Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases

Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases | 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 Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases Markus Kleinberger, Didem Çifçi, Christina Paiato, Erwin Tomasich, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5000054/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Acta Neuropathologica Communications → Version 1 posted 10 You are reading this latest preprint version Abstract BACKGROUND Tumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) have increasingly been reported to impact the brain metastatic process of solid tumors. However, data on intra-individual differences between primary tumor and brain metastasis (BM), as well as their correlation with clinical outcome parameters, is scarce. METHODS We retrospectively identified patients who received resection of the primary tumor and BM between 01/1990 and 10/2022. Density quantification of TAMs (CD68 + , CD163 + ) and TILs (CD3 + , CD8 + , CD45RO + , FOXP3 + ) was performed by immunohistochemical staining of matched tumor tissue samples. Images were processed with QuPath software and heterogeneity of generated heatmaps was measured by Shannon Entropy. Time-to-BM (TTBM) was defined as the time from diagnosis of the primary tumor until the first diagnosis of BM. RESULTS In total, 104 patients (46.2% female; median age 57.3 years at BM diagnosis) were included: 78/104 (75%) non-small cell lung cancer, 18/104 (17%) breast cancer, 8/104 (8%) renal cell carcinomas. Densities of CD3 + (p<0.001), CD8 + (p<0.001) and FOXP3 + -TILs (p=0.045) were higher in primary tumor samples, while CD68 + (p=0.025) and CD163 + -TAM densities (p<0.001) were higher in the matched BM. Higher CD3 + -TILs and CD163 + -TAMs densities in primary tumors were associated with shorter TTBM (p=0.039 and p=0.024, respectively). Higher entropies of CD3 + (p<0.001) and FOXP3 + (p=0.002) TILs were observed in primary tumors compared to BM. Longer TTBM was associated with higher entropy of FOXP3 + TILs (p=0.031) and lower entropy in CD163 + TAMs (p=0.040). No significant associations of immune cell densities or entropies with OS after BM diagnosis were found. DISCUSSION By utilizing a unique cohort of matched primary tumor and BM tissue samples, we could demonstrate higher TIL densities in primary tumors and higher TAM densities in BM, respectively. Higher cell densities of CD3 + -TILs and CD163 + -TAMs in primary tumors were associated with shorter TTBM, while a larger difference between CD3 + and CD8 + densities between primary tumor and BM was associated with longer TTBM. Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The tumor microenvironment (TME) represents an ecosystem consisting of a variety of malignant and non-malignant cells, organized through highly complex cell-cell interactions, subcellular communication and crosstalk 1 . Intra-tumoral immune cells like tumor-infiltrating lymphocytes (TILs) or tumor-associated macrophages (TAMs) are the main effector cells of the inflammatory microenvironment 2,3 . Particularly in the face of novel treatment options the presence of specific immune cell subtypes within the unique TME of brain metastases has demonstrated associations with higher intracranial response rates to immune checkpoint inhibitors (ICI) 4 . However, concerning the high heterogeneity within the TME additional information about the spatial distribution is essential to sufficiently analyze immune cell infiltration pattern and to put them into perspective with biological traits 5 . Besides the intratumoral genetic and epigenetic characteristics the localization and co-localization within the TME are pivotal for cellular crosstalk of cancer and immune effector cells 6,7 . Although previous studies addressed the characterization of the TME in high-level resolution, in-depth information on differences between tumor sites is missing due to the infrequent availability of more than one tumor sample from the same patient 8-14 . In the current study, we aimed to improve the knowledge on intrapersonal differences and their association with their clinical course by using a unique cohort of 104 patients with matched tissue samples deriving from the primary tumor and the brain metastasis site. Material and methods Patient cohort Patients diagnosed with histologically confirmed and intracranial metastasized non-small cell lung cancer (NSCLC), breast cancer (BC) and renal cell carcinoma (RCC) were retrospectively identified from the Vienna Brain Metastasis Registry. Patients with tissue samples of the primary tumor and BM via tumor resection or excision between 01/1990 and 12/2022 at the Medical University of Vienna were included. Patients with insufficient formalin-fixed and paraffin-embedded (FFPE) tumor samples (e.g., fine needle aspiration) were excluded. FFPE specimens available for scientific purposes were obtained from the Department of Pathology and Department of Neurology, Division of Neuropathology and Neurochemistry. Clinical data were extracted from the Vienna Brain Metastasis Registry and electronic patient charts. This retrospective exploratory study was approved by the Ethics Committee of the Medical University of Vienna (EC approvals no. 1692/2022, 1895/2022) in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study the necessity of written informed consent was waived by the competent authorities. An illustrated workflow is presented in Figure 1. Immunohistochemistry FFPE tumor blocks of primary and matched BM were prepared by cutting in 4µm thick sections. Consecutive immunohistochemical staining was performed on a Ventana Benchmark ULTRA machine (Roche Diagnostics, Rotkreuz, Switzerland) by using the following antibodies: CD3 (SP7, Epredia, US), CD8 (C8/144B, Dako, US), CD45RO (UCHL1, Dako, US), FOXP3 (206D, BioLegend, US), CD68 (KP1, Dako, US), CD163 (MRQ-26, Cell Marque, US) (Figure 2). Heat-induced antigen retrieval was done by incubating with Tris-EDTA-based Cell Conditioning 1 (CC1) buffer (Roche Diagnostics, Rotkreuz, Switzerland) for all used antibodies. The protein expression was visualized using the UltraView DAB IHC Detection Kit (Ventana) and counterstained with Hematoxylin and Bluing Reagent (Ventana). The used protocol including the specific conditions for each antibody is provided in Supplementary Table 1. Image analysis Stained slides were digitalized by using NanoZoomer S60 slide scanner (Hamamatsu Photonics, Hamamatsu City, Japan) with 40x magnification. For further analysis, QuPath software was used as previously described 15 . Briefly, whole slide images of the same entity were loaded into batches and underwent semi-automatic tissue detection. Artifacts such as anthracotic pigments or hemosiderin, as well as healthy adjacent tissue were carefully cropped to avoid affecting accuracy of automatic processing (Supplementary Fig. 1). After positive cell detection the tissue area was subclassified in tumor, necrosis, and gaps. Densities deriving from positive cells and tumor maps were exported automatically by a Groovy script in QuPath. Ultimately, measurements of density by positive cells per mm 2 tumor were exported. Shannon entropy Prior to further analysis, the tumor maps were subjected to Gaussian blurring to refine the tumor region annotations. A Gaussian kernel with a sigma value of 15 pixels was applied to each tumor map. The selection of the sigma value was based on qualitative assessment through visual inspection to achieve a balance between smoothing the tumor boundaries while retaining their essential characteristics. Binary tumor masks were derived from the Gaussian-blurred tumor maps through a thresholding operation. The threshold value was set to the 75th percentile of pixel intensity values within each blurred tumor map. Pixels with intensities exceeding this threshold were assigned a value of 1, representing tumorous regions, while those below were assigned a value of 0, representing non-tumorous regions. This operation effectively segmented and extracted only the regions of interest, i.e., the tumor regions, from the original density heatmaps. Shannon entropy was computed for the extracted tumorous regions within the density heatmaps to quantify the heterogeneity of tumor density. Shannon entropy (H) is a statistical measure of randomness or uncertainty within a given region 31 . For each isolated tumor region, the entropy was calculated using the formula: n is the number of discrete intensity levels in the density values within the tumor region, and p ( xi ) represents the probability of each intensity level occurring within the region. The Shannon entropy thus provided a numerical representation of the spatial heterogeneity of tumor density within each tumor region. Statistical analysis Patient characteristics, cell densities and entropy indices are given as absolute numbers, percentage, median and range. Cell densities were measured in number of positive stained cells per mm 2 . To accommodate variations across different staining groups and to ensure consistency of entropy values across all cancer types, the calculated entropy values underwent additional normalization through min-max normalization. This technique standardized the Shannon entropy index values within each staining group to a range of 0 (low heterogeneity) to 1 (high heterogeneity). This step ensured that the entropy values were tailored to the staining groups facilitating comparisons across diverse cancer types. Difference in cell densities and Shannon entropy between primary tumors and matched BM are calculated by subtraction for all immune cell subpopulations. Time-to-BM (TTBM) is measured between histologically confirmed diagnosis of primary tumor and BM. In case the BM is diagnosed prior to the primary tumor TTBM is zero. Patients with diagnosis of BM within 2 months after primary tumor diagnosis are considered as synchronously diagnosed in accordance with previous studies 16,17 . Overall survival after BM is calculated between the date of histological diagnosis of the BM and death. A Wilcoxon signed rank test was used to test for a statistically significant difference between the median values of Primary Tumor and BM samples. To account for the treatment administered in-between and the differences among entities, we applied a linear mixed model including these variables and the sample origin as a fixed effect with interaction terms and a random effect for each patient. In this, as well as the subsequent analyses, we used a square root transformation on the data to meet the normality assumption. A spearman rank correlation coefficient was calculated to quantify the association between TTBM and difference in density/entropy. Additionally, a linear regression model was constructed to model the TTBM based on density/entropy in BM and primary tumor. The association between OS after BM and the density/entropy differences were inspected using Cox regression models and the difference in density/entropy as sole predictor variable. To test if the association is irrespective of Glasgow Prognostic Score (GPA), we constructed an additional model with an interaction term. Results were considered significant at a two-sided p-value < 0.05. As the exploratory analyses within this study are aimed at the generation of hypotheses, no adjustment for multiple testing was applied 18 . Statistical analysis and data visualization