Macroscopic Color Variation in Clear Cell Renal Cell Carcinoma: A Novel Prognostic Indicator Reflecting the Tumor Microenvironment

Macroscopic Color Variation in Clear Cell Renal Cell Carcinoma: A Novel Prognostic Indicator Reflecting the Tumor Microenvironment | 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 Macroscopic Color Variation in Clear Cell Renal Cell Carcinoma: A Novel Prognostic Indicator Reflecting the Tumor Microenvironment Nozomi Tsujio, Chisato Ohe, Masanori Shiohara, Nao Yukimatsu, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7556339/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract [Background] Clear cell renal cell carcinoma (ccRCC) exhibits heterogeneous macroscopic colors, ranging from golden yellow to pale tan or white, demonstrating intratumoral heterogeneity in cellular composition. This study aimed to establish a macroscopic color scoring system for ccRCC and evaluate its correlation with pathological prognostic factors and previously reported histological parameters linked to treatment response-related gene signatures. [Methods] We retrospectively analyzed macroscopic color variation in 309 localized ccRCC cases, categorizing it into three levels: score 1 (golden yellow), score 2 (yellow to pale tan), and score 3 (grey to white). The prognostic significance of macroscopic color scores and their associations with pathological prognostic features were evaluated. [Results] Macroscopic color score was significantly correlated with TNM stage (p = 0.009), tumor size (p = 0.001), World Health Organization (WHO)/International Society of Urologic Pathology (ISUP) grade, sarcomatoid/rhabdoid features, tumor-type necrosis, cytological phenotype, vascularity-based architectural classification, and immunophenotype (p < 0.001). Higher scores were associated with adverse histological parameters, decreased vascularity, and increased immune infiltration, with the strongest correlations observed for WHO/ISUP grade and vascularity-based architectural classification. Although the C-index, a measure of predictive accuracy, of macroscopic score was lower than that of histological parameters, it effectively stratified recurrence-free survival into three distinct groups (hazard ratio, 2.69; p = 0.022 for score 2; hazard ratio, 6.53; p < 0.001 for score 3). [Conclusion] Macroscopic color variation in ccRCC may reflect underlying morphological and molecular heterogeneity. The macroscopic color scoring system may be valuable for guiding tissue sampling and risk stratification, potentially informing treatment strategies and diagnostic imaging approaches. clear cell renal cell carcinoma macroscopic color intratumoral heterogeneity tissue sampling prognostic marker risk stratification Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Clear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), exhibits morphological heterogeneity in cellular composition. Biallelic inactivation of the von Hippel-Lindau (VHL) gene, a hallmark of ccRCC, is associated with clear cell proliferation and abundant vascularity [ 1 ]. Multiregional sequencing studies have highlighted the complex intratumoral heterogeneity of RCC, revealing that tumor cell proliferation can lead to the evolution of spatially and temporally distinct subclones [ 2 ]. These subclones harbor unique mutations and somatic copy number alterations that contribute to the development of more aggressive phenotypes [ 3 , 4 ]. Recent research suggests that integrated molecular and morphological evolutionary models hold promise for improving prognostic and therapeutic strategies in ccRCC [ 5 , 6 ]. Kapur et al. developed an architectural evolutionary model based on hematoxylin and eosin (H&E) and CD31 immunohistochemical staining. They found that architectural patterns with high vascularity were associated with indolent prognosis, whereas those with low vascularity were correlated with more aggressive disease [ 6 ]. Building on previous research, our group established a vascularity-based architectural classification that focuses on architectural patterns reflecting the background vasculature [ 7 ]. We previously revealed the associations among neoplastic cells, vascularity-based architectural classification, and immunophenotypes in ccRCC [ 8 ]. This classification could assess patient prognosis and showed correlations with angiogenesis-related gene signatures predictive of response to tyrosine kinase inhibitors (TKIs) and immune-related gene signatures predictive of response to immune checkpoint inhibitors (ICIs), as identified in the IMmotion 150 study [ 9 ]. However, this classification was limited because vascularity-architectural categories were assessed only in areas with the highest nucleolar grade. Therefore, a method to quantify the proportion of each category is needed to develop practical tools to inform treatment decisions in routine clinical practice. The macroscopic color of ccRCC varies widely, ranging from golden yellow to pale tan or white, reflecting intratumoral heterogeneity [ 10 ]. Pathologists have often noted a correlation between grossly white, firm areas and high-grade histological components such as sarcomatoid or rhabdoid features [ 11 ]. However, to date, no studies have systematically evaluated the relationship between macroscopic color and key histological parameters, such as conventional prognostic factors and histological features related to the tumor microenvironment, including vasculature and immune cells. In the present study, we established a scoring system for macroscopic color variation in ccRCC and investigated its correlation with the pathological prognostic factors. This study underscores the importance of gross pathological assessment, demonstrating how appropriate tissue sampling based on macroscopic findings can improve patient prognosis by refining the risk stratification and guiding individualized treatment strategies. Materials and Methods Data collection This retrospective study was approved by the Institutional Review Board of Osaka Metropolitan University Hospital (IRB# 2024-003) in accordance with the Declaration of Helsinki. The initial cohort comprised of 340 patients who underwent partial or radical nephrectomy for ccRCC at our institution between 2013 and 2020. After excluding 31 patients whose tumors were not confirmed to be ccRCC upon review, 309 localized, surgically treated ccRCC cases (cT1-4N0-1M0) were included in the final analysis, and none had received any prior therapy. Pathological prognostic factors, including the 2017 TNM stage [ 12 ], World Health Organization (WHO)/International Society of Urologic Pathology (ISUP) nucleolar grade, sarcomatoid/rhabdoid features, and tumor-type necrosis, were evaluated based on the 2022 WHO classification criteria [ 1 ]. Macroscopic evaluation Representative photographs of formalin-fixed gross surgical specimens were evaluated by two pathologists (N.T. and C.O.) who were blinded to clinical and pathological information. In partial nephrectomy cases, the specimens were evaluated by sectioning through the plane of the greatest diameter. In radical nephrectomy cases, specimens were evaluated using an initial longitudinal section, followed by thin transverse sections. Macroscopic color variation in ccRCC was categorized into three levels based on the following scoring system: score 1, uniform golden yellow (Fig. 1a-c), often with cyst formation and hemorrhage; score 2, yellow to pale tan (Fig. 1d-f); and score 3, grey to white (Fig. 1g-i). Areas exhibiting color alterations due to hemorrhage and infarction, hyalinization (Fig. 1b), myxoid degeneration (Fig. 1c), or tumor-type necrosis (Fig. 1h) were excluded from the evaluation. The highest score recorded was assigned to each case, regardless of the proportion of high-score components; thus, areas with scores of 2 and 3 could include areas with lower scores. Histological evaluation All hematoxylin and eosin (H&E)-stained slides were pathologically re-evaluated by two pathologists (N.T. and C.O.) who were blinded to clinical and macroscopic information. All ccRCC diagnoses require the presence of a typical ccRCC morphology, characterized by small, thin-walled vascular networks and/or diffuse membranous positivity for carbonic anhydrase 9 (CA9), detected by immunohistochemistry [ 1 ]. In addition to conventional pathological prognostic factors, the following histological features were evaluated according to previously described methods [ 7 , 13 – 15 ]: (i) cytological phenotype, categorized as clear, mixed (some tumor cells are clear, and some have eosinophilic cytoplasm), or eosinophilic [ 13 ] (Suppl. Figure 1a); ii) vascularity-based architectural classification [ 7 ] (Suppl. Figure 1b), and (iii) immunophenotype, classified as cold, immune-low, excluded, or hot, considering both the degree and distribution of tumor-associated immune cells (TAICs) [ 14 , 15 ](Suppl. Figure 1c). Cytological phenotypes and architectural classifications were evaluated in areas with the highest WHO/ISUP grades. Immunophenotypes were evaluated on a whole slide, including the areas with the highest WHO/ISUP grade. The vascularity-based architectural classification was divided into three categories as follows: category 1, characterized by an enriched vascular network, including compact/small nested, macrocystic/microcystic, and tubular/acinar patterns; category 2, characterized by a widely spaced vascular network, including alveolar/large nested, thick trabecular/insular, and papillary/pseudopapillary patterns; and category 3, characterized by scattered vascularity without a distinct vascular network, including solid sheets, sarcomatoid, and rhabdoid patterns (Suppl. Figure 1b). Immunophenotypes were categorized based on the location and density of TAICs into four types: cold (no TAICs), immune-low (focal or low TAICs regardless of their location), excluded (diffuse or high peritumoral TAICs), and hot (diffuse or high intratumoral TAICs) (Suppl. Figure 1c). Statistical analysis The study outcome measure was recurrence-free survival (RFS), defined as the time from surgery to local or distant metastasis initially detected on imaging. All continuous data are presented as median and interquartile range (IQRs). Statistical analyses were performed using EZR version 1.68 (Saitama Medical Center) [ 16 ]. RFS was assessed using the Kaplan-Meier method with log-rank and Cox proportional hazards models. Harrell’s concordance index (C-index) was used to evaluate the predictive accuracy of the Cox models. Cramer's V test was performed to examine the significant