Navigating the Current Landscape of Non-Clear Cell Renal Cell Carcinoma: A Review of the Literature

Identifying potential risk genes for clear cell renal cell carcinoma with deep reinforcement learning

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Post-marketing surveillance data for avelumab + axitinib treatment in patients with advanced renal cell carcinoma in Japan: Subgroup analyses by pathological classification

OBJECTIVE

Clinical trials have demonstrated the efficacy and safety of avelumab + axitinib in patients with advanced clear cell renal cell carcinoma (ccRCC). However, information is limited regarding the activity of avelumab + axitinib in patients with non-clear cell RCC (nccRCC). In Japan, post-marketing surveillance (PMS) of patients with RCC receiving avelumab + axitinib treatment in general clinical practice was undertaken. We report ad hoc analyses of PMS data according to RCC pathological classification.

METHODS

Of 328 patients with RCC who received ≥1 dose of avelumab and were enrolled between December 2019 and May 2021, 271 (82.6%) had ccRCC, 22 (6.7%) had nccRCC, and 35 (10.7%) had missing or unknown RCC pathology. Among patients with nccRCC, pathological subtypes were papillary in 12 (3.7%), translocation in 3 (0.9%), acquired cystic disease associated in 3 (0.9%), chromophobe in 2 (0.6%), mucinous tubular and spindle cell in 1 (0.3%), and Bellini duct in 1 (0.3%).

RESULTS

Among patients with ccRCC or nccRCC, any-grade adverse drug reactions of safety specifications occurred in 140 (51.7%) and 15 (68.2%), and of grade ≥3 in 48 (17.7%) and 6 (27.3%), respectively. The objective response rate in patients with ccRCC or nccRCC was 36.9% and 22.7%, respectively; in patients with papillary tumors, it was 33.3%. Median overall survival was not reached in patients with ccRCC or nccRCC, and 12-month overall survival rates were 86.8% and 76.7%, respectively.

CONCLUSIONS

Overall, subgroup analyses of PMS data suggest that avelumab + axitinib improved clinical outcomes in nccRCC in addition to ccRCC.

CRYAB is upregulated and predicts clinical prognosis in kidney renal clear cell carcinoma

Clear cell renal cell carcinoma (KIRC) is the most prevalent subtype of renal cell carcinoma (RCC), accounting for 70% to 80% of all RCC cases. The CRYAB (αB-crystallin) gene is broadly expressed across various human tissues, yet its role in KIRC progression remains unclear. This study aims to elucidate the function of CRYAB in KIRC progression and to assess its potential as a biomarker for early diagnosis, therapeutic targeting, and prognosis. In our report, we found that CRYAB was dramatically upregulated in KIRC, and its expression was associated with TNM stage, pathological stage, and age. Also, patients with higher CRYAB expression exhibited poor survival and prognosis. CRYAB overexpression led to enhanced tumor cell proliferation. Vice versa, CRYAB downregulation resulted in decreased cell proliferation in vitro. Mechanistically, Gene set enrichment analysis plots showed the enrichment of cell survival. Consistently, these effects were associated with increased AKT signaling and BCL-2 expression. Furthermore, we also observed that CRYAB expression levels were negatively correlated with immunocyte infiltration. In conclusion, these findings suggested that CRYAB could be regarded as a latent biomarker for early diagnosis, therapeutic targeting, and prognosis.

A framework for target discovery in rare cancers

While large-scale functional genetic screens have uncovered numerous cancer dependencies, rare cancers are poorly represented in such efforts and the landscape of dependencies in many rare cancers remains obscure. We performed genome-scale CRISPR knockout screens in an exemplar rare cancer, TFE3-translocation renal cell carcinoma (tRCC), revealing previously unknown tRCC-selective dependencies in pathways related to mitochondrial biogenesis, oxidative metabolism, and kidney lineage specification. To generalize to other rare cancers in which experimental models may not be readily available, we employed machine learning to infer gene dependencies in a tumor or cell line based on its transcriptional profile. By applying dependency prediction to alveolar soft part sarcoma (ASPS), a distinct rare cancer also driven by TFE3 translocations, we discovered and validated that MCL1 represents a dependency in ASPS but not tRCC. Finally, we applied our model to predict gene dependencies in tumors from the TCGA (11,373 tumors; 28 lineages) and multiple additional rare cancers (958 tumors across 16 types, including 13 distinct subtypes of kidney cancer), nominating potentially actionable vulnerabilities in several poorly-characterized cancer types. Our results couple unbiased functional genetic screening with a predictive model to establish a landscape of candidate vulnerabilities across cancers, including several rare cancers currently lacking in potential targets.