were performed using R 4.2.2 (The R Project for Statistical Computing, Vienna, Austria). The graphical abstract (Fig. 1) was illustrated with Biorender.com. Results Patients characteristics Overall, 104 patients with matched samples of the primary tumor and the BM samples were available: 78/104 (75%) non-small cell lung cancer, 18/104 (17%) breast cancer, 8/104 (8%) renal cell carcinomas. This population comprised of 56/104 males (53.8%) and 48/104 females (46.2%), whereas all breast cancer patients were female. The median age at BM diagnosis was 57 years (range 28 – 77). For detailed information concerning patients’ characteristics refer to Table 1. Difference of cell density and entropy between primary tumor and matched BM Overall, higher TIL density was found in primary tumors, while BM presented with a higher fraction of TAMs (Figure 3, Table 2). In detail, using Wilcoxon signed rank test of the immune cell densities we observed significantly higher densities of CD3 + (median 589 vs 63 cells/mm 2 , p<0.001), CD8 + (median 207 vs 33 cells/mm 2 , p<0.001) and FOXP3 + -TILs (median 163 vs 69 cells/mm 2 , p=0.045) in primary tumors. In contrast, higher CD68 + (median 866 vs 1081 cells/mm 2 , p=0.025,) and CD163 + -TAM densities were observed in matched BM (median 444 vs 811 cells/mm 2 , p<0.001). These differences were significant irrespective of applied systemic treatment or entity (all p<0.001). In CD45RO + TILs (median 361 vs 283 cells/mm 2 , p=0.3), no significant differences were observed between primary tumors and BM. Overall, higher entropy levels were observed in primary tumors compared to matched BM, arguing for a more heterogeneous infiltration throughout the primary tumor compared to BM (Figure 4). In detail, significantly higher entropy values in primary tumors were observed for CD3 + (median 0.8 vs 0.61, p<0.001) and FOXP3 + (median 0.50 vs 0.41, p=0.002) TILs. No significant difference in TAM entropy levels between primary and matched BM were present. Detailed summary of immune cell densities and entropies are shown in Table 2 and graphically in Figures 3 and 4, as well as Supplementary Figures 2-7. Association of difference in immune cell densities and entropy between primary tumor and BM and time to BM (TTBM) Next, we aimed to investigate whether the observed differences in TIL and TAM densities and entropies between primary tumor and matched BM are associated with the clinical course of these patients. Twenty-eight patients (26.9%) were synchronously diagnosed with primary tumor and BM. The median TTBM in the non-synchronously diagnosed patients (76/104; 73.1%) was 18.9 months (range 2.4 – 119.5). In the non-synchronously diagnose population and separated by entity, median TTBM in NSCLC patients was 15.4 months (range 2.4 – 73.4), 27.6 months in RCC (range 2.8 – 119.5) and 29.3 (range 13.1 – 64.6) in BC patients. Of all non-synchronously diagnosed patients 51/82 (62.2%) received systemic treatment between the diagnosis of primary tumor and BM. In treated patients, the median TTBM was 18.6 months (range 0 – 119.5) and in non-treated patients 13.9 months (range 0.2 – 73.4; p=0.21). A difference in CD3 + (p=0.033) and CD8 + (p=0.008), but not for CD68 + (p=0.8), CD163 + (p=0.7), FOXP3 + (p=0.5) or CD45RO + (p=0.4) infiltration between primary tumor and matched BM showed significant positive associations with TTBM. Higher immune cell densities in primary tumors of CD3 + -TILs (p=0.039) and CD163 + -TAMs were associated with shorter TTBM (p=0.024). Heterogeneity of primary tumors showed positive association with FOXP3 + TILs (p=0.031) and a negative association for CD163 + -TAMs (p=0.04) with TTBM. The difference in CD68 + entropy between primary tumor and BM was positively associated with TTBM (p=0.043). Data on synchronously and non-synchronously diagnosed patients are displayed in Supplementary Table 2. Association of immune cell densities and entropy with OS after BM diagnosis The differences in TIL and TAM density between the primary tumor and matched BM did not correlate with survival after BM diagnosis (p>0.05; Supplementary Figures 2 and 4). Further, also the difference in TIL and TAM entropies between primary tumor and matched BM did not correlate with survival after BM diagnosis (p>0.05; Supplementary Figure 3 and 5). However, higher GPA was significantly associated with lower overall survival (OS) after BM (p<0.01). Discussion Changes in the inflammatory microenvironment upon progression are likely to impact the clinical course as well as the response to immune-modulating therapies in patients with solid cancers. Here, we investigated the changes in the inflammatory microenvironment in a unique and large cohort of patients with matched extra- and intracranial tumor tissue. Overall, we could show that the inflammatory microenvironment composition changes from primary tumor to BM as the TME composition of the primary tumors presented with a TIL dominating infiltration, while in the BM more TAMs were observed. Further, higher CD3 + TIL and CD163 + TAM densities in the primary tumor were associated with shorter TTBM development. Our data of higher TAM density in BM compared to the matched primary tumor well supports studies demonstrating a potentially pro-tumorigenic phenotype of TAM in BM 8 and contributing to the formation of CNS metastases by modulating vessel integrity and function 19 . Importantly, in contrast to the previous body of evidence, we investigated the TAM composition and density in a particular large human cohort. In line, we strongly add to the preclinical evidence supporting the exploration of TAM as a therapeutic target, particularly in the context of BM development. In contrast to TAMs, we could demonstrate significantly lower cell densities of CD3 + , CD8 + and FOXP3 + -TILs in BMs compared to their primary tumor origin. Other than macrophages, TILs have recently been analyzed intensively for their cell densities and spatial distribution with solid tumor tissue 20 . Particularly in the face of immune-checkpoint inhibitors utilizing their treatment effects via TILs, the lower densities within BM might explain worse intracranial response rates 21 , as well as frequently observed mixed response indicated by shrinking of extracranial lesion, yet intracranial disease progression 22 . To address the issue of heterogeneity of infiltration in addition to density, we used Shannon Entropy deriving from heatmaps of tissue slides to objectively quantify heterogeneity of immune cell infiltration. Generally, we could show that higher entropy levels were present in primary tumors compared to matched BM indicating a more homogenously distribution in BM. Particularly, CD3 + and FOXP3 + TILs showed significantly more heterogenous infiltration in primary tumors. A more homogenous distribution of FOXP3 + regulatory T-cells in primary tumors was associated with shorter TTBM, compared to homogenously distributed CD163 + -TAMs in primary tumors being associated with longer time-to-BM. By using subtraction of entropy and cell density, we showed that a higher difference in CD3 + and CD8 + -TIL densities, as well as CD68 + entropies between primary tumor and matched BM were associated with longer TTBM. Therefore, entropy may demonstrate a new method to objectively quantify heterogeneity within the TME, emphasizing the importance of localization of inflammation. For correct interpretation of our findings, several limitations of this study must be addressed: Our cohort comprises a relatively large population of matched tumor tissue samples. (i) Samples were recruited over a long period of time, therefore anti-cancer treatment standards have changed, potentially affecting survival parameters. However, we addressed this confounder by putting treatment agents and modalities in multivariable analysis. (ii) The used entropy values only provide information about the heterogeneity of the infiltration yet no specific data about localization. (iii) Additionally, we only included patients who developed BM during the observation period, hence potential patients with longer TTBM exceeding this period were not included. (iv) The retrospective design of our study precludes definite conclusions, (v) as well as the skewed distribution of tumor entities which did not allow us to test for entity-specific correlation or genetic subpopulations. Nevertheless, byutilizing this unique cohort of matched primary tumor and BM FFPE whole slide tissue samples, we could address the missing gap of intraindividual spatial and longitudinal changes in the TME of solid tumors. In summary, we demonstrated higher TIL densities in primary tumors, compared to higher TAM densities in BM. Higher cell densities of CD3 + TILs and CD163 + TAMs in primary tumors showed significant associations with shorter TTBM, while a larger difference between CD3 + and CD8 + differences between primary tumor and BM was associated with longer TTBM. Differences in immune cell densities indicate potentially diverting impact of the innate and the adaptive immune system in the process of brain metastatic seeding. Additional, high-dimensional tissue analysis of the presented matched cohort is planned to further gain insight in the intraindividual differences of metastatic diseases. List of abbreviations BC Breast cancer BM Brain metastasis CC1 Cell Conditioning 1 CNS Central nervous system EC Ethics committee FFPE Formalin-fixed and paraffin-embedded GMCSF Granulocyte-macrophage colony-stimulating factor GPA Glasgow prognostic score H Shannon Entropy ICI Immune-checkpoint inhibitor MCSF Macrophage colony-stimulating factor NSCLC Non-small cell lung cancer OS Overall survival RCC Renal cell carcinoma TAM Tumor-associated macrophages TIL Tumor-infiltrating lymphocytes TME Tumor microenvironment TTBM Time-to-brain metastasis Declarations Ethics approval and consent to participate This study was approved by the Ethics Committees of the Medical University of Vienna (approval no. 1692/2022, 1895/2022). Consent for publication Not applicable. Availability of data and material Further data will be shared upon reasonable request to the corresponding author. Competing interests M.J.M. received research funding from Bristol-Myers Squibb and travel support from Pierre Fabre. A.S.B. received research support from Daiichi Sankyo, Roche, and honoraria for lectures, consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. Z.S. is a former employee of Boehringer Ingelheim and Turbine AI. M.P. received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen. J.N.K declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore he holds shares in StratifAI GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius. Funding The financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged. Authors' contributions M.K. conceived the present idea, designed, and performed the analysis, contributed to sample preparation, interpretated of the results and wrote the manuscript. D.C. performed the analysis of density maps to obtain entropy values and contributed to the interpretation of the results and critical review of the manuscript. C.P. contributed to sample preparation, interpretation of the results and critical review of the manuscript. E.T. contributed to sample preparation, interpretation of the results and critical review of the manuscript. M.J.M. contributed to the imaging analysis with QuPath, the interpretation of the results and critical review of the manuscript. A.S. contributed to obtaining the patient characteristics, the interpretation of the results and critical review of the manuscript. Z.S. contributed to the interpretation of the results and critical review of the manuscript. Z.I.C. helped supervise the project, design the computational framework and the analysis to obtain entropy values and contributed to the interpretation of the results and critical review of the manuscript. L.B. contributed to the statistical analysis, the interpretation of the results and critical review of the manuscript. J.H. contributed to the interpretation of the results and critical review of the manuscript. L.M. contributed to the interpretation of the results and critical review of the manuscript. G.H. contributed to the interpretation of the results and critical review of the manuscript. M.P. helped supervise the project and contributed to interpretation of the results and helped writing the manuscript. J.N.K helped supervise the project, design the computational framework and the analysis to obtain entropy values and contributed to the interpretation of the results and critical review of the manuscript. A.S.B. devised the main conceptual ideas, helped designing the analysis, contributed to the interpretation of the results and helped writing the manuscript. All named authors meet all four criteria for (co)-authorship provided by the Good Scientific Practice guidelines of the Medical University of Vienna. