Multilevel Genomics-Based Taxonomy of Renal Cell Carcinoma

MOICS, a novel classier deciphering immune heterogeneity and aid precise management of clear cell renal cell carcinoma at multiomics level

Recent studies have indicated that the tumor immune microenvironment plays a pivotal role in the initiation and progression of clear cell renal cell carcinoma (ccRCC). However, the characteristics and heterogeneity of tumor immunity in ccRCC, particularly at the multiomics level, remain poorly understood. We analyzed immune multiomics datasets to perform a consensus cluster analysis and validate the clustering results across multiple internal and external ccRCC datasets; and identified two distinctive immune phenotypes of ccRCC, which we named multiomics immune-based cancer subtype 1 (MOICS1) and subtype 2 (MOICS2). The former, MOICS1, is characterized by an immune-hot phenotype with poor clinical outcomes, marked by significant proliferation of CD4+ and CD8+ T cells, fibroblasts, and high levels of immune inhibitory signatures; the latter, MOICS2, exhibits an immune-cold phenotype with favorable clinical characteristics, characterized by robust immune activity and high infiltration of endothelial cells and immune stimulatory signatures. Besides, a significant negative correlation between immune infiltration and angiogenesis were identified. We further explored the mechanisms underlying these differences, revealing that negatively regulated endopeptidase activity, activated cornification, and neutrophil degranulation may promote an immune-deficient phenotype, whereas enhanced monocyte recruitment could ameliorate this deficiency. Additionally, significant differences were observed in the genomic landscapes between the subtypes: MOICS1 exhibited mutations in TTN, BAP1, SETD2, MTOR, MUC16, CSMD3, and AKAP9, while MOICS2 was characterized by notable alterations in the TGF-β pathway. Overall, our work demonstrates that multi-immune omics remodeling analysis enhances the understanding of the immune heterogeneity in ccRCC and supports precise patient management.

Comprehensive proteogenomic characterization of rare kidney tumors

Non-clear cell renal cell carcinomas (non-ccRCCs) encompass diverse malignant and benign tumors. Refinement of differential diagnosis biomarkers, markers for early prognosis of aggressive disease, and therapeutic targets to complement immunotherapy are current clinical needs. Multi-omics analyses of 48 non-ccRCCs compared with 103 ccRCCs reveal proteogenomic, phosphorylation, glycosylation, and metabolic aberrations in RCC subtypes. RCCs with high genome instability display overexpression of IGF2BP3 and PYCR1. Integration of single-cell and bulk transcriptome data predicts diverse cell-of-origin and clarifies RCC subtype-specific proteogenomic signatures. Expression of biomarkers MAPRE3, ADGRF5, and GPNMB differentiates renal oncocytoma from chromophobe RCC, and PIGR and SOSTDC1 distinguish papillary RCC from MTSCC. This study expands our knowledge of proteogenomic signatures, biomarkers, and potential therapeutic targets in non-ccRCC.

The potential role of precision medicine to alleviate racial disparities in prostate, bladder and renal urological cancer care

Background

Racial disparities in oncological outcomes resulting from differences in social determinants of health (SDOH) and tumour biology are well described in prostate cancer (PCa) but similar inequities exist in bladder (BCa) and renal cancers (RCCs). Precision medicine (PM) aims to provide personalized treatment based on individual patient characteristics and has the potential to reduce these inequities in GU cancers.

Objective

This article aims to review the current evidence outlining racial disparities in GU cancers and explore studies demonstrating improved oncological outcomes when PM is applied to racially diverse patient populations.

Evidence acquisition

Evidence was obtained from Pubmed and Web of Science using keywords prostate, bladder and renal cancer, racial disparity and precision medicine. Because limited studies were found, preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines were not applied but rather related articles were studied to explore existing debates, identify the current status and speculate on future applications.

Results

Evidence suggests addressing SDOH for PCa can reverse racial inequities in oncological outcomes but differences in incidence remain. Similar disparities in BCa and RCC are seen, and it would be reasonable to suggest achieving parity in SDOH for all races would do the same. Research applying a PM approach to different ethnicities is lacking although in African Americans (AAs) with metastatic castrate-resistant prostate cancer (mCRPCa) better outcomes have been shown with androgen receptor inhibitors, radium-223 and sipuleucel. Exploiting the abscopal effect with targeted radiation therapy (RT) and immunotherapy has promise but requires further study, as does defining actionable mutations in specific patient groups to tailor treatments as appropriate.

Conclusion

For all GU cancers, the historical underrepresentation of ethnic minorities in clinical trials still exists and there is an urgent need for recruitment strategies to address this. PM is a promising development with the potential to reduce inequities in GU cancers, however, both improved understanding of race-specific tumour biology, and enhanced recruitment of minority populations into clinical trials are required. Without this, the benefits of PM will be limited.

Established and emerging biomarkers of immunotherapy in renal cell carcinoma

Immunotherapies, such as immune checkpoint inhibitors, have heralded impressive progress for patient care in renal cell carcinoma (RCC). Despite this success, some patients' disease fails to respond, and other patients experience significant side effects. Thus, development of biomarkers is needed to ensure that patients can be selected to maximize benefit from immunotherapies. Improving clinicians' ability to predict which patients will respond to immunotherapy and which are most at risk of adverse events - namely through clinical biomarkers - is indispensable for patient safety and therapeutic efficacy. Accordingly, an evolving suite of therapeutic biomarkers continues to be investigated. This review discusses biomarkers for immunotherapy in RCC, highlighting current practices and emerging innovations, aiming to contribute to improved outcomes for patients with RCC.

Hybrid Oncocytic Tumors (HOTs) in Birt-Hogg-Dubé Syndrome Patients-A Tale of Two Cities: Sequencing Analysis Reveals Dual Lineage Markers Capturing the 2 Cellular Populations of HOT

Birt-Hogg-Dubé (BHD) syndrome is associated with an increased risk of multifocal renal tumors, including hybrid oncocytic tumor (HOT) and chromophobe renal cell carcinoma (chRCC). HOT exhibits heterogenous histologic features overlapping with chRCC and benign renal oncocytoma, posing challenges in diagnosis of HOT and renal tumor entities resembling HOT. In this study, we performed integrative analysis of bulk and single-cell RNA sequencing data from renal tumors and normal kidney tissues, and nominated candidate biomarkers of HOT, L1CAM, and LINC01187 , which are also lineage-specific markers labeling the principal cell and intercalated cell lineages of the distal nephron, respectively. Our findings indicate the principal cell lineage marker L1CAM and intercalated cell lineage marker LINC01187 to be expressed mutually exclusively in a unique checkered pattern in BHD-associated HOTs, and these 2 lineage markers collectively capture the 2 distinct tumor epithelial populations seen to co-exist morphologically in HOTs. We further confirmed that the unique checkered expression pattern of L1CAM and LINC01187 distinguished HOT from chRCC, renal oncocytoma, and other major and rare renal cell carcinoma subtypes. We also characterized the histopathologic features and immunophenotypic features of oncocytosis in the background kidney of patients with BHD, as well as the intertumor and intratumor heterogeneity seen within HOT. We suggest that L1CAM and LINC01187 can serve as stand-alone diagnostic markers or as a panel for the diagnosis of HOT. These lineage markers will inform future studies on the evolution and interaction between the 2 transcriptionally distinct tumor epithelial populations in such tumors.

