Phase II Exploratory Study of Neoadjuvant Disitamab Vedotin and Penpulimab in HER2-low Stage II-III Breast Cancer (NeoPanDa04)

Phase II Exploratory Study of Neoadjuvant Disitamab Vedotin and Penpulimab in HER2-low Stage II-III Breast Cancer (NeoPanDa04) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Phase II Exploratory Study of Neoadjuvant Disitamab Vedotin and Penpulimab in HER2-low Stage II-III Breast Cancer (NeoPanDa04) Ting Luo, Xiaoxiao Liu, Yuting Song, Lei Liu, Aaron Qi Zhang, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7869437/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract HER2-low breast cancer lacks effective targeted options in the curative setting. We evaluated the efficacy, safety, and exploratory biomarker correlates of neoadjuvant disitamab vedotin (HER2-targeted antibody–drug conjugate) plus penpulimab (PD-1 inhibitor) in stage Ⅱ–Ⅲ disease (NCT05726175). In a prospective single-arm study, patients with newly diagnosed stage II–III HER2-low breast cancer (IHC 1 + or 2+/FISH−) received disitamab vedotin plus penpulimab every 3 weeks for six cycles before surgery. The primary end point was pathologic complete response (pCR). Secondary end points included objective response rate (ORR) and safety. Exploratory analyses incorporated multi-omic profiling (proteomics, multiplex immunofluorescence, RNA sequencing) and integrative modeling to derive predictive biomarkers. In the per-protocol set, pCR was 25.0% (4/16) and ORR 56.3% (9/16). At surgery, 31.3% achieved residual cancer burden (RCB) 0–1. Numerically higher pCR rates were seen in PD-L1–positive versus –negative tumors (33.3% v 14.3%) and in HER2 IHC 2+/FISH − versus IHC 1 + tumors (37.5% v 12.5%). Treatment was generally well tolerated: grade ≥ 3 events occurred in 25%, with no treatment-related deaths. Exploratory multi-omics yielded a baseline response prediction model (BRPscore; CCL19 + M2/M1 macrophage ratio) that correctly discriminated all pCR cases with strong discriminatory capacity (AUC 0.89), and identified MCP-1 as the most consistent biomarker across timepoints. In conclusion, neoadjuvant disitamab vedotin plus penpulimab produced a 25% pCR with manageable safety in stage II–III HER2-low breast cancer. The integration of a BRPscore with MCP-1 as a dynamic biomarker provides proof-of-concept for biomarker-driven patient selection and adaptive monitoring, supporting further randomized evaluation of ADC–ICI combinations. Health sciences/Oncology/Cancer/Breast cancer Health sciences/Oncology/Cancer/Cancer therapy/Targeted therapies Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction According to the latest data from the National Cancer Center of China (2022), breast cancer ranks sixth in incidence and fifth in cancer-related mortality among women, whereas globally, it represents the most common malignancy in high human development index (HDI) countries 1 . Despite a relatively lower incidence in China, patients frequently present at younger ages and with more advanced disease, leading to inferior outcomes, thereby underscoring the urgent need for improved prevention and treatment strategies. HER2-positive breast cancer is associated with aggressive biology, low sensitivity to chemotherapy, and poor prognosis 2 . The development of HER2-targeted therapies has transformed outcomes for this subgroup 3 . Multiple targeted agents, including monoclonal antibodies, tyrosine kinase inhibitors, and antibody–drug conjugates (ADCs), have been approved for HER2-positive breast cancer 2 , 4 , 5 . However, the therapeutic benefit of these approaches is limited in tumors with low HER2 expression, representing an important area of ongoing investigation. More than half of HER2-negative metastatic breast cancers (mBC) demonstrate low-level HER2 expression, defined as IHC 1 + or IHC 2 + with ISH negativity 6 , 7 . HER2-low tumors can be further classified by hormone receptor (HR) status into HR-positive and triple-negative breast cancer (TNBC) subtypes 8 . For HR-positive disease, current management largely follows that of HR-positive breast cancer in general, with recommended neoadjuvant regimens including anthracycline–taxane chemotherapy or endocrine therapy; however, the overall pathologic complete response (pCR) rate remains below 10%. For HER2-low TNBC, NCCN guidelines recommend anthracycline–taxane combinations as the backbone of neoadjuvant therapy. Reported pCR rates range from 28–35% with combination therapy, compared with approximately 20% with anthracyclines alone and 12% with taxanes alone 9 . Addition of platinum increases pCR to about 50% but at the expense of greater toxicity and reduced tolerability 10 , 11 . More recently, KEYNOTE-522 and IMpassion031 showed that adding immune checkpoint inhibitors to chemotherapy significantly improved pCR and event-free survival 12 , 13 , and these regimens are now recommended for TNBC by the Chinese Society of Clinical Oncology (CSCO) guidelines. Nevertheless, immunotherapy in the neoadjuvant setting has not yet been approved in China, highlighting an unmet need for safer and more effective regimens. ADCs combine a monoclonal antibody, a chemical linker, and a cytotoxic payload, thus uniting target specificity with potent cell killing 13 . Encouraging clinical results have been reported with several ADCs in HER2-low breast cancer, including DS-8201 (T-DXd), disitamab vedotin (RC48), and sacituzumab govitecan 6 , 14 , 15 , 16 , 17 . Preclinical studies suggest synergy between ADCs and PD-1/PD-L1 blockade, with combinations demonstrating enhanced antitumor activity in HER2-expressing models 18 . Furthermore, chemotherapy has been shown to augment tumor immunogenicity, thereby providing a rationale for combination strategies with immune checkpoint inhibitors. Trials such as IMpassion130/131 and KEYNOTE-355 demonstrated meaningful efficacy and manageable safety for chemo-immunotherapy in metastatic TNBC 19 , 20 , 21 , KEYNOTE-522 confirmed that adding pembrolizumab to neoadjuvant carboplatin and paclitaxel improved pCR by 13.6% (51.2% vs 64.8%) 12 , while IMpassion031 showed a 16.5% absolute increase in pCR with atezolizumab plus chemotherapy versus placebo (57.6% vs 41.1%, P = 0.0044) 13 . Taken together, these findings provide a strong rationale for evaluating antibody–drug conjugates in combination with immune checkpoint inhibitors as neoadjuvant therapy for HER2-low breast cancer. On this basis, we conducted an exploratory single-arm clinical study of RC48 plus penpulimab in patients with HER2-low early or locally advanced breast cancer, with the primary objective of assessing efficacy and safety. In addition, multi-omic profiling—including proteomic, transcriptomic, and immune-cell analyses—was integrated to identify biomarkers of response and to develop predictive models for patient stratification in the neoadjuvant setting. Results Patient Disposition and Baseline Characteristics From 14 August 2023 to 7 August 2024, 50 patients were screened and 20 met eligibility and initiated neoadjuvant therapy; these 20 patients constituted both the intent-to-treat (ITT) and safety populations (Fig. 1 A). Enrolled patients began to accept RC48 plus penpulimab every 3 weeks (RC48 2.0 mg/kg IV; penpulimab 200 mg IV) for up to 6 cycles (Fig. 1 B). Sixteen patients (80%) completed all planned neoadjuvant cycles and proceeded to surgery. Four patients (20%) could not be evaluabled for the primary end point: one discontinued because of an adverse event, one withdrew consent at the patient’s request, and two discontinued for other reasons (disease progression and per investigator discretion) (Fig. 1 A). Serial biospecimen collection was performed as prespecified (Fig. 1 B): pretreatment tumor biopsy and blood (pre-NAT), on-treatment blood at cycle 3 day 1 (C3D1) and cycle 5 day 1 (C5D1), and post-NAT tumor and blood at surgery. Paired tissues and blood were processed for standard pathology and exploratory translational analyses, including Olink proteomics, immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and RNA sequencing (RNA-seq). Baseline characteristics of the ITT population (N = 20) are summarized in Table 1 . The median age was 43.5 years (range, 33–68), and all patients were female with ECOG performance status of 0. Half of the tumors were HER2 IHC 1 + and half were IHC 2+/FISH−. Most patients had high proliferative disease, with KI-67 > 20% in 80%. Hormone receptor positivity was common (ER 90%, PR 85%). The majority presented with T2 (74.5%) and node-positive (85%) disease, with 65% at stage II and 35% at stage III. PD-L1 CPS was > 1 in 55% of patients. Table 1 Baseline Demographic and Disease Characteristics (ITT Population) Patient Characteristic All ITT Patients (N = 20) Median age, years 43.5 (33, 68) Female, n (%) 20 (100) ECOG-PS , n (%) 0 20(100) 1 0 HER2, n (%) IHC 1+ 10 (50.0) IHC 2+/FISH- 10 (50.0) KI-67 score, n (%) ≤ 20% 4 (20.0) >20% 16 (80.0) HR, n (%) ER+ 18 (90.0) PR+ 17 (85.0) T stage, n (%) T1 2 (10.0) T2 13(74.5) T3 1(5.0) T4 4(20.0) N stage, n (%) N0 3 (15.0) N1 14 (70.0) N2 3 (15.0) N3 0 Stage (cTNM classificationd), n (%) Ⅱ 13(65.0) Ⅲ 7(35.0) PDL1, n (%) CPS>1 11(55.0) CPS ≤ 1 9(45.0) Data are No. (%) unless otherwise indicated. Age is median (range). Abbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; HR, hormone receptor; ER, estrogen receptor; PR, progesterone receptor; cTNM, clinical tumor–node–metastasis; PD-L1, programmed death-ligand 1; CPS, combined positive score. Efficacy and Safety In the evaluable population (per protocol set, baseline characteristics summarized in table S1 ), the pathological complete response (pCR) (ypT0/is, ypN0) rate was 25% (4 of 16), and the objective response rate (ORR) was 56.3% (9/16) (Fig. 1 C). Longitudinal assessments showed that most patients experienced early and sustained tumor shrinkage, with maximum reductions approaching 75% and no progressive disease (PD) was observed in this cohort (Fig. 1 D). Waterfall plots confirmed substantial reductions in tumor burden across the cohort, and responses were observed irrespective of PD-L1 expression levels (Fig. 1 E). Additionally, results in the ITT population are provided in Fig. S1 A and S1B. At surgery, 31.25% of patients achieved residual cancer burden (RCB) 0–1, while 68.75% were classified as RCB-3 (Fig. 1 F). However, no significant differences in CD4⁺, CD8⁺, or TIL percentages were observed between RCB 0–1 and RCB-3 groups (Fig. 1 G). Exploratory subgroup analysis showed that the pCR rate was 22.2% in stage Ⅱ and 28.6% in stage Ⅲ disease; 28.6% among LN⁺ and 0% among LN⁻ patients. By PD-L1 expression, pCR was 14.3% for CPS < 1 and markedly higher (75.0%) for CPS ≥ 10. By ER status, pCR was 50.0% in ER⁺<10% and 21.4% in ER⁺≥10%; notably, patients with ER⁺≥10% and PD-L1 CPS ≥ 10 achieved a pCR rate of 66.7%. For HER2 subgroups, pCR was 37.5% in HER2 2+ / FISH − and 12.5% in HER2 1 + tumors (Fig. S1 C). Safety outcomes were assessed in the ITT population (N = 20), all (100%) experienced ≥ 1 treatment-emergent adverse event (TEAE) and 5 (25%) had grade ≥ 3 TEAEs. One patient (5%) had treatment delay/discontinuation due to an AE. Hematologic events were mostly low grade: neutropenia 4 (20%), leukopenia 3 (15%), and anemia 3 (15%) (all grade 1–2). Common laboratory/Metabolic abnormalities included ALT increase 12 (60%; grade ≥ 3, 1 [5%]), AST increase 10 (50%; grade ≥ 3, 1 [5%]), hypertriglyceridemia 10 (50%; grade ≥ 3, 1 [5%]), and hyperglycemia 6 (30%). Frequent gastrointestinal/dermatologic events were constipation 12 (60%), nausea 11 (55%), rash 11 (55%), pruritus 14 (70%), diarrhea 4 (20%), and vomiting 3 (15%). Other TEAEs included urinary tract infection 7 (35%; grade ≥ 3, 1 [5%]), fatigue 8 (40%), pain 8 (40%), anorexia 5 (25%), paresthesia 8 (40%), and abnormal urinalysis 5 (25%; grade ≥ 3, 1 [5%]).