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Jin MZ, Jin WL: The updated landscape of tumor microenvironment and drug repurposing. 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NA .. not applicable NSCLC n=78 (75%) Breast Cancer n=18 (17.3%) RCC n=8 (7.7%) All n=104 (100%) Sex male 50 (64.1%) 0 7 (87.5%) 57 (53.8%) female 28 (35.9%) 18 (100%) 1 (12.5%) 47 (46.2%) Age diagnosis BM median 58.1 46 54.5 57 [years] range 44-77 28-73 44-69 28-77 Year of BM diagnosis 1990-2000 15 (19.2%) 3 (16.7%) 5 (62.5%) 23 (22.1%) 2001-2010 33 (42.3%) 6 (33.3%) 3 (37.5%) 42 (40.4%) 2011-2020 30 (38.5%) 9 (50.0%) 0 39 (37.5%) Immunohistochemistry ER positive NA 8 (44.4%) NA 8 (7.7%) PR positive NA 4 (22.2%) NA 4 (3.8%) HER2 positive NA 3 (16.7%) NA 3 (2.9%) Histopathology Adenocarcinoma 61 (78.3%) 61 (58.7%) Squamous Cell Carcinoma 14 (17.9%) 14 (13.5%) Large Cell Carcinoma 3 (3.8%) 3 (2.9%) Ductal 15 (83.3%) 15 (14.4%) Lobular 2 (11.1%) 2 (1.9%) Adenocystic 1 (5.6%) 1 (0.9%) Clear Cell Carcinoma 8 (100%) 8 (7.7%) Subtypes Luminal 8 (44.4%) 8 (7.7%) HER2 1 (5.6%) 1 (0.9%) TNBC 9 (50%) 9 (8.6%) Grading G1 1 (1.3%) 1 (5.6%) 0 2 (1.9%) G2 23 (29.5%) 5 (27.7%) 5 (62.5%) 33 (31.7%) G3 26 (33.4%) 12 (66.7%) 0 38 (36.6%) G4 1 (1.3%) 0 2 (25.0%) 3 (2.9%) unknown 27 (34.5%) 0 1 (12.5%) 28 (26.9%) Stage at primary diagnosis Synchronous 26 (33.4%) 1 (5.6%) 1 (12.5%) 28 (26.9%) Neoadjuvant BM therapy Chemotherapy 33 (42.3%) 16 (88.9%) 5 (62.5%) 54 (51.9%) Radiotherapy 5 (6.4%) 0 1 (12.5%) 6 (5.8%) Time-to-BM median median 44.8 126.2 118.1 57.1 [weeks] range 0-320.0 0-281.3 3.6-520.6 0-520.6 Overall survival median 62.4 28.0 61.7 53.7 from BM diagnosis range 3.0-787.1 0.9-164.4 1.9-262.9 0.9-787.1 [weeks] censored 12 (15.4%) 3 (16.7%) 0 15 (14.4%) Table 2. Immune cell densities and entropy values. BC...breast cancer, RCC...renal cell carcinoma, TAMs...Tumor-associated macrophages, TILs...Tumor-infiltrating lymphocytes TAMs TILs Cell densities [cell/mm 2 ] CD68 CD163 CD3 CD8 FOXP3 CD45RO Overall Primary median 866.0 443.9 589.3 207.4 162.9 361.2 range 3.8-7738.9 51.1-1785.5 2.5-6037.9 2.3-9005.4 2.2- 1222.3 8.3- 7449.2 BM median 1081.0 810.9 62.7 33.4 68.9 282.8 range 79.3-9261.0 14.3-5109.6 0.4- 2322.3 0.0-1758.5 1.5- 2480.7 2.6- 5341.1 NSCLC Primary median 866.0 472.5 639.8 1989.0 165.7 439.4 range 3.8- 6417.6 65.7- 1785.5 26.7- 2347.9 10.5- 1516.8 13.0- 1021.2 8.8- 2747.0 BM median 1047.5 775.9 66.4 26.3 66.9 344.1 range 79.3- 9261.0 14.3- 2879.8 0.5- 2322.3 0.5- 1486.2 1.5- 2480.7 2.6- 5341.1 BC Primary median 306.3 300.6 360.7 388.0 151.1 160.0 range 98.9- 7738.9 51.1- 1359.1 2.5- 6037.9 2.3- 9005.4 2.2- 1222.3 8.3- 7449.2 BM median 961.5 978.7 22.6 32.2 55.2 93.2 range 237.6- 4483.2 155.6- 2752.5 5.8- 158.9 1.6- 135.4 5.7- 1757.4 6.7- 1917.5 RCC Primary median 1613.4 575.3 490.1 540.2 76.0 142.4 range 425.7- 2163.8 238.3- 988.7 72.3- 1374.1 69.2- 2206.0 11.9- 945.5 43.0- 671.1 BM median 1928.8 1468.0 181.9 169.2 92.8 199.8 range 871.3- 5520.9 447.6- 5109.6 11.4- 1441.6 0.0- 1758.5 6.3- 673.4 49.9- 2470.4 Shannon Entropy [index] CD68 CD163 CD3 CD8 FOXP3 CD45RO Overall Primary median 0.43 0.51 0.42 0.24 0.34 0.34 range 0.0-1.0 0.12-0.77 0.11-1.0 0.0-1.0 0.00-1.0 0.06-0.81 BM median 0.44 0.67 0.31 0.19 0.27 0.38 range 0.09-0.82 0.0-1.0 0.0-0.92 0.0-0.66 0.0-1.0 0.0-1.0 NSCLC Primary median 0.45 0.53 0.42 0.23 0.36 0.35 range 0.11-0.8 0.32-0.77 0.11-0.7 0.0-0.49 0.12-0.64 0.07-0.81 BM median 0.46 0.70 0.34 0.19 0.27 0.38 range 0.09-0.8 0.0-0.94 0.0-0.92 0.05-0.66 0.0-1.0 0.0-1.0 BC Primary median 0.23 0.4 0.37 0.25 0.28 0.33 range 0.0-1.0 0.12-0.67 0.15-1.0 0.0-1.0 0.0-1.0 0.06-0.65 BM median 0.31 0.17 0.27 0.15 0.27 0.35 range 0.09-0.62 0.0-0.79 0.05-0.5 0.08-0.25 0.12-0.87 0.01-0.68 RCC Primary median 0.42 0.48 0.56 0.36 0.17 0.18 range 0.11-0.62 0.32-0.59 0.31-0.72 0.14-0.59 0.08-0.52 0.1-0.43 BM median 0.37 0.63 0.37 0.25 0.25 0.31 range 0.26-0.82 0.54-1.0 0.3-0.74 0.0-0.5 0.1-0.38 0.11-0.72 Additional Declarations Competing interest reported. M.J.M. received research funding from Bristol-Myers Squibb and travel support from Pierre Fabre. A.S.B. received research support from Daiichi Sankyo, Roche, and honoraria for lectures, consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. Z.S. is a former employee of Boehringer Ingelheim and Turbine AI. M.P. received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp & Dome, Tocagen. J.N.K declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore he holds shares in StratifAI GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius. Supplementary Files SupplFig1Removal.docx Supplementary Figure 1 – Manual removal of artefacts to avoid false positive detection. SupplFig2Density.docx Supplementary Figure 2 – Differences in cell densities between primary tumor and matched BM. SupplFig3Density.docx Supplementary Figure 3 – Differences in cell entropies between primary tumor and matched BM. SupplFig4SqrtDensity.docx Supplementary Figure 4 – Differences in cell densities between primary tumor and matched BM on a square root scale. SupplFig5SqrtEntropy.docx Supplementary Figure 5 – Differences in cell entropies between primary tumor and matched BM on a square root scale. SupplFig6SqrtDensity.pdf Supplementary Figure 6 – Square root cell densities of primary tumors and matched BM. SupplFig7SqrtEntropy.pdf Supplementary Figure 7 – Spatial heterogeneity of primary tumors and matched BM measured by squared Shannon entropy. SupplTable1IHCAntibodies.docx Supplementary Table 1 – Overview of used antibody clones for IHC stainings with Ventana. SupplTable2Synch.docx Supplementary Table 2 – Immune cell densities and entropy values in patients with synchronous and non-synchronous diagnosis. Cite Share Download PDF Status: Published Journal Publication published 19 Feb, 2025 Read the published version in Acta Neuropathologica Communications → Version 1 posted Editorial decision: Revision requested 07 Jan, 2025 Reviews received at journal 21 Dec, 2024 Reviews received at journal 15 Dec, 2024 Reviewers agreed at journal 12 Dec, 2024 Reviewers agreed at journal 12 Dec, 2024 Reviewers agreed at journal 30 Nov, 2024 Reviewers invited by journal 22 Oct, 2024 Editor assigned by journal 02 Sep, 2024 Submission checks completed at journal 02 Sep, 2024 First submitted to journal 29 Aug, 2024 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. 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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-5000054","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":352239774,"identity":"4c330059-1cab-497f-89bf-48b473bf028b","order_by":0,"name":"Markus Kleinberger","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Markus","middleName":"","lastName":"Kleinberger","suffix":""},{"id":352239775,"identity":"77f54f01-622a-4d8a-b65f-13973cab4472","order_by":1,"name":"Didem Çifçi","email":"","orcid":"","institution":"Else Kroener Fresenius Center for Digital Health, Medical Faculty Carl Gustav Carus, Technical University Dresden, Dresden, Germany","correspondingAuthor":false,"prefix":"","firstName":"Didem","middleName":"","lastName":"Çifçi","suffix":""},{"id":352239776,"identity":"babf1261-1117-4c3f-b4a9-fec5ec5d2ecd","order_by":2,"name":"Christina Paiato","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Christina","middleName":"","lastName":"Paiato","suffix":""},{"id":352239777,"identity":"db996e85-406c-46c3-b2c0-545f1fe506c1","order_by":3,"name":"Erwin Tomasich","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Erwin","middleName":"","lastName":"Tomasich","suffix":""},{"id":352239778,"identity":"8ebc69bc-ab07-4b47-84ff-b4682e46da0a","order_by":4,"name":"Maximilian Johannes Mair","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Maximilian","middleName":"Johannes","lastName":"Mair","suffix":""},{"id":352239779,"identity":"14a2910b-64b7-42d4-aacb-aaeb243ecc74","order_by":5,"name":"Ariane Steindl","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Ariane","middleName":"","lastName":"Steindl","suffix":""},{"id":352239781,"identity":"9a0b393e-a4a0-44eb-99ff-32464d159c59","order_by":6,"name":"Zoltán Spiró","email":"","orcid":"","institution":"Division of Oncology, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Christian Doppler Laboratory for Personalized Immunotherapy, Department of Medicine I, Medical University of Vienna, Vienna, Austria; Precision Medicine Technologies, CBmed GmbH, Graz, Austria","correspondingAuthor":false,"prefix":"","firstName":"Zoltán","middleName":"","lastName":"Spiró","suffix":""},{"id":352239783,"identity":"cddb1587-19d7-4d5a-a47c-f98d88651fe6","order_by":7,"name":"Zunamys I. 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(2) After scanning digitalized slides were further analyzed with QuPath. Tissue was detected automatically, and false positive contamination was removed manually. (3) Positive cell detection for tissue annotations. (4) Pixel classification for regions of interest (tumor, necrosis, gap). Tumor maps generated and exported by automatic script. (5) Generating density maps for positive cells. (6) Entropy indices were calculated by using density and tumor map. Shannon entropy and cell densities [cells/mm\u003csup\u003e2\u003c/sup\u003e] were correlated with patient characteristics and outcome parameters. Graphical abstract was designed with Biorender.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/57ad648d6e068d525be97a7a.png"},{"id":66949201,"identity":"b318c57f-5e2f-47e3-9ac3-5e888bf82a5d","added_by":"auto","created_at":"2024-10-18 10:01:57","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":727406,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmunohistochemical staining of TILs and TAMs in primary tumor and matched brain metastasis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/acff180184a7d09d5d323588.png"},{"id":66948196,"identity":"421f6249-c65a-45cc-898b-45345b72adda","added_by":"auto","created_at":"2024-10-18 09:53:58","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":28506,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImmune cell densities of primary tumors and matched brain metastases.