Renal cancer: new models and approach for personalizing therapy

Chronic arsenic exposure induces malignant transformation of human HaCaT cells through both deterministic and stochastic changes in transcriptome expression

Biological processes are inherently stochastic, i.e., are partially driven by hard to predict random probabilistic processes. Carcinogenesis is driven both by stochastic and deterministic (predictable non-random) changes. However, very few studies systematically examine the contribution of stochastic events leading to cancer development. In differential gene expression studies, the established data analysis paradigms incentivize expression changes that are uniformly different across the experimental versus control groups, introducing preferential inclusion of deterministic changes at the expense of stochastic processes that might also play a crucial role in the process of carcinogenesis. In this study, we applied simple computational techniques to quantify: (i) The impact of chronic arsenic (iAs) exposure as well as passaging time on stochastic gene expression and (ii) Which genes were expressed deterministically and which were expressed stochastically at each of the three stages of cancer development. Using biological coefficient of variation as an empirical measure of stochasticity we demonstrate that chronic iAs exposure consistently suppressed passaging related stochastic gene expression at multiple time points tested, selecting for a homogenous cell population that undergo transformation. Employing multiple balanced removal of outlier data, we show that chronic iAs exposure induced deterministic and stochastic changes in the expression of unique set of genes, that populate largely unique biological pathways. Together, our data unequivocally demonstrate that both deterministic and stochastic changes in transcriptome-wide expression are critical in driving biological processes, pathways and networks towards clonal selection, carcinogenesis, and tumor heterogeneity.

Advanced in vitro models for renal cell carcinoma therapy design

Renal cell carcinoma (RCC) and its principal subtype, clear cell RCC, are the most diagnosed kidney cancer. Despite substantial improvement over the last decades, current pharmacological intervention still fails to achieve long-term therapeutic success. RCC is characterized by a high intra- and inter-tumoral heterogeneity and is heavily influenced by the crosstalk of the cells composing the tumor microenvironment, such as cancer-associated fibroblasts, endothelial cells and immune cells. Moreover, multiple physicochemical properties such as pH, interstitial pressure or oxygenation may also play an important role. These elements are often poorly recapitulated in in vitro models used for drug development. This inadequate recapitulation of the tumor is partially responsible for the current lack of an effective and curative treatment. Therefore, there are needs for more complex in vitro or ex vivo drug screening models. In this review, we discuss the current state-of-the-art of RCC models and suggest strategies for their further development.

Postoperative Metabolic Phenoreversion in Clear Cell Renal Cell Carcinoma

The ultimate goal of surgical treatment in cancer is to remove the tumor mass for restoring a healthy state. A 16-lipid panel that discriminated healthy controls from clear cell renal cell carcinoma (ccRCC) patients in a prior study was evaluated in the present work in paired-serum samples collected from patients (n = 41) before and after nephrectomy. Changes in the lipid and metabolite fingerprints from ccRCC patients were investigated and compared with fingerprints from healthy individuals obtained by means of ultra-performance liquid chromatography-high-resolution mass spectrometry. The lipid panel differentiated phenotypes associated with metabolic restoration after surgery, representing a serum signature of phenoreversion to a healthy metabolic state. In particular, PC 16:0/0:0, PC 18:2/18:2, and linoleic acid allowed discriminating serum samples from ccRCC patients with poor prognosis from those with an improved outcome during the follow-up period. Ratios of PC 16:0/0:0 and PC 18:2/18:2 with linoleic acid levels may contribute as prognostic tools to support decision-making during the patient follow-up care. The preliminary character of these results should be validated with larger cohorts, including subjects with different ethnicities, life style, and diets. MetaboLights study references: MTBLS1839, MTBLS3838, and MTBLS4629.

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes' standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages.

