Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

Clark, David; Dhanasekaran, Saravana M.; Petralia, Francesca; Pan, Jianbo; Song, Xiaoyu; Hu, Yingwei; Leprevost, Felipe da Veiga; Reva, Boris; Lih, Tung-Shing Mamie; Chang, Hui-Yin; Ma, Wanli; Huang, Chen; Ricketts, Christopher J.; Chen, Lijun; Krek, Azra; Li, Yize; Rykunov, Dmitry; Li, Qing Kay; Chen, Lin S.; Özbek, Umut; Vasaikar, Suhas; Wu, Yige; Yoo, Seungyeul; Chowdhury, Shrabanti; Wyczalkowski, Matthew A.; Ji, Jiayi; Schnaubelt, Michael; Kong, Andy T.; Sethuraman, Sunantha; Avtonomov, Dmitry M.; Ao, Minghui; Colaprico, Antonio; Cao, Song; Cho, Kyung-Cho; Kalayci, Selim; Ma, Shiyong; Liu, Wenke; Ruggles, Kelly V.; Calinawan, Anna; Gümüş, Zeynep H.; Geiszler, Daniel; Kawaler, Emily; Teo, Guo Ci; Wen, Bo; Zhang, Yuping; Keegan, Sarah; Li, Kai; Chen, Feng; Pierorazio, Phillip M.; Chen, Xi Steven; Pavlovich, Christian P.; Hakimi, A. Ari; Bromiński, Gabriel; Hsieh, James J.; Antczak, Andrzej; Omelchenko, Tatiana; Lubiński, Jan; Wiznerowicz, Maciej; Linehan, W. Marston; Kinsinger, Christopher R.; Thiagarajan, Mathangi; Boja, Emily S.; Mesri, Mehdi; Hiltke, Tara; Robles, Ana I.; Rodriguez, Henry; Qian, Jiang; Fenyö, David; Zhang, Bing; Li, Ding; Schadt, Eric E.; Chinnaiyan, Arul M.; Zhang, Zhen; Omenn, Gilbert S.; Cieślik, Marcin; Chan, Daniel W.; Nesvizhskii, Alexey I.; Wang, Pei; Zhang, Hui; Hashimi, A. Samad; Pico, Alexander; Karpova, Alla; Charamut, Alyssa; Paulovich, Amanda G.; Perou, Amy M.; Malovannaya, Anna; Marrero-Oliveras, Annette; Agarwal, Anupriya; Hindenach, Barbara; Pruetz, Barbara L.; Kim, Beom-Jun; Druker, Brian J.; Newton, Chelsea J.; Birger, Chet; Jones, Corbin D.; Tognon, Cristina E.; Mani, D. R.; Valley, Dana R.; Rohrer, Daniel C.; Zhou, Daniel Cui; Tansil, Darlene; Chesla, David; Heiman, David I.; Wheeler, David A.; Tan, Donghui; Chan, Doug W.; Demir, Emek; Malc, Ewa; Modugno, Francesmary; Getz, Gaddy; Hostetter, Galen; Wilson, George; Hart, Gerald W.; Zhu, Heng; Liu, Hongwei; Culpepper, Houston; Sun, Hua; Zhou, Hua; Day, Jacob; Suh, James; Huang, Jasmine; McDermott, Jason; Whiteaker, Jeffrey R.; Tyner, Jeffrey W.; Eschbacher, Jennifer; Chen, Jin; McGee, John P.; Zhu, Jun; Ketchum, Karen A.; Rodland, Karin D.; Clauser, Karl R.; Robinson, Karna; Krug, Karsten; Hoadley, Katherine A.; Um, Ki Sung; Elburn, Kim; Holloway, Kimberly; Wang, Liang-Bo; Blumenberg, Lili M.; Hannick, Linda; Qi, Liqun; Sokoll, Lori J.; Cornwell, MacIntosh; Loriaux, Marc; Domagalski, Marcin J.; Gritsenko, Marina; Anderson, Matthew L.; Monroe, Matthew E.; Ellis, Matthew J.; Dyer, Maureen A.; Anurag, Meenakshi; Burke, Meghan C.; Borucki, Melissa; Gillette, Michael A.; Birrer, Michael J.; Lewis, Michael R.; Ittmann, Michael; Smith, Michael J.; Vernon, Michael; Chaikin, Michelle; Chheda, Milan G.; Khan, Munziba; Roche, Nancy; Edwards, Nathan; Vatanian, Negin; Tignor, Nicole; Beckmann, Noam D.; Grady, Pamela; Castro, Patricia; Piehowski, Paul; McGarvey, Peter B.; Mieczkowski, Piotr A.; Hariharan, Pushpa; Gao, Qingsong; Dhir, Rajiv; Kothadia, Ramani B.; Thangudu, Ratna R.; Montgomery, Rebecca; Jayasinghe, Reyka G.; Smith, Richard; Edwards, Robert A.; Zelt, Robert; Bremner, Ross M.; Liu, Ruiyang; Hong, Runyu; Mareedu, Sailaja; Payne, Samuel H.; Cottingham, Sandra; Markey, Sanford P.; Jewell, Scott D.; Patel, Shalin; Satpathy, Shankha; Richey, Shannon; Davies, Sherri R.; Cai, Shuang; Boca, Simina M.; Patil, Snehal; Sengupta, Sohini; Carter, Sonya; Gabriel, Stacey; Thomas, Stefani N.; Young, Stephanie; Stein, Stephen E.; Carr, Steven A.; Foltz, Steven M.; Hilsenbeck, S. G.; Krubit, Tanya; Liu, Tao; Skelly, Tara; Westbrook, Thomas F.; Borate, Uma; Velvulou, Uma; Petyuk, Vladislav; Bocik, William; Chen, Xi; Shi, Yan; Geffen, Yifat; Lu, Yihao; Wang, Ying; Maruvka, Yosef E.; Li, Zhi; Shi, Zhiao; Tu, Zhidong

