpreter (BVI) (Illumina Inc., San Diego, CA, USA). Filters were applied to restrict coverage depths to \u0026ge;\u0026thinsp;100 reads and a VAF of \u0026ge;\u0026thinsp;2.5%. Sequencing results focused on variant types such as base substitutions, short insertions or deletions (SNP, MNP, INDEL), and their translational consequences on proteins. Potential pathogenicity was evaluated using in silico algorithms (PolyPhen and SIFT) along with other variant annotation tools (ClinVar, RefSeq, COSMIC, My Cancer Genome). Variants were classified as pathogenic, likely pathogenic, and of uncertain significance or benign. All variants included are described in Supplementary Table S4\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis.\u003c/h2\u003e\u003cp\u003eAll clinical, pathological, and molecular data were collected and curated into a study dataset prior to analysis. Descriptive and inferential statistical analyses were performed using RStudio software (version 2024.12.0.467) (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eCategorical variables were summarized as absolute frequencies and percentages, and their associations were assessed with Pearson\u0026rsquo;s chi-square test or, when expected cell counts were \u0026lt;\u0026thinsp;5, with the nonparametric Fisher\u0026rsquo;s exact test. Numerical variables were presented as means with standard deviations (SD) or medians with interquartile ranges (IQR), depending on distribution. Between-group comparisons were performed using two-sample t tests when normality assumptions were met or with the Wilcoxon rank-sum test as a nonparametric alternative. For survival outcomes, Kaplan\u0026ndash;Meier (KM) estimators were used to calculate and visualize survival probabilities over time. Differences between survival curves were compared using the log-rank test. In addition, Cox proportional hazards regression models were applied to estimate hazard ratios (HR) and 95% confidence intervals (CI) for disease-free and overall survival. Survival analyses were implemented using the Survminer and Survival packages in R (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e)\u003c/p\u003e\u003cp\u003eGiven the relatively small sample size (n\u0026thinsp;=\u0026thinsp;43), a post hoc power analysis was conducted using G*Power (version 3.1) (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e) to evaluate whether the study was sufficiently powered to detect significant differences. With a two-tailed α of 0.05 and assuming a high effect size, the achieved statistical power was 0.81.\u003c/p\u003e\u003cp\u003eAll tests were two-sided, and p-values\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eTNBC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eTriple negative breast cancer\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eNAC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eNeoadjuvant chemotherapy\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eOS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eOverall survival\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003epCR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003ePathological complete response\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eIHC\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eImmunohistochemistry\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eER\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eEstrogen receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003ePR\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eProgesterone receptor\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eHER2\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eHuman epidermal growth factor receptor-2.\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization:\u003c/strong\u003e J.C. \u003cstrong\u003eMethodology:\u003c/strong\u003e J.C., J.R.B.M., R.B.R, \u003cstrong\u003eClinical data acquisition:\u003c/strong\u003e J.S.R., C.A.P.O., J.B.L., J.M.G.B. \u003cstrong\u003ePathology review:\u003c/strong\u003e C.A.P.O., J.B.L., J.M.G.B. \u0026nbsp;\u003cstrong\u003eGenomic sequencing and data generation:\u0026nbsp;\u003c/strong\u003eJ.C., J.R.B., L.A., R.B.R. \u003cstrong\u003eBioinformatics and statistical analysis:\u003c/strong\u003e C.H.O., J.P., A.F. \u003cstrong\u003eData curation:\u003c/strong\u003e L.S., A.D. \u003cstrong\u003eVisualization:\u003c/strong\u003e J.C., J.S.R. \u003cstrong\u003eFunding acquisition:\u003c/strong\u003e J.C., J.M.G.B. \u003cstrong\u003eSupervision:\u003c/strong\u003e J.C. \u003cstrong\u003eWriting – original draft:\u003c/strong\u003e J.C., J.R.B.M. \u003cstrong\u003eWriting – review \u0026amp; editing:\u003c/strong\u003e All authors. All authors approved the manuscript and accepted responsibility for its contents.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This work was supported by the ECMOR Research Chair (English Cathedra of Modern Oncology Research) established at CEU Cardenal Herrera University in collaboration with Hospital de Dénia and Charity Cancer Care Jávea, and by the UCH-CEU Research Program for Consolidated Research Groups. The funding institutions had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors wish to express their sincere gratitude to the Cancer Care Jávea Association for their invaluable support, both through fundraising efforts and through their continuous commitment to patient care and advocacy. Their contribution made this research possible. We also extend our special thanks to Dr. Ignacio Pérez-Roger for his dedication to the establishment of the ECMOR Research Chair at CEU Cardenal Herrera University (CEU-UCH) and for his dedicated support for this project.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare no conflicts of interest.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e: The datasets generated and analyzed during the current study are from the corresponding and first author upon request.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLehmann, B. D. et al. Identification of human triple-negative breast cancer subtypes and preclinical models for selection of targeted therapies. \u003cem\u003eJ. Clin. 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Methods\u003c/em\u003e. \u003cb\u003e41\u003c/b\u003e, 1149\u0026ndash;1160 (2009).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"triple-negative breast cancer, NGS, breastfeeding, neoadjuvant chemotherapy, mutational profiling, KIT, NOTCH1, CDKN2A, therapeutic response","lastPublishedDoi":"10.21203/rs.3.rs-8140522/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8140522/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBreastfeeding has been associated with a reduced risk of developing triple-negative breast cancer (TNBC), but its impact on tumor biology and treatment response remains unclear. We analyzed 43 patients with TNBC treated with taxane-based neoadjuvant chemotherapy (NAC), integrating detailed reproductive histories with clinicopathological features and targeted next-generation sequencing of pre- and post-treatment tumor samples. Long-term breastfeeding (\u0026ge;\u0026thinsp;6 months) was significantly associated with higher pathological complete response (pCR) rates (57% vs. 27%), as well as a lower mutational burden and reduced prevalence of alterations in KIT, NOTCH1, CDKN2A, and KDR. Non-responding tumors were enriched in KDR mutations suggesting a potential role in chemoresistance. NOTCH1 and CDKN2A mutations correlated with increased mutational burden, indicating enhanced genomic instability These findings suggest that extended breastfeeding may imprint lasting biological effects that shape mammary epithelial stability, immune surveillance, and subsequent chemosensitivity. Recent mechanistic evidence supports this concept, showing durable T-cell\u0026ndash;mediated immunoprotection and epigenetic/metabolic programming induced by lactation. Although limited by sample size, our results provide the first clinical and genomic evidence linking breastfeeding duration with therapy response in TNBC and underscore the relevance of reproductive history in shaping tumor behavior and treatment outcomes.\u003c/p\u003e","manuscriptTitle":"Long-Term Breastfeeding is associated with Improved Pathological Response and Reduced Oncogenic Mutations in Triple Negative Breast Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:17:53","doi":"10.21203/rs.3.rs-8140522/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"37144051403681751714848946785089832712","date":"2026-03-18T13:52:42+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-11-25T16:48:08+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-20T11:16:21+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-18T14:26:41+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-18T14:25:25+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-11-18T03:35:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a2ad179-e9f6-4eb0-ad9d-a5c707e90f1c","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":58607176,"name":"Biological sciences/Cancer"},{"id":58607177,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2025-12-01T08:17:53+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:17:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8140522","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8140522","identity":"rs-8140522","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}