(Table S2 ) Adverse events considered ADC-related were frequent, led by alopecia 18 (90%), with paresthesia 8 (40%), insomnia 5 (25%), and allergic dermatitis 1 (5%). Prespecified immune-related AEs included hypothyroidism 4 (20%), hyperthyroidism 3 (15%; grade ≥ 3, 1 [5%]), immune-mediated myocarditis 1 (5%; grade 3), urticaria 1 (5%), and proteinuria 1 (5%). Overall, the regimen was associated primarily with low-grade toxicities; high-grade events were infrequent and encompassed laboratory abnormalities and isolated immune-mediated myocarditis. Baseline Correlates of Pathologic Response To explore baseline determinants of response, we compared pre-treatment profiles between patients who achieved pCR and those who did not using Olink proteomics, multiplex immunofluorescence (mIF), immunohistochemistry (IHC), and RNA sequencing. Proteomic analysis identified seven proteins (IL13, CSF-1, uPA, TNFRSF9, β-NGF, CCL19, and CCL11) that were significantly elevated in the pCR group (Fig. 2 A–C, Fig. S2 ). KEGG analysis of these proteins suggested enrichment of immune- and signaling-related pathways, such as cytokine–cytokine receptor interaction, IL-17 signaling and NF-κB signaling (Fig. 2 D). Immune profiling further demonstrated distinct patterns between groups. Non-pCR tumors showed greater M2 macrophage infiltration and higher M2/M1 ratios, while pCR tumors contained more abundant tumor-infiltrating lymphocytes (TILs) (Fig. 2 E–F). Other immune-cell differences were observed but did not reach significance, likely reflecting the limited sample size (Fig. S3 ). RNA sequencing revealed broad transcriptional divergence at baseline, with differentially expressed genes in the pCR group mapping to KEGG pathway (Fig. 2 G–I, Fig. S4). These findings highlight potential immune and signaling mechanisms that may contribute to therapeutic sensitivity. Derivation and Performance of a Baseline Predictive Score To identify underlying baseline correlates of response, we integrated proteomic, immune-cell, and transcriptomic features. Immune composition inferred by xCell showed distinct patterns between groups (Fig. 3 A), with pCR tumors enriched for CD4⁺ naive T cells and CD8⁺ effector memory T cells (Fig. 3 B–C). GSEA demonstrated preferential activation of chemokine and TNF/NF-κB signaling in pCR tumors (Fig. 3 D). Proteomic and transcriptomic analyses both revealed concordant pathway trends, with significance predominantly observed in the proteome, particularly in cytokine–cytokine receptor interaction, IL-17, NF-κB, and chemokine signaling pathways (Fig. 3 E). Then, we mapped the previous seven pCR-enriched proteins (Fig. 2 B) to cell types using reference single-cell datasets and four proteins (Fig. 3 F ) —CCL19, uPA, TNFRSF9, and CSF-1—were prioritized based on their immune relevance and receptor–ligand connectivity, providing a focused panel of candidate biomarkers for further analysis. To integrate baseline protein, immune-cell, and pathway features, we performed multivariable logistic regression using the four prioritized proteins together with identified immune-cell subsets and pathways (Fig. 3 G). In multivariable logistic regression, only CCL19 and the M2/M1 ratio remained independent predictors, yielding a two-variable model with an AUC of 0.89 (95% CI, 0.70–1.00) and good calibration (Fig. 3 H–I). On this basis, we derived a Baseline Response Predictive score (BRPscore) incorporating these two factors for patient stratification (Fig. 3 J). Using the pre-specified BRPscore threshold, patients were stratified into BRPscore-High and -Low groups. All pCR cases (3/3, 100%) were observed in the BRPscore-High group, whereas the most of non-pCR patients were observed in BRPscore-Low group (Fig. 3 K–L). As a benchmark, we applied two commonly used PD-L1 thresholds (1 and 10). At the 1 cut-off, there remained substantial overlap between pCR and non-pCR, with limited discriminatory ability (Fig. 3 M). At the 10 cut-off, separation improved in non-PCR group, still with 75% (3/4) of pCR classified into the PD-L1–High group, this performance was still inferior to the BRPscore (Fig. 3 N). To further test model validity, we included one patient with progressive disease (PD) who had available data (Fig. 3 O). This patient exhibited much lower BRPscore than non-pCR cases, consistent with model predictions. Although limited by small numbers, this observation supports the discriminatory capacity of the BRPscore. Rapid Phase Molecular Dynamics To further explore the biological basis of treatment response, we first compared proteomic at cycle 3 day 1 (C3D1) with baseline to capture the rapid response phase. In the pCR group, 10 proteins increased and 2 decreased, whereas 33 proteins increased in the non-pCR group (Fig. 4 A–C). Venn analysis of these proteins (Fig. 4 D): 5 proteins uniquely changed in pCR (IL-22RA1, 4E-BP1, CD6, MCP-1, MCP-3), 26 uniquely changed in non-pCR, and 7 shared by both groups. Among the non-pCR–specific proteins, 7 proteins (Beta-NGF, IL-10RB, IL-17C, IL-4, NT-3, OPG, and uPA) exhibited the opposite direction of change in pCR. Within the shared set of 7 proteins, only SLAMF1 changed in opposite directions between pCR and non-pCR. In total, 13 proteins were identified as potential signatures, including 5 uniquely altered in pCR, 7 in non-pCR, and 1 shared protein with divergent changes between groups. Among the 13 proteins, MCP-1 and IL4 are shown as examples (Fig. 4 E-F). KEGG analysis of those 13 proteins suggested enrichment patterns in cytokine/cytokine-receptor interaction, PI3K–AKT, and NF-κB/TNF-related signaling (Fig. 4 G). RNA-seq at C3D1 identified 697 differentially expressed transcripts in pCR tumors (503 up, 194 down) and 484 in non-pCR tumors (244 up, 240 down) relative to baseline (Fig. 4 H–J). Venn mapping of C3D1 versus pre-NAT DEGs (Fig. 4 K) defined four analytic subsets: (i) 481 genes uniquely upregulated in pCR; (ii) 187 genes uniquely downregulated in pCR; (iii) 5 genes upregulated in pCR but downregulated in non-pCR; and (iv) 3 genes showing the inverse pattern (up in non-pCR and down in pCR). These four subsets (481, 187, 5, and 3 genes) were carried forward for immune-related pathway enrichment and compared with the proteomic profiles (Fig. 4 L–M). xCell deconvolution highlighted B-cell compartments as the main on-treatment signal (C3D1 v.s. pre-NAT) (Fig. S5). In the non-pCR group, total B cells were significantly reduced at C3D1 ( P = 0.043) with non-significant downward trends in naïve and memory subsets. In the pCR group, all B-cell subsets showed non-significant upward trends, likely limited by sample size. Next, we mapped the previous potential signatures from Fig. 4 D to cell types using public single-cell references. Ultimately, only 6 candidates (4E-BP1, CD6, MCP-1, IL-10RB, uPA and SLAMF1) aligned with B-cell lineages (Fig. 4 N), with their cross-modal correlations illustrated in Fig. 4 O. Proteomic changes were less pronounced at a later timepoint (post-NAT v.s. C5D1; Fig. S6), supporting C3D1 as a critical window of early immune and signaling adaptation during which therapeutic modulation may influence efficacy. Longitudinal Proteomic Programs and Candidate Biomarkers Finally, we investigated sequential proteomic and transcriptomic changes across all treatment stages. At the protein level, time-series analysis identified 13 candidates showing sustained increases across treatment stages (Fig. 5 A), consistent with STEM-type modeling. Integration with repeated-measures ANOVA confirmed a subset of 11 proteins with significant or trend-level phase–response interactions (Fig. 5 B). KEGG enrichment of these 11 dynamic proteins pointed to immune- and inflammation-related pathways (Fig. 5 C). Representative trajectories for CD40 and MCP-1 are shown (Fig. 5 D–E). Parallel RNA-seq demonstrated stage-dependent transcriptional changes in the same directions (Fig. S7). At the post-NAT stage, volcano and heatmap analyses identified seven proteins—TNFRSF9, IL15RA, CD40, MCP-1, CST5, NT-3, and 4E-BP1—with overall higher levels in pCR tumors compared with non-pCR (Fig. 5 F–G). KEGG analysis based on the seven proteins are shown in Fig. 5 H. Integrated KEGG pathways of proteins identified from different groups (rapid-response, full-course and outcome) are also shown in Fig. 5 I. A three-way Venn diagram (Fig. 5 J) identified MCP-1 as the only protein shared across these three groups. TNFRSF9, IL-15RA, IL-18R1, and CD40 shared across full-course and outcome analyses. We then mapped these five proteins to immune pathways to visualize regulatory linkages and inter-protein relationships, revealing convergent functional connectivity via distinct pathways (Fig. 5 K). Further protein–protein interaction (PPI) analysis placed the five key proteins within two immune modules—the TNF/TNFR co-stimulatory axis (TNFSF9–TNFRSF9, CD40) and the common γ-chain cytokine axis (IL-2/IL-15 with IL2RA/IL2RB/IL15RA). Notably, MCP-1 occupied a central, highly connected position, linking nodes across both modules and suggesting a bridging role between chemokine recruitment and co-stimulatory/cytokine signaling (Fig. 5 L). In addition, in non-pCR cases, mIF comparing pre-NAT and post-NAT showed a broad downward shift across multiple immune lineages, whereas CD8⁺ T-cell density exhibited a non-significant upward trend (Fig. S8A and S8B). M2/M1 ratio trended lower post-treatment, however, PD-1 + CD8 + T cells increased significantly, indicating greater fuctional exhaustion despite CD8 T cell abundance (Fig. S8A). Discussion In this single-arm, single-center exploratory trial, we evaluated RC48 plus penpulimab as neoadjuvant therapy for patients with early or locally advanced, ER-positive/HER2-low breast cancer. Among 16 evaluable patients, a pCR rate of 25% was achieved with a manageable safety profile, representing a clinically meaningful improvement compared with historical data. Prior studies have consistently shown that HER2-low tumors, particularly HR-positive subtypes, are less responsive to standard chemotherapy with reported pCR rates typically below 10% 22 . Thus, the pCR rate observed in our study suggests a promising therapeutic signal in this difficult-to-treat population. While T-DXd has demonstrated substantial activity in metastatic HER2-low disease 7 , neoadjuvant studies such as TRIO-US B-12 TALENT reported modest efficacy, with pCR rates below 6%. These findings highlight that ADCs alone may not be sufficient in the curative-intent setting. By contrast, our study introduces a chemotherapy-sparing ADC–ICI combination strategy, achieving a numerically higher pCR rate than observed with ADC monotherapy. This concept is further supported by large phase III immunotherapy trials such as KEYNOTE-756 23 and CheckMate 7FL 24 , which suggest potential benefit of immune checkpoint inhibitors in ER-positive/HER2-negative breast cancer, though efficacy in Asian subgroups has been less pronounced. The 25% pCR rate in our cohort, achieved without conventional cytotoxic chemotherapy, provides early validation for a “de-chemotherapy” approach in HER2-low disease. If confirmed, this strategy could offer a novel therapeutic option with improved tolerability. The safety profile of the combination was consistent with the known toxicities of each agent, with no unexpected adverse events or treatment-related deaths. Common events included pruritus, liver enzyme elevations, and constipation, while grade ≥ 3 toxicities were infrequent. These results support the feasibility of integrating this regimen into the neoadjuvant setting, in contrast to some ICI-based regimens where treatment-related deaths have been reported. Beyond clinical endpoints, this study also provides important translational insights. At baseline, multi-omic integration enabled the development of a Baseline Response Predictive (BRP) model that combined CCL19 and the M2/M1 macrophage ratio, achieving an AUC of 0.89. Importantly, all patients who achieved pCR were correctly classified as BRP-high, suggesting this model may serve as a powerful stratification tool prior to therapy. This finding emphasizes that pre-treatment immune contexture can inform patient selection and guide clinical decision-making in HER2-low disease. Dynamic molecular profiling revealed that the most pronounced biological divergence occurred at the rapid-response phase (C3D1). During this window, early immune signaling and cytokine modulation were observed, with MCP-1 emerging as a key marker. Notably, MCP-1 was consistently identified across baseline, early on-treatment, and longitudinal analyses, positioning it as a central biomarker that bridges chemokine recruitment with co-stimulatory and cytokine signaling pathways. The persistence of MCP-1 across all analytic layers underscores its potential as both a pharmacodynamic marker and a therapeutic target. Taken together, these results suggest a two-step framework for future trial design: (i) baseline stratification using the BRPscore to identify patients most likely to benefit; and (ii) dynamic monitoring of MCP-1 and other rapid-response markers (e.g., TNFRSF9, IL-15RA, CD40) to refine early treatment adaptation. This integrative approach could improve trial efficiency, enable biomarker-driven patient selection, and ultimately accelerate the development of chemotherapy-sparing regimens. This study has limitations. The small sample size and single-arm design preclude definitive conclusions, and comparisons with historical controls may introduce bias. Only short-term outcomes such as pCR were available, whereas long-term survival endpoints (EFS, OS) remain immature. The exploratory BRP scoring system also requires external validation in larger cohorts, and heterogeneity in HR status and tumor burden should be further addressed. The BRPscore and MCP-1 findings, while compelling, require external validation in larger, independent cohorts and functional studies to confirm mechanistic relevance. In summary, this study demonstrates the feasibility and potential efficacy of a chemotherapy-sparing ADC–ICI regimen in HER2-low early breast cancer. By integrating a baseline predictive model (BRPscore) with dynamic on-treatment biomarkers such as MCP-1, we provide a proof-of-concept that biomarker-driven strategies can inform patient selection and adaptive monitoring. Future randomized studies with larger populations and mature survival follow-up will be essential to confirm these promising signals and to define the role of this combination in clinical practice. Methods Study Design and Patient Population This was a single-arm, single-center exploratory clinical trial (ClinicalTrials.gov identifier:NCT05726175 ) evaluating RC48 in combination with penpulimab as neoadjuvant therapy for patients with HER2-low early or locally advanced breast cancer (). The protocol was approved by the Ethics Committee of West China Hospital (approval number [2022 (1990)] and conducted in accordance with Good Clinical Practice and the Declaration of Helsinki. All patients provided written informed consent before enrollment (August 2023 to August 2024). Key eligibility criteria included women aged 18–75 years with histologically confirmed invasive breast cancer, ECOG performance status of 0–1, and clinical stage cT1N1–2 or cT2–4Nx with HER2 IHC 1 + or 2+/FISH−. Major exclusion criteria were inflammatory, metastatic, or bilateral breast cancer; prior antitumor therapy within 12 months; clinically significant cardiac disease; prior exposure to PD-1/PD-L1 or CTLA-4 inhibitors; and a history of neurologic or psychiatric disorders, including seizure, dementia, or substance abuse. Treatment and Study Schema Patients received RC48 2.0 mg/kg intravenously plus penpulimab 200 mg intravenously every 3 weeks as neoadjuvant therapy, for up to 6 cycles, followed by surgery. Serial biospecimens were collected at prespecified time points: tumor biopsy and blood before treatment (pre-NAT), peripheral blood at cycle 3 day 