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<journal-meta>
<journal-id journal-id-type="publisher-id">Front. Pharmacol.</journal-id>
<journal-title-group>
<journal-title>Frontiers in Pharmacology</journal-title>
<abbrev-journal-title abbrev-type="pubmed">Front. Pharmacol.</abbrev-journal-title>
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<issn pub-type="epub">1663-9812</issn>
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<publisher-name>Frontiers Media S.A.</publisher-name>
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<article-id pub-id-type="publisher-id">1752328</article-id>
<article-id pub-id-type="doi">10.3389/fphar.2026.1752328</article-id>
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<subject>Original Research</subject>
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<title-group>
<article-title>Tumour microenvironment and HER2-low status dictate response and resistance to anthracycline-taxane chemotherapy in premenopausal TNBC: a retrospective multicohort study</article-title>
<alt-title alt-title-type="left-running-head">Cai et al.</alt-title>
<alt-title alt-title-type="right-running-head">
<ext-link ext-link-type="uri" xlink:href="https://doi.org/10.3389/fphar.2026.1752328">10.3389/fphar.2026.1752328</ext-link>
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<name>
<surname>Cai</surname>
<given-names>Shuanglong</given-names>
</name>
<xref ref-type="aff" rid="aff1">
<sup>1</sup>
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<sup>&#x2020;</sup>
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<surname>Yu</surname>
<given-names>Shaohong</given-names>
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<sup>2</sup>
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<surname>Zhou</surname>
<given-names>Quan</given-names>
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<sup>3</sup>
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<given-names>Fangyuan</given-names>
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<xref ref-type="aff" rid="aff4">
<sup>4</sup>
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<sup>5</sup>
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<sup>6</sup>
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<aff id="aff1">
<label>1</label>
<institution>Department of Thyroid and Breast Surgery, Comprehensive Breast Health Center, The First Affiliated Hospital of Lishui University, Lishui People&#x2019;s Hospital</institution>, <city>Lishui</city>, <state>Zhejiang</state>, <country country="CN">China</country>
</aff>
<aff id="aff2">
<label>2</label>
<institution>Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences</institution>, <city>Beijing</city>, <country country="CN">China</country>
</aff>
<aff id="aff3">
<label>3</label>
<institution>Department of Gynecology, Fujian Provincial Hospital, Fuzhou University Affiliated Provincial Hospital</institution>, <city>Fuzhou</city>, <state>Fujian</state>, <country country="CN">China</country>
</aff>
<aff id="aff4">
<label>4</label>
<institution>Department of Cardiology, Fuzhou University Affiliated Provincial Hospital, Shengli Clinical Medical College of Fujian Medical University, Fujian Provincial Hospital</institution>, <city>Fuzhou</city>, <state>Fujian</state>, <country country="CN">China</country>
</aff>
<aff id="aff5">
<label>5</label>
<institution>Fujian Provincial Key Laboratory of Cardiovascular Disease, Fujian Cardiovascular Institute, Fujian Provincial Center for Geriatrics, Fujian Clinical Medical Research Center for Cardiovascular Diseases</institution>, <city>Fuzhou</city>, <state>Fujian</state>, <country country="CN">China</country>
</aff>
<aff id="aff6">
<label>6</label>
<institution>Fujian Heart Failure Center Alliance</institution>, <city>Fuzhou</city>, <state>Fujian</state>, <country country="CN">China</country>
</aff>
<author-notes>
<corresp id="c001">
<label>&#x2a;</label>Correspondence: Xiaoxin Zheng, <email xlink:href="mailto:729607425@qq.com">729607425@qq.com</email>; Yong Shi, <email xlink:href="mailto:shiyong111@163.com">shiyong111@163.com</email>; Jingdan Li, <email xlink:href="mailto:lijingdan@foxmil.com">lijingdan@foxmil.com</email>
</corresp>
<fn fn-type="equal" id="fn001">
<label>&#x2020;</label>
<p>These authors have contributed equally to this work and share first authorship</p>
</fn>
<fn fn-type="equal" id="fn002">
<label>&#x2021;</label>
<p>These authors have contributed equally to this work</p>
</fn>
</author-notes>
<pub-date publication-format="electronic" date-type="pub" iso-8601-date="2026-02-09">
<day>09</day>
<month>02</month>
<year>2026</year>
</pub-date>
<pub-date publication-format="electronic" date-type="collection">
<year>2026</year>
</pub-date>
<volume>17</volume>
<elocation-id>1752328</elocation-id>
<history>
<date date-type="received">
<day>23</day>
<month>11</month>
<year>2025</year>
</date>
<date date-type="rev-recd">
<day>05</day>
<month>01</month>
<year>2026</year>
</date>
<date date-type="accepted">
<day>13</day>
<month>01</month>
<year>2026</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright &#xa9; 2026 Cai, Yu, Zhou, Kuang, Rao, Fang, Zheng, Shi and Li.</copyright-statement>
<copyright-year>2026</copyright-year>
<copyright-holder>Cai, Yu, Zhou, Kuang, Rao, Fang, Zheng, Shi and Li</copyright-holder>
<license>
<ali:license_ref start_date="2026-02-09">https://creativecommons.org/licenses/by/4.0/</ali:license_ref>
<license-p>This is an open-access article distributed under the terms of the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution License (CC BY)</ext-link>. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.</license-p>
</license>
</permissions>
<abstract>
<sec>
<title>Background</title>
<p>Premenopausal women with triple-negative breast cancer (TNBC) exhibit considerable heterogeneity in their response to standard anthracycline and taxane-based chemotherapy, yet the underlying mechanisms remain poorly understood. We aimed to investigate the combined role of the tumour immune microenvironment and HER2-low status in predicting chemosensitivity and intrinsic resistance in this specific population.</p>
</sec>
<sec>
<title>Methods</title>
<p>We retrospectively analysed data from 767 premenopausal patients with TNBC across two Chinese medical centres. All patients underwent primary surgery followed by adjuvant chemotherapy based on anthracyclines and taxanes. Patients were randomly assigned to training and internal validation cohorts. Independent predictors of DFS were identified using multivariable Cox proportional hazards regression, and a nomogram was constructed accordingly. The model&#x2019;s discrimination was assessed using the concordance index (C-index) and time-dependent receiver operating characteristic (ROC) curve analysis, while calibration was evaluated with calibration curves.</p>
