1031 Activation of Mtor in Renal Cell Carcinoma Due to Increased Phosphorylation Rather Than Protein Over Expression

Kruck, Stephan; Hennenlotter, Joerg; Merseburger, Axel S.; Ohneseit, Petra; Schilling, Dieter; Bedke, Jens; Kuczyk, Markus A.; Sievert, Karl‐Dietrich; Stenzl, Arnulf

doi:10.1016/s1569-9056(09)61015-0

Cited by 9 publications

(20 citation statements)

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“…Patient data for AJCC pathological stage 9 and histological grade for each cancer type was obtained from the standardized clinical dataset, the TCGA Pan-Cancer Clinical Data Resource 79 T,N, and M staging was obtained from the GDC 80 database using the R/Bioconductor 81 package recount2. 77 Unique patient IDs were matched using R for each cancer type to include only primary tumor samples for clinical analyses (Sample code “01”).…”

Section: Methodsmentioning

confidence: 99%

“…For clinical analysis, Tumor, Node, Metastasis (TNM) staging system developed by American Joint Committee on Cancer AAJC 9 was used. The TNM system classifies cancer: T: by the size, which is further grouped into t1- (t1a, t1a1, t1b, t1b1), t2- (t2a, t2a1, t2a2, t2b, t2c), t3- (t3a, t3b, t3c) and t4- (t4a, t4b, t4c, t4d, t4e).…”

Section: Methodsmentioning

confidence: 99%

“…We define “clinically relevant” as having a correlation or relationship to critical clinical benchmarks, such as tumor grade and American Joint Committee on Cancer (AJCC) tumor, node, metastasis (TNM) staging. 9 In short, we have analyzed and highlighted kinases whose mRNA expression levels appear to be prognostic and associated with the progression of cancer. Since kinase activity does not always correspond with mRNA expression levels (e.g., mTOR oncogenic activity is due to an increase in activation via phosphorylation 10 ), we also leveraged TCGA genomic data and prioritized kinases that confer a selective advantage to tumor development as measured by the accrual and clustering of mutations at specific regions of their amino acid sequence.…”

Section: Introductionmentioning

confidence: 99%

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The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

Essegian

Khurana

Stathias

et al. 2020

Cell Reports Medicine

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Summary The approval of the first kinase inhibitor, Gleevec, ushered in a paradigm shift for oncological treatment—the use of genomic data for targeted, efficacious therapies. Since then, over 48 additional small-molecule kinase inhibitors have been approved, solidifying the case for kinases as a highly druggable and attractive target class. Despite the role deregulated kinase activity plays in cancer, only 8% of the kinome has been effectively “drugged.” Moreover, 24% of the 634 human kinases are understudied. We have developed a comprehensive scoring system that utilizes differential gene expression, pathological parameters, overall survival, and mutational hotspot analysis to rank and prioritize clinically relevant kinases across 17 solid tumor cancers from The Cancer Genome Atlas. We have developed the clinical kinase index (CKI) app ( http://cki.ccs.miami.edu ) to facilitate interactive analysis of all kinases in each cancer. Collectively, we report that understudied kinases have potential clinical value as biomarkers or drug targets that warrant further study.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

Essegian

Khurana

Stathias

et al. 2020

Cell Reports Medicine

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“…Other factors such as posttranslational modifications (PTMs) have been shown to contribute to development of diseases [21]. In future, we will extend the rank node operator to consider factors such as expression levels and PTMs in the ranking.…”

Section: Discussionmentioning

confidence: 99%

Relational operators for prioritizing candidate biomarkers in high-throughput differential expression data

Onsongo

Xie²,

Griffin³

et al. 2010

Proceedings of the First ACM International Conference on Bioinformatics and Computational Biology

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Recent developments in high-throughput proteomics technologies have made it possible to detect and identify low abundance proteins. These technologies provide a new window through which proteomes can be analyzed. Despite holding great promise, the contribution of mass spectrometry based proteomics in identifying novel diagnostic biomarkers has been disappointing. This failure has, in part, been attributed to the lack of effective strategies for determining candidate biomarkers that justify more expensive and timeconsuming validation studies. An approach that bridges the gap between unbiased experimental paradigm emphasizing comprehensive characterizations of proteins and a candidatedriven paradigm would overcome this limitation [38]. To this end, we have developed database operators that extend the database management systems to analyze highthroughput proteomics and genomics data. By analyzing differentially expressed genes and proteins using pathway databases, these operators take advantage of established expert domain knowledge in pathway annotation to prioritize candidate biomarkers. They provide a systematic way of bridging the gap between unbiased experimental paradigm and candidate-driven paradigm. To test the operators, we analyzed a dataset of salivary proteins differentially expressed between pre-malignant and malignant oral lesions. Six proteins are identified as candidate biomarkers worth of validation studies. A literature search reveals these high priority * Accompanying software and supplementary data available at candidate biomarkers interact with proteins implicated in cancer development highlighting their potential utility as biomarkers demonstrating the effectiveness of our operators. The developed operators will help overcome one of the main challenges of high-throughput computational techniques; provide a systematic way of bridging the gap between unbiased data driven approach and hypothesis driven approach to prioritize candidate biomarkers worth of more expensive and time consuming validation studies.

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“…mTOR pathway has also been shown to be upregulated in RCC and is the second major pathway targeted by current therapeutic options. Moreover, alteration at the level of mTOR activity promotes the invasiveness and metastatic potential in a variety of malignancies such as glioblastoma, small cell lung cancer, osteosarcoma, pancreatic cancer, leukemia, breast, colon cancer and RCC [ 9 ]. Therefore, the combined effects of mTOR inhibition with p38 MAPK inhibition can help in the treatment of RCC by inducing apoptosis and decreasing cell proliferation.…”

Section: Introductionmentioning

confidence: 99%

Combination of mTOR and MAPK Inhibitors—A Potential Way to Treat Renal Cell Carcinoma

et al. 2016

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Renal cell carcinoma (RCC) is the most common neoplasm that occurs in the kidney and is marked by a unique biology, with a long history of poor response to conventional cancer treatments. In the past few years, there have been significant advancements to understand the biology of RCC. This has led to the introduction of novel targeted therapies in the management of patients with metastatic disease. Patients treated with targeted therapies for RCC had shown positive impact on overall survival, however, no cure is possible and patients need to undergo treatment for long periods of time, which raises challenges to manage the associated adverse events. Moreover, many patients may not respond to it and even response may not last long enough in the responders. Many inhibitors of the Mammalian target of Rapamycin (mTOR) signaling pathway are currently being used in treatment of advanced RCC. Studies showed that inhibitions of mTOR pathways induce Mitogen-Activated Protein Kinase (MAPK) escape cell death and cells become resistant to mTOR inhibitors. Because of this, there is a need to inhibit both pathways with their inhibitors comparatively for a better outcome and treatment of patients with RCC.

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1031 Activation of Mtor in Renal Cell Carcinoma Due to Increased Phosphorylation Rather Than Protein Over Expression

Cited by 9 publications

References 0 publications

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

The Clinical Kinase Index: A Method to Prioritize Understudied Kinases as Drug Targets for the Treatment of Cancer

Relational operators for prioritizing candidate biomarkers in high-throughput differential expression data

Combination of mTOR and MAPK Inhibitors—A Potential Way to Treat Renal Cell Carcinoma

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