Molecular Characterization of TFE3-Rearranged Renal Cell Carcinoma: A Comparative Study With Papillary and Clear Cell Renal Cell Carcinomas

TFE3-rearranged renal cell carcinoma (rRCC) is a rare subtype of renal cell carcinomas belonging to the MiT family translocation RCC. To further elucidate the co-alterations that occur along with TFE3 fusions in rRCC, we characterized the genomic, transcriptional, and immune landscapes in comparison to clear cell (ccRCC) and papillary renal cell carcinoma (pRCC). Next-generation sequencing of RNA (whole transcriptome) and DNA (592-gene panel or whole exome) for rRCC (N = 20), pRCC (N = 20), and ccRCC samples (N = 392) was performed. Patients with rRCC were significantly younger and more frequently female (median 44.5 years, 75.0% female) as compared with patients with pRCC (68.5 years, 25.0% female; P < .05) and ccRCC (62.0 years, 27.8% female; P < .05). A total of 8 unique fusion partners were observed, including a novel fusion with SRRM2::TFE3 in 2 patients. ccRCC exhibited significantly higher mutation rates of VHL (0% rRCC, 0% pRCC, 78.7% ccRCC; P < .05) and PBMR1 (0% rRCC, 5.0% pRCC, 49.4% ccRCC; P < .05). The genomic landscapes of rRCC were sparse with no mutations occurring with a prevalence higher than 10% other than pTERT (18.2% rRCC, 0% pRCC, 9.2% ccRCC). rRCC were associated with significantly less M1 macrophages (0.8%) as compared with pRCC (1.4%) and ccRCC (2.7%) (P < .05), suggesting a cold tumor-immune microenvironment. However, rRCC were more commonly PD-L1+ (rRCC 50%, pRCC 19.0%, ccRCC 12.2%; P < .05). Gene set enrichment analysis showed that rRCC are enriched in genes related to oxidative phosphorylation when compared with both ccRCC and pRCC. Despite having a colder tumor-immune microenvironment than pRCC and ccRCC, increased PDL1+ rates in rRCC suggest a potential benefit from immune checkpoint inhibitor therapy.

Genomic and epigenomic integrative subtypes of renal cell carcinoma in a Japanese cohort

Renal cell carcinoma (RCC) comprises several histological types characterised by different genomic and epigenomic aberrations; however, the molecular pathogenesis of each type still requires further exploration. We perform whole-genome sequencing of 128 Japanese RCC cases of different histology to elucidate the significant somatic alterations and mutagenesis processes. We also perform transcriptomic and epigenomic sequencing to identify distinguishing features, including assay for transposase-accessible chromatin sequencing (ATAC-seq) and methyl sequencing. Genomic analysis reveals that the mutational signature differs among the histological types, suggesting that different carcinogenic factors drive each histology. From the ATAC-seq results, master transcription factors are identified for each histology. Furthermore, clear cell RCC is classified into three epi-subtypes, one of which expresses highly immune checkpoint molecules with frequent loss of chromosome 14q. These genomic and epigenomic features may lead to the development of effective therapeutic strategies for RCC.

Molecular insight into renal cancer and latest therapeutic approaches to tackle it: an updated review

Renal cell carcinoma (RCC) is one of the most lethal genitourinary cancers, with the highest mortality rate, and may remain undetected throughout its development. RCC can be sporadic or hereditary. Exploring the underlying genetic abnormalities in RCC will have important implications for understanding the origins of nonhereditary renal cancers. The treatment of RCC has evolved over centuries from the era of cytokines to targeted therapy to immunotherapy. A surgical cure is the primary treatment modality, especially for organ-confined diseases. Furthermore, the urologic oncology community focuses on nephron-sparing surgical approaches and ablative procedures when small renal masses are detected incidentally in conjunction with interventional radiologists. In addition to new combination therapies approved for RCC treatment, several trials have been conducted to investigate the potential benefits of certain drugs. This may lead to durable responses and more extended survival benefits for patients with metastatic RCC (mRCC). Several approved drugs have reduced the mortality rate of patients with RCC by targeting VEGF signaling and mTOR. This review better explains the signaling pathways involved in the RCC progression, oncometabolites, and essential biomarkers in RCC that can be used for its diagnosis. Further, it provides an overview of the characteristics of RCC carcinogenesis to assist in combating treatment resistance, as well as details about the current management and future therapeutic options. In the future, multimodal and integrated care will be available, with new treatment options emerging as we learn more about the disease.

Candidate biomarkers for treatment benefit from sunitinib in patients with advanced renal cell carcinoma using mass spectrometry-based (phospho)proteomics

The tyrosine kinase inhibitor sunitinib is an effective first-line treatment for patients with advanced renal cell carcinoma (RCC). Hypothesizing that a functional read-out by mass spectrometry-based (phospho, p-)proteomics will identify predictive biomarkers for treatment outcome of sunitinib, tumor tissues of 26 RCC patients were analyzed. Eight patients had primary resistant (RES) and 18 sensitive (SENS) RCC. A 78 phosphosite signature (p < 0.05, fold-change > 2) was identified; 22 p-sites were upregulated in RES (unique in RES: BCAR3, NOP58, EIF4A2, GDI1) and 56 in SENS (35 unique). EIF4A1/EIF4A2 were differentially expressed in RES at the (p-)proteome and, in an independent cohort, transcriptome level. Inferred kinase activity of MAPK3 (p = 0.026) and EGFR (p = 0.045) as determined by INKA was higher in SENS. Posttranslational modifications signature enrichment analysis showed that different p-site-centric signatures were enriched (p < 0.05), of which FGF1 and prolactin pathways in RES and, in SENS, vanadate and thrombin treatment pathways, were most significant. In conclusion, the RCC (phospho)proteome revealed differential p-sites and kinase activities associated with sunitinib resistance and sensitivity. Independent validation is warranted to develop an assay for upfront identification of patients who are intrinsically resistant to sunitinib.

Comprehensive analyses of immune tumor microenvironment in papillary renal cell carcinoma

BACKGROUND

Papillary renal cell carcinoma (pRCC) is the most common non-clear cell RCC, and associated with poor outcomes in the metastatic setting. In this study, we aimed to comprehensively evaluate the immune tumor microenvironment (TME), largely unknown, of patients with metastatic pRCC and identify potential therapeutic targets.

METHODS

We performed quantitative gene expression analysis of TME using Microenvironment Cell Populations-counter (MCP-counter) methodology, on two independent cohorts of localized pRCC (n=271 and n=98). We then characterized the TME, using immunohistochemistry (n=38) and RNA-sequencing (RNA-seq) (n=30) on metastatic pRCC from the prospective AXIPAP trial cohort.

RESULTS

Unsupervised clustering identified two "TME subtypes", in each of the cohorts: the "immune-enriched" and the "immune-low". Within AXIPAP trial cohort, the "immune-enriched" cluster was significantly associated with a worse prognosis according to the median overall survival to 8 months (95% CI, 6 to 29) versus 37 months (95% CI, 20 to NA, p=0.001). The two immune signatures, Teff and JAVELIN Renal 101 Immuno signature, predictive of response to immune checkpoint inhibitors (CPI) in clear cell RCC, were significantly higher in the "immune-enriched" group (adjusted p<0.05). Finally, five differentially overexpressed genes were identified, corresponding mainly to B lymphocyte populations.

CONCLUSION

For the first time, using RNA-seq and immunohistochemistry, we have highlighted a specific immune TME subtype of metastatic pRCC, significantly more infiltrated with T and B immune population. This "immune-enriched" group appears to have a worse prognosis and could have a potential predictive value for response to immunotherapy, justifying the confirmation of these results in a cohort of metastatic pRCC treated with CPI and in combination with targeted therapies.

TRIAL REGISTRATION NUMBER

NCT02489695.

Characterization of tumor microenvironment and sensitive chemotherapy drugs based on cuproptosis-related signatures in renal cell carcinoma

Cuproptosis is a novel type of copper-induced cell death and is considered as a new therapeutic target for many cancers. Distant metastases occur in about 40% of patients with advanced renal cell carcinoma (RCC), with a poor 5-year prognosis of about 10%. Through a series of comprehensive analyses, four differentially expressed cuproptosis-related lncRNAs (DECRLs) were identified as candidate biomarkers for RCC. The risk model constructed by using these four DECRLs can better predict the prognosis of patients with RCC, which is determined by the receiver operating characteristic (Time dependent area under curve value at 1-year, 3-year, 5-year, and 10-year were 0.82, 0.80, 0.76, and 0.73 respectively). There were significant differences in immune status between high-risk and low-risk RCC patients. The differentially expressed gene enrichment terms between high- and low-risk patients was also dominated by immune-related terms. The risk score was also correlated with immunotherapy as measured by the tumor immune dysfunction and exclusion (TIDE) score. In addition, we also found that the sensitivity of many chemotherapy drugs varies widely between high- and low-risk patients. The sensitivity of the three chemotherapy drugs (AZD4547, Vincristine, and WEHI-539) varied among high- and low-risk patients, and was significantly negatively correlated with risk values, suggesting that they could be used as clinical treatment drugs for RCC. Our study not only obtained four potential biomarkers, but also provided guidance for immunotherapy and chemotherapy treatment of RCC, as well as new research strategies for the screening of other cancer biomarkers and sensitive drugs.