</sec>
<sec>
<title>Results</title>
<p>Multivariable analysis identified lower stromal tumour-infiltrating lymphocyte (sTIL) expression levels, and HER2 IHC 2&#x2b;/FISH-negative status as independent factors associated with poorer DFS, besides that T3/T4 staging, higher N staging. A nomogram integrating these four variables demonstrated excellent predictive accuracy for DFS, with a C-index of 0.862 in the training set and 0.861 in the validation set. The area under the ROC curve (AUC) for predicting 3-year DFS was 0.907 and 0.908 in the training and validation sets, respectively.</p>
</sec>
<sec>
<title>Conclusion</title>
<p>Our findings reveal that an immune-poor tumour microenvironment and HER2-low biology are key, complementary determinants of intrinsic resistance to standard chemotherapy in premenopausal TNBC. These readily available biomarkers provide a mechanistic rationale for patient stratification, suggesting that sTIL-low tumours might benefit from immunomodulatory strategies, while HER2-low tumours represent a candidate population for novel antibody-drug conjugates. This paradigm shift from empirical to biomarker-informed therapy could help overcome chemoresistance in this high-risk group.</p>
</sec>
</abstract>
<kwd-group>
<kwd>anthracycline-taxanechemotherapy</kwd>
<kwd>biomarkers</kwd>
<kwd>chemoresistance</kwd>
<kwd>HER2-low</kwd>
<kwd>premenopausal triple-negative breast cancer</kwd>
<kwd>stromal tumor infiltrating lymphocytes (sTILs)</kwd>
</kwd-group>
<funding-group>
<funding-statement>The author(s) declared that financial support was received for this work and/or its publication. This study was supported in part by grants from the Doctoral Research Initiation Fund (No. 2025bs001) from Lishui Hospital of Wenzhou Medical University, the First Affiliated Hospital of Lishui University, and Lishui People&#x2019;s Hospital, Zhejiang Province, Zhejiang Provincial Medical and Health Science and Technology Plan Project (No. 2022KY1439), Green Valley Medical Talents Cultivation Project in Lishui, Zhejiang Province, and the Natural Science Foundation of Fujian Province (2025J01080).</funding-statement>
</funding-group>
<counts>
<fig-count count="14"/>
<table-count count="2"/>
<equation-count count="0"/>
<ref-count count="48"/>
<page-count count="16"/>
</counts>
<custom-meta-group>
<custom-meta>
<meta-name>section-at-acceptance</meta-name>
<meta-value>Pharmacology of Anti-Cancer Drugs</meta-value>
</custom-meta>
</custom-meta-group>
</article-meta>
</front>
<body>
<sec sec-type="intro" id="s1">
<label>1</label>
<title>Introduction</title>
<p>Breast cancer remains the most commonly diagnosed malignancy and the leading cause of cancer-related mortality among women worldwide (<xref ref-type="bibr" rid="B7">Bray et al., 2024</xref>). Triple-negative breast cancer (TNBC), characterized by the absence of estrogen receptor, progesterone receptor, and HER2 amplification, represents a particularly aggressive subtype with limited therapeutic options and a heightened propensity for early recurrence (<xref ref-type="bibr" rid="B6">Bianchini et al., 2016</xref>; <xref ref-type="bibr" rid="B15">Foulkes et al., 2010</xref>). Within this heterogeneous disease, premenopausal patients often exhibit distinct clinicopathological and molecular features, including enriched immunogenic signatures and homologous recombination deficiency, which may influence both tumour behaviour and therapeutic response (<xref ref-type="bibr" rid="B3">Azim et al., 2012</xref>; <xref ref-type="bibr" rid="B5">Bareche et al., 2018</xref>).</p>
<p>Anthracycline and taxane-based chemotherapy remains the cornerstone of adjuvant treatment for early-stage TNBC (<xref ref-type="bibr" rid="B23">Liedtke et al., 2008</xref>). However, intrinsic and acquired chemoresistance significantly undermines its efficacy, leading to disparate clinical outcomes (<xref ref-type="bibr" rid="B17">Gonzalez-Angulo et al., 2011</xref>; <xref ref-type="bibr" rid="B45">Wahba and El-Hadaad, 2015</xref>). The mechanisms underlying chemoresistance are multifactorial, encompassing tumour-intrinsic factors such as apoptotic evasion, enhanced DNA damage repair, and cancer stem cell persistence, as well as microenvironmental influences including immune contexture and stromal composition (<xref ref-type="bibr" rid="B19">Holohan et al., 2013</xref>; <xref ref-type="bibr" rid="B38">Shibue and Weinberg, 2017</xref>; <xref ref-type="bibr" rid="B18">Gottesman, 2002</xref>).</p>
<p>Emerging evidence underscores the prognostic significance of stromal tumour-infiltrating lymphocytes (sTILs) in TNBC, where higher densities correlate with improved pathological complete response and survival following chemotherapy (<xref ref-type="bibr" rid="B24">M Geurts et al., 2024</xref>; <xref ref-type="bibr" rid="B33">Salgado et al., 2015</xref>). Concurrently, The biological and clinical significance of HER2-low breast cancer, defined as immunohistochemistry (IHC) 1&#x2b; or IHC 2&#x2b; with negative <italic>in situ</italic> hybridization (FISH), has recently come into focus. Supported by evidence from clinical trials of novel anti-HER2 antibody&#x2013;drug conjugates, this subset has emerged as a therapeutically actionable entity (<xref ref-type="bibr" rid="B34">Saura et al., 2025</xref>; <xref ref-type="bibr" rid="B28">Modi et al., 2025</xref>). Growing preliminary evidence further suggests that HER2-low tumours may represent a distinct biological subtype with unique therapeutic vulnerabilities (<xref ref-type="bibr" rid="B40">Tarantino et al., 2020</xref>; <xref ref-type="bibr" rid="B2">Agostinetto et al., 2021</xref>). Recent studies, including those by Tarantino P and Schettini F et al., have begun to delineate the underlying biology of HER2-low breast cancer across subtypes, including triple-negative breast cancer (<xref ref-type="bibr" rid="B41">Tarantino et al., 2023</xref>; <xref ref-type="bibr" rid="B36">Schettini et al., 2025</xref>).</p>
<p>Despite these advances, validated biomarkers for stratifying chemosensitivity in premenopausal TNBC remain scarce. Most existing prognostic tools neither account for the unique biology of this demographic nor integrate readily accessible biomarkers reflective of tumour-immune interplay (<xref ref-type="bibr" rid="B39">Sparano et al., 2018</xref>; <xref ref-type="bibr" rid="B30">Parker et al., 2009</xref>). Therefore, we aimed to develop and validate a clinically applicable nomogram that incorporates key clinicopathological and microenvironmental variables-specifically sTIL density and HER2-low status-to predict disease-free survival (DFS) in premenopausal women with TNBC treated with anthracycline-taxane chemotherapy.</p>