First-Line Immune Combination Therapies for Nonclear Cell Versus Clear Cell Metastatic Renal Cell Carcinoma: Real-World Multicenter Data From Germany

INTRODUCTION

The aim was to compare treatment outcomes of clear cell metastatic renal cell carcinoma (ccmRCC) versus non-ccmRCC (nccmRCC) patients who received first-line immune combination therapies.

MATERIALS AND METHODS

Within our retrospective multi-institutional consecutive database of eight tertiary-care centers, we identified mRCC patients treated with first-line immune combination therapies between 11/2017 and 12/2022. Using log-rank analysis and multivariable Cox regression, we tested for differences in overall survival (OS) and progression-free survival (PFS) of nccmRCC versus ccmRCC patients. Covariables consisted of age at diagnosis, sex, International Metastatic Renal Cell Carcinoma Database Consortium risk groups, Eastern Cooperative Oncology Group status, and sarcomatoid feature.

RESULTS

Of 289 study patients, 39 (13%) patients harbored nccmRCC. Median OS was 37 months versus not reached for ccmRCC versus nccmRCC patients (P = .6). Median PFS was 13 versus 15 months (P = .9). Multivariable Cox regression models did not identify nccmRCC as an independent predictor of higher overall mortality in mRCC patients (hazard ratio [HR]: 1.23; P = .6) or a higher progression rate (HR: 1.0; P = 1.0).

CONCLUSION

In our real-world multi-institutional study, no differences in OS and PFS between ccmRCC and nccmRCC patients receiving first-line immune combination treatment were observed, even after adjustment for important patient and tumor characteristics. More prospective trials in nccmRCC patients are needed.

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing

BACKGROUND

Despite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC.

METHODS

The gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity.

RESULTS

The mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells.

CONCLUSION

ADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

Cuproptosis gene-related, neural network-based prognosis prediction and drug-target prediction for KIRC

BACKGROUND

Kidney renal clear cell carcinoma (KIRC), as a common case in renal cell carcinoma (RCC), has the risk of postoperative recurrence, thus its prognosis is poor and its prognostic markers are usually based on imaging methods, which have the problem of low specificity. In addition, cuproptosis, as a novel mode of cell death, has been used as a biomarker to predict disease in many cancers in recent years, which also provides an important basis for prognostic prediction in KIRC. For postoperative patients with KIRC, an important means of preventing disease recurrence is pharmacological treatment, and thus matching the appropriate drug to the specific patient's target is also particularly important. With the development of neural networks, their predictive performance in the field of medical big data has surpassed that of traditional methods, and this also applies to the field of prognosis prediction and drug-target prediction.

OBJECTIVE

The purpose of this study is to screen for cuproptosis genes related to the prognosis of KIRC and to establish a deep neural network (DNN) model for patient risk prediction, while also developing a personalized nomogram model for predicting patient survival. In addition, sensitivity drugs for KIRC were screened, and a graph neural network (GNN) model was established to predict the targets of the drugs, in order to discover potential drug action sites and provide new treatment ideas for KIRC.

METHODS

We used the Cancer Genome Atlas (TCGA) database, International Cancer Genome Consortium (ICGC) database, and DrugBank database for our study. Differentially expressed genes (DEGs) were screened using TCGA data, and then a DNN-based risk prediction model was built and validated using ICGC data. Subsequently, the differences between high- and low-risk groups were analyzed and KIRC-sensitive drugs were screened, and finally a GNN model was trained using DrugBank data to predict the relevant targets of these drugs.