1 (C3D1) and cycle 5 day 1 (C5D1), and tumor tissue and blood at surgery (post-NAT). Paired tumor and blood samples were used for standard pathology and exploratory translational analyses, including Olink proteomics, immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and RNA sequencing (RNA-seq). Endpoints and assessments The primary endpoint was pathologic complete response (pCR; ypT0/is ypN0) in the intention-to-treat population, defined as no residual invasive cancer in breast or regional lymph nodes post-neoadjuvant therapy, allowing ductal carcinoma in situ. Secondary endpoints included efficacy outcomes: breast pathologic complete response (bpCR; ypT0/is), residual cancer burden (RCB) score, objective response rate (ORR), disease control rate (DCR), invasive disease-free survival (iDFS), and event-free survival (EFS). Safety was evaluated via ECOG performance status, vital signs, physical examinations, laboratory tests, adverse events (AEs), serious AEs, and quality-of-life assessments, per NCI-CTCAE v5.0. Exploratory endpoints assessed biomarkers in blood and tumor tissue, including PD-L1 expression, tumor-infiltrating lymphocytes, circulating tumor cells, and immune cell subsets (e.g., CD4 + and CD8 + cells). Clinical response was evaluated through physical examination and breast imaging (ultrasound and enhanced MRI) at baseline, every two cycles during neoadjuvant therapy, and pre-surgery. Systemic staging included chest, abdominal, pelvic, and additional imaging (e.g., cranial, bone scans) per institutional protocols, maintaining consistent imaging conditions (e.g., contrast use, scan thickness). Pathologic response was assessed in surgical specimens, with total pathologic complete response (pCR; ypT0/is ypN0) defined as no residual invasive cancer in breast or regional lymph nodes, allowing ductal carcinoma in situ. Breast pathologic complete response (bpCR; ypT0/is) and residual cancer burden (RCB) were evaluated, with RCB scores calculated using validated parameters (tumor size, cellularity, lymph node involvement) via the MD Anderson RCB calculator. Safety was monitored in all patients receiving at least one dose of study treatment. Assessments included ECOG performance status, vital signs, physical examinations, 12-lead electrocardiography, echocardiography (if clinically indicated), and laboratory tests (hematology, serum biochemistry, thyroid function, urinalysis, stool analysis, coagulation). Adverse events (AEs) and serious AEs were graded per NCI-CTCAE v5.0, with the highest grade per event recorded. Exploratory biomarker analyses were conducted on tumor and peripheral blood samples collected pretreatment, during treatment (cycles 2, 4, 6), and post-surgery. Biomarkers included, but were not limited to, PD-L1 expression, tumor-infiltrating lymphocytes, circulating tumor cells, and immune cell subsets (e.g., CD4 + and CD8 + T cells), assessed via immunohistochemistry and other molecular techniques. Multiplex Immunofluorescence (mIF) mIF was performed using a predefined antibody–fluorophore panel by Shanghai KR Pharmtech (Shanghai, China). FFPE tissue sections (4 µm) were deparaffinized, rehydrated, and subjected to antigen retrieval with EDTA buffer (pH 9.0) or citrate buffer (pH 6.0). Immunostaining was performed on a Krast 600 platform (Kuoran Biotech, Shanghai, China), following automated standard protocols. Each round of staining involved incubation with primary antibodies (e.g. CD8, CD68, CD163, FOXP3, CD56, PANCK) and tyramide signal amplification (TSA)-based fluorophores (Opal 480, 520, 570, 620, 690, TSA-DIG 780). After each staining cycle, signal removal and reactivation were automatically performed to prevent cross-reactivity. Nuclei were counterstained with DAPI (CST, catalog # 4083S, 1:1000 ). The primary antibodies and their respective concentrations and sources are detailed in Table S3 . Slides were scanned on a KR-HT5 multispectral scanner (KR Pharmtech) to generate qptiff images under controlled conditions (10–30°C, ≤ 70% humidity). Scanning was performed using the Opal Polaris 5–7 color protocol with optimized exposure times (0–100 ms). Images were processed using HT5 View for spectral unmixing and inForm software (Akoya Biosciences) for tissue segmentation and marker quantification. ROIs were selected based on staining patterns or tumor regions, and positivity thresholds were defined using automatic scoring and visual verification. All mIF assays included positive and negative controls, and slides were stored in the dark at 4°C. Scanning occurred within two weeks to maintain signal integrity. Olink Proteomics Methodology Proteins were quantified using the Olink® Target 96 Inflammation panel (Table S4, Olink Proteomics AB, Uppsala, Sweden) following the manufacturer's protocol. The Proximity Extension Assay (PEA) technology, as previously described (Assarsson et al., 2014), enables the simultaneous analysis of 92 analytes using just 1 µL of each sample. Briefly, pairs of oligonucleotide-labeled antibody probes bind to their target proteins, and when the probes are in close proximity, the oligonucleotides hybridize. The addition of DNA polymerase initiates proximity-dependent DNA polymerization, generating a unique PCR target sequence. This DNA sequence is then detected and quantified using a microfluidic real-time PCR instrument (Signature Q100, LC-Bio Technology CO., Ltd., Hangzhou, China). Data were quality-controlled and normalized using an internal extension control and an inter-plate control to account for intra- and inter-run variability. The final results are reported as Normalized Protein eXpression (NPX) values, which are expressed on a log2 scale where higher NPX values correspond to increased protein expression. All assay validation data, including detection limits and intra- and inter-assay precision, are available on the manufacturer's website ( www.olink.com ). To further explore the potential cellular origins of Olink activity molecules that exhibit significant differences across groups, we analyzed the single-cell transcriptomic dataset (GSE176078) comprising 26 primary breast cancer samples from the NCBI database using Seurat. The analysis parameters were kept consistent with those of the original study. The predominant cellular sources of each activity molecule were identified based on the expression patterns of their corresponding encoding genes. RNA Sequencing (RNA-seq) RNA-seq was performed on the Illumina NovaSeq X Plus platform. Raw reads from all samples were aligned to the human reference genome (Ensembl v112) using HISAT2 (version 2.2.1; https://daehwankimlab.github.io/hisat2/ ). The mapped reads of each sample were assembled into transcriptomes using StringTie (version 2.1.6; http://ccb.jhu.edu/software/stringtie/ ) with default parameters. Subsequently, transcriptomes from all samples were merged to generate a comprehensive transcriptome using gffcompare (version 0.9.8; http://ccb.jhu.edu/software/stringtie/gffcompare.shtml ). Gene expression levels were estimated using StringTie and Ballgown (version 3.3.3; http://www.bioconductor.org/packages/release/bioc/html/ballgown.html ) by calculating the FPKM (fragments per kilobase of transcript per million mapped reads) values for each mRNA. Differential expression analysis of genes was performed using DESeq2 between two groups, and edgeR (for pairwise comparison) was used for additional validation. The genes with the parameter of P value < 0.05 and absolute fold change ≥ 1.5 were considered differentially expressed (DEGs). The DEGs were further subjected to functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO enrichment was performed to identify significantly enriched terms in molecular function, cellular component, and biological process categories using hypergeometric testing (p < 0.05). KEGG pathway analysis was similarly conducted to identify significantly enriched pathways (p < 0.05). Immune infiltration analysis was conducted by TIMER, a deconvolution approach built on constrained least-squares regression with cancer-type–aware signature selection. The method estimates infiltration levels for six major immune lineages (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, dendritic cells) from bulk RNA-seq data 25 , 26 . In this study, estimates and figures were generated via TIMER2.0 under its breast-carcinoma, standardized workflow; results from other TIMER2.0 modules (CIBERSORT, quanTIseq, xCell, MCP-counter, EPIC) served as sensitivity analyses to confirm directional consistency. All enrichment analyses were performed using OmicStudio tools ( https://www.omicstudio.cn/tool ). Visualizations were generated using R (version 4.1.3) and the ggplot2 package (version 3.3.3). Statistical Analysis The statistical analysis of this single-arm exploratory trial was primarily descriptive. Continuous variables were summarized using the mean, standard deviation, median, minimum, and maximum values. Categorical and ordinal variables were summarized using counts, percentages, and two-sided 95% confidence intervals calculated by exact binomial methods. The primary endpoint was the pathologic complete response (pCR; ypT0/is ypN0) in the intention-to-treat population. Secondary endpoints included objective response rate (ORR), residual cancer burden (RCB), and safety. Exploratory analyses evaluated potential predictive biomarkers using multi-omic data. Exploratory biomarker analyses, including proteomic and transcriptomic profiling, were descriptive and hypothesis-generating. Pathway enrichment was analyzed using hypergeometric testing on KEGG terms, and protein–protein interaction networks were constructed using STRING. Unless otherwise specified, all statistical tests were two-sided with a significance level of α = 0.05. Statistical analyses were performed using SPSS version 22.0 (IBM Corp) and R version 4.1.0 (R Foundation for Statistical Computing). Declarations Competing interests The authors declare no competing interests. Author contributions Every author involved in the study reviewed and sanctioned the finalized manuscript. Yuting Song, Lei Liu, Aaron Qi Zhang and Xin Xie wrote the clinical trial implementation, overseeing data acquisition, statistical analysis, figure generation, and manuscript drafting. Shibao Li assisted in revising the writing. Collection and assembly of data: Chunying Zhuang, Xiaorong Zhong, Yan Cheng, Dan Zheng. Bing Wei served as the guide for pathological reading. Ping He, Xi Yan, Tinglun Tian, Jie Chen contributed to patient recruitment and therapeutic management. Juanjuan Li assisted in data analysis of the RNA-seq and Olink. Xiaoxiao Liu, Shibao Li and Ting Luo were responsible for the experimental design data validation and manuscript revision. All the authors have read and approved the article. Every author involved in the study reviewed and sanctioned the finalized manuscript. Acknowledgements RemeGen, Ltd (Yantai, China) and Chia Tai Tianqing Pharmaceutical Group Co., Ltd were the study sponsors. We would like to express our appreciation to the participants and their families for their invaluable participation in this study. We are grateful to Shuaitong Chen, Jiali Ren and Siyuan Shen (LC-Bio Technology Co., Ltd.) for his support and assistance in the data analysis. This research was made possible through the support of the following funding sources: Young Talent Program of The Affiliated Hospital of Xuzhou Medical University. Advanced Program of The Affiliated Hospital of Xuzhou Medical University (PYJH2024210). Medical Research Project of Jiangsu Provincial Health Commission (H2023050). National Natural Science Foundation of China (No. 32301278). Data availability The raw data have been deposited in the Genome Sequence Archive for Human (GSA-Human) at the National Genomics Data Center (NGDC) under BioProject accession number PRJCA042227. Datasets generated in this study are available from the corresponding author upon request via email at [email protected] , use for commercial purposes is prohibited. All requests will be reviewed by the corresponding author within 2 weeks. Prior to data sharing, a signed data access agreement with the sponsor is required. References Han B , et al. Cancer incidence and mortality in China, 2022. J Natl Cancer Cent 4 , 47-53 (2024). Swain SM, Shastry M, Hamilton E. Targeting HER2-positive breast cancer: advances and future directions. Nat Rev Drug Discov 22 , 101-126 (2023). Waks AG, Winer EP. Breast Cancer Treatment: A Review. JAMA 321 , 288-300 (2019). Modi S , et al. Trastuzumab Deruxtecan in Previously Treated HER2-Positive Breast Cancer. N Engl J Med 382 , 610-621 (2020). Figueroa-Magalhães MC, Jelovac D, Connolly R, Wolff AC. Treatment of HER2-positive breast cancer. Breast 23 , 128-136 (2014). Tarantino P , et al. ESMO expert consensus statements (ECS) on the definition, diagnosis, and management of HER2-low breast cancer. Ann Oncol 34 , 645-659 (2023). Modi S , et al. Trastuzumab Deruxtecan in Previously Treated HER2-Low Advanced Breast Cancer. N Engl J Med 387 , (2022). Schalper KA, Kumar S, Hui P, Rimm DL, Gershkovich P. A retrospective population-based comparison of HER2 immunohistochemistry and fluorescence in situ hybridization in breast carcinomas: impact of 2007 American Society of Clinical Oncology/College of American Pathologists criteria. Arch Pathol Lab Med 138 , 213-219 (2014). Cortazar P , et al. Pathological complete response and long-term clinical benefit in breast cancer: the CTNeoBC pooled analysis. Lancet 384 , 164-172 (2014). Loibl S , et al. Survival analysis of carboplatin added to an anthracycline/taxane-based neoadjuvant chemotherapy and HRD score as predictor of response-final results from GeparSixto. Ann Oncol 29 , 2341-2347 (2018). Shepherd JH , et al. CALGB 40603 (Alliance): Long-Term Outcomes and Genomic Correlates of Response and Survival After Neoadjuvant Chemotherapy With or Without Carboplatin and Bevacizumab in Triple-Negative Breast Cancer. J Clin Oncol 40 , 1323-1334 (2022). Schmid P , et al. Pembrolizumab for Early Triple-Negative Breast Cancer. N Engl J Med 382 , 810-821 (2020). Mittendorf EA , et al. Neoadjuvant atezolizumab in combination with sequential nab-paclitaxel and anthracycline-based chemotherapy versus placebo and chemotherapy in patients with early-stage triple-negative breast cancer (IMpassion031): a randomised, double-blind, phase 3 trial. Lancet 396 , 1090-1100 (2020). Doi T , et al. Safety, pharmacokinetics, and antitumour activity of trastuzumab deruxtecan (DS-8201), a HER2-targeting antibody-drug conjugate, in patients with advanced breast and gastric or gastro-oesophageal tumours: a phase 1 dose-escalation study. Lancet Oncol 18 , 1512-1522 (2017). Wang J , et al. Disitamab vedotin, a HER2-directed antibody-drug conjugate, in patients with HER2-overexpression and HER2-low advanced breast cancer: a phase I/Ib study. Cancer Commun (Lond) 44 , 833-851 (2024). Rugo HS , et al. Sacituzumab Govitecan in Hormone Receptor-Positive/Human Epidermal Growth Factor Receptor 2-Negative Metastatic Breast Cancer. J Clin Oncol 40 , 3365-3376 (2022). Bardia A , et al. Sacituzumab Govitecan in Metastatic Triple-Negative Breast Cancer. N Engl J Med 384 , 1529-1541 (2021). Huang L , et al. A HER2 target antibody drug conjugate combined with anti-PD-(L)1 treatment eliminates hHER2+ tumors in hPD-1 transgenic mouse model and contributes immune memory formation. Breast Cancer Res Treat 191 , 51-61 (2022). Schmid P , et al. Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol 21 , 44-59 (2020). Miles D , et al. Primary results from IMpassion131, a double-blind, placebo-controlled, randomised phase III trial of first-line paclitaxel with or without atezolizumab for unresectable locally advanced/metastatic triple-negative breast cancer. Ann Oncol 32 , (2021). Cortes J , et al. Pembrolizumab plus chemotherapy versus placebo plus chemotherapy for previously untreated locally recurrent inoperable or metastatic triple-negative breast cancer (KEYNOTE-355): a randomised, placebo-controlled, double-blind, phase 3 clinical trial. Lancet 396 , 1817-1828 (2020). von Minckwitz G , et al. Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): a randomised phase 2 trial. Lancet Oncol 15 , 747-756 (2014). Cardoso F , et al. Pembrolizumab and chemotherapy in high-risk, early-stage, ER+/HER2- breast cancer: a randomized phase 3 trial. Nat Med 31 , 442-448 (2025). Loi S , et al. Neoadjuvant nivolumab and chemotherapy in early estrogen receptor-positive breast cancer: a randomized phase 3 trial. Nat Med 31 , 433-441 (2025). Additional Declarations There is NO Competing Interest. Supplementary Files supplementarytables.pdf Supplementary Tables Sfigurelegends.pdf S Figure legends Researchprotocol.pdf Research protocol Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7869437","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":534018263,"identity":"5caa25ab-48c3-4d73-9263-64a5c090e33d","order_by":0,"name":"Ting 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1","display":"","copyAsset":false,"role":"figure","size":472998,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy design, patient disposition, and efficacy outcomes.\u003cbr\u003e\n \u003c/strong\u003e(A) Patient disposition. Of 50 screened patients, 20 were enrolled and received RC48 plus penpulimab neoadjuvant therapy; 16 completed treatment and 4 discontinued (adverse event, n = 1; withdrawal, n = 1; other reasons, n = 2).\u003cbr\u003e\n(B) Study schema showing treatment and sample collection at baseline, cycle 3 day 1 (C3D1), cycle 5 day 1 (C5D1), and post-treatment surgery.\u003cbr\u003e\n(C) In the evaluable cohort (n = 16), the pathologic complete response (pCR; ypT0/is, ypN0) rate was 25% (4/16) and the objective response rate (ORR) was 56.3% (9/16).\u003cbr\u003e\n(D) Spider plot of tumor size change over time in the evaluable cohort; dashed lines indicate RECIST thresholds.\u003cbr\u003e\n(E) Waterfall plot of best percentage change from baseline in the evaluable cohort, color-coded by response and annotated with PD-L1 status.\u003c/p\u003e\n\u003cp\u003e(F) Residual cancer burden (RCB) distribution at surgery (RCB 0–1, 31.25%; RCB-3, 68.75%).\u003cbr\u003e\n(G) Immune infiltration by RCB class; no significant differences were observed in CD4⁺, CD8⁺, or total TILs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: AE, adverse event; C3D1/C5D1, cycle 3/5 day 1; IV, intravenous; NAT, neoadjuvant therapy; IHC, immunohistochemistry; mIF, multiplex immunofluorescence; RC48, disitamab vedotin; pCR, pathologic complete response; ORR, objective response rate; PR, partial response; SD, stable disease; PD, progressive disease; RECIST, Response Evaluation Criteria in Solid Tumors; RCB, residual cancer burden.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG.1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/339cf818196fa6c2e1a36afe.jpg"},{"id":97142605,"identity":"ddf6a09e-1354-4111-a00d-0c5214da9cd3","added_by":"auto","created_at":"2025-12-01 10:07:45","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":467295,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBaseline proteomic, immune-cell, and transcriptomic features associated with pCR.\u003cbr\u003e\n \u003c/strong\u003e(A) Number of differentially expressed proteins between pCR and non-pCR tumors at baseline by Olink analysis.\u003cbr\u003e\n(B) Volcano plot showing significantly upregulated proteins in the pCR group (IL13, CSF-1, uPA, TNFRSF9, β-NGF, CCL19, CCL11).\u003cbr\u003e\n(C) Heatmap of the seven proteins across individual patients, and (D) KEGG analysis of these proteins.\u003cbr\u003e\n(E) Baseline immune profiling by mIF and IHC.\u003c/p\u003e\n\u003cp\u003e(F) Boxplots showing increased M2 macrophages and M2/M1 ratios in non-pCR tumors and higher TILs density in pCR tumors.\u003cbr\u003e\n(G) Number of DEGs between baseline pCR and non-pCR tumors.\u003cbr\u003e\n(H) Volcano plot of DEGs and (I) KEGG analysis of these DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: DEG, differentially expressed gene; mIF, multiplex immunofluorescence; IHC, immunohistochemistry; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG.2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/50d8db078813e1b1adcb48e2.jpg"},{"id":97131564,"identity":"7f37fea2-b27e-44d3-b9f9-50ef562db9f1","added_by":"auto","created_at":"2025-12-01 08:43:04","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":771885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBaseline integrative analysis and development of the BRPscore.\u003c/strong\u003e\u003cbr\u003e\n(A) xCell deconvolution of baseline RNA-seq revealed distinct immune-cell patterns between pCR and non-pCR tumors.\u003cbr\u003e\n(B–C) Boxplots of selected immune subsets showing enrichment of CD4⁺ naive T cells and CD8⁺ effector memory T cells in pCR tumors.\u003cbr\u003e\n(D) GSEA indicated preferential activation of chemokine and TNF/NF-κB signaling in pCR tumors.\u003cbr\u003e\n(E) KEGG pathway analysis of proteome- and transcriptome-derived features showed significance predominantly in the proteome.\u003c/p\u003e\n\u003cp\u003e(F) Protein–cell mapping identified four (TNFRSF9, IL13, CSF-1, and CCL19) of seven pCR-associated proteins as immune-relevant biomarkers.\u003cbr\u003e\n(G) ROC screening of prioritized proteins, immune-cell subsets, and pathways.\u003cbr\u003e\n(H–I) Multivariable logistic regression retained CCL19 and the M2/M1 ratio as independent predictors, yielding a two-variable model (AUC 0.89, 95% CI 0.70–1.00).\u003cbr\u003e\n(J) Formula of the Baseline Response Predictive score (BRPscore) derived from these two variables.\u003cbr\u003e\n(K–L) Stratification by BRPscore (High vs Low) showed all pCR events confined to the High group. N=12, number of patients who completed NAT with all available data for Olink proteomics and multiplex immunofluorescence (mIF).\u003cbr\u003e\n(M–N) Comparison of different PD-L1 scores cutoffs, with the PD-L1 0–10 scheme providing superior discrimination.\u003cbr\u003e\n(O) Per-patient predicted probabilities showed that progressive disease (PD) cases had the lowest scores, consistent with model predictions. N=13, number of all patients with all available data for Olink proteomics and mIF.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: pCR, pathologic complete response; ROC, receiver operating characteristic; AUC, area under the ROC curve; BRPscore, Baseline Response Predictive score; GSEA, gene set enrichment analysis; KEGG, Kyoto Encyclopedia of Genes and Genomes; mIF, multiplex immunofluorescence; NAT, neoadjuvant therapy; PD, progressive disease.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG.3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/27fa93d0979ce9130122ec91.jpg"},{"id":97142370,"identity":"81471bd8-500b-40ab-8f63-05cc7a74405a","added_by":"auto","created_at":"2025-12-01 10:07:33","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":724441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProteomic and transcriptomic dynamics during the rapid response phase (C3D1).\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Numbers of proteins significantly altered at C3D1 relative to baseline in pCR and non-pCR tumors.\u003c/p\u003e\n\u003cp\u003e(B–C) Heatmaps of differentially expressed proteins in pCR and non-pCR groups, respectively.(D) Venn diagram shows the intersection of proteins with significant changes at C3D1 stage in both groups.\u003c/p\u003e\n\u003cp\u003e(E) MCP1 protein curves with significant changes in pCR group alone at C3D1.\u003c/p\u003e\n\u003cp\u003e(F) IL-4 protein curves with significant changes in non-pCR group alone at C3D1.\u003c/p\u003e\n\u003cp\u003e(G) KEGG enrichment analysis of 13 proteins. 5 proteins (IL-22 RA1, 4E-BP1, CD6, MCP-1, and MCP-3) were significantly changed alone in the pCR group; among the 26 proteins with significant changes in non-pCR alone, 7 proteins (Beta-NGF, IL-1ORB, IL-17C, IL4, NT-3, OPG, and UPA) showed an opposite trend in pCR group; among the 7 proteins that underwent significant changes in non-pCR and pCR patients at C3D1 stage, only 1 protein (SLAMF1) showed an opposite trend between the two groups.\u003c/p\u003e\n\u003cp\u003e(H) Numbers of DEGs at C3D1 versus baseline in pCR and non-pCR tumors and (I–J) Volcano plots of DEGs in the two groups.\u003cbr\u003e\n(K) Venn diagram shows the intersection of DEGs in different group at C3D1 stage.\u003c/p\u003e\n\u003cp\u003e(L) KEGG enriched immune-related signaling pathways for the selected DEGs, which are within the regions represented by red and blue numbers in the K plot.\u003cbr\u003e\n(M) Integrated pathway analysis comparing proteomic and transcriptomic alterations at C3D1.\u003cbr\u003e\n(N) Ligand–receptor mapping of 13 potential signatures (identified from D) to immune-cell origins, highlighting 6 candidates (uPA, MCP-1, CD6, SLAMF1, IL-10RB, and 4E-BP1) associated with B-cell subsets.\u003cbr\u003e\n(O) Cross-modal heatmap showing correlations between immune-related DEGs and 6 proteins and their associated B-cell subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: C3D1, cycle 3 day 1; DEG, differentially expressed gene; KEGG, Kyoto\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEncyclopedia of Genes and Genomes; NAT, neoadjuvant therapy; NF-κB, nuclear factor kappa B;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003epCR, pathologic complete response.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG.4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/669eaef43179b14709f0bfbe.jpg"},{"id":97142495,"identity":"5f29519c-5b30-4c28-95f9-262821dd6148","added_by":"auto","created_at":"2025-12-01 10:07:38","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":516191,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eContinuous proteomic and transcriptomic dynamics across treatment phases.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Heatmap of time-series clustering (STEM) showing 13 proteins with sustained increases across treatment.\u003c/p\u003e\n\u003cp\u003e(B) Venn diagram integrating repeated-measures ANOVA and STEM analysis, identifying 11 proteins with phase-dependent changes and (C) KEGG enrichment of these 11 proteins. 11 proteins: CCL3, CCL4, CD40, HGF, IL-12B, IL-15RA, IL-18R1, IL8, MCP-1, TNFB, and TNFRSF9.\u003c/p\u003e\n\u003cp\u003e(D–E) Longitudinal expression trajectories of CD40 and MCP-1 across treatment stages.\u003c/p\u003e\n\u003cp\u003e(F–G) Volcano plot and heatmap of proteins between pCR and non-pCR patients at the post-NAT stage and (H) KEGG pathways of these proteins.\u003c/p\u003e\n\u003cp\u003e(I) Integrated KEGG pathway analysis of proteins identified from rapid-response, full-course, and post-treatment comparisons. Rapid-response: pre-NAT—C3D1; full-course: pre-NAT—C3D1—C5D1—post-NAT; and outcome analyses: post-pCR \u003cem\u003ev.s.\u003c/em\u003epost-non-pCR.\u003c/p\u003e\n\u003cp\u003e(J) Screening cross-proteins by three-way Venn diagram. 1 triple cross protein: MCP-1; 4 double cross proteins: TNFRSF9, IL-15RA, IL-18R1, CD40.\u003c/p\u003e\n\u003cp\u003e(K) Pathway network map of 5 candidate proteins (two or more intersections of venn analysis) and their enrichment pathways.\u003c/p\u003e\n\u003cp\u003e(L) Protein–protein interaction network shows the proteins screened by Venn and the prediction of the protein with the strongest interaction with them.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAbbreviations: ANOVA, analysis of variance; DEG, differentially expressed gene; KEGG, Kyoto\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEncyclopedia of Genes and Genomes; NAT, neoadjuvant therapy; pCR, pathologic complete\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eresponse; STEM, Short Time-series Expression Miner.