</sec>
<sec sec-type="methods" id="s2">
<label>2</label>
<title>Methods</title>
<sec id="s2-1">
<label>2.1</label>
<title>Study design and patient cohort</title>
<p>This retrospective, multicentre cohort study consecutively enrolled 767 female patients with TNBC from Peking Union Medical College Hospital and Lishui People&#x2019;s Hospital of Zhejiang Province between 1 Jan 2016, and 31 Dec 2021 (<xref ref-type="fig" rid="F1">Figure 1</xref>).</p>
<fig id="F1" position="float">
<label>FIGURE 1</label>
<caption>
<p>Patient selection flowchart for this study.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g001.tif">
<alt-text content-type="machine-generated">Flowchart illustrating patient selection criteria and study groups from Peking Union Medical College Hospital and Lishui People&#x27;s Hospital database between January 1, 2016, and December 31, 2021. Patients included are premenopausal women with newly diagnosed, operable, primary unilateral invasive breast cancer and a triple-negative subtype. Those excluded had metastatic disease at diagnosis, a history of neoadjuvant chemotherapy, or incomplete data. A total of 767 patients treated with postoperative adjuvant chemotherapy were enrolled, divided into training sets (548 patients) and validation sets (219 patients).</alt-text>
</graphic>
</fig>
<p>Inclusion Criteria: (1) Histopathologically confirmed invasive breast carcinoma; (2) Negative for estrogen receptor, progesterone receptor, and HER2 (defined as ER/PR &#x3c;1% positive cells, HER2 IHC 0 or 1&#x2b;, or 2&#x2b; with negative FISH); (3) Premenopausal status at diagnosis; (4) Underwent curative-intent surgery (modified radical mastectomy or breast-conserving surgery) at initial diagnosis; (5) Received adjuvant chemotherapy based on anthracyclines and taxanes (sequential or combination regimens) postoperatively; (6) Had complete clinical, pathological, and follow-up data.</p>
<p>Exclusion Criteria: (1) Bilateral breast cancer or concurrent other malignancies; (2) History of neoadjuvant chemotherapy; (3) Presence of distant metastasis at diagnosis; (4) Incomplete follow-up data.</p>
</sec>
<sec id="s2-2">
<label>2.2</label>
<title>Variable definition and data collection</title>
<p>Collected variables included: age at diagnosis, family history of breast cancer, surgical approach, postoperative pathological T staging, postoperative pathological N staging, tumor grade, lymph-vascular invasion, sTIL expression levels, Her2 expression levels, Ki-67 index, CK5/6 expression, EGFR expression, and receipt of radiotherapy.</p>
<p>HER2 Status Assessment: HER2 protein expression was evaluated by IHC, and equivocal cases (IHC 2&#x2b;) were reflexively tested by FISH according to the American Society of Clinical Oncology/College of American Pathologists (ASCO/CAP) guidelines (<xref ref-type="bibr" rid="B47">Wolff et al., 2023</xref>). To ensure consistency and mitigate inter-institutional variability-a known challenge in HER2-low scoring&#x2014;all IHC and FISH results underwent a centralized review by two experienced breast pathologists who were blinded to patient outcomes. HER2-low status was defined according to the standard definition as IHC 1&#x2b; or IHC 2&#x2b;/FISH-negative. For primary and sensitivity analyses, patients were categorized into three groups: HER2 IHC 0, HER2 IHC 1&#x2b;, and HER2 IHC 2&#x2b;/FISH-negative.</p>
<p>sTIL Assessment: sTILs were evaluated by two experienced pathologists blinded to clinical outcomes, according to international guidelines. sTILs were defined as the percentage of tumour stroma area occupied by mononuclear inflammatory cells (lymphocytes and plasma cells). sTIL density was categorised into three groups: low (&#x2264;10%), intermediate (10%&#x2013;40%), and high (&#x3e;40%).</p>
</sec>
<sec id="s2-3">
<label>2.3</label>
<title>Follow-up strategy</title>
<p>Follow-up started from the date of initial breast cancer diagnosis and ended on 1 June 2025. Follow-up methods included outpatient visits and telephone interviews. Data collected encompassed: local or regional recurrence, distant metastasis, time to first recurrence or metastasis, site of first recurrence or metastasis, and vital status at the last follow-up.</p>
<p>Disease-free survival (DFS) was defined as the time from surgery date to the first occurrence of any of the following events: local recurrence, regional recurrence, distant metastasis, contralateral breast cancer, or death from any cause.</p>
</sec>
<sec id="s2-4">
<label>2.4</label>
<title>Statistical analysis</title>
<p>Analyses were performed using R software (version 4.3.1). The patient cohort was randomly split into a training set (70%) and an internal validation set (30%). Categorical variables are presented as numbers (percentages) and compared using the &#x3c7;<sup>2</sup> test or Fisher&#x2019;s exact test. Continuous variables are presented as mean &#xb1; standard deviation or median (interquartile range) based on their distribution and compared using the t-test or Mann-Whitney U test.</p>
<p>In the training set, univariable Cox regression analysis (P &#x3c; 0.05) was used to screen prognostic factors associated with DFS. Significant variables were subsequently included in a multivariable Cox proportional hazards regression model to identify independent prognostic factors. Results are presented as hazard ratios (HRs) with 95% confidence intervals (CIs).</p>
<p>Based on the final independent prognostic factors, a nomogram for predicting 1-year, 3-year, and 5-year DFS probabilities was constructed using the rms package. The model&#x2019;s discrimination was assessed using the C-index and time-dependent AUC analysis. Calibration curves were plotted to evaluate the agreement between predicted probabilities and observed outcomes (assessed by the Kaplan-Meier method). For key prognostic variables, Kaplan-Meier survival curves were generated, and differences between groups were compared using the log-rank test.</p>
<p>All tests were two-sided, and a P value &#x3c;0.05 was considered statistically significant.</p>
</sec>
</sec>
<sec sec-type="results" id="s3">
<label>3</label>
<title>Results</title>
<sec id="s3-1">
<label>3.1</label>
<title>Patient characteristics and follow-up</title>