Characterization of Intercalated Cell Markers KIT and LINC01187 in Chromophobe Renal Cell Carcinoma and Other Renal Neoplasms

Introduction. Chromophobe renal cell carcinoma (chromophobe RCC) is the third major subcategory of renal tumors after clear cell RCC and papillary RCC, accounting for approximately 5% of all RCC subtypes. Other oncocytic neoplasms seen commonly in surgical pathology practice include the eosinophilic variant of chromophobe RCC, renal oncocytoma, and low-grade oncocytic unclassified RCC. Methods. In our recent next-generation sequencing based study, we nominated a lineage-specific novel biomarker LINC01187 (long intergenic non-protein coding RNA 1187) which was found to be enriched in chromophobe RCC. Like KIT (cluster of differentiation 117; CD117), a clinically utilized chromophobe RCC related biomarker, LINC01187 is expressed in intercalated cells of the nephron. In this follow-up study, we performed KIT immunohistochemistry and LINC01187 RNA in situ hybridization (RNA-ISH) on a cohort of chromophobe RCC and other renal neoplasms, characterized the expression patterns, and quantified the expression signals of the two biomarkers in both primary and metastatic settings. Results. LINC01187, in comparison to KIT, exhibits stronger and more uniform expression within tumors while maintaining temporal and spatial consistency. LINC01187 also is devoid of intra-tumoral heterogeneous expression pattern, a phenomenon commonly noted with KIT. Conclusions. LINC01187 expression can augment the currently utilized KIT assay and help facilitate easy microscopic analyses in routine surgical pathology practice.

Interferon signaling promotes tolerance to chromosomal instability during metastatic evolution in renal cancer

Molecular routes to metastatic dissemination are critical determinants of aggressive cancers. Through in vivo CRISPR-Cas9 genome editing, we generated somatic mosaic genetically engineered models that faithfully recapitulate metastatic renal tumors. Disruption of 9p21 locus is an evolutionary driver to systemic disease through the rapid acquisition of complex karyotypes in cancer cells. Cross-species analysis revealed that recurrent patterns of copy number variations, including 21q loss and dysregulation of the interferon pathway, are major drivers of metastatic potential. In vitro and in vivo genomic engineering, leveraging loss-of-function studies, along with a model of partial trisomy of chromosome 21q, demonstrated a dosage-dependent effect of the interferon receptor genes cluster as an adaptive mechanism to deleterious chromosomal instability in metastatic progression. This work provides critical knowledge on drivers of renal cell carcinoma progression and defines the primary role of interferon signaling in constraining the propagation of aneuploid clones in cancer evolution.

Distinguishing Renal Cell Carcinoma From Normal Kidney Tissue Using Mass Spectrometry Imaging Combined With Machine Learning

PURPOSE

Accurately distinguishing renal cell carcinoma (RCC) from normal kidney tissue is critical for identifying positive surgical margins (PSMs) during partial and radical nephrectomy, which remains the primary intervention for localized RCC. Techniques that detect PSM with higher accuracy and faster turnaround time than intraoperative frozen section (IFS) analysis can help decrease reoperation rates, relieve patient anxiety and costs, and potentially improve patient outcomes.

MATERIALS AND METHODS

Here, we extended our combined desorption electrospray ionization mass spectrometry imaging (DESI-MSI) and machine learning methodology to identify metabolite and lipid species from tissue surfaces that can distinguish normal tissues from clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC) tissues.

RESULTS

From 24 normal and 40 renal cancer (23 ccRCC, 13 pRCC, and 4 chRCC) tissues, we developed a multinomial lasso classifier that selects 281 total analytes from over 27,000 detected molecular species that distinguishes all histological subtypes of RCC from normal kidney tissues with 84.5% accuracy. On the basis of independent test data reflecting distinct patient populations, the classifier achieves 85.4% and 91.2% accuracy on a Stanford test set (20 normal and 28 RCC) and a Baylor-UT Austin test set (16 normal and 41 RCC), respectively. The majority of the model's selected features show consistent trends across data sets affirming its stable performance, where the suppression of arachidonic acid metabolism is identified as a shared molecular feature of ccRCC and pRCC.

CONCLUSION

Together, these results indicate that signatures derived from DESI-MSI combined with machine learning may be used to rapidly determine surgical margin status with accuracies that meet or exceed those reported for IFS.

Stage Migration in Renal Malignancies in COVID Era: A Single-Center Analysis

The COVID-19 disease, caused by SARS-CoV-2 virus, attained the status of a pandemic by March 2020. There was apprehension among patients suffering from renal malignancies about balancing cancer treatment and preventing COVID-19 infection transmission. We analyzed 184 patients with renal malignancies retrospectively, who presented to our institute over 2 years: 91 patients of renal malignancies in pre-COVID era (March 2019-Feb 2020) and 93 patients in COVID era (March 2020-Feb 2021). The parameters analyzed were age, tumor size, clinical presentation, clinical stage, pathological stage, nuclear grade, and presence of metastasis. Level of significance was kept at 95%, and p value <0.05 was considered significant. The age of patients was comparable in both groups (p: 0.381). Clinical presentation was also similar in both groups whereas there were more cases diagnosed during routine evaluation in pre-COVID era (p: 0.022). Tumor size was 5.84 ± 3.03cm vs. 7.10±3.83cm (p: 0.017) in pre-COVID vs. COVID era, respectively. Patients in COVID era had significantly higher clinical stage (p = 0.041), pathological stage (p =0.027), nuclear grade (p = 0.007), and presence of metastasis (p = 0.005) as compared to pre-COVID era. Patients, who underwent Nephron-sparing surgery, also had higher pathological stage in COVID era. COVID overshadowed the management of renal malignancies. There was a clear shift and stage migration in patients of renal malignancies in COVID era as compared to pre-COVID era, probably because of less routine health check-ups and patients deferring hospital visits due to fear of contracting COVID infection.

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.

Cancer-cell-derived fumarate suppresses the anti-tumor capacity of CD8+ T cells in the tumor microenvironment

Metabolic alterations in the microenvironment significantly modulate tumor immunosensitivity, but the underlying mechanisms remain obscure. Here, we report that tumors depleted of fumarate hydratase (FH) exhibit inhibition of functional CD8⁺ T cell activation, expansion, and efficacy, with enhanced malignant proliferative capacity. Mechanistically, FH depletion in tumor cells accumulates fumarate in the tumor interstitial fluid, and increased fumarate can directly succinate ZAP70 at C96 and C102 and abrogate its activity in infiltrating CD8⁺ T cells, resulting in suppressed CD8⁺ T cell activation and anti-tumor immune responses in vitro and in vivo. Additionally, fumarate depletion by increasing FH expression strongly enhances the anti-tumor efficacy of anti-CD19 CAR T cells. Thus, these findings demonstrate a role for fumarate in controlling TCR signaling and suggest that fumarate accumulation in the tumor microenvironment (TME) is a metabolic barrier to CD8⁺ T cell anti-tumor function. And potentially, fumarate depletion could be an important strategy for tumor immunotherapy.

Single-cell Deconvolution of a Specific Malignant Cell Population as a Poor Prognostic Biomarker in Low-risk Clear Cell Renal Cell Carcinoma Patients

BACKGROUND

Intratumor heterogeneity (ITH) is a key feature in clear cell renal cell carcinomas (ccRCCs) that impacts outcomes such as aggressiveness, response to treatments, or recurrence. In particular, it may explain tumor relapse after surgery in clinically low-risk patients who did not benefit from adjuvant therapy. Recently, single-cell RNA sequencing (scRNA-seq) has emerged as a powerful tool to unravel expression ITH (eITH) and might enable better assessment of clinical outcomes in ccRCC.

OBJECTIVE

To explore eITH in ccRCC with a focus on malignant cells (MCs) and assess its relevance to improve prognosis for low-risk patients.

DESIGN, SETTING, AND PARTICIPANTS

We performed scRNA-seq on tumor samples from five untreated ccRCC patients ranging from pT1a to pT3b. Data were complemented with a published dataset composed of pairs of matched normal and ccRCC samples.

INTERVENTION

Radical or partial nephrectomy on untreated ccRCC patients.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Viability and cell type proportions were determined by flow cytometry. Following scRNA-seq, a functional analysis was performed and tumor progression trajectories were inferred. A deconvolution approach was applied on an external cohort, and Kaplan-Meier survival curves were estimated with respect to the prevalence of malignant clusters.

