The Cancer Genome Atlas Comprehensive Molecular Characterization of Renal Cell Carcinoma

Sensitivity of VHL mutant kidney cancers to HIF2 inhibitors does not require an intact p53 pathway

VHL tumor suppressor gene inactivation is a hallmark of clear cell renal cell carcinoma (ccRCC), the most common form of kidney cancer, and promotes tumor growth by stabilizing the hypoxia-inducible factor 2 (HIF2) transcription factor. HIF2 inhibitors appear to be helpful for some, but not all, ccRCC patients in clinical trials. Previous preclinical and clinical data suggested that only ccRCCs that can activate the p53 tumor suppressor in response to DNA damage would respond to HIF2 inhibitors. Here, we show that an intact p53 pathway is neither necessary nor sufficient for the sensitivity of ccRCCs to HIF2 inhibitors, suggesting that it would be premature to use p53 status to determine which ccRCC patients should be treated with a HIF2 inhibitor.

Inactivation of the VHL tumor suppressor gene is the signature initiating event in clear cell renal cell carcinoma (ccRCC), which is the most common form of kidney cancer. The VHL tumor suppressor protein marks hypoxia-inducible factor 1 (HIF1) and HIF2 for proteasomal degradation when oxygen is present. The inappropriate accumulation of HIF2 drives tumor formation by VHL tumor suppressor protein (pVHL)–defective ccRCC. Belzutifan, a first-in-class allosteric HIF2 inhibitor, has advanced to phase 3 testing for advanced ccRCC and is approved for ccRCCs arising in patients with VHL disease, which is caused by germline VHL mutations. HIF2 can suppress p53 function in some settings and preliminary data suggested that an intact p53 pathway, as measured by activation in response to DNA damage, was necessary for HIF2 dependence. Here, we correlated HIF2 dependence and p53 status across a broader collection of ccRCC cell lines. We also genetically manipulated p53 function in ccRCC lines that were or were not previously HIF2-dependent and then assessed their subsequent sensitivity to HIF2 ablation using CRISPR-Cas9 or the HIF2 inhibitor PT2399, which is closely related to belzutifan. From these studies, we conclude that p53 status does not dictate HIF2 dependence, at least in preclinical models, and thus is unlikely to be a useful biomarker for predicting which ccRCC patients will respond to HIF2 inhibitors.

Elongin C (ELOC/TCEB1)-associated von Hippel-Lindau disease

Around 95% of patients with clinical features that meet the diagnostic criteria for von Hippel-Lindau disease (VHL) have a detectable inactivating germline variant in VHL. The VHL protein (pVHL) functions as part of the E3 ubiquitin ligase complex comprising pVHL, elongin C, elongin B, cullin 2 and ring box 1 (VCB-CR complex), which plays a key role in oxygen sensing and degradation of hypoxia-inducible factors. To date, only variants in VHL have been shown to cause VHL disease. We undertook trio analysis by whole-exome sequencing in a proband with VHL disease but without a detectable VHL mutation. Molecular studies were also performed on paired DNA extracted from the proband's kidney tumour and blood and bioinformatics analysis of sporadic renal cell carcinoma (RCC) dataset was undertaken. A de novo pathogenic variant in ELOC NM_005648.4(ELOC):c.236A>G (p.Tyr79Cys) gene was identified in the proband. ELOC encodes elongin C, a key component [C] of the VCB-CR complex. The p.Tyr79Cys substitution is a mutational hotspot in sporadic VHL-competent RCC and has previously been shown to mimic the effects of pVHL deficiency on hypoxic signalling. Analysis of an RCC from the proband showed similar findings to that in somatically ELOC-mutated RCC (expression of hypoxia-responsive proteins, no somatic VHL variants and chromosome 8 loss). These findings are consistent with pathogenic ELOC variants being a novel cause for VHL disease and suggest that genetic testing for ELOC variants should be performed in individuals with suspected VHL disease with no detectable VHL variant.

Phase II Trial of Cabozantinib Plus Nivolumab in Patients With Non-Clear-Cell Renal Cell Carcinoma and Genomic Correlates

PURPOSE

To assess the efficacy and safety of cabozantinib plus nivolumab in a phase II trial in patients with non-clear-cell renal cell carcinoma (RCC).

PATIENTS AND METHODS

Patients had advanced non-clear-cell renal carcinoma who underwent 0-1 prior systemic therapies excluding prior immune checkpoint inhibitors. Patients received cabozantinib 40 mg once daily plus nivolumab 240 mg once every 2 weeks or 480 mg once every 4 weeks. Cohort 1 enrolled patients with papillary, unclassified, or translocation-associated RCC; cohort 2 enrolled patients with chromophobe RCC. The primary end point was objective response rate (ORR) by RECIST 1.1; secondary end points included progression-free survival, overall survival, and safety. Next-generation sequencing results were correlated with response.

RESULTS

A total of 47 patients were treated with a median follow-up of 13.1 months. Objective response rate for cohort 1 (n = 40) was 47.5% (95% CI, 31.5 to 63.9), with median progression-free survival of 12.5 months (95% CI, 6.3 to 16.4) and median overall survival of 28 months (95% CI, 16.3 to not evaluable). In cohort 2 (n = 7), no responses were observed; one patient had stable disease > 1 year. Grade 3/4 treatment-related adverse events were observed in 32% treated patients. Cabozantinib and nivolumab were discontinued because of toxicity in 13% and 17% of patients, respectively. Common mutations included NF2 and FH in cohort 1 and TP53 and PTEN in cohort 2. Objective responses were seen in 10/12 patients with either NF2 or FH mutations.

CONCLUSION

Cabozantinib plus nivolumab showed promising efficacy in most non-clear-cell RCC variants tested in this trial, particularly those with prominent papillary features, whereas treatment effects were limited in chromophobe RCC. Genomic findings in non-clear-cell RCC variants warrant further study as predictors of response.

Therapeutic potential of CDK4/6 inhibitors in renal cell carcinoma

The treatment of advanced and metastatic kidney cancer has entered a golden era with the addition of more therapeutic options, improved survival and new targeted therapies. Tyrosine kinase inhibitors, mammalian target of rapamycin (mTOR) inhibitors and immune checkpoint blockade have all been shown to be promising strategies in the treatment of renal cell carcinoma (RCC). However, little is known about the best therapeutic approach for individual patients with RCC and how to combat therapeutic resistance. Cancers, including RCC, rely on sustained replicative potential. The cyclin-dependent kinases CDK4 and CDK6 are involved in cell-cycle regulation with additional roles in metabolism, immunogenicity and antitumour immune response. Inhibitors of CDK4 and CDK6 are now commonly used as approved and investigative treatments in breast cancer, as well as several other tumours. Furthermore, CDK4/6 inhibitors have been shown to work synergistically with other kinase inhibitors, including mTOR inhibitors, as well as with immune checkpoint inhibitors in preclinical cancer models. The effect of CDK4/6 inhibitors in kidney cancer is relatively understudied compared with other cancers, but the preclinical studies available are promising. Collectively, growing evidence suggests that targeting CDK4 and CDK6 in kidney cancer, alone and in combination with current therapeutics including mTOR and immune checkpoint inhibitors, might have therapeutic benefit and should be further explored.

Clinical validation of a 90-gene expression test for tumor tissue of origin diagnosis: a large-scale multicenter study of 1417 patients

Background

Once malignancy tumors were diagnosed, the determination of tissue origin and tumor type is critical for clinical management. Although the significant advance in imaging techniques and histopathological approaches, the diagnosis remains challenging in patients with metastatic and poorly differentiated or undifferentiated tumors. Gene expression profiling has been demonstrated the ability to classify multiple tumor types. The present study aims to assess the performance of a 90-gene expression test for tumor classification (i.e. the determination of tumor tissue of origin) in real clinical settings.

Methods

Formalin-fixed paraffin-embedded samples and associated clinicopathologic information were collected from three cancer centers between January 2016 and January 2021. A total of 1417 specimens that met quality control criteria (RNA quality, tumor cell content ≥ 60% and so on) were analyzed by the 90-gene expression test to identify the tumor tissue of origin. The performance was evaluated by comparing the test results with histopathological diagnosis.