<p>The final cohort comprised 767 premenopausal TNBC patients with a median follow-up of 76.0 months. The median age was 49.0 years. The cohort was randomly divided into training (n &#x3d; 548) and internal validation (n &#x3d; 219) sets. Baseline clinicopathological characteristics were largely balanced between the training and validation sets, except for family history, postoperative pathological T staging, Ki-67 index, and EGFR status (all P &#x3c; 0.05, <xref ref-type="table" rid="T1">Table 1</xref>). All other variables showed no significant differences (all P &#x3e; 0.05, <xref ref-type="table" rid="T1">Table 1</xref>). In the training set, 420 DFS events and 128 deaths were recorded over a median follow-up of 73.7 months; the validation set experienced 170 DFS events and 49 deaths over 75.4 months.</p>
<table-wrap id="T1" position="float">
<label>TABLE 1</label>
<caption>
<p>The clinical-pathological characteristics of TNBC patients in both the training and validation sets.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th align="left">Variables</th>
<th align="left">Training sets (n &#x3d; 548)</th>
<th align="left">Validation sets (n &#x3d; 219)</th>
<th align="left">P Value</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td align="left">Age at diagnosis</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.531</td>
</tr>
<tr>
<td align="left">&#x3c;50 years</td>
<td align="center">451 (82.30%)</td>
<td align="center">176 (80.37%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">&#x2265;50 years</td>
<td align="center">97 (17.70%)</td>
<td align="center">43 (19.63%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Family history</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.020</td>
</tr>
<tr>
<td align="left">No</td>
<td align="center">469 (85.58%)</td>
<td align="center">201 (91.78%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="center">79 (14.42%)</td>
<td align="center">18 (8.22%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Surgical approach</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.396</td>
</tr>
<tr>
<td align="left">Radical surgery</td>
<td align="center">412 (75.18%)</td>
<td align="center">171 (78.08%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Breast-conserving surgery</td>
<td align="center">136 (24.82%)</td>
<td align="center">48 (21.92%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Postoperative pathological T staging</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">T1</td>
<td align="center">172 (31.39%)</td>
<td align="center">91 (41.55%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">T2</td>
<td align="center">264 (48.18%)</td>
<td align="center">65 (29.68%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">T3&#x2b;T4</td>
<td align="center">112 (20.44%)</td>
<td align="center">63 (28.77%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Postoperative pathological N staging</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.183</td>
</tr>
<tr>
<td align="left">N0</td>
<td align="center">391 (71.35%)</td>
<td align="center">153 (69.86%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">N1</td>
<td align="center">109 (19.89%)</td>
<td align="center">47 (21.46%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">N2</td>
<td align="center">27 (4.93%)</td>
<td align="center">16 (7.31%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">N3</td>
<td align="center">21 (3.83%)</td>
<td align="center">3 (1.37%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Tumor grade</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.170</td>
</tr>
<tr>
<td align="left">G1&#x2b;G2</td>
<td align="center">347 (63.32%)</td>
<td align="center">127 (57.99%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">G3</td>
<td align="center">201 (36.68%)</td>
<td align="center">92 (42.01%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Lymph-vascular invasion</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.088</td>
</tr>
<tr>
<td align="left">No</td>
<td align="center">283 (51.64%)</td>
<td align="center">128 (58.45%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="center">265 (48.36%)</td>
<td align="center">91 (41.55%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">sTIL expression levels</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.427</td>
</tr>
<tr>
<td align="left">Low</td>
<td align="center">117 (21.35%)</td>
<td align="center">38 (17.35%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Intermediate</td>
<td align="center">162 (29.56%)</td>
<td align="center">71 (32.42%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">High</td>
<td align="center">269 (49.09%)</td>
<td align="center">110 (50.23%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Her2 expression levels</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.522</td>
</tr>
<tr>
<td align="left">IHC 0</td>
<td align="center">218 (39.78%)</td>
<td align="center">95 (43.38%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">IHC 1&#x2b;</td>
<td align="center">169 (30.84%)</td>
<td align="center">59 (26.94%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">IHC 2&#x2b;/Fish-</td>
<td align="center">161 (29.38%)</td>
<td align="center">65 (29.68%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Ki67</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.005</td>
</tr>
<tr>
<td align="left">Ki67 &#x2264; 20</td>
<td align="center">22 (4.01%)</td>
<td align="center">20 (9.13%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Ki67 &#x3e; 20</td>
<td align="center">526 (95.99%)</td>
<td align="center">199 (90.87%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">CK5/6</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.995</td>
</tr>
<tr>
<td align="left">Negative</td>
<td align="center">115 (20.99%)</td>
<td align="center">46 (21.00%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Positive</td>
<td align="center">433 (79.01%)</td>
<td align="center">173 (79.00%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">EGFR</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="center">0.031</td>
</tr>
<tr>
<td align="left">Negative</td>
<td align="center">91 (16.61%)</td>
<td align="center">51 (23.29%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Positive</td>
<td align="center">457 (83.39%)</td>
<td align="center">168 (76.71%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Radiation therapy status</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">0.731</td>
</tr>
<tr>
<td align="left">No</td>
<td align="left">442 (80.66%)</td>
<td align="left">179 (81.74%)</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="left">106 (19.34%)</td>
<td align="left">40 (18.26%)</td>
<td align="left">&#x200b;</td>
</tr>
</tbody>
</table>
<table-wrap-foot>
<fn>
<p>Family history, HBOC-related cancer history.</p>
</fn>
<fn>
<p>sTIL, stromal tumor-infiltrating lymphocytes; IHC, mmunohistochemistry.</p>
</fn>
<fn>
<p>CK5/6, cytokeratin 5/6; HER2, human epidermal growth factor receptor 2.</p>
</fn>
<fn>
<p>FISH, fluorescence <italic>in situ</italic> hybridization; EGFR, epidermal growth factor receptor.</p>
