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Patient-derived xenografts (PDX) are high-fidelity cancer models typically credentialled by genomics, transcriptomics and proteomics. Characterization of metabolic reprogramming, a hallmark of cancer, is less frequent. Dysregulated metabolism is a key feature of clear cell renal cell carcinoma (ccRCC) and authentic preclinical models are needed to evaluate novel imaging and therapeutic approaches targeting metabolism. We characterized 5 PDX from high-grade or metastatic ccRCC by multiparametric magnetic resonance imaging (MRI) and steady state metabolic profiling and flux analysis. Similar to MRI of clinical ccRCC, T2-weighted images of orthotopic tumors of most PDX were homogeneous. The increased hyperintense (cystic) areas observed in one PDX mimicked the cystic phenotype typical of some RCC. The negligible hypointense (necrotic) areas of PDX grown under the highly vascularized renal capsule are beneficial for preclinical studies. Mean apparent diffusion coefficient (ADC) values were equivalent to those of ccRCC in human patients. Hyperpolarized (HP) [1-13C]pyruvate MRI of PDX showed high glycolytic activity typical of high-grade primary and metastatic ccRCC with considerable intra- and inter-tumoral variability, as has been observed in clinical HP MRI of ccRCC. Comparison of steady state metabolite concentrations and metabolic flux in [U-13C]glucose-labeled tumors highlighted the distinctive phenotypes of two PDX with elevated levels of numerous metabolites and increased fractional enrichment of lactate and/or glutamate, capturing the metabolic heterogeneity of glycolysis and the TCA cycle in clinical ccRCC. Culturing PDX cells and reimplanting to generate xenografts (XEN), or passaging PDX in vivo, altered some imaging and metabolic characteristics while transcription remained like that of the original PDX. These findings show that PDX are realistic models of ccRCC for imaging and metabolic studies but that the plasticity of metabolism must be considered when manipulating PDX for preclinical studies.

PURPOSE: Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes. MATERIALS AND METHODS: Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues. RESULTS: From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC. CONCLUSION: Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS. Combining mass spectrometry imaging and machine learning enables successful diagnosis of renal cell carcinoma from normal kidney tissues. [ABSTRACT FROM AUTHOR]

Background: Clear cell renal cell carcinoma (ccRCC), the predominant subtype of kidney cancer, possesses characteristic alterations to multiple metabolic pathways, including the accumulation of cytosolic lipid droplets. However, the pathways that drive lipid droplet accumulation in ccRCC cells and their importance to cancer biology remain poorly understood. Methods: We sought to identify the carbon sources necessary for lipid droplet accumulation using Oil red O staining and isotope-tracing lipidomics. The role of the acyl-CoA synthetase (ACSL) family members, an important group of lipid metabolic enzymes, was investigated using siRNA and drug mediated inhibition. CTB and XTT assays were performed to determine the effect of ACSL3 knockdown and lipid starvation on ccRCC cell viability and shRNA was used to study the effect of ACSL3 in an orthotopic mouse model. The relationship between ferroptosis susceptibility of ccRCC and ACSL3 controlled lipid metabolism was examined using CTB and FACS-based assays. The importance of 5-LOX in ferroptosis susceptibility in ccRCC was shown with XTT survival assays, and the expression level and predictive value of 5-LOX in TCGA ccRCC data was assessed. Results: We found that ccRCC cells obtain the necessary substrates for lipid droplet accumulation by metabolizing exogenous serum derived lipids and not through de novo lipogenesis. We show that this metabolism of exogenous fatty acids into lipid droplets requires the enzyme acyl-CoA synthetase 3 (ACSL3) and not other ACSL family proteins. Importantly, genetic or pharmacologic suppression of ACSL3 is cytotoxic to ccRCC cells in vitro and causes a reduction of tumor weight in an orthotopic mouse model. Conversely, ACSL3 inhibition decreases the susceptibility of ccRCC cells to ferroptosis, a non-apoptotic form of cell death involving lipid peroxidation. The sensitivity of ccRCC to ferroptosis is also highly dependent on the composition of exogenous fatty acids and on 5-lipoxygenase (5-LOX), a leukotriene producing enzyme which produces lipid peroxides that have been implicated in other cancers but not in ccRCC. Conclusions: ACSL3 regulates the accumulation of lipid droplets in ccRCC and is essential for tumor growth. In addition, ACSL3 also modulates ferroptosis sensitivity in a manner dependent on the composition of exogenous fatty acids. Both functions of ACSL3 could be exploited for ccRCC therapy. [ABSTRACT FROM AUTHOR]