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"FIG.5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/f5c54d751628e080f15dbf53.jpg"},{"id":101397920,"identity":"db91f192-5430-496c-a0c9-8896e66ec10c","added_by":"auto","created_at":"2026-01-29 09:38:08","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4026784,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/96a0116f-68b4-4f50-ac09-45190691cb4f.pdf"},{"id":97131562,"identity":"c88e1945-ba32-467d-9e40-264f9551755f","added_by":"auto","created_at":"2025-12-01 08:43:04","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":263378,"visible":true,"origin":"","legend":"Supplementary Tables","description":"","filename":"supplementarytables.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/b1f8b854355a876704e2321c.pdf"},{"id":97131570,"identity":"4846f6a9-ef4d-4004-a631-2550cf3f76b7","added_by":"auto","created_at":"2025-12-01 08:43:04","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":1500920,"visible":true,"origin":"","legend":"S Figure legends","description":"","filename":"Sfigurelegends.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/042d9b63f2265503e4e6f623.pdf"},{"id":97141717,"identity":"c36e114e-28a1-4d47-8651-f21c27747075","added_by":"auto","created_at":"2025-12-01 10:06:54","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":1340697,"visible":true,"origin":"","legend":"Research protocol","description":"","filename":"Researchprotocol.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7869437/v1/ff7e1a38d15f46e40fa30468.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Phase II Exploratory Study of Neoadjuvant Disitamab Vedotin and Penpulimab in HER2-low Stage II-III Breast Cancer (NeoPanDa04)","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAccording to the latest data from the National Cancer Center of China (2022), breast cancer ranks sixth in incidence and fifth in cancer-related mortality among women, whereas globally, it represents the most common malignancy in high human development index (HDI) countries\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Despite a relatively lower incidence in China, patients frequently present at younger ages and with more advanced disease, leading to inferior outcomes, thereby underscoring the urgent need for improved prevention and treatment strategies. HER2-positive breast cancer is associated with aggressive biology, low sensitivity to chemotherapy, and poor prognosis\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. The development of HER2-targeted therapies has transformed outcomes for this subgroup\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Multiple targeted agents, including monoclonal antibodies, tyrosine kinase inhibitors, and antibody\u0026ndash;drug conjugates (ADCs), have been approved for HER2-positive breast cancer\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. However, the therapeutic benefit of these approaches is limited in tumors with low HER2 expression, representing an important area of ongoing investigation.\u003c/p\u003e\u003cp\u003eMore than half of HER2-negative metastatic breast cancers (mBC) demonstrate low-level HER2 expression, defined as IHC 1\u0026thinsp;+\u0026thinsp;or IHC 2\u0026thinsp;+\u0026thinsp;with ISH negativity\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. HER2-low tumors can be further classified by hormone receptor (HR) status into HR-positive and triple-negative breast cancer (TNBC) subtypes\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. For HR-positive disease, current management largely follows that of HR-positive breast cancer in general, with recommended neoadjuvant regimens including anthracycline\u0026ndash;taxane chemotherapy or endocrine therapy; however, the overall pathologic complete response (pCR) rate remains below 10%. For HER2-low TNBC, NCCN guidelines recommend anthracycline\u0026ndash;taxane combinations as the backbone of neoadjuvant therapy. Reported pCR rates range from 28\u0026ndash;35% with combination therapy, compared with approximately 20% with anthracyclines alone and 12% with taxanes alone\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Addition of platinum increases pCR to about 50% but at the expense of greater toxicity and reduced tolerability\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e. More recently, KEYNOTE-522 and IMpassion031 showed that adding immune checkpoint inhibitors to chemotherapy significantly improved pCR and event-free survival\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and these regimens are now recommended for TNBC by the Chinese Society of Clinical Oncology (CSCO) guidelines. Nevertheless, immunotherapy in the neoadjuvant setting has not yet been approved in China, highlighting an unmet need for safer and more effective regimens.\u003c/p\u003e\u003cp\u003eADCs combine a monoclonal antibody, a chemical linker, and a cytotoxic payload, thus uniting target specificity with potent cell killing\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. Encouraging clinical results have been reported with several ADCs in HER2-low breast cancer, including DS-8201 (T-DXd), disitamab vedotin (RC48), and sacituzumab govitecan\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. Preclinical studies suggest synergy between ADCs and PD-1/PD-L1 blockade, with combinations demonstrating enhanced antitumor activity in HER2-expressing models\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eFurthermore, chemotherapy has been shown to augment tumor immunogenicity, thereby providing a rationale for combination strategies with immune checkpoint inhibitors. Trials such as IMpassion130/131 and KEYNOTE-355 demonstrated meaningful efficacy and manageable safety for chemo-immunotherapy in metastatic TNBC\u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, KEYNOTE-522 confirmed that adding pembrolizumab to neoadjuvant carboplatin and paclitaxel improved pCR by 13.6% (51.2% vs 64.8%)\u003csup\u003e12\u003c/sup\u003e, while IMpassion031 showed a 16.5% absolute increase in pCR with atezolizumab plus chemotherapy versus placebo (57.6% vs 41.1%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0044)\u003csup\u003e13\u003c/sup\u003e .\u003c/p\u003e\u003cp\u003eTaken together, these findings provide a strong rationale for evaluating antibody\u0026ndash;drug conjugates in combination with immune checkpoint inhibitors as neoadjuvant therapy for HER2-low breast cancer. On this basis, we conducted an exploratory single-arm clinical study of RC48 plus penpulimab in patients with HER2-low early or locally advanced breast cancer, with the primary objective of assessing efficacy and safety. In addition, multi-omic profiling\u0026mdash;including proteomic, transcriptomic, and immune-cell analyses\u0026mdash;was integrated to identify biomarkers of response and to develop predictive models for patient stratification in the neoadjuvant setting.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatient Disposition and Baseline Characteristics\u003c/h2\u003e\u003cp\u003eFrom 14 August 2023 to 7 August 2024, 50 patients were screened and 20 met eligibility and initiated neoadjuvant therapy; these 20 patients constituted both the intent-to-treat (ITT) and safety populations (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Enrolled patients began to accept RC48 plus penpulimab every 3 weeks (RC48 2.0 mg/kg IV; penpulimab 200 mg IV) for up to 6 cycles (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). Sixteen patients (80%) completed all planned neoadjuvant cycles and proceeded to surgery. Four patients (20%) could not be evaluabled for the primary end point: one discontinued because of an adverse event, one withdrew consent at the patient\u0026rsquo;s request, and two discontinued for other reasons (disease progression and per investigator discretion) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Serial biospecimen collection was performed as prespecified (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB): pretreatment tumor biopsy and blood (pre-NAT), on-treatment blood at cycle 3 day 1 (C3D1) and cycle 5 day 1 (C5D1), and post-NAT tumor and blood at surgery. Paired tissues and blood were processed for standard pathology and exploratory translational analyses, including Olink proteomics, immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and RNA sequencing (RNA-seq).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eBaseline characteristics of the ITT population (N\u0026thinsp;=\u0026thinsp;20) are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 43.5 years (range, 33\u0026ndash;68), and all patients were female with ECOG performance status of 0. Half of the tumors were HER2 IHC 1\u0026thinsp;+\u0026thinsp;and half were IHC 2+/FISH\u0026minus;. Most patients had high proliferative disease, with KI-67\u0026thinsp;\u0026gt;\u0026thinsp;20% in 80%. Hormone receptor positivity was common (ER 90%, PR 85%). The majority presented with T2 (74.5%) and node-positive (85%) disease, with 65% at stage II and 35% at stage III. PD-L1 CPS was \u0026gt;\u0026thinsp;1 in 55% of patients.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\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\u003eBaseline Demographic and Disease Characteristics (ITT Population)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\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\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatient Characteristic\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAll ITT Patients (N\u0026thinsp;=\u0026thinsp;20)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMedian age, years\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e43.5\u0026nbsp;(33,\u0026nbsp;68)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20\u0026nbsp;(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eECOG-PS ,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20(100)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHER2, \u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC 1+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026nbsp;(50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIHC 2+/FISH-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10\u0026nbsp;(50.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKI-67 score, \u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026le;\u0026thinsp;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4\u0026nbsp;(20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026gt;20%\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e16\u0026nbsp;(80.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHR, \u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e18\u0026nbsp;(90.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e17\u0026nbsp;(85.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT stage,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2\u0026nbsp;(10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(74.5)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1(5.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eT4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e4(20.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN stage,\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026nbsp;(15.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e14\u0026nbsp;(70.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e3\u0026nbsp;(15.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage (cTNM classificationd),\u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e13(65.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7(35.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePDL1, \u0026nbsp;n\u0026nbsp;(%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPS\u0026gt;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e11(55.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCPS\u0026thinsp;\u0026le;\u0026thinsp;1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9(45.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eData are No. (%) unless otherwise indicated. Age is median (range).\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"2\"\u003e\u003cem\u003eAbbreviations: ECOG PS, Eastern Cooperative Oncology Group performance status; HER2, human epidermal growth factor receptor 2; IHC, immunohistochemistry; FISH, fluorescence in situ hybridization; HR, hormone receptor; ER, estrogen receptor; PR, progesterone receptor; cTNM, clinical tumor\u0026ndash;node\u0026ndash;metastasis; PD-L1, programmed death-ligand 1; CPS, combined positive score.\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eEfficacy and Safety\u003c/h3\u003e\n\u003cp\u003eIn the evaluable population (per protocol set, baseline characteristics summarized in table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e), the pathological complete response (pCR) (ypT0/is, ypN0) rate was 25% (4 of 16), and the objective response rate (ORR) was 56.3% (9/16) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Longitudinal assessments showed that most patients experienced early and sustained tumor shrinkage, with maximum reductions approaching 75% and no progressive disease (PD) was observed in this cohort (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Waterfall plots confirmed substantial reductions in tumor burden across the cohort, and responses were observed irrespective of PD-L1 expression levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE). Additionally, results in the ITT population are provided in Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eA and S1B.