Screening of possible biomarkers and therapeutic targets in kidney renal clear cell carcinoma: Evidence from bioinformatic analysis

Renal cell carcinoma (RCC), as one of the most common urological malignancies, has many histologic and molecular subtypes, among which clear cell renal cell carcinoma (ccRCC) is one of the most common causes of tumor-related deaths. However, the molecular mechanism of ccRCC remains unclear. In order to identify the candidate genes that may exist in the occurrence and development of ccRCC, microarray datasets GSE6344, GSE16441, GSE36895, GSE53757 and GSE76351 had been downloaded from Gene Expression Omnibus (GEO) database. Apart from that, the differentially expressed genes (DEGs) were screened through Bioinformatics & Evolutionary Genomics. In addition, the protein-protein interaction network (PPI) was constructed, and the module analysis was performed using STRING and Cytoscape. By virtue of DAVID online database, GO/KEGG enrichment analysis of DEGs was performed. Consequently, a total of 118 DEGs were screened, including 24 up-regulated genes and 94 down-regulated genes. The plug-in MCODE of Cytoscape was adopted to analyze the most significant modules of DEGs. What’s more, the genes with degree greater than 10 in DEGs were selected as the hub genes. The overall survival (OS) and disease progression free survival (DFS) of 9 hub genes were analyzed through GEPIA2 online platform. As shown by the survival analysis, SLC34A1, SLC12A3, SLC12A1, PLG, and ENO2 were closely related to the OS of ccRCC, whereas SLC34A1 and LOX were closely related to DFS. Among 11 SLC members, 6 SLC members were highly expressed in non-cancerous tissues (SLC5A2, SLC12A1, SLC12A3, SLC34A1, SLC34A2, SLC34A3). Besides, SLC12A5 and SLC12A7 were highly expressed in ccRCC. Furthermore, SLC12A1-A7, SLC34A1 and SLC34A3 were closely related to OS, whereas SLC12A2/A4/A6/A7 and SLC34A1/A3 were closely related to DFS. In addition, 5 algorithms were used to analyze hub genes, the overlapping genes were AQP2 and KCNJ1. To sum up, hub gene can help us understand the molecular mechanism of the occurrence and development of ccRCC, thereby providing a theoretical basis for the diagnosis and targeted therapy of ccRCC.

Construction and Characterization of n6-Methyladenosine-Related lncRNA Prognostic Signature and Immune Cell Infiltration in Kidney Renal Clear Cell Carcinoma

Background. Kidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear. Methods. We extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package “glmnet.” Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis. Results. We found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. $\text{Risk\hspace{0.17em}score}=AC015813.1\ast \left(0.0086\right)+EMX2OS\ast \left(-0.0101\right)+LINC00173\ast \left(0.0309\right)+PWAR5\ast \left(-0.0146\right)+SNHG1\ast \left(0.0043\right).$ The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628. Conclusions. In conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

Experimental and computational modeling for signature and biomarker discovery of renal cell carcinoma progression

BACKGROUND

Renal Cell Carcinoma (RCC) is difficult to treat with 5-year survival rate of 10% in metastatic patients. Main reasons of therapy failure are lack of validated biomarkers and scarce knowledge of the biological processes occurring during RCC progression. Thus, the investigation of mechanisms regulating RCC progression is fundamental to improve RCC therapy.

METHODS

In order to identify molecular markers and gene processes involved in the steps of RCC progression, we generated several cell lines of higher aggressiveness by serially passaging mouse renal cancer RENCA cells in mice and, concomitantly, performed functional genomics analysis of the cells. Multiple cell lines depicting the major steps of tumor progression (including primary tumor growth, survival in the blood circulation and metastatic spread) were generated and analyzed by large-scale transcriptome, genome and methylome analyses. Furthermore, we performed clinical correlations of our datasets. Finally we conducted a computational analysis for predicting the time to relapse based on our molecular data.

RESULTS

Through in vivo passaging, RENCA cells showed increased aggressiveness by reducing mice survival, enhancing primary tumor growth and lung metastases formation. In addition, transcriptome and methylome analyses showed distinct clustering of the cell lines without genomic variation. Distinct signatures of tumor aggressiveness were revealed and validated in different patient cohorts. In particular, we identified SAA2 and CFB as soluble prognostic and predictive biomarkers of the therapeutic response. Machine learning and mathematical modeling confirmed the importance of CFB and SAA2 together, which had the highest impact on distant metastasis-free survival. From these data sets, a computational model predicting tumor progression and relapse was developed and validated. These results are of great translational significance.