RESULTS AND LIMITATIONS

We analyzed 54 812 cells and identified 35 cell subpopulations. The eITH analysis revealed that each tumor contained various degrees of clonal diversity. The transcriptomic signatures of MCs in one particularly heterogeneous sample were used to design a deconvolution-based strategy that allowed the risk stratification of 310 low-risk ccRCC patients.

CONCLUSIONS

We described eITH in ccRCCs, and used this information to establish significant cell population-based prognostic signatures and better discriminate ccRCC patients. This approach has the potential to improve the stratification of clinically low-risk patients and their therapeutic management.

PATIENT SUMMARY

We sequenced the RNA content of individual cell subpopulations composed of clear cell renal cell carcinomas and identified specific malignant cells the genetic information of which can be used to predict tumor progression.

meth-SemiCancer: a cancer subtype classification framework via semi-supervised learning utilizing DNA methylation profiles

BACKGROUND

Identification of the cancer subtype plays a crucial role to provide an accurate diagnosis and proper treatment to improve the clinical outcomes of patients. Recent studies have shown that DNA methylation is one of the key factors for tumorigenesis and tumor growth, where the DNA methylation signatures have the potential to be utilized as cancer subtype-specific markers. However, due to the high dimensionality and the low number of DNA methylome cancer samples with the subtype information, still, to date, a cancer subtype classification method utilizing DNA methylome datasets has not been proposed.

RESULTS

In this paper, we present meth-SemiCancer, a semi-supervised cancer subtype classification framework based on DNA methylation profiles. The proposed model was first pre-trained based on the methylation datasets with the cancer subtype labels. After that, meth-SemiCancer generated the pseudo-subtypes for the cancer datasets without subtype information based on the model's prediction. Finally, fine-tuning was performed utilizing both the labeled and unlabeled datasets.

CONCLUSIONS

From the performance comparison with the standard machine learning-based classifiers, meth-SemiCancer achieved the highest average F1-score and Matthews correlation coefficient, outperforming other methods. Fine-tuning the model with the unlabeled patient samples by providing the proper pseudo-subtypes, encouraged meth-SemiCancer to generalize better than the supervised neural network-based subtype classification method. meth-SemiCancer is publicly available at https://github.com/cbi-bioinfo/meth-SemiCancer .

High-throughput and targeted drug screens identify pharmacological candidates against MiT-translocation renal cell carcinoma

BACKGROUND

MiT-Renal Cell Carcinoma (RCC) is characterized by genomic translocations involving microphthalmia-associated transcription factor (MiT) family members TFE3, TFEB, or MITF. MiT-RCC represents a specific subtype of sporadic RCC that is predominantly seen in young patients and can present with heterogeneous histological features making diagnosis challenging. Moreover, the disease biology of this aggressive cancer is poorly understood and there is no accepted standard of care therapy for patients with advanced disease. Tumor-derived cell lines have been established from human TFE3-RCC providing useful models for preclinical studies.

METHODS

TFE3-RCC tumor derived cell lines and their tissues of origin were characterized by IHC and gene expression analyses. An unbiased high-throughput drug screen was performed to identify novel therapeutic agents for treatment of MiT-RCC. Potential therapeutic candidates were validated in in vitro and in vivo preclinical studies. Mechanistic assays were conducted to confirm the on-target effects of drugs.

RESULTS

The results of a high-throughput small molecule drug screen utilizing three TFE3-RCC tumor-derived cell lines identified five classes of agents with potential pharmacological efficacy, including inhibitors of phosphoinositide-3-kinase (PI3K) and mechanistic target of rapamycin (mTOR), and several additional agents, including the transcription inhibitor Mithramycin A. Upregulation of the cell surface marker GPNMB, a specific MiT transcriptional target, was confirmed in TFE3-RCC and evaluated as a therapeutic target using the GPNMB-targeted antibody-drug conjugate CDX-011. In vitro and in vivo preclinical studies demonstrated efficacy of the PI3K/mTOR inhibitor NVP-BGT226, Mithramycin A, and CDX-011 as potential therapeutic options for treating advanced MiT-RCC as single agents or in combination.

CONCLUSIONS

The results of the high-throughput drug screen and validation studies in TFE3-RCC tumor-derived cell lines have provided in vitro and in vivo preclinical data supporting the efficacy of the PI3K/mTOR inhibitor NVP-BGT226, the transcription inhibitor Mithramycin A, and GPNMB-targeted antibody-drug conjugate CDX-011 as potential therapeutic options for treating advanced MiT-RCC. The findings presented here should provide the basis for designing future clinical trials for patients with MiT-driven RCC.

Identification of basement membrane-related prognostic signature for predicting prognosis, immune response and potential drug prediction in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is a malignant neoplasm of the kidney and is highly interesting due to its increasing incidence. Many studies have shown that the basement membrane (BM) plays an important role in the development of cancer, and structural and functional changes in the BM can be observed in most renal lesions. However, the role of BM in the malignant progression of PRCC and its impact on prognosis has not been fully studied. Therefore, this study aimed to explore the functional and prognostic value of basement membrane-associated genes (BMs) in PRCC patients. We identified differentially expressed BMs between PRCC tumor samples and normal tissue and systematically explored the relevance of BMs to immune infiltration. Moreover, we constructed a risk signature based on these differentially expressed genes (DEGs) using Lasso regression analysis and demonstrated their independence using Cox regression analysis. Finally, we predicted 9 small molecule drugs with the potential to treat PRCC and compared the differences in sensitivity to commonly used chemotherapeutic agents between high and low-risk groups to better target patients for more precise treatment planning. Taken together, our study suggested that BMs might play a crucial role in the development of PRCC, and these results might provide new insights into the treatment of PRCC.

Identification and validation of NFIA as a novel prognostic marker in renal cell carcinoma

Prognostic tools are an essential component of the clinical management of patients with renal cell carcinoma (RCC). Although tumour stage and grade can provide important information, they fail to consider patient- and tumour-specific biology. In this study, we set out to find a novel molecular marker of RCC by using hepatocyte nuclear factor 4A (HNF4A), a transcription factor implicated in RCC progression and malignancy, as a blueprint. Through transcriptomic analyses, we show that the nuclear factor I A (NFIA)-driven transcription network is active in primary RCC and that higher levels of NFIA confer a survival benefit. We validate our findings using immunohistochemical staining and analysis of a 363-patient tissue microarray (TMA), showing for the first time that NFIA can independently predict poor cancer-specific survival in clear cell RCC (ccRCC) patients (hazard ratio = 0.46, 95% CI = 0.24-0.85, p value = 0.014). Furthermore, we confirm the association of HNF4A with higher grades and stages in ccRCC in our TMA cohort. We present novel data that show HNF4A protein expression does not confer favourable prognosis in papillary RCC, confirming our survival analysis with publicly available HNF4A RNA expression data. Further work is required to elucidate the functional role of NFIA in RCC as well as the testing of these markers on patient material from diverse multi-centre cohorts, to establish their value for the prognostication of RCC.

Sunitinib resistance in renal cell carcinoma: From molecular mechanisms to predictive biomarkers

Currently, renal cell carcinoma (RCC) is the most prevalent type of kidney cancer. Targeted therapy has replaced radiation therapy and chemotherapy as the main treatment option for RCC due to the lack of significant efficacy with these conventional therapeutic regimens. Sunitinib, a drug used to treat gastrointestinal tumors and renal cell carcinoma, inhibits the tyrosine kinase activity of a number of receptor tyrosine kinases, including vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), c-Kit, rearranged during transfection (RET) and fms-related receptor tyrosine kinase 3 (Flt3). Although sunitinib has been shown to be efficacious in the treatment of patients with advanced RCC, a significant number of patients have primary resistance to sunitinib or acquired drug resistance within the 6-15 months of therapy. Thus, in order to develop more efficacious and long-lasting treatment strategies for patients with advanced RCC, it will be crucial to ascertain how to overcome sunitinib resistance that is produced by various drug resistance mechanisms. In this review, we discuss: 1) molecular mechanisms of sunitinib resistance; 2) strategies to overcome sunitinib resistance and 3) potential predictive biomarkers of sunitinib resistance.

