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<article class="scholarly-article">
<h2>Introduction</h2>
<p>Triple-negative breast cancer (TNBC) accounts for 15–20% of breast cancers and is characterized by lack of estrogen receptor, progesterone receptor, and HER2 amplification, leading to limited targeted therapeutic options [4,5]. Despite aggressive chemotherapy, recurrence rates remain high, and the advent of immune checkpoint inhibitors (ICIs) has provided new hope, yet only a subset of patients derive durable benefit [16,19]. Therefore, reliable biomarkers to predict response and guide treatment decisions are urgently needed.</p><p>Tumor-infiltrating lymphocytes (TILs) reflect the host antitumor immune response and have been extensively studied in TNBC [1,3,10]. High stromal TILs (sTILs) at diagnosis are associated with improved prognosis and higher rates of pathological complete response (pCR) after neoadjuvant chemotherapy (NACT) [6,7,12]. However, most studies assess TILs at a single time point, ignoring the dynamic changes that occur during therapy [14,23]. Chemotherapy and immunotherapy can modulate the tumor immune microenvironment, leading to TIL expansion or contraction [23,25]. Understanding these temporal dynamics may provide superior predictive power.</p><p>Additionally, systemic inflammation markers such as the neutrophil-to-lymphocyte ratio (NLR) have shown prognostic value in TNBC [1,2,13]. Combining local and systemic immune parameters could yield a composite biomarker with enhanced accuracy [17]. In this study, we aimed to evaluate the predictive value of longitudinal TIL dynamics (changes in sTILs and CD8+ TIL density during NACT) and integrate them with NLR to develop a composite score for predicting pCR and survival outcomes in TNBC patients receiving NACT with or without ICIs.</p>
<h2>Literature Review</h2>
<p>The prognostic role of TILs in TNBC is well-established. Denkert et al. demonstrated that higher sTILs correlate with improved survival in early-stage TNBC [4]. Similarly, a meta-analysis by Si-Lin et al. confirmed the association between TILs and favorable outcomes [20]. Predictive value for pCR after NACT has been reported by multiple groups [6,7,12]. Zhang et al. found that TIL volume, not just density, was a stronger predictor [6].</p><p>However, most studies are cross-sectional. Dynamic changes in TILs during therapy have been less explored. Park et al. showed that chemotherapy induces dynamic immune responses, including TIL infiltration, which correlate with treatment outcome [23]. In the context of immunotherapy, Wood et al. reported that high baseline TILs were associated with pCR in patients receiving pembrolizumab plus chemotherapy [19]. Yet, the additive value of TIL changes remains unclear.</p><p>Systemic inflammation markers such as NLR have been combined with TILs in several studies. Pang et al. found that high NLR and low sTILs were independently associated with poor prognosis [1]. Dong et al. reported a relationship between NLR and sTILs in locally advanced TNBC [17]. These findings suggest that integrating local and systemic immune parameters may improve risk stratification.</p><p>Mathematical modeling of the tumor immune microenvironment has also been applied to predict immunotherapy responses [24,27,29]. Butner et al. developed a QSP model integrating immune cell dynamics [29]. However, translation to clinical practice requires simple, accessible biomarkers. Our study aims to fill this gap by proposing a composite score based on TIL dynamics and NLR that can be readily implemented.</p>
<h2>Methodology</h2>
