A large‐scale assay library for targeted protein quantification in renal cell carcinoma tissues

PROTEOMICS ◽  
2021 ◽  
pp. 2100228
Author(s):  
Petr Lapcik ◽  
Lucia Janacova ◽  
Pavla Bouchalova ◽  
David Potesil ◽  
Jan Podhorec ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Protein Quantification

Exploiting Synthetic Lethal Network for Precision Treatment of Clear Cell Renal Cell Carcinoma

2021 ◽  
Author(s):  
Zhicheng Liu ◽  
Dongxu Lin ◽  
Linmeng Zhang ◽  
Chen Yang ◽  
Bin Guo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Target Genes ◽  
Clear Cell ◽  
Therapeutic Potential ◽  
Synthetic Lethality ◽  
Treatment Modalities ◽  
Cell Renal Cell Carcinoma

Abstract Background The emerging of targeted therapies has revolutionized the treatment modalities of advanced clear cell renal cell carcinoma (ccRCC) over the past fifteen years. However, lack of personalized treatment limits the development of effective clinical guidelines and improvement of patient prognosis. In this study, large-scale genomic profiles of ccRCC cohorts were exploited for conducting an integrative analysis. Method Based on synthetic lethality (SL), a concept that simultaneous losses of two genes cause cell death while a single loss does not, we sought to develop a computational pipeline to infer potential SL partners of ccRCC. Drug response prediction were received from three pharmacological databases to select agents which are likely to be effective in precisely treating patients with target gene mutation. Results We developed a credible method to identify SL pairs and determined a list of 72 candidate pairs which might be utilized to selectively eliminate tumors with genetic aberrations through SL partners of specific mutations. Further analysis identified BRD4 and PRKDC as novel medicine targets for patients with BAP1 mutations. After mapping these target genes to comprehensive drug datasets, two agents (BI-2536 and PI-103) were found to have considerable therapeutic potential in BAP1 mutant tumors. Conclusion Overall, our findings provide insight into the overview of ccRCC mutation patterns and offer novel opportunities for improving individualized cancer treatment.

scholarly journals The epigenetic landscape of clear-cell renal cell carcinoma

2015 ◽  
Vol 2 (3) ◽  
pp. 90-104 ◽  
Author(s):  
Katarzyna Kluzek ◽  
Hans Antonius Bluyssen ◽  
Joanna Wesoly
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Kidney Tumors ◽  
Cell Renal Cell Carcinoma ◽  
Chromosome 3P ◽  
Recurrent Mutations ◽  
Novel Therapeutic Strategies

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of all kidney tumors. During the last few years, epigenetics has emerged as an important mechanism in ccRCC pathogenesis. Recent reports, involving large-scale methylation and sequencing analyses, have identified genes frequently inactivated by promoter methylation and recurrent mutations in genes encoding chromatin regulatory proteins. Interestingly, three of detected genes (PBRM1, SETD2 and BAP1) are located on chromosome 3p, near the VHL gene, inactivated in over 80% ccRCC cases. This suggests that 3p alterations are an essential part of ccRCC pathogenesis. Moreover, most of the proteins encoded by these genes cooperate in histone H3 modifications. The aim of this review is to summarize the latest discoveries shedding light on deregulation of chromatin machinery in ccRCC. Newly described ccRCC-specific epigenetic alterations could potentially serve as novel diagnostic and prognostic biomarkers and become an object of novel therapeutic strategies.

scholarly journals A 2/1 sunitinib dosing schedule provides superior antitumor effectiveness and less toxicity than a 4/2 schedule for metastatic renal cell carcinoma: A systematic review and meta-analysis

2019 ◽  
Author(s):  
Huan Deng ◽  
Lianli Zeng ◽  
Qian Wu ◽  
Li Wang ◽  
Zhengdong Hong ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Meta Analysis ◽  
Objective Response ◽  
Initial Dosage ◽  
Cochrane Library ◽  
Platelet Disorder