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/c738a010d83a972f3262711f.png"},{"id":66948197,"identity":"e952e1fc-a14d-4139-9ab9-4d39f5baa61e","added_by":"auto","created_at":"2024-10-18 09:53:59","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSpatial heterogeneity of immune cells of primary tumors and matched BM measured by Shannon entropy.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/fb03e640ecc3a14b8d22ba93.png"},{"id":77052672,"identity":"72aa45f9-cd2a-4241-b9e1-d0977139d031","added_by":"auto","created_at":"2025-02-24 16:22:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2375959,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/3fd9457b-f779-450a-9d53-bfff0ab59106.pdf"},{"id":66948188,"identity":"4acf22a1-b88b-4892-a881-e097ec3a05eb","added_by":"auto","created_at":"2024-10-18 09:53:56","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":9022641,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1 – Manual removal of artefacts to avoid false positive detection.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig1Removal.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/5d50893ee710d4ec22e42248.docx"},{"id":66948239,"identity":"c1c634f2-1ce1-4751-b5cc-254d0ca36606","added_by":"auto","created_at":"2024-10-18 09:54:00","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":83023,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2 – Differences in cell densities between primary tumor and matched BM.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig2Density.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/b4363e25bef5ccc6cf7d0fd8.docx"},{"id":66948238,"identity":"0f9c1c35-ecdb-4619-8316-fea7bba74d5c","added_by":"auto","created_at":"2024-10-18 09:54:00","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":115075,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3 – Differences in cell entropies between primary tumor and matched BM.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig3Density.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/b436704136e920be9f1bedb5.docx"},{"id":66948187,"identity":"f0d26623-38aa-4bf3-9cf9-e96834f67ab7","added_by":"auto","created_at":"2024-10-18 09:53:56","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":110920,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4 – Differences in cell densities between primary tumor and matched BM on a square root scale.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig4SqrtDensity.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/2c45e1e74cc092fe4e382b15.docx"},{"id":66948189,"identity":"638ed394-6cb4-46f7-974b-6d083b6d3b45","added_by":"auto","created_at":"2024-10-18 09:53:56","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":108716,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5 – Differences in cell entropies between primary tumor and matched BM on a square root scale.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig5SqrtEntropy.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/2c096ffd13d26f2200e46bf0.docx"},{"id":66948194,"identity":"02e98afb-12b2-40e9-bcaf-4b7ea4866ad9","added_by":"auto","created_at":"2024-10-18 09:53:58","extension":"pdf","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":2087801,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 6 – Square root cell densities of primary tumors and matched BM.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig6SqrtDensity.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/c3fd40c2132dd44e58ec84c7.pdf"},{"id":66948240,"identity":"60f0f319-910c-42c3-993b-0e8c1dca54f5","added_by":"auto","created_at":"2024-10-18 09:54:01","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":1870243,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 7 – Spatial heterogeneity of primary tumors and matched BM measured by squared Shannon entropy.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplFig7SqrtEntropy.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/9d6ad32660c499d38e491415.pdf"},{"id":66948174,"identity":"9635b414-2b6b-4013-96a3-d8ad746d1fa9","added_by":"auto","created_at":"2024-10-18 09:53:55","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":15551,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 1 – Overview of used antibody clones for IHC stainings with Ventana.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplTable1IHCAntibodies.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/7d482a1d4cd72a9c79910319.docx"},{"id":66948190,"identity":"a400cf6b-8f1a-4235-9a6c-3ebce7f78caf","added_by":"auto","created_at":"2024-10-18 09:53:57","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":21300,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Table 2 – Immune cell densities and entropy values in patients with synchronous and non-synchronous diagnosis.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"SupplTable2Synch.docx","url":"https://assets-eu.researchsquare.com/files/rs-5000054/v1/af3d772cae6d1fb765cc9965.docx"}],"financialInterests":"Competing interest reported. M.J.M. received research funding from Bristol-Myers Squibb and travel support from Pierre Fabre.\nA.S.B. received research support from Daiichi Sankyo, Roche, and honoraria for lectures, consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie. \nZ.S. is a former employee of Boehringer Ingelheim and Turbine AI.\nM.P. received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp \u0026 Dome, Tocagen. \nJ.N.K declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore he holds shares in StratifAI GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius.","formattedTitle":"Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases","fulltext":[{"header":"Introduction ","content":"\u003cp\u003eThe tumor microenvironment (TME) represents an ecosystem consisting of a variety of malignant and non-malignant cells, organized through highly complex cell-cell interactions, subcellular communication and crosstalk\u003csup\u003e1\u003c/sup\u003e. Intra-tumoral immune cells like tumor-infiltrating lymphocytes (TILs) or tumor-associated macrophages (TAMs) are the main effector cells of the inflammatory microenvironment\u003csup\u003e2,3\u003c/sup\u003e. Particularly in the face of novel treatment options the presence of specific immune cell subtypes within the unique TME of brain metastases has demonstrated associations with higher intracranial response rates to immune checkpoint inhibitors (ICI)\u003csup\u003e4\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHowever, concerning the high heterogeneity within the TME additional information about the spatial distribution is essential to sufficiently analyze immune cell infiltration pattern and to put them into perspective with biological traits\u003csup\u003e5\u003c/sup\u003e. Besides the intratumoral genetic and epigenetic characteristics the localization and co-localization within the TME are pivotal for cellular crosstalk of cancer and immune effector cells\u003csup\u003e6,7\u003c/sup\u003e. Although previous studies addressed the characterization of the TME in high-level resolution, in-depth information on differences between tumor sites is missing due to the infrequent availability of more than one tumor sample from the same patient\u0026nbsp;\u003csup\u003e8-14\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn the current study, we aimed to improve the knowledge on intrapersonal differences and their association with their clinical course by using a unique cohort of 104 patients with matched tissue samples deriving from the primary tumor and the brain metastasis site. \u0026nbsp;\u003c/p\u003e"},{"header":"Material and methods ","content":"\u003cp\u003e\u003cstrong\u003ePatient cohort\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients diagnosed with histologically confirmed and intracranial metastasized non-small cell lung cancer (NSCLC), breast cancer (BC) and renal cell carcinoma (RCC) were retrospectively identified from the Vienna Brain Metastasis Registry. Patients with tissue samples of the primary tumor and BM via tumor resection or excision between 01/1990 and 12/2022 at the Medical University of Vienna were included. Patients with insufficient formalin-fixed and paraffin-embedded (FFPE) tumor samples (e.g., fine needle aspiration) were excluded. FFPE specimens available for scientific purposes were obtained from the Department of Pathology and Department of Neurology, Division of Neuropathology and Neurochemistry.\u0026nbsp;Clinical data were extracted from the Vienna Brain Metastasis Registry and electronic patient charts. This retrospective exploratory study was approved by the Ethics Committee of the Medical University of Vienna (EC approvals no. 1692/2022, 1895/2022) in accordance with the Declaration of Helsinki. Due to the retrospective nature of the study the necessity of written informed consent was waived by the competent authorities. An illustrated workflow is presented in Figure 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFFPE tumor blocks of primary and matched BM were prepared by cutting in 4\u0026micro;m thick sections. Consecutive immunohistochemical staining was performed on a Ventana Benchmark ULTRA machine (Roche Diagnostics, Rotkreuz, Switzerland) by using the following antibodies: CD3 (SP7, Epredia, US), CD8 (C8/144B, Dako, US), CD45RO (UCHL1, Dako, US), FOXP3 (206D, BioLegend, US), CD68 (KP1, Dako, US), CD163 (MRQ-26, Cell Marque, US) (Figure 2).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHeat-induced antigen retrieval was done by incubating with Tris-EDTA-based Cell Conditioning 1 (CC1) buffer (Roche Diagnostics, Rotkreuz, Switzerland) for all used antibodies. The protein expression was visualized using the UltraView DAB IHC Detection Kit (Ventana) and counterstained with Hematoxylin and Bluing Reagent (Ventana). The used protocol including the specific conditions for each antibody is provided in Supplementary Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImage analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStained slides were digitalized by using NanoZoomer S60 slide scanner (Hamamatsu Photonics, Hamamatsu City, Japan) with 40x magnification. For further analysis, QuPath software was used as previously described\u003csup\u003e15\u003c/sup\u003e. Briefly, whole slide images of the same entity were loaded into batches and underwent semi-automatic tissue detection. Artifacts such as anthracotic pigments or hemosiderin, as well as healthy adjacent tissue were carefully cropped to avoid affecting accuracy of automatic processing (Supplementary Fig. 1). After\u0026nbsp;positive cell detection the tissue area was subclassified in tumor, necrosis, and gaps. Densities deriving from positive cells and tumor maps were exported automatically by a Groovy script in QuPath. Ultimately, measurements of density by positive cells per mm\u003csup\u003e2\u003c/sup\u003e tumor were exported.