Results

The 1417 samples represent 21 main tumor types classified by common tissue origins and anatomic sites. Overall, the 90-gene expression test reached an accuracy of 94.4% (1338/1417, 95% CI: 0.93 to 0.96). Among different tumor types, sensitivities were ranged from 74.2% (head&neck tumor) to 100% (adrenal carcinoma, mesothelioma, and prostate cancer). Sensitivities for the most prevalent cancers of lung, breast, colorectum, and gastroesophagus are 95.0%, 98.4%, 93.9%, and 90.6%, respectively. Moreover, specificities for all 21 tumor types are greater than 99%.

Conclusions

These findings showed robust performance of the 90-gene expression test for identifying the tumor tissue of origin and support the use of molecular testing as an adjunct to tumor classification, especially to those poorly differentiated or undifferentiated tumors in clinical practice.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03318-6.

RNA-binding protein MEX3A controls G1/S transition via regulating the RB/E2F pathway in clear cell renal cell carcinoma

MEX3A is an RNA-binding protein that mediates mRNA decay through binding to 3′ untranslated regions. However, its role and mechanism in clear cell renal cell carcinoma remain unknown. In this study, we found that MEX3A expression was transcriptionally activated by ETS1 and upregulated in clear cell renal cell carcinoma. Silencing MEX3A markedly reduced clear cell renal cell carcinoma cell proliferation in vitro and in vivo. Inhibiting MEX3A induced G1/S cell-cycle arrest. Gene set enrichment analysis revealed that E2F targets are the central downstream pathways of MEX3A. To identify MEX3A targets, systematic screening using enhanced cross-linking and immunoprecipitation sequencing, and RNA-immunoprecipitation sequencing assays were performed. A network of 4,000 genes was identified as potential targets of MEX3A. Gene ontology analysis of upregulated genes bound by MEX3A indicated that negative regulation of the cell proliferation pathway was highly enriched. Further assays indicated that MEX3A bound to the CDKN2B 3′ untranslated region, promoting its mRNA degradation. This leads to decreased levels of CDKN2B and an uncontrolled cell cycle in clear cell renal cell carcinoma, which was confirmed by rescue experiments. Our findings revealed that MEX3A acts as a post-transcriptional regulator of abnormal cell-cycle progression in clear cell renal cell carcinoma.

Germline and sporadic mTOR pathway mutations in low-grade oncocytic tumor of the kidney

Low-grade oncocytic tumor (LOT) of the kidney is a recently described entity with poorly understood pathogenesis. Using next-generation sequencing (NGS) and complementary approaches, we provide insight into its biology. We describe 22 LOT corresponding to 7 patients presenting with a median age of 75 years (range 63-86 years) and male to female ratio 2:5. All 22 tumors demonstrated prototypical microscopic features. Tumors were well-circumscribed and solid. They were composed of sheets of tumor cells in compact nests. Tumor cells had eosinophilic cytoplasm, round to oval nuclei (without nuclear membrane irregularities), focal subtle perinuclear halos, and occasional binucleation. Sharply delineated edematous stromal islands were often observed. Tumor cells were positive for PAX8, negative for CD117, and exhibited diffuse and strong cytokeratin-7 expression. Six patients presented with pT1 tumors. At a median follow-up of 29 months, four patients were alive without recurrence (three patients had died from unrelated causes). All tumors were originally classified as chromophobe renal cell carcinoma, eosinophilic variant (chRCC-eo). While none of the patients presented with known syndromic features, one patient with multiple bilateral LOTs was subsequently found to have a likely pathogenic germline TSC1 mutation. Somatic, likely activating, mutations in MTOR and RHEB were identified in all other evaluable LOTs. As assessed by phospho-S6 and phospho-4E-BP1, mTOR complex 1 (mTORC1) was activated across all cases but to different extent. MTOR mutant LOT exhibited lower levels of mTORC1 activation, possibly related to mTORC1 dimerization and the preservation of a wild-type MTOR copy (retained chromosome 1). Supporting its distinction from related entities, gene expression analyses showed that LOT clustered separately from classic chRCC, chRCC-eo, and RO. In summary, converging mTORC1 pathway mutations, mTORC1 complex activation, and a distinctive gene expression signature along with characteristic phenotypic features support LOT designation as a distinct entity with both syndromic and non-syndromic cases associated with an indolent course.

Low-grade oncocytic renal tumor (LOT): mutations in mTOR pathway genes and low expression of FOXI1

Low-grade oncocytic renal tumor (LOT) is an emerging provisional entity, described as rare solid renal oncocytic/eosinophilic tumor sharing diffuse CK7 and negative CD117 immunoprofile. The links between LOT and other eosinophilic chromophobe like-renal cell carcinomas (RCC) are currently discussed. We sequenced tumoral DNA with a next generation sequencing panel for kidney cancer and carried out immunohistochemical analyses with CK7, CD117, SDHB, 4EBP1-P, S6K-P, and FOXI1 antibodies in a series of ten cases of LOT (9 females, 1 male; mean age at surgery: 66 years, 42.3 to 83.4) retrospectively diagnosed from a cohort of 272 tumors initially classified as chromophobe RCC (CHRCC). All LOT were single, without known hereditary predisposition, classified stage pT1 (70%), pT2 (20%) or pT3a (10%). Morphological features were similar to previous descriptions and clinical behavior was indolent for the six cases with available follow-up. We identified genetic variations in mTOR pathway related genes in 80% of cases, MTOR (7 cases) or TSC1 (1 case). Expression of FOXI1 was absent in all cases. In 9 LOT, 4EBP1-P and S6K-P were overexpressed, suggesting mTOR pathway activation.Our data highlights the major role of mTOR pathway in tumorigenesis of LOT mostly due to activating MTOR gene variations. Absence of FOXI1 expression is a strong argument to distinguish LOT from eosinophilic CHRCC and to bring them closer to other recently described FOXI1 negative eosinophilic-CHRCC like with MTOR/TSC mutations. Altogether, our data argue to consider LOT as a distinct entity with a favorable clinical outcome. However, in case of metastasis, an accurate diagnosis of LOT would be essential for the patient's management and could allow targeted therapy.

Construction and Verification of a Combined Hypoxia and Immune Index for Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most aggressive malignancies in humans. Hypoxia-related genes are now recognized as a reflection of poor prognosis in cancer patients with cancer. Meanwhile, immune-related genes play an important role in the occurrence and progression of ccRCC. Nevertheless, reliable prognostic indicators based on hypoxia and immune status have not been well established in ccRCC. The aims of this study were to develop a new gene signature model using bioinformatics and open databases and to validate its prognostic value in ccRCC. The data used for the model structure can be accessed from The Cancer Genome Atlas database. Univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the hypoxia- and immune-related genes associated with prognostic risk, which were used to develop a characteristic model of prognostic risk. Kaplan-Meier and receiver-operating characteristic curve analyses were performed as well as independent prognostic factor analyses and correlation analyses of clinical characteristics in both the training and validation cohorts. In addition, differences in tumor immune cell infiltrates were compared between the high and low risk groups. Overall, 30 hypoxia- and immune-related genes were identified, and five hypoxia- and immune-related genes (EPO, PLAUR, TEK, TGFA, TGFB1) were ultimately selected. Survival analysis showed that the high-risk score on the hypoxia- and immune-related gene signature was significantly associated with adverse survival outcomes. Furthermore, clinical ccRCC samples from our medical center were used to validate the differential expression of the five genes in tumor tissue compared to normal tissue through quantitative real-time polymerase chain reaction (qRT-PCR). However, more clinical trials are needed to confirm these results, and future experimental studies must verify the potential mechanism behind the predictive value of the hypoxia- and immune-related gene signature.

LiKidMiRs: A ddPCR-Based Panel of 4 Circulating miRNAs for Detection of Renal Cell Carcinoma

Simple Summary

Early detection of renal cell carcinoma (RCC) significantly increases the likelihood of curative treatment, avoiding the need of adjuvant therapies, associated side effects and comorbidities. Thus, we aimed to discover circulating microRNAs that might aid in early, minimally invasive, RCC detection/diagnosis.