CONCLUSION

A combination of experimental and mathematical modeling was able to generate meaningful data for the prediction of the clinical evolution of RCC.

Integrated analysis on the N6-methyladenosine-related long noncoding RNAs prognostic signature, immune checkpoints, and immune cell infiltration in clear cell renal cell carcinoma

Background

Patients with advanced clear cell renal cell carcinoma (ccRCC) have a poor prognosis and lack effective prognostic biomarkers. N6‐methyladenosine‐related lncRNAs (m6A‐related long noncoding RNAs [lncRNAs]) have been confirmed to be associated with the development of multiple tumors, but its role in ccRCC is not clear.

Methods

Gene expression data and clinical information of ccRCC patients were extracted from The Cancer Genome Atlas Database. The prognostic m6A‐related lncRNAs were obtained by Pearson's correlation analysis and univariate Cox regression analysis. Afterward, the cluster classification and its correlation with prognosis, clinical characteristics, and immunity were analyzed. LASSO regression was used to establish the prognostic risk model. The predictive performance of the prognostic model was evaluated and validated by survival analysis and receiver operating characteristic curve analysis, et al. The expression of immune checkpoints and immune cell infiltration in patients with different risks were systematically analyzed.

Results

A total of 27 prognostic m6A‐related lncRNAs were identified. These m6A‐related lncRNAs were differentially expressed between tumor and normal tissues. Among them, 24 high‐risk m6A‐related lncRNAs were overexpressed in Cluster 2 and correlated with poor prognosis, low stromal score, high expression of immune checkpoints, and immunosuppressive cells infiltration. Based upon, a prognostic risk model composed of seven m6A‐related lncRNAs was constructed. After a series of analyses, it was proved that this model had good sensitivity and specificity, and could predict the prognosis of patients with different clinical stratification. The expression of PD‐1, PD‐L1, CTLA‐4, LAG‐3, TIM‐3, and TIGIT were significantly increased in the high‐risk patients, and there was a correlation between the risk score and immune cell infiltration.

Conclusions

The seven m6A‐related lncRNAs prognostic risk signature showed reliable prognostic predictive power for ccRCC and was associated with the expression of immune checkpoints and immune cell infiltration. This seven m6A‐related lncRNAs signature will be helpful in managing ccRCC and guiding individualized immunotherapy.

Development of a mechanically matched silk scaffolded 3D clear cell renal cell carcinoma model

Development of a 3D, biomaterials-based model for clear cell renal cell carcinoma (ccRCC) would be advantageous for understanding disease progression in vitro. This study demonstrated the development of lyophilized silk scaffolds that mechanically match the experimentally determined Young's modulus for ex vivo ccRCC samples and normal kidney tissue. Scaffolds fabricated from silk solutions ranging from 3 to 12% (w/v) were evaluated through mechanical testing. Following mechanical characterization of ccRCC samples, it was demonstrated that 6% silk scaffolds mechanically matched ccRCC samples. No impact of pathological grade and stage on the calculated ccRCC modulus was observed and all tumors evaluated mechanically matched the 6% silk scaffold formulation. Stratifying tissue specimens based upon histological observations (e.g. evidence of high levels of collagen deposition) resulted in no significant differences between groups. To investigate the impact of a mechanically matched culturing environment on in vitro ccRCC disease characteristics a model ccRCC cell line, 786-O, was utilized. Scaffolded 786-O cells demonstrated increased lipid droplet accumulation, a hallmark of ccRCC, compared to standard two-dimensional (2D) culture conditions. Additionally, scaffolded 786-O cells demonstrated increased expression of genes associated with ccRCC aggressiveness (ex. VEGFA, TNF, and IL-6) or immune markers under investigation as therapeutic targets (ex. PDL1, CTLA4). Comparison with 786-O cells grown on non-mechanically matched scaffolds demonstrated that these improved ccRCC characteristics were driven by scaffold modulus. Overall, our findings support the use of silk scaffolds in replicating physiologic tumor behavior for clear cell renal cell carcinoma and provide a platform for investigating disease progression.