RESULTS

A prognostic model was built by screening 10 significantly different cuproptosis-related genes, the model had an AUC of 0.739 on the training set (TCGA data) and an AUC of 0.707 on the validation set (ICGC data), which demonstrated a good predictive performance. Based on the prognostic model in this paper, patients were also classified into high- and low-risk groups, and functional analyses were performed. In addition, 251 drugs were screened for sensitivity, and four drugs were ultimately found to have high sensitivity, with 5-Fluorouracil having the best inhibitory effect, and subsequently their corresponding targets were also predicted by GraphSAGE, with the most prominent targets including Cytochrome P450 2D6, UDP-glucuronosyltransferase 1A, and Proto-oncogene tyrosine-protein kinase receptor Ret. Notably, the average accuracy of GraphSAGE was 0.817 ± 0.013, which was higher than that of GAT and GTN.

CONCLUSION

Our KIRC risk prediction model, constructed using 10 cuproptosis-related genes, had good independent prognostic ability. In addition, we screened four highly sensitive drugs and predicted relevant targets for these four drugs that might treat KIRC. Finally, literature research revealed that four drug-target interactions have been demonstrated in previous studies and the remaining targets are potential sites of drug action for future research.

Treatment Landscape of Renal Cell Carcinoma

OPINION STATEMENT

The treatment landscape of renal cell carcinoma (RCC) has evolved significantly over the past three decades. Active surveillance and tumor ablation are alternatives to extirpative therapy in appropriately selected patients. Stereotactic body radiation therapy (SBRT) is an emerging noninvasive alternative to treat primary RCC tumors. The advent of immune checkpoint inhibitors (ICIs) has greatly improved the overall survival of advanced RCC, and now the ICI-based doublet (dual ICI-ICI doublet; or ICI in combination with a vascular endothelial growth factor tyrosine kinase inhibitor, ICI-TKI doublet) has become the standard frontline therapy. Based on unprecedented outcomes in the metastatic with ICIs, they are also being explored in the neoadjuvant and adjuvant setting for patients with high-risk disease. Adjuvant pembrolizumab has proven efficacy to reduce the risk of RCC recurrence after nephrectomy. Historically considered a radioresistant tumor, SBRT occupies an expanding role to treat RCC with oligometastasis or oligoprogression in combination with systemic therapy. Furthermore, SBRT is being investigated in combination with ICI-doublet in the advanced disease setting. Lastly, given the treatment paradigm is shifting to adopt ICIs at earlier disease course, the prospective studies guiding treatment sequencing in the post-ICI setting is maturing. The effort is ongoing in search of predictive biomarkers to guide optimal treatment option in RCC.

The Metastasis Pattern of Renal Cell Carcinoma Is Influenced by Histologic Subtype, Grade, and Sarcomatoid Differentiation

Background and Objectives: Metastasis is a major cause of death in renal cell carcinoma (RCC) patients; therefore, a better understanding of the metastatic process and the ability to predict metastasis in advance is important for treating patients with RCC. This study aimed to investigate whether histological subtypes of RCC and other factors, such as nuclear grade and sarcomatoid differentiation, could predict the probability and location of metastases in patients with RCC. Materials and Methods: Cases of clear-cell, papillary, chromophobe, and sarcomatoid RCC were retrieved and analyzed from the Surveillance, Epidemiology, and End Results databases. Results: When comparing the metastatic patterns among the three histologic subtypes, patients with clear-cell RCC were significantly more likely to have brain and lung metastases. Moreover, patients with papillary RCC were significantly less likely to develop bone metastases and more likely to develop lymph node metastases. Patients with chromophobe RCC are significantly more likely to develop liver metastases. As the nuclear grade increased, there was also a significantly increased tendency for clear-cell RCC to metastasize to the lungs. Patients with sarcomatoid RCC had a higher rate of metastasis, with a significantly higher probability of metastasis to the bone and lungs, than those with all three histological subtypes did. Conclusions: Histological subtype, nuclear grade, and sarcomatoid differentiation were significant predictors of metastasis in patients with RCC.