correlations between the variables. A two-sided p < 0.05 was considered statistically significant. Results Clinicopathological characteristics The clinicopathological features of the 309 patients with localized ccRCC are summarized in Table 1 . The median age at ccRCC diagnosis was 66 years (IQR, 54.0–74.0). A total of 95 tumors (30.7%) were high stage (TNM stage III/IV), and 121 tumors (39.2%) were high-grade (WHO/ISUP grade 3/4). During a median follow-up of 49 months (IQR, 29–64 months), 37 patients (12.0%) experienced recurrence, and 12 patients (3.9%) died of ccRCC. Based on the results of macroscopic color scoring, the numbers) patients had scores of 1, 2, 3, 86 (27.8%), and 45 (14.6%), respectively. Cytological phenotype evaluation classified 109 patients (35.3%) as having a clear phenotype, 169 patients (54.7%) as having a mixed phenotype, and 31 patients (10.0%) as having an eosinophilic phenotype. The results of the vascularity-based architectural classification were as follows: 118 patients (38.2%) in category 1, 141 patients (45.6%) in category 2, and 50 patients (16.2%) in category 3. Immunophenotype evaluation classified 76 patients (24.6%) as cold type, 181 patients (58.6%) as immune-low type, 10 patients (3.2%) as excluded type, and 42 patients (13.6%) as hot type. Table 1 Clinicopathological characteristics of 309 cases with localized ccRCCs Variables Age, years, median (IQR) 66 (54.0–74.0) Gender, n (%) Female 89 (28.8) Male 220 (71.2) TNM stage, n (%) Stage I 209 (67.6) Stage II 5 (1.6) Stage III 94 (30.4) Stage IV 1 (0.3) Tumor size, n (%) ≦ 4 cm 189 (61.2) 4 cm <, ≦ 7 cm 78 (25.2) 7 cm <, ≦ 10 cm 33 (10.7) 10 cm < 9 (2.9) Macroscopic color score, n (%) Score 0 178 (57.6) Score 1 86 (27.8) Score 2 45 (14.6) WHO/ISUP grade, n (%) Grade 1 49 (15.9) Grade 2 139 (45.0) Grade 3 71 (23.0) Grade 4 50 (16.2) Sarcomatoid/rhabdoid features, n (%) Absence 277 (89.6) Presence 32 (10.4) Necrosis, n (%) Absence 268 (86.7) Presence 41 (13.3) Cytological phenotype, n (%) Clear 109 (35.3) Mixed 169 (54.7) Eosinophilic 31 (10.0) Vascularity-based architectural classification, n (%) Category 1 118 (38.2) Category 2 141 (45.6) Category 3 50 (16.2) Immunophenotype, n (%) Cold 76 (24.6) Immune-low 181 (58.6) Excluded 10 (3.2) Hot 42 (13.6) Recurrence, n (%) 37 (12.0) Cancer-specific mortality, n (%) 12 (3.9) ccRCC, clear cell renal cell carcinoma; IQR, interquartile range; WHO, World Health Organization; ISUP, International Society of Urological Pathology Association of macroscopic color score with histological parameters Macroscopic color score was significantly associated with TNM stage (p = 0.009), tumor size (p = 0.001), WHO/ISUP grade, sarcomatoid/rhabdoid features, tumor-type necrosis, cytological phenotype, vascularity-based architectural classification, and immunophenotype (p < 0.001 for all others; Fig. 2). Among the parameters examined, the WHO/ISUP grade showed the strongest association with the macroscopic color score (Cramer's V = 0.34; p < 0.001). A moderate association was found for the vascularity-based architectural classification (Cramer's V = 0.29; p < 0.001). The cytological phenotype (Cramer's V = 0.24; p < 0.001) and immunophenotype (Cramer's V = 0.21; p < 0.001) showed weak but significant associations. Supplementary Fig. 2 shows the correlation between representative macroscopic color tones and their corresponding histological features. Prognostic significance of macroscopic color score and histological parameters Kaplan-Meier survival analysis showed that the 5-year RFS rates differed significantly according to the macroscopic color score: 93.6% for score 1, 81.3% for score 2 (HR, 2.69; p = 0.022), and 63.2% for score 3 (HR, 6.53; p < 0.001) (Fig. 3). Similar results were observed for the WHO/ISUP grade (Suppl. Figure 3a): 97.6% for grade 1/2, 81.6% for grade 3 (HR, 5.43; p < 0.001), and 46.2% for grade 4 (HR, 22.39; p < 0.001). The 5-year RFS rates also differed according to cytological phenotype (Suppl. Figure 3b): 97.8% for the clear phenotype, 86.8% for the mixed phenotype (HR, 5.65; p = 0.021), and 37.0% for the eosinophilic phenotype (HR, 45.13; p < 0.001). In terms of the vascularity-based architectural classification (Suppl. Figure 3c), the 5-year RFS rates were 98.0% for category 1, 88.7% for category 2 (hazard ratio [HR], 4.85; p = 0.039), and 48.3% for category 3 (HR, 36.23; p < 0.001). In terms of the immunophenotype (Suppl. Figure 3d), the 5-year RFS rates were 93.9% for the cold type, 88.4% for the immune-low type (HR, 1.70; p = 0.34), 50.0% for the excluded type (HR, 11.12; p < 0.001), and 66.5% for the hot type (HR, 6.84; p < 0.001). Among the parameters examined, the WHO/ISUP grade demonstrated the highest C-index for predicting RFS, outperforming vascularity-based architectural classification, cytological phenotype, immunophenotype, and macroscopic color score (0.818 vs. 0.816, 0.789, 0.729, and 0.709, respectively). Discussion This study demonstrated a significant correlation between macroscopic color variation in ccRCC and conventional pathological prognostic factors, as well as histological parameters linked to tumor aggressiveness and microenvironment. Notably, the strongest association was observed with WHO/ISUP grade, followed by vascularity-based architectural classification. Although the C-index of the macroscopic score was lower than that of the histological parameters, it effectively stratified the prognosis into three distinct groups. To the best of our knowledge, this is the first report of a macroscopic color scoring system for ccRCC that provides several important insights. First, this study demonstrated that macroscopic color variation in ccRCC can stratify prognosis. Thus far, color detection in RCC has primarily been used as a diagnostic tool to aid in the identification of histological subtypes. For instance, the yellow color observed in clear cell RCC indicates intracytoplasmic lipids; the yellow to brown color observed in papillary RCC indicates foamy macrophages or hemorrhage; the beige or pale tan color observed in chromophobe renal tumors indicates the presence of abundant mitochondria; and the grey to white color observed in collecting duct carcinoma indicates desmoplasia (Suppl. Figure 4) [ 1 , 10 ]. Macroscopic studies of ccRCC have predominantly focused on tumor morphology and infiltration patterns [ 17 ]; however, our analysis, which focused solely on color using a simple, easily applied evaluation method, highlights the clinical significance of macroscopic color variation. The strong correlation with WHO/ISUP grade is logical, as WHO/ISUP grade 3 tumors are often characterized by prominent nucleoli and frequently exhibit eosinophilic cytoplasm containing abundant mitochondria [ 18 ]. These mitochondria-rich eosinophilic features are consistent with the pale tan color observed in chromophobe RCC. In contrast, WHO/ISUP grade 4 tumors, which frequently exhibit sarcomatoid or rhabdoid features, tend to have a gray-to-white appearance [ 11 ]. Second, the significant association between tumor size and macroscopic score further supports the notion that intratumoral heterogeneity is associated with tumor aggressiveness. Although prior research has demonstrated a relationship between clonal evolution, genetic and histological intratumoral heterogeneity, and tumor aggressiveness in ccRCC [ 2 – 6 ], an association with macroscopic findings has not been reported. Histological studies have revealed that smaller ccRCC tumors (≤ 4 cm) tend to exhibit less architectural diversity [ 6 ], which is consistent with our observation of a predominance of a macroscopic score of 1 for these smaller tumors. Therefore, based on the results of this study, clonal evolution in ccRCC can be inferred through macroscopic intratumoral heterogeneity. Third, our findings suggest that macroscopic color can predict prognosis and may reflect the tumor microenvironment, which is crucial for treatment selection with TKIs and ICIs. Our previous studies showed that vascularity-based architectural classification [ 7 ] and cytological phenotype [ 13 , 19 ] correlated with angiogenesis- and immune-related gene signatures predictive of response to TKIs and ICIs, respectively. In the present study, we observed significant differences in vascularity-based architectural classification based on the macroscopic color score; tumors with abundant vascular networks tended to have a low color score, whereas those with decreased or sparse vascularity were associated with a high color score. Given that our previous study demonstrated a correlation between vascularity-based architectural classification and cytological phenotype [ 7 ], a similar trend in macroscopic findings is reasonable. Specifically, a clear or eosinophilic phenotype could indicate the presence of cytoplasmic lipids (for golden yellow color) or abundant mitochondria (for light tan color), respectively. Furthermore, vascularity-based architectural classification exhibits an inverse correlation with inflammation, as evaluated by histology and mRNA expression levels [ 7 ]. Therefore, although microscopic evaluation may provide a more precise assessment, the tumor microenvironment can be inferred to some extent from macroscopic images. Given the growing trend of using TKI + ICI or ICI combination therapies [ 20 – 22 ], it is crucial to understand intratumoral heterogeneity. The correlation between macroscopic findings and histological parameters linked to treatment response-related gene signatures suggests that macroscopic assessment may provide insights into overall tumor composition (Fig. 4). With the approval of HIF2α inhibitors for advanced ccRCC [ 23 ], treatment decisions for advanced RCC require careful consideration when selecting TKIs, ICIs, and HIF2α inhibitors [ 24 , 25 ]. Our previous research indicated that HIF2α was expressed immunohistochemically and at the mRNA level in category 1 (compact/small nested and micro/macrocystic) tumors, which may be associated with a clear cell type and a "cold" immunophenotype corresponding to a macroscopic score of 1. Although our study focused on localized RCC without preoperative treatment, macroscopic evaluation of nephrectomy specimens after neoadjuvant therapy may help to predict which agents are likely to be effective against residual tumor components. Therefore, further investigation of the relationship between macroscopic findings and treatment responses is warranted. Fourth, although macroscopic scoring independently stratifies prognosis into three distinct groups, the prioritization of areas with color scores of 2 