Caspase 4 Overexpression as a Prognostic Marker in Clear Cell Renal Cell Carcinoma: A Study Based on the Cancer Genome Atlas Data Mining

The dysregulation of caspase 4 (CASP4) expression is related to the occurrence, development, and outcome of many malignant tumors; however, its role in clear cell renal cell carcinoma (ccRCC) remains unclear. Herein, we investigated the expression of CASP4 in tumor tissues and its relationship with clinical prognosis, immune infiltration, and drug sensitivity status of ccRCC patients. Oncomine and The Cancer Genome Atlas (TCGA) databases were used to determine CASP4 mRNA expression in ccRCC patients. The correlation between CASP4 expression and disease prognosis was evaluated using Kaplan–Meier analysis. Related pathways were obtained from TCGA database via gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). Meanwhile, genes co-expressing with CASP4 in ccRCC were investigated. Finally, we analyzed the proportion of tumor-infiltrating immune cells (TICs) using the CIBERSORT computational method and assessed CASP4 methylation and its relationship with drug sensitivity. Immunohistochemical analysis of 30 paired ccRCC and adjacent normal tissues confirmed the in silico results. CASP4 mRNA expression in ccRCC was significantly higher than that in the normal tissues, positively correlated with clinicopathological features (clinical stage and pathological grade), and negatively correlated with patient overall survival (OS). GSEA and GSVA showed that the genes in the CASP4-high expression group were primarily enriched in immune-related activities. Moreover, CIBERSORT analysis of TIC proportions revealed that activated CD4 memory T cells were positively correlated with CASP4 expression. Notably, methylation analysis revealed that the abnormal upregulation of CASP4 might be caused by hypomethylation. Finally, we found that the abnormal expression of CASP4 may be related to tumor drug resistance. Overall, our study shows that CASP4 is overexpressed in ccRCC and is an important factor affecting disease prognosis. Hence, CASP4 may serve as a potential prognostic biomarker and therapeutic target in ccRCC.

Genomic Fabric Remodeling in Metastatic Clear Cell Renal Cell Carcinoma (ccRCC): A New Paradigm and Proposal for a Personalized Gene Therapy Approach

Simple Summary

We applied the genomic fabric principles for personalized gene therapy to a case of clear cell renal cell carcinoma (ccRCC). Despite decades of research, the process of finding the molecular mechanisms responsible for the disease and, more importantly, the therapeutic solution is still a work in progress. We analyzed the transcriptomes of the chest wall metastasis, two distinct cancer nodules, and the cancer-free surrounding tissue in the surgically removed right kidney of a Fuhrman grade 3 metastatic ccRCC patient. The studies revealed that even histopathologically equally classified cancer nodules from the same kidney have different transcriptomic topologies, requiring tailored therapeutic solutions not only for each patient but even for each cancer nodule. We identified death-associated protein kinase 3 (DAPK3); transcription activation suppressor (TASOR); family with sequence similarity 27, member C, long non-coding RNA (FAM27C); and UDP-N-acetylglucosaminyltransferase subunit (ALG13) as the gene master regulators of the four profiled regions and proposed molecular mechanisms by which expression manipulation of TASOR and ALG13 may selectively destroy the cancer cells without affecting many of the normal cells.

Abstract

Published transcriptomic data from surgically removed metastatic clear cell renal cell carcinoma samples were analyzed from the genomic fabric paradigm (GFP) perspective to identify the best targets for gene therapy. GFP considers the transcriptome as a multi-dimensional mathematical object constrained by a dynamic set of expression controls and correlations among genes. Every gene in the chest wall metastasis, two distinct cancer nodules, and the surrounding normal tissue of the right kidney was characterized by three independent measures: average expression level, relative expression variation, and expression correlation with each other gene. The analyses determined the cancer-induced regulation, control, and remodeling of the chemokine and vascular endothelial growth factor (VEGF) signaling, apoptosis, basal transcription factors, cell cycle, oxidative phosphorylation, renal cell carcinoma, and RNA polymerase pathways. Interestingly, the three cancer regions exhibited different transcriptomic organization, suggesting that the gene therapy should not be personalized only for every patient but also for each major cancer nodule. The gene hierarchy was established on the basis of gene commanding height, and the gene master regulators DAPK3,TASOR, FAM27C and ALG13 were identified in each profiled region. We delineated the molecular mechanisms by which TASOR overexpression and ALG13 silencing would selectively affect the cancer cells with little consequences for the normal cells.

