Spatial Mapping of Tamoxifen and its Metabolites in Breast Cancer Tissue via MALDI-MSI: Novel Insights into Endocrine Therapy Response

Spatial Mapping of Tamoxifen and its Metabolites in Breast Cancer Tissue via MALDI-MSI: Novel Insights into Endocrine Therapy Response | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatial Mapping of Tamoxifen and its Metabolites in Breast Cancer Tissue via MALDI-MSI: Novel Insights into Endocrine Therapy Response Chikage Kato, Sae Kitano, Takushi Yamamoto, Ikoi Omatsu, Nagisa Hirotani, and 7 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8321696/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 A fundamental question in tumor pharmacology is how systemically administered drugs reach and distribute within tumor tissues. In this study, we applied matrix-assisted laser desorption/ionization–based mass spectrometry imaging to surgical specimens from 16 patients with hormone receptor–positive, human epidermal growth factor receptor 2-negative breast cancer who received neoadjuvant tamoxifen therapy. Spatial distribution and heterogeneity of tamoxifen, together with its major metabolites N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen, and endoxifen were directly visualized and correlated with histopathological features and therapeutic response. Tamoxifen was consistently detected in cancer regions regardless of histological subtype; however, its intratumoral intensity and spatial uniformity varied, reflecting stromal composition and tissue architecture. Cases with more homogeneous and higher intensity of tamoxifen distribution tended to show decreased Ki-67 expression and histopathological response. Among the metabolites, significant intratumoral accumulation of 4-hydroxy-tamoxifen, and endoxifen —but not N-desmethyl-tamoxifen—was associated with histopathological response. This study provides the first direct demonstration that intratumoral distribution of tamoxifen and its active metabolites in human breast cancer is spatially heterogeneous and linked to therapeutic efficacy. Mass spectrometry imaging-based visualization of drug pharmacokinetics at the molecular and spatial level offers a transformative approach for understanding and predicting treatment response, introducing drug distribution as a new dimension in personalized oncology with broad implications for tumor pharmacology. Personalized Medicine Drug distribution Heterogeneity Mass spectrometry imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Cancer remains one of the leading causes of mortality worldwide, posing a major global health challenge 1 . Among the therapeutic strategies developed to address this challenge, systemic therapy plays a central role in modern oncology. In particular, the primary systemic treatment (PST) has been established as a standard approach that broadens treatment options—by enabling tumor downstaging to facilitate less extensive surgery, or by providing salvage opportunities for otherwise inoperable disease 2 – 7 . Nevertheless, accurately predicting therapeutic responses remain challenging owing to the pronounced biological heterogeneity of tumors 8 , 9 . From a clinicopathological standpoint, numerous biomarkers have been explored to refine treatment selection, including proliferation markers 10 , immune cell infiltration profiles 11 , genomic signatures 12 , and liquid biopsy–based indicators 13 . However, despite these extensive efforts, the predictive performance of such biomarkers remains limited 14 , 15 . At the core of this challenge lies one of the most fundamental yet unresolved questions in tumor pharmacology— do administered drugs actually reach their targets within the tumor, and how are they spatially distributed? Despite the vast number of clinical trials evaluating therapeutic efficacy, our understanding of drug bioavailability and intratumoral pharmacokinetics remains limited. This critical knowledge gap represents a major barrier to elucidating the mechanisms of drug resistance and non-responsiveness in cancer therapy. To date, mass spectrometry imaging (MSI) has been widely applied in cancer research for molecular-level diagnosis and subtype classification 16 – 21 for elucidating intratumoral heterogeneity 17 , 18 , 22 , and for exploring cancer metabolism 23 – 25 . More recently, MSI has attracted increasing attention as a powerful approach to investigate the relationship between intratumoral pharmacokinetics and therapeutic efficacy 26 . Unlike conventional pharmacokinetic assays, MSI enables label-free, high–spatial-resolution visualization of drugs and their metabolites directly on tissue sections 27 . This capability allows spatial mapping of drug concentration gradients, diffusion barriers, and metabolic distributions in direct correspondence with tissue architecture 28 , 29 . By bridging drug administration with pharmacologic effect in both the spatial and molecular dimensions, MSI provides a transformative platform to decipher intratumoral pharmacology. In this study, we applied MSI to surgical specimens from patients with hormone receptor (HR)–positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer who received neoadjuvant endocrine therapy with tamoxifen. By directly visualizing the intratumoral distribution and metabolism of tamoxifen, we demonstrate that MSI can decipher the “black box” of intratumoral pharmacokinetics underlying drug delivery and action in human tumors. Our findings highlight MSI as a powerful translational approach to deepen understanding of drug–tumor interactions and to provide a framework for investigating pharmacologic heterogeneity and resistance mechanisms in breast cancer and potentially beyond. 2. Materials and Methods 2.1. Reagents The primary reagents used in this study were as follows. Tamoxifen (TAM; Cat. #13258) was purchased from Cayman Chemical (Ann Arbor, MI, USA), and letrozole (Cat. #S1235) was purchased from Selleck Chemicals (Houston, TX, USA). For matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), three types of matrix solutions were prepared:α-cyano-4-hydroxycinnamic acid (CHCA; 10 mg/mL in 60% acetonitrile with 0.1% trifluoroacetic acid), 2,5-dihydroxybenzoic acid (DHB; 10 mg/mL in 70% methanol with 0.1% trifluoroacetic acid), and 9-aminoacridine (9-AA; 4 mg/mL in 70% methanol). Each drug was dissolved in methanol or ethanol at a final concentration of 100 µM and mixed with the prepared matrix solution at a 1:1 ratio. A 1 µL aliquot of the mixture was spotted onto an indium–tin oxide (ITO)-coated glass slide and air-dried to prepare standard samples for MALDI-TOFMS analysis. 2.2. Patients and tissue sample collection The study included 16 patients diagnosed with HR–positive, HER2-negative breast cancer (stage I–III) at University Hospital, Kyoto Prefectural University of Medicine, who received oral TAM ( Nolvadex® , AstraZeneca, UK) at a dose of 20 mg once daily for at least four weeks before undergoing curative surgery between January 2024 and May 2025. Breast cancer diagnosis and assessment of therapeutic response were confirmed by histopathological evaluation independently conducted by two experienced pathologists. During surgery, a portion of the resected tumor tissue was excised (approximately 10mm × 10mm), rapidly frozen on dry ice, and stored as fresh-frozen specimens for subsequent analyses. 2.3. Sample preparation and histological staining Fresh-frozen tumor specimens were sectioned at a thickness of 10 µm using a cryostat (CM1950; Leica Biosystems, Wetzlar, Germany) and mounted onto ITO-coated glass slides (Matsunami Glass Ind., Osaka, Japan) at − 20°C for MALDI-TOFMS analysis. A uniform coating of CHCA matrix was applied to each section at an approximate thickness of 0.7 µm using the iMLayer device (Shimadzu, Kyoto, Japan). For Cases 1 and 2, serial sections from the same specimen were subjected to hematoxylin and eosin (H&E) staining. For Cases 3–16, to enable more accurate visualization of intratumoral drug distribution, H&E staining was performed on the same sections after MALDI-TOFMS analysis, following matrix removal. Overlay images combining H&E-stained histology and MSI drug-distribution maps were generated using IMAGEREVEAL MS software (Shimadzu, Kyoto, Japan). 2.4. Mass spectrometry imaging and data analysis Mass spectrometry imaging was performed using an iMScope QT instrument (Shimadzu, Kyoto, Japan). The acquisition parameters were as follows: a mass-to-charge ( m/z ) range of 450–900, positive-ion mode, spatial resolution 10 µm, laser repetition rate 1000 Hz, laser diameter approximately 10 µm, and laser power 40 (arbitrary units). Images corresponding to each m/z peak were generated using IMAGEREVEAL MS software. The spatial distributions of TAM and its metabolites—N-desmethyltamoxifen (NDMTAM), 4-hydroxytamoxifen (4OHTAM), and endoxifen (ENX)—were visualized and analyzed using IMAGEREVEAL MS (Shimadzu Corporation, Kyoto, Japan). The m/z values corresponding to each compound were as follows: TAM ([M + H]⁺ = 372.23), NDMTAM ([M + H]⁺ = 358.22), 4OHTAM ([M + H]⁺ = 388.23), and ENX ([M + H]⁺ = 374.21). All spectra were normalized by total ion current (TIC). Referring to the H&E-stained sections, ten regions of interest (ROIs; each measuring 5 × 5 pixels, i.e., 25 pixels per ROI) were randomly selected within both cancer and non-cancer regions. The mean signal intensity for each ion species within each ROI was extracted. Data visualization and statistical analyses were performed using GraphPad Prism (version 10.5.0 ; Boston, MA, USA). Differences in TAM and metabolite accumulation between cancer and non-cancer regions (250 pixels per region) were assessed using the Mann–Whitney U test. Statistical significance was set at P < 0.01 for intensity comparisons. The coefficient of variation (CV = [standard deviation (SD)/mean] × 100%) for TAM intensity across ten ROIs in the cancer regions was calculated as an indicator of heterogeneity. Associations between the presence of metabolite accumulation and pathological treatment response were analyzed using Fisher’s exact test, with P < 0.05 considered statistically significant. 2.5. Ethical approval All study procedures were approved by the Ethics Committee of Kyoto Prefectural University of Medicine (approval number: ERB-C-400-7, ERB-C-2804-1). Written informed consent or opt-out consent was obtained from all participants prior to inclusion. Patients were fully informed that non-participation would not result in any disadvantage, that withdrawal from the study was permitted at any time, and that personal information would be protected to ensure anonymity. All experiments were conducted in accordance with the approved institutional guidelines and ethical standards. 