Pan-cancer molecular subtypes of metastasis reveal distinct and evolving transcriptional programs

Molecular mechanisms underlying cancer metastasis span diverse tissues of origin. Here, we synthesize and collate the transcriptomes of patient-derived xenografts and patient tumor metastases, and these data collectively represent 38 studies and over 3,000 patients and 4,000 tumors. We identify four expression-based subtypes of metastasis transcending tumor lineage. The first subtype has extensive copy alterations, higher expression of MYC transcriptional targets and DNA repair genes, and bromodomain inhibitor response association. The second subtype has higher expression of genes involving metabolism and prostaglandin synthesis and regulation. The third subtype has evidence of neuronal differentiation, higher expression of DNA and histone methylation genes and EZH2 transcriptional targets, and BCL2 inhibitor response association. The fourth subtype has higher expression of immune checkpoint and Notch pathway genes. The metastasis subtypes reflect expression differences from paired primaries, with subtype switching being common. These subtypes facilitate understanding of the molecular underpinnings of metastases beyond tissue-oriented domains, with therapeutic implications.

Epigenomic charting and functional annotation of risk loci in renal cell carcinoma

While the mutational and transcriptional landscapes of renal cell carcinoma (RCC) are well-known, the epigenome is poorly understood. We characterize the epigenome of clear cell (ccRCC), papillary (pRCC), and chromophobe RCC (chRCC) by using ChIP-seq, ATAC-Seq, RNA-seq, and SNP arrays. We integrate 153 individual data sets from 42 patients and nominate 50 histology-specific master transcription factors (MTF) to define RCC histologic subtypes, including EPAS1 and ETS-1 in ccRCC, HNF1B in pRCC, and FOXI1 in chRCC. We confirm histology-specific MTFs via immunohistochemistry including a ccRCC-specific TF, BHLHE41. FOXI1 overexpression with knock-down of EPAS1 in the 786-O ccRCC cell line induces transcriptional upregulation of chRCC-specific genes, TFCP2L1, ATP6V0D2, KIT, and INSRR, implicating FOXI1 as a MTF for chRCC. Integrating RCC GWAS risk SNPs with H3K27ac ChIP-seq and ATAC-seq data reveals that risk-variants are significantly enriched in allelically-imbalanced peaks. This epigenomic atlas in primary human samples provides a resource for future investigation.

Copper Death Inducer, FDX1, as a Prognostic Biomarker Reshaping Tumor Immunity in Clear Cell Renal Cell Carcinoma

BACKGROUND

Progress in the diagnosis and treatment of clear cell renal cell carcinoma (ccRCC) has significantly prolonged patient survival. However, ccRCC displays an extreme heterogenous characteristic and metastatic tendency, which limit the benefit of targeted or immune therapy. Thus, identifying novel biomarkers and therapeutic targets for ccRCC is of great importance.

METHOD

Pan cancer datasets, including the expression profile, DNA methylation, copy number variation, and single nucleic variation, were introduced to decode the aberrance of copper death regulators (CDRs). Then, FDX1 was systematically analyzed in ccRCC to evaluate its impact on clinical characteristics, prognosis, biological function, immune infiltration, and therapy response. Finally, in vivo experiments were utilized to decipher FDX1 in ccRCC malignancy and its role in tumor immunity.

RESULT

Copper death regulators were identified at the pancancer level, especially in ccRCC. FDX1 played a protective role in ccRCC, and its expression level was significantly decreased in tumor tissues, which might be regulated via CNV events. At the molecular mechanism level, FDX1 positively regulated fatty acid metabolism and oxidative phosphorylation. In addition, FDX1 overexpression restrained ccRCC cell line malignancy and enhanced tumor immunity by increasing the secretion levels of IL2 and TNFγ.

CONCLUSIONS

Our research illustrated the role of FDX1 in ccRCC patients' clinical outcomes and its impact on tumor immunity, which could be treated as a promising target for ccRCC patients.

Renal Carcinoma and Angiogenesis: Therapeutic Target and Biomarkers of Response in Current Therapies

Due to the aberrant hypervascularization and the high immune infiltration of renal tumours, current therapeutic regimens of renal cell carcinoma (RCC) target angiogenic or immunosuppressive pathways or both. Tumour angiogenesis plays an essential role in tumour growth and immunosuppression. Indeed, the aberrant vasculature promotes hypoxia and can also exert immunosuppressive functions. In addition, pro-angiogenic factors, including VEGF-A, have an immunosuppressive action on immune cells. Despite the progress of treatments in RCC, there are still non responders or acquired resistance. Currently, no biomarkers are used in clinical practice to guide the choice between the different available treatments. Considering the role of angiogenesis in RCC, angiogenesis-related markers are interesting candidates. They have been studied in the response to antiangiogenic drugs (AA) and show interest in predicting the response. They have been less studied in immunotherapy alone or combined with AA. In this review, we will discuss the role of angiogenesis in tumour growth and immune escape and the place of angiogenesis-targeted biomarkers to predict response to current therapies in RCC.

Clinical proteomics towards multiomics in cancer

Recent technological advancements in mass spectrometry (MS)-based proteomics technologies have accelerated its application to study greater and greater numbers of human tumor specimens. Over the last several years, the Clinical Proteomic Tumor Analysis Consortium, the International Cancer Proteogenome Consortium, and others have generated MS-based proteomic profiling data combined with corresponding multiomics data on thousands of human tumors to date. Proteomic data sets in the public domain can be re-examined by other researchers with different questions in mind from what the original studies explored. In this review, we examine the increasing role of proteomics in studying cancer, along with the potential for previous studies and their associated data sets to contribute to improving the diagnosis and treatment of cancer in the clinical setting. We also explore publicly available proteomics and multi-omics data from cancer cell line models to show how such data may aid in identifying therapeutic strategies for cancer subsets.

Tumors masquerading as type 2 papillary renal cell carcinoma: pathologists' ever-expanding differential diagnosis for a heterogeneous group of entities

Although papillary renal cell carcinoma has historically been classified as either type 1 or type 2, data from The Cancer Genome Atlas (TCGA) has demonstrated significant genomic heterogeneity in tumors classified as "type 2 papillary renal cell carcinoma" (T2PRCC). Papillary renal cell carcinoma is expected to have a favorable clinical course compared to clear cell renal cell carcinoma (CCRCC). However, tumors with poor outcome more similar to CCRCC were included in the T2PRCC cohort studied by the TCGA. The differential diagnosis for T2PRCC includes a variety of other renal tumors, including aggressive entities such as TFE3 translocation-associated renal cell carcinoma, TFEB-amplified renal cell carcinoma, fumarate hydratase-deficient renal cell carcinoma, high-grade CCRCC, and collecting duct carcinoma. Accurate classification of these tumors is important for prognostication and selection of therapy.

HIRA loss transforms FH-deficient cells

Fumarate hydratase (FH) is a mitochondrial enzyme that catalyzes the reversible hydration of fumarate to malate in the tricarboxylic acid (TCA) cycle. Germline mutations of FH lead to hereditary leiomyomatosis and renal cell carcinoma (HLRCC), a cancer syndrome characterized by a highly aggressive form of renal cancer. Although HLRCC tumors metastasize rapidly, FH-deficient mice develop premalignant cysts in the kidneys, rather than carcinomas. How Fh1-deficient cells overcome these tumor-suppressive events during transformation is unknown. Here, we perform a genome-wide CRISPR-Cas9 screen to identify genes that, when ablated, enhance the proliferation of Fh1-deficient cells. We found that the depletion of the histone cell cycle regulator (HIRA) enhances proliferation and invasion of Fh1-deficient cells in vitro and in vivo. Mechanistically, Hira loss activates MYC and its target genes, increasing nucleotide metabolism specifically in Fh1-deficient cells, independent of its histone chaperone activity. These results are instrumental for understanding mechanisms of tumorigenesis in HLRCC and the development of targeted treatments for patients.