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShannon entropy\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePrior to further analysis, the tumor maps were subjected to Gaussian blurring to refine the tumor region annotations. A Gaussian kernel with a sigma value of 15 pixels was applied to each tumor map. The selection of the sigma value was based on qualitative assessment through visual inspection to achieve a balance between smoothing the tumor boundaries while retaining their essential characteristics. Binary tumor masks were derived from the Gaussian-blurred tumor maps through a thresholding operation. The threshold value was set to the 75th percentile of pixel intensity values within each blurred tumor map. Pixels with intensities exceeding this threshold were assigned a value of 1, representing tumorous regions, while those below were assigned a value of 0, representing non-tumorous regions. This operation effectively segmented and extracted only the regions of interest, i.e., the tumor regions, from the original density heatmaps.\u003c/p\u003e\n\u003cp\u003eShannon entropy was computed for the extracted tumorous regions within the density heatmaps to quantify the heterogeneity of tumor density. Shannon entropy (H) is a statistical measure of randomness or uncertainty within a given region\u003csup\u003e31\u003c/sup\u003e. For each isolated tumor region, the entropy was calculated using the formula:\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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\" height=\"42\" width=\"217\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003en\u003c/em\u003e is the number of discrete intensity levels in the density values within the tumor region, and \u003cem\u003ep\u003c/em\u003e(\u003cem\u003exi\u003c/em\u003e) represents the probability of each intensity level occurring within the region. The Shannon entropy thus provided a numerical representation of the spatial heterogeneity of tumor density within each tumor region.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatient characteristics, cell densities and entropy indices are given as absolute numbers, percentage, median and range. Cell densities were measured in number of positive stained cells per mm\u003csup\u003e2\u003c/sup\u003e. To accommodate variations across different staining groups and to ensure consistency of entropy values across all cancer types, the calculated entropy values underwent additional normalization through min-max normalization. This technique standardized the Shannon entropy index values within each staining group to a range of 0 (low heterogeneity) to 1 (high heterogeneity). This step ensured that the entropy values were tailored to the staining groups facilitating comparisons across diverse cancer types. Difference in cell densities and Shannon entropy between primary tumors and matched BM are calculated by subtraction for all immune cell subpopulations. Time-to-BM (TTBM) is measured between histologically confirmed diagnosis of primary tumor and BM. In case the BM is diagnosed prior to the primary tumor TTBM is zero. Patients with diagnosis of BM within 2 months after primary tumor diagnosis are considered as synchronously diagnosed in accordance with previous studies\u003csup\u003e16,17\u003c/sup\u003e. Overall survival after BM is calculated \u0026nbsp;between the date of histological diagnosis of the BM and death. \u0026nbsp;\u0026nbsp;A Wilcoxon signed rank test was used to test for a statistically significant difference between the median values of Primary Tumor and BM samples. To account for the treatment administered in-between and the differences among entities, we applied a linear mixed model including these variables and the sample origin as a fixed effect with interaction terms and a random effect for each patient. In this, as well as the subsequent analyses, we used a square root transformation on the data to meet the normality assumption. A spearman rank correlation coefficient was calculated to quantify the association between TTBM and difference in density/entropy. Additionally, a linear regression model was constructed to model the TTBM based on density/entropy in BM and primary tumor. The association between OS after BM and the density/entropy differences were inspected using Cox regression models and the difference in density/entropy as sole predictor variable. To test if the association is irrespective of Glasgow Prognostic Score (GPA), we constructed an additional model with an interaction term. Results were considered significant at a two-sided p-value \u0026lt; 0.05. As the exploratory analyses within this study are aimed at the generation of hypotheses, no adjustment for multiple testing was applied\u003csup\u003e18\u003c/sup\u003e. Statistical analysis and data visualization were performed using R 4.2.2 (The R Project for Statistical Computing, Vienna, Austria). The graphical abstract (Fig. 1) was illustrated with Biorender.com.\u0026nbsp;\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003e\u003cu\u003ePatients characteristics\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 104 patients with matched samples of the primary tumor and the BM samples were available: 78/104\u0026nbsp;(75%)\u0026nbsp;non-small cell lung cancer, 18/104 (17%) breast cancer, 8/104 (8%) renal cell carcinomas. This population comprised of 56/104 males (53.8%) and 48/104 females (46.2%), whereas all breast cancer patients were female. The median age at BM diagnosis was 57 years (range 28 \u0026ndash; 77). \u0026nbsp;For detailed information concerning patients\u0026rsquo; characteristics refer to Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eDifference of cell density and entropy between primary tumor and matched BM\u0026nbsp;\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eOverall, higher TIL density was found in primary tumors, while BM presented with a higher fraction of TAMs (Figure 3, Table 2). In detail, using Wilcoxon signed rank test of the immune cell densities we observed significantly higher densities of CD3\u003csup\u003e+\u003c/sup\u003e (median \u0026nbsp;589 vs 63 cells/mm\u003csup\u003e2\u003c/sup\u003e, p\u0026lt;0.001), CD8\u003csup\u003e+\u003c/sup\u003e (median 207 vs 33 cells/mm\u003csup\u003e2\u003c/sup\u003e, p\u0026lt;0.001) and FOXP3\u003csup\u003e+\u003c/sup\u003e-TILs \u0026nbsp;(median \u0026nbsp;163 vs 69 cells/mm\u003csup\u003e2\u003c/sup\u003e, p=0.045) in primary tumors. In contrast, higher CD68\u003csup\u003e+\u003c/sup\u003e (median 866 vs 1081 cells/mm\u003csup\u003e2\u003c/sup\u003e, p=0.025,) and CD163\u003csup\u003e+\u003c/sup\u003e-TAM densities were observed in matched BM (median 444 vs 811 cells/mm\u003csup\u003e2\u003c/sup\u003e, p\u0026lt;0.001). These differences were\u003csup\u003e\u0026nbsp;\u003c/sup\u003esignificant irrespective of applied systemic treatment or entity (all p\u0026lt;0.001). In CD45RO\u003csup\u003e+\u003c/sup\u003e TILs (median 361 vs 283 cells/mm\u003csup\u003e2\u003c/sup\u003e, p=0.3), no significant differences were observed between primary tumors and BM.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOverall, higher entropy levels were observed in primary tumors compared to matched BM, arguing for a more heterogeneous infiltration throughout the primary tumor compared to BM (Figure 4). In detail, significantly higher entropy values in primary tumors were observed for CD3\u003csup\u003e+\u003c/sup\u003e (median 0.8 vs 0.61, p\u0026lt;0.001) and FOXP3\u003csup\u003e+\u003c/sup\u003e (median 0.50 vs 0.41, p=0.002) TILs. No significant difference in TAM entropy levels between primary and matched BM were present. Detailed summary of immune cell densities and entropies are shown in Table 2 and graphically in Figures 3 and 4, as well as Supplementary Figures 2-7.\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAssociation of difference in immune cell densities and entropy between primary tumor and BM and time to BM (TTBM)\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eNext, we aimed to investigate whether the observed differences in TIL and TAM densities and entropies between primary tumor and matched BM are associated with the clinical course of these patients. Twenty-eight patients (26.9%) were synchronously diagnosed with primary tumor and BM. The median TTBM in the non-synchronously diagnosed patients (76/104; 73.1%) was 18.9 months (range 2.4 \u0026ndash; 119.5). In the non-synchronously diagnose population and separated by entity, median TTBM in NSCLC patients was 15.4 months (range 2.4 \u0026ndash; 73.4), 27.6 months in RCC (range 2.8 \u0026ndash; 119.5) and 29.3 (range 13.1 \u0026ndash; 64.6) in BC patients. Of all non-synchronously diagnosed patients 51/82 (62.2%) received systemic treatment between the diagnosis of primary tumor and BM. In treated patients, the median TTBM was 18.6 months (range 0 \u0026ndash; 119.5) and in non-treated patients 13.9 months (range 0.2 \u0026ndash; 73.4; p=0.21).\u003c/p\u003e\n\u003cp\u003eA difference in CD3\u003csup\u003e+\u003c/sup\u003e (p=0.033) and CD8\u003csup\u003e+\u003c/sup\u003e (p=0.008), but not for CD68\u003csup\u003e+\u003c/sup\u003e (p=0.8), CD163\u003csup\u003e+\u003c/sup\u003e (p=0.7), FOXP3\u003csup\u003e+\u003c/sup\u003e (p=0.5) or CD45RO\u003csup\u003e+\u003c/sup\u003e (p=0.4) infiltration between primary tumor and matched BM showed significant positive associations with TTBM. Higher immune cell densities in primary tumors of CD3\u003csup\u003e+\u003c/sup\u003e-TILs (p=0.039) and CD163\u003csup\u003e+\u003c/sup\u003e-TAMs were associated with shorter TTBM (p=0.024). Heterogeneity of primary tumors showed positive association with FOXP3\u003csup\u003e+\u003c/sup\u003e TILs (p=0.031) and a negative association for CD163\u003csup\u003e+\u003c/sup\u003e-TAMs (p=0.04) with TTBM. The difference in CD68\u003csup\u003e+\u003c/sup\u003e entropy between primary tumor and BM was positively associated with TTBM (p=0.043). Data on synchronously and non-synchronously diagnosed patients are displayed in Supplementary Table 2.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cu\u003eAssociation of immune cell densities and entropy with OS after BM diagnosis\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eThe differences in TIL and TAM density between the primary tumor and matched BM did not correlate with survival after BM diagnosis (p\u0026gt;0.05; Supplementary Figures 2 and 4). Further, also the difference in TIL and TAM entropies between primary tumor and matched BM did not correlate with survival after BM diagnosis (p\u0026gt;0.05; Supplementary Figure 3 and 5). However, higher GPA was significantly associated with lower overall survival (OS) after BM (p\u0026lt;0.01).\u003c/p\u003e"},{"header":"Discussion ","content":"\u003cp\u003eChanges in the inflammatory microenvironment upon progression are likely to impact the clinical course as well as the response to immune-modulating therapies in patients with solid cancers. Here, we investigated the changes in the inflammatory microenvironment in a unique and large cohort of patients with matched extra- and intracranial tumor tissue. Overall, we could show that the inflammatory microenvironment composition changes from primary tumor to BM as the TME composition of the primary tumors presented with a TIL dominating infiltration, while in the BM more TAMs were observed. Further, higher CD3\u003csup\u003e+\u003c/sup\u003e TIL and CD163\u003csup\u003e+\u003c/sup\u003e TAM densities in the primary tumor were associated with shorter TTBM development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur data of higher TAM density in BM compared to the matched primary tumor well supports studies demonstrating a potentially pro-tumorigenic phenotype of TAM in BM\u003csup\u003e8\u003c/sup\u003e and contributing to the formation of CNS metastases by modulating vessel integrity and function\u003csup\u003e19\u003c/sup\u003e. Importantly, in contrast to the previous body of evidence, we investigated the TAM composition and density in a particular large human cohort. In line, we strongly add to the preclinical evidence supporting the exploration of TAM as a therapeutic target, particularly in the context of BM development.