\u003c/p\u003e\u003cp\u003eAt surgery, 31.25% of patients achieved residual cancer burden (RCB) 0\u0026ndash;1, while 68.75% were classified as RCB-3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF). However, no significant differences in CD4⁺, CD8⁺, or TIL percentages were observed between RCB 0\u0026ndash;1 and RCB-3 groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eG). Exploratory subgroup analysis showed that the pCR rate was 22.2% in stage Ⅱ and 28.6% in stage Ⅲ disease; 28.6% among LN⁺ and 0% among LN⁻ patients. By PD-L1 expression, pCR was 14.3% for CPS\u0026thinsp;\u0026lt;\u0026thinsp;1 and markedly higher (75.0%) for CPS\u0026thinsp;\u0026ge;\u0026thinsp;10. By ER status, pCR was 50.0% in ER⁺\u0026lt;10% and 21.4% in ER⁺\u0026ge;10%; notably, patients with ER⁺\u0026ge;10% and PD-L1 CPS\u0026thinsp;\u0026ge;\u0026thinsp;10 achieved a pCR rate of 66.7%. For HER2 subgroups, pCR was 37.5% in HER2 2+ / FISH\u0026thinsp;\u0026minus;\u0026thinsp;and 12.5% in HER2 1\u0026thinsp;+\u0026thinsp;tumors (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003eC).\u003c/p\u003e\u003cp\u003eSafety outcomes were assessed in the ITT population (N\u0026thinsp;=\u0026thinsp;20), all (100%) experienced\u0026thinsp;\u0026ge;\u0026thinsp;1 treatment-emergent adverse event (TEAE) and 5 (25%) had grade\u0026thinsp;\u0026ge;\u0026thinsp;3 TEAEs. One patient (5%) had treatment delay/discontinuation due to an AE. Hematologic events were mostly low grade: neutropenia 4 (20%), leukopenia 3 (15%), and anemia 3 (15%) (all grade 1\u0026ndash;2). Common laboratory/Metabolic abnormalities included ALT increase 12 (60%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]), AST increase 10 (50%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]), hypertriglyceridemia 10 (50%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]), and hyperglycemia 6 (30%). Frequent gastrointestinal/dermatologic events were constipation 12 (60%), nausea 11 (55%), rash 11 (55%), pruritus 14 (70%), diarrhea 4 (20%), and vomiting 3 (15%). Other TEAEs included urinary tract infection 7 (35%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]), fatigue 8 (40%), pain 8 (40%), anorexia 5 (25%), paresthesia 8 (40%), and abnormal urinalysis 5 (25%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]).(Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eAdverse events considered ADC-related were frequent, led by alopecia 18 (90%), with paresthesia 8 (40%), insomnia 5 (25%), and allergic dermatitis 1 (5%). Prespecified immune-related AEs included hypothyroidism 4 (20%), hyperthyroidism 3 (15%; grade\u0026thinsp;\u0026ge;\u0026thinsp;3, 1 [5%]), immune-mediated myocarditis 1 (5%; grade 3), urticaria 1 (5%), and proteinuria 1 (5%).\u003c/p\u003e\u003cp\u003eOverall, the regimen was associated primarily with low-grade toxicities; high-grade events were infrequent and encompassed laboratory abnormalities and isolated immune-mediated myocarditis.\u003c/p\u003e\n\u003ch3\u003eBaseline Correlates of Pathologic Response\u003c/h3\u003e\n\u003cp\u003eTo explore baseline determinants of response, we compared pre-treatment profiles between patients who achieved pCR and those who did not using Olink proteomics, multiplex immunofluorescence (mIF), immunohistochemistry (IHC), and RNA sequencing.\u003c/p\u003e\u003cp\u003eProteomic analysis identified seven proteins (IL13, CSF-1, uPA, TNFRSF9, β-NGF, CCL19, and CCL11) that were significantly elevated in the pCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u0026ndash;C, Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e). KEGG analysis of these proteins suggested enrichment of immune- and signaling-related pathways, such as cytokine\u0026ndash;cytokine receptor interaction, IL-17 signaling and NF-κB signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eImmune profiling further demonstrated distinct patterns between groups. Non-pCR tumors showed greater M2 macrophage infiltration and higher M2/M1 ratios, while pCR tumors contained more abundant tumor-infiltrating lymphocytes (TILs) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE\u0026ndash;F). Other immune-cell differences were observed but did not reach significance, likely reflecting the limited sample size (Fig. \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eRNA sequencing revealed broad transcriptional divergence at baseline, with differentially expressed genes in the pCR group mapping to KEGG pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eG\u0026ndash;I, Fig. S4). These findings highlight potential immune and signaling mechanisms that may contribute to therapeutic sensitivity.\u003c/p\u003e\n\u003ch3\u003eDerivation and Performance of a Baseline Predictive Score\u003c/h3\u003e\n\u003cp\u003eTo identify underlying baseline correlates of response, we integrated proteomic, immune-cell, and transcriptomic features. Immune composition inferred by xCell showed distinct patterns between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), with pCR tumors enriched for CD4⁺ naive T cells and CD8⁺ effector memory T cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u0026ndash;C). GSEA demonstrated preferential activation of chemokine and TNF/NF-κB signaling in pCR tumors (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Proteomic and transcriptomic analyses both revealed concordant pathway trends, with significance predominantly observed in the proteome, particularly in cytokine\u0026ndash;cytokine receptor interaction, IL-17, NF-κB, and chemokine signaling pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThen, we mapped the previous seven pCR-enriched proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) to cell types using reference single-cell datasets and four proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e\u0026mdash;CCL19, uPA, TNFRSF9, and CSF-1\u0026mdash;were prioritized based on their immune relevance and receptor\u0026ndash;ligand connectivity, providing a focused panel of candidate biomarkers for further analysis.\u003c/p\u003e\u003cp\u003eTo integrate baseline protein, immune-cell, and pathway features, we performed multivariable logistic regression using the four prioritized proteins together with identified immune-cell subsets and pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG). In multivariable logistic regression, only CCL19 and the M2/M1 ratio remained independent predictors, yielding a two-variable model with an AUC of 0.89 (95% CI, 0.70\u0026ndash;1.00) and good calibration (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eH\u0026ndash;I). On this basis, we derived a Baseline Response Predictive score (BRPscore) incorporating these two factors for patient stratification (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eJ).\u003c/p\u003e\u003cp\u003eUsing the pre-specified BRPscore threshold, patients were stratified into BRPscore-High and -Low groups. All pCR cases (3/3, 100%) were observed in the BRPscore-High group, whereas the most of non-pCR patients were observed in BRPscore-Low group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eK\u0026ndash;L). As a benchmark, we applied two commonly used PD-L1 thresholds (1 and 10). At the 1 cut-off, there remained substantial overlap between pCR and non-pCR, with limited discriminatory ability (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eM). At the 10 cut-off, separation improved in non-PCR group, still with 75% (3/4) of pCR classified into the PD-L1\u0026ndash;High group, this performance was still inferior to the BRPscore (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eN). To further test model validity, we included one patient with progressive disease (PD) who had available data (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eO). This patient exhibited much lower BRPscore than non-pCR cases, consistent with model predictions.\u003c/p\u003e\u003cp\u003eAlthough limited by small numbers, this observation supports the discriminatory capacity of the BRPscore.\u003c/p\u003e\n\u003ch3\u003eRapid Phase Molecular Dynamics\u003c/h3\u003e\n\u003cp\u003eTo further explore the biological basis of treatment response, we first compared proteomic at cycle 3 day 1 (C3D1) with baseline to capture the rapid response phase. In the pCR group, 10 proteins increased and 2 decreased, whereas 33 proteins increased in the non-pCR group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u0026ndash;C). Venn analysis of these proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD): 5 proteins uniquely changed in pCR (IL-22RA1, 4E-BP1, CD6, MCP-1, MCP-3), 26 uniquely changed in non-pCR, and 7 shared by both groups. Among the non-pCR\u0026ndash;specific proteins, 7 proteins (Beta-NGF, IL-10RB, IL-17C, IL-4, NT-3, OPG, and uPA) exhibited the opposite direction of change in pCR. Within the shared set of 7 proteins, only SLAMF1 changed in opposite directions between pCR and non-pCR. In total, 13 proteins were identified as potential signatures, including 5 uniquely altered in pCR, 7 in non-pCR, and 1 shared protein with divergent changes between groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAmong the 13 proteins, MCP-1 and IL4 are shown as examples (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE-F). KEGG analysis of those 13 proteins suggested enrichment patterns in cytokine/cytokine-receptor interaction, PI3K\u0026ndash;AKT, and NF-κB/TNF-related signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG).\u003c/p\u003e\u003cp\u003eRNA-seq at C3D1 identified 697 differentially expressed transcripts in pCR tumors (503 up, 194 down) and 484 in non-pCR tumors (244 up, 240 down) relative to baseline (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH\u0026ndash;J). Venn mapping of C3D1 versus pre-NAT DEGs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eK) defined four analytic subsets: (i) 481 genes uniquely upregulated in pCR; (ii) 187 genes uniquely downregulated in pCR; (iii) 5 genes upregulated in pCR but downregulated in non-pCR; and (iv) 3 genes showing the inverse pattern (up in non-pCR and down in pCR). These four subsets (481, 187, 5, and 3 genes) were carried forward for immune-related pathway enrichment and compared with the proteomic profiles (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eL\u0026ndash;M).\u003c/p\u003e\u003cp\u003exCell deconvolution highlighted B-cell compartments as the main on-treatment signal (C3D1 \u003cem\u003ev.s.\u003c/em\u003e pre-NAT) (Fig. S5). In the non-pCR group, total B cells were significantly reduced at C3D1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.043) with non-significant downward trends in na\u0026iuml;ve and memory subsets. In the pCR group, all B-cell subsets showed non-significant upward trends, likely limited by sample size. Next, we mapped the previous potential signatures from Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD to cell types using public single-cell references. Ultimately, only 6 candidates (4E-BP1, CD6, MCP-1, IL-10RB, uPA and SLAMF1) aligned with B-cell lineages (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eN), with their cross-modal correlations illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eO.\u003c/p\u003e\u003cp\u003eProteomic changes were less pronounced at a later timepoint (post-NAT \u003cem\u003ev.s.\u003c/em\u003e C5D1; Fig. S6), supporting C3D1 as a critical window of early immune and signaling adaptation during which therapeutic modulation may influence efficacy.\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eLongitudinal Proteomic Programs and Candidate Biomarkers\u003c/h2\u003e\u003cp\u003eFinally, we investigated sequential proteomic and transcriptomic changes across all treatment stages. At the protein level, time-series analysis identified 13 candidates showing sustained increases across treatment stages (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), consistent with STEM-type modeling. Integration with repeated-measures ANOVA confirmed a subset of 11 proteins with significant or trend-level phase\u0026ndash;response interactions (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB). KEGG enrichment of these 11 dynamic proteins pointed to immune- and inflammation-related pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Representative trajectories for CD40 and MCP-1 are shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD\u0026ndash;E). Parallel RNA-seq demonstrated stage-dependent transcriptional changes in the same directions (Fig. S7).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAt the post-NAT stage, volcano and heatmap analyses identified seven proteins\u0026mdash;TNFRSF9, IL15RA, CD40, MCP-1, CST5, NT-3, and 4E-BP1\u0026mdash;with overall higher levels in pCR tumors compared with non-pCR (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eF\u0026ndash;G). KEGG analysis based on the seven proteins are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eH. Integrated KEGG pathways of proteins identified from different groups (rapid-response, full-course and outcome) are also shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eI.\u003c/p\u003e\u003cp\u003eA three-way Venn diagram (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eJ) identified MCP-1 as the only protein shared across these three groups. TNFRSF9, IL-15RA, IL-18R1, and CD40 shared across full-course and outcome analyses. We then mapped these five proteins to immune pathways to visualize regulatory linkages and inter-protein relationships, revealing convergent functional connectivity via distinct pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eK). Further protein\u0026ndash;protein interaction (PPI) analysis placed the five key proteins within two immune modules\u0026mdash;the TNF/TNFR co-stimulatory axis (TNFSF9\u0026ndash;TNFRSF9, CD40) and the common γ-chain cytokine axis (IL-2/IL-15 with IL2RA/IL2RB/IL15RA). Notably, MCP-1 occupied a central, highly connected position, linking nodes across both modules and suggesting a bridging role between chemokine recruitment and co-stimulatory/cytokine signaling (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eL).