Dendritic Cells or Macrophages? The Microenvironment of Human Clear Cell Renal Cell Carcinoma Imprints a Mosaic Myeloid Subtype Associated with Patient Survival

Since their initial description by Elie Metchnikoff, phagocytes have sparked interest in a variety of biologic disciplines. These important cells perform central functions in tissue repair and immune activation as well as tolerance. Myeloid cells can be immunoinhibitory, particularly in the tumor microenvironment, where their presence is generally associated with poor patient prognosis. These cells are highly adaptable and plastic, and can be modulated to perform desired functions such as antitumor activity, if key programming molecules can be identified. Human clear cell renal cell carcinoma (ccRCC) is considered immunogenic; yet checkpoint blockades that target T cell dysfunction have shown limited clinical efficacy, suggesting additional layers of immunoinhibition. We previously described "enriched-in-renal cell carcinoma" (erc) DCs that were often found in tight contact with dysfunctional T cells. Using transcriptional profiling and flow cytometry, we describe here that ercDCs represent a mosaic cell type within the macrophage continuum co-expressing M1 and M2 markers. The polarization state reflects tissue-specific signals that are characteristic of RCC and renal tissue homeostasis. ErcDCs are tissue-resident with increasing prevalence related to tumor grade. Accordingly, a high ercDC score predicted poor patient survival. Within the profile, therapeutic targets (VSIG4, NRP1, GPNMB) were identified with promise to improve immunotherapy.

Cellular milieu in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is globally the most prevalent renal cancer. The cells of origin in ccRCC have been identified as proximal tubular epithelial cells (PTEC); however, the transcriptomic pathways resulting in the transition from normal to malignant PTEC state have remained unclear. Immunotherapy targeting checkpoints have revolutionized the management of ccRCC, but a sustained clinical response is achieved in only a minority of ccRCC patients. This indicates that our understanding of the mechanisms involved in the malignant transition and resistance to immune checkpoint therapy in ccRCC is unclear. This review examines recent single-cell transcriptomics studies of ccRCC to clarify the transition of PTEC in ccRCC development, and the immune cell types, states, and interactions that may limit the response to targeted immune therapy, and finally suggests stromal cells as key drivers in recurrent and locally invasive ccRCC. These and future single-cell transcriptomics studies will continue to clarify the cellular milieu in the ccRCC microenvironment, thus defining actional clinical, therapeutic, and prognostic characteristics of ccRCC.

Proteogenomic, Epigenetic, and Clinical Implications of Recurrent Aberrant Splice Variants in Clear Cell Renal Cell Carcinoma

BACKGROUND

Alternative mRNA splicing can be dysregulated in cancer, resulting in the generation of aberrant splice variants (SVs). Given the paucity of actionable genomic mutations in clear cell renal cell carcinoma (ccRCC), aberrant SVs may be an avenue to novel mechanisms of pathogenesis.

OBJECTIVE

To identify and characterize aberrant SVs enriched in ccRCC.

DESIGN, SETTING, AND PARTICIPANTS

Using RNA-seq data from the Cancer Cell Line Encyclopedia, we identified neojunctions uniquely expressed in ccRCC. Candidate SVs were then checked for expression across normal tissue in the Genotype-Tissue Expression Project and primary tumor tissue from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and our institutional Total Cancer Care database.

OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS

Clinicopathologic, genomic, and survival data were available for all cohorts. Epigenetic data were available for the TCGA and CPTAC cohorts. Proteomic data were available for the CPTAC cohort. The association of aberrant SV expression with these variables was examined using the Kruskal-Wallis test, pairwise t test, Spearman correlation test, and Cox regression analysis.

RESULTS AND LIMITATIONS

Our pipeline identified 16 ccRCC-enriched SVs. EGFR, HPCAL1-SV and RNASET2-SV expression was negatively correlated with gene-specific CpG methylation. We derived a survival risk score based primarily on the expression of five SVs (RNASET2, FGD1, PDZD2, COBLL1, and PTPN14), which was consistent and applicable across multiple cohorts on multivariate analysis. The splicing factor RBM4, which modulates splicing of HIF-1α, exhibited significantly lower expression at the protein level in the high-risk group, as defined by our SV-based score.

CONCLUSIONS

We describe 16 aberrant SVs enriched in ccRCC, many of which are associated with disease biology and/or clinical outcomes. This study provides a novel strategy for identifying and characterizing disease-specific aberrant SVs.

PATIENT SUMMARY

We describe a method to identify disease targets and biomarkers using transcriptomic analysis beyond somatic mutations or gene expression. Kidney tumors express unique splice variants that may provide additional prognostic information following surgery.

Accurate detection of benign and malignant renal tumor subtypes with MethylBoostER: An epigenetic marker-driven learning framework

Current gold standard diagnostic strategies are unable to accurately differentiate malignant from benign small renal masses preoperatively; consequently, 20% of patients undergo unnecessary surgery. Devising a more confident presurgical diagnosis is key to improving treatment decision-making. We therefore developed MethylBoostER, a machine learning model leveraging DNA methylation data from 1228 tissue samples, to classify pathological subtypes of renal tumors (benign oncocytoma, clear cell, papillary, and chromophobe RCC) and normal kidney. The prediction accuracy in the testing set was 0.960, with class-wise ROC AUCs >0.988 for all classes. External validation was performed on >500 samples from four independent datasets, achieving AUCs >0.89 for all classes and average accuracies of 0.824, 0.703, 0.875, and 0.894 for the four datasets. Furthermore, consistent classification of multiregion samples (N = 185) from the same patient demonstrates that methylation heterogeneity does not limit model applicability. Following further clinical studies, MethylBoostER could facilitate a more confident presurgical diagnosis to guide treatment decision-making in the future.

A novel molecular signature identifies mixed subtypes in renal cell carcinoma with poor prognosis and independent response to immunotherapy

Background

Renal cell carcinoma (RCC) is a heterogeneous disease comprising histologically defined subtypes. For therapy selection, precise subtype identification and individualized prognosis are mandatory, but currently limited. Our aim was to refine subtyping and outcome prediction across main subtypes, assuming that a tumor is composed of molecular features present in distinct pathological subtypes.

Methods

Individual RCC samples were modeled as linear combination of the main subtypes (clear cell (ccRCC), papillary (pRCC), chromophobe (chRCC)) using computational gene expression deconvolution. The new molecular subtyping was compared with histological classification of RCC using the Cancer Genome Atlas (TCGA) cohort (n = 864; ccRCC: 512; pRCC: 287; chRCC: 65) as well as 92 independent histopathologically well-characterized RCC. Predicted continuous subtypes were correlated to cancer-specific survival (CSS) in the TCGA cohort and validated in 242 independent RCC. Association with treatment-related progression-free survival (PFS) was studied in the JAVELIN Renal 101 (n = 726) and IMmotion151 trials (n = 823). CSS and PFS were analyzed using the Kaplan–Meier and Cox regression analysis.

Results

One hundred seventy-four signature genes enabled reference-free molecular classification of individual RCC. We unambiguously assign tumors to either ccRCC, pRCC, or chRCC and uncover molecularly heterogeneous tumors (e.g., with ccRCC and pRCC features), which are at risk of worse outcome. Assigned proportions of molecular subtype-features significantly correlated with CSS (ccRCC (P = 4.1E − 10), pRCC (P = 6.5E − 10), chRCC (P = 8.6E − 06)) in TCGA. Translation into a numerical RCC-R(isk) score enabled prognosis in TCGA (P = 9.5E − 11). Survival modeling based on the RCC-R score compared to pathological categories was significantly improved (P = 3.6E − 11). The RCC-R score was validated in univariate (P = 3.2E − 05; HR = 3.02, 95% CI: 1.8–5.08) and multivariate analyses including clinicopathological factors (P = 0.018; HR = 2.14, 95% CI: 1.14–4.04). Heterogeneous PD-L1-positive RCC determined by molecular subtyping showed increased PFS with checkpoint inhibition versus sunitinib in the JAVELIN Renal 101 (P = 3.3E − 04; HR = 0.52, 95% CI: 0.36 − 0.75) and IMmotion151 trials (P = 0.047; HR = 0.69, 95% CI: 0.48 − 1). The prediction of PFS significantly benefits from classification into heterogeneous and unambiguous subtypes in both cohorts (P = 0.013 and P = 0.032).

Conclusion

Switching from categorical to continuous subtype classification across most frequent RCC subtypes enables outcome prediction and fosters personalized treatment strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13073-022-01105-y.