</fn>
</table-wrap-foot>
</table-wrap>
</sec>
<sec id="s3-2">
<label>3.2</label>
<title>Prognostic nomogram for disease-free survival</title>
<p>The multivariate Cox regression revealed that postoperative pathological T staging (T3&#x2b;T4,HR &#x3d; 3.434,95%CI:1.975-5.97,P &#x3c; 0.001), postoperative pathological N staging (N1,HR &#x3d; 3.204,95%CI:2.101-4.885,P &#x3c; 0.001; N2,HR &#x3d; 5.284,95%CI:2.507-11.139,P &#x3c; 0.001; N3,HR &#x3d; 7.587,95%CI:4.381-13.14,P &#x3c; 0.001),sTIL expression levels (sTIL intermediate expression, HR &#x3d; 0.408, 95% CI: 0.275-0.604,P &#x3c; 0.001; sTIL high expression, HR &#x3d; 0.044, 95% CI: 0.021-0.091,P &#x3c; 0.001), and Her2 expression levels (IHC 2&#x2b;/Fish-, HR &#x3d; 2.392, 95% CI: 1.513-3.782,P &#x3c; 0.001) were independent predictive factors for DFS in premenopausal TNBC patients (<xref ref-type="table" rid="T2">Table 2</xref>). A nomogram integrating these variables was constructed to estimate 1-, 3-, and 5-year DFS probabilities (<xref ref-type="fig" rid="F2">Figure 2</xref>). The model exhibited excellent discrimination, with C-indices of 0.862 (training) and 0.861 (validation). Time-dependent receiver operating characteristic (ROC) analysis further confirmed its robust predictive accuracy, with area under the curve (AUC) values for 3-year DFS of 0.907 and 0.908 in the training and validation sets, respectively (<xref ref-type="fig" rid="F3">Figures 3</xref>, <xref ref-type="fig" rid="F4">4</xref>). Calibration curves demonstrated close alignment between predicted and observed outcomes across all time points (<xref ref-type="fig" rid="F5">Figures 5</xref>&#x2013;<xref ref-type="fig" rid="F10">10</xref>). Kaplan-Meier analyses visually affirmed the prognostic stratification afforded by each variable (<xref ref-type="fig" rid="F11">Figures 11</xref>&#x2013;<xref ref-type="fig" rid="F14">14</xref>).</p>
<table-wrap id="T2" position="float">
<label>TABLE 2</label>
<caption>
<p>COX Univariate and Multivariate Analysis of Disease-free survival for TNBC patients.</p>
</caption>
<table>
<thead valign="top">
<tr>
<th rowspan="2" align="center">Variables</th>
<th colspan="3" align="center">Univariate analyses</th>
<th colspan="3" align="center">Multivariate analyses</th>
</tr>
<tr>
<th align="center">HR</th>
<th align="center">95%CI</th>
<th align="center">P</th>
<th align="center">HR</th>
<th align="center">95%CI</th>
<th align="center">P</th>
</tr>
</thead>
<tbody valign="top">
<tr>
<td colspan="7" align="left">Age at diagnosis</td>
</tr>
<tr>
<td align="left">&#x3c;50 years</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">&#x2265;50 years</td>
<td align="left">0.445</td>
<td align="left">0.246&#x2013;0.807</td>
<td align="left">0.008</td>
<td align="left">0.605</td>
<td align="left">0.326&#x2013;1.124</td>
<td align="left">0.112</td>
</tr>
<tr>
<td colspan="7" align="left">Family history</td>
</tr>
<tr>
<td align="left">No</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="left">0.586</td>
<td align="left">0.323&#x2013;1.062</td>
<td align="left">0.078</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td colspan="7" align="left">Surgical approach</td>
</tr>
<tr>
<td align="left">Radical surgery</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Breast-conserving surgery</td>
<td align="left">0.964</td>
<td align="left">0.644&#x2013;1.445</td>
<td align="left">0.86</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td colspan="7" align="left">Postoperative pathological T staging</td>
</tr>
<tr>
<td align="left">T1</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">T2</td>
<td align="left">1.6</td>
<td align="left">0.985&#x2013;2.600</td>
<td align="left">0.058</td>
<td align="left">1.623</td>
<td align="left">0.969&#x2013;2.719</td>
<td align="left">0.066</td>
</tr>
<tr>
<td align="left">T3&#x2b;T4</td>
<td align="left">4.264</td>
<td align="left">2.596&#x2013;7.004</td>
<td align="left">&#x3c;0.001</td>
<td align="left">3.434</td>
<td align="left">1.975&#x2013;5.97</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td colspan="7" align="left">Postoperative pathological N staging</td>
</tr>
<tr>
<td align="left">N0</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">N1</td>
<td align="left">3.882</td>
<td align="left">2.609&#x2013;5.776</td>
<td align="left">&#x3c;0.001</td>
<td align="left">3.204</td>
<td align="left">2.101&#x2013;4.885</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">N2</td>
<td align="left">2.967</td>
<td align="left">1.462&#x2013;6.021</td>
<td align="left">0.003</td>
<td align="left">5.284</td>
<td align="left">2.507&#x2013;11.139</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">N3</td>
<td align="left">19.799</td>
<td align="left">11.683&#x2013;33.554</td>
<td align="left">&#x3c;0.001</td>
<td align="left">7.587</td>
<td align="left">4.381&#x2013;13.14</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td colspan="7" align="left">Tumor grade</td>
</tr>
<tr>
<td align="left">G1&#x2b;G2</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">G3</td>
<td align="left">2.242</td>
<td align="left">1.583&#x2013;3.176</td>
<td align="left">&#x3c;0.001</td>
<td align="left">1.341</td>
<td align="left">0.923&#x2013;1.948</td>
<td align="left">0.123</td>
</tr>
<tr>
<td colspan="7" align="left">Lymph-vascular invasion</td>
</tr>
<tr>
<td align="left">No</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="left">1</td>
<td align="left">0.707&#x2013;1.414</td>
<td align="left">1</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td colspan="7" align="left">sTIL expression levels</td>
</tr>
<tr>
<td align="left">Low</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Intermediate</td>
<td align="left">0.494</td>
<td align="left">0.344&#x2013;0.710</td>
<td align="left">&#x3c;0.001</td>
<td align="left">0.408</td>
<td align="left">0.275&#x2013;0.604</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td align="left">High</td>
<td align="left">0.041</td>
<td align="left">0.021&#x2013;0.083</td>
<td align="left">&#x3c;0.001</td>
<td align="left">0.044</td>
<td align="left">0.021&#x2013;0.091</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td colspan="7" align="left">Her2 expression levels</td>
</tr>
<tr>
<td align="left">IHC 0</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">IHC 1&#x2b;</td>
<td align="left">1.695</td>
<td align="left">1.074&#x2013;2.674</td>
<td align="left">0.023</td>
<td align="left">1.177</td>
<td align="left">0.734&#x2013;1.887</td>
<td align="left">0.499</td>
</tr>
<tr>
<td align="left">IHC 2&#x2b;/Fish-</td>
<td align="left">2.298</td>
<td align="left">1.488&#x2013;3.549</td>
<td align="left">&#x3c;0.001</td>
<td align="left">2.392</td>
<td align="left">1.513&#x2013;3.782</td>