PI3K/AKT/mTOR Dysregulation and Reprogramming Metabolic Pathways in Renal Cancer: Crosstalk with the VHL/HIF Axis

Renal cell carcinoma (RCC) represents 85-95% of kidney cancers and is the most frequent type of renal cancer in adult patients. It accounts for 3% of all cancer cases and is in 7th place among the most frequent histological types of cancer. Clear cell renal cell carcinoma (ccRCC), accounts for 75% of RCCs and has the most kidney cancer-related deaths. One-third of the patients with ccRCC develop metastases. Renal cancer presents cellular alterations in sugars, lipids, amino acids, and nucleic acid metabolism. RCC is characterized by several metabolic dysregulations including oxygen sensing (VHL/HIF pathway), glucose transporters (GLUT 1 and GLUT 4) energy sensing, and energy nutrient sensing cascade. Metabolic reprogramming represents an important characteristic of the cancer cells to survive in nutrient and oxygen-deprived environments, to proliferate and metastasize in different body sites. The phosphoinositide 3-kinase-AKT-mammalian target of the rapamycin (PI3K/AKT/mTOR) signaling pathway is usually dysregulated in various cancer types including renal cancer. This molecular pathway is frequently correlated with tumor growth and survival. The main aim of this review is to present renal cancer types, dysregulation of PI3K/AKT/mTOR signaling pathway members, crosstalk with VHL/HIF axis, and carbohydrates, lipids, and amino acid alterations.

ANO4 Expression Is a Potential Prognostic Biomarker in Non-Metastasized Clear Cell Renal Cell Carcinoma

Background: Over the past decade, transcriptome profiling has elucidated many pivotal pathways involved in oncogenesis. However, a detailed comprehensive map of tumorigenesis remains an enigma to solve. Propelled research has been devoted to investigating the molecular drivers of clear cell renal cell carcinoma (ccRCC). To add another piece to the puzzle, we evaluated the role of anoctamin 4 (ANO4) expression as a potential prognostic biomarker in non-metastasized ccRCC. Methods: A total of 422 ccRCC patients with the corresponding ANO4 expression and clinicopathological data were obtained from The Cancer Genome Atlas Program (TCGA). Differential expression across several clinicopathological variables was performed. The Kaplan-Meier method was used to assess the impact of ANO4 expression on the overall survival (OS), progression-free interval (PFI), disease-free interval (DFI), and disease-specific survival (DSS). Univariate and multivariate Cox logistic regression analyses were conducted to identify independent factors modulating the aforementioned outcomes. Gene set enrichment analysis (GSEA) was used to discern a set of molecular mechanisms involved in the prognostic signature. Tumor immune microenvironment was estimated using xCell. Results: ANO4 expression was upregulated in tumor samples compared to normal kidney tissue. Albeit the latter finding, low ANO4 expression is associated with advanced clinicopathological variables such as tumor grade, stage, and pT. In addition, low ANO4 expression is linked to shorter OS, PFI, and DSS. Multivariate Cox logistic regression analysis identified ANO4 expression as an independent prognostic variable in OS (HR: 1.686, 95% CI: 1.120-2.540, p = 0.012), PFI (HR: 1.727, 95% CI: 1.103-2.704, p = 0.017), and DSS (HR: 2.688, 95% CI: 1.465-4.934, p = 0.001). GSEA identified the following pathways to be enriched within the low ANO4 expression group: epithelial-mesenchymal transition, G2-M checkpoint, E2F targets, estrogen response, apical junction, glycolysis, hypoxia, coagulation, KRAS, complement, p53, myogenesis, and TNF-α signaling via NF-κB pathways. ANO4 expression correlates significantly with monocyte (ρ = -0.1429, p = 0.0033) and mast cell (ρ = 0.1598, p = 0.001) infiltration. Conclusions: In the presented work, low ANO4 expression is portrayed as a potential poor prognostic factor in non-metastasized ccRCC. Further experimental studies should be directed to shed new light on the exact molecular mechanisms involved.