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn contrast to TAMs, we could demonstrate significantly lower cell densities of CD3\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e and FOXP3\u003csup\u003e+\u003c/sup\u003e-TILs in BMs compared to their primary tumor origin. Other than\u0026nbsp;macrophages, TILs have recently been analyzed intensively for their cell densities and spatial distribution with solid tumor tissue\u003csup\u003e20\u003c/sup\u003e. Particularly in the face of immune-checkpoint inhibitors utilizing their treatment effects via TILs, the lower densities within BM might explain worse intracranial response rates\u003csup\u003e21\u003c/sup\u003e, as well as frequently observed mixed response indicated by shrinking of extracranial lesion, yet intracranial disease progression\u003csup\u003e22\u003c/sup\u003e.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo address the issue of heterogeneity of infiltration in addition to density, we used Shannon Entropy deriving from heatmaps of tissue slides to objectively quantify heterogeneity of immune cell infiltration. Generally, we could show that higher entropy levels were present in primary tumors compared to matched BM indicating a more homogenously distribution in BM. Particularly,\u0026nbsp;CD3\u003csup\u003e+\u003c/sup\u003e and FOXP3\u003csup\u003e+\u003c/sup\u003e TILs showed significantly more heterogenous infiltration in primary tumors. A more homogenous distribution of FOXP3\u003csup\u003e+\u003c/sup\u003e regulatory T-cells in primary tumors was associated with shorter TTBM, compared to homogenously distributed CD163\u003csup\u003e+\u003c/sup\u003e-TAMs in primary tumors being associated with longer time-to-BM. By using subtraction of entropy and cell density, we showed that a higher difference in\u0026nbsp;CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e-TIL densities, as well as CD68\u003csup\u003e+\u003c/sup\u003e entropies between primary tumor and matched BM were associated with longer TTBM.\u0026nbsp;Therefore, entropy may demonstrate a new method to objectively quantify heterogeneity within the TME, emphasizing the importance of localization of inflammation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFor correct interpretation of our findings, several limitations of this study must be addressed: Our cohort comprises a relatively large population of matched tumor tissue samples. (i) Samples were recruited over a long period of time, therefore anti-cancer treatment standards have changed, potentially affecting survival parameters. However, we addressed this confounder by putting treatment agents and modalities in multivariable analysis. (ii) The used entropy values only provide information about the heterogeneity of the infiltration yet no specific data about localization. (iii)\u0026nbsp;Additionally, we only included patients who developed BM during the observation period, hence potential patients with longer TTBM exceeding this period were not included. (iv) The retrospective design of our study precludes definite conclusions, (v) as well as the skewed distribution of tumor entities which did not allow us to test for entity-specific correlation or genetic subpopulations. Nevertheless, byutilizing this unique cohort of matched primary tumor and BM FFPE whole slide tissue samples, we could address the missing gap of intraindividual spatial and longitudinal changes in the TME of solid tumors.\u003c/p\u003e\n\u003cp\u003eIn summary, we demonstrated higher TIL densities in primary tumors, compared to higher TAM densities in BM. Higher cell densities of CD3\u003csup\u003e+\u0026nbsp;\u003c/sup\u003eTILs and CD163\u003csup\u003e+\u003c/sup\u003eTAMs in primary tumors showed significant associations with\u0026nbsp;shorter TTBM, while a larger difference between CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e differences between primary tumor and BM was associated with longer TTBM. Differences in immune cell densities indicate potentially diverting impact of the innate and the adaptive immune system in the process of brain metastatic seeding. Additional, high-dimensional tissue analysis of the presented matched cohort is planned to further gain insight in the intraindividual differences of metastatic diseases.\u003c/p\u003e"},{"header":"List of abbreviations ","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"604\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eBreast cancer\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eBrain metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCC1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eCell Conditioning 1\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCNS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eCentral nervous system\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eEthics committee\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFFPE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eFormalin-fixed and paraffin-embedded\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGMCSF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eGranulocyte-macrophage colony-stimulating factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGPA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eGlasgow prognostic score\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eH\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eShannon Entropy\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eICI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eImmune-checkpoint inhibitor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMCSF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eMacrophage colony-stimulating factor\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eNon-small cell lung cancer\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eOverall survival\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCC\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eRenal cell carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eTumor-associated macrophages\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTIL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eTumor-infiltrating lymphocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTME\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eTumor microenvironment\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 26.534%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTTBM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 73.466%;\"\u003e\n \u003cp\u003eTime-to-brain metastasis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committees of the Medical University of Vienna (approval no. 1692/2022, 1895/2022).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFurther data will be shared upon reasonable request to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.J.M. received research funding from Bristol-Myers Squibb and travel support from Pierre Fabre.\u003c/p\u003e\n\u003cp\u003eA.S.B. received research support from Daiichi Sankyo, Roche, and honoraria for lectures, consultation or advisory board participation from Roche, Bristol-Meyers Squibb, Merck, Daiichi Sankyo as well as travel support from Roche, Amgen and AbbVie.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eZ.S. is a former employee of Boehringer Ingelheim and Turbine AI.\u003c/p\u003e\n\u003cp\u003eM.P. received honoraria for lectures, consultation, or advisory board participation from the following for-profit companies: Bayer, Bristol-Myers Squibb, Novartis, Gerson Lehrman Group (GLG), CMC Contrast, GlaxoSmithKline, Mundipharma, Roche, BMJ Journals, MedMedia, Astra Zeneca, AbbVie, Lilly, Medahead, Daiichi Sankyo, Sanofi, Merck Sharp \u0026amp; Dome, Tocagen.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJ.N.K declares consulting services for Bioptimus, France; Owkin, France; DoMore Diagnostics, Norway; Panakeia, UK; AstraZeneca, UK; Scailyte, Switzerland; Mindpeak, Germany; and MultiplexDx, Slovakia. Furthermore he holds shares in StratifAI GmbH, Germany, has received a research grant by GSK, and has received honoraria by AstraZeneca, Bayer, Daiichi Sankyo, Eisai, Janssen, MSD, BMS, Roche, Pfizer and Fresenius.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe financial support by the Austrian Federal Ministry for Digital and Economic Affairs, the National Foundation for Research, Technology and Development and the Christian Doppler Research Association is gratefully acknowledged.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.K. conceived the present idea, designed, and performed the analysis,\u0026nbsp;contributed to sample preparation, interpretated of the results and\u0026nbsp;wrote the manuscript.\u003c/p\u003e\n\u003cp\u003eD.C. performed the analysis of density maps to obtain entropy values and contributed to the\u0026nbsp;interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eC.P.\u0026nbsp;contributed to sample preparation, interpretation of the results and critical review of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eE.T.\u0026nbsp;contributed to sample preparation, interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eM.J.M. contributed to the imaging analysis with QuPath,\u0026nbsp;the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eA.S. contributed to obtaining the patient characteristics,\u0026nbsp;the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eZ.S.\u0026nbsp;contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eZ.I.C. helped supervise the project,\u0026nbsp;design the computational framework and the analysis to obtain entropy values and contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eL.B.\u0026nbsp;contributed to the statistical analysis, the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eJ.H.\u0026nbsp;contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eL.M.\u0026nbsp;contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eG.H.\u0026nbsp;contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eM.P. helped supervise the project and\u0026nbsp;contributed to interpretation of the results and helped\u0026nbsp;writing the manuscript.\u003c/p\u003e\n\u003cp\u003eJ.N.K helped supervise the project,\u0026nbsp;design the computational framework and the analysis to obtain entropy values and contributed to the interpretation of the results and critical review of the manuscript.\u003c/p\u003e\n\u003cp\u003eA.S.B. devised the main conceptual ideas, helped designing the analysis,\u0026nbsp;contributed to the interpretation of the results and helped\u0026nbsp;writing the manuscript.\u003c/p\u003e\n\u003cp\u003eAll named authors meet all four criteria for (co)-authorship provided by the Good Scientific Practice guidelines of the Medical University of Vienna.