or 3 within our system underscores the prognostic significance of these high-scoring regions, similar to the WHO/ISUP grading system [ 26 ]. Given that adjuvant treatment is indicated for patients exhibiting pathological high-risk features (e.g., pT3a or higher, grade 4 nuclear atypia, or sarcomatoid differentiation) [ 27 , 28 ], features frequently associated with a macroscopic score of 3 and inadequate sampling of these areas pose a significant risk of underestimating relevant pathological prognostic factors. Therefore, meticulous macroscopic evaluation and comprehensive tissue sampling are essential for an accurate risk assessment. Fifth, although the WHO/ISUP grade is based on nucleolar characteristics, the correlation between the macroscopic score and architectural classification, which reflects the microenvironment, is clinically significant. Vascular networks can be identified by contrast enhancement and can potentially be assessed using contrast-enhanced CT [ 29 , 30 ]. Although contrast enhancement patterns differ between ccRCC and chromophobe RCC [ 31 ], as the category of ccRCC increases, it exhibits vascular network morphology similar to that of chromophobe RCC. Validation of the correlation between macroscopic images, vascularity-based architectural classification, and radiographic images could allow assessment of the ccRCC tumor microenvironment, which is relevant for treatment selection, solely with radiographic images. These insights may also lead to improved sampling strategies during tumor biopsy, thereby obtaining useful prognostic information. Despite the promising findings of this study, several limitations must be acknowledged. As this was a retrospective, single-center study, our findings may be subject to bias and limited generalizability. The variable slice widths used by different pathologists may have missed high-scoring areas in the macrophotographs. Although areas exhibiting color alterations due to hemorrhage, infarction, tumor-type necrosis, hyalinization, or myxoid degeneration were excluded from the evaluation, these features are sometimes difficult to identify on macrophotographs and may lead to interobserver variability. The subjective nature of macroscopic color evaluation and relatively simple three-tiered scoring system may not capture the full spectrum of colors observed in ccRCC. The lack of molecular data restricts elucidation of the underlying molecular mechanisms. Furthermore, a limited follow-up period may not have detected late recurrences. Finally, our study focused exclusively on localized ccRCC, and the applicability of our findings to advanced diseases remains unclear. Future research should focus on validating these findings in larger, multi-institutional cohorts, developing objective color measurement techniques, integrating molecular data, and extending the follow-up period. Despite these limitations, our study provides a valuable foundation for future investigations of the role of macroscopic tumor characteristics in ccRCC management. Conclusion In conclusion, this study emphasizes the importance of macroscopic evaluation of ccRCC. By incorporating macroscopic color scoring into routine pathological assessments, we can refine risk stratification, guide individualized treatment strategies, and potentially improve patient prognosis. Further investigation is needed to determine the utility of macroscopic color scoring in the context of neoadjuvant therapy and advanced ccRCC. Declarations Ethics approval and consent to participate The study design was approved by the Osaka Metropolitan University Hospital Institutional Review Board (IRB# 2024-003) in accordance with the Declaration of Helsinki. The requirement for individual informed consent was waived due to the retrospective nature of the study. Instead, informed consent was obtained as an opt-out method on the institutional website ( https://www.omu.ac.jp/med/med_ethics/igakukei/information/ ), and no patient expressed refusal. Consent for publication Not Applicable. Competing Interests CO has received honoraria as a speaker from Merck Sharp and Dohme, while the remaining authors declare no conflicts of interest related to this work. Funding This study was supported by the Japan Society for the Promotion of Science KAKENHI Fund (grant #24K10144 to CO). Author Contribution Conceptualization: NT, CO; Methodology: NT, CO, MS, MK, and RU; Formal analysis and investigation: NT, CO, MS; Funding acquisition: CO; Resources: NY, TY, Supervision: JU, KK; Writing - original draft preparation: NT, CO; Writing - review & editing and final approval of the manuscript, all authors. Acknowledgements Not applicable. Data Availability The datasets analyzed in the current study are available from the corresponding author upon reasonable request. References WHO Classification of Tumours Editorial Board. WHO Classification of Tumors, urinary and male genital tumors. 5th ed. Lyon: IARC; 2022. Gerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883–92. Turajlic S, Xu H, Litchfield K, Rowan A, Chambers T, Lopez JI, et al. Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal. Cell. 2018;173:581–e594512. Li Y, Lih TM, Dhanasekaran SM, Mannan R, Chen L, Cieslik M, et al. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell. 2023;41:139–e163117. Cai Q, Christie A, Rajaram S, Zhou Q, Araj E, Chintalapati S, et al. Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type. EBioMedicine. 2020;51:102526. Kapur P, Christie A, Rajaram S, Brugarolas J. What morphology can teach us about renal cell carcinoma clonal evolution. Kidney Cancer J. 2020;18:68–76. Ohe C, Yoshida T, Amin MB, Atsumi N, Ikeda J, Saiga K, et al. Development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma: correlation with conventional pathological prognostic factors, gene expression patterns, and clinical outcomes. Mod Pathol. 2022;35:816–24. Ohe C, Yoshida T, Amin MB, Smith SC, Shiohara M, Tsujio N et al. Comparison of Histologic Parameters and Predictive Gene Signatures in Clear Cell Renal Cell Carcinoma Response to Systemic Therapy. Pathol Int. 2025; 75: 267–77. McDermott DF, Huseni MA, Atkins MB, Motzer RJ, Rini BI, Escudier B, et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat Med. 2018;24:749–57. Amin MB, Tickoo SK, editors. Diagnostic pathology. Genitourinary. 2nd ed. Philadelphia: Elsevier; 2016. López JI. Intratumor heterogeneity in clear cell renal cell carcinoma: a review for the practicing pathologist. Apmis. 2016;124:153–9. Brierley JD, Gospodarowicz MK, Wittekind C, editors. TNM classification of malignant tumours. 8th ed. West Sussex: John Wiley & Sons, Inc.; 2017. Yoshida T, Ohe C, Ikeda J, Atsumi N, Ohsugi H, Sugi M et al. Eosinophilic features in clear cell renal cell carcinoma correlate with outcomes of immune checkpoint and angiogenesis blockade. J Immunother Cancer. 2021; 9. Ohe C, Yoshida T, Ikeda J, Tsuzuki T, Ohashi R, Ohsugi H et al. Histologic-Based Tumor-Associated Immune Cells Status in Clear Cell Renal Cell Carcinoma Correlates with Gene Signatures Related to Cancer Immunity and Clinical Outcomes. Biomedicines. 2022; 10. Shiohara M, Ohe C, Tsujio N, Uno R, Kohashi K. Correlation of histological immunophenotype in papillary renal cell carcinoma with gene signatures related to the therapeutic effect of systemic therapy. Pathol Res Pract. 2025;266:155764. Kanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl. 2013;48:452–8. Jeong SU, Park JM, Shin SJ, Lee J, Song C, Go H, et al. Prognostic Significance of Macroscopic Appearance in Clear Cell Renal Cell Carcinoma and Its Metastasis-Predicting Model. Pathol Int. 2017;67:610–9. Nilsson H, Lindgren D, Axelson H, Brueffer C, Saal LH, Lundgren J, et al. Features of increased malignancy in eosinophilic clear cell renal cell carcinoma. J Pathol. 2020;252:384–97. Ohe C, Yoshida T, Amin MB, Uno R, Atsumi N, Yasukochi Y, et al. Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma. Hum Pathol. 2023;131:68–78. Motzer RJ, Tannir NM, McDermott DF, Arén Frontera O, Melichar B, Choueiri TK, et al. Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med. 2018;378:1277–90. Cetin B, Wabl CA, Gumusay O. Reshaping Treatment Paradigms for Advanced Renal Cell Cancer Patients and Improving Patient Management: Optimal Management for Renal Cell Cancer Patients. Curr Treat Options Oncol. 2022;23:609–29. Tran J, Ornstein MC. Clinical Review on the Management of Metastatic Renal Cell Carcinoma. JCO Oncol Pract. 2022;18:187–96. Choueiri TK, Powles T, Peltola K, de Velasco G, Burotto M, Suarez C, et al. Belzutifan versus Everolimus for Advanced Renal-Cell Carcinoma. N Engl J Med. 2024;391:710–21. Nguyen CB, Oh E, Bahar P, Vaishampayan UN, Else T, Alva AS. Novel Approaches with HIF-2α Targeted Therapies in Metastatic Renal Cell Carcinoma. Cancers (Basel). 2024; 16. Cardenas LM, Deluce JE, Khan S, Gulam O, Maleki Vareki S, Fernandes R, et al. Next Wave of Targets in the Treatment of Advanced Renal Cell Carcinoma. Curr Oncol. 2022;29:5426–41. Delahunt B, Eble JN, Egevad L, Samaratunga H. Grading of renal cell carcinoma. Histopathology. 2019;74:4–17. Choueiri TK, Tomczak P, Park SH, Venugopal B, Ferguson T, Chang YH, et al. Adjuvant Pembrolizumab after Nephrectomy in Renal-Cell Carcinoma. N Engl J Med. 2021;385:683–94. Choueiri TK, Tomczak P, Park SH, Venugopal B, Ferguson T, Symeonides SN, et al. Overall Survival with Adjuvant Pembrolizumab in Renal-Cell Carcinoma. N Engl J Med. 2024;390:1359–71. Mains JR, Donskov F, Pedersen EM, Madsen HH, Rasmussen F. Dynamic Contrast-Enhanced Computed Tomography-Derived Blood Volume and Blood Flow Correlate With Patient Outcome in Metastatic Renal Cell Carcinoma. Invest Radiol. 2017;52:103–10. Zhang B, Wu Q, Qiu X, Ding X, Wang J, Li J, et al. Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma. BMC Cancer. 2021;21:874. Young JR, Margolis D, Sauk S, Pantuck AJ, Sayre J, Raman SS. Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT. Radiology. 2013;267:444–53. Additional Declarations Competing interest reported. CO has received honoraria as a speaker from Merck Sharp and Dohme, while the remaining authors declare no conflicts of interest related to this work. Supplementary Files SupplementaryInformationfinal.