3. Result 3.1. Ionization characteristics of reagents To assess the ionization efficiency of the agents used in endocrine therapy for breast cancer, we evaluated tamoxifen (TAM) and letrozole as representative drugs. Each compound was analyzed using three commonly employed matrices—α-cyano-4-hydroxycinnamic acid (CHCA), 2,5-dihydroxybenzoic acid (DHB), and 9-aminoacridine (9-AA)—to determine their ionization behavior and imaging feasibility in MALDI-based mass spectrometry (Fig. 1 ). For TAM, a distinct peak at m/z 372.23 was observed when CHCA was used, whereas no corresponding signal was detected with the matrix alone (Fig. 1 A). In contrast, the peak intensity was markedly lower (approximately 1/50) with DHB compared to CHCA, and no interfering matrix peaks were detected around m/z 372.23 (Fig. 1 B). No TAM-derived peaks were observed when using 9-AA as the matrix (Fig. 1 C). Letrozole showed only a weak peak at m/z 286.11 with CHCA (Fig. 1 D) and was undetectable with DHB or 9-AA (Fig. 1 E and F). Thus, while both agents were ionizable with CHCA, the ionization efficiency of letrozole was considerably lower than that of TAM, indicating that detection in tissue sections would be challenging. Consequently, TAM was selected for subsequent human tissue analyses. 3.2. Intratumoral distribution of tamoxifen in human breast cancer tissues We next analyzed surgical specimens from breast cancer patients who had received oral TAM as neoadjuvant endocrine therapy, using the iMScope QT to visualize the intratumoral distribution of TAM following breast-conserving surgery or total mastectomy. Clinical characteristics of all 16 patients are summarized in Table 1. The analyzed tumors consisted of 13 cases of invasive breast carcinoma of no special type (IBC-NST) and three cases of mucinous carcinoma as a special histological subtype. The median duration of preoperative TAM administration was 65 days (range, 31–89 days). All patients were confirmed to have estrogen receptor (ER)–positive breast cancer before treatment, and postoperative pathological diagnoses remained unchanged. Progesterone receptor (PgR) expression was also positive in all cases before treatment; after TAM administration, decrease of ≥ 20% in PgR expression was observed in eight cases. Ki-67 expression decreased by more than 5% in nine cases. To confirm whether orally administered TAM could be detected within human breast cancer tissues, two representative cases of IBC-NST were analyzed: Case 1, with low-to-moderate stromal content, and Case 2, with abundant stromal content. A clear peak corresponding to the m/z of TAM was detected in both analyzed cases, demonstrating that orally administered TAM was absorbed and distributed within the tumor tissue, with its spatial localization and signal intensity differences readily visualized (Fig. 2 A). To evaluate this distribution, the spatial pattern of cancer cells identified on hematoxylin and eosin (H&E)–stained serial sections was compared with the mass spectrometry imaging (MSI) map of TAM. In Case 1, TAM exhibited a cord-like distribution corresponding to the characteristic trabecular arrangement of cancer cells, whereas in Case 2, where H&E staining revealed abundant stromal components centrally and scattered cancer cells on both sides, TAM intensity was weaker in the center and markedly stronger in the bilateral tumor regions (Fig. 2 A). Because serial sections were used in both cases, slight positional discrepancies of several tens of micrometers between sections made precise comparison of drug distribution and cancer cell localization difficult. Such intersectional misalignment hampers accurate assessment of drug localization relative to cancer cells or stromal components. To overcome this limitation, the matrix was removed from the same MALDI-analyzed section after MS acquisition, followed by H&E staining and image co-registration of the MSI and H&E data (Fig. 2 B). The matrix was removed using a stepwise dilution method with 70% ethanol to preserve cellular morphology. This approach enabled more accurate determination of TAM signal intensity within cancer and non-cancer regions (Fig. 2 C). 3.3. TAM distribution across histological subtypes We next evaluated the spatial distribution and heterogeneity of TAM within cancer and non-cancer regions across three histological subtypes: IBC-NST with abundant stroma, IBC-NST with scant stroma, and mucinous carcinoma. Intratumoral heterogeneity of drug penetration was quantified as the coefficient of variation (CV) of TAM signal intensity. For each distribution parameter, the relationship between TAM localization and indicators of therapeutic response was assessed based on histopathological and immunohistochemical alterations, including changes in Ki-67 expression and tissue morphology. 3.3.1. IBC-NST with abundant stromal component (scirrhous appearance) Among the analyzed IBC-NST cases, six tumors were pathologically classified as having a scirrhous appearance, characterized by a low tumor-to-stroma ratio and infiltrative growth (Fig. 3 ). In all six cases, TAM was predominantly distributed within the scattered cancer cell regions, with significantly higher signal intensities compared with the surrounding stroma-rich, non-cancer regions. The median CV, representing intratumoral heterogeneity of TAM distribution, was 104.02 (range, 80.0–151.3). Cases with lower CV values indicating more uniform intratumoral TAM distribution (Cases 6, 8, 9, and 12) corresponded to those showing decreases in the proliferation marker Ki-67 (Fig. 3 A, B, C, E, and G). Among them, only Case 9, which exhibited the highest TAM intensity and the lowest CV, demonstrated distinct histopathological response (Fig. 3 G). 3.3.2. IBC-NST with scant stromal component Next, we analyzed five cases characterized by a relatively high tumor-to-stroma ratio with sparse stromal components (Fig. 4 ). In all cases, the TAM signal intensity was significantly higher in cancer regions than in non-cancer regions, similar to those with abundant stromal components. To further assess the relationship between intratumoral heterogeneity and therapeutic response, these cases were subdivided into two morphological patterns: two with a solid pattern , defined by poorly developed glandular structures and densely packed tumor nests (Fig. 4 A and B), and three with a tubule-forming pattern , showing more distinct glandular architecture with moderate cellular and structural atypia (Fig. 4 C, D, and E). In the solid pattern, Case 16 exhibited relatively uniform TAM distribution (low CV) and decrease in Ki-67 expression; however, the mean TAM intensity was comparatively low, and no histopathological responses were observed. In contrast, within the tubule-forming pattern, Cases 7 and 15—both showing lower CV values—demonstrated higher mean TAM intensities accompanied by decrease in Ki-67 and histopathological responses (Fig. 4 F). 3.3.3. Special type (mucinous carcinoma) Among the special histological subtypes, three cases of mucinous carcinoma were analyzed, comprising two cases of type A (Fig. 5 A and B) and one mixed-type case (type B; Fig. 5 C). Similar to the 13 cases of IBC-NST, TAM accumulation was significantly higher in cancer regions compared with non-cancer regions. Notably, in type A tumors, TAM distribution was markedly low within the abundant mucin pools (Fig. 5 A and B). In contrast, the mixed-type (type B) tumor, which contained less mucin, exhibited a higher mean TAM intensity with a lower CV, accompanied by decrease in Ki-67 expression and distinct histopathological response (Fig. 5 C and D). Across all histological subtypes—including IBC-NST with abundant or sparse stroma and mucinous carcinoma—TAM consistently accumulated preferentially within cancer regions. However, the intensity and spatial heterogeneity of TAM distribution varied among subtypes, potentially reflecting differences in tumor cell density and stromal architecture. Cases exhibiting more uniform and higher TAM intensity (i.e., lower CV and higher mean signal) tended to show decreases in Ki-67 expression and histopathological responses suggestive of therapeutic response, indicating that both the efficiency and uniformity of intratumoral drug penetration may contribute to treatment efficacy. Given that TAM is metabolized in vivo to potent active metabolites such as 4-hydroxytamoxifen and endoxifen, we next investigated the spatial distribution of these metabolites and their association with therapeutic outcomes. 3.4. Distribution characteristics of tamoxifen metabolites TAM is metabolized by CYP3A4/5 to N-desmethyltamoxifen (NDMTAM) and by CYP2D6 to 4-hydroxytamoxifen (4OHTAM). Through sequential metabolism by both CYP pathways, the active metabolite endoxifen (ENX) is generated. Both 4OHTAM and ENX have been reported to exhibit 10- to 100-fold stronger anti-estrogenic activity at the ER compared with the parent compound 30 – 32 . Because TAM itself possesses relatively weak ER binding and antagonistic properties and is considered to act largely as a “prodrug,” we evaluated the intratumoral distribution of its major metabolites in addition to TAM. Representative MS ion images of TAM metabolites are shown for the 10 cases without histopathological responses (non-pathological responder group) (Fig. 6 A) and the four cases exhibiting degenerative features, such as nuclear condensation (pathological responder group) (Fig. 6 C). NDMTAM, which has been reported to possess pharmacologic activity comparable to the parent compound, displayed a distribution pattern largely consistent with that of TAM in all cases and exhibited significantly higher signal intensities in cancer regions (Fig. 6 A, B, C, and D). This finding indicates that NDMTAM distribution does not contribute meaningfully to the presence or absence of treatment-related histopathological alterations (Fig. 6 E). For 4OHTAM, among the non-pathological responder group, nine cases showed no significant regional difference in signal intensity between cancer and non-cancer regions, whereas only one case (Case 16) exhibited significant enrichment within cancer regions (Fig. 6 A and B). Among pathological responder group, three cases (Cases 7, 9, and 10) demonstrated significant accumulation of 4OHTAM within cancer regions, while one case showed no difference (Fig. 6 C and D). For ENX, nine of the ten non-pathological responder cases showed no significant difference in signal intensity, with only one case (Case 6) exhibiting enrichment within cancer regions, which did not coincide with the case showing significant accumulation of 4OHTAM (Fig. 6 A and B). In contrast, among the responder group, three cases demonstrated significant accumulation of ENX within cancer regions, coinciding with the same cases that showed significant 4OHTAM accumulation in the pathological responder group (Fig. 6 C and D). When assessing the association between metabolite accumulation within cancer regions and the presence of histopathological responses, we found that enrichment of either 4OHTAM or ENX was individually and significantly correlated with treatment-related histopathological alterations. Notably, cases exhibiting significant accumulation of both 4OHTAM and ENX showed the strongest association with histopathological responses (Fig. 6 E). These findings suggest that intratumoral accumulation of the more potent active metabolites—4OHTAM and ENX—may serve as potential stratification factors for tamoxifen-based therapy in both preoperative and postoperative settings. 