Abstract

Background: Decreased renal cell cancer-related mortality is an important societal goal, embodied by efforts to develop effective biomarkers enabling early detection and increasing the likelihood of curative treatment. Herein, we sought to develop a new biomarker for early and minimally invasive detection of renal cell carcinoma (RCC) based on a microRNA panel assessed by ddPCR. Methods: Plasma samples from patients with RCC (n = 124) or oncocytomas (n = 15), and 64 healthy donors, were selected. Hsa-miR-21-5p, hsa-miR-126-3p, hsa-miR-155-5p and hsa-miR-200b-3p levels were evaluated using a ddPCR protocol. Results: RCC patients disclosed significantly higher circulating levels of hsa-miR-155-5p compared to healthy donors, whereas the opposite was observed for hsa-miR-21-5p levels. Furthermore, hsa-miR-21-5p and hsa-miR-155-5p panels detected RCC with high sensitivity (82.66%) and accuracy (71.89%). The hsa-miR-126-3p/hsa-miR-200b-3p panel identified the most common RCC subtype (clear cell, ccRCC) with 74.78% sensitivity. Conclusion: Variable combinations of plasma miR levels assessed by ddPCR enable accurate detection of RCC in general, and of ccRCC. These findings, if confirmed in larger studies, provide evidence for a novel ancillary tool which might aid in early detection of RCC.

The Next Paradigm Shift in the Management of Clear Cell Renal Cancer: Radiogenomics—Definition, Current Advances, and Future Directions

With improved molecular characterization of clear cell renal cancer and advances in texture analysis as well as machine learning, diagnostic radiology is primed to enter personalized medicine with radiogenomics: the identification of relationships between tumor image features and underlying genomic expression. By developing surrogate image biomarkers, clinicians can augment their ability to non-invasively characterize a tumor and predict clinically relevant outcomes (i.e., overall survival; metastasis-free survival; or complete/partial response to treatment). It is thus important for clinicians to have a basic understanding of this nascent field, which can be difficult due to the technical complexity of many of the studies. We conducted a review of the existing literature for radiogenomics in clear cell kidney cancer, including original full-text articles until September 2021. We provide a basic description of radiogenomics in diagnostic radiology; summarize existing literature on relationships between image features and gene expression patterns, either computationally or by radiologists; and propose future directions to facilitate integration of this field into the clinical setting.

Deciphering the etiology and role in oncogenic transformation of the CpG island methylator phenotype: a pan-cancer analysis

Numerous cancer types have shown to present hypermethylation of CpG islands, also known as a CpG island methylator phenotype (CIMP), often associated with survival variation. Despite extensive research on CIMP, the etiology of this variability remains elusive, possibly due to lack of consistency in defining CIMP. In this work, we utilize a pan-cancer approach to further explore CIMP, focusing on 26 cancer types profiled in the Cancer Genome Atlas (TCGA). We defined CIMP systematically and agnostically, discarding any effects associated with age, gender or tumor purity. We then clustered samples based on their most variable DNA methylation values and analyzed resulting patient groups. Our results confirmed the existence of CIMP in 19 cancers, including gliomas and colorectal cancer. We further showed that CIMP was associated with survival differences in eight cancer types and, in five, represented a prognostic biomarker independent of clinical factors. By analyzing genetic and transcriptomic data, we further uncovered potential drivers of CIMP and classified them in four categories: mutations in genes directly involved in DNA demethylation; mutations in histone methyltransferases; mutations in genes not involved in methylation turnover, such as KRAS and BRAF; and microsatellite instability. Among the 19 CIMP-positive cancers, very few shared potential driver events, and those drivers were only IDH1 and SETD2 mutations. Finally, we found that CIMP was strongly correlated with tumor microenvironment characteristics, such as lymphocyte infiltration. Overall, our results indicate that CIMP does not exhibit a pan-cancer manifestation; rather, general dysregulation of CpG DNA methylation is caused by heterogeneous mechanisms.

Molecular Characterization of m6A Modifications in Non-Clear Cell Renal Cell Carcinoma and Potential Relationship with Pathological Types

Background

N6-Methyladenosine (m6A) modification is a eukaryotic mRNA modification that modulates the fate of modified RNA and, therefore, the expression of proteins. m6A modifications are associated with important roles in several cancers. Most studies related to m6A modification are based on clear cell renal cell carcinoma (ccRCC) and little is known about its role in non-ccRCC.

Methods

We summarized the molecular features of different m6A modification patterns in non-ccRCC based on The Cancer Genome Atlas database and correlated them with phenotypes such as immune patterns and prognosis. We also computed the m6Ascore and assessed its prognostic value using multivariate Cox regression analysis.

Results

We found the immune-excluded phenotype to be predominant in non-ccRCC patients. We also found that in non-clear cell carcinoma, different m6A modification profiles determine different immune patterns and are associated with different prognosis. m6AgeneCluser typing strongly associated with pathological status. Based on our findings, we suggest that the m6Ascore can be used as an independent prognostic value for prognostic assessment in non-ccRCC.

Conclusion

This study confirms the important role of m6A modifications in non-ccRCC, reveals the heterogeneity of tumor immunity, and highlights the promise of non-ccRCC therapy.

Histologic-Based Tumor-Associated Immune Cells Status in Clear Cell Renal Cell Carcinoma Correlates with Gene Signatures Related to Cancer Immunity and Clinical Outcomes

The three-tier immunophenotype (desert, excluded, and inflamed) and the four-tier immunophenotype (cold, immunosuppressed, excluded, and hot) have been linked to prognosis and immunotherapy response. This study aims to evaluate whether immunophenotypes of clear cell renal cell carcinoma, identified on hematoxylin and eosin-stained slides, correlate with gene expression signatures related to cancer immunity, and clinical outcomes. We evaluated tumor-associated immune cells (TAICs) status using three methodologies: three-tier immunophenotype based on the location of TAICs, four-tier immunophenotype considering both the location and degree of TAICs and inflammation score focusing only on the degree of TAICs, using a localized clear cell renal cell carcinoma cohort (n = 436) and The Cancer Genome Atlas (TCGA)-KIRC cohort (n = 162). We evaluated the association of the TAICs status assessed by three methodologies with CD8 and PD-L1 immunohistochemistry and immune gene expression signatures by TCGA RNA-sequencing data. All three methodologies correlated with immunohistochemical and immune gene expression signatures. The inflammation score and the four-tier immunophenotype showed similarly higher accuracy in predicting recurrence-free survival and overall survival compared to the three-tier immunophenotype. In conclusion, a simple histologic assessment of TIACs may predict clinical outcomes and immunotherapy responses.

Identification of Key Proteins from the Alternative Lengthening of Telomeres-Associated Promyelocytic Leukemia Nuclear Bodies Pathway

Simple Summary

The alternative lengthening of telomeres is a telomere maintenance mechanism used by some cancer types to elongate their telomeres without the aid of telomerase. This mechanism contributes to the proliferation and immortality of cancer cells. One of the hallmarks of this mechanism is the interaction with promyelocytic leukemia nuclear bodies, which are suspected to be the key places where telomere extension occurs. Despite the discovery of some mechanisms, elements, key genes, and proteins from the pathway, the alternative lengthening of telomeres mechanism is still poorly understood, and it is highly associated with a poor prognosis. In this study, we combined multiomics approaches with genomic, transcriptomic, and proteomic analyses of 71 genes/proteins related to promyelocytic leukemia nuclear bodies in more than 10,000 cancer samples from The Cancer Genome Atlas Consortium. As a result, 13 key proteins were proposed as candidates for future experimental studies that will validate these proteins as therapeutic markers, which will improve the understanding and treatment of these type of cancers.

Abstract

Alternative lengthening of telomeres-associated promyelocytic leukemia nuclear bodies (APBs) are a hallmark of telomere maintenance. In the last few years, APBs have been described as the main place where telomeric extension occurs in ALT-positive cancer cell lines. A different set of proteins have been associated with APBs function, however, the molecular mechanisms behind their assembly, colocalization, and clustering of telomeres, among others, remain unclear. To improve the understanding of APBs in the ALT pathway, we integrated multiomics analyses to evaluate genomic, transcriptomic and proteomic alterations, and functional interactions of 71 APBs-related genes/proteins in 32 Pan-Cancer Atlas studies from The Cancer Genome Atlas Consortium (TCGA). As a result, we identified 13 key proteins which showed distinctive mutations, interactions, and functional enrichment patterns across all the cancer types and proposed this set of proteins as candidates for future ex vivo and in vivo analyses that will validate these proteins to improve the understanding of the ALT pathway, fill the current research gap about APBs function and their role in ALT, and be considered as potential therapeutic targets for the diagnosis and treatment of ALT-positive cancers in the future.

