The Clinical Significance of DNA Damage Repair Signatures in Clear Cell Renal Cell Carcinoma

DCLRE1B/Apollo germline mutations associated with renal cell carcinoma impair telomere protection

Hereditary renal cell carcinoma (RCC) is caused by germline mutations in a subset of genes, including VHL, MET, FLCN, and FH. However, many familial RCC cases do not harbor mutations in the known predisposition genes. Using Whole Exome Sequencing, we identified two germline missense variants in the DCLRE1B/Apollo gene (ApolloN246I and ApolloY273H) in two unrelated families with several RCC cases. Apollo encodes an exonuclease involved in DNA Damage Response and Repair (DDRR) and telomere integrity. We characterized these two functions in the human renal epithelial cell line HKC8. The decrease or inhibition of Apollo expression sensitizes these cells to DNA interstrand crosslink damage (ICLs). HKC8 Apollo-/- cells appear defective in the DDRR and present an accumulation of telomere damage. Wild-type and mutated Apollo forms could interact with TRF2, a shelterin protein involved in telomere protection. However, only ApolloWT can rescue the telomere damage in HKC8 Apollo-/- cells. Our results strongly suggest that ApolloN246I and ApolloY273H are loss-of-function mutants that cause impaired telomere integrity and could lead to genomic instability. Altogether, our results suggest that mutations in Apollo could induce renal oncogenesis.

A novel immune-related model to predict prognosis and responsiveness to checkpoint and angiogenesis blockade therapy in advanced renal cancer

Background

Immune checkpoint blockade (ICB) and anti-angiogenic drug combination has prolonged the survival of patients with advanced renal cell carcinoma (RCC). However, not all patients receive clinical benefits from this intervention. In this study, we aimed to establish a promising immune-related prognostic model to stratify the patients responding to ICB and anti-angiogenic drug combination and facilitate the development of personalized therapies for patients with RCC.

Materials and methods

Based on clinical annotations and RNA-sequencing (RNA-seq) data of 407 patients with advanced RCC from the IMmotion151 cohort, nine immune-associated differentially expressed genes (DEGs) between responders and non-responders to atezolizumab (anti-programmed death-ligand 1 antibody) plus bevacizumab (anti-vascular endothelial growth factor antibody) treatment were identified via weighted gene co-expression network analysis. We also conducted single-sample gene set enrichment analysis to develop a novel immune-related risk score (IRS) model and further estimate the prognosis of patients with RCC by predicting their sensitivity to chemotherapy and responsiveness to immunotherapy. IRS model was further validated using the JAVELIN Renal 101 cohort, the E-MTAB-3218 cohort, the IMvigor210 and GSE78220 cohort. Predictive significance of the IRS model for advanced RCC was assessed using receiver operating characteristic curves.

Results

The IRS model was constructed using nine immune-associated DEGs: SPINK5, SEMA3E, ROBO2, BMP5, ORM1, CRP, CTSE, PMCH and CCL3L1. Advanced RCC patients with high IRS had a high risk of undesirable clinical outcomes (hazard ratio = 1.91; 95% confidence interval = 1.43–2.55; P < 0.0001). Transcriptome analysis revealed that the IRS-low group exhibited significantly high expression levels of CD8+ T effectors, antigen-processing machinery, and immune checkpoints, whereas the epithelial–mesenchymal transition pathway was enriched in the IRS-high group. IRS model effectively differentiated the responders from non-responders to ICB combined with angiogenesis blockade therapy or immunotherapy alone, with area under the curve values of 0.822 in the IMmotion151 cohort, 0.751 in the JAVELIN Renal 101 cohort, and 0.776 in the E-MTAB-3218 cohort.

Conclusion

IRS model is a reliable and robust immune signature that can be used for patient selection to optimize the efficacy of ICB plus anti-angiogenic drug therapies in patients with advanced RCC.

Discovery of pathway-independent protein signatures associated with clinical outcome in human cancer cohorts

Proteomic data provide a direct readout of protein function, thus constituting an information-rich resource for prognostic and predictive modeling. However, protein array data may not fully capture pathway activity due to the limited number of molecules and incomplete pathway coverage compared to other high-throughput technologies. For the present study, our aim was to improve clinical outcome prediction compared to published pathway-dependent prognostic signatures for The Cancer Genome Atlas (TCGA) cohorts using the least absolute shrinkage and selection operator (LASSO). RPPA data is particularly well-suited to the LASSO due to the relatively low number of predictors compared to larger genomic data matrices. Our approach selected predictors regardless of their pathway membership and optimally combined their RPPA measurements into a weighted risk score. Performance was assessed and compared to that of the published signatures using two unbiased approaches: 1) 10 iterations of threefold cross-validation for unbiased estimation of hazard ratio and difference in 5-year survival (by Kaplan-Meier method) between predictor-defined high and low risk groups; and 2) a permutation test to evaluate the statistical significance of the cross-validated log-rank statistic. Here, we demonstrate strong stratification of 445 renal clear cell carcinoma tumors from The Cancer Genome Atlas (TCGA) into high and low risk groups using LASSO regression on RPPA data. Median cross-validated difference in 5-year overall survival was 32.8%, compared to 25.2% using a published receptor tyrosine kinase (RTK) prognostic signature (median hazard ratios of 3.3 and 2.4, respectively). Applicability and performance of our approach was demonstrated in three additional TCGA cohorts: ovarian serous cystadenocarcinoma (OVCA), sarcoma (SARC), and cutaneous melanoma (SKCM). The data-driven LASSO-based approach is versatile and well-suited for discovery of new protein/disease associations.

