Improving our understanding of papillary renal cell carcinoma with integrative genomic analysis

Integrative analysis of DNA methylation and gene expression in papillary renal cell carcinoma

Patterns of DNA methylation are significantly altered in cancers. Interpreting the functional consequences of DNA methylation requires the integration of multiple forms of data. The recent advancement in the next-generation sequencing can help to decode this relationship and in biomarker discovery. In this study, we investigated the methylation patterns of papillary renal cell carcinoma (PRCC) and its relationship with the gene expression using The Cancer Genome Atlas (TCGA) multi-omics data. We found that the promoter and body of tumor suppressor genes, microRNAs and gene clusters and families, including cadherins, protocadherins, claudins and collagens, are hypermethylated in PRCC. Hypomethylated genes in PRCC are associated with the immune function. The gene expression of several novel candidate genes, including interleukin receptor IL17RE and immune checkpoint genes HHLA2, SIRPA and HAVCR2, shows a significant correlation with DNA methylation. We also developed machine learning models using features extracted from single and multi-omics data to distinguish early and late stages of PRCC. A comparative study of different feature selection algorithms, predictive models, data integration techniques and representations of methylation data was performed. Integration of both gene expression and DNA methylation features improved the performance of models in distinguishing tumor stages. In summary, our study identifies PRCC driver genes and proposes predictive models based on both DNA methylation and gene expression. These results on PRCC will aid in targeted experiments and provide a strategy to improve the classification accuracy of tumor stages.

Genomic landscape analyses of reprogrammed cells using integrative and non-integrative methods reveal variable cancer-associated alterations

Recent development of cell reprogramming technologies brought a major hope for future cell therapy applications by the use of these cells or their derivatives. For this purpose, one of the major requirements is the absence of genomic alterations generating a risk of cell transformation. Here we analyzed by microarray-based comparative genomic hybridization human iPSC generated by two non-integrative and one integrative method at pluripotent stage as well as in corresponding teratomas. We show that all iPSC lines exhibit copy number variations (CNV) of several genes deregulated in oncogenesis. These cancer-associated genomic alterations were more pronounced in virally programmed hiPSCs and their derivative teratoma as compared to those found in iPSC generated by mRNA-mediated reprogramming. Bioinformatics analysis showed the involvement of these genes in human leukemia and carcinoma. We conclude that genetic screening should become a standard procedure to ensure that hiPSCs are free from cancer-associated genomic alterations before clinical use.

The molecular characterization and therapeutic strategies of papillary renal cell carcinoma

Introduction: Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behavior. In this review, we summarize the current progression on pRCC in molecular level. Our findings highlight the need for molecular markers to accurately subtype pRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Areas covered: This review highlights the need for molecular markers to accurately subtype PRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Expert commentary: There are mainly two subtypes of pRCC based on histology. However, little is known about the genetic characterization of the sporadic forms of pRCC and there are currently no standard forms of therapy for patients with advanced disease. Both MET inhibitors and immunotherapy may be effective in advanced pRCC treatment. Therefore, understanding the molecular basis of pRCC and identifying the main goal of treatment is crucial for the selection of the best strategy.

High proliferation rate and TNM stage but not histomorphological subtype are independent prognostic markers for overall survival in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is currently divided in 2 subtypes. We reviewed a large cohort of PRCC and correlated subtype, morphological features and diagnostic marker expression with overall survival (OS) to uncover differences between the 2 subtypes. Three hundred seventy-six renal tumors initially diagnosed as PRCC with clinical and survival data were collected from the participating centers. Two hundred forty-six tumors were classified as PRCC1 (65.4%) and 130 as PRCC2 (34.6%) and graded according to the 2016 World Health Organization/International Society of Urological Pathology grading system. Morphological features (abundant cytoplasm, necrosis, fibrous stroma, foamy macrophages and psammoma bodies) were noted. Immunohistochemical stains (MIB1, p53, Racemase, EMA, CK7, CK20, E-Cadherin) were performed using tissue microarrays. χ²-Tests, log-rank tests and uni- and multivariate Cox regression analysis were performed. Both subtypes displayed different morphological features and immunohistochemical profiles: abundant cytoplasm was more frequent in PRCC2, while foamy macrophages were more common in PRCC1. Abundant cytoplasm and presence of psammoma bodies were associated with poorer OS. PRCC1 showed more frequent CK7 expression, PRCC2 more frequent E-Cadherin, p53 and higher MIB1 expression (>15%). Expression of Racemase and CK7 was associated with better OS, while high MIB1 (>15%) was associated with poorer OS. In multivariate analysis, the only independent predictors of OS were proliferation (MIB1), tumor stage, metastasis and age at surgery. Subtype was not an independent prognostic factor. Therefore, PRCC subtype on its own is not suitable for estimating survival. More data focusing on PRCC tumor biology is needed to define prognostic subgroups, especially in PRCC2.