Deep Learning-Based Classification of Epithelial-Mesenchymal Transition for Predicting Response to Therapy in Clear Cell Renal Cell Carcinoma

Epithelial-mesenchymal transition (EMT) profoundly impacts prognosis and immunotherapy of clear cell renal cell carcinoma (ccRCC). However, not every patient is tested for EMT status because this requires additional genetic studies. In this study, we developed an EMT gene signature to classify the H&E-stained slides from The Cancer Genome Atlas (TCGA) into epithelial and mesenchymal subtypes, then we trained a deep convolutional neural network to classify ccRCC which according to our EMT subtypes accurately and automatically and to further predict genomic data and prognosis. The clinical significance and multiomics analysis of the EMT signature was investigated. Patient cohorts from TCGA (n = 252) and whole slide images were used for training, testing, and validation using an algorithm to predict the EMT subtype. Our approach can robustly distinguish features predictive of the EMT subtype in H&E slides. Visualization techniques also detected EMT-associated histopathological features. Moreover, EMT subtypes were characterized by distinctive genomes, metabolic states, and immune components. Deep learning convolutional neural networks could be an extremely useful tool for predicting the EMT molecular classification of ccRCC tissue. The underlying multiomics information can be crucial in applying the appropriate and tailored targeted therapy to the patient.

T and NK cell abundance defines two distinct subgroups of renal cell carcinoma

Renal cell carcinoma (RCC) is considered as an immunogenic cancer. Because not all patients respond to current immunotherapies, we aimed to investigate the immunological heterogeneity of RCC tumors. We analyzedthe immunophenotype of the circulating, tumor, and matching adjacent healthy kidney immune cells from 52 nephrectomy patients with multi-parameter flow cytometry. Additionally, we studied the transcriptomic and mutation profiles of 20 clear cell RCC (ccRCC) tumors with bulk RNA sequencing and a customized pan-cancer gene panel. The tumor samples clustered into two distinct subgroups defined by the abundance of intratumoral CD3+ T cells (CD3^high, 25/52) and NK cells (NK^high, 27/52). CD3^high tumors had an overall higher frequency of tumor infiltrating lymphocytes and PD-1 expression on the CD8+ T cells compared to NK^high tumors. The tumor infiltrating T and NK cells had significantly elevated expression levels of LAG-3, PD-1, and HLA-DR compared to the circulating immune cells. Transcriptomic analysis revealed increased immune signaling (IFN-γ, TNF-α via NF-κB, and T cell receptor signaling) and kidney metabolism pathways in the CD3^high subgroup. Genomic analysis confirmed the typical ccRCC mutation profile including VHL, PBRM1, and SETD2 mutations, and revealed PBRM1 as a uniquely mutated gene in the CD3^high subgroup. Approximately half of the RCC tumors have a high infiltration of NK cells associated with a lower number of tumor infiltrating lymphocytes, lower PD-1 expression, a distinct transcriptomic and mutation profile, providing insights to the immunological heterogeneity of RCC which may impact treatment responses to immunological therapies.

Integrative clinical and molecular characterization of translocation renal cell carcinoma

Translocation renal cell carcinoma (tRCC) is a poorly characterized subtype of kidney cancer driven by MiT/TFE gene fusions. Here, we define the landmarks of tRCC through an integrative analysis of 152 patients with tRCC identified across genomic, clinical trial, and retrospective cohorts. Most tRCCs harbor few somatic alterations apart from MiT/TFE fusions and homozygous deletions at chromosome 9p21.3 (19.2% of cases). Transcriptionally, tRCCs display a heightened NRF2-driven antioxidant response that is associated with resistance to targeted therapies. Consistently, we find that outcomes for patients with tRCC treated with vascular endothelial growth factor receptor inhibitors (VEGFR-TKIs) are worse than those treated with immune checkpoint inhibitors (ICI). Using multiparametric immunofluorescence, we find that the tumors are infiltrated with CD8⁺ T cells, though the T cells harbor an exhaustion immunophenotype distinct from that of clear cell RCC. Our findings comprehensively define the clinical and molecular features of tRCC and may inspire new therapeutic hypotheses.

Establishment of a Prognostic Prediction and Drug Selection Model for Patients with Clear Cell Renal Cell Carcinoma by Multiomics Data Analysis

METHODS

This study was based on the multiomics data (including mRNA, lncRNA, miRNA, methylation, and WES) of 258 ccRCC patients from TCGA database. Firstly, we screened the feature values that had impact on the prognosis and obtained two subtypes. Then, we used 10 algorithms to achieve multiomics clustering and conducted pseudotiming analysis to further validate the robustness of our clustering method, based on which the two subtypes of ccRCC patients were further subtyped. Meanwhile, the immune infiltration was compared between the two subtypes, and drug sensitivity and potential drugs were analyzed. Furthermore, to analyze the heterogeneity of patients at the multiomics level, biological functions between two subtypes were compared. Finally, Boruta and PCA methods were used for dimensionality reduction and cluster analysis to construct a renal cancer risk model based on mRNA expression.

RESULTS

A prognosis predicting model of ccRCC was established by dividing patients into the high- and low-risk groups. It was found that overall survival (OS) and progression-free interval (PFI) were significantly different between the two groups (p < 0.01). The area under the OS time-dependent ROC curve for 1, 3, 5, and 10 years in the training set was 0.75, 0.72, 0.71, and 0.68, respectively.

CONCLUSION

The model could precisely predict the prognosis of ccRCC patients and may have implications for drug selection for ccRCC patients.

Integrated Analysis Of Immunotherapy Treated Clear Cell Renal Cell Carcinomas: An Exploratory Study

Molecular or immunological differences between responders and nonresponders to immune checkpoint inhibitors (ICIs) of clear cell renal cell carcinomas (ccRCCs) remain incompletely understood. To address this question, we performed next-generation sequencing, methylation analysis, genome wide copy number analysis, targeted RNA sequencing and T-cell receptor sequencing, and we studied frequencies of tumor-infiltrating CD8+ T cells, presence of tertiary lymphoid structures (TLS) and PD-L1 expression in 8 treatment-naive ccRCC patients subsequently treated with ICI (3 responders, 5 nonresponders). Unexpectedly, we identified decreased frequencies of CD8+ tumor-infiltrating T cells and TLS, and a decreased expression of PD-L1 in ICI responders when compared with nonresponders. However, neither tumor-specific genetic alterations nor gene expression profiles correlated with response to ICI or the observed immune features. Our results underline the challenge to stratify ccRCC patients for immunotherapy based on routinely available pathologic primary tumor material, even with advanced technologies. Our findings emphasize the analysis of pretreated metastatic tissue in line with recent observations describing treatment effects on the tumor microenvironment. In addition, our data call for further investigation of additional parameters in a larger ccRCC cohort to understand the mechanistic implications of the observed differences in tumor-infiltrating CD8+ T cells, TLS, and PD-L1 expression.

A germline 1;3 translocation disrupting the VHL gene: a novel genetic cause for von Hippel-Lindau

Von Hippel-Lindau (VHL) disease is an autosomal dominant hereditary tumour susceptibility disease caused by germline pathogenic variation of the VHL tumour suppressor gene. Affected individuals are at risk of developing multiple malignant and benign tumours in a number of organs.In this report, a male patient in his 20s who presented to the Urologic Oncology Branch at the National Cancer Institute with a clinical diagnosis of VHL was found to have multiple cerebellar haemangioblastomas, bilateral epididymal cysts, multiple pancreatic cysts, and multiple, bilateral renal tumours and cysts. The patient had no family history of VHL and was negative for germline VHL mutation by standard genetic testing. Further genetic analysis demonstrated a germline balanced translocation between chromosomes 1 and 3, t(1;3)(p36.3;p25) with a breakpoint on chromosome 3 within the second intron of the VHL gene. This created a pathogenic germline alteration in VHL by a novel mechanism that was not detectable by standard genetic testing.Karyotype analysis is not commonly performed in existing genetic screening protocols for patients with VHL. Based on this case, protocols should be updated to include karyotype analysis in patients who are clinically diagnosed with VHL but demonstrate no detectable mutation by existing genetic testing.

