Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers

Chen, Ken; Chandrashekar, Darshan S.; Varambally, Sooryanarayana; Creighton, Chad J.

doi:10.1038/s41467-019-13528-0

Cited by 385 publications

(376 citation statements)

References 47 publications

(93 reference statements)

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“…1 E). We also analyzed proteomics data on clear cell renal cancer from the National Cancer Institute (NCI)–sponsored Clinical Proteomic Tumor Analysis Consortium, which was recently reported using the UALCAN analysis portal (Chen et al, 2019; Clark et al, 2019). These data clearly demonstrate reduced PRDM16 protein in RCC and therefore validate both our immunoblot and immunohistochemistry analyses (Fig.…”

Section: Resultsmentioning

confidence: 99%

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

Kundu

Nam

Shelar

et al. 2020

Journal of Experimental Medicine

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Analysis of transcriptomic data demonstrates extensive epigenetic gene silencing of the transcription factor PRDM16 in renal cancer. We show that restoration of PRDM16 in RCC cells suppresses in vivo tumor growth. RNaseq analysis reveals that PRDM16 imparts a predominantly repressive effect on the RCC transcriptome including suppression of the gene encoding semaphorin 5B (SEMA5B). SEMA5B is a HIF target gene highly expressed in RCC that promotes in vivo tumor growth. Functional studies demonstrate that PRDM16’s repressive properties, mediated by physical interaction with the transcriptional corepressors C-terminal binding proteins (CtBP1/2), are required for suppression of both SEMA5B expression and in vivo tumor growth. Finally, we show that reconstitution of RCC cells with a PRDM16 mutant unable to bind CtBPs nullifies PRDM16’s effects on both SEMA5B repression and tumor growth suppression. Collectively, our data uncover a novel epigenetic basis by which HIF target gene expression is amplified in kidney cancer and a new mechanism by which PRDM16 exerts its tumor suppressive effects.

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Section: Resultsmentioning

confidence: 99%

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

Kundu

Nam

Shelar

et al. 2020

Journal of Experimental Medicine

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“…UALCAN ( http://ualcan.path.uab.edu/ ) is an online tool for analyzing cancer transcriptome data, which is based on public cancer transcriptome data (TCGA and MET500 transcriptome sequencing) 19 . The "CPTAC analysis" module of UALCAN provides protein expression analysis option using data from Clinical Proteomic Tumor Analysis Consortium (CPTAC) Confirmatory/Discovery dataset 20 . The comparison of RRM2 protein expression between LUAD and normal lung was examined in UALCAN.…”

Section: Methodsmentioning

confidence: 99%

Independent prognostic implications of RRM2 in lung adenocarcinoma

Luo

Cao

et al. 2020

J. Cancer

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Background: Ribonucleoside-diphosphate reductase subunit M2 ( RRM2 ) is the catalytic subunit of ribonucleotide reductase and modulates the enzymatic activity, which is essential for DNA replication and repair. However, the role of RRM2 in lung adenocarcinoma (LUAD) remains unclear. Methods: In this study, we explored the expression pattern and prognostic value of RRM2 in LUAD across TCGA, GEO, Oncomine, UALCAN, PrognoScan, and Kaplan-Meier Plotter, and confirmed its independent prognostic value via Cox analyses. LinkedOmics and GEPIA2 were applied to investigate co-expression and functional networks associated with RRM2 . Besides, we used TIMER to assess the correlation between RRM2 and the main six types of tumor-infiltrating immune cells. Lastly, the correlations between immune signatures of immunomodulators, chemokines, and 28 tumor-infiltrating lymphocytes (TILs) and RRM2 were examined by tumor purity-corrected partial Spearman's rank correlation coefficient through TIMER portal. Results: RRM2 was found upregulated in tumor tissues in TCGA-LUAD, and validated in multiple independent cohorts. Moreover, whether in TCGA or other cohorts, high RRM2 expression was found to be associated with poor survival. Cox analyses showed that high RRM2 expression was an independent risk factor for overall survival, disease-specific survival, and progression-free survival of LUAD. Functional network analysis suggested that RRM2 regulates RNA transport, oocyte meiosis, spliceosome, ribosome biogenesis in eukaryotes, and cellular senescence signaling through pathways involving multiple cancer-related kinases and E2F family. Also, RRM2 expression correlated with infiltrating levels of B cells, CD4+ T cells, and neutrophils. Subsequent analysis found that B cells and dendritic cells could predict the outcome of LUAD. B cells were identified as an independent risk factor among six types of immune cells through Cox analyses. At last, the correlation analysis showed RRM2 correlated with 67.68% (624/922) of the immune signatures we performed. Conclusion: Our research showed that RRM2 could independently predict the prognosis of LUAD and was associated with immune infiltration. In particular, the tight relationship between RRM2 and B cell marker genes are the potential epicenter of the immune response and one of the critical factors affecting the prognosis. Our findings laid the foundation for further research on the immunomodulatory role of RRM2 in LUAD.

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“…The data generated from RPPA studies have been deposited in "The Cancer Proteome Atlas (TCPA)" (Li et al, 2017). However, non-specificity of the antibodies and low-throughput are the major limitations of RPPA compared to mass-spectrometry based techniques (Chen et al, 2019). To overcome this barrier, TCGA samples from different cancer types were subjected to LC-MS/MS-based mass-spectrometry analysis resulting in the identification and quantification of thousands of proteins.…”

Section: Omics Type Technique Application Referencesmentioning

confidence: 99%

Integration of Online Omics-Data Resources for Cancer Research

et al. 2020

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The manifestations of cancerous phenotypes necessitate alterations at different levels of information-flow from genome to proteome. The molecular alterations at different information processing levels serve as the basis for the cancer phenotype to emerge. To understand the underlying mechanisms that drive the acquisition of cancer hallmarks it is required to interrogate cancer cells using multiple levels of information flow represented by different omics-such as genomics, epigenomics, transcriptomics, and proteomics. The advantage of multi-omics data integration comes with a trade-off in the form of an added layer of complexity originating from inherently diverse types of omics-datasets that may pose a challenge to integrate the omics-data in a biologically meaningful manner. The plethora of cancer-specific online omics-data resources, if able to be integrated efficiently and systematically, may facilitate the generation of new biological insights for cancer research. In this review, we provide a comprehensive overview of the online single-and multi-omics resources that are dedicated to cancer. We catalog various online omics-data resources such as The Cancer Genome Atlas (TCGA) along with various TCGA-associated data portals and tools for multi-omics analysis and visualization, the International Cancer Genome Consortium (ICGC), Catalogue of Somatic Mutations in Cancer (COSMIC), The Pathology Atlas, Gene Expression Omnibus (GEO), and PRoteomics IDEntifications (PRIDE). By comparing the strengths and limitations of the respective online resources, we aim to highlight the current biological and technological challenges and possible strategies to overcome these challenges. We outline the available schemes for the integration of the multi-omics dimensions for stratifying cancer patients and biomarker prediction based on the integrated molecular-signatures of cancer. Finally, we propose the multi-omics driven systems-biology approaches to realize the potential of precision onco-medicine as the

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Pan-cancer molecular subtypes revealed by mass-spectrometry-based proteomic characterization of more than 500 human cancers

Cited by 385 publications

References 47 publications

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

Independent prognostic implications of RRM2 in lung adenocarcinoma

Integration of Online Omics-Data Resources for Cancer Research

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