Thymic Epithelial Tumors as a Model of Networking: Development of a Synergistic Strategy for Clinical and Translational Research Purposes

Among the group of thymic epithelial tumors (TET), thymomas often show either uncertain or explicit malignant biological behavior, local invasiveness, and intrathoracic relapse and are often difficult to manage. From the initial stages, thymic carcinomas tend to show aggressive behavior and extrathoracic spread. Moreover, the interplay of epithelial cells and thymocytes in thymomas causes complex immune derangement and related systemic autoimmune diseases. Due to their rare occurrence and to the limited funding opportunities available for rare tumors, it is challenging to make advances in clinical and translational research in TET. The authors of this paper are all members of a multidisciplinary clinical and research thoracic tumor team. Strong input was given to the team by long-standing expertise in TET in the Pathology Department. In addition, thanks to the collaboration between research units at our Institute as well as to national collaborations, over the last 10 years we were able to perform several tissue-based research studies. The most recent studies focused on microRNA and on functional studies on the thymic carcinoma cell line 1889c. The recent implementation of our biobank now provides us with a new tool for networking collaborative research activities. Moreover, the participation in a worldwide community such as ITMIG (International Thymic Malignancy Interest Group) has allowed us to significantly contribute toward fundamental projects/research both in tissue-based studies (The Cancer Genome Atlas) and in clinical studies (TNM staging of TET). Our achievements derive from constant commitment and long-standing experience in diagnosis and research in TET. New perspectives opened up due to the establishment of national [the Italian Collaborative Group for ThYmic MalignanciEs (TYME)] and European reference networks such as EURACAN, for an empowered joint clinical action in adult solid rare tumors. The challenge we face still lies in the advancement of clinical and basic science in thymic epithelial malignancies.

Organoids as a new model for improving regenerative medicine and cancer personalized therapy in renal diseases

The pressure towards innovation and creation of new model systems in regenerative medicine and cancer research has fostered the development of novel potential therapeutic applications. Kidney injuries provoke a high request of organ transplants making it the most demanding system in the field of regenerative medicine. Furthermore, renal cancer frequently threaten patients’ life and aggressive forms still remain difficult to treat. Ethical issues related to the use of embryonic stem cells, has fueled research on adult, patient-specific pluripotent stem cells as a model for discovery and therapeutic development, but to date, normal and cancerous renal experimental models are lacking. Several research groups are focusing on the development of organoid cultures. Since organoids mimic the original tissue architecture in vitro, they represent an excellent model for tissue engineering studies and cancer therapy testing. We established normal and tumor renal cell carcinoma organoids previously maintained in a heterogeneous multi-clone stem cell-like enriching medium. Starting from adult normal kidney specimens, we were able to isolate and propagate organoid 3D-structures composed of both differentiated and undifferentiated cells while expressing nephron specific markers. Furthermore, we were capable to establish organoids derived from cancer tissues although with a success rate inferior to that of their normal counterpart. Cancer cultures displayed epithelial and mesenchymal phenotype while retaining tumor specific markers. Of note, tumor organoids recapitulated neoplastic masses when orthotopically injected into immunocompromised mice. Our data suggest an innovative approach of long-term establishment of normal- and cancer-derived renal organoids obtained from cultures of fleshly dissociated adult tissues. Our results pave the way to organ replacement pioneering strategies as well as to new models for studying drug-induced nephrotoxicity and renal diseases. Along similar lines, deriving organoids from renal cancer patients opens unprecedented opportunities for generation of preclinical models aimed at improving therapeutic treatments.