Abstract Background The standard sunitinib schedule to treat metastatic renal cell carcinoma (mRCC) is 4 weeks on/2 weeks off (4/2). However, some studies revealed intolerable adverse events (AEs) in patients on this schedule. An alternative schedule, 2 weeks on/1 week off (2/1), may overcome this issue. This meta-analysis was performed to compare the effectiveness and toxicity between the 2/1 and 4/2 sunitinib dosing schedules. Methods We acquired relevant studies by searching PubMed, ScienceDirect, the Cochrane Library, Scopus, Ovid MEDLINE, Embase, Web of Science, and Google Scholar. Our main endpoints included overall survival (OS), progression-free survival (PFS), objective response rate (ORR), disease control rate (DCR), and AEs. Results We identified 9 medium- and high-quality studies. Both schedules were effective for mRCC, with comparable OS and similar ORR. However, the 2/1 schedule had better PFS (hazard ratio (HR) = 0.81, 95% confidence interval [CI]: 0.66-0.99, P= 0.04), higher DCR (risk rate (RR) = 1.22, 95% CI: 1.01-1.47, P= 0.04) and fewer dosage interruptions (RR= 0.60, 95% CI: 0.43-0.84, P= 0.003). Additionally, the 2/1 schedule elicited fewer specific severe AEs, including thrombocytopenia/platelet disorder, hand-foot syndrome, hypertension and fatigue. In our subanalysis, PFS was better among East Asians using the 2/1 schedule than among other populations (HR= 0.75, 95% CI: 0.58-0.98, P= 0.03), and patients administered an initial dosage of 50 mg/d on the 2/1 schedule had superior PFS (HR= 0.76, 95% CI: 0.59-0.97, P= 0.03) than those others. Conclusions These findings suggest that the 2/1 schedule is more suitable for mRCC than 4/2, due to superior PFS, better DCR and fewer AEs. Nevertheless, more large-scale studies with good quality are needed.

scholarly journals Machine Learning Approach to Predict the Probability of Recurrence after Surgery for Renal Cell Carcinoma: Prediction Model Development Study (Preprint)

2020 ◽  
Author(s):  
HyungMin Kim ◽  
Sun Jung Lee ◽  
So Jin Park ◽  
In Young Choi ◽  
Sung-Hoo Hong
Keyword(s):  
Machine Learning ◽  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Tertiary Care ◽  
Analytical Data ◽  
Machine Learning Algorithms ◽  
High Recurrence Rate ◽  
A Value

BACKGROUND Renal cell carcinoma (RCC) has a high recurrence rate of 20–30 % after nephrectomy for clinically localized disease, and more than 40 % of patients eventually die of the disease, making regular monitoring and constant management of utmost importance. OBJECTIVE The objective of this study was to develop an algorithm that predicts the probability of recurrence within 5 and 10 years of RCC. METHODS Data from 6,849 Korean RCC patients were collected from 8 tertiary care hospitals listed in the KOrean Renal Cell Carcinoma (KORCC) web-based database (DB). To predict RCC recurrence, 2,814 analytical data were extracted from the DB. Eight machine learning algorithms were used to predict the probability of RCC recurrence, and the results were compared. RESULTS Within five years of surgery, the highest area under the receiver operating characteristic curve (AUROC) was obtained from the naive Bayes (NB) model, with a value of 0.836. Within 10 years of surgery, the highest AUROC was obtained from the NB model, with a value of 0.784. CONCLUSIONS An algorithm was developed that predicts the probability of RCC recurrence within 5 and 10 years using the KORCC DB, a large-scale RCC cohort in Korea. It is expected that the developed algorithm will help clinicians manage prognosis and establish customized treatment strategies for patients with RCC after surgery.

scholarly journals A Proteogenomic Atlas of Clear Cell Renal Cell Carcinoma in a Chinese Population

2021 ◽  
Author(s):  
Chen Ding ◽  
Yuan-Yuan Qu ◽  
Jinwen Feng ◽  
Xiaohui Wu ◽  
Lin Bai ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Genomic Analysis ◽  
Genomic Alterations ◽  
Cell Renal Cell Carcinoma ◽  
Genetic Features ◽  
One Carbon Metabolism

Abstract Renal cell carcinoma (RCC) is among the top 10 malignant carcinomas1. Clear cell (cc)RCC, accounting for ~ 75% of RCC cases, is an aggressive histological RCC subtype. In the last decade, large-scale multiomics studies have profoundly enhanced our understanding of this disease2,3. However, despite the differences of genomic alterations between Western and Eastern ccRCC4,5, these studies mostly focused on patients in Western populations. Here we conducted a comprehensive proteogenomic analysis of 232 tumor and adjacent non-tumor tissue pairs from Chinese ccRCC patients. Genomic analysis revealed unique genetic features of Chinese ccRCC and distinct mutation patterns associated with copy number alterations. Based on proteomic profiles, ccRCC showed extensive metabolic dysregulation, especially in one-carbon metabolism. We classified ccRCC into three subtypes (GP1–3), among which the most aggressive GP1 exhibited dominant immune response, metastasis, and metabolic imbalance, linking the proteomic features, genomic alterations, and clinical outcomes of ccRCC. Nicotinamide N-methyltransferase (NNMT) and NNMT mediated protein homocysteinylation were identified as a poor prognosis indicator and a drug target for GP1, respectively. We demonstrated that NNMT induces DNA-dependent protein kinase catalytic subunit (DNA-PKcs) homocysteinylation, increases DNA repair, and promotes tumor growth in ccRCC. Treatment of N-acetyl-cysteine (NAC), an inhibitor of homocysteinylation, markedly reduced the NNMT overexpression induced radioresistance of tumor cells. This study provided valuable insights into the biological underpinnings and prognosis assessment of ccRCC, revealing a targetable metabolic vulnerability.