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7556339","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":529705560,"identity":"5d611ed7-e1b0-4064-88f3-b2caad0bf517","order_by":0,"name":"Nozomi Tsujio","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Nozomi","middleName":"","lastName":"Tsujio","suffix":""},{"id":529705561,"identity":"2e219a69-4313-481e-8e83-fd9284d143bd","order_by":1,"name":"Chisato Ohe","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAz0lEQVRIiWNgGAWjYDACCSD+2IBgMzA2EKGFcSbJWph5kbUQBPzSzQc/2+44LMcgdvjgDYYaOwbm2QSskZxzLFk698xhYwbptGQLhmPJDIxzDuDXYnAjx4w5t+1w4v7bOWYSDGwHGBhnJBDSkv+N2RKopUE6/5sEwz+itOSwMTOCteSwSTC2EaFFckaasWRvWzrIL8YWiX3JPAT9wi+R/PDDzzZrOQbp5Ic3PnyzkzMkFGJQ0AyhgE7iMZxBlA6GOgRTnrgIHQWjYBSMghEEAKjAP5u/vxA1AAAAAElFTkSuQmCC","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":true,"prefix":"","firstName":"Chisato","middleName":"","lastName":"Ohe","suffix":""},{"id":529705562,"identity":"bdaf1118-b7db-4e0a-b028-78b720d8766c","order_by":2,"name":"Masanori Shiohara","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Masanori","middleName":"","lastName":"Shiohara","suffix":""},{"id":529705563,"identity":"03b009e3-c483-4148-93a2-626b6c62137a","order_by":3,"name":"Nao Yukimatsu","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Nao","middleName":"","lastName":"Yukimatsu","suffix":""},{"id":529705564,"identity":"dd01702f-6fcf-4220-b280-a7daed490371","order_by":4,"name":"Takeshi Yamasaki","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Takeshi","middleName":"","lastName":"Yamasaki","suffix":""},{"id":529705565,"identity":"7f7f5a79-06b1-4183-bab7-1afe6b17d2be","order_by":5,"name":"Masahiro Kato","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Kato","suffix":""},{"id":529705566,"identity":"e280e8c5-cf68-4b88-847d-bb2a47f3831e","order_by":6,"name":"Rena Uno","email":"","orcid":"","institution":"Hyogo Cancer Center","correspondingAuthor":false,"prefix":"","firstName":"Rena","middleName":"","lastName":"Uno","suffix":""},{"id":529705567,"identity":"50a28706-ab4e-40a5-a34a-a9a48e00aa5f","order_by":7,"name":"Junji Uchida","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Junji","middleName":"","lastName":"Uchida","suffix":""},{"id":529705568,"identity":"5db74406-263f-4476-9986-8e20f5ad09af","order_by":8,"name":"Kenichi Kohashi","email":"","orcid":"","institution":"Osaka Metropolitan University","correspondingAuthor":false,"prefix":"","firstName":"Kenichi","middleName":"","lastName":"Kohashi","suffix":""}],"badges":[],"createdAt":"2025-09-07 12:53:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7556339/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7556339/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":93574094,"identity":"7d8c7fd4-6c41-484d-a3d9-238a23cbb6f5","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"tif","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2109284,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/f0048d7868a706c29566671b.tif"},{"id":93575144,"identity":"9c4a1767-2fbb-4bc1-8301-a54569493cd3","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":57895,"visible":true,"origin":"","legend":"","description":"","filename":"DiagnosticpathologyTsujioMacroscopiccolorscoringsystem0928.docx","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/127254e01fb937f54787dd11.docx"},{"id":93575150,"identity":"6e019f71-c227-49b9-a830-f78691157674","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"tif","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209816,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/fc0ad8c9e18d35e9f104ae97.tif"},{"id":93574090,"identity":"2c11560c-988f-4d2e-b5d7-289f3c41be30","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":21459,"visible":true,"origin":"","legend":"","description":"","filename":"TsujioTable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/caef91d81fa0c7ced2b4e3df.docx"},{"id":93575148,"identity":"07a05211-a607-4f6c-b304-c0975eb24edc","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"tif","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106720,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/356bb36d453963285574162d.tif"},{"id":93574103,"identity":"87198b98-5cad-49fe-a73a-b5950bf81bef","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"tif","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":744338,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4new.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/a61e3ec9abea2142b74f07ad.tif"},{"id":93575146,"identity":"1492b736-bd63-49fe-bd77-0e167fae2c27","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"json","order_by":6,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10165,"visible":true,"origin":"","legend":"","description":"","filename":"7e36b4f8778c4c79a4644c969f09416a.json","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/108f0cd9423578cf4c23c070.json"},{"id":93574105,"identity":"7e4615b9-06e1-4664-a46c-47e57722a3c4","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"pdf","order_by":7,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":1952437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/e09c8ba48ab14503ff2cf5c4.pdf"},{"id":93574099,"identity":"72ae1b38-4077-48a3-8e90-68c4fc45fffd","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"xml","order_by":8,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":91117,"visible":true,"origin":"","legend":"","description":"","filename":"7e36b4f8778c4c79a4644c969f09416a1enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/88c4a7f110eede0d25ffed2e.xml"},{"id":93575155,"identity":"7e09ddf8-02ac-4676-b142-f3d017ba852a","added_by":"auto","created_at":"2025-10-15 09:22:33","extension":"tif","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":2109284,"visible":true,"origin":"","legend":"","description":"","filename":"Figure1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/2d3c654623c177a2021f1b7a.tif"},{"id":93575151,"identity":"e0197441-4c7d-4528-9d57-cfd9fda44b5e","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"tif","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":209816,"visible":true,"origin":"","legend":"","description":"","filename":"Figure2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/5893e95fd5814fe8420fb065.tif"},{"id":93575152,"identity":"bb016029-82b1-438e-bdc2-1a2fb3368d9f","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"tif","order_by":11,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":106720,"visible":true,"origin":"","legend":"","description":"","filename":"Figure3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/a314055fe705fba2b12460e2.tif"},{"id":93574109,"identity":"82392c7a-72f0-400d-b0e6-091304b13463","added_by":"auto","created_at":"2025-10-15 09:14:33","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":744338,"visible":true,"origin":"","legend":"","description":"","filename":"Figure4new.tif","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/f1b23ee632859a2bed1aeaf0.tif"},{"id":93575153,"identity":"294adbae-0bb2-4b4c-871f-a73c51ba6415","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"png","order_by":13,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":314213,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/0effb4abc699c697a037e6db.png"},{"id":93574097,"identity":"8e8662a1-3b5e-4690-b07c-fbe3796236a4","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"png","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":32308,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/4a81b5de0b9db00f1de1395d.png"},{"id":93575154,"identity":"a0942cbd-ffd3-40b5-b08b-21f454f05b8f","added_by":"auto","created_at":"2025-10-15 09:22:33","extension":"png","order_by":15,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":13961,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/f6252556d75809fd4ed965b4.png"},{"id":93574101,"identity":"e8e2d1e0-2f53-4f2a-8943-11725a3fdd61","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"png","order_by":16,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111894,"visible":true,"origin":"","legend":"","description":"","filename":"OnlineFigure4new.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/5b8c990497930b8a4793b782.png"},{"id":93574111,"identity":"3cffe851-890e-4167-a09c-a05faaf2d8e4","added_by":"auto","created_at":"2025-10-15 09:14:33","extension":"xml","order_by":17,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":88842,"visible":true,"origin":"","legend":"","description":"","filename":"7e36b4f8778c4c79a4644c969f09416a1structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/e48d6314dfb59fa49211a936.xml"},{"id":93574107,"identity":"cb6b2ab1-82ab-40bf-af9c-c09b41ccb5a5","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"html","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":97545,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/c1f3d1f312ed2a84eee0b91d.html"},{"id":93574089,"identity":"52183402-537c-4f90-bd99-93c09732e262","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1291128,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative macroscopic images of formalin-fixed clear cell renal cell carcinoma categorized by the macroscopic color scoring system: (a-c) Score 1, characterized by a typical golden yellow appearance and frequent cyst formation; (d-f) Score 2, characterized by a yellow to pale tan color; (g-i) Score 3, characterized by a grey to white, firm appearance suggestive of dedifferentiation. The dotted areas marked with asterisks indicate regions excluded from evaluation due to hyalinization (b), myxoid degeneration (c) and necrosis (h). Tumors evaluated as score 3 contained yellow to pale tan areas corresponding to Scores 1–2, indicating intratumoral heterogeneity (g-i).\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/3a93f52738fc69daadff6519.png"},{"id":93575145,"identity":"1aa9a239-eeb5-465d-b09a-4184e0573471","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":645627,"visible":true,"origin":"","legend":"\u003cp\u003eAssociation of macroscopic color score with histological parameters. The percentage of each macroscopic color score with histological parameters was determined\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/c2bc258607304be6871a7558.png"},{"id":93576300,"identity":"08df254c-bfcd-43d5-8e0f-a4bd45ccb02e","added_by":"auto","created_at":"2025-10-15 09:30:32","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":230413,"visible":true,"origin":"","legend":"\u003cp\u003ePrognostic significance of macroscopic color score. Kaplan-Meier curves for macroscopic color score. RFS, recurrence-free survival; HR, hazard ratio; CI, confidence interval\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/97a39e575e3a6710bfefab06.png"},{"id":93574091,"identity":"6469c5d0-007f-4f24-af73-107901c1f962","added_by":"auto","created_at":"2025-10-15 09:14:32","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":583186,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic representation illustrating the associations between macroscopic color variation and histological parameters, including WHO/ISUP grade, cytological phenotype, vascularity-based architectural classification, and immunophenotype. Neoplastic cell nuclei are depicted in purple, the nucleoli in dark red, the intervening vascular network in brown, and tumor-associated immune cells in blue\u003c/p\u003e","description":"","filename":"Figure4new.png","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/238caf625432aa3fa1f438ae.png"},{"id":97140435,"identity":"09b9a530-49db-4df9-9d29-e4530453cc79","added_by":"auto","created_at":"2025-12-01 10:05:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3581893,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/f194825e-23e1-4c63-98a8-00a6dcf0f91d.pdf"},{"id":93575149,"identity":"407ddde8-3e2e-4fdf-ad67-0c0fb93c9071","added_by":"auto","created_at":"2025-10-15 09:22:32","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1952437,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformationfinal.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7556339/v1/fe1fab27ec40643abdec7b16.pdf"}],"financialInterests":"Competing interest reported. CO has received honoraria as a speaker from Merck Sharp and Dohme, while the remaining authors declare no conflicts of interest related to this work.","formattedTitle":"Macroscopic Color Variation in Clear Cell Renal Cell Carcinoma: A Novel Prognostic Indicator Reflecting the Tumor Microenvironment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eClear cell renal cell carcinoma (ccRCC), the most common subtype of renal cell carcinoma (RCC), exhibits morphological heterogeneity in cellular composition. Biallelic inactivation of the von Hippel-Lindau \u003cem\u003e(VHL)\u003c/em\u003e gene, a hallmark of ccRCC, is associated with clear cell proliferation and abundant vascularity [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Multiregional sequencing studies have highlighted the complex intratumoral heterogeneity of RCC, revealing that tumor cell proliferation can lead to the evolution of spatially and temporally distinct subclones [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. These subclones harbor unique mutations and somatic copy number alterations that contribute to the development of more aggressive phenotypes [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eRecent research suggests that integrated molecular and morphological evolutionary models hold promise for improving prognostic and therapeutic strategies in ccRCC [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Kapur et al. developed an architectural evolutionary model based on hematoxylin and eosin (H\u0026amp;E) and CD31 immunohistochemical staining. They found that architectural patterns with high vascularity were associated with indolent prognosis, whereas those with low vascularity were correlated with more aggressive disease [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eBuilding on previous research, our group established a vascularity-based architectural classification that focuses on architectural patterns reflecting the background vasculature [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. We previously revealed the associations among neoplastic cells, vascularity-based architectural classification, and immunophenotypes in ccRCC [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This classification could assess patient prognosis and showed correlations with angiogenesis-related gene signatures predictive of response to tyrosine kinase inhibitors (TKIs) and immune-related gene signatures predictive of response to immune checkpoint inhibitors (ICIs), as identified in the IMmotion 150 study [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, this classification was limited because vascularity-architectural categories were assessed only in areas with the highest nucleolar grade. Therefore, a method to quantify the proportion of each category is needed to develop practical tools to inform treatment decisions in routine clinical practice.\u003c/p\u003e\u003cp\u003eThe macroscopic color of ccRCC varies widely, ranging from golden yellow to pale tan or white, reflecting intratumoral heterogeneity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Pathologists have often noted a correlation between grossly white, firm areas and high-grade histological components such as sarcomatoid or rhabdoid features [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, to date, no studies have systematically evaluated the relationship between macroscopic color and key histological parameters, such as conventional prognostic factors and histological features related to the tumor microenvironment, including vasculature and immune cells.\u003c/p\u003e\u003cp\u003eIn the present study, we established a scoring system for macroscopic color variation in ccRCC and investigated its correlation with the pathological prognostic factors. This study underscores the importance of gross pathological assessment, demonstrating how appropriate tissue sampling based on macroscopic findings can improve patient prognosis by refining the risk stratification and guiding individualized treatment strategies.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003e This retrospective study was approved by the Institutional Review Board of Osaka Metropolitan University Hospital (IRB# 2024-003) in accordance with the Declaration of Helsinki. The initial cohort comprised of 340 patients who underwent partial or radical nephrectomy for ccRCC at our institution between 2013 and 2020. After excluding 31 patients whose tumors were not confirmed to be ccRCC upon review, 309 localized, surgically treated ccRCC cases (cT1-4N0-1M0) were included in the final analysis, and none had received any prior therapy. Pathological prognostic factors, including the 2017 TNM stage [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], World Health Organization (WHO)/International Society of Urologic Pathology (ISUP) nucleolar grade, sarcomatoid/rhabdoid features, and tumor-type necrosis, were evaluated based on the 2022 WHO classification criteria [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eMacroscopic evaluation\u003c/h3\u003e\n\u003cp\u003eRepresentative photographs of formalin-fixed gross surgical specimens were evaluated by two pathologists (N.T. and C.O.) who were blinded to clinical and pathological information. In partial nephrectomy cases, the specimens were evaluated by sectioning through the plane of the greatest diameter. In radical nephrectomy cases, specimens were evaluated using an initial longitudinal section, followed by thin transverse sections. Macroscopic color variation in ccRCC was categorized into three levels based on the following scoring system: score 1, uniform golden yellow (Fig.\u0026nbsp;1a-c), often with cyst formation and hemorrhage; score 2, yellow to pale tan (Fig.\u0026nbsp;1d-f); and score 3, grey to white (Fig.\u0026nbsp;1g-i). Areas exhibiting color alterations due to hemorrhage and infarction, hyalinization (Fig.\u0026nbsp;1b), myxoid degeneration (Fig.\u0026nbsp;1c), or tumor-type necrosis (Fig.\u0026nbsp;1h) were excluded from the evaluation. The highest score recorded was assigned to each case, regardless of the proportion of high-score components; thus, areas with scores of 2 and 3 could include areas with lower scores.\u003c/p\u003e\n\u003ch3\u003eHistological evaluation\u003c/h3\u003e\n\u003cp\u003eAll hematoxylin and eosin (H\u0026amp;E)-stained slides were pathologically re-evaluated by two pathologists (N.T. and C.O.) who were blinded to clinical and macroscopic information. All ccRCC diagnoses require the presence of a typical ccRCC morphology, characterized by small, thin-walled vascular networks and/or diffuse membranous positivity for carbonic anhydrase 9 (CA9), detected by immunohistochemistry [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In addition to conventional pathological prognostic factors, the following histological features were evaluated according to previously described methods [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]: (i) cytological phenotype, categorized as clear, mixed (some tumor cells are clear, and some have eosinophilic cytoplasm), or eosinophilic [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] (Suppl. Figure\u0026nbsp;1a); ii) vascularity-based architectural classification [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] (Suppl. Figure\u0026nbsp;1b), and (iii) immunophenotype, classified as cold, immune-low, excluded, or hot, considering both the degree and distribution of tumor-associated immune cells (TAICs) [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e](Suppl. Figure\u0026nbsp;1c). Cytological phenotypes and architectural classifications were evaluated in areas with the highest WHO/ISUP grades. Immunophenotypes were evaluated on a whole slide, including the areas with the highest WHO/ISUP grade. The vascularity-based architectural classification was divided into three categories as follows: category 1, characterized by an enriched vascular network, including compact/small nested, macrocystic/microcystic, and tubular/acinar patterns; category 2, characterized by a widely spaced vascular network, including alveolar/large nested, thick trabecular/insular, and papillary/pseudopapillary patterns; and category 3, characterized by scattered vascularity without a distinct vascular network, including solid sheets, sarcomatoid, and rhabdoid patterns (Suppl. Figure\u0026nbsp;1b). Immunophenotypes were categorized based on the location and density of TAICs into four types: cold (no TAICs), immune-low (focal or low TAICs regardless of their location), excluded (diffuse or high peritumoral TAICs), and hot (diffuse or high intratumoral TAICs) (Suppl. Figure\u0026nbsp;1c).