\u003c/p\u003e\u003cp\u003eIn addition, in non-pCR cases, mIF comparing pre-NAT and post-NAT showed a broad downward shift across multiple immune lineages, whereas CD8⁺ T-cell density exhibited a non-significant upward trend (Fig. S8A and S8B). M2/M1 ratio trended lower post-treatment, however, PD-1\u003csup\u003e+\u003c/sup\u003e CD8\u003csup\u003e+\u003c/sup\u003eT cells increased significantly, indicating greater fuctional exhaustion despite CD8 T cell abundance (Fig. S8A).\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003e In this single-arm, single-center exploratory trial, we evaluated RC48 plus penpulimab as neoadjuvant therapy for patients with early or locally advanced, ER-positive/HER2-low breast cancer. Among 16 evaluable patients, a pCR rate of 25% was achieved with a manageable safety profile, representing a clinically meaningful improvement compared with historical data. Prior studies have consistently shown that HER2-low tumors, particularly HR-positive subtypes, are less responsive to standard chemotherapy with reported pCR rates typically below 10%\u003csup\u003e22\u003c/sup\u003e. Thus, the pCR rate observed in our study suggests a promising therapeutic signal in this difficult-to-treat population.\u003c/p\u003e\u003cp\u003eWhile T-DXd has demonstrated substantial activity in metastatic HER2-low disease\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, neoadjuvant studies such as TRIO-US B-12 TALENT reported modest efficacy, with pCR rates below 6%. These findings highlight that ADCs alone may not be sufficient in the curative-intent setting. By contrast, our study introduces a chemotherapy-sparing ADC\u0026ndash;ICI combination strategy, achieving a numerically higher pCR rate than observed with ADC monotherapy. This concept is further supported by large phase III immunotherapy trials such as KEYNOTE-756\u003csup\u003e23\u003c/sup\u003e and CheckMate 7FL\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, which suggest potential benefit of immune checkpoint inhibitors in ER-positive/HER2-negative breast cancer, though efficacy in Asian subgroups has been less pronounced. The 25% pCR rate in our cohort, achieved without conventional cytotoxic chemotherapy, provides early validation for a \u0026ldquo;de-chemotherapy\u0026rdquo; approach in HER2-low disease. If confirmed, this strategy could offer a novel therapeutic option with improved tolerability.\u003c/p\u003e\u003cp\u003eThe safety profile of the combination was consistent with the known toxicities of each agent, with no unexpected adverse events or treatment-related deaths. Common events included pruritus, liver enzyme elevations, and constipation, while grade\u0026thinsp;\u0026ge;\u0026thinsp;3 toxicities were infrequent. These results support the feasibility of integrating this regimen into the neoadjuvant setting, in contrast to some ICI-based regimens where treatment-related deaths have been reported.\u003c/p\u003e\u003cp\u003eBeyond clinical endpoints, this study also provides important translational insights. At baseline, multi-omic integration enabled the development of a Baseline Response Predictive (BRP) model that combined CCL19 and the M2/M1 macrophage ratio, achieving an AUC of 0.89. Importantly, all patients who achieved pCR were correctly classified as BRP-high, suggesting this model may serve as a powerful stratification tool prior to therapy. This finding emphasizes that pre-treatment immune contexture can inform patient selection and guide clinical decision-making in HER2-low disease.\u003c/p\u003e\u003cp\u003eDynamic molecular profiling revealed that the most pronounced biological divergence occurred at the rapid-response phase (C3D1). During this window, early immune signaling and cytokine modulation were observed, with MCP-1 emerging as a key marker. Notably, MCP-1 was consistently identified across baseline, early on-treatment, and longitudinal analyses, positioning it as a central biomarker that bridges chemokine recruitment with co-stimulatory and cytokine signaling pathways. The persistence of MCP-1 across all analytic layers underscores its potential as both a pharmacodynamic marker and a therapeutic target.\u003c/p\u003e\u003cp\u003eTaken together, these results suggest a two-step framework for future trial design: (i) baseline stratification using the BRPscore to identify patients most likely to benefit; and (ii) dynamic monitoring of MCP-1 and other rapid-response markers (e.g., TNFRSF9, IL-15RA, CD40) to refine early treatment adaptation. This integrative approach could improve trial efficiency, enable biomarker-driven patient selection, and ultimately accelerate the development of chemotherapy-sparing regimens.\u003c/p\u003e\u003cp\u003eThis study has limitations. The small sample size and single-arm design preclude definitive conclusions, and comparisons with historical controls may introduce bias. Only short-term outcomes such as pCR were available, whereas long-term survival endpoints (EFS, OS) remain immature. The exploratory BRP scoring system also requires external validation in larger cohorts, and heterogeneity in HR status and tumor burden should be further addressed. The BRPscore and MCP-1 findings, while compelling, require external validation in larger, independent cohorts and functional studies to confirm mechanistic relevance.\u003c/p\u003e\u003cp\u003eIn summary, this study demonstrates the feasibility and potential efficacy of a chemotherapy-sparing ADC\u0026ndash;ICI regimen in HER2-low early breast cancer. By integrating a baseline predictive model (BRPscore) with dynamic on-treatment biomarkers such as MCP-1, we provide a proof-of-concept that biomarker-driven strategies can inform patient selection and adaptive monitoring. Future randomized studies with larger populations and mature survival follow-up will be essential to confirm these promising signals and to define the role of this combination in clinical practice.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eStudy Design and Patient Population\u003c/h2\u003e\u003cp\u003eThis was a single-arm, single-center exploratory clinical trial \u003cb\u003e(ClinicalTrials.gov identifier:NCT05726175\u003c/b\u003e) evaluating RC48 in combination with penpulimab as neoadjuvant therapy for patients with HER2-low early or locally advanced breast cancer (). The protocol was approved by the Ethics Committee of West China Hospital \u003cb\u003e(approval number [2022 (1990)]\u003c/b\u003e and conducted in accordance with Good Clinical Practice and the Declaration of Helsinki. All patients provided written informed consent before enrollment (August 2023 to August 2024).\u003c/p\u003e\u003cp\u003eKey eligibility criteria included women aged 18\u0026ndash;75 years with histologically confirmed invasive breast cancer, ECOG performance status of 0\u0026ndash;1, and clinical stage cT1N1\u0026ndash;2 or cT2\u0026ndash;4Nx with HER2 IHC 1\u0026thinsp;+\u0026thinsp;or 2+/FISH\u0026minus;. Major exclusion criteria were inflammatory, metastatic, or bilateral breast cancer; prior antitumor therapy within 12 months; clinically significant cardiac disease; prior exposure to PD-1/PD-L1 or CTLA-4 inhibitors; and a history of neurologic or psychiatric disorders, including seizure, dementia, or substance abuse.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\u003ch2\u003eTreatment and Study Schema\u003c/h2\u003e\u003cp\u003ePatients received RC48 2.0 mg/kg intravenously plus penpulimab 200 mg intravenously every 3 weeks as neoadjuvant therapy, for up to 6 cycles, followed by surgery. Serial biospecimens were collected at prespecified time points: tumor biopsy and blood before treatment (pre-NAT), peripheral blood at cycle 3 day 1 (C3D1) and cycle 5 day 1 (C5D1), and tumor tissue and blood at surgery (post-NAT). Paired tumor and blood samples were used for standard pathology and exploratory translational analyses, including Olink proteomics, immunohistochemistry (IHC), multiplex immunofluorescence (mIF), and RNA sequencing (RNA-seq).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003eEndpoints and assessments\u003c/h2\u003e\u003cp\u003eThe primary endpoint was pathologic complete response (pCR; ypT0/is ypN0) in the intention-to-treat population, defined as no residual invasive cancer in breast or regional lymph nodes post-neoadjuvant therapy, allowing ductal carcinoma in situ.\u003c/p\u003e\u003cp\u003eSecondary endpoints included efficacy outcomes: breast pathologic complete response (bpCR; ypT0/is), residual cancer burden (RCB) score, objective response rate (ORR), disease control rate (DCR), invasive disease-free survival (iDFS), and event-free survival (EFS). Safety was evaluated via ECOG performance status, vital signs, physical examinations, laboratory tests, adverse events (AEs), serious AEs, and quality-of-life assessments, per NCI-CTCAE v5.0.\u003c/p\u003e\u003cp\u003eExploratory endpoints assessed biomarkers in blood and tumor tissue, including PD-L1 expression, tumor-infiltrating lymphocytes, circulating tumor cells, and immune cell subsets (e.g., CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e cells).\u003c/p\u003e\u003cp\u003eClinical response was evaluated through physical examination and breast imaging (ultrasound and enhanced MRI) at baseline, every two cycles during neoadjuvant therapy, and pre-surgery. Systemic staging included chest, abdominal, pelvic, and additional imaging (e.g., cranial, bone scans) per institutional protocols, maintaining consistent imaging conditions (e.g., contrast use, scan thickness). Pathologic response was assessed in surgical specimens, with total pathologic complete response (pCR; ypT0/is ypN0) defined as no residual invasive cancer in breast or regional lymph nodes, allowing ductal carcinoma in situ. Breast pathologic complete response (bpCR; ypT0/is) and residual cancer burden (RCB) were evaluated, with RCB scores calculated using validated parameters (tumor size, cellularity, lymph node involvement) via the MD Anderson RCB calculator.\u003c/p\u003e\u003cp\u003eSafety was monitored in all patients receiving at least one dose of study treatment. Assessments included ECOG performance status, vital signs, physical examinations, 12-lead electrocardiography, echocardiography (if clinically indicated), and laboratory tests (hematology, serum biochemistry, thyroid function, urinalysis, stool analysis, coagulation). Adverse events (AEs) and serious AEs were graded per NCI-CTCAE v5.0, with the highest grade per event recorded.\u003c/p\u003e\u003cp\u003eExploratory biomarker analyses were conducted on tumor and peripheral blood samples collected pretreatment, during treatment (cycles 2, 4, 6), and post-surgery. Biomarkers included, but were not limited to, PD-L1 expression, tumor-infiltrating lymphocytes, circulating tumor cells, and immune cell subsets (e.g., CD4\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e T cells), assessed via immunohistochemistry and other molecular techniques.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003eMultiplex Immunofluorescence (mIF)\u003c/h2\u003e\u003cp\u003emIF was performed using a predefined antibody\u0026ndash;fluorophore panel by Shanghai KR Pharmtech (Shanghai, China). FFPE tissue sections (4 \u0026micro;m) were deparaffinized, rehydrated, and subjected to antigen retrieval with EDTA buffer (pH 9.0) or citrate buffer (pH 6.0). Immunostaining was performed on a Krast 600 platform (Kuoran Biotech, Shanghai, China), following automated standard protocols.\u003c/p\u003e\u003cp\u003eEach round of staining involved incubation with primary antibodies (e.g. CD8, CD68, CD163, FOXP3, CD56, PANCK) and tyramide signal amplification (TSA)-based fluorophores (Opal 480, 520, 570, 620, 690, TSA-DIG 780). After each staining cycle, signal removal and reactivation were automatically performed to prevent cross-reactivity. Nuclei were counterstained with DAPI (CST, catalog # 4083S, 1:1000 ). The primary antibodies and their respective concentrations and sources are detailed in Table \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eSlides were scanned on a KR-HT5 multispectral scanner (KR Pharmtech) to generate qptiff images under controlled conditions (10\u0026ndash;30\u0026deg;C, \u0026le;\u0026thinsp;70% humidity). Scanning was performed using the Opal Polaris 5\u0026ndash;7 color protocol with optimized exposure times (0\u0026ndash;100 ms).\u003c/p\u003e\u003cp\u003eImages were processed using HT5 View for spectral unmixing and inForm software (Akoya Biosciences) for tissue segmentation and marker quantification. ROIs were selected based on staining patterns or tumor regions, and positivity thresholds were defined using automatic scoring and visual verification.\u003c/p\u003e\u003cp\u003eAll mIF assays included positive and negative controls, and slides were stored in the dark at 4\u0026deg;C. Scanning occurred within two weeks to maintain signal integrity.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003eOlink Proteomics Methodology\u003c/h2\u003e\u003cp\u003eProteins were quantified using the Olink\u0026reg; Target 96 Inflammation panel (Table S4, Olink Proteomics AB, Uppsala, Sweden) following the manufacturer's protocol. The Proximity Extension Assay (PEA) technology, as previously described (Assarsson et al., 2014), enables the simultaneous analysis of 92 analytes using just 1 \u0026micro;L of each sample. Briefly, pairs of oligonucleotide-labeled antibody probes bind to their target proteins, and when the probes are in close proximity, the oligonucleotides hybridize. The addition of DNA polymerase initiates proximity-dependent DNA polymerization, generating a unique PCR target sequence. This DNA sequence is then detected and quantified using a microfluidic real-time PCR instrument (Signature Q100, LC-Bio Technology CO., Ltd., Hangzhou, China).\u003c/p\u003e\u003cp\u003eData were quality-controlled and normalized using an internal extension control and an inter-plate control to account for intra- and inter-run variability. The final results are reported as Normalized Protein eXpression (NPX) values, which are expressed on a log2 scale where higher NPX values correspond to increased protein expression.