A 17-Gene Expression Signature for Early Identification of Poor Prognosis in Clear Cell Renal Cell Carcinoma

The Identification of reliable Biomarkers able to predict the outcome after nephrectomy of patients with clear cell renal cell carcinoma (ccRCC) is an unmet need. The gene expression analysis in tumor tissues represents a promising tool for better stratification of ccRCC subtypes and patients' evaluation.

METHODS

In our study we retrospectively analyzed using Next-Generation expression analysis (NanoString), the expression of a gene panel in tumor tissue from 46 consecutive patients treated with nephrectomy for non-metastatic ccRCC at two Italian Oncological Centres. Significant differences in expression levels of selected genes was sought. Additionally, we performed a univariate and a multivariate analysis on overall survival according to Cox regression model.

RESULTS

A 17-gene expression signature of patients with a recurrence-free survival (RFS) < 1 year (unfavorable genomic signature (UGS)) and of patients with a RFS > 5 years (favorable genomic signature (FGS)) was identified and resulted in being significantly correlated with overall survival of the patients included in this analysis (HR 51.37, p < 0.0001).

CONCLUSIONS

The identified Genomic Signatures may serve as potential biomarkers for prognosis prediction of non-metastatic RCC and could drive both follow-up and treatment personalization in RCC management.

DNA Methylation in INA, NHLH2, and THBS4 Is Associated with Metastatic Disease in Renal Cell Carcinoma

The detection of DNA methylation in primary tumor tissues could be relevant for early stratification of aggressive renal cell carcinomas (RCCs) as a basis for future personalized adjuvant therapy. Methylated TCGA KIRC based candidate CpG loci in INA, NHLH2, and THBS4 that are possibly associated with RCC metastasis were evaluated by pyrosequencing in 154 paired normal adjacent and primary tumor tissues, as well as in 202 metastatic tissues. Statistical analysis was carried out by bivariate logistic regression for group comparisons, log rank survival analysis, and unsupervised and supervised analysis for the classification of tumors. Increased methylation of INA, NHLH2, and THBS4 loci were significantly associated with distant metastasis in primary tumors (p < 0.05), tissue-specific hypermethylation in metastatic (p = 7.88 × 10^-8, 5.57 × 10^-10, 2.06 × 10^-7) and tumor tissues (p = 3.72 × 10^-24, 3.17 × 10^-13, 1.58 × 10^-19), and shortened progression free survival in patients (p = 0.03). Combined use of CpG site-specific methylation permits the discrimination of tissues with metastatic disease and reveals a significant contribution of CpG sites in all genes to the statistical classification model. Thus, metastasis in RCC is significantly associated with methylation alterations in INA, NHLH2, and THBS4 loci, providing independent information for the potential early detection of aggressive renal cancers as a rationale for stratifying patients to adjuvant therapies.

Immune cell mediated cabozantinib resistance for patients with renal cell carcinoma

Renal cell carcinoma (RCC) is the third most common genitourinary cancer in the USA. Despite recent advances in the treatment for advanced and metastatic clear cell RCC (ccRCC), the 5-year relative survival rate for the distant disease remains at 12%. Cabozantinib, a tyrosine kinase inhibitor (TKI), which is one of the first-line therapies approved to treat advanced ccRCC as a single agent, is now being investigated as a combination therapy with newer immunotherapeutic agents. However, not much is known about how cabozantinib modulates the immune system. Here, we present a high throughput tri-culture model that incorporates cancer cells, endothelial cells, and patient-derived immune cells to study the effect of immune cells from patients with ccRCC on angiogenesis and cabozantinib resistance. We show that circulating immune cells from patients with ccRCC induce cabozantinib resistance via increased secretion of a set of pro-angiogenic factors. Using multivariate partial least square regression modeling, we identified CD4+ T cell subsets that are correlated with cabozantinib resistance and report the changes in the frequency of these populations in ccRCC patients who are undergoing cabozantinib therapy. These findings provide a potential set of biomarkers that should be further investigated in the current TKI-immunotherapy combination clinical trials to improve personalized treatments for patients with ccRCC.

Clinicopathological and Molecular Prognostic Classifier for Intermediate/High-Risk Clear Cell Renal Cell Carcinoma

Simple Summary

In this report, we identified biomarkers for tumor progression from tissue samples of intermediate/high-risk ccRCC. Using the molecular findings and the clinical data, we developed an improved prognostic model which could help to provide better individualized management recommendations.

Abstract

The probability of tumor progression in intermediate/high-risk clear cell renal cell carcinoma (ccRCC) is highly variable, underlining the lack of predictive accuracy of the current clinicopathological factors. To develop an accurate prognostic classifier for these patients, we analyzed global gene expression patterns in 13 tissue samples from progressive and non-progressive ccRCC using Illumina Hi-seq 4000. Expression levels of 22 selected differentially expressed genes (DEG) were assessed by nCounter analysis in an independent series of 71 ccRCCs. A clinicopathological-molecular model for predicting tumor progression was developed and in silico validated in a total of 202 ccRCC patients using the TCGA cohort. A total of 1202 DEGs were found between progressive and non-progressive intermediate/high-risk ccRCC in RNAseq analysis, and seven of the 22 DEGs selected were validated by nCounter. Expression of HS6ST2, pT stage, tumor size, and ISUP grade were found to be independent prognostic factors for tumor progression. A risk score generated using these variables was able to distinguish patients at higher risk of tumor progression (HR 7.27; p < 0.001), consistent with the results obtained from the TCGA cohort (HR 2.74; p < 0.002). In summary, a combined prognostic algorithm was successfully developed and validated. This model may aid physicians to select high-risk patients for adjuvant therapy.

Identification of CCL4 as an Immune-Related Prognostic Biomarker Associated With Tumor Proliferation and the Tumor Microenvironment in Clear Cell Renal Cell Carcinoma

The last decade has witnessed revolutionary advances taken in immunotherapy for various malignant tumors. However, immune-related molecules and their characteristics in the prediction of clinical outcomes and immunotherapy response in clear cell renal cell carcinoma (ccRCC) remain largely unclear. C-C Motif Chemokine Ligand 4 (CCL4) was extracted from the intersection analysis of common differentially expressed genes (DEGs) of four microarray datasets from the Gene Expression Omnibus database and immune-related gene lists in the ImmPort database using Cytoscape plug-ins and univariate Cox regression analysis. Subsequential analysis revealed that CCL4 was highly expressed in ccRCC patients, and positively correlated with multiple clinicopathological characteristics, such as grade, stage and metastasis, while negatively with overall survival (OS). We performed gene set enrichment analysis (GSEA) and gene set variant analysis (GSVA) with gene sets coexpressed with CCL4, and observed that gene sets positively related to CCL4 were enriched in tumor proliferation and immune-related pathways while metabolic activities in the negatively one. To further explore the correlation between CCL4 and immune-related biological process, the CIBERSORT algorithm, ESTIMATE method, and tumor mutational burden (TMB) score were employed to evaluate the tumor microenvironment (TME) characteristics of each sample and confirmed that high CCL4 expression might give rise to high immune cell infiltration. Moreover, correlation analysis revealed that CCL4 was positively correlated with common immune checkpoint genes, such as programmed cell death protein 1 (PD-1), cytotoxic T-lymphocyte-associated protein 4 (CTLA4), and lymphocyte activating 3 (LAG3). Overall, this study demonstrated that CCL4 might serve as a potential immune-related prognostic biomarker to predict clinical outcomes and immunotherapy response in ccRCC. Moreover, CCL4 might contribute to TME modulation, indicating the mechanism CCL4 involved in tumor proliferation and metastasis, which could provide novel therapeutic perceptions for ccRCC patients.