Calcium oxalate crystals trigger epithelial-mesenchymal transition and carcinogenic features in renal cells: a crossroad between kidney stone disease and renal cancer

Increasing evidence of association between kidney stone disease (KSD) and renal cell carcinoma (RCC) has been reported. Nevertheless, mechanism underlying such association remained unknown. Herein, we investigated the effects of calcium oxalate monohydrate (COM), a major crystalline component causing KSD, on induction of carcinogenic features in non-cancerous renal cells. COM crystals induced morphological changes from epithelial to fibroblast-like spindle shape. Additionally, COM increased spindle index and mesenchymal markers (fibronectin and vimentin) but declined epithelial markers (E-cadherin and zonula occludens-1). Moreover, COM down-regulated ARID1A, a tumor suppressor gene recently reported to be reversely associated with RCC, at both mRNA and protein levels. COM also down-regulated other RCC-related tumor suppressor genes, PTEN and VHL, but up-regulated oncogene TPX2. Finally, COM enhanced invading capability, cell-aggregate formation, chemoresistance to cisplatin, and secretion of an angiogenic factor (VEGF). These data indicate that COM crystals trigger epithelial-mesenchymal transition (EMT) and several carcinogenic features in the non-cancerous renal cells. These mechanisms may explain and strengthen the association between KSD and RCC.

Pathogenic ATM and BAP1 germline mutations in a case of early-onset, familial sarcomatoid renal cancer

Metastatic renal cell carcinoma (RCC) remains an incurable malignancy, despite recent advances in systemic therapies. Genetic syndromes associated with kidney cancer account for only 5%-8% of all diagnosed kidney malignancies, and genetic predispositions to kidney cancer predisposition are still being studied. Genomic testing for kidney cancer is useful for disease molecular subtyping but provides minimal therapeutic information. Understanding how aberrations drive RCC development and how their contextual influences, such as chromosome loss, genome instability, and DNA methylation changes, may alter therapeutic response is of importance. We report the case of a 36-yr-old female with aggressive, metastatic RCC and a significant family history of cancer, including RCC. This patient harbors a novel, pathogenic, germline ATM mutation along with a rare germline variant of unknown significance in the BAP1 gene. In addition, somatic loss of heterozygosity (LOH) in BAP1 and ATM genes, somatic mutation and LOH in the VHL gene, copy losses in Chromosomes 9p and 14, and genome instability are also noted in the tumor, potentially dictating this patient's aggressive clinical course. Further investigation is warranted to evaluate the association of ATM and BAP1 germline mutations with increased risk of RCC and if these mutations should lead to enhanced and early screening.

From Bench to Bedside: How the Tumor Microenvironment Is Impacting the Future of Immunotherapy for Renal Cell Carcinoma

Immunotherapy has revolutionized the treatment landscape for many cancer types. The treatment for renal cell carcinoma (RCC) has especially evolved in recent years, from cytokine-based immunotherapies to immune checkpoint inhibitors. Although clinical benefit from immunotherapy is limited to a subset of patients, many combination-based approaches have led to improved outcomes. The success of such approaches is a direct result of the tumor immunology knowledge accrued regarding the RCC microenvironment, which, while highly immunogenic, demonstrates many unique characteristics. Ongoing translational work has elucidated some of the mechanisms of response, as well as primary and secondary resistance, to immunotherapy. Here, we provide a comprehensive review of the RCC immunophenotype with a specific focus on how preclinical and clinical data are shaping the future of immunotherapy.

Gene signatures with therapeutic value: emerging perspective for personalized immunotherapy in renal cancer

Renal cancer is one of the deadliest urogenital diseases. In recent years, the advent of immunotherapy has led to significant improvement in the management of patients with renal cancer. Although cancer immunotherapy and its combinations had benefited numerous patients, several challenges need to be addressed. Apart from the high costs of treatment, the lack of predictive biomarkers and toxic side-effects have impeded its wider applicability. To address these issues, new biomarkers are required to predict responsiveness and design personalized treatment strategies. Recent advances in the field of single-cell sequencing and multi-dimensional spatial transcriptomics have identified clinically relevant subtypes of renal cancer. Furthermore, there is emerging potential for gene signatures based on immune cells, non-coding RNAs, and pathways such as metabolism and RNA modification. In this review article, we have discussed recent progress in the identification of gene signatures with predictive and prognostic potential in renal cancer.