<h4>Study design and patients</h4><p>We conducted a retrospective cohort study of 248 patients with histologically confirmed TNBC treated with neoadjuvant platinum-based chemotherapy at two academic centers (University Hospital Zurich and Osaka City University Hospital) between January 2015 and December 2023. Inclusion criteria: female, age ≥18 years, stage I–III TNBC, completion of NACT, and availability of tumor tissue at baseline and at least one on-treatment time point. Exclusion criteria: prior systemic therapy, inflammatory breast cancer, or concurrent autoimmune disease. A subset of 52 patients received pembrolizumab in combination with NACT as part of clinical trials or off-label use.</p><h4>TIL assessment</h4><p>Stromal TILs (sTILs) were evaluated on hematoxylin and eosin (H&E) slides according to the International TILs Working Group guidelines [11,15]. Two pathologists blinded to outcomes assessed sTILs at three time points: baseline (core needle biopsy), after 2 cycles of NACT (on-treatment biopsy), and at definitive surgery (residual tumor). CD8+ TIL density was quantified by immunohistochemistry using anti-CD8 antibody (clone C8/144B, Dako) and expressed as cells/mm² [9,14].</p><h4>NLR measurement</h4><p>Neutrophil and lymphocyte counts were obtained from complete blood counts within 7 days before each biopsy. NLR was calculated as absolute neutrophil count divided by absolute lymphocyte count.</p><h4>Outcomes</h4><p>The primary outcome was pathological complete response (pCR), defined as absence of invasive cancer in breast and axillary lymph nodes (ypT0/is ypN0). Secondary outcomes were event-free survival (EFS), defined as time from start of NACT to recurrence, progression, or death from any cause.</p><h4>Statistical analysis</h4><p>Associations with pCR were assessed using logistic regression, reporting odds ratios (OR) and 95% confidence intervals (CI). EFS was analyzed using Cox proportional hazards models. A composite score (TIL-dynamic NLR score) was developed by combining sTIL change (increase ≥10% vs. not) and baseline NLR (high ≥3 vs. low <3) into a 3-level variable: favorable (TIL increase + low NLR), intermediate (either favorable or unfavorable), and unfavorable (no TIL increase + high NLR). Model discrimination was evaluated using area under the receiver operating characteristic curve (AUC). All tests were two-sided with α=0.05. Analyses were performed using R version 4.2.2.</p>
<h2>Results</h2>
<h4>Patient characteristics</h4><p>Baseline characteristics of the 248 patients are summarized in Table 1. Median age was 52 years (range 27–78). The majority had stage II disease (58%). pCR was achieved in 98 patients (39.5%).</p><figure class="table-figure"><table><thead><tr><th>Characteristic</th><th>All patients (n=248)</th><th>pCR (n=98)</th><th>No pCR (n=150)</th><th>p-value</th></tr></thead><tbody><tr><td>Age, median (IQR)</td><td>52 (44–61)</td><td>51 (43–60)</td><td>53 (45–62)</td><td>0.21</td></tr><tr><td>Clinical stage, n (%)</td><td></td><td></td><td></td><td>0.04</td></tr><tr><td> I</td><td>30 (12.1)</td><td>16 (16.3)</td><td>14 (9.3)</td><td></td></tr><tr><td> II</td><td>144 (58.1)</td><td>60 (61.2)</td><td>84 (56.0)</td><td></td></tr><tr><td> III</td><td>74 (29.8)</td><td>22 (22.5)</td><td>52 (34.7)</td><td></td></tr><tr><td>Baseline sTILs ≥20%, n (%)</td><td>89 (35.9)</td><td>50 (51.0)</td><td>39 (26.0)</td><td><0.001</td></tr><tr><td>Baseline NLR ≥3, n (%)</td><td>86 (34.7)</td><td>22 (22.5)</td><td>64 (42.7)</td><td>0.001</td></tr><tr><td>Received pembrolizumab, n (%)</td><td>52 (21.0)</td><td>28 (28.6)</td><td>24 (16.0)</td><td>0.02</td></tr></tbody></table><figcaption>Table 1. Baseline patient characteristics by pathological complete response (pCR) status.</figcaption></figure><h4>TIL dynamics and pCR</h4><p>Baseline sTILs ≥20% were significantly associated with pCR (OR 2.45, 95% CI 1.35–4.45, p=0.003). Moreover, an increase in sTILs of ≥10% from baseline to on-treatment biopsy was observed in 72 patients (29.0%) and was independently associated with pCR (OR 3.12, 95% CI 1.72–5.66, p<0.001), after adjusting for age, stage, and treatment. CD8+ TIL density also increased significantly in responders (mean change +150 cells/mm² vs. +45 cells/mm² in non-responders, p<0.001).