\u003c/p\u003e\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003cp\u003eThe study outcome measure was recurrence-free survival (RFS), defined as the time from surgery to local or distant metastasis initially detected on imaging. All continuous data are presented as median and interquartile range (IQRs). Statistical analyses were performed using EZR version 1.68 (Saitama Medical Center) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. RFS was assessed using the Kaplan-Meier method with log-rank and Cox proportional hazards models. Harrell\u0026rsquo;s concordance index (C-index) was used to evaluate the predictive accuracy of the Cox models. Cramer's V test was performed to examine the significant correlations between the variables. A two-sided p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eClinicopathological characteristics\u003c/h2\u003e\u003cp\u003eThe clinicopathological features of the 309 patients with localized ccRCC are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age at ccRCC diagnosis was 66 years (IQR, 54.0\u0026ndash;74.0). A total of 95 tumors (30.7%) were high stage (TNM stage III/IV), and 121 tumors (39.2%) were high-grade (WHO/ISUP grade 3/4). During a median follow-up of 49 months (IQR, 29\u0026ndash;64 months), 37 patients (12.0%) experienced recurrence, and 12 patients (3.9%) died of ccRCC. Based on the results of macroscopic color scoring, the numbers) patients had scores of 1, 2, 3, 86 (27.8%), and 45 (14.6%), respectively. Cytological phenotype evaluation classified 109 patients (35.3%) as having a clear phenotype, 169 patients (54.7%) as having a mixed phenotype, and 31 patients (10.0%) as having an eosinophilic phenotype. The results of the vascularity-based architectural classification were as follows: 118 patients (38.2%) in category 1, 141 patients (45.6%) in category 2, and 50 patients (16.2%) in category 3. Immunophenotype evaluation classified 76 patients (24.6%) as cold type, 181 patients (58.6%) as immune-low type, 10 patients (3.2%) as excluded type, and 42 patients (13.6%) as hot type.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eClinicopathological characteristics of 309 cases with localized ccRCCs\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"2\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eVariables\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge, years, median (IQR)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e66 (54.0\u0026ndash;74.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGender, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFemale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e89 (28.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMale\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e220 (71.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTNM stage, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage I\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e209 (67.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage II\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 (1.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage III\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e94 (30.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage IV\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (0.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTumor size, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e≦\u0026thinsp;4 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e189 (61.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4 cm \u0026lt;, ≦ 7 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e78 (25.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7 cm \u0026lt;, ≦ 10 cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e33 (10.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e10 cm \u0026lt;\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e9 (2.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMacroscopic color score, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScore 0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e178 (57.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScore 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e86 (27.8)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eScore 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e45 (14.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWHO/ISUP grade, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e49 (15.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e139 (45.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e71 (23.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGrade 4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (16.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSarcomatoid/rhabdoid features, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e277 (89.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e32 (10.4)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNecrosis, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAbsence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e268 (86.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePresence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e41 (13.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCytological phenotype, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eClear\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e109 (35.3)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMixed\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e169 (54.7)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEosinophilic\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e31 (10.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVascularity-based architectural classification, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e118 (38.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e141 (45.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCategory 3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e50 (16.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunophenotype, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCold\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e76 (24.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmune-low\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e181 (58.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExcluded\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e10 (3.2)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHot\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e42 (13.6)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrence, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e37 (12.0)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCancer-specific mortality, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e12 (3.9)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e\u003cp\u003eccRCC, clear cell renal cell carcinoma; IQR, interquartile range; WHO, World Health Organization; ISUP, International Society of Urological Pathology\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eAssociation of macroscopic color score with histological parameters\u003c/h3\u003e\n\u003cp\u003eMacroscopic color score was significantly associated with TNM stage (p\u0026thinsp;=\u0026thinsp;0.009), tumor size (p\u0026thinsp;=\u0026thinsp;0.001), WHO/ISUP grade, sarcomatoid/rhabdoid features, tumor-type necrosis, cytological phenotype, vascularity-based architectural classification, and immunophenotype (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for all others; Fig.\u0026nbsp;2). Among the parameters examined, the WHO/ISUP grade showed the strongest association with the macroscopic color score (Cramer's V\u0026thinsp;=\u0026thinsp;0.34; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). A moderate association was found for the vascularity-based architectural classification (Cramer's V\u0026thinsp;=\u0026thinsp;0.29; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The cytological phenotype (Cramer's V\u0026thinsp;=\u0026thinsp;0.24; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and immunophenotype (Cramer's V\u0026thinsp;=\u0026thinsp;0.21; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) showed weak but significant associations. Supplementary Fig.\u0026nbsp;2 shows the correlation between representative macroscopic color tones and their corresponding histological features.\u003c/p\u003e\n\u003ch3\u003ePrognostic significance of macroscopic color score and histological parameters\u003c/h3\u003e\n\u003cp\u003eKaplan-Meier survival analysis showed that the 5-year RFS rates differed significantly according to the macroscopic color score: 93.6% for score 1, 81.3% for score 2 (HR, 2.69; p\u0026thinsp;=\u0026thinsp;0.022), and 63.2% for score 3 (HR, 6.53; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;3). Similar results were observed for the WHO/ISUP grade (Suppl. Figure\u0026nbsp;3a): 97.6% for grade 1/2, 81.6% for grade 3 (HR, 5.43; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 46.2% for grade 4 (HR, 22.39; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The 5-year RFS rates also differed according to cytological phenotype (Suppl. Figure\u0026nbsp;3b): 97.8% for the clear phenotype, 86.8% for the mixed phenotype (HR, 5.65; p\u0026thinsp;=\u0026thinsp;0.021), and 37.0% for the eosinophilic phenotype (HR, 45.13; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of the vascularity-based architectural classification (Suppl. Figure\u0026nbsp;3c), the 5-year RFS rates were 98.0% for category 1, 88.7% for category 2 (hazard ratio [HR], 4.85; p\u0026thinsp;=\u0026thinsp;0.039), and 48.3% for category 3 (HR, 36.23; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). In terms of the immunophenotype (Suppl. Figure\u0026nbsp;3d), the 5-year RFS rates were 93.9% for the cold type, 88.4% for the immune-low type (HR, 1.70; p\u0026thinsp;=\u0026thinsp;0.34), 50.0% for the excluded type (HR, 11.12; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and 66.5% for the hot type (HR, 6.84; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Among the parameters examined, the WHO/ISUP grade demonstrated the highest C-index for predicting RFS, outperforming vascularity-based architectural classification, cytological phenotype, immunophenotype, and macroscopic color score (0.818 vs. 0.816, 0.789, 0.729, and 0.709, respectively).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrated a significant correlation between macroscopic color variation in ccRCC and conventional pathological prognostic factors, as well as histological parameters linked to tumor aggressiveness and microenvironment. Notably, the strongest association was observed with WHO/ISUP grade, followed by vascularity-based architectural classification. Although the C-index of the macroscopic score was lower than that of the histological parameters, it effectively stratified the prognosis into three distinct groups. To the best of our knowledge, this is the first report of a macroscopic color scoring system for ccRCC that provides several important insights.