Sunitinib for metastatic renal cell carcinoma: A systematic review and meta-analysis of real world and clinical trials data.

2019 ◽  
Vol 37 (7_suppl) ◽  
pp. 650-650
Author(s):  
Michael Moran ◽  
Dana J. Nickens ◽  
Katherine Adcock ◽  
Arial Desscan ◽  
Meg Bennetts ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Clinical Practice ◽  
Cell Carcinoma ◽  
Metastatic Renal Cell Carcinoma ◽  
Real World ◽  
Renal Cell ◽  
Large Scale ◽  
Meta Analysis ◽  
Sensitivity Analyses ◽  
Line Treatment

650 Background: Randomized controlled trials (RCTs) are the basis of approval for medical interventions, but may not fully reflect populations seen in clinical practice. Sunitinib is a widely used 1st-line treatment for patients (pts) with metastatic renal cell carcinoma (mRCC). This is the first large-scale meta-analysis to evaluate the efficacy of sunitinib using the novel approach of combining RCTs and real-world data (RWD). Methods: PubMed, Ovid, MEDLINE and EMBASE were searched from 2000-2017 for RCTs and RWD studies of sunitinib as 1st-line treatment in pts with mRCC. Eligible studies contained a cohort of ≥50 adult pts per study arm. The meta-analysis combined RWD and RCT study arms, adjusting for data type (RCT or RWD). Recorded outcomes were: median progression-free survival (mPFS), median overall survival (mOS) and objective response rate (ORR). A random effects model to account for study heterogeneity was applied to each endpoint. Sensitivity analyses evaluated the robustness of the overall estimate. Results: Of the studies that met eligibility criteria, mPFS, mOS and ORR were reported by 18, 19 and 15 studies, respectively. Combined RWD and RCT analyses are presented in the Table. Reported mPFS (RWD, 7.5–11.0; RCTs, 5.6–15.1 months) and ORR data (RWD, 14.0–34.6%; RCTs, 18.8–46.9%) were consistent with the overall estimates. Reported mOS showed greater variation in RWD (6.8–33.2 months) compared with RCTs (21.8–31.5 months). Sensitivity analyses showed no evidence of lack of robustness for mPFS, mOS or ORR. Interpretation of these results is limited by differences in trial design and cohort characteristics. Conclusions: This novel, large-scale meta-analysis validates sunitinib as an effective 1st-line treatment for pts with mRCC in both RCTs and everyday clinical practice. [Table: see text]

scholarly journals Metabolomic Profiling in Renal Cell Carcinoma Patients: News and Views

Cancers ◽  
2021 ◽  
Vol 13 (20) ◽  
pp. 5229
Author(s):  
Gaetano Aurilio ◽  
Matteo Santoni ◽  
Francesco Massari ◽  
Alessia Cimadamore ◽  
Alessandro Rizzo ◽  
...  
Keyword(s):  
Renal Cell Carcinoma ◽  
Cell Carcinoma ◽  
Renal Cell ◽  
Large Scale ◽  
Clear Cell ◽  
Clinical Investigation ◽  
Treatment Strategies ◽  
Akt Signaling ◽  
Tyrosine Kinase Domain ◽  
Metabolomic Profiling

Background: We address novelty regarding metabolomic profiling in renal cell carcinoma (RCC) patients, in an attempt to postulate potential treatment strategies. Methods: A large-scale literature search in existing scientific websites focusing on the keywords “renal cell carcinoma”, “clear cell histology”, “papillary histology”, “metabolomic profiling”, and “therapeutics” was performed. Results: The PI3K/Akt signaling pathway is key in clear cell RCC metabolism and accordingly several drugs are presently available for routine use in clinical practice. Along this line, new treatment combinations against PI3K/Akt family members are currently under clinical investigation. On the other hand, new developed targets such as c-Met tyrosine kinase domain, glutathione (GSH) metabolism, and histone deacetylases enzymes (HDAC), as well as therapeutic strategies targeting them are currently being tested in clinical trials and here discussed. Conclusions: In RCC patients, the PI3K/Akt signaling is still the most effective targetable pathway. Targeting other metabolic pathways such as c-Met, GSH, and HDAC appears to be a promising approach and deserve further insights.

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