Integrated TCR repertoire analysis and single-cell transcriptomic profiling of tumor-infiltrating T cells in renal cell carcinoma identifies shared and tumor-restricted expanded clones with unique phenotypes

Objective responses of metastatic renal cell carcinoma (RCC) associated with systemic immunotherapies suggest the potential for T-cell-mediated tumor clearance. Recent analyses associate clonally expanded T cells present in the tumor at diagnosis with responses to immune checkpoint inhibitors (ICIs). To identify and further characterize tumor-associated, clonally expanded T cells, we characterized the density, spatial distribution, T-cell receptor (TCR) repertoire, and transcriptome of tumor-infiltrating T cells from 14 renal tumors at the time of resection and compared them with T cells in peripheral blood and normal adjacent kidney. Multiplex immunohistochemistry revealed that T-cell density was higher in clear cell RCC (ccRCC) than in other renal tumor histologies with spatially nonuniform T-cell hotspots and exclusion zones. TCR repertoire analysis also revealed increased clonal expansion in ccRCC tumors compared with non-clear cell histologies or normal tissues. Expanded T-cell clones were most frequently CD8+ with some detectable in peripheral blood or normal kidney and others found exclusively within the tumor. Divergent expression profiles for chemokine receptors and ligands and the Ki67 proliferation marker distinguished tumor-restricted T-cell clones from those also present in blood suggesting a distinct phenotype for subsets of clonally expanded T cells that also differed for upregulated markers of T-cell activation and exhaustion. Thus, our single-cell level stratification of clonally expanded tumor infiltrating T-cell subpopulations provides a framework for further analysis. Future studies will address the spatial orientation of these clonal subsets within tumors and their association with treatment outcomes for ICIs or other therapeutic modalities.

Driver Gene Alterations in Malignant Progression of Gastric Cancer

The identification of driver genes is of great importance in modern medical research. It is also an essential factor in the development of individualization and has a positive effect on understanding the causes of cancer. Gene mutations are the primary cause of the outcomes of the process of tumorigenesis. Driver genes can be used as therapeutic targets for tumor-specific mutation-dependent overexpression. This study sought to identify mutation-based driver genes in gastric cancer (GC) by applying comprehensive gene expression and copy number analysis. Multiplatform analysis was used to identify four major genomic subtypes of GC. The most prominent cancer-related variations observed in this cohort were TTN mutations (found in 56% of tumors), followed by TP53 (51%), MUC16 (7%), and LRP1B (6%) mutations. In our analysis, mutation characteristics were mainly related to the DNA mismatch repair system. In addition, 34 candidate driver oncogenes were identified in GC. Further research identified six GC-related driver genes associated with the levels of immune infiltration of different immune cells and the majority of immune markers. Our mutation-based study of driver oncogenes identified potential drug targets in GC.

Vasopressin Receptor Type-2 Mediated Signaling in Renal Cell Carcinoma Stimulates Stromal Fibroblast Activation

Vasopressin type-2 receptor (V2R) is ectopically expressed and plays a pathogenic role in clear cell renal cell carcinoma (ccRCC) tumor cells. Here we examined how V2R signaling within human ccRCC tumor cells (Caki1 cells) stimulates stromal cancer-associated fibroblasts (CAFs). We found that cell culture conditioned media from Caki1 cells increased activation, migration, and proliferation of fibroblasts in vitro, which was inhibited by V2R gene silencing in Caki1 cells. Analysis of the conditioned media and mRNA of the V2R gene silenced and control Caki1 cells showed that V2R regulates the production of CAF-activating factors. Some of these factors were also found to be regulated by YAP in these Caki1 cells. YAP expression colocalized and correlated with V2R expression in ccRCC tumor tissue. V2R gene silencing or V2R antagonist significantly reduced YAP in Caki1 cells. Moreover, the V2R antagonist reduced YAP expression and myofibroblasts in mouse xenograft tumors. These results suggest that V2R plays an important role in secreting pro-fibrotic factors that stimulate fibroblast activation by a YAP-dependent mechanism in ccRCC tumors. Our results demonstrate a novel role for the V2R-YAP axis in the regulation of myofibroblasts in ccRCC and a potential therapeutic target.

Clinical, Brain, and Multilevel Clustering in Early Psychosis and Affective Stages

Importance

Approaches are needed to stratify individuals in early psychosis stages beyond positive symptom severity to investigate specificity related to affective and normative variation and to validate solutions with premorbid, longitudinal, and genetic risk measures.

Objective

To use machine learning techniques to cluster, compare, and combine subgroup solutions using clinical and brain structural imaging data from early psychosis and depression stages.

Design, Setting, and Participants

A multisite, naturalistic, longitudinal cohort study (10 sites in 5 European countries; including major follow-up intervals at 9 and 18 months) with a referred patient sample of those with clinical high risk for psychosis (CHR-P), recent-onset psychosis (ROP), recent-onset depression (ROD), and healthy controls were recruited between February 1, 2014, to July 1, 2019. Data were analyzed between January 2020 and January 2022.

Main Outcomes and Measures

A nonnegative matrix factorization technique separately decomposed clinical (287 variables) and parcellated brain structural volume (204 gray, white, and cerebrospinal fluid regions) data across CHR-P, ROP, ROD, and healthy controls study groups. Stability criteria determined cluster number using nested cross-validation. Validation targets were compared across subgroup solutions (premorbid, longitudinal, and schizophrenia polygenic risk scores). Multiclass supervised machine learning produced a transferable solution to the validation sample.

Results

There were a total of 749 individuals in the discovery group and 610 individuals in the validation group. Individuals included those with CHR-P (n = 287), ROP (n = 323), ROD (n = 285), and healthy controls (n = 464), The mean (SD) age was 25.1 (5.9) years, and 702 (51.7%) were female. A clinical 4-dimensional solution separated individuals based on positive symptoms, negative symptoms, depression, and functioning, demonstrating associations with all validation targets. Brain clustering revealed a subgroup with distributed brain volume reductions associated with negative symptoms, reduced performance IQ, and increased schizophrenia polygenic risk scores. Multilevel results distinguished between normative and illness-related brain differences. Subgroup results were largely validated in the external sample.

Conclusions and Relevance

The results of this longitudinal cohort study provide stratifications beyond the expression of positive symptoms that cut across illness stages and diagnoses. Clinical results suggest the importance of negative symptoms, depression, and functioning. Brain results suggest substantial overlap across illness stages and normative variation, which may highlight a vulnerability signature independent from specific presentations. Premorbid, longitudinal, and genetic risk validation suggested clinical importance of the subgroups to preventive treatments.

PBX1, EMCN and ERG are associated with the sub-clusters and the prognosis of VHL mutant clear cell renal cell carcinoma

The molecular heterogeneity of primary clear cell renal cell carcinoma (ccRCC) has been reported. However, the classifications of Von Hippel-Lindau (VHL) mutant ccRCC are unclear. Here, VHL mutant ccRCC from The Cancer Genome Atlas and E-MTAB-1980 datasets were divided into two sub-clusters through non-negative matrix factorization algorithm. Most VHL mutant ccRCC patients in sub-cluster2 were with pathological T1 stage and VHL mutant ccRCC patients in sub-cluster1 were with decreased overall survival. DNA replication and homologous recombination scores were higher, while, WNT signaling pathway and regulation of autophagy scores were lower in sub-cluster1 VHL mutant ccRCC. Moreover, PBX1 transcriptional scores and mRNA expressions were lower in sub-cluster1 VHL mutant ccRCC patients and were associated with the overall survival of VHL mutant ccRCC. Furthermore, PBX1 associated genes EMCN and ERG were down-regulated in sub-cluster1 VHL mutant ccRCC and overall survival was decreased in EMCN or ERG lowly expressed VHL mutant ccRCC patients. Also, PBX1 and EMCN were down-regulated in ccRCC tissues, compared with normal kidney tissues. At last, we constructed risk models based on PBX1, EMCN and EGR expression features. With the increase of the risk score, the number of death of VHL mutant ccRCC patients was increased.

Proteogenomic characterization of 2002 human cancers reveals pan-cancer molecular subtypes and associated pathways

Mass-spectrometry-based proteomic data on human tumors—combined with corresponding multi-omics data—present opportunities for systematic and pan-cancer proteogenomic analyses. Here, we assemble a compendium dataset of proteomics data of 2002 primary tumors from 14 cancer types and 17 studies. Protein expression of genes broadly correlates with corresponding mRNA levels or copy number alterations (CNAs) across tumors, but with notable exceptions. Based on unsupervised clustering, tumors separate into 11 distinct proteome-based subtypes spanning multiple tissue-based cancer types. Two subtypes are enriched for brain tumors, one subtype associating with MYC, Wnt, and Hippo pathways and high CNA burden, and another subtype associating with metabolic pathways and low CNA burden. Somatic alteration of genes in a pathway associates with higher pathway activity as inferred by proteome or transcriptome data. A substantial fraction of cancers shows high MYC pathway activity without MYC copy gain but with mutations in genes with noncanonical roles in MYC. Our proteogenomics survey reveals the interplay between genome and proteome across tumor lineages.