Clear cell renal cell carcinoma (ccRCC) is the most common and lethal subtype of kidney cancer. Intraoperative frozen section (IFS) analysis is used to confirm the diagnosis during partial nephrectomy. However, surgical margin evaluation using IFS analysis is time consuming and unreliable, leading to relatively low utilization. In our study, we demonstrated the use of desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) as a molecular diagnostic and prognostic tool for ccRCC. DESI‐MSI was conducted on fresh‐frozen 23 normal tumor paired nephrectomy specimens of ccRCC. An independent validation cohort of 17 normal tumor pairs was analyzed. DESI‐MSI provides two‐dimensional molecular images of tissues with mass spectra representing small metabolites, fatty acids and lipids. These tissues were subjected to histopathologic evaluation. A set of metabolites that distinguish ccRCC from normal kidney were identified by performing least absolute shrinkage and selection operator (Lasso) and log‐ratio Lasso analysis. Lasso analysis with leave‐one‐patient‐out cross‐validation selected 57 peaks from over 27,000 metabolic features across 37,608 pixels obtained using DESI‐MSI of ccRCC and normal tissues. Baseline Lasso of metabolites predicted the class of each tissue to be normal or cancerous tissue with an accuracy of 94 and 76%, respectively. Combining the baseline Lasso with the ratio of glucose to arachidonic acid could potentially reduce scan time and improve accuracy to identify normal (82%) and ccRCC (88%) tissue. DESI‐MSI allows rapid detection of metabolites associated with normal and ccRCC with high accuracy. As this technology advances, it could be used for rapid intraoperative assessment of surgical margin status. What's new? Intraoperative frozen section (IFS) analysis is used in clear cell renal cell carcinoma (ccRCC) to confirm diagnosis during partial nephrectomy. Surgical margin evaluation using IFS analysis is time‐consuming and unreliable, however, leading to relatively low utilization. Here, the authors evaluated the feasibility of using desorption electrospray ionization mass spectrometry imaging (DESI‐MSI) in assessing ccRCC in core tissues. Their combined predictive model consisting of differentially expressed metabolites and the ratio of glucose to arachidonic acid could discriminate cancer vs. normal tissue with an accuracy of 85%. DESI‐MSI thus demonstrates promise as a method to improve detection of positive surgical margins intraoperatively. [ABSTRACT FROM AUTHOR]

Androgen receptor (AR) is expressed in normal murine and human kidneys of both genders, but its physiologic role is uncertain. Several studies showed loss of AR in renal cell carcinoma (RCC) in conjunction with increasing clinical stage and pathological grade, but others found that higher AR expression correlated with worse outcomes. Limited functional studies with renal cell lines suggested tumor-promoting activity of AR. In this study, we queried transcriptomic, proteomic, epigenetic and survival data from The Cancer Genome Atlas (TCGA) to evaluate AR expression and its association with overall survival in three subtypes of RCC (clear cell [ccRCC], papillary [pRCC], and chromophobe [chRCC]). We found that although there was no significant difference in AR mRNA expression in ccRCC of males vs. females, AR protein expression in ccRCC was significantly higher in male compared to female patients. More importantly, higher expression of AR at both transcript and protein levels was associated with improved overall survival in both genders with ccRCC, but did not predict survival of either gender with pRCC or chRCC. Genes whose transcript levels were associated with AR mRNA levels significantly overlapped between ccRCC and pRCC, but not with chRCC, suggesting a similar transcriptional program mediated by AR in ccRCC and pRCC. Ingenuity pathway analysis also identified overlapping pathways and upstream regulators enriched in AR-associated genes in ccRCC and pRCC. Hypermethylation of CpG sites located in the promoter and first exon of AR was associated with loss of AR expression and poor overall survival. Our findings support a tumor suppressor role for AR in both genders that might be exploited to decrease the incidence or progression of ccRCC. [ABSTRACT FROM AUTHOR]

