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Renal-cell carcinoma is considered to be a radioresistant tumour, but this notion might be wrong. If given in a few (even single) fractions, but at a high fraction dose, stereotactic body radiotherapy becomes increasingly important in the management of renal-cell carcinoma, both in primary settings and in treatment of oligometastatic disease. There is an established biological rationale for the radiosensitivity of renal-cell carcinoma to stereotactic body radiotherapy based on the ceramide pathway, which is activated only when a high dose per fraction is given. Apart from the direct effect of stereotactic body radiotherapy on renal-cell carcinoma, stereotactic body radiotherapy can also induce an abscopal effect. This effect, caused by immunological processes, might be enhanced when targeted drugs and stereotactic body radiotherapy are combined. Therefore, rigorous, prospective randomised trials involving a multidisciplinary scientific panel are needed urgently.

Anti-angiogenic (AA) therapy is a cornerstone of metastatic clear cell renal cell carcinoma (ccRCC) treatment, but not everyone responds, and predictive biomarkers are lacking. CD31, a marker of vasculature, is insufficient, and the Angioscore, an RNA-based angiogenesis quantification method, is costly, associated with delays, difficult to standardize, and does not account for tumor heterogeneity. Here, we developed an interpretable deep learning (DL) model that predicts the Angioscore directly from ubiquitous histopathology slides yielding a visual vascular network (H&E DL Angio). H&E DL Angio achieves a strong correlation with the Angioscore across multiple cohorts (spearman correlations of 0.77 and 0.73). Using this approach, we found that angiogenesis inversely correlates with grade and stage and is associated with driver mutation status. Importantly, DL Angio expediently predicts AA response in both a real-world and IMmotion150 trial cohorts, out-performing CD31, and closely approximating the Angioscore (c-index 0.66 vs 0.67) at a fraction of the cost. Angioscore is a transcriptome based predicter of treatment response in clear cell renal cell carcinoma. Here, the authors use deep learning to infer the Angioscore from histopathology slides using clinical trial data.

Kidney renal clear cell carcinoma (KIRC), as one of the most angiogenically active urological tumors, has not yet been fully elucidated its molecular mechanisms. Molecules interacting with CasL-Like 2 (MICAL-L2), a unique oxidoreductase, is known to be involved in cytoskeletal regulation. However, its role and mechanism in tumor angiogenesis remain unclear. This study aims to reveal the specific mechanism by which MICAL-L2 regulates KIRC angiogenesis through cytoskeletal dynamics and to explore its clinical translational value and significance. Bioinformatics database analysis showed that MICAL-L2 was significantly overexpressed in various solid tumors and was closely associated with shorter overall survival in KIRC patients. Gene set enrichment analysis demonstrated that MICAL-L2 expression was significantly correlated with members of the hypoxia-inducible factor (HIF) family and was closely related to angiogenesis pathways. Analysis of human KIRC cell lines revealed that the upregulation of MICAL-L2 stimulated the release of various pro-angiogenic cytokines, primarily through the activation of a novel mTOR/HIF1α/VEGF pathway, promoting tumor angiogenesis. Further studies using cultured KIRC cell lines unveiled that MICAL-L2 co-localized with F-actin in the cytoplasm and promoted tumor angiogenesis remodeling in vitro. Additionally, analysis of clinical datasets indicated that high expression of MICAL-L2 was associated with drug resistance to various anti-angiogenic agents and actin inhibitors in cancer. This study reveals a new mechanism in which MICAL-L2, under the tumor hypoxic environment, promotes KIRC angiogenesis and progression by activating the “cytoskeletal remodeling-mTOR/HIF1α/VEGF signaling pathway” using integrated analysis of clinical samples and cell models, providing new theoretical evidence and potential intervention targets for the development of combination therapies targeting the tumor microenvironment.