BRCA1-Associated Protein 1 (BAP-1) as a Prognostic and Predictive Biomarker in Clear Cell Renal Cell Carcinoma: A Systematic Review

BACKGROUND: The gene that encodes BRCA1-associated protein 1 (BAP1) has been reported to be dysregulated in several human cancers such as uveal melanoma, malignant pleural mesothelioma, hepatocellular carcinoma, thymic epithelial tumors, and clear-cell renal cell carcinoma (ccRCC). The gene is located on the human chromosome 3p21.3, encoding a deubiquitinase and acts as a classic two-hit tumor suppressor gene. BAP1 predominantly resides in the nucleus, where it interacts with several chromatin-associated factors, as well as regulates calcium signaling in the cytoplasm. As newer therapies continue to evolve for the management of RCC, it is important to understand the role of BAP1 mutation as a prognostic and predictive biomarker. OBJECTIVE: We aimed to systematically evaluate the role of BAP1 mutations in patients with RCC in terms of its impact on prognosis and its role as a predictive biomarker. METHODS: Following PRISMA guidelines, we performed a systematic literature search using PubMed and Embase through March 2021. Titles and abstracts were screened to identify articles for full-text and then a descriptive review was performed. RESULTS: A total of 490 articles were initially identified. Ultimately 71 articles that met our inclusion criteria published between 2012–2021 were included in the analysis. Data were extracted and organized to reflect the role of BAP1 alterations as a marker of prognosis as well as a marker of response to treatments, such as mTOR inhibitors, VEGF tyrosine kinase inhibitors, and immune checkpoint inhibitors. CONCLUSIONS: Alterations in BAP1 appear to be uniformly associated with poor prognosis in patients with RCC. Knowledge gaps remain with regard to the predictive relevance of BAP1 alterations, especially in the context of immunotherapy. Prospective studies are required to more precisely ascertain the predictive value of BAP1 alterations in RCC.

Novel emerging biomarkers to immunotherapy in kidney cancer

The treatment of metastatic renal cell carcinoma has significantly evolved in recent years, particularly with the advent of novel immune checkpoint inhibitors (ICI). Despite the striking benefits observed on a population level, outcomes vary and some patients do not respond to ICI-based regimens, ultimately require salvage therapies. An ever deeper understanding of the disease biology mediated by the development of multiple high-throughput molecular omics has led to significant progress in biomarkers discovery. But despite growing insights into the molecular underpinnings of the tumor microenvironment, biomarkers have not been integrated successfully into clinical practice. In this review, we discuss some of the novel emerging predictive biomarkers to ICIs in metastatic renal cell carcinoma.

Nucleolar Proteins and Non-Coding RNAs: Roles in Renal Cancer

Renal cell cancer is the most frequent kidney malignancy. Most RCC cases are classified as clear cell renal cell carcinoma (ccRCC), characterized by high aggressiveness and poor prognosis for patients. ccRCC aggressiveness is defined by classification systems based on changes in morphology of nucleoli, the membraneless substructures of nuclei. The latter act as the sites of ribosome biogenesis as well as the hubs that trap and immobilize proteins, preventing their action in other cellular compartments. Thereby, nucleoli control cellular functioning and homeostasis. Nucleoli are also the sites of activity of multiple noncoding RNAs, including snoRNAs, IGS RNA, and miRNAs. Recent years have brought several remarkable discoveries regarding the role of nucleolar non-coding RNAs, in particular snoRNAs, in ccRCC. The expression of snoRNAs is largely dysregulated in ccRCC tumors. snoRNAs, such as SNHG1, SNHG4 and SNHG12, act as miRNA sponges, leading to aberrant expression of oncogenes and tumor suppressors, and directly contributing to ccRCC development and progression. snoRNAs can also act without affecting miRNA functioning, by altering the expression of key oncogenic proteins such as HIF1A. snoRNAs are also potentially useful biomarkers of ccRCC progression. Here, we comprehensively discuss the role of nucleolar proteins and non-coding RNAs in ccRCC.

ASS1 and ASL suppress growth in clear cell renal cell carcinoma via altered nitrogen metabolism

BACKGROUND

Kidney cancer is a common adult malignancy in the USA. Clear cell renal cell carcinoma (ccRCC), the predominant subtype of kidney cancer, is characterized by widespread metabolic changes. Urea metabolism is one such altered pathway in ccRCC. The aim of this study was to elucidate the contributions of urea cycle enzymes, argininosuccinate synthase 1 (ASS1), and argininosuccinate lyase (ASL) towards ccRCC progression.

METHODS

We employed a combination of computational, genetic, and metabolomic tools along with in vivo animal models to establish a tumor-suppressive role for ASS1 and ASL in ccRCC.

RESULTS

We show that the mRNA and protein expression of urea cycle enzymes ASS1 and ASL are reduced in ccRCC tumors when compared to the normal kidney. Furthermore, the loss of ASL in HK-2 cells (immortalized renal epithelial cells) promotes growth in 2D and 3D growth assays, while combined re-expression of ASS1 and ASL in ccRCC cell lines suppresses growth in 2D, 3D, and in vivo xenograft models. We establish that this suppression is dependent on their enzymatic activity. Finally, we demonstrate that conservation of cellular aspartate, regulation of nitric oxide synthesis, and pyrimidine production play pivotal roles in ASS1+ASL-mediated growth suppression in ccRCC.

CONCLUSIONS

ccRCC tumors downregulate the components of the urea cycle including the enzymes argininosuccinate synthase 1 (ASS1) and argininosuccinate lyase (ASL). These cytosolic enzymes lie at a critical metabolic hub in the cell and are involved in aspartate catabolism and arginine and nitric oxide biosynthesis. Loss of ASS1 and ASL helps cells redirect aspartate towards pyrimidine synthesis and support enhanced proliferation. Additionally, reduced levels of ASS1 and ASL might help regulate nitric oxide (NO) generation and mitigate its cytotoxic effects. Overall, our work adds to the understanding of urea cycle enzymes in a context-independent of ureagenesis, their role in ccRCC progression, and uncovers novel potential metabolic vulnerabilities in ccRCC.

Microenvironment-associated gene HSD11B1 may serve as a prognostic biomarker in clear cell renal cell carcinoma: a study based on TCGA, RT‑qPCR, Western blotting, and immunohistochemistry

Clear cell renal cell carcinoma (ccRCC) is one of the most common malignant tumors worldwide. The clinical treatment of ccRCC is strongly associated with the tumor microenvironment (TME). Identifying potential markers of ccRCC is important to improve prognosis. Therefore, in the present study, the levels of immune/stromal components and the proportion of tumor-infiltrating immune cells (TIICs) were determined in 611 ccRCC samples using the ESTIMATE and CIBERSORT analytical tools. Subsequently, hydroxysteroid 11-beta dehydrogenase-1 (HSD11B1) was identified by univariate Cox regression analysis, protein-protein interaction (PPI) networks and clinical survival analysis to be associated with ccRCC prognosis. At the same time, the abundance of HSD11B1 increased significantly in ccRCC was verified by western blotting, RT‑qPCR and immunostaining analysis. Furthermore, Gene Set Enrichment Analysis (GSEA) and TME suggested that HSD11B1 was involved in TME immune-related status. Taken together, the results of the present study demonstrated that HSD11B1 is a potential prognostic biomarker associated with immune cell infiltration in ccRCC.

Cost-Effectiveness Analysis of First-Line Nivolumab Plus Cabozantinib for Advanced Renal Cell Carcinoma in the United States

INTRODUCTION

Nivolumab plus cabozantinib improved progression-free survival and overall survival compared with sunitinib in the first-line treatment of advanced renal cell carcinoma (RCC) according to CheckMate 9ER study.

METHODS

A Markov model was developed to compare the costs and effectiveness of nivolumab plus cabozantinib with those of sunitinib in the first-line treatment of advanced RCC. Primary outcomes were costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratio (ICER). Model uncertainty was assessed in univariable and probabilistic sensitivity analyses.

RESULTS

The total cost per patient was $681,425 for nivolumab plus cabozantinib and $256,302 for sunitinib. The incremental QALY for nivolumab plus cabozantinib was 0.49 compared with sunitinib. The ICER for nivolumab plus cabozantinib was $863,720 per QALY gained versus sunitinib. The results remained robust in univariable and probabilistic sensitivity analyses.

CONCLUSIONS

On the basis of a willingness-to-pay threshold of $150,000, nivolumab plus cabozantinib was not cost-effective under current drug pricing in the first-line treatment of advanced RCC from a US payer's perspective.