4. Discussion In this study, we applied mass spectrometry imaging (MSI) to human breast cancer specimens and directly visualized the spatial distribution of tamoxifen (TAM) and its active metabolites following systemic administration. Our analysis revealed three key advances: (i) direct molecular mapping of systemically delivered drugs within human tumors under real clinical conditions, (ii) identification of cancer cell–selective yet heterogeneous intratumoral drug distribution, and (iii) visualization of pharmacologically active metabolites associated with treatment response. These findings position MSI as a powerful tool for addressing the fundamental pharmacological question of whether, and how, anticancer agents actually reach their targets within solid tumors. TAM is a selective estrogen receptor modulator that exerts its effect through high-affinity binding to the estrogen receptor (ER). Végvári et al. demonstrated that the localization pattern of TAM differs according to ER status in ex vivo soaked tissues 33 . In our study, TAM distribution was analyzed exclusively in patients with ER-high tumors, revealing strong affinity and selectivity for cancer cells under physiological, in vivo conditions. Importantly, even within ER-rich, histologically defined tumor regions, TAM distribution exhibited pronounced spatial heterogeneity. This phenomenon challenges the long-standing assumption of uniform intratumoral drug exposure underlying most clinical pharmacologic studies. Our data suggest that differences in tumor architecture, cellular density, and extracellular matrix (ECM) composition, and the resultant diffusion barriers, have substantial impact on local drug penetration. The ECM acts not only as a physical barrier but also modulates drug resistance and immune response; degradation or inhibition of ECM synthesis has been reported to enhance the efficacy of cytotoxic drugs such as doxorubicin, cisplatin, gemcitabine, and eribulin 34 – 41 . In line with these findings, our results provide a new perspective by demonstrating that receptor-mediated agents such as TAM can also exhibit heterogeneous intratumoral diffusion and distribution. In highly cellular tumors, excessive local receptor density rather than ECM impedance may limit homogeneous drug access. Previous reports of heterogeneous paclitaxel distribution across xenograft models of mesothelioma, ovarian, colorectal, and breast cancer support the notion that histopathological features profoundly affect drug permeability 42 . Clinically, our observations emphasize that the degree of heterogeneity—and the extent of uniform high-concentration exposure within cancer regions—may directly influence therapeutic response. Even for endocrine therapies, improving drug delivery efficiency to cancer cells may represent a critical direction for future drug development. Additional contributors to intratumoral drug heterogeneity likely include regional ER variability 43 and drug-efflux transporters 44 , warranting further high-resolution investigation. Beyond the parent compound, our data demonstrate that MSI can capture intratumoral accumulation of pharmacologically active metabolites, particularly endoxifen (ENX). Although detected only in part of the tumor area, ENX accumulation correlated with histopathological response, suggesting that local metabolite distribution may more directly reflect endocrine efficacy than systemic exposure. Prior efforts to stratify TAM therapy using CYP2D6 genotyping or serum metabolite concentrations 45 – 47 have produced inconsistent results, prompting increased attention to ENX monitoring 48 . Studies have proposed various serum thresholds (e.g., 5.97 ng/mL 49 , 5.2 ng/mL 50 , 3.3 ng/mL 51 ) associated with reduced recurrence risk, while others found no correlation between endoxifen levels or tamoxifen activity scores and objective response, clinical benefit, or progression-free survival 52 . In addition to ENX, 4-hydroxytamoxifen (4OHTAM)—another potent active metabolite—has also been reported to have clinical relevance. A recent study demonstrated that lower serum 4OHTAM concentrations (≤ 3.26 nM) were significantly associated with decreased breast cancer–specific survival 53 . These findings collectively indicate that insufficient systemic exposure to either 4OHTAM or ENX may compromise endocrine efficacy; however, our spatial imaging results further suggest that even adequate serum levels may not ensure sufficient intratumoral accumulation. Together, these data underscore the importance of assessing metabolite-level pharmacokinetics in situ to better understand therapeutic variability in tamoxifen response. These observations highlight the importance of capturing metabolite-level pharmacokinetics in situ , rather than relying solely on serum measurements. Such inconsistencies likely arise because circulating metabolite levels do not necessarily reflect local drug availability within the tumor microenvironment. Even with high systemic exposure, ECM composition and cellular density may limit intratumoral accumulation, thereby attenuating the anti-estrogenic effect and clinical efficacy. Although our sample size was limited, direct visualization of intratumoral endoxifen provides spatially resolved pharmacokinetic information unattainable through serum monitoring and may serve as a proximal biomarker of endocrine response. Longitudinal studies are warranted to determine whether local metabolite abundance predicts prognosis or recurrence risk. The implications of these findings extend beyond endocrine therapy to tumor pharmacology at large. The concept of drug heterogeneity is likely applicable to cytotoxic agents, molecularly targeted therapies, and immunomodulatory drugs. Interacting determinants—including cellularity, receptor expression, transporter activity, and ECM remodeling—collectively shape intratumoral drug exposure. Future research should integrate MSI with ultra-high-resolution MSI, spatial transcriptomics, and multiplex histopathology to bridge drug localization with molecular determinants of sensitivity and resistance. Several limitations should be acknowledged. First, matrix effects in MALDI-TOFMS cannot be completely eliminated, limiting absolute quantification between samples; therefore, our analysis relied on relative intra-tumoral comparisons. Integration with LC-MS/MS will allow quantitative validation. Second, this study involved a limited cohort and short-term outcomes; larger and longitudinal cohorts are necessary to assess clinical associations. Third, the current spatial resolution does not enable subcellular localization, restricting evaluation of receptor engagement and downstream pharmacologic effects. Future advances in spatial resolution and multimodal integration—including immunostaining and spatial omics—will be essential to resolve these aspects. In conclusion, this study provides the first direct evidence in human breast cancer specimens that intratumoral distributions of tamoxifen and its active metabolites are heterogeneous and may relate to therapeutic efficacy. MSI-based visualization of intratumoral pharmacokinetics at molecular and spatial dimensions has the potential to fundamentally transform the understanding and prediction of drug response. By introducing drug distribution as a new dimension of tumor heterogeneity, our findings broaden the scope of precision oncology beyond endocrine therapy and into a unified framework for spatial pharmacology in cancer. Abbreviations PST primary systemic treatment MSI Mass Spectrometry Imaging HR hormone receptor HER2 human epidermal growth factor receptor 2 TAM Tamoxifen MALDI-TOFMS Matrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry CHCA a-Cyano-4-hydroxycinnamic acid DHB 2,5-dihydroxybenzoic acid 9-AA 9-Aminoacridine ITO indium–tin oxide H&E hematoxylin and eosin m/z mass-to-charge NDMTAM N-desmetyl-tamoxifen 4OHTAM 4-hydroxy-tamoxifen ENX endoxifen ROI region of interest CV coefficient of variation SD standard deviation TIC total ion current IBC-NST invasive breast carcinoma of no special type ER estrogen receptor PgR progesterone receptor ECM extracellular matrix Declarations Authors’ Disclosures Chikage Kato has received research funding from Shimadzu Corporation. Yasuto Naoi has received research funding from Ono Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Eizai Co., Ltd., Eli Lilly Co., Ltd. and AstraZeneca PLC. CRediT authorship contribution statement Chikage Kato : Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing–original draft, Visualization, Funding acquisition; Sae Kitano : Investigation, Resources, Writing-review & editing; Takushi Yamamoto : Methodology, Validation, Investigation, Writing-review & editing; Ikoi Omatsu : Validation, Resources, Writing-review & editing; Nagisa Hirotani : Investigation, Writing-review & editing; Akira Watanabe : Validation, Writing-review & editing; Maiko Nishida : Validation, Writing-review & editing; Midori Morita : Resources, Writing-review & editing; Koichi Sakaguchi : Writing-review & editing; Eiichi Konishi : Validation, Writing-review & editing; Mitsutoshi Setou : Writing–review and editing, Supervision; Yasuto Naoi : Writing–review and editing, Supervision. Ethics statement The patient samples were utilized under protocol ERB-C-400-7 and the clinical records were extracted under protocol ERB-C-2804-1, both of which were approved by the Institutional Review Board of Kyoto Prefectural University of Medicine. The opt-out method was applied to obtain consent from all patients in this study. Acknowledgments The authors acknowledge the following funding support: 23K06720 (C. Kato, Grant-in-Aid for Scientific Research [C]) and ENT M Dr. Noboru and Teruko Asano Foundation for Basic Medical Research Grant. Data availability The data generated during the current study are available from the corresponding author upon reasonable request. References Naghavi M et al (2024) Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021. 