Machine learning models to predict the progression from early to late stages of papillary renal cell carcinoma

Papillary Renal Cell Carcinoma (PRCC) is a heterogeneous disease with variations in disease progression and clinical outcomes. The advent of next generation sequencing techniques (NGS) has generated data from patients that can be analysed to develop a predictive model. In this study, we have adopted a machine learning approach to identify biomarkers and build classifiers to discriminate between early and late stages of PRCC from gene expression profiles. A machine learning pipeline incorporating different feature selection algorithms and classification models is developed to analyse RNA sequencing dataset (RNASeq). Further, to get a reliable feature set, we extracted features from different partitions of the training dataset and aggregated them into feature sets for classification. We evaluated the performance of different algorithms on the basis of 10-fold cross validation and independent test dataset. 10-fold cross validation was also performed on a microarray dataset of PRCC. A random forest based feature selection (varSelRF) yielded minimum number of features (104) and a best performance with area under Precision Recall curve (PR-AUC) of 0.804, MCC (Matthews Correlation Coefficient) of 0.711 and accuracy of 88% with Shrunken Centroid classifier on a test dataset. We identified 80 genes that are consistently altered between stages by different feature selection algorithms. The extracted features are related to cellular components - centromere, kinetochore and spindle, and biological process mitotic cell cycle. These observations reveal potential mechanisms for an increase in chromosome instability in the late stage of PRCC. Our study demonstrates that the gene expression profiles can be used to classify stages of PRCC.

Identifying biomarkers of papillary renal cell carcinoma associated with pathological stage by weighted gene co-expression network analysis

Although papillary renal cell carcinoma (PRCC) accounts for 10%–15% of renal cell carcinoma (RCC), no predictive molecular biomarker is currently applicable to guiding disease stage of PRCC patients. The mRNASeq data of PRCC and adjacent normal tissue in The Cancer Genome Atlas was analyzed to identify 1148 differentially expressed genes, on which weighted gene co-expression network analysis was performed. Then 11 co-expressed gene modules were identified. The highest association was found between blue module and pathological stage (r = 0.45) by Pearson's correlation analysis. Functional enrichment analysis revealed that biological processes of blue module focused on nuclear division, cell cycle phase, and spindle (all P < 1e-10). All 40 hub genes in blue module can distinguish localized (pathological stage I, II) from non-localized (pathological stage III, IV) PRCC (P < 0.01). A good molecular biomarker for pathological stage of RCC must be a prognostic gene in clinical practice. Survival analysis was performed to reversely validate if hub genes were associated with pathological stage. Survival analysis unveiled that all hub genes were associated with patient prognosis (P < 0.01). The validation cohort GSE2748 verified that 30 hub genes can differentiate localized from non-localized PRCC (P < 0.01), and 18 hub genes are prognosis-associated (P < 0.01).

ROC curve indicated that the 17 hub genes exhibited excellent diagnostic efficiency for localized and non-localized PRCC (AUC > 0.7). These hub genes may serve as a biomarker and help to distinguish different pathological stages for PRCC patients.

Renal cell carcinoma: the search for a reliable biomarker

One particular challenge in the treatment of kidney tumors is the range of histologies and tumor phenotypes a renal mass can represent. A kidney tumor can range from benign (e.g., oncocytoma) to a clinically indolent malignancy (e.g., papillary type I, chromophobe) to aggressive disease [e.g., papillary type II or high-grade clear cell renal cell carcinoma (ccRCC)]. Even among various subtypes, kidney cancers are genetically diverse with variable prognoses and treatment response rates. Therefore, the key to proper treatment is the differentiation of these subtypes. Currently, a wide array of diagnostic, prognostic, and predictive biomarkers exist that may help guide the individualized care of kidney cancer patients. This review will discuss the various serum, urine, imaging, and immunohistological biomarkers available in practice.

Renal cell carcinoma: molecular characterization and evolving treatment paradigms

PURPOSE OF REVIEW

The treatment landscape of advanced renal cell carcinoma (RCC) continues to shift as both new targeted therapies and immunotherapies show efficacy in treating the disease. Contemporary insights into the molecular characterization of RCC are likely to fuel the development of additional therapies. This review summarizes recent advancements in the biologic characterization of RCC and discusses newly approved therapies and ongoing studies in the treatment of advanced RCC.

RECENT FINDINGS

The Cancer Genome Atlas has now completed comprehensive molecular characterization of clear cell, papillary, and chromophobe RCC, providing insights into the biology of these entities. Two new 'targeted' therapies, cabozantinib and lenvatinib, as well as a novel immune checkpoint inhibitor, the programed death 1 inhibitor nivolumab, have recently been approved for the treatment of metastatic RCC. Although some of these newer therapies are associated with prolongation of survival, there are few long-term responders and the quest for more durable treatment strategies continues.

SUMMARY

The addition of several new agents effective in metastatic RCC has resulted in improvements in overall survival; however, there are few avenues to durable responses or cure. Ongoing studies as well advances in our understanding of the molecular alterations underlying distinct forms of RCC promise further therapeutic advances and have the potential to alter the current treatment paradigm.

The radiologist's role in the management of papillary renal cell carcinoma

Papillary carcinoma is the second most common renal cell carcinoma. It has a better prognosis than the more frequent clear cell carcinoma, although this does not hold true for advanced cases, because no specific treatment exists. It presents as a circumscribed peripheral tumor (small and homogeneously solid or larger and cystic/hemorrhagic) or as an infiltrating lesion that invades the veins, which has a worse prognosis. Due to their low vascular density, papillary renal cell carcinomas enhance less than other renal tumors, and this facilitates their characterization. On computed tomography, they might not enhance conclusively, and in these cases they are impossible to distinguish from hyperattenuating cysts. Contrast-enhanced ultrasonography and magnetic resonance imaging are more sensitive for detecting vascularization. Other characteristics include a specific vascular pattern, hypointensity on T2-weighted images, restricted water diffusion, and increased signal intensity in opposed phase images. We discuss the genetic, histologic, clinical, and radiological aspects of these tumors in which radiologists play a fundamental role in management.