ObjectivesVarious imaging methods have been evaluated regarding non-invasive differentiation of renal cell carcinoma (RCC) subtypes. Dual-energy computed tomography (DECT) allows iodine concentration (IC) analysis as a correlate of tissue perfusion. Microvascular density (MVD) in histopathology specimens is evaluated to determine intratumoral vascularization. The objective of this study was to assess the potential of IC and MVD regarding the differentiation between papillary and clear cell RCC and between well- and dedifferentiated tumors. Further, we aimed to investigate a possible correlation between these parameters.MethodsDECT imaging series of 53 patients with clear cell RCC (ccRCC) and 15 with papillary RCC (pRCC) were analyzed regarding IC. Histology samples were stained using CD31/CD34 monoclonal antibodies; MVD was evaluated digitally. Statistical analysis included performance of Mann-Whitney U test, ROC analysis, and Spearman rank correlation.ResultsAnalysis of IC demonstrated significant differences between ccRCC and pRCC (p < 0.001). A cutoff value of ≤ 3.1 mg/ml at IC analysis allowed identification of pRCC with an accuracy of 86.8%. Within the ccRCC subgroup, G1/G2 tumors could significantly be differentiated from G3/G4 carcinomas (p = 0.045). A significant positive correlation between IC and MVD could be determined for the entire RCC cohort and the ccRCC subgroup. Limitations include the small percentage of pRCCs.ConclusionsIC analysis is a useful method to differentiate pRCC from ccRCC. The significant positive correlation between IC and MVD indicates valid representation of tumor perfusion by DECT.Key Points• Analysis of iodine concentration using DECT imaging could reliably distinguish papillary from clear cell subtypes of renal cell cancer (RCC).• A cutoff value of 3.1 mg/ml allowed a distinction between papillary and clear cell RCCs with an accuracy of 86.8%.• The positive correlation with microvascular density in tumor specimens indicates correct display of perfusion by iodine concentration analysis.

Ultrasound microvessel imaging (UMI), when applied with ultrafast planewave acquisitions, has demonstrated superior blood signal sensitivity in comparison to conventional Doppler imaging. Here we propose a high spatial resolution and ultra-sensitive UMI that is based on conventional line-by-line high-frequency ultrasound imagers and singular value decomposition (SVD) clutter filtering for the visualization and quantification of tumor microvasculature and perfusion. The technique was applied to a chicken embryo tumor model of renal cell carcinoma that was treated with two FDA-approved anti-angiogenic agents at clinically relevant dosages. We demonstrate the feasibility of 3D evaluation with UMI to achieve highly sensitive detection of microvasculature using conventional line-by-line ultrasound imaging on a preclinical and commercially available high-frequency ultrasound device without software or hardware modifications. Quantitative parameters (vascularization index and fractional moving blood volume) derived from UMI images provide significantly improved evaluation of anti-angiogenic therapy response as compared with conventional power Doppler imaging, using histological analysis and immunohistochemistry as the reference standard. This proof-of-concept study demonstrates that high-frequency UMI is a low-cost, contrast-agent-free, easily applicable, accessible, and quantitative imaging tool for tumor characterization, which may be very useful for preclinical evaluation and longitudinal monitoring of anti-cancer treatment.

The microRNA let-7d has been reported to be a tumor suppressor in renal cell carcinoma (RCC). Tumor-associated macrophages (TAM) are M2-polarized macrophages that can enhance tumor growth and angiogenesis in many human cancers. However, the role of let-7d in TAM-associated RCC progression remains elusive. First, we observed a strongly inverse correlation between let-7d expression and microvessel density in RCC tissues. Furthermore, the proliferation, migration, and tube formation of HUVECs were significantly inhibited by conditioned medium from a coculture system of the phorbol myristate acetate pretreated human THP-1 macrophages and let-7d-overexpressing RCC cells. Moreover, the proportion of M2 macrophages was significantly lower in the group that was cocultured with let-7d-overexpressing RCC cells. Subcutaneous xenografts formed by the injection of let-7d-overexpressing RCC cells together with THP-1 cells resulted in a significant decrease in the M2 macrophage ratio and microvessel density compared with those formed by the injection of control RCC cells with THP-1 cells. In silico and experimental analysis revealed interleukin-10 (IL-10) and IL-13 as let-7d target genes. Importantly, the addition of IL-10 and IL-13 counteracted the inhibitory effects of the conditioned medium from the coculture system with let-7d-overexpressing RCC cells in vitro. Additionally, overexpression of IL-10 and IL-13 reversed the effects of let-7d on macrophage M2 polarization and tumor angiogenesis in vivo. Finally, the expression of IL-10 and IL-13 were inversely correlated with the expression of let-7d in RCC clinical specimens. These results suggest that let-7d may inhibit intratumoral macrophage M2 polarization and subsequent tumor angiogenesis by targeting IL-10 and IL-13.