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Trends Pharmacol Sci 43:740–753 Ntshangase S et al (2019) Mass Spectrometry Imaging Demonstrates the Regional Brain Distribution Patterns of Three First-Line Antiretroviral Drugs. ACS Omega 4:21169–21177 Passarelli MK et al (2017) The 3D OrbiSIMS—label-free metabolic imaging with subcellular lateral resolution and high mass-resolving power. Nat Methods 14:1175–1183 Sarkaria JN, Miller EM, Parker CJ, Jordan VC, Mulcahy RT (1994) 4-Hydroxytamoxifen, an active metabolite of tamoxifen, does not alter the radiation sensitivity of MCF-7 breast carcinoma cells irradiatedin vitro. Breast Cancer Res Treat 30:159–165 Helland T et al (2015) The Active Tamoxifen Metabolite Endoxifen (4OHNDtam) Strongly Down-Regulates Cytokeratin 6 (CK6) in MCF-7 Breast Cancer Cells. PLoS ONE 10:e0122339 Woo HI et al (2017) Variations in plasma concentrations of tamoxifen metabolites and the effects of genetic polymorphisms on tamoxifen metabolism in Korean patients with breast cancer. 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Nanomed (Lond) 12:2137–2148 Sanchez-Spitman AB et al (2019) Clinical pharmacokinetics and pharmacogenetics of tamoxifen and endoxifen. Expert Rev Clin Pharmacol 12:523–536 Brauch H, MüRdter TE, Eichelbaum M, Schwab M (2009) Pharmacogenomics of Tamoxifen Therapy. Clin Chem 55:1770–1782 Hertz DL et al (2017) CYP2D6 genotype is not associated with survival in breast cancer patients treated with tamoxifen: results from a population-based study. Breast Cancer Res Treat 166:277–287 De Schultink V, Huitema AHM, A.D.R., Beijnen JH (2018) Therapeutic Drug Monitoring of endoxifen as an alternative for CYP2D6 genotyping in individualizing tamoxifen therapy. Breast 42:38–40 Madlensky L et al (2011) Tamoxifen Metabolite Concentrations, CYP2D6 Genotype, and Breast Cancer Outcomes. Clin Pharmacol Ther 89:718–725 Saladores P et al (2015) Tamoxifen metabolism predicts drug concentrations and outcome in premenopausal patients with early breast cancer. Pharmacogenomics J 15:84–94 Helland T et al (2017) Serum concentrations of active tamoxifen metabolites predict long-term survival in adjuvantly treated breast cancer patients. Breast Cancer Res 19 Neven P et al (2018) Tamoxifen Metabolism and Efficacy in Breast Cancer: A Prospective Multicenter Trial. Clin Cancer Res 24:2312–2318 Helland T et al (2021) Low Z-4OHtam concentrations are associated with adverse clinical outcome among early stage premenopausal breast cancer patients treated with adjuvant tamoxifen. Mol Oncol 15:957–967 Tables Table 1 is available in the Supplementary Files section. Additional Declarations The authors declare no competing interests. Supplementary Files 20251109Table1.tif Table 1. Clinicopathological characteristics pre and post neoadjuvant tamoxifen-based endocrine therapy (NET) in 16 hormone receptor-positive, human epidermal growth receptor 2–negative breast cancer patients. 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-8321696","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":557901835,"identity":"3e0c5ea8-9039-4f73-8bdb-153adafe3a97","order_by":0,"name":"Chikage Kato","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Chikage","middleName":"","lastName":"Kato","suffix":""},{"id":557901836,"identity":"a4a4e16f-4ffc-4c6c-9319-72e14d828c2d","order_by":1,"name":"Sae 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11:01:21","extension":"html","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":128380,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/d69186efbaf190c44c553890.html"},{"id":98218322,"identity":"63a18666-adf2-4f28-8f4b-882d08f064f2","added_by":"auto","created_at":"2025-12-15 11:01:21","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":318661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIonization characteristics of tamoxifen and letrozole across three MALDI matrices.\u003c/strong\u003e\u003cbr\u003e\n(A) Mass spectra of tamoxifen (TAM) acquired with α-cyano-4-hydroxycinnamic acid (CHCA) and CHCA-only control.\u003cbr\u003e\n(B) Mass spectra of TAM acquired with 2,5-dihydroxybenzoic acid (DHB) and DHB-only control.\u003cbr\u003e\n(C) Mass spectra of TAM acquired with 9-aminoacridine (9-AA) and 9-AA–only control.\u003c/p\u003e\n\u003cp\u003e(D) Mass spectra of letrozole (LET)\u003cstrong\u003e \u003c/strong\u003eacquired with CHCA.\u003cbr\u003e\n(E) Mass spectra of LET acquired with DHB.\u003cbr\u003e\n(F) Mass spectra of LET acquired with 9-AA.\u003c/p\u003e","description":"","filename":"20251109Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/93c0d6286670c3d1d992866b.png"},{"id":98432470,"identity":"cebcf8f7-091e-4aeb-afc1-24ec1d68bb56","added_by":"auto","created_at":"2025-12-17 16:49:35","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2170113,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDetection of orally administered TAM in human breast cancer tissues.\u003c/strong\u003e\u003cbr\u003e\n(A) Hematoxylin and eosin (H\u0026amp;E)–stained image of IBC-NST (left), optical image of the serial section (middle), mass spectrometry imaging (MSI) map at \u003cem\u003em/z\u003c/em\u003e372.23 corresponding to TAM (right), and representative mass spectra obtained from regions with high and low TAM signal intensities. The upper panel (Case 1) represents IBC-NST with low-to-moderate stromal content, whereas the lower panel (Case 2) shows IBC-NST with abundant stromal content. White bar indicates 100 µm. Black arrowheads indicate the \u003cem\u003em/z\u003c/em\u003e 372.23 peak corresponding to TAM.\u003c/p\u003e\n\u003cp\u003e(B) The upper panel shows MSI map of TAM (\u003cem\u003em/z\u003c/em\u003e372.23) (left), the merged image of TAM–MSI and H\u0026amp;E staining from the same section (middle), and the H\u0026amp;E image (right). White bar indicates 100 µm. The lower panel presents an enlarged view of the tumor–stroma interface in the H\u0026amp;E image, together with representative mass spectra comparing TAM signal intensities between cancer and non-cancer regions. White bar indicates 25 µm. Black arrowheads indicate the \u003cem\u003em/z\u003c/em\u003e 372.23 peak corresponding to TAM.\u003c/p\u003e","description":"","filename":"20251109Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/2d80e4812c05e1a3c6411fb8.png"},{"id":98432404,"identity":"94a89284-2d9d-4b74-aa00-70833e7ae8ea","added_by":"auto","created_at":"2025-12-17 16:49:31","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1368691,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTAM accumulation in cancer versus non-cancer regions in IBC-NST with abundant stromal components (scirrhous appearance).\u003c/strong\u003e\u003cbr\u003e\n(A–F) MSI maps of TAM (\u003cem\u003em/z\u003c/em\u003e 372.23), merged images, and distribution of ten cancer-region ROIs (red) and ten non-cancer ROIs (blue) for Cases 6, 8, 9, 12, and 14. Box plots compare TAM intensity between regions. White bar indicates 100 µm. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003cbr\u003e\n(G) Table showing, for each case, the mean, the standard deviation (SD), and the coefficient of variation (CV) of TAM intensity in cancer regions, together with the absolute change in Ki-67, and the presence or absence of histopathological response.\u003c/p\u003e","description":"","filename":"20251109Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/609e033b4926d429a3017119.png"},{"id":98218323,"identity":"13590eb0-34c4-447f-be1d-6b5f5ae1d524","added_by":"auto","created_at":"2025-12-15 11:01:21","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1107861,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTAM accumulation in cancer versus non-cancer regions in IBC-NST with scant or intermediate stromal components (solid and tubule-forming appearances).\u003c/strong\u003e\u003cbr\u003e\n(A, B) MSI maps of TAM (\u003cem\u003em/z\u003c/em\u003e 372.23), merged images, and distribution of ten cancer-region ROIs (red) and ten non-cancer ROIs (blue) for IBC-NST with solid-pattern. White bar indicates 100 µm. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003c/p\u003e\n\u003cp\u003e(C, D, E) MSI maps of TAM (\u003cem\u003em/z\u003c/em\u003e 372.23), merged images, and distribution of ten cancer-region ROIs (red) and ten non-cancer ROIs (blue) for IBC-NST with tubule-forming pattern. Cases 5, 7, and 15. Box plots compare TAM intensity. White bar indicates 100 µm. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003cbr\u003e\n(F) Table showing, for each case, the mean, the standard deviation (SD), and the coefficient of variation (CV) of TAM intensity in cancer regions, together with the morphological patterns, the absolute change in Ki-67, and the presence or absence of histopathological response.\u003c/p\u003e","description":"","filename":"20251109Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/ace15ae8b577fa93e5e7d7ed.png"},{"id":98432840,"identity":"ed63c546-689a-4e92-ad44-45e2f96c7373","added_by":"auto","created_at":"2025-12-17 16:50:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":635384,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTAM accumulation in cancer, non-cancer, and mucinous lake regions in mucinous carcinoma.\u003c/strong\u003e\u003cbr\u003e\n(A, B) MSI maps of TAM (\u003cem\u003em/z\u003c/em\u003e 372.23), merged images, and distribution of ten ROIs each within cancer regions (red), non-cancer regions (blue), and mucus lakes regions (green) for Cases 3 and 13. White bar indicates 100 µm. Data represent mean values from ten ROIs (25 pixels each) within cancer, non-cancer and mucus lake regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.0001; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01.\u003cbr\u003e\n(C) MSI map and merged image for Case 10 with cancer-region ROIs (red) and non-cancer ROIs (blue). White bar indicates 100 µm. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001.\u003cbr\u003e\n(D) Table showing, for each case, the mean, the standard deviation (SD), and the coefficient of variation (CV) of TAM intensity in cancer regions, together with the morphological patterns, the absolute change in Ki-67, and the presence or absence of histopathological response.\u003c/p\u003e","description":"","filename":"20251109Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/e9d8d3f92c3867bad6bfc10f.png"},{"id":98218324,"identity":"05f8b1e6-976d-43fb-8f6b-23511b4c8b06","added_by":"auto","created_at":"2025-12-15 11:01:21","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":995755,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial distribution of TAM metabolites and their association with histopathological response.\u003c/strong\u003e\u003cbr\u003e\n(A) MSI maps of N-desmethyltamoxifen (NDMTAM; left), 4-hydroxytamoxifen (4OHTAM; middle), and endoxifen (ENX; right) in ten non-pathological responder cases. White bar indicates 100 µm.