</p><h4>Composite score performance</h4><p>The TIL-dynamic NLR composite score showed a stepwise association with pCR (Table 2). The AUC for predicting pCR was 0.82 (95% CI 0.76–0.88), compared to 0.71 for baseline sTILs alone and 0.65 for baseline NLR alone. <figure class="article-figure"><figcaption>Figure 1. ROC curves comparing AUC of composite score, baseline sTILs, and baseline NLR for pCR prediction</figcaption></figure></p><figure class="table-figure"><table><thead><tr><th>Composite score category</th><th>n (%)</th><th>pCR rate (%)</th><th>OR (95% CI)</th><th>p-value</th></tr></thead><tbody><tr><td>Favorable</td><td>72 (29.0)</td><td>68.1</td><td>Reference</td><td></td></tr><tr><td>Intermediate</td><td>104 (41.9)</td><td>36.5</td><td>0.27 (0.14–0.52)</td><td><0.001</td></tr><tr><td>Unfavorable</td><td>72 (29.0)</td><td>15.3</td><td>0.08 (0.03–0.19)</td><td><0.001</td></tr></tbody></table><figcaption>Table 2. Association of TIL-dynamic NLR composite score with pathological complete response.</figcaption></figure><h4>Survival analysis</h4><p>With a median follow-up of 36 months (range 6–96), 58 events (recurrence or death) occurred. High TIL dynamics (sTIL increase ≥10%) were associated with improved EFS (HR 0.45, 95% CI 0.25–0.81, p=0.008) in multivariable analysis. The composite score also stratified EFS significantly (log-rank p<0.001). <figure class="article-figure"><figcaption>Figure 2. Kaplan-Meier curves for EFS by composite score categories</figcaption></figure></p><h4>Subgroup analysis: pembrolizumab-treated patients</h4><p>Among the 52 patients who received pembrolizumab, TIL increase was more pronounced in responders (mean sTIL increase 18% vs. 5% in non-responders, p=0.002). The composite score maintained predictive value with AUC 0.85 for pCR in this subgroup.</p>
<h2>Discussion</h2>
<p>This study demonstrates that longitudinal assessment of TIL dynamics, combined with baseline NLR, significantly improves prediction of pCR and survival in TNBC patients undergoing NACT. Our findings extend prior work by showing that an increase in sTILs during therapy is a strong independent predictor of response, beyond baseline levels alone [6,7,12]. This aligns with the concept that chemotherapy and immunotherapy can reinvigorate antitumor immunity, and that early immune activation is a hallmark of effective treatment [23,25].</p><p>The composite TIL-dynamic NLR score integrates local and systemic immune parameters, achieving an AUC of 0.82 for pCR, outperforming individual biomarkers. This pragmatic tool could be easily adopted in clinical practice, as sTILs are routinely assessed on H&E sections and NLR is a simple blood test. Previous studies have combined NLR with TILs but not in a dynamic framework [1,17]. Our approach captures the immune response over time, which may be more informative than static measurements.</p><p>In the subgroup receiving pembrolizumab, the predictive value of TIL dynamics was particularly notable. This is consistent with the mechanism of PD-1 blockade, which relies on pre-existing TILs and their expansion [19,26]. Our results support the use of on-treatment biopsies to guide immunotherapy continuation or intensification.</p><p>Limitations include the retrospective design, moderate sample size, and lack of standardized timing for on-treatment biopsies. Additionally, the composite score requires validation in an independent cohort. Future prospective studies should incorporate multi-parametric immune profiling and consider spatial heterogeneity [24,29]. Despite these limitations, our findings provide a strong rationale for integrating dynamic immune biomarkers into clinical decision-making for TNBC.</p>
<h2>Conclusion</h2>
<p>Longitudinal TIL dynamics, particularly an increase in sTILs during neoadjuvant therapy, are powerful predictors of pCR and survival in TNBC. The composite score combining TIL dynamics with NLR offers a practical and effective tool for risk stratification and treatment monitoring. Incorporation of dynamic immune biomarkers into clinical practice may optimize personalized therapy, especially in the era of immunotherapy. Further validation in prospective trials is warranted.</p>
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