mTOR is a rational target in renal cell carcinoma (RCC) because of its role in disease progression. However, the effects of temsirolimus, the only first-generation mTOR inhibitor approved by the FDA for first-line treatment of metastatic RCC, on tumor reduction and progression-free survival are minimal. Second-generation mTOR inhibitors have not been evaluated on RCC. We compared the effects of temsirolimus and MLN0128, a potent second-generation mTOR inhibitor, on RCC growth and metastasis using a realistic patient-derived tissue slice graft (TSG) model. TSGs were derived from three fresh primary RCC specimens by subrenal implantation of precision-cut tissue slices into immunodeficient mice that were randomized and treated with MLN0128, temsirolimus, or placebo. MLN0128 consistently suppressed primary RCC growth, monitored by magnetic resonance imaging (MRI), in three TSG cohorts for up to 2 months. Temsirolimus, in contrast, only transiently inhibited the growth of TSGs in one of two cohorts before resistance developed. In addition, MLN0128 reduced liver metastases, determined by human-specific quantitative polymerase chain reaction, in two TSG cohorts, whereas temsirolimus failed to have any significant impact. Moreover, MLN0128 decreased levels of key components of the two mTOR subpathways including TORC1 targets 4EBP1, p-S6K1, HIF1α and MTA1 and the TORC2 target c-Myc, consistent with dual inhibition. Our results demonstrated that MLN0128 is superior to temsirolimus in inhibiting primary RCC growth as well as metastases, lending strong support for further clinical development of dual mTOR inhibitors for RCC treatment. [ABSTRACT FROM AUTHOR]

Abstract: Objective: Discovery of curative therapies for renal cell carcinoma (RCC) is hampered by lack of authentic preclinical models. Tumorgrafts, generated by direct implantation of patient-derived tissues into mice, have demonstrated superior ability to predict therapeutic response. We evaluated “tissue slice grafts” (TSGs) as an improved tumorgraft model of RCC. Materials and methods: Cores of fresh RCC were precision-cut at 300µm and implanted under the renal capsule of RAG2−/−γC−/− mice. Engraftment rate, histology, biomarker expression, genetic fidelity, and metastatic potential were evaluated. Magnetic resonance imaging (MRI) was tested as a noninvasive method to measure tumor volume, and response to a targeted therapy was determined. Results: All 13 cases of RCC engrafted and displayed characteristic histology and biomarkers. TSG volume quantified noninvasively by MRI highly correlated with graft weights, providing a unique tool for monitoring orthotopic growth. Moreover, in 2 cases, cancer cells from TSGs metastasized to clinically relevant sites, including bone. Microarray analysis and DNA sequencing demonstrated a high degree of correlation of global gene expression and von Hippel-Lindau (VHL) status between TSGs and parental tumors. Treatment of TSGs with sunitinib significantly decreased graft weight and mean vessel density compared with controls. Conclusion: The TSG model of RCC faithfully recapitulates tumor pathology, gene expression, genetic mutation, and drug response. The high engraftment rate and metastatic potential of this authentic model, in conjunction with the ability to generate large first-generation animal cohorts and to quantitate tumor volume at the orthotopic site by MRI, proffer significant advantages compared with other preclinical platforms. [Copyright &y& Elsevier]