\u003c/p\u003e\n\u003cp\u003e(B) Scatter plots showing intensities of NDMTAM, 4OHTAM, and ENX in non-pathological responder cases. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ns, not significant.\u003cbr\u003e\n(C) MSI maps of NDMTAM (left), 4OHTAM (middle), and ENX (right) in four pathological responder cases. White bar indicates 100 µm.\u003c/p\u003e\n\u003cp\u003e(D) Scatter plots showing signal intensities of NDMTAM, 4OHTAM, and ENX in pathological responder cases. Data represent mean values from ten ROIs (25 pixels each) within cancer and non-cancer regions, analyzed using the Mann–Whitney U test. ****\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.0001; ***\u003cem\u003eP\u003c/em\u003e\u0026lt; 0.001; **\u003cem\u003eP\u003c/em\u003e \u0026lt; 0.01; ns, not significant.\u003cbr\u003e\n(E) Association between TAM metabolites accumulation and histopathological response. Interleaved bar graphs show the relationship between the significant accumulation in cancer region for each metabolite (NDMTAM, 4OHTAM, ENX, and combined 4OHTAM and ENX) and histopathological response.\u003c/p\u003e","description":"","filename":"20251109Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/1a333d70a31d91cbb666b963.png"},{"id":98622389,"identity":"f790dc8b-9031-4780-a70a-1c22a7c033da","added_by":"auto","created_at":"2025-12-19 16:53:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7152470,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/6a9d1e67-fde7-44ac-8210-1138daa23bc0.pdf"},{"id":98218319,"identity":"45c41f94-e5a9-437d-a6d4-81d750f09e9a","added_by":"auto","created_at":"2025-12-15 11:01:21","extension":"tif","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1460882,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e Clinicopathological characteristics pre and post neoadjuvant tamoxifen-based endocrine therapy (NET) in 16 hormone receptor-positive, human epidermal growth receptor 2–negative breast cancer patients.\u003c/p\u003e","description":"","filename":"20251109Table1.tif","url":"https://assets-eu.researchsquare.com/files/rs-8321696/v1/d462d14739214d1a1de4dfa9.tif"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eSpatial Mapping of Tamoxifen and its Metabolites in Breast Cancer Tissue via MALDI-MSI: Novel Insights into Endocrine Therapy Response\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eCancer remains one of the leading causes of mortality worldwide, posing a major global health challenge\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Among the therapeutic strategies developed to address this challenge, systemic therapy plays a central role in modern oncology. In particular, the primary systemic treatment (PST) has been established as a standard approach that broadens treatment options\u0026mdash;by enabling tumor downstaging to facilitate less extensive surgery, or by providing salvage opportunities for otherwise inoperable disease\u003csup\u003e\u003cspan additionalcitationids=\"CR3 CR4 CR5 CR6\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Nevertheless, accurately predicting therapeutic responses remain challenging owing to the pronounced biological heterogeneity of tumors\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. From a clinicopathological standpoint, numerous biomarkers have been explored to refine treatment selection, including proliferation markers\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, immune cell infiltration profiles\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, genomic signatures\u003csup\u003e\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, and liquid biopsy\u0026ndash;based indicators\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. However, despite these extensive efforts, the predictive performance of such biomarkers remains limited\u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAt the core of this challenge lies one of the most fundamental yet unresolved questions in tumor pharmacology\u0026mdash;\u003cem\u003edo administered drugs actually reach their targets within the tumor, and how are they spatially distributed?\u003c/em\u003e Despite the vast number of clinical trials evaluating therapeutic efficacy, our understanding of drug bioavailability and intratumoral pharmacokinetics remains limited. This critical knowledge gap represents a major barrier to elucidating the mechanisms of drug resistance and non-responsiveness in cancer therapy.\u003c/p\u003e \u003cp\u003eTo date, mass spectrometry imaging (MSI) has been widely applied in cancer research for molecular-level diagnosis and subtype classification\u003csup\u003e\u003cspan additionalcitationids=\"CR17 CR18 CR19 CR20\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e for elucidating intratumoral heterogeneity\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e,\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e,\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, and for exploring cancer metabolism\u003csup\u003e\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. More recently, MSI has attracted increasing attention as a powerful approach to investigate the relationship between intratumoral pharmacokinetics and therapeutic efficacy\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Unlike conventional pharmacokinetic assays, MSI enables label-free, high\u0026ndash;spatial-resolution visualization of drugs and their metabolites directly on tissue sections\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. This capability allows spatial mapping of drug concentration gradients, diffusion barriers, and metabolic distributions in direct correspondence with tissue architecture\u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e,\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u003c/sup\u003e. By bridging drug administration with pharmacologic effect in both the spatial and molecular dimensions, MSI provides a transformative platform to decipher intratumoral pharmacology.\u003c/p\u003e \u003cp\u003eIn this study, we applied MSI to surgical specimens from patients with hormone receptor (HR)\u0026ndash;positive, human epidermal growth factor receptor 2 (HER2)-negative breast cancer who received neoadjuvant endocrine therapy with tamoxifen. By directly visualizing the intratumoral distribution and metabolism of tamoxifen, we demonstrate that MSI can decipher the \u0026ldquo;black box\u0026rdquo; of intratumoral pharmacokinetics underlying drug delivery and action in human tumors. Our findings highlight MSI as a powerful translational approach to deepen understanding of drug\u0026ndash;tumor interactions and to provide a framework for investigating pharmacologic heterogeneity and resistance mechanisms in breast cancer and potentially beyond.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Reagents\u003c/h2\u003e \u003cp\u003eThe primary reagents used in this study were as follows. Tamoxifen (TAM; Cat. #13258) was purchased from Cayman Chemical (Ann Arbor, MI, USA), and letrozole (Cat. #S1235) was purchased from Selleck Chemicals (Houston, TX, USA). For matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOFMS), three types of matrix solutions were prepared:α-cyano-4-hydroxycinnamic acid (CHCA; 10 mg/mL in 60% acetonitrile with 0.1% trifluoroacetic acid), 2,5-dihydroxybenzoic acid (DHB; 10 mg/mL in 70% methanol with 0.1% trifluoroacetic acid), and 9-aminoacridine (9-AA; 4 mg/mL in 70% methanol). Each drug was dissolved in methanol or ethanol at a final concentration of 100 \u0026micro;M and mixed with the prepared matrix solution at a 1:1 ratio. A 1 \u0026micro;L aliquot of the mixture was spotted onto an indium\u0026ndash;tin oxide (ITO)-coated glass slide and air-dried to prepare standard samples for MALDI-TOFMS analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Patients and tissue sample collection\u003c/h2\u003e \u003cp\u003eThe study included 16 patients diagnosed with HR\u0026ndash;positive, HER2-negative breast cancer (stage I\u0026ndash;III) at University Hospital, Kyoto Prefectural University of Medicine, who received oral TAM (\u003cem\u003eNolvadex\u0026reg;\u003c/em\u003e, AstraZeneca, UK) at a dose of 20 mg once daily for at least four weeks before undergoing curative surgery between January 2024 and May 2025. Breast cancer diagnosis and assessment of therapeutic response were confirmed by histopathological evaluation independently conducted by two experienced pathologists. During surgery, a portion of the resected tumor tissue was excised (approximately 10mm \u0026times; 10mm), rapidly frozen on dry ice, and stored as fresh-frozen specimens for subsequent analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Sample preparation and histological staining\u003c/h2\u003e \u003cp\u003eFresh-frozen tumor specimens were sectioned at a thickness of 10 \u0026micro;m using a cryostat (CM1950; Leica Biosystems, Wetzlar, Germany) and mounted onto ITO-coated glass slides (Matsunami Glass Ind., Osaka, Japan) at \u0026minus;\u0026thinsp;20\u0026deg;C for MALDI-TOFMS analysis. A uniform coating of CHCA matrix was applied to each section at an approximate thickness of 0.7 \u0026micro;m using the iMLayer device (Shimadzu, Kyoto, Japan). For Cases 1 and 2, serial sections from the same specimen were subjected to hematoxylin and eosin (H\u0026amp;E) staining. For Cases 3\u0026ndash;16, to enable more accurate visualization of intratumoral drug distribution, H\u0026amp;E staining was performed on the same sections after MALDI-TOFMS analysis, following matrix removal. Overlay images combining H\u0026amp;E-stained histology and MSI drug-distribution maps were generated using IMAGEREVEAL MS software (Shimadzu, Kyoto, Japan).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Mass spectrometry imaging and data analysis\u003c/h2\u003e \u003cp\u003eMass spectrometry imaging was performed using an \u003cem\u003eiMScope QT\u003c/em\u003e instrument (Shimadzu, Kyoto, Japan). The acquisition parameters were as follows: a mass-to-charge (\u003cem\u003em/z\u003c/em\u003e) range of 450\u0026ndash;900, positive-ion mode, spatial resolution 10 \u0026micro;m, laser repetition rate 1000 Hz, laser diameter approximately 10 \u0026micro;m, and laser power 40 (arbitrary units). Images corresponding to each \u003cem\u003em/z\u003c/em\u003e peak were generated using \u003cem\u003eIMAGEREVEAL MS\u003c/em\u003e software. The spatial distributions of TAM and its metabolites\u0026mdash;N-desmethyltamoxifen (NDMTAM), 4-hydroxytamoxifen (4OHTAM), and endoxifen (ENX)\u0026mdash;were visualized and analyzed using \u003cem\u003eIMAGEREVEAL MS\u003c/em\u003e (Shimadzu Corporation, Kyoto, Japan). The \u003cem\u003em/z\u003c/em\u003e values corresponding to each compound were as follows: TAM ([M\u0026thinsp;+\u0026thinsp;H]⁺ = 372.23), NDMTAM ([M\u0026thinsp;+\u0026thinsp;H]⁺ = 358.22), 4OHTAM ([M\u0026thinsp;+\u0026thinsp;H]⁺ = 388.23), and ENX ([M\u0026thinsp;+\u0026thinsp;H]⁺ = 374.21). All spectra were normalized by total ion current (TIC). Referring to the H\u0026amp;E-stained sections, ten regions of interest (ROIs; each measuring 5 \u0026times; 5 pixels, i.e., 25 pixels per ROI) were randomly selected within both cancer and non-cancer regions. The mean signal intensity for each ion species within each ROI was extracted. Data visualization and statistical analyses were performed using GraphPad Prism (version 10.5.0 ; Boston, MA, USA). Differences in TAM and metabolite accumulation between cancer and non-cancer regions (250 pixels per region) were assessed using the Mann\u0026ndash;Whitney U test. Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01 for intensity comparisons. The coefficient of variation (CV = [standard deviation (SD)/mean] \u0026times; 100%) for TAM intensity across ten ROIs in the cancer regions was calculated as an indicator of heterogeneity. Associations between the presence of metabolite accumulation and pathological treatment response were analyzed using Fisher\u0026rsquo;s exact test, with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 considered statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Ethical approval\u003c/h2\u003e \u003cp\u003e All study procedures were approved by the Ethics Committee of Kyoto Prefectural University of Medicine (approval number: ERB-C-400-7, ERB-C-2804-1). Written informed consent or opt-out consent was obtained from all participants prior to inclusion. Patients were fully informed that non-participation would not result in any disadvantage, that withdrawal from the study was permitted at any time, and that personal information would be protected to ensure anonymity. All experiments were conducted in accordance with the approved institutional guidelines and ethical standards.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Result","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Ionization characteristics of reagents\u003c/h2\u003e \u003cp\u003eTo assess the ionization efficiency of the agents used in endocrine therapy for breast cancer, we evaluated tamoxifen (TAM) and letrozole as representative drugs. Each compound was analyzed using three commonly employed matrices\u0026mdash;α-cyano-4-hydroxycinnamic acid (CHCA), 2,5-dihydroxybenzoic acid (DHB), and 9-aminoacridine (9-AA)\u0026mdash;to determine their ionization behavior and imaging feasibility in MALDI-based mass spectrometry (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor TAM, a distinct peak at \u003cem\u003em/z\u003c/em\u003e 372.23 was observed when CHCA was used, whereas no corresponding signal was detected with the matrix alone (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). In contrast, the peak intensity was markedly lower (approximately 1/50) with DHB compared to CHCA, and no interfering matrix peaks were detected around \u003cem\u003em/z\u003c/em\u003e 372.23 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). No TAM-derived peaks were observed when using 9-AA as the matrix (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC).\u003c/p\u003e \u003cp\u003eLetrozole showed only a weak peak at \u003cem\u003em/z\u003c/em\u003e 286.11 with CHCA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD) and was undetectable with DHB or 9-AA (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE and F). Thus, while both agents were ionizable with CHCA, the ionization efficiency of letrozole was considerably lower than that of TAM, indicating that detection in tissue sections would be challenging. Consequently, TAM was selected for subsequent human tissue analyses.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Intratumoral distribution of tamoxifen in human breast cancer tissues\u003c/h2\u003e \u003cp\u003eWe next analyzed surgical specimens from breast cancer patients who had received oral TAM as neoadjuvant endocrine therapy, using the \u003cem\u003eiMScope QT\u003c/em\u003e to visualize the intratumoral distribution of TAM following breast-conserving surgery or total mastectomy. Clinical characteristics of all 16 patients are summarized in Table\u0026nbsp;1. The analyzed tumors consisted of 13 cases of invasive breast carcinoma of no special type (IBC-NST) and three cases of mucinous carcinoma as a special histological subtype. The median duration of preoperative TAM administration was 65 days (range, 31\u0026ndash;89 days). All patients were confirmed to have estrogen receptor (ER)\u0026ndash;positive breast cancer before treatment, and postoperative pathological diagnoses remained unchanged. Progesterone receptor (PgR) expression was also positive in all cases before treatment; after TAM administration, decrease of \u0026ge;\u0026thinsp;20% in PgR expression was observed in eight cases. Ki-67 expression decreased by more than 5% in nine cases.\u003c/p\u003e \u003cp\u003eTo confirm whether orally administered TAM could be detected within human breast cancer tissues, two representative cases of IBC-NST were analyzed: Case 1, with low-to-moderate stromal content, and Case 2, with abundant stromal content. A clear peak corresponding to the \u003cem\u003em/z\u003c/em\u003e of TAM was detected in both analyzed cases, demonstrating that orally administered TAM was absorbed and distributed within the tumor tissue, with its spatial localization and signal intensity differences readily visualized (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). To evaluate this distribution, the spatial pattern of cancer cells identified on hematoxylin and eosin (H\u0026amp;E)\u0026ndash;stained serial sections was compared with the mass spectrometry imaging (MSI) map of TAM. In Case 1, TAM exhibited a cord-like distribution corresponding to the characteristic trabecular arrangement of cancer cells, whereas in Case 2, where H\u0026amp;E staining revealed abundant stromal components centrally and scattered cancer cells on both sides, TAM intensity was weaker in the center and markedly stronger in the bilateral tumor regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eBecause serial sections were used in both cases, slight positional discrepancies of several tens of micrometers between sections made precise comparison of drug distribution and cancer cell localization difficult. Such intersectional misalignment hampers accurate assessment of drug localization relative to cancer cells or stromal components. To overcome this limitation, the matrix was removed from the same MALDI-analyzed section after MS acquisition, followed by H\u0026amp;E staining and image co-registration of the MSI and H\u0026amp;E data (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). The matrix was removed using a stepwise dilution method with 70% ethanol to preserve cellular morphology. This approach enabled more accurate determination of TAM signal intensity within cancer and non-cancer regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3. TAM distribution across histological subtypes\u003c/h2\u003e \u003cp\u003eWe next evaluated the spatial distribution and heterogeneity of TAM within cancer and non-cancer regions across three histological subtypes: IBC-NST with abundant stroma, IBC-NST with scant stroma, and mucinous carcinoma. Intratumoral heterogeneity of drug penetration was quantified as the coefficient of variation (CV) of TAM signal intensity. For each distribution parameter, the relationship between TAM localization and indicators of therapeutic response was assessed based on histopathological and immunohistochemical alterations, including changes in Ki-67 expression and tissue morphology.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. IBC-NST with abundant stromal component (scirrhous appearance)\u003c/h2\u003e \u003cp\u003eAmong the analyzed IBC-NST cases, six tumors were pathologically classified as having a scirrhous appearance, characterized by a low tumor-to-stroma ratio and infiltrative growth (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In all six cases, TAM was predominantly distributed within the scattered cancer cell regions, with significantly higher signal intensities compared with the surrounding stroma-rich, non-cancer regions. The median CV, representing intratumoral heterogeneity of TAM distribution, was 104.02 (range, 80.0\u0026ndash;151.3). Cases with lower CV values indicating more uniform intratumoral TAM distribution (Cases 6, 8, 9, and 12) corresponded to those showing decreases in the proliferation marker Ki-67 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, B, C, E, and G). Among them, only Case 9, which exhibited the highest TAM intensity and the lowest CV, demonstrated distinct histopathological response (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eG).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. IBC-NST with scant stromal component\u003c/h2\u003e \u003cp\u003eNext, we analyzed five cases characterized by a relatively high tumor-to-stroma ratio with sparse stromal components (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). In all cases, the TAM signal intensity was significantly higher in cancer regions than in non-cancer regions, similar to those with abundant stromal components. To further assess the relationship between intratumoral heterogeneity and therapeutic response, these cases were subdivided into two morphological patterns: two with a \u003cem\u003esolid pattern\u003c/em\u003e, defined by poorly developed glandular structures and densely packed tumor nests (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA and B), and three with a \u003cem\u003etubule-forming pattern\u003c/em\u003e, showing more distinct glandular architecture with moderate cellular and structural atypia (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC, D, and E). In the solid pattern, Case 16 exhibited relatively uniform TAM distribution (low CV) and decrease in Ki-67 expression; however, the mean TAM intensity was comparatively low, and no histopathological responses were observed. In contrast, within the tubule-forming pattern, Cases 7 and 15\u0026mdash;both showing lower CV values\u0026mdash;demonstrated higher mean TAM intensities accompanied by decrease in Ki-67 and histopathological responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Special type (mucinous carcinoma)\u003c/h2\u003e \u003cp\u003eAmong the special histological subtypes, three cases of mucinous carcinoma were analyzed, comprising two cases of type A (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B) and one mixed-type case (type B; Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC). Similar to the 13 cases of IBC-NST, TAM accumulation was significantly higher in cancer regions compared with non-cancer regions. Notably, in type A tumors, TAM distribution was markedly low within the abundant mucin pools (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA and B). In contrast, the mixed-type (type B) tumor, which contained less mucin, exhibited a higher mean TAM intensity with a lower CV, accompanied by decrease in Ki-67 expression and distinct histopathological response (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC and D).