<td align="left">&#x3c;0.001</td>
</tr>
<tr>
<td colspan="7" align="left">Ki67</td>
</tr>
<tr>
<td align="left">Ki67 &#x2264; 20</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
</tr>
<tr>
<td align="left">Ki67 &#x3e; 20</td>
<td align="left">0.326</td>
<td align="left">0.184&#x2013;0.579</td>
<td align="left">&#x3c;0.001</td>
<td align="left">0.759</td>
<td align="left">0.384&#x2013;1.498</td>
<td align="left">0.426</td>
</tr>
<tr>
<td colspan="7" align="left">CK5/6</td>
</tr>
<tr>
<td align="left">Negative</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Positive</td>
<td align="left">0.813</td>
<td align="left">0.543&#x2013;1.219</td>
<td align="left">0.317</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td colspan="7" align="left">EGFR</td>
</tr>
<tr>
<td align="left">Negative</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Positive</td>
<td align="left">1.088</td>
<td align="left">0.675&#x2013;1.754</td>
<td align="left">0.729</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td colspan="7" align="left">Radiation therapy status</td>
</tr>
<tr>
<td align="left">No</td>
<td align="left">1</td>
<td align="left">&#x200b;</td>
<td align="left">&#x200b;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
<tr>
<td align="left">Yes</td>
<td align="left">1.004</td>
<td align="left">0.648&#x2013;1.554</td>
<td align="left">0.987</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
<td align="left">&#x2014;</td>
</tr>
</tbody>
</table>
</table-wrap>
<fig id="F2" position="float">
<label>FIGURE 2</label>
<caption>
<p>Prognostic nomogram for predicting disease-free survival time of premenopausal TNBC patients.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g002.tif">
<alt-text content-type="machine-generated">Nomogram for predicting disease-free survival (DFS) probabilities, including scales for postoperative pathological T and N staging, sTIL and Her2 expression levels. It provides total points with corresponding 1-year, 3-year, and 5-year DFS probabilities.</alt-text>
</graphic>
</fig>
<fig id="F3" position="float">
<label>FIGURE 3</label>
<caption>
<p>Roc curve of the training set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g003.tif">
<alt-text content-type="machine-generated">ROC curve comparing model performance at different time intervals with three colored lines: yellow for one year (AUC 0.790), blue for three years (AUC 0.907), and red for five years (AUC 0.909). Sensitivity is plotted against one minus specificity.</alt-text>
</graphic>
</fig>
<fig id="F4" position="float">
<label>FIGURE 4</label>
<caption>
<p>Roc curve of the validation set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g004.tif">
<alt-text content-type="machine-generated">ROC curve plot showing sensitivity versus one minus specificity. Three lines represent different time intervals: 1-year in orange (AUC: 0.841), 3-year in blue (AUC: 0.908), and 5-year in red (AUC: 0.918). A diagonal reference line is included.</alt-text>
</graphic>
</fig>
<fig id="F5" position="float">
<label>FIGURE 5</label>
<caption>
<p>Calibration curve of the training set at 1&#xa0;Year.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g005.tif">
<alt-text content-type="machine-generated">Calibration plot showing predicted probability on the x-axis and actual probability on the y-axis. Data points are near the diagonal line, indicating good model calibration. Error bars represent uncertainty.</alt-text>
</graphic>
</fig>
<fig id="F6" position="float">
<label>FIGURE 6</label>
<caption>
<p>Calibration curve of the training set at 3&#xa0;Years.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g006.tif">
<alt-text content-type="machine-generated">Calibration plot showing predicted versus actual probabilities with a dashed diagonal reference line. Data points with error bars are shown along the solid line, indicating prediction accuracy. The actual probabilities range from zero to one.</alt-text>
</graphic>
</fig>
<fig id="F7" position="float">
<label>FIGURE 7</label>
<caption>
<p>Calibration curve of the training set at 5&#xa0;Years.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g007.tif">
<alt-text content-type="machine-generated">Scatter plot displaying actual probability versus predicted probability with a line of best fit. Blue data points with error bars show deviation from the diagonal line, indicating prediction accuracy. Axis ranges from 0.0 to 1.0.</alt-text>
</graphic>
</fig>
<fig id="F8" position="float">
<label>FIGURE 8</label>
<caption>
<p>Calibration curve of the validation set at 1&#xa0;Years.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g008.tif">
<alt-text content-type="machine-generated">Calibration plot showing actual probability versus predicted probability. Data points are plotted along with vertical error bars indicating variability. A diagonal dotted line represents perfect calibration where predicted equals actual probability.</alt-text>
</graphic>
</fig>
<fig id="F9" position="float">
<label>FIGURE 9</label>
<caption>
<p>Calibration curve of the validation set at 3&#xa0;Years.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g009.tif">
<alt-text content-type="machine-generated">Calibration plot showing predicted probability on the x-axis and actual probability on the y-axis. Points with error bars closely follow the diagonal line, indicating good model calibration.</alt-text>
</graphic>
</fig>
<fig id="F10" position="float">
<label>FIGURE 10</label>
<caption>
<p>Calibration curve of the validation set at 5&#xa0;Years.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g010.tif">
<alt-text content-type="machine-generated">Calibration plot showing predicted versus actual probabilities, with data points connected by lines. Error bars are present, indicating uncertainty. The dashed diagonal line represents perfect calibration where predicted and actual probabilities match.</alt-text>
</graphic>
</fig>
<fig id="F11" position="float">
<label>FIGURE 11</label>
<caption>
<p>KM curves stratified by T staging in the Training Set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g011.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival curve showing survival probability over time in months for three groups: T1 in red, T2 in blue, and T3+T4 in green. T1 and T2 have higher survival probabilities than T3+T4. The p-value is less than 0.0001.</alt-text>
</graphic>
</fig>
<fig id="F12" position="float">
<label>FIGURE 12</label>
<caption>
<p>KM curves stratified by N staging in the Training Set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g012.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival curves show four groups (N0 in red, N1 in blue, N2 in green, N3 in black) over time in months. Survival probability decreases most rapidly for N3 and least for N2. A p-value is less than 0.0001, indicating statistical significance.</alt-text>