\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eJin MZ, Jin WL: The updated landscape of tumor microenvironment and drug repurposing. Signal Transduct Target Ther 5:166, 2020\u003c/li\u003e\n\u003cli\u003eBader JE, Voss K, Rathmell JC: Targeting Metabolism to Improve the Tumor Microenvironment for Cancer Immunotherapy. Mol Cell 78:1019-1033, 2020\u003c/li\u003e\n\u003cli\u003eBejarano L, Jordāo MJC, Joyce JA: Therapeutic targeting of the tumor microenvironment. Cancer Discovery 11:933-959, 2021\u003c/li\u003e\n\u003cli\u003eWischnewski V, Maas RR, Aruffo PG, et al: Phenotypic diversity of T cells in human primary and metastatic brain tumors revealed by multiomic interrogation. Nat Cancer 4:908-924, 2023\u003c/li\u003e\n\u003cli\u003eYang B, Li X, Zhang W, et al: Spatial heterogeneity of infiltrating T cells in high-grade serous ovarian cancer revealed by multi-omics analysis. Cell Rep Med 3:100856, 2022\u003c/li\u003e\n\u003cli\u003eDentro SC, Leshchiner I, Haase K, et al: Characterizing genetic intra-tumor heterogeneity across 2,658 human cancer genomes. Cell 184:2239-2254 e39, 2021\u003c/li\u003e\n\u003cli\u003eTomasich E, Muhlbacher J, Woran K, et al: Immune cell distribution and DNA methylation signatures differ between tumor and stroma enriched compartment in pancreatic ductal adenocarcinoma. Transl Res 271:40-51, 2024\u003c/li\u003e\n\u003cli\u003eBiermann J, Melms JC, Amin AD, et al: Dissecting the treatment-naive ecosystem of human melanoma brain metastasis. Cell 185:2591-2608 e30, 2022\u003c/li\u003e\n\u003cli\u003eChevrier S, Levine JH, Zanotelli VRT, et al: An Immune Atlas of Clear Cell Renal Cell Carcinoma. Cell 169:736-749 e18, 2017\u003c/li\u003e\n\u003cli\u003eGonzalez H, Mei W, Robles I, et al: Cellular architecture of human brain metastases. Cell 185:729-745 e20, 2022\u003c/li\u003e\n\u003cli\u003eJackson HW, Fischer JR, Zanotelli VRT, et al: The single-cell pathology landscape of breast cancer. Nature 578:615-620, 2020\u003c/li\u003e\n\u003cli\u003eKarimi E, Yu MW, Maritan SM, et al: Single-cell spatial immune landscapes of primary and metastatic brain tumours. Nature 614:555-563, 2023\u003c/li\u003e\n\u003cli\u003eLeader AM, Grout JA, Maier BB, et al: Single-cell analysis of human non-small cell lung cancer lesions refines tumor classification and patient stratification. Cancer Cell 39:1594-1609 e12, 2021\u003c/li\u003e\n\u003cli\u003eRisom T, Glass DR, Averbukh I, et al: Transition to invasive breast cancer is associated with progressive changes in the structure and composition of tumor stroma. Cell 185:299-310 e18, 2022\u003c/li\u003e\n\u003cli\u003eBankhead P, Loughrey MB, Fernandez JA, et al: QuPath: Open source software for digital pathology image analysis. Sci Rep 7:16878, 2017\u003c/li\u003e\n\u003cli\u003eJiang K, Parker M, Materi J, et al: Epidemiology and survival outcomes of synchronous and metachronous brain metastases: a retrospective population-based study. 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J Hematol Oncol 16:121, 2023\u003c/li\u003e\n\u003cli\u003eRauwerdink DJW, Molina G, Frederick DT, et al: Mixed Response to Immunotherapy in Patients with Metastatic Melanoma. Ann Surg Oncol 27:3488-3497, 2020\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Clinical and histopathological patient characteristics.\u0026nbsp;\u003c/strong\u003eNA .. not applicable\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"633\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n=78 (75%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBreast Cancer\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;n=18 (17.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003en=8 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAll\u003cbr\u003e\u0026nbsp;\u003c/strong\u003en=104 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e50 (64.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e7 (87.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e57 (53.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003efemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e28 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e18 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e47 (46.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge diagnosis BM\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e58.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e54.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e[years] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e44-77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e28-73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e44-69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e28-77\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of BM diagnosis\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e1990-2000\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e15 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e3 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e23 (22.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e2001-2010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e33 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e6 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e3 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e42 (40.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e2011-2020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e30 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e9 (50.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e39 (37.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImmunohistochemistry\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eER positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e8 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e8 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003ePR positive\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e4 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e4 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eHER2 positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e3 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003eNA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e3 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathology\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAdenocarcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e61 (78.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e61 (58.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eSquamous Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e14 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e14 (13.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eLarge Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e3 (3.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e3 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eDuctal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e15 (83.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e15 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eLobular\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e2 (11.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e2 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eAdenocystic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eClear Cell Carcinoma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e8 (100%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e8 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubtypes\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eLuminal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e8 (44.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e8 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eHER2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e1 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eTNBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e9 (50%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e9 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGrading\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eG1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e2 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u0026nbsp;G2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e23 (29.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e5 (27.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e33 (31.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eG3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e26 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e12 (66.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e38 (36.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eG4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e1 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e2 (25.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e3 (2.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 43.2859%;\"\u003e\n \u003cp\u003eunknown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e27 (34.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e28 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStage at primary diagnosis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eSynchronous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e26 (33.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1 (5.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e28 (26.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNeoadjuvant BM therapy\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eChemotherapy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e33 (42.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e16 (88.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e5 (62.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e54 (51.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; Radiotherapy \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e5 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e0\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e1 (12.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e6 (5.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTime-to-BM median\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e44.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e126.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e118.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e57.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e[weeks] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e0-320.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e0-281.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e3.6-520.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e0-520.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall survival\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e62.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e28.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e61.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003e53.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003efrom BM diagnosis \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; range\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e3.0-787.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e0.9-164.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e1.9-262.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e0.9-787.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 43.2859%;\"\u003e\n \u003cp\u003e[weeks] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;censored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e12 (15.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 14.8499%;\"\u003e\n \u003cp\u003e3 (16.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 13.4281%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 15.0079%;\"\u003e\n \u003cp\u003e15 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Immune cell densities and entropy values.