Molecular Mechanisms of Resistance to Immunotherapy and Antiangiogenic Treatments in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common histological subtype arising from renal cell carcinomas. This tumor is characterized by a predominant angiogenic and immunogenic microenvironment that interplay with stromal, immune cells, and tumoral cells. Despite the obscure prognosis traditionally related to this entity, strategies including angiogenesis inhibition with tyrosine kinase inhibitors (TKIs), as well as the enhancement of the immune system with the inhibition of immune checkpoint proteins, such as PD-1/PDL-1 and CTLA-4, have revolutionized the treatment landscape. This approach has achieved a substantial improvement in life expectancy and quality of life from patients with advanced ccRCC. Unfortunately, not all patients benefit from this success as most patients will finally progress to these therapies and, even worse, approximately 5 to 30% of patients will primarily progress. In the last few years, preclinical and clinical research have been conducted to decode the biological basis underlying the resistance mechanisms regarding angiogenic and immune-based therapy. In this review, we summarize the insights of these molecular alterations to understand the resistance pathways related to the treatment with TKI and immune checkpoint inhibitors (ICIs). Moreover, we include additional information on novel approaches that are currently under research to overcome these resistance alterations in preclinical studies and early phase clinical trials.

Mutation Profile Variability in the Primary Tumor and Multiple Pulmonary Metastases of Clear Cell Renal Cell Carcinoma. A Review of the Literature and Analysis of Four Metastatic Cases

(1) Background: There are limited data concerning inter-tumoral and inter-metastatic heterogeneity in clear cell renal cell carcinoma (CCRCC). The aim of our study was to review published data and to examine mutation profile variability in primary and multiple pulmonary metastases (PMs) in our cohort of four patients with metastatic CCRCC. (2) Methods: Four patients were enrolled in this study. The clinical characteristics, types of surgeries, histopathologic results, immunohistochemical and genetic evaluations of corresponding primary tumor and PMs, and follow-up data were recorded. (3) Results: In our series, the most commonly mutated genes were those in the canonically dysregulated VHL pathway, which were detected in both primary tumors and corresponding metastasis. There were genetic profile differences between primary and metastatic tumors, as well as among particular metastases in one patient. (4) Conclusions: CCRCC shows heterogeneity between the primary tumor and its metastasis. Such mutational changes may be responsible for suboptimal treatment outcomes in targeted therapy settings.

A renal cell carcinoma tumorgraft platform to advance precision medicine

Renal cell carcinoma (RCC) encompasses a heterogenous group of tumors, but representative preclinical models are lacking. We previously showed that patient-derived tumorgraft (TG) models recapitulate the biology and treatment responsiveness. Through systematic orthotopic implantation of tumor samples from 926 ethnically diverse individuals into non-obese diabetic (NOD)/severe combined immunodeficiency (SCID) mice, we generate a resource comprising 172 independently derived, stably engrafted TG lines from 148 individuals. TG lines are characterized histologically and genomically (whole-exome [n = 97] and RNA [n = 102] sequencing). The platform features a variety of histological and oncogenotypes, including TCGA clades further corroborated through orthogonal metabolomic analyses. We illustrate how it enables a deeper understanding of RCC biology; enables the development of tissue- and imaging-based molecular probes; and supports advances in drug development.

ISPRF: a machine learning model to predict the immune subtype of kidney cancer samples by four genes

Background

Clear cell renal cell carcinoma (ccRCC) is the most common type of renal cell carcinoma (RCC). Immunotherapy, especially anti-PD-1, is becoming a pillar of ccRCC treatment. However, precise biomarkers and robust models are needed to select the proper patients for immunotherapy.

Methods

A total of 831 ccRCC transcriptomic profiles were obtained from 6 datasets. Unsupervised clustering was performed to identify the immune subtypes among ccRCC samples based on immune cell enrichment scores. Weighted correlation network analysis (WGCNA) was used to identify hub genes distinguishing subtypes and related to prognosis. A machine learning model was established by a random forest (RF) algorithm and used on an open and free online website to predict the immune subtype.

Results

In the identified immune subtypes, subtype2 was enriched in immune cell enrichment scores and immunotherapy biomarkers. WGCNA analysis identified four hub genes related to immune subtypes, CTLA4, FOXP3, IFNG, and CD19. The RF model was constructed by mRNA expression of these four hub genes, and the value of area under the receiver operating characteristic curve (AUC) was 0.78. Subtype2 patients in the independent validation cohort had a better drug response and prognosis for immunotherapy treatment. Moreover, an open and free website was developed by the RF model (https://immunotype.shinyapps.io/ISPRF/).

Conclusions

The current study constructs a model and provides a free online website that could identify suitable ccRCC patients for immunotherapy, and it is an important step forward to personalized treatment.

Overexpressing IFITM family genes predict poor prognosis in kidney renal clear cell carcinoma

Background

The interferon-inducible transmembrane (IFITM) proteins are localized in the endolysosomal and plasma membranes, conferring cellular immunity to various infections. However, the relationship with carcinogenesis remains poorly elucidated. In the present study, we investigated the role of IFITM in kidney renal clear cell carcinoma (KIRC).

Methods

We utilized the online databases of Oncomine, UALCAN and Human Protein Atlas to analyze the expression of IFITMs and validate their levels in human KIRC cells by qPCR and western blot. Furthermore, we evaluated prognostic significance with the Gene Expression Profiling Interactive Analysis tool (Kaplan-Meier (KM) Plotter) and delineated the immune cell infiltration profile related to IFITMs with the TIMER2.0 database.

Results

IFITMs were overexpressed in KIRC and varied in subtypes and tumor grades. High expression of IFITMs indicated a poor prognosis and more immune cell infiltration, especially endothelial cells and cancer-associated fibroblasts. IFITMs were associated with immune genes, which correlated with poor prognosis of renal clear cell carcinoma. We also explored the enriched network of IFITMs co-occurrence genes and their targeted transcription factors and miRNA. The expression of IFITMs correlated with hub mutated genes of KIRC.

Conclusions

IFITMs play a crucial role in the oncogenesis of KIRC and could be a potential surrogate marker for treatment response to targeted therapies.

Updates on Immunotherapy and Immune Landscape in Renal Clear Cell Carcinoma

Several clinicopathological features of clear cell renal cell carcinomas (ccRCC) contribute to make an "atypical" cancer, including resistance to chemotherapy, sensitivity to anti-angiogenesis therapy and ICIs despite a low mutational burden, and CD8+ T cell infiltration being the predictor for poor prognosis-normally CD8+ T cell infiltration is a good prognostic factor in cancer patients. These "atypical" features have brought researchers to investigate the molecular and immunological mechanisms that lead to the increased T cell infiltrates despite relatively low molecular burdens, as well as to decipher the immune landscape that leads to better response to ICIs. In the present study, we summarize the past and ongoing pivotal clinical trials of immunotherapies for ccRCC, emphasizing the potential molecular and cellular mechanisms that lead to the success or failure of ICI therapy. Single-cell analysis of ccRCC has provided a more thorough and detailed understanding of the tumor immune microenvironment and has facilitated the discovery of molecular biomarkers from the tumor-infiltrating immune cells. We herein will focus on the discussion of some major immune cells, including T cells and tumor-associated macrophages (TAM) in ccRCC. We will further provide some perspectives of using molecular and cellular biomarkers derived from these immune cell types to potentially improve the response rate to ICIs in ccRCC patients.

Rational Development of Liquid Biopsy Analysis in Renal Cell Carcinoma

Simple Summary

Among patients affected by renal cell carcinoma (RCC), the most common type of kidney cancer, it remains difficult to identify those who are at high risk for relapse or metastasis. This is in part due to the absence of reliable clinical biomarkers and robust methods to capture them. The aim of our study was to develop an improved assay to capture prognostic genomic biomarkers in circulating tumor DNA (ctDNA) in RCC. For this purpose, we first established a next generation sequencing (NGS) assay, targeting genes that are tailored for RCC and that are largely excluded from commercially available assays. Next, we showed the reliable performance of this assay to detect prognostic gene mutations in tumor DNA isolated from plasma, and from extracellular vesicles. Thus, our study provides a resource to facilitate ctDNA analysis for precision medicine in RCC.