\u003c/p\u003e\u003cp\u003eFirst, this study demonstrated that macroscopic color variation in ccRCC can stratify prognosis. Thus far, color detection in RCC has primarily been used as a diagnostic tool to aid in the identification of histological subtypes. For instance, the yellow color observed in clear cell RCC indicates intracytoplasmic lipids; the yellow to brown color observed in papillary RCC indicates foamy macrophages or hemorrhage; the beige or pale tan color observed in chromophobe renal tumors indicates the presence of abundant mitochondria; and the grey to white color observed in collecting duct carcinoma indicates desmoplasia (Suppl. Figure\u0026nbsp;4) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Macroscopic studies of ccRCC have predominantly focused on tumor morphology and infiltration patterns [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]; however, our analysis, which focused solely on color using a simple, easily applied evaluation method, highlights the clinical significance of macroscopic color variation. The strong correlation with WHO/ISUP grade is logical, as WHO/ISUP grade 3 tumors are often characterized by prominent nucleoli and frequently exhibit eosinophilic cytoplasm containing abundant mitochondria [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. These mitochondria-rich eosinophilic features are consistent with the pale tan color observed in chromophobe RCC. In contrast, WHO/ISUP grade 4 tumors, which frequently exhibit sarcomatoid or rhabdoid features, tend to have a gray-to-white appearance [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSecond, the significant association between tumor size and macroscopic score further supports the notion that intratumoral heterogeneity is associated with tumor aggressiveness. Although prior research has demonstrated a relationship between clonal evolution, genetic and histological intratumoral heterogeneity, and tumor aggressiveness in ccRCC [\u003cspan additionalcitationids=\"CR3 CR4 CR5\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], an association with macroscopic findings has not been reported. Histological studies have revealed that smaller ccRCC tumors (\u0026le;\u0026thinsp;4 cm) tend to exhibit less architectural diversity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], which is consistent with our observation of a predominance of a macroscopic score of 1 for these smaller tumors. Therefore, based on the results of this study, clonal evolution in ccRCC can be inferred through macroscopic intratumoral heterogeneity.\u003c/p\u003e\u003cp\u003eThird, our findings suggest that macroscopic color can predict prognosis and may reflect the tumor microenvironment, which is crucial for treatment selection with TKIs and ICIs. Our previous studies showed that vascularity-based architectural classification [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and cytological phenotype [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] correlated with angiogenesis- and immune-related gene signatures predictive of response to TKIs and ICIs, respectively. In the present study, we observed significant differences in vascularity-based architectural classification based on the macroscopic color score; tumors with abundant vascular networks tended to have a low color score, whereas those with decreased or sparse vascularity were associated with a high color score. Given that our previous study demonstrated a correlation between vascularity-based architectural classification and cytological phenotype [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], a similar trend in macroscopic findings is reasonable. Specifically, a clear or eosinophilic phenotype could indicate the presence of cytoplasmic lipids (for golden yellow color) or abundant mitochondria (for light tan color), respectively. Furthermore, vascularity-based architectural classification exhibits an inverse correlation with inflammation, as evaluated by histology and mRNA expression levels [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, although microscopic evaluation may provide a more precise assessment, the tumor microenvironment can be inferred to some extent from macroscopic images.\u003c/p\u003e\u003cp\u003eGiven the growing trend of using TKI\u0026thinsp;+\u0026thinsp;ICI or ICI combination therapies [\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], it is crucial to understand intratumoral heterogeneity. The correlation between macroscopic findings and histological parameters linked to treatment response-related gene signatures suggests that macroscopic assessment may provide insights into overall tumor composition (Fig.\u0026nbsp;4). With the approval of HIF2α inhibitors for advanced ccRCC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], treatment decisions for advanced RCC require careful consideration when selecting TKIs, ICIs, and HIF2α inhibitors [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Our previous research indicated that HIF2α was expressed immunohistochemically and at the mRNA level in category 1 (compact/small nested and micro/macrocystic) tumors, which may be associated with a clear cell type and a \"cold\" immunophenotype corresponding to a macroscopic score of 1. Although our study focused on localized RCC without preoperative treatment, macroscopic evaluation of nephrectomy specimens after neoadjuvant therapy may help to predict which agents are likely to be effective against residual tumor components. Therefore, further investigation of the relationship between macroscopic findings and treatment responses is warranted.\u003c/p\u003e\u003cp\u003eFourth, although macroscopic scoring independently stratifies prognosis into three distinct groups, the prioritization of areas with color scores of 2 or 3 within our system underscores the prognostic significance of these high-scoring regions, similar to the WHO/ISUP grading system [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Given that adjuvant treatment is indicated for patients exhibiting pathological high-risk features (e.g., pT3a or higher, grade 4 nuclear atypia, or sarcomatoid differentiation) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], features frequently associated with a macroscopic score of 3 and inadequate sampling of these areas pose a significant risk of underestimating relevant pathological prognostic factors. Therefore, meticulous macroscopic evaluation and comprehensive tissue sampling are essential for an accurate risk assessment.\u003c/p\u003e\u003cp\u003eFifth, although the WHO/ISUP grade is based on nucleolar characteristics, the correlation between the macroscopic score and architectural classification, which reflects the microenvironment, is clinically significant. Vascular networks can be identified by contrast enhancement and can potentially be assessed using contrast-enhanced CT [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Although contrast enhancement patterns differ between ccRCC and chromophobe RCC [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], as the category of ccRCC increases, it exhibits vascular network morphology similar to that of chromophobe RCC. Validation of the correlation between macroscopic images, vascularity-based architectural classification, and radiographic images could allow assessment of the ccRCC tumor microenvironment, which is relevant for treatment selection, solely with radiographic images. These insights may also lead to improved sampling strategies during tumor biopsy, thereby obtaining useful prognostic information.\u003c/p\u003e\u003cp\u003eDespite the promising findings of this study, several limitations must be acknowledged. As this was a retrospective, single-center study, our findings may be subject to bias and limited generalizability. The variable slice widths used by different pathologists may have missed high-scoring areas in the macrophotographs. Although areas exhibiting color alterations due to hemorrhage, infarction, tumor-type necrosis, hyalinization, or myxoid degeneration were excluded from the evaluation, these features are sometimes difficult to identify on macrophotographs and may lead to interobserver variability. The subjective nature of macroscopic color evaluation and relatively simple three-tiered scoring system may not capture the full spectrum of colors observed in ccRCC. The lack of molecular data restricts elucidation of the underlying molecular mechanisms. Furthermore, a limited follow-up period may not have detected late recurrences. Finally, our study focused exclusively on localized ccRCC, and the applicability of our findings to advanced diseases remains unclear. Future research should focus on validating these findings in larger, multi-institutional cohorts, developing objective color measurement techniques, integrating molecular data, and extending the follow-up period. Despite these limitations, our study provides a valuable foundation for future investigations of the role of macroscopic tumor characteristics in ccRCC management.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study emphasizes the importance of macroscopic evaluation of ccRCC. By incorporating macroscopic color scoring into routine pathological assessments, we can refine risk stratification, guide individualized treatment strategies, and potentially improve patient prognosis. Further investigation is needed to determine the utility of macroscopic color scoring in the context of neoadjuvant therapy and advanced ccRCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design was approved by the Osaka Metropolitan University Hospital Institutional Review Board (IRB# 2024-003) in accordance with the Declaration of Helsinki. The requirement for individual informed consent was waived due to the retrospective nature of the study. Instead, informed consent was obtained as an opt-out method on the institutional website (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.omu.ac.jp/med/med_ethics/igakukei/information/\u003c/span\u003e\u003c/span\u003e), and no patient expressed refusal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\u003c/p\u003e\n\u003ch2\u003eCompeting Interests\u003c/h2\u003e\n\u003cp\u003eCO has received honoraria as a speaker from Merck Sharp and Dohme, while the remaining authors declare no conflicts of interest related to this work.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the Japan Society for the Promotion of Science KAKENHI Fund (grant #24K10144 to CO).\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: NT, CO; Methodology: NT, CO, MS, MK, and RU; Formal analysis and investigation: NT, CO, MS; Funding acquisition: CO; Resources: NY, TY, Supervision: JU, KK; Writing - original draft preparation: NT, CO; Writing - review \u0026amp; editing and final approval of the manuscript, all authors.