\u003c/p\u003e\u003cp\u003eAll assay validation data, including detection limits and intra- and inter-assay precision, are available on the manufacturer's website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e\u003ca href=\"http://www.olink.com\" target=\"_blank\"\u003ewww.olink.com\u003c/a\u003e\u003c/span\u003e\u003cspan address=\"http://www.olink.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTo further explore the potential cellular origins of Olink activity molecules that exhibit significant differences across groups, we analyzed the single-cell transcriptomic dataset (GSE176078) comprising 26 primary breast cancer samples from the NCBI database using Seurat. The analysis parameters were kept consistent with those of the original study. The predominant cellular sources of each activity molecule were identified based on the expression patterns of their corresponding encoding genes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003eRNA Sequencing (RNA-seq)\u003c/h2\u003e\u003cp\u003eRNA-seq was performed on the Illumina NovaSeq X Plus platform. Raw reads from all samples were aligned to the human reference genome (Ensembl v112) using HISAT2 (version 2.2.1; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://daehwankimlab.github.io/hisat2/\u003c/span\u003e\u003cspan address=\"https://daehwankimlab.github.io/hisat2/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The mapped reads of each sample were assembled into transcriptomes using StringTie (version 2.1.6; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ccb.jhu.edu/software/stringtie/\u003c/span\u003e\u003cspan address=\"http://ccb.jhu.edu/software/stringtie/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with default parameters. Subsequently, transcriptomes from all samples were merged to generate a comprehensive transcriptome using gffcompare (version 0.9.8; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://ccb.jhu.edu/software/stringtie/gffcompare.shtml\u003c/span\u003e\u003cspan address=\"http://ccb.jhu.edu/software/stringtie/gffcompare.shtml\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eGene expression levels were estimated using StringTie and Ballgown (version 3.3.3; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/release/bioc/html/ballgown.html\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/packages/release/bioc/html/ballgown.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by calculating the FPKM (fragments per kilobase of transcript per million mapped reads) values for each mRNA.\u003c/p\u003e\u003cp\u003eDifferential expression analysis of genes was performed using DESeq2 between two groups, and edgeR (for pairwise comparison) was used for additional validation. The genes with the parameter of P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute fold change\u0026thinsp;\u0026ge;\u0026thinsp;1.5 were considered differentially expressed (DEGs).\u003c/p\u003e\u003cp\u003eThe DEGs were further subjected to functional enrichment analysis using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways. GO enrichment was performed to identify significantly enriched terms in molecular function, cellular component, and biological process categories using hypergeometric testing (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). KEGG pathway analysis was similarly conducted to identify significantly enriched pathways (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003eImmune infiltration analysis was conducted by TIMER, a deconvolution approach built on constrained least-squares regression with cancer-type\u0026ndash;aware signature selection. The method estimates infiltration levels for six major immune lineages (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, dendritic cells) from bulk RNA-seq data \u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. In this study, estimates and figures were generated via TIMER2.0 under its breast-carcinoma, standardized workflow; results from other TIMER2.0 modules (CIBERSORT, quanTIseq, xCell, MCP-counter, EPIC) served as sensitivity analyses to confirm directional consistency.\u003c/p\u003e\u003cp\u003eAll enrichment analyses were performed using OmicStudio tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omicstudio.cn/tool\u003c/span\u003e\u003cspan address=\"https://www.omicstudio.cn/tool\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Visualizations were generated using R (version 4.1.3) and the ggplot2 package (version 3.3.3).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eStatistical Analysis\u003c/h2\u003e\u003cp\u003eThe statistical analysis of this single-arm exploratory trial was primarily descriptive. Continuous variables were summarized using the mean, standard deviation, median, minimum, and maximum values. Categorical and ordinal variables were summarized using counts, percentages, and two-sided 95% confidence intervals calculated by exact binomial methods.\u003c/p\u003e\u003cp\u003eThe primary endpoint was the pathologic complete response (pCR; ypT0/is ypN0) in the intention-to-treat population. Secondary endpoints included objective response rate (ORR), residual cancer burden (RCB), and safety. Exploratory analyses evaluated potential predictive biomarkers using multi-omic data.\u003c/p\u003e\u003cp\u003eExploratory biomarker analyses, including proteomic and transcriptomic profiling, were descriptive and hypothesis-generating. Pathway enrichment was analyzed using hypergeometric testing on KEGG terms, and protein\u0026ndash;protein interaction networks were constructed using STRING.\u003c/p\u003e\u003cp\u003eUnless otherwise specified, all statistical tests were two-sided with a significance level of α\u0026thinsp;=\u0026thinsp;0.05. Statistical analyses were performed using SPSS version 22.0 (IBM Corp) and R version 4.1.0 (R Foundation for Statistical Computing).\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\u003ch2\u003eAuthor contributions\u003c/h2\u003e\u003cp\u003eEvery author involved in the study reviewed and sanctioned the finalized manuscript. Yuting Song, Lei Liu, Aaron Qi Zhang and Xin Xie wrote the clinical trial implementation, overseeing data acquisition, statistical analysis, figure generation, and manuscript drafting. Shibao Li assisted in revising the writing. Collection and assembly of data: Chunying Zhuang, Xiaorong Zhong, Yan Cheng, Dan Zheng. Bing Wei served as the guide for pathological reading. Ping He, Xi Yan, Tinglun Tian, Jie Chen contributed to patient recruitment and therapeutic management. Juanjuan Li assisted in data analysis of the RNA-seq and Olink. Xiaoxiao Liu, Shibao Li and Ting Luo were responsible for the experimental design data validation and manuscript revision. All the authors have read and approved the article. Every author involved in the study reviewed and sanctioned the finalized manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eRemeGen, Ltd (Yantai, China) and Chia Tai Tianqing Pharmaceutical Group Co., Ltd were the study sponsors. We would like to express our appreciation to the participants and their families for their invaluable participation in this study. We are grateful to Shuaitong Chen, Jiali Ren and Siyuan Shen (LC-Bio Technology Co., Ltd.) for his support and assistance in the data analysis. This research was made possible through the support of the following funding sources: Young Talent Program of The Affiliated Hospital of Xuzhou Medical University. Advanced Program of The Affiliated Hospital of Xuzhou Medical University (PYJH2024210). Medical Research Project of Jiangsu Provincial Health Commission (H2023050). National Natural Science Foundation of China (No. 32301278).\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e\u003cp\u003eThe raw data have been deposited in the Genome Sequence Archive for Human (GSA-Human) at the National Genomics Data Center (NGDC) under BioProject accession number PRJCA042227. Datasets generated in this study are available from the corresponding author upon request via email at [email protected], use for commercial purposes is prohibited. All requests will be reviewed by the corresponding author within 2 weeks. Prior to data sharing, a signed data access agreement with the sponsor is required.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eHan B\u003cem\u003e, et al.\u003c/em\u003e Cancer incidence and mortality in China, 2022. \u003cem\u003eJ Natl Cancer Cent\u003c/em\u003e \u003cstrong\u003e4\u003c/strong\u003e, 47-53 (2024).\u003c/li\u003e\n\u003cli\u003eSwain SM, Shastry M, Hamilton E. Targeting HER2-positive breast cancer: advances and future directions. \u003cem\u003eNat Rev Drug Discov\u003c/em\u003e \u003cstrong\u003e22\u003c/strong\u003e, 101-126 (2023).\u003c/li\u003e\n\u003cli\u003eWaks AG, Winer EP. 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\u003cstrong\u003e40\u003c/strong\u003e, 3365-3376 (2022).\u003c/li\u003e\n\u003cli\u003eBardia A\u003cem\u003e, et al.\u003c/em\u003e Sacituzumab Govitecan in Metastatic Triple-Negative Breast Cancer. \u003cem\u003eN Engl J Med\u003c/em\u003e \u003cstrong\u003e384\u003c/strong\u003e, 1529-1541 (2021).\u003c/li\u003e\n\u003cli\u003eHuang L\u003cem\u003e, et al.\u003c/em\u003e A HER2 target antibody drug conjugate combined with anti-PD-(L)1 treatment eliminates hHER2+\u0026thinsp;tumors in hPD-1 transgenic mouse model and contributes immune memory formation. \u003cem\u003eBreast Cancer Res Treat\u003c/em\u003e \u003cstrong\u003e191\u003c/strong\u003e, 51-61 (2022).\u003c/li\u003e\n\u003cli\u003eSchmid P\u003cem\u003e, et al.\u003c/em\u003e Atezolizumab plus nab-paclitaxel as first-line treatment for unresectable, locally advanced or metastatic triple-negative breast cancer (IMpassion130): updated efficacy results from a randomised, double-blind, placebo-controlled, phase 3 trial. 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Neoadjuvant carboplatin in patients with triple-negative and HER2-positive early breast cancer (GeparSixto; GBG 66): a randomised phase 2 trial. \u003cem\u003eLancet Oncol\u003c/em\u003e \u003cstrong\u003e15\u003c/strong\u003e, 747-756 (2014).\u003c/li\u003e\n\u003cli\u003eCardoso F\u003cem\u003e, et al.\u003c/em\u003e Pembrolizumab and chemotherapy in high-risk, early-stage, ER+/HER2- breast cancer: a randomized phase 3 trial. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 442-448 (2025).\u003c/li\u003e\n\u003cli\u003eLoi S\u003cem\u003e, et al.\u003c/em\u003e Neoadjuvant nivolumab and chemotherapy in early estrogen receptor-positive breast cancer: a randomized phase 3 trial. \u003cem\u003eNat Med\u003c/em\u003e \u003cstrong\u003e31\u003c/strong\u003e, 433-441 (2025).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7869437/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7869437/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eHER2-low breast cancer lacks effective targeted options in the curative setting. We evaluated the efficacy, safety, and exploratory biomarker correlates of neoadjuvant disitamab vedotin (HER2-targeted antibody\u0026ndash;drug conjugate) plus penpulimab (PD-1 inhibitor) in stage Ⅱ\u0026ndash;Ⅲ disease (NCT05726175). In a prospective single-arm study, patients with newly diagnosed stage II\u0026ndash;III HER2-low breast cancer (IHC 1\u0026thinsp;+\u0026thinsp;or 2+/FISH\u0026minus;) received disitamab vedotin plus penpulimab every 3 weeks for six cycles before surgery. The primary end point was pathologic complete response (pCR). Secondary end points included objective response rate (ORR) and safety. Exploratory analyses incorporated multi-omic profiling (proteomics, multiplex immunofluorescence, RNA sequencing) and integrative modeling to derive predictive biomarkers. In the per-protocol set, pCR was 25.0% (4/16) and ORR 56.3% (9/16). At surgery, 31.3% achieved residual cancer burden (RCB) 0\u0026ndash;1. Numerically higher pCR rates were seen in PD-L1\u0026ndash;positive versus \u0026ndash;negative tumors (33.3% v 14.3%) and in HER2 IHC 2+/FISH\u0026thinsp;\u0026minus;\u0026thinsp;versus IHC 1\u0026thinsp;+\u0026thinsp;tumors (37.5% v 12.5%). Treatment was generally well tolerated: grade\u0026thinsp;\u0026ge;\u0026thinsp;3 events occurred in 25%, with no treatment-related deaths. Exploratory multi-omics yielded a baseline response prediction model (BRPscore; CCL19\u0026thinsp;+\u0026thinsp;M2/M1 macrophage ratio) that correctly discriminated all pCR cases with strong discriminatory capacity (AUC 0.89), and identified MCP-1 as the most consistent biomarker across timepoints. In conclusion, neoadjuvant disitamab vedotin plus penpulimab produced a 25% pCR with manageable safety in stage II\u0026ndash;III HER2-low breast cancer. The integration of a BRPscore with MCP-1 as a dynamic biomarker provides proof-of-concept for biomarker-driven patient selection and adaptive monitoring, supporting further randomized evaluation of ADC\u0026ndash;ICI combinations.\u003c/p\u003e","manuscriptTitle":"Phase II Exploratory Study of Neoadjuvant Disitamab Vedotin and Penpulimab in HER2-low Stage II-III Breast Cancer (NeoPanDa04)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-01 08:42:59","doi":"10.21203/rs.3.rs-7869437/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5533d80c-3707-4a3b-8b6a-5a7bfb908df7","owner":[],"postedDate":"December 1st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56781004,"name":"Health sciences/Oncology/Cancer/Breast cancer"},{"id":56781005,"name":"Health sciences/Oncology/Cancer/Cancer therapy/Targeted therapies"}],"tags":[],"updatedAt":"2026-01-28T12:13:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-01 08:42:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7869437","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7869437","identity":"rs-7869437","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}