Pan-cancer proteomics analysis enables the analysis of protein expression across multiple cancer types. Here, the authors compare proteomics from 14 cancer types and show 11 distinct subtypes across multiple cancer types. Proteome data could link higher pathway activity levels with somatic alteration of specific genes in the pathway.

The genomic landscape of pediatric renal cell carcinomas

Pediatric renal cell carcinomas (RCC) differ from their adult counterparts not only in histologic subtypes but also in clinical characteristics and outcome. However, the underlying biology is still largely unclear. For this reason, we performed whole-exome and transcriptome sequencing analyses on a cohort of 25 pediatric RCC patients with various histologic subtypes, including 10 MiT family translocation (MiT) and 10 papillary RCCs. In this cohort of pediatric RCC, we find only limited genomic overlap with adult RCC, even within the same histologic subtype. Recurrent somatic mutations in genes not previously reported in RCC were detected, such as in CCDC168, PLEKHA1, VWF, and MAP3K9. Our papillary pediatric RCCs, which represent the largest cohort to date with comprehensive molecular profiling in this age group, appeared as a distinct genomic subtype differing in terms of gene mutations and gene expression patterns not only from MiT-RCC but also from their adult counterparts.

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WES and RNA-seq of 25 pediatric RCCs with various histologic subtypes

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Detected only limited genomic overlap with adult RCC

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Revealed recurrent somatic mutations in genes not previously reported in RCC

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Discovery of a CRK-PITPNA fusion gene in a pediatric papillary RCC

Size‑based isolation and detection of renal carcinoma cells from whole blood

Renal cell carcinoma (RCC) is a tumour type with an indolent growth pattern and rather vague symptoms. The present study developed a platform for liquid biopsy of RCC based upon the isolation of circulating tumour cells (CTCs). Founded on the observation that RCC tumour cells are considerably larger than leucocytes, the present study employed a microfluidics-based system for isolation of RCC CTCs from whole blood. Using this system, it was revealed that 66% of spiked-in RCC tumour cells could be retrieved using this approach. Furthermore, it was demonstrated that these cells could be molecularly detected with digital PCR using RCC-specific genes down to one tumour cell, whilst avoiding detection in samples lacking tumour cells. Finally, subtype specific transcripts were identified to distinguish the different subtypes of RCC, which were then validated in patient tumours. The present study established a novel workflow for the isolation of RCC CTCs from whole blood, with the potential to detect these cells irrespective of subtype.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

Fumarate inhibits PTEN to promote tumorigenesis and therapeutic resistance of type2 papillary renal cell carcinoma

Fumarate is an oncometabolite. However, the mechanism underlying fumarate-exerted tumorigenesis remains unclear. Here, utilizing human type2 papillary renal cell carcinoma (PRCC2) as a model, we show that fumarate accumulates in cells deficient in fumarate hydratase (FH) and inhibits PTEN to activate PI3K/AKT signaling. Mechanistically, fumarate directly reacts with PTEN at cysteine 211 (C211) to form S-(2-succino)-cysteine. Succinated C211 occludes tethering of PTEN with the cellular membrane, thereby diminishing its inhibitory effect on the PI3K/AKT pathway. Functionally, re-expressing wild-type FH or PTEN C211S phenocopies an AKT inhibitor in suppressing tumor growth and sensitizing PRCC2 to sunitinib. Analysis of clinical specimens indicates that PTEN C211 succination levels are positively correlated with AKT activation in PRCC2. Collectively, these findings elucidate a non-metabolic, oncogenic role of fumarate in PRCC2 via direct post-translational modification of PTEN and further reveal potential stratification strategies for patients with FH loss by combinatorial AKTi and sunitinib therapy.

Epstein-Barr virus infection is associated with the nuclear factor-kappa B p65 signaling pathway in renal cell carcinoma

BACKGROUND

There have been few studies regarding viral involvement in the pathogenesis of renal cell carcinoma (RCC). The aim of this study was to examine the possible association of Epstein-Barr virus (EBV) infection with clinicopathological features and cellular biomarkers including p53, p16INK4a, Ki-67 and nuclear factor-kappa B (NF-κB) in RCC tumors.

METHODS

In this prospective study, 122 histologically confirmed Formalin-fixed Paraffin-embedded RCC tissue specimens along with 96 specimens of their corresponding peritumoral tissues and 23 samples of blunt renal injuries were subjected to nested polymerase chain reaction (nPCR) in order to amplify EBV DNA sequences. The expression of p53, p16INK4a, Ki-67 and NF-κB was investigated by immunohistochemistry (IHC) assay. Statistical analysis was employed to demonstrate the possible associations.

RESULTS

Infection with EBV was found to be significantly associated with RCC. Our results indicate that p65 NF-κB signaling pathway is probably involved in EBV-mediated RCC pathogenesis. Moreover, we found p53, Ki-67 and cytoplasmic NF-κB expression to be associated with tumor nuclear grade in RCC patients. The expression of p53 and Ki-67 was associated with primary tumor category as well. In addition, p53 overexpression was significantly more frequent among nonconventional RCC tumors than the conventional histologic type.

CONCLUSIONS

Infection with EBV is likely to play an important role in the development of RCC through the constitutive and permanent activation of NF-κB p65 signaling pathway. However, more experiments and supporting data are required to reach a decisive conclusion.

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.

Identification of Molecular Subtypes in Head and Neck Squamous Cell Carcinoma Based on Dysregulated Immune LncRNAs

Long noncoding RNAs (lncRNAs) perform indispensable functions in cancer pathologies and are involved in the onset and progression of multiple cancers. Multiple platforms were performed to comprehensively analyze the head and neck squamous cell carcinoma (HNSCC) for determining molecular subtypes. Molecular subtypes were clustered and analyzed by the “ConsensusClusterPlus” R package. The Limma software was utilized to screen for differentially expressed genes (DEGs). Functional enrichment analyses, including Gene Set Enrichment Analysis (GSEA), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO), were performed on the three database resources. Seventeen lncRNAs were determined as HNSCC-specific immune lncRNAs that were dysregulated. Our research identified and redefined two distinct molecular subtypes, C1 (230 samples) and C2 (269 samples). Moreover, the C1 subtype had a higher survival rate than the C2 subtype in HNSCC samples, as well as a prolonged median survival duration with activated immune response. 1531 DEGs, including 529 upmodulated genes and 1002 downmodulated genes, were identified in the above two subtypes. Functional enrichment analysis revealed that upmodulated genes in C2 were associated with tumorigenesis and development, while downregulated genes in C2 were associated with immune response. By comparing with the existing immunophenotyping group, it found that C1 had more overlaps with the existing Atypical and Basal, and C2 and Classical and Mesenchymal had a high degree of coincidence. On the basis of lncRNA, there were significant differences in the aspect of prognostic and immunological characteristics in the two identified molecular subtypes of HNSCC.

Single-cell chromatin accessibility landscape in kidney identifies additional cell-of-origin in heterogenous papillary renal cell carcinoma

Papillary renal cell carcinoma (pRCC) is the most heterogenous renal cell carcinoma. Patient survival varies and no effective therapies for advanced pRCC exist. Histological and molecular characterization studies have highlighted the heterogeneity of pRCC tumours. Recent studies identified the proximal tubule (PT) cell as a cell-of-origin for pRCC. However, it remains elusive whether other pRCC subtypes have different cell-of-origin. Here, by obtaining genome-wide chromatin accessibility profiles of normal human kidney cells using single-cell transposase-accessible chromatin-sequencing and comparing the profiles with pRCC samples, we discover that besides PT cells, pRCC can also originate from kidney collecting duct principal cells. We show pRCCs with different cell-of-origin exhibit different molecular characteristics and clinical behaviors. Further, metabolic reprogramming appears to mediate the progression of pRCC to the advanced state. Here, our results suggest that determining cell-of-origin and monitoring origin-dependent metabolism could potentially be useful for early diagnosis and treatment of pRCC.