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAcross all histological subtypes\u0026mdash;including IBC-NST with abundant or sparse stroma and mucinous carcinoma\u0026mdash;TAM consistently accumulated preferentially within cancer regions. However, the intensity and spatial heterogeneity of TAM distribution varied among subtypes, potentially reflecting differences in tumor cell density and stromal architecture. Cases exhibiting more uniform and higher TAM intensity (i.e., lower CV and higher mean signal) tended to show decreases in Ki-67 expression and histopathological responses suggestive of therapeutic response, indicating that both the efficiency and uniformity of intratumoral drug penetration may contribute to treatment efficacy. Given that TAM is metabolized in vivo to potent active metabolites such as 4-hydroxytamoxifen and endoxifen, we next investigated the spatial distribution of these metabolites and their association with therapeutic outcomes.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Distribution characteristics of tamoxifen metabolites\u003c/h2\u003e \u003cp\u003eTAM is metabolized by CYP3A4/5 to N-desmethyltamoxifen (NDMTAM) and by CYP2D6 to 4-hydroxytamoxifen (4OHTAM). Through sequential metabolism by both CYP pathways, the active metabolite endoxifen (ENX) is generated. Both 4OHTAM and ENX have been reported to exhibit 10- to 100-fold stronger anti-estrogenic activity at the ER compared with the parent compound\u003csup\u003e\u003cspan additionalcitationids=\"CR31\" citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Because TAM itself possesses relatively weak ER binding and antagonistic properties and is considered to act largely as a \u0026ldquo;prodrug,\u0026rdquo; we evaluated the intratumoral distribution of its major metabolites in addition to TAM. Representative MS ion images of TAM metabolites are shown for the 10 cases without histopathological responses (non-pathological responder group) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA) and the four cases exhibiting degenerative features, such as nuclear condensation (pathological responder group) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). NDMTAM, which has been reported to possess pharmacologic activity comparable to the parent compound, displayed a distribution pattern largely consistent with that of TAM in all cases and exhibited significantly higher signal intensities in cancer regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, B, C, and D). This finding indicates that NDMTAM distribution does not contribute meaningfully to the presence or absence of treatment-related histopathological alterations (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor 4OHTAM, among the non-pathological responder group, nine cases showed no significant regional difference in signal intensity between cancer and non-cancer regions, whereas only one case (Case 16) exhibited significant enrichment within cancer regions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B). Among pathological responder group, three cases (Cases 7, 9, and 10) demonstrated significant accumulation of 4OHTAM within cancer regions, while one case showed no difference (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D). For ENX, nine of the ten non-pathological responder cases showed no significant difference in signal intensity, with only one case (Case 6) exhibiting enrichment within cancer regions, which did not coincide with the case showing significant accumulation of 4OHTAM (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA and B). In contrast, among the responder group, three cases demonstrated significant accumulation of ENX within cancer regions, coinciding with the same cases that showed significant 4OHTAM accumulation in the pathological responder group (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC and D).\u003c/p\u003e \u003cp\u003eWhen assessing the association between metabolite accumulation within cancer regions and the presence of histopathological responses, we found that enrichment of either 4OHTAM or ENX was individually and significantly correlated with treatment-related histopathological alterations. Notably, cases exhibiting significant accumulation of both 4OHTAM and ENX showed the strongest association with histopathological responses (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). These findings suggest that intratumoral accumulation of the more potent active metabolites\u0026mdash;4OHTAM and ENX\u0026mdash;may serve as potential stratification factors for tamoxifen-based therapy in both preoperative and postoperative settings.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eIn this study, we applied mass spectrometry imaging (MSI) to human breast cancer specimens and directly visualized the spatial distribution of tamoxifen (TAM) and its active metabolites following systemic administration. Our analysis revealed three key advances: (i) direct molecular mapping of systemically delivered drugs within human tumors under real clinical conditions, (ii) identification of cancer cell\u0026ndash;selective yet heterogeneous intratumoral drug distribution, and (iii) visualization of pharmacologically active metabolites associated with treatment response. These findings position MSI as a powerful tool for addressing the fundamental pharmacological question of whether, and how, anticancer agents actually reach their targets within solid tumors.\u003c/p\u003e \u003cp\u003eTAM is a selective estrogen receptor modulator that exerts its effect through high-affinity binding to the estrogen receptor (ER). V\u0026eacute;gv\u0026aacute;ri et al. demonstrated that the localization pattern of TAM differs according to ER status in ex vivo soaked tissues\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. In our study, TAM distribution was analyzed exclusively in patients with ER-high tumors, revealing strong affinity and selectivity for cancer cells under physiological, in vivo conditions. Importantly, even within ER-rich, histologically defined tumor regions, TAM distribution exhibited pronounced spatial heterogeneity. This phenomenon challenges the long-standing assumption of uniform intratumoral drug exposure underlying most clinical pharmacologic studies. Our data suggest that differences in tumor architecture, cellular density, and extracellular matrix (ECM) composition, and the resultant diffusion barriers, have substantial impact on local drug penetration. The ECM acts not only as a physical barrier but also modulates drug resistance and immune response; degradation or inhibition of ECM synthesis has been reported to enhance the efficacy of cytotoxic drugs such as doxorubicin, cisplatin, gemcitabine, and eribulin\u003csup\u003e\u003cspan additionalcitationids=\"CR35 CR36 CR37 CR38 CR39 CR40\" citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In line with these findings, our results provide a new perspective by demonstrating that receptor-mediated agents such as TAM can also exhibit heterogeneous intratumoral diffusion and distribution. In highly cellular tumors, excessive local receptor density rather than ECM impedance may limit homogeneous drug access. Previous reports of heterogeneous paclitaxel distribution across xenograft models of mesothelioma, ovarian, colorectal, and breast cancer support the notion that histopathological features profoundly affect drug permeability\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e. Clinically, our observations emphasize that the degree of heterogeneity\u0026mdash;and the extent of uniform high-concentration exposure within cancer regions\u0026mdash;may directly influence therapeutic response. Even for endocrine therapies, improving drug delivery efficiency to cancer cells may represent a critical direction for future drug development. Additional contributors to intratumoral drug heterogeneity likely include regional ER variability\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e and drug-efflux transporters\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e, warranting further high-resolution investigation.\u003c/p\u003e \u003cp\u003eBeyond the parent compound, our data demonstrate that MSI can capture intratumoral accumulation of pharmacologically active metabolites, particularly endoxifen (ENX). Although detected only in part of the tumor area, ENX accumulation correlated with histopathological response, suggesting that local metabolite distribution may more directly reflect endocrine efficacy than systemic exposure. Prior efforts to stratify TAM therapy using CYP2D6 genotyping or serum metabolite concentrations\u003csup\u003e\u003cspan additionalcitationids=\"CR46\" citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e have produced inconsistent results, prompting increased attention to ENX monitoring\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Studies have proposed various serum thresholds (e.g., 5.97 ng/mL\u003csup\u003e49\u003c/sup\u003e, 5.2 ng/mL\u003csup\u003e50\u003c/sup\u003e, 3.3 ng/mL\u003csup\u003e51\u003c/sup\u003e) associated with reduced recurrence risk, while others found no correlation between endoxifen levels or tamoxifen activity scores and objective response, clinical benefit, or progression-free survival\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. In addition to ENX, 4-hydroxytamoxifen (4OHTAM)\u0026mdash;another potent active metabolite\u0026mdash;has also been reported to have clinical relevance. A recent study demonstrated that lower serum 4OHTAM concentrations (\u0026le;\u0026thinsp;3.26 nM) were significantly associated with decreased breast cancer\u0026ndash;specific survival\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. These findings collectively indicate that insufficient systemic exposure to either 4OHTAM or ENX may compromise endocrine efficacy; however, our spatial imaging results further suggest that even adequate serum levels may not ensure sufficient intratumoral accumulation. Together, these data underscore the importance of assessing metabolite-level pharmacokinetics \u003cem\u003ein situ\u003c/em\u003e to better understand therapeutic variability in tamoxifen response.