\u0026nbsp;\u003c/strong\u003eBC...breast cancer, RCC...renal cell carcinoma, TAMs...Tumor-associated macrophages, TILs...Tumor-infiltrating lymphocytes\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" align=\"\" width=\"635\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 18.8327%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 21.8457%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTAMs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" style=\"width: 40.1726%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTILs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"bottom\" style=\"width: 18.8327%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCell densities [cell/mm\u003csup\u003e2\u003c/sup\u003e] \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD163\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOXP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD45RO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e866.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e443.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e589.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n 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\u003cp\u003e4483.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e155.6-\u003c/p\u003e\n \u003cp\u003e2752.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e5.8-\u003c/p\u003e\n \u003cp\u003e158.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e1.6-\u003c/p\u003e\n \u003cp\u003e135.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e5.7-\u003c/p\u003e\n \u003cp\u003e1757.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e6.7-\u003c/p\u003e\n \u003cp\u003e1917.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRCC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e1613.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e575.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e490.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e540.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e76.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e142.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e425.7-\u003c/p\u003e\n \u003cp\u003e2163.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e238.3-\u003c/p\u003e\n \u003cp\u003e988.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e72.3-\u003c/p\u003e\n \u003cp\u003e1374.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e69.2-\u003c/p\u003e\n \u003cp\u003e2206.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e11.9-\u003c/p\u003e\n \u003cp\u003e945.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e43.0-\u003c/p\u003e\n \u003cp\u003e671.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e1928.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e1468.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e181.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e169.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e92.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e199.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e871.3-\u003c/p\u003e\n \u003cp\u003e5520.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e447.6-\u003c/p\u003e\n \u003cp\u003e5109.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e11.4-\u003c/p\u003e\n \u003cp\u003e1441.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.0-\u003c/p\u003e\n \u003cp\u003e1758.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e6.3-\u003c/p\u003e\n \u003cp\u003e673.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e49.9-\u003c/p\u003e\n \u003cp\u003e2470.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" style=\"width: 29.0298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eShannon Entropy [index]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD68\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD163\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD8\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eFOXP3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCD45RO\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.12-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.11-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.00-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.06-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.09-0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.0-0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.0-0.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNSCLC\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.11-0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.32-0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.11-0.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.0-0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.12-0.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.07-0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 11.2894%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 9.3834%;\"\u003e\n \u003cp\u003eBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n 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style=\"width: 9.3834%;\"\u003e\n \u003cp\u003ePrimary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003emedian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 9.3834%;\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.4097%;\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 8.2105%;\"\u003e\n \u003cp\u003erange\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 10.8495%;\"\u003e\n \u003cp\u003e0.0-1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd 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\u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"acta-neuropathologica-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anec","sideBox":"Learn more about [Acta Neuropathologica Communications](https://actaneurocomms.biomedcentral.com/)","snPcode":"40478","submissionUrl":"https://submission.springernature.com/new-submission/40478/3","title":"Acta Neuropathologica Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-5000054/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5000054/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBACKGROUND \u003c/strong\u003eTumor-infiltrating lymphocytes (TILs) and tumor-associated macrophages (TAMs) have increasingly been reported to impact the brain metastatic process of solid tumors. However, data on intra-individual differences between primary tumor and brain metastasis (BM), as well as their correlation with clinical outcome parameters, is scarce.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMETHODS \u003c/strong\u003eWe retrospectively identified patients who received resection of the primary tumor and BM between 01/1990 and 10/2022. Density quantification of TAMs (CD68\u003csup\u003e+\u003c/sup\u003e, CD163\u003csup\u003e+\u003c/sup\u003e) and TILs (CD3\u003csup\u003e+\u003c/sup\u003e, CD8\u003csup\u003e+\u003c/sup\u003e, CD45RO\u003csup\u003e+\u003c/sup\u003e, FOXP3\u003csup\u003e+\u003c/sup\u003e) was performed by immunohistochemical staining of matched tumor tissue samples. Images were processed with QuPath software and heterogeneity of generated heatmaps was measured by Shannon Entropy. Time-to-BM (TTBM) was defined as the time from diagnosis of the primary tumor until the first diagnosis of BM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRESULTS \u003c/strong\u003eIn total, 104 patients (46.2% female; median age 57.3 years at BM diagnosis) were included: 78/104 (75%) non-small cell lung cancer, 18/104 (17%) breast cancer, 8/104 (8%) renal cell carcinomas. Densities of CD3\u003csup\u003e+\u003c/sup\u003e (p\u0026lt;0.001), CD8\u003csup\u003e+\u003c/sup\u003e (p\u0026lt;0.001) and FOXP3\u003csup\u003e+\u003c/sup\u003e-TILs (p=0.045) were higher in primary tumor samples, while CD68\u003csup\u003e+\u003c/sup\u003e (p=0.025) and CD163\u003csup\u003e+\u003c/sup\u003e-TAM densities (p\u0026lt;0.001) were higher in the matched BM. Higher CD3\u003csup\u003e+\u003c/sup\u003e-TILs and CD163\u003csup\u003e+\u003c/sup\u003e-TAMs densities in primary tumors were associated with shorter TTBM (p=0.039 and p=0.024, respectively). Higher entropies of CD3\u003csup\u003e+\u003c/sup\u003e (p\u0026lt;0.001) and FOXP3\u003csup\u003e+\u003c/sup\u003e (p=0.002) TILs were observed in primary tumors compared to BM. Longer TTBM was associated with higher entropy of FOXP3\u003csup\u003e+\u003c/sup\u003e TILs (p=0.031) and lower entropy in CD163\u003csup\u003e+\u003c/sup\u003e TAMs (p=0.040). No significant associations of immune cell densities or entropies with OS after BM diagnosis were found.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDISCUSSION\u003c/strong\u003e By utilizing a unique cohort of matched primary tumor and BM tissue samples, we could demonstrate higher TIL densities in primary tumors and higher TAM densities in BM, respectively. Higher cell densities of CD3\u003csup\u003e+\u003c/sup\u003e-TILs and CD163\u003csup\u003e+\u003c/sup\u003e-TAMs in primary tumors were associated with shorter TTBM, while a larger difference between CD3\u003csup\u003e+\u003c/sup\u003e and CD8\u003csup\u003e+\u003c/sup\u003e densities between primary tumor and BM was associated with longer TTBM.\u003c/p\u003e","manuscriptTitle":"Density and entropy of immune cells within the tumor microenvironment of primary tumors and matched brain metastases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 09:53:40","doi":"10.21203/rs.3.rs-5000054/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-01-07T17:59:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-21T18:47:28+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-12-15T14:38:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"329086618812869463967091722901292622503","date":"2024-12-12T15:11:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"123690063544594344979464278159726901702","date":"2024-12-12T06:47:14+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319793068869573781091475807934336275232","date":"2024-11-30T21:46:04+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-23T02:23:45+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-02T21:50:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-02T07:38:28+00:00","index":"","fulltext":""},{"type":"submitted","content":"Acta Neuropathologica Communications","date":"2024-08-29T21:48:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"acta-neuropathologica-communications","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"anec","sideBox":"Learn more about [Acta Neuropathologica Communications](https://actaneurocomms.biomedcentral.com/)","snPcode":"40478","submissionUrl":"https://submission.springernature.com/new-submission/40478/3","title":"Acta Neuropathologica Communications","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"f964ab7d-fdf5-4adc-9c30-b8b4688f276a","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-02-24T16:03:53+00:00","versionOfRecord":{"articleIdentity":"rs-5000054","link":"https://doi.org/10.1186/s40478-025-01939-8","journal":{"identity":"acta-neuropathologica-communications","isVorOnly":false,"title":"Acta Neuropathologica Communications"},"publishedOn":"2025-02-19 15:57:55","publishedOnDateReadable":"February 19th, 2025"},"versionCreatedAt":"2024-10-18 09:53:40","video":"","vorDoi":"10.1186/s40478-025-01939-8","vorDoiUrl":"https://doi.org/10.1186/s40478-025-01939-8","workflowStages":[]},"version":"v1","identity":"rs-5000054","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5000054","identity":"rs-5000054","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}