</graphic>
</fig>
<fig id="F13" position="float">
<label>FIGURE 13</label>
<caption>
<p>KM curves stratified by sTIL expression levels in the Training Set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g013.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival curve displays survival probabilities over time for three risk groups: low (red), intermediate (blue), and high (green). The high-risk group maintains the highest survival probability throughout. The p-value is less than 0.0001, indicating significant differences between groups.</alt-text>
</graphic>
</fig>
<fig id="F14" position="float">
<label>FIGURE 14</label>
<caption>
<p>KM curves stratified by Her2 expression levels in the Training Set.</p>
</caption>
<graphic xlink:href="fphar-17-1752328-g014.tif">
<alt-text content-type="machine-generated">Kaplan-Meier survival plot showing survival probability over time in months for three immunohistochemistry (IHC) groups: IHC 0 (red line), IHC 1+ (blue line), and IHC 2+/Fish- (green line). The x-axis represents time in months, and the y-axis shows survival probability. The significant p-value is 0.00064.</alt-text>
</graphic>
</fig>
</sec>
</sec>
<sec sec-type="discussion" id="s4">
<label>4</label>
<title>Discussion</title>
<p>In this large, multicentre retrospective study, we developed and validated a prognostic nomogram specifically tailored for premenopausal women with TNBC receiving anthracycline&#x2013;taxane chemotherapy. The model-incorporating T stage, N stage, sTIL density, and HER2 immunohistochemistry (IHC) status-demonstrated high predictive accuracy and clinical utility, addressing a critical unmet need in this high-risk population.</p>
<p>Our findings confirm the prognostic relevance of the tumour immune microenvironment in this cohort. Low sTIL density was independently associated with worse disease-free survival, corroborating previous studies that have consistently linked lower sTIL levels with inferior outcomes in patients treated with DNA-damaging chemotherapies (<xref ref-type="bibr" rid="B24">M Geurts et al., 2024</xref>; <xref ref-type="bibr" rid="B12">Denkert et al., 2018</xref>; <xref ref-type="bibr" rid="B1">Adams et al., 2019</xref>). Clinically, these results align with the concept that tumours with low sTILs frequently exhibit an immunologically &#x201c;cold&#x201d; phenotype, which may inform future therapeutic strategies aimed at enhancing antitumour immunity in such patients (<xref ref-type="bibr" rid="B37">Sharma et al., 2017</xref>; <xref ref-type="bibr" rid="B13">Emens and Middleton, 2015</xref>).</p>
<p>We also observed that HER2 IHC 2&#x2b;/fluorescence <italic>in situ</italic> hybridization (FISH)-negative status&#x2014;but not IHC 1&#x2b;&#x2014;independently predicted poorer disease-free survival, challenging several earlier reports that associated HER2-zero expression with inferior outcomes in TNBC (<xref ref-type="bibr" rid="B26">Miglietta et al., 2025</xref>; <xref ref-type="bibr" rid="B48">Won and Spruck, 2020</xref>). This discrepancy may reflect variations in patient selection, treatment setting, or HER2 scoring methodology. Multivariable analysis under the standard HER2-low definition (IHC 1&#x2b; or IHC 2&#x2b;/FISH-negative) indicated that the prognostic risk was predominantly driven by the IHC 2&#x2b;/FISH-negative subgroup. This suggests that HER2-low TNBC may not represent a homogeneous entity and that IHC 2&#x2b;/FISH-negative tumours could harbour distinct biological properties&#x2014;a hypothesis that warrants further validation.</p>
<p>Growing preclinical and clinical evidence indicates that HER2-low breast cancers exhibit altered receptor trafficking, activation of compensatory signalling pathways (e.g., PI3K/AKT, MAPK), and specific tumour&#x2013;stroma interactions, which may collectively compromise chemotherapy efficacy (<xref ref-type="bibr" rid="B40">Tarantino et al., 2020</xref>; <xref ref-type="bibr" rid="B35">Schettini et al., 2021</xref>; <xref ref-type="bibr" rid="B27">Modi et al., 2022</xref>). Translationaly, the recent efficacy of novel antibody&#x2013;drug conjugates such as trastuzumab deruxtecan in HER2-low metastatic breast cancer underscores the clinical relevance of this subset and highlights its potential susceptibility to targeted approaches (<xref ref-type="bibr" rid="B4">Banerji et al., 2019</xref>; <xref ref-type="bibr" rid="B16">Gennari et al., 2021</xref>). Our data suggest that premenopausal patients with HER2-low TNBC&#x2014;particularly those with IHC 2&#x2b;/FISH-negative disease&#x2014;represent a candidate population for adjuvant antibody&#x2013;drug conjugate trials. Such a strategy could eventually shift the therapeutic paradigm from empirical chemotherapy toward biomarker-guided targeted treatment.</p>
<p>Moreover, TNBC is a molecularly heterogeneous disease, encompassing distinct subtypes such as those described by Lehmann BD et al. (basal-like, immunomodulatory, mesenchymal, etc.) (<xref ref-type="bibr" rid="B22">Lehmann et al., 2011</xref>). The prognostic role of sTILs and HER2-low status may vary across these subtypes, a nuance not captured in our retrospective analysis. Future prospective studies integrating transcriptomic subtyping with tumour microenvironment features are warranted to refine personalised prognostic models.</p>
<p>By restricting our analysis to premenopausal women, we minimised confounding from age-related biological differences, such as the higher prevalence of PIK3CA mutations in older patients (<xref ref-type="bibr" rid="B10">Ciriello et al., 2015</xref>; <xref ref-type="bibr" rid="B8">Cancer Genome Atlas Network, 2012</xref>). This enhances the biological coherence and clinical applicability of our model for this specific demographic. The nomogram&#x2019;s performance (C-index &#x3e;0.86) surpasses that of many conventional prognostic tools and genomic assays (<xref ref-type="bibr" rid="B29">Paik et al., 2004</xref>; <xref ref-type="bibr" rid="B9">Cardoso et al., 2019</xref>), supporting its utility in routine practice for risk-adapted therapeutic decision-making.</p>
<p>From a translational perspective, our model provides a rationale for personalised adjuvant strategies. High-risk patients-those with low sTILs and HER2-low tumours-may benefit from treatment intensification, including capecitabine maintenance (<xref ref-type="bibr" rid="B46">Wang et al., 2021</xref>; <xref ref-type="bibr" rid="B25">Masuda et al., 2017</xref>) or PARP inhibition in BRCA-mutated cases (<xref ref-type="bibr" rid="B20">J Tutt et al., 2021</xref>; <xref ref-type="bibr" rid="B32">Robson et al., 2017</xref>). Conversely, low-risk patients could be spared unnecessary toxicity. Furthermore, this tool may facilitate refined patient selection for clinical trials evaluating immunomodulatory agents or novel antibody&#x2013;drug conjugates in the early-stage setting.</p>