Venous tumour thrombus (VTT), where the primary tumour invades the renal vein and inferior vena cava, affects 10–15% of renal cell carcinoma (RCC) patients. Curative surgery for VTT is high-risk, but neoadjuvant therapy may improve outcomes. The NAXIVA trial demonstrated a 35% VTT response rate after 8 weeks of neoadjuvant axitinib, a VEGFR-directed therapy. However, understanding non-response is critical for better treatment. Here we show that response to axitinib in this setting is characterised by a distinct and predictable set of features. We conduct a multiparametric investigation of samples collected during NAXIVA using digital pathology, flow cytometry, plasma cytokine profiling and RNA sequencing. Responders have higher baseline microvessel density and increased induction of VEGF-A and PlGF during treatment. A multi-modal machine learning model integrating features predict response with an AUC of 0.868, improving to 0.945 when using features from week 3. Key predictive features include plasma CCL17 and IL-12. These findings may guide future treatment strategies for VTT, improving the clinical management of this challenging scenario. Venous tumour thrombus can occur within renal cell carcinoma, and can require complex additional surgery and treatment. Here, the authors analyse multiparametric data from patients treated with axitinib and develop a machine learning model to predict neoadjuvant treatment response.

Background The APLNR (apelin receptor) has been shown to be an essential gene for cancer immunotherapy, with deficiency in APLNR leading to immunotherapy failure. The aim of this study is to investigate the expression of APLN (apelin) and APLNR in patients with renal cell carcinoma (RCC), and its association with clinicopathological parameters and survival. Methods Three well-characterised patient cohorts with RCC were used: Study cohort 1 (clear-cell RCC; APLN/APLNR mRNA expression; n  = 166); TCGA validation cohort (clear-cell RCC; APLN/APLNR mRNA expression; n  = 481); Study cohort 2 (all RCC subtypes; APLNR protein expression/immunohistochemistry; n  = 300). Associations between mRNA/protein expression and clinicopathological variables/patients’ survival were tested statistically. Results While APLN showed only very weak association with tumour histological grade (TCGA cohort), APLNR/mRNA protein expression correlate significantly with ccRCC aggressiveness. APLNR is expressed in tumour vasculature and tumour cells at different levels, and these expression levels associate with tumour aggressiveness in opposing directions. APLNR expression was negatively correlated with PD-L1 expression by tumour cells in a subset of patients with ccRCC. APLNR expression in either compartment is an independent prognostic factor for survival of patients with ccRCC. Conclusion The APLNR/APLN-system appears to play an important role in ccRCC, warranting further clinical investigation.

While TR4 nuclear receptor plays key roles to promote prostate cancer progression, its roles to alter the progression of clear cell renal cell carcinoma (ccRCC), remains unclear. Here, we demonstrate that TR4 can promote the ccRCC cell vasculogenic mimicry (VM) formation and its associated metastasis via modulating the miR490-3p/vimentin (VIM) signals. Mechanism dissection revealed that TR4 might increase the oncogene VIM expression via decreasing the miR-490-3p expression through direct binding to the TR4-response-elements (TR4REs) on the promoter region of miR-490-3p, which might then directly target the 3′ UTR of VIM-mRNA to increase its protein expression. Preclinical studies using the in vivo mouse model with xenografted RCC Caki-1 cells into the sub-renal capsule of nude mice also found that TR4 could promote the ccRCC VM and its associated metastasis via modulating the miR490-3p/VIM signals. Together, results from preclinical studies using multiple RCC cell lines and the in vivo mouse model all conclude that TR4 may play a key role to promote ccRCC VM formation and metastasis and targeting the newly identified TR4/miR-490-3p/VIM signals with small molecules may help us to develop a new therapeutic approach to better suppress the ccRCC metastasis.

Chloride intracellular channel protein 1 (CLIC1) is known as a promoter of cancer progression, metastasis, and angiogenesis. Thus, CLIC1 could be a future therapeutic target. This study aimed to evaluate the effect of anti-CLIC1 antibodies on tumour cells and vessels of human renal cell carcinoma (RCC) in rabbit cornea and chick embryo chorioallantoic membrane (CAM) models. Human cc-RCC xenografts on rabbit cornea and CAM surface were performed. Anti-CLIC1 antibodies were applied for 5 consecutive days on both tumor models. We comparatively evaluated treated and untreated tumors by combining ultrasonography with microscopic techniques. RCC implants rapidly recruited blood vessels and had an exponential growth rate on both tumor models. Anti-CLIC1 antibodies suppressed tumor growth by inducing tumor cell necrosis. Tumor vessels regressed rapidly but not completely during anti-CLIC1 antibodies based therapy. Anti-CLIC1 antibodies induced tumor necrosis and tumor vasculature regression in human cc-RCC xenografts in both in vivo experimental models.