\u003c/p\u003e \u003cp\u003eThese observations highlight the importance of capturing metabolite-level pharmacokinetics \u003cem\u003ein situ\u003c/em\u003e, rather than relying solely on serum measurements. Such inconsistencies likely arise because circulating metabolite levels do not necessarily reflect local drug availability within the tumor microenvironment. Even with high systemic exposure, ECM composition and cellular density may limit intratumoral accumulation, thereby attenuating the anti-estrogenic effect and clinical efficacy. Although our sample size was limited, direct visualization of intratumoral endoxifen provides spatially resolved pharmacokinetic information unattainable through serum monitoring and may serve as a proximal biomarker of endocrine response. Longitudinal studies are warranted to determine whether local metabolite abundance predicts prognosis or recurrence risk.\u003c/p\u003e \u003cp\u003eThe implications of these findings extend beyond endocrine therapy to tumor pharmacology at large. The concept of drug heterogeneity is likely applicable to cytotoxic agents, molecularly targeted therapies, and immunomodulatory drugs. Interacting determinants\u0026mdash;including cellularity, receptor expression, transporter activity, and ECM remodeling\u0026mdash;collectively shape intratumoral drug exposure. Future research should integrate MSI with ultra-high-resolution MSI, spatial transcriptomics, and multiplex histopathology to bridge drug localization with molecular determinants of sensitivity and resistance.\u003c/p\u003e \u003cp\u003eSeveral limitations should be acknowledged. First, matrix effects in MALDI-TOFMS cannot be completely eliminated, limiting absolute quantification between samples; therefore, our analysis relied on relative intra-tumoral comparisons. Integration with LC-MS/MS will allow quantitative validation. Second, this study involved a limited cohort and short-term outcomes; larger and longitudinal cohorts are necessary to assess clinical associations. Third, the current spatial resolution does not enable subcellular localization, restricting evaluation of receptor engagement and downstream pharmacologic effects. Future advances in spatial resolution and multimodal integration\u0026mdash;including immunostaining and spatial omics\u0026mdash;will be essential to resolve these aspects.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides the first direct evidence in human breast cancer specimens that intratumoral distributions of tamoxifen and its active metabolites are heterogeneous and may relate to therapeutic efficacy. MSI-based visualization of intratumoral pharmacokinetics at molecular and spatial dimensions has the potential to fundamentally transform the understanding and prediction of drug response. By introducing drug distribution as a new dimension of tumor heterogeneity, our findings broaden the scope of precision oncology beyond endocrine therapy and into a unified framework for spatial pharmacology in cancer.\u003c/p\u003e "},{"header":"Abbreviations","content":"\u003cp\u003ePST\u003c/p\u003e\n\u003cp\u003eprimary systemic treatment\u003c/p\u003e\n\u003cp\u003eMSI\u003c/p\u003e\n\u003cp\u003eMass Spectrometry Imaging\u003c/p\u003e\n\u003cp\u003eHR\u003c/p\u003e\n\u003cp\u003ehormone receptor\u003c/p\u003e\n\u003cp\u003eHER2\u003c/p\u003e\n\u003cp\u003ehuman epidermal growth factor receptor 2\u003c/p\u003e\n\u003cp\u003eTAM\u003c/p\u003e\n\u003cp\u003eTamoxifen\u003c/p\u003e\n\u003cp\u003eMALDI-TOFMS\u003c/p\u003e\n\u003cp\u003eMatrix Assisted Laser Desorption/Ionization Time of Flight Mass Spectrometry\u003c/p\u003e\n\u003cp\u003eCHCA\u003c/p\u003e\n\u003cp\u003ea-Cyano-4-hydroxycinnamic acid\u003c/p\u003e\n\u003cp\u003eDHB\u003c/p\u003e\n\u003cp\u003e2,5-dihydroxybenzoic acid\u003c/p\u003e\n\u003cp\u003e9-AA\u003c/p\u003e\n\u003cp\u003e9-Aminoacridine\u003c/p\u003e\n\u003cp\u003eITO\u003c/p\u003e\n\u003cp\u003eindium–tin oxide\u003c/p\u003e\n\u003cp\u003eH\u0026amp;E\u003c/p\u003e\n\u003cp\u003ehematoxylin and eosin\u003c/p\u003e\n\u003cp\u003e\u003cem\u003em/z\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003emass-to-charge\u003c/p\u003e\n\u003cp\u003eNDMTAM\u003c/p\u003e\n\u003cp\u003eN-desmetyl-tamoxifen\u003c/p\u003e\n\u003cp\u003e4OHTAM\u003c/p\u003e\n\u003cp\u003e4-hydroxy-tamoxifen\u003c/p\u003e\n\u003cp\u003eENX\u003c/p\u003e\n\u003cp\u003eendoxifen\u003c/p\u003e\n\u003cp\u003eROI\u003c/p\u003e\n\u003cp\u003eregion of interest\u003c/p\u003e\n\u003cp\u003eCV\u003c/p\u003e\n\u003cp\u003ecoefficient of variation\u003c/p\u003e\n\u003cp\u003eSD\u003c/p\u003e\n\u003cp\u003estandard deviation\u003c/p\u003e\n\u003cp\u003eTIC\u003c/p\u003e\n\u003cp\u003etotal ion current\u003c/p\u003e\n\u003cp\u003eIBC-NST\u003c/p\u003e\n\u003cp\u003einvasive breast carcinoma of no special type\u003c/p\u003e\n\u003cp\u003eER\u003c/p\u003e\n\u003cp\u003eestrogen receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003ePgR\u003c/p\u003e\n\u003cp\u003eprogesterone receptor\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eECM\u003c/p\u003e\n\u003cp\u003eextracellular matrix\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eAuthors\u0026rsquo; Disclosures\u003c/b\u003e \u003c/p\u003e \u003cp\u003eChikage Kato has received research funding from Shimadzu Corporation. Yasuto Naoi has received research funding from Ono Pharmaceutical Co., Ltd., Daiichi Sankyo Co., Ltd., Eizai Co., Ltd., Eli Lilly Co., Ltd. and AstraZeneca PLC.\u003c/p\u003e \u003cp\u003e \u003cb\u003eCRediT authorship contribution statement\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eChikage Kato\u003c/b\u003e: Conceptualization, Methodology, Validation, Formal analysis, Investigation, Resources, Data curation, Writing\u0026ndash;original draft, Visualization, Funding acquisition; \u003cb\u003eSae Kitano\u003c/b\u003e: Investigation, Resources, Writing-review \u0026amp; editing; \u003cb\u003eTakushi Yamamoto\u003c/b\u003e: Methodology, Validation, Investigation, Writing-review \u0026amp; editing; \u003cb\u003eIkoi Omatsu\u003c/b\u003e: Validation, Resources, Writing-review \u0026amp; editing; \u003cb\u003eNagisa Hirotani\u003c/b\u003e: Investigation, Writing-review \u0026amp; editing; \u003cb\u003eAkira Watanabe\u003c/b\u003e: Validation, Writing-review \u0026amp; editing; \u003cb\u003eMaiko Nishida\u003c/b\u003e: Validation, Writing-review \u0026amp; editing; \u003cb\u003eMidori Morita\u003c/b\u003e: Resources, Writing-review \u0026amp; editing; \u003cb\u003eKoichi Sakaguchi\u003c/b\u003e: Writing-review \u0026amp; editing; \u003cb\u003eEiichi Konishi\u003c/b\u003e: Validation, Writing-review \u0026amp; editing; \u003cb\u003eMitsutoshi Setou\u003c/b\u003e: Writing\u0026ndash;review and editing, Supervision; \u003cb\u003eYasuto Naoi\u003c/b\u003e: Writing\u0026ndash;review and editing, Supervision.\u003c/p\u003e\u003cp\u003e \u003ch2\u003eEthics statement\u003c/h2\u003e \u003cp\u003eThe patient samples were utilized under protocol ERB-C-400-7 and the clinical records were extracted under protocol ERB-C-2804-1, both of which were approved by the Institutional Review Board of Kyoto Prefectural University of Medicine. The opt-out method was applied to obtain consent from all patients in this study.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eAcknowledgments\u003c/h2\u003e \u003cp\u003eThe authors acknowledge the following funding support: 23K06720 (C. Kato, Grant-in-Aid for Scientific Research [C]) and ENT M Dr. Noboru and Teruko Asano Foundation for Basic Medical Research Grant.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe data generated during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eNaghavi M et al (2024) Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990\u0026ndash;2021: a systematic analysis for the Global Burden of Disease Study 2021. 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Mol Oncol 15:957\u0026ndash;967\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTable 1 is available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Kyoto Prefectural University of Medicine","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":"Drug distribution, Heterogeneity, Mass spectrometry imaging","lastPublishedDoi":"10.21203/rs.3.rs-8321696/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8321696/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eA fundamental question in tumor pharmacology is how systemically administered drugs reach and distribute within tumor tissues. In this study, we applied matrix-assisted laser desorption/ionization\u0026ndash;based mass spectrometry imaging to surgical specimens from 16 patients with hormone receptor\u0026ndash;positive, human epidermal growth factor receptor 2-negative breast cancer who received neoadjuvant tamoxifen therapy. Spatial distribution and heterogeneity of tamoxifen, together with its major metabolites N-desmethyl-tamoxifen, 4-hydroxy-tamoxifen, and endoxifen were directly visualized and correlated with histopathological features and therapeutic response. Tamoxifen was consistently detected in cancer regions regardless of histological subtype; however, its intratumoral intensity and spatial uniformity varied, reflecting stromal composition and tissue architecture. Cases with more homogeneous and higher intensity of tamoxifen distribution tended to show decreased Ki-67 expression and histopathological response. Among the metabolites, significant intratumoral accumulation of 4-hydroxy-tamoxifen, and endoxifen \u0026mdash;but not N-desmethyl-tamoxifen\u0026mdash;was associated with histopathological response. This study provides the first direct demonstration that intratumoral distribution of tamoxifen and its active metabolites in human breast cancer is spatially heterogeneous and linked to therapeutic efficacy. Mass spectrometry imaging-based visualization of drug pharmacokinetics at the molecular and spatial level offers a transformative approach for understanding and predicting treatment response, introducing drug distribution as a new dimension in personalized oncology with broad implications for tumor pharmacology.\u003c/p\u003e","manuscriptTitle":"Spatial Mapping of Tamoxifen and its Metabolites in Breast Cancer Tissue via MALDI-MSI: Novel Insights into Endocrine Therapy Response","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-15 11:01:16","doi":"10.21203/rs.3.rs-8321696/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":"581b0e4a-f4c8-4dcc-b5af-27faf62cafc9","owner":[],"postedDate":"December 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":59453116,"name":"Personalized Medicine"}],"tags":[],"updatedAt":"2025-12-15T11:01:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-15 11:01:16","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8321696","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8321696","identity":"rs-8321696","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}