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003eThe datasets analyzed in the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWHO Classification of Tumours Editorial Board. WHO Classification of Tumors, urinary and male genital tumors. 5th ed. Lyon: IARC; 2022.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eGerlinger M, Rowan AJ, Horswell S, Math M, Larkin J, Endesfelder D, et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. N Engl J Med. 2012;366:883\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTurajlic S, Xu H, Litchfield K, Rowan A, Chambers T, Lopez JI, et al. Tracking Cancer Evolution Reveals Constrained Routes to Metastases: TRACERx Renal. Cell. 2018;173:581\u0026ndash;e594512.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eLi Y, Lih TM, Dhanasekaran SM, Mannan R, Chen L, Cieslik M, et al. Histopathologic and proteogenomic heterogeneity reveals features of clear cell renal cell carcinoma aggressiveness. Cancer Cell. 2023;41:139\u0026ndash;e163117.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCai Q, Christie A, Rajaram S, Zhou Q, Araj E, Chintalapati S, et al. Ontological analyses reveal clinically-significant clear cell renal cell carcinoma subtypes with convergent evolutionary trajectories into an aggressive type. EBioMedicine. 2020;51:102526.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKapur P, Christie A, Rajaram S, Brugarolas J. What morphology can teach us about renal cell carcinoma clonal evolution. Kidney Cancer J. 2020;18:68\u0026ndash;76.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhe C, Yoshida T, Amin MB, Atsumi N, Ikeda J, Saiga K, et al. Development and validation of a vascularity-based architectural classification for clear cell renal cell carcinoma: correlation with conventional pathological prognostic factors, gene expression patterns, and clinical outcomes. Mod Pathol. 2022;35:816\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhe C, Yoshida T, Amin MB, Smith SC, Shiohara M, Tsujio N et al. Comparison of Histologic Parameters and Predictive Gene Signatures in Clear Cell Renal Cell Carcinoma Response to Systemic Therapy. Pathol Int. 2025; 75: 267\u0026ndash;77.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMcDermott DF, Huseni MA, Atkins MB, Motzer RJ, Rini BI, Escudier B, et al. Clinical activity and molecular correlates of response to atezolizumab alone or in combination with bevacizumab versus sunitinib in renal cell carcinoma. Nat Med. 2018;24:749\u0026ndash;57.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eAmin MB, Tickoo SK, editors. Diagnostic pathology. Genitourinary. 2nd ed. Philadelphia: Elsevier; 2016.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eL\u0026oacute;pez JI. Intratumor heterogeneity in clear cell renal cell carcinoma: a review for the practicing pathologist. Apmis. 2016;124:153\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eBrierley JD, Gospodarowicz MK, Wittekind C, editors. TNM classification of malignant tumours. 8th ed. West Sussex: John Wiley \u0026amp; Sons, Inc.; 2017.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoshida T, Ohe C, Ikeda J, Atsumi N, Ohsugi H, Sugi M et al. Eosinophilic features in clear cell renal cell carcinoma correlate with outcomes of immune checkpoint and angiogenesis blockade. J Immunother Cancer. 2021; 9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhe C, Yoshida T, Ikeda J, Tsuzuki T, Ohashi R, Ohsugi H et al. Histologic-Based Tumor-Associated Immune Cells Status in Clear Cell Renal Cell Carcinoma Correlates with Gene Signatures Related to Cancer Immunity and Clinical Outcomes. Biomedicines. 2022; 10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eShiohara M, Ohe C, Tsujio N, Uno R, Kohashi K. Correlation of histological immunophenotype in papillary renal cell carcinoma with gene signatures related to the therapeutic effect of systemic therapy. Pathol Res Pract. 2025;266:155764.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eKanda Y. Investigation of the freely available easy-to-use software 'EZR' for medical statistics. Bone Marrow Transpl. 2013;48:452\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eJeong SU, Park JM, Shin SJ, Lee J, Song C, Go H, et al. Prognostic Significance of Macroscopic Appearance in Clear Cell Renal Cell Carcinoma and Its Metastasis-Predicting Model. Pathol Int. 2017;67:610\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNilsson H, Lindgren D, Axelson H, Brueffer C, Saal LH, Lundgren J, et al. Features of increased malignancy in eosinophilic clear cell renal cell carcinoma. J Pathol. 2020;252:384\u0026ndash;97.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eOhe C, Yoshida T, Amin MB, Uno R, Atsumi N, Yasukochi Y, et al. Deep learning-based predictions of clear and eosinophilic phenotypes in clear cell renal cell carcinoma. Hum Pathol. 2023;131:68\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMotzer RJ, Tannir NM, McDermott DF, Ar\u0026eacute;n Frontera O, Melichar B, Choueiri TK, et al. Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med. 2018;378:1277\u0026ndash;90.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCetin B, Wabl CA, Gumusay O. Reshaping Treatment Paradigms for Advanced Renal Cell Cancer Patients and Improving Patient Management: Optimal Management for Renal Cell Cancer Patients. Curr Treat Options Oncol. 2022;23:609\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eTran J, Ornstein MC. Clinical Review on the Management of Metastatic Renal Cell Carcinoma. JCO Oncol Pract. 2022;18:187\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoueiri TK, Powles T, Peltola K, de Velasco G, Burotto M, Suarez C, et al. Belzutifan versus Everolimus for Advanced Renal-Cell Carcinoma. N Engl J Med. 2024;391:710\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eNguyen CB, Oh E, Bahar P, Vaishampayan UN, Else T, Alva AS. Novel Approaches with HIF-2α Targeted Therapies in Metastatic Renal Cell Carcinoma. Cancers (Basel). 2024; 16.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eCardenas LM, Deluce JE, Khan S, Gulam O, Maleki Vareki S, Fernandes R, et al. Next Wave of Targets in the Treatment of Advanced Renal Cell Carcinoma. Curr Oncol. 2022;29:5426\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eDelahunt B, Eble JN, Egevad L, Samaratunga H. Grading of renal cell carcinoma. Histopathology. 2019;74:4\u0026ndash;17.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoueiri TK, Tomczak P, Park SH, Venugopal B, Ferguson T, Chang YH, et al. Adjuvant Pembrolizumab after Nephrectomy in Renal-Cell Carcinoma. N Engl J Med. 2021;385:683\u0026ndash;94.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eChoueiri TK, Tomczak P, Park SH, Venugopal B, Ferguson T, Symeonides SN, et al. Overall Survival with Adjuvant Pembrolizumab in Renal-Cell Carcinoma. N Engl J Med. 2024;390:1359\u0026ndash;71.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eMains JR, Donskov F, Pedersen EM, Madsen HH, Rasmussen F. Dynamic Contrast-Enhanced Computed Tomography-Derived Blood Volume and Blood Flow Correlate With Patient Outcome in Metastatic Renal Cell Carcinoma. Invest Radiol. 2017;52:103\u0026ndash;10.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eZhang B, Wu Q, Qiu X, Ding X, Wang J, Li J, et al. Effect of spectral CT on tumor microvascular angiogenesis in renal cell carcinoma. BMC Cancer. 2021;21:874.\u003c/span\u003e\u003c/li\u003e\u003cli\u003e\u003cspan\u003eYoung JR, Margolis D, Sauk S, Pantuck AJ, Sayre J, Raman SS. Clear cell renal cell carcinoma: discrimination from other renal cell carcinoma subtypes and oncocytoma at multiphasic multidetector CT. Radiology. 2013;267:444\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"clear cell renal cell carcinoma, macroscopic color, intratumoral heterogeneity, tissue sampling, prognostic marker, risk stratification","lastPublishedDoi":"10.21203/rs.3.rs-7556339/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7556339/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"[Background] Clear cell renal cell carcinoma (ccRCC) exhibits heterogeneous macroscopic colors, ranging from golden yellow to pale tan or white, demonstrating intratumoral heterogeneity in cellular composition. This study aimed to establish a macroscopic color scoring system for ccRCC and evaluate its correlation with pathological prognostic factors and previously reported histological parameters linked to treatment response-related gene signatures. [Methods] We retrospectively analyzed macroscopic color variation in 309 localized ccRCC cases, categorizing it into three levels: score 1 (golden yellow), score 2 (yellow to pale tan), and score 3 (grey to white). The prognostic significance of macroscopic color scores and their associations with pathological prognostic features were evaluated. [Results] Macroscopic color score was significantly correlated with TNM stage (p = 0.009), tumor size (p = 0.001), World Health Organization (WHO)/International Society of Urologic Pathology (ISUP) grade, sarcomatoid/rhabdoid features, tumor-type necrosis, cytological phenotype, vascularity-based architectural classification, and immunophenotype (p \u003c 0.001). Higher scores were associated with adverse histological parameters, decreased vascularity, and increased immune infiltration, with the strongest correlations observed for WHO/ISUP grade and vascularity-based architectural classification. Although the C-index, a measure of predictive accuracy, of macroscopic score was lower than that of histological parameters, it effectively stratified recurrence-free survival into three distinct groups (hazard ratio, 2.69; p = 0.022 for score 2; hazard ratio, 6.53; p \u003c 0.001 for score 3). [Conclusion] Macroscopic color variation in ccRCC may reflect underlying morphological and molecular heterogeneity. The macroscopic color scoring system may be valuable for guiding tissue sampling and risk stratification, potentially informing treatment strategies and diagnostic imaging approaches.","manuscriptTitle":"Macroscopic Color Variation in Clear Cell Renal Cell Carcinoma: A Novel Prognostic Indicator Reflecting the Tumor Microenvironment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 09:14:27","doi":"10.21203/rs.3.rs-7556339/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b48a394-5fbc-40ff-8437-df424034ad69","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-12-01T06:09:05+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 09:14:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7556339","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7556339","identity":"rs-7556339","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}