Abstract

Renal cell carcinoma (RCC) is known for its variable clinical behavior and outcome, including heterogeneity in developing relapse or metastasis. Recent data highlighted the potential of somatic mutations as promising biomarkers for risk stratification in RCC. Likewise, the analysis of circulating tumor DNA (ctDNA) for such informative somatic mutations (liquid biopsy) is considered an important advance for precision oncology in RCC, allowing to monitor molecular disease evolution in real time. However, our knowledge about the utility of ctDNA analysis in RCC is limited, in part due to the lack of RCC-appropriate assays for ctDNA analysis. Here, by interrogating different blood compartments in xenograft models, we identified plasma cell-free (cf) DNA and extracellular vesicles (ev) DNA enriched for RCC-associated ctDNA. Additionally, we developed sensitive targeted sequencing and bioinformatics workflows capable of detecting somatic mutations in RCC-relevant genes with allele frequencies ≥ 0.5%. Applying this assay to patient-matched tumor and liquid biopsies, we captured tumor mutations in cf- and ev-DNA fractions isolated from the blood, highlighting the potentials of both fractions for ctDNA analysis. Overall, our study presents an RCC-appropriate sequencing assay and workflow for ctDNA analysis and provides a proof of principle as to the feasibility of detecting tumor-specific mutations in liquid biopsy in RCC patients.

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.

Differential Immunohistochemical and Molecular Profiling of Conventional and Aggressive Components of Chromophobe Renal Cell Carcinoma: Pitfalls for Diagnosis

Chromophobe renal cell carcinoma (ChRCC) is a relatively rare subtype of RCC with a characteristic histologic appearance. Most ChRCC are slow growing, but sarcomatoid differentiation and metastases can occur, indicative of aggressive behavior and poor prognosis. Herein, we characterize ten ChRCC with aggressive components, defined as sarcomatoid change and/or metastasis. Immunohistochemistry and next-generation sequencing was performed on available formalin-fixed paraffin-embedded tissue, with differential profiling of conventional and aggressive components. All ten cases showed a conventional component of renal tumor morphologically consistent with ChRCC: three had sarcomatoid change, four had metastases, and three had both sarcomatoid change and metastases. In the primary conventional components, a typical ChRCC IHC pattern (CK7+, CD117+ and CAIX-) was observed in 8/10 cases; 2 cases had rare CK7 staining. In the aggressive components, CD117 and/or CK7 was lost in 7/10 cases; 3 cases showed loss of both. Two of 10 cases showed significant CAIX staining in the aggressive component. All 7 cases that had molecular profiling performed showed characteristic chromosomal losses reported for ChRCC, though two cases showed additional complex copy number alterations in the aggressive component only. Recurrent TP53 mutations (TP53m) were also seen; however surprisingly, the conventional and aggressive components had no shared TP53m: a TP53m was private to aggressive components in 2 cases; to the conventional component in 1 case; and in 4 cases, components demonstrated different TP53m. Of the 21 pathogenic alterations identified in 7 tumors, only a PTEN splicing alteration was shared between both components in one case. In conclusion, ChRCC can have IHC staining patterns and molecular profile that differ between conventional and aggressive components. Interpretation of stains on metastases or small biopsies to determine histologic subtype can be misleading. The lack of shared pathogenic mutations between the two components supports a model in which aggressive ChRCC can have convergent subclones with different TP53m.

Inhibition of DCLK1 with DCLK1-IN-1 Suppresses Renal Cell Carcinoma Invasion and Stemness and Promotes Cytotoxic T-Cell-Mediated Anti-Tumor Immunity

Simple Summary

In this study, we found that the novel small molecule kinase inhibitor DCLK1-IN-1 not only inhibited DCLK1 phosphorylation, stemness, and EMT-related properties of RCC cells but also revealed its potential as an immunotherapy agent and potential combination therapy with anti-PD1 against RCC in immune co-culture experiments.

Abstract

The approval of immune checkpoint inhibitors has expanded treatment options for renal cell carcinoma (RCC), but new therapies that target RCC stemness and promote anti-tumor immunity are needed. Previous findings demonstrate that doublecortin-like kinase 1 (DCLK1) regulates stemness and is associated with RCC disease progression. Herein, we demonstrate that small-molecule kinase inhibitor DCLK1-IN-1 strongly inhibits DCLK1 phosphorylation and downregulates pluripotency factors and cancer stem cell (CSC) or epithelial-mesenchymal transition (EMT)-associated markers including c-MET, c-MYC, and N-Cadherin in RCC cell lines. Functionally, DCLK1-IN-1 treatment resulted in significantly reduced colony formation, migration, and invasion. Additionally, assays using floating or Matrigel spheroid protocols demonstrated potent inhibition of stemness. An analysis of clinical populations showed that DCLK1 predicts RCC survival and that its expression is correlated with reduced CD8+ cytotoxic T-cell infiltration and increases in M2 immunosuppressive macrophage populations. The treatment of RCC cells with DCLK1-IN-1 significantly reduced the expression of immune checkpoint ligand PD-L1, and co-culture assays using peripheral blood monocytes (PBMCs) or T-cell expanded PBMCs demonstrated a significant increase in immune-mediated cytotoxicity alone or in combination with anti-PD1 therapy. Together, these findings demonstrate broad susceptibility to DCLK1 kinase inhibition in RCC using DCLK1-IN-1 and provide the first direct evidence for DCLK1-IN-1 as an immuno-oncology agent.

Targeted Quantification of Carbon Metabolites Identifies Metabolic Progression Markers and an Undiagnosed Case of SDH-Deficient Clear Cell Renal Cell Carcinoma in a German Cohort

Renal cell carcinoma (RCC) is among the 10 most common cancer entities and can be categorised into distinct subtypes by differential expression of Krebs cycle genes. We investigated the predictive value of several targeted metabolites with regards to tumour stages and patient survival in an unselected cohort of 420 RCCs. Unsupervised hierarchical clustering of metabolite ratios identified two main clusters separated by α-ketoglutarate (α-KG) levels and sub-clusters with differential levels of the oncometabolite 2-hydroxyglutarate (2HG). Sub-clusters characterised by high 2HG were enriched in higher tumour stages, suggesting metabolite profiles might be suitable predictors of tumour stage or survival. Bootstrap forest models based on single metabolite signatures showed that lactate, 2HG, citrate, aspartate, asparagine, and glutamine better predicted the cancer-specific survival (CSS) of clear cell RCC patients, whereas succinate and α-ketoglutarate were better CSS predictors for papillary RCC patients. Additionally, this assay identifies rare cases of tumours with SDHx mutations, which are caused predominantly by germline mutations and which predispose to development of different neoplasms. Hence, analysis of selected metabolites should be further evaluated for potential utility in liquid biopsies, which can be obtained using less invasive methods and potentially facilitate disease monitoring for both patients and caregivers.

Determinants of anti-PD-1 response and resistance in clear cell renal cell carcinoma

ADAPTeR is a prospective, phase II study of nivolumab (anti-PD-1) in 15 treatment-naive patients (115 multiregion tumor samples) with metastatic clear cell renal cell carcinoma (ccRCC) aiming to understand the mechanism underpinning therapeutic response. Genomic analyses show no correlation between tumor molecular features and response, whereas ccRCC-specific human endogenous retrovirus expression indirectly correlates with clinical response. T cell receptor (TCR) analysis reveals a significantly higher number of expanded TCR clones pre-treatment in responders suggesting pre-existing immunity. Maintenance of highly similar clusters of TCRs post-treatment predict response, suggesting ongoing antigen engagement and survival of families of T cells likely recognizing the same antigens. In responders, nivolumab-bound CD8+ T cells are expanded and express GZMK/B. Our data suggest nivolumab drives both maintenance and replacement of previously expanded T cell clones, but only maintenance correlates with response. We hypothesize that maintenance and boosting of a pre-existing response is a key element of anti-PD-1 mode of action.

DNA Methylation Profiling: An Emerging Paradigm for Cancer Diagnosis

Histomorphology has been a mainstay of cancer diagnosis in anatomic pathology for many years. DNA methylation profiling is an additional emerging tool that will serve as an adjunct to increase accuracy of pathological diagnosis. Genome-wide interrogation of DNA methylation signatures, in conjunction with machine learning methods, has allowed for the creation of clinical-grade classifiers, most prominently in central nervous system and soft tissue tumors. Tumor DNA methylation profiling has led to the identification of new entities and the consolidation of morphologically disparate cancers into biologically coherent entities, and it will progressively become mainstream in the future. In addition, DNA methylation patterns in circulating tumor DNA hold great promise for minimally invasive cancer detection and classification. Despite practical challenges that accompany any new technology, methylation profiling is here to stay and will become increasingly utilized as a cancer diagnostic tool across a range of tumor types. Expected final online publication date for the Annual Review of Pathology: Mechanisms of Disease, Volume 17 is January 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.