</sec>
<sec id="s5">
<label>5</label>
<title>Limitations</title>
<p>Several limitations of our study warrant consideration. First, its retrospective design introduces potential selection bias, despite multicentre enrolment. Second, all participants were of Chinese ancestry; external validation in diverse ethnic and geographic populations is essential to confirm generalisability (<xref ref-type="bibr" rid="B14">Eniu et al., 2008</xref>). Third, although patients were randomly assigned to training and validation sets, some baseline variables (e.g., family history, postoperative pathological T staging, Ki-67 index, and EGFR status) showed uneven distributions. While our model maintained high predictive performance across cohorts, such imbalances could theoretically introduce selection bias or affect generalizability. Future validation in larger, multi-ethnic cohorts is warranted to confirm the model&#x2019;s stability. Fourth, although HER2 and sTIL assessments followed international guidelines and underwent central review, HER2 scoring reproducibility remains a challenge in TNBC, and sTIL evaluation is semi-quantitative. Future iterations could benefit from digital pathology, automated image analysis, or standardized immune-based assays to improve objectivity and reproducibility (<xref ref-type="bibr" rid="B21">Klauschen et al., 2018</xref>; <xref ref-type="bibr" rid="B44">Vanguri et al., 2022</xref>). Fifth, we did not incorporate germline BRCA status, a known determinant of TNBC prognosis and treatment response (<xref ref-type="bibr" rid="B43">Tutt et al., 2010</xref>; <xref ref-type="bibr" rid="B42">Tung et al., 2020</xref>). Integrating genomic and transcriptomic data in future prospective studies may further refine predictive accuracy and biological insight (<xref ref-type="bibr" rid="B11">Curtis et al., 2012</xref>; <xref ref-type="bibr" rid="B31">Pereira et al., 2016</xref>).</p>
</sec>
<sec sec-type="conclusion" id="s6">
<label>6</label>
<title>Conclusion</title>
<p>In conclusion, we have identified and validated low stromal TILs and HER2-low status as key biomarkers of intrinsic resistance to anthracycline-taxane chemotherapy in premenopausal TNBC. Beyond prognostication, these factors illuminate distinct biological pathways underlying treatment failure-an immunosuppressive microenvironment and the unique biology of HER2-low tumors. This mechanistic insight provides a compelling rationale for future pharmacological strategies: leveraging immunotherapy to inflame sTIL-low tumors and deploying novel ADCs to target HER2-low subtypes. Our findings thus offer a practical framework for patient stratification and pave the way for more personalized, mechanism-driven adjuvant therapy trials in this challenging disease.</p>
</sec>
</body>
<back>
<sec sec-type="data-availability" id="s7">
<title>Data availability statement</title>
<p>The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.</p>
</sec>
<sec sec-type="ethics-statement" id="s8">
<title>Ethics statement</title>
<p>The studies involving humans were approved by Medical Ethics Committee of Peking Union Medical College Hospital and Lishui People&#x2019;s Hospital of Zhejiang Province. The studies were conducted in accordance with the local legislation and institutional requirements. The participants provided their written informed consent to participate in this study.</p>
</sec>
<sec sec-type="author-contributions" id="s9">
<title>Author contributions</title>
<p>SC: Writing &#x2013; review and editing, Writing &#x2013; original draft. SY: Formal Analysis, Writing &#x2013; original draft. QZ: Writing &#x2013; review and editing, Methodology. FK: Formal Analysis, Writing &#x2013; review and editing. LR: Resources, Writing &#x2013; review and editing. ZF: Formal Analysis, Writing &#x2013; review and editing. XZ: Project administration, Funding acquisition, Supervision, Validation, Writing &#x2013; review and editing. YS: Project administration, Formal Analysis, Supervision, Writing &#x2013; review and editing. JL: Project administration, Formal Analysis, Supervision, Validation, Writing &#x2013; review and editing.</p>
</sec>
<ack>
<title>Acknowledgements</title>
<p>We thank all the authors listed for their contributions to this study.</p>
</ack>
<sec sec-type="COI-statement" id="s11">
<title>Conflict of interest</title>
<p>The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.</p>
</sec>
<sec sec-type="ai-statement" id="s12">
<title>Generative AI statement</title>
<p>The author(s) declared that generative AI was not used in the creation of this manuscript.</p>
<p>Any alternative text (alt text) provided alongside figures in this article has been generated by Frontiers with the support of artificial intelligence and reasonable efforts have been made to ensure accuracy, including review by the authors wherever possible. If you identify any issues, please contact us.</p>
</sec>
<sec sec-type="disclaimer" id="s13">
<title>Publisher&#x2019;s note</title>
<p>All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.</p>
</sec>
<fn-group>
<fn fn-type="custom" custom-type="edited-by">
<p>
<bold>Edited by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/2822061/overview">Zhendong Shi</ext-link>, Tianjin Medical University Cancer Institute and Hospital, China</p>
</fn>
<fn fn-type="custom" custom-type="reviewed-by">
<p>
<bold>Reviewed by:</bold> <ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3297199/overview">Tanzeel Huma</ext-link>, Wake Forest University, United States</p>
<p>
<ext-link ext-link-type="uri" xlink:href="https://loop.frontiersin.org/people/3309012/overview">Xuliren Wang</ext-link>, Fudan University, China</p>
</fn>
</fn-group>
<fn-group>
<fn fn-type="abbr" id="abbrev1">
<label>Abbreviations:</label>
<p>TNBC, Triple-Negative Breast Cancer; DFS, Disease-Free Survival; sTILs, Stromal Tumor-Infiltrating Lymphocytes; IHC, Immunohistochemistry; HER2, Human Epidermal Growth Factor Receptor 2; FISH, Fluorescence In Situ Hybridization; ER, Estrogen Receptor; PR, Progesterone Receptor; ROC, Receiver Operating Characteristic; AUC, Area Under the ROC Curve; C-index, Concordance Index; HR, Hazard Ratio; CI, Confidence Interval; ADC, :Antibody-Drug Conjugate; BRCA, Breast Cancer gene; PIK3CA, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha; PARP, Poly(ADP-ribose) Polymerase.</p>
</fn>
</fn-group>
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