doi:10.1016/j.cell.2019.10.007

Cited by 453 publications

(328 citation statements)

References 190 publications

Supporting

Mentioning

314

Contrasting

Order By: Relevance

“…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

View full text Add to dashboard Cite

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.

show abstract

Section: Resultsmentioning

confidence: 99%

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

Kundu

Nam

Shelar

et al. 2020

Journal of Experimental Medicine

View full text Add to dashboard Cite

show abstract

“…Renal cell carcinoma (RCC), one of the top ten most commonly diagnosed cancers worldwide, is dominated by the clear cell histological subtype, which makes up 75% of all RCC cases [1]. To…”

Section: Ccrcc Data Descriptionmentioning

confidence: 99%

“…In addition, through ProTrack users can efficiently generate visual heatmaps of customized combinations of multi-omic data from CPTAC and perform an initial query of such associations. In this paper, we focus on introducing ProTrack for the CPTAC clear cell renal cell carcinoma (ccRCC) study [1], importantly similar tools for CPTAC proteogenomic studies of other cancer types, will be made available as and when the corresponding data is released by the consortium or upon publication.…”

mentioning

confidence: 99%

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

Calinawan

Song

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

The Clinical Proteomic Tumor Analysis Consortium (CPTAC) initiative has generated extensive multi-omics data resources of deep proteogenomic profiles for multiple cancer types. To enable the broader community of biological and medical researchers to intuitively query, explore, and download data and analysis results from various CPTAC projects, we built a prototype user-friendly web application called "ProTrack" with the CPTAC clear cell renal cell carcinoma (ccRCC) data set ( http://ccrcc.cptac-data-view.org ). Here we describe the salient features of this application which provides a dynamic, comprehensive, and granular visualization of the rich proteogenomic data. Statement of SignificanceThe CPTAC initiative (https://proteomics.cancer.gov/) has generated multi-omics data for multiple cancer types to understand the proteogenomic aberrations of these malignancies. Collectively this effort has so far produced a large data resource for the research community, including high-throughput profiles for proteome, phosphoproteome, whole exome, whole genome, transcriptome, and DNA methylome. To make this valuable data-resource useful to the larger research community, there is a pressing need for development of user-friendly, readily accessible, and easily shared analytic and visualization tools for aligning multi-omics data and exploring alterations in key cancer genes, to drive and support new biological hypotheses. To bridge this gap, we have developed CPTAC ProTrack, an interactive web application which uses a multilayered, client-server architecture in order to deliver an interactive web experience to any user of a modern web-browser. This tool is intentionally designed accessible for researchers, biologists, and clinicians who are interested in multi-omic data without any need to code.

show abstract

“…We first wanted to know how these measurements relate to each other to determine the extent that one measurement can stand as a proxy for overall LINE-1 "activity." We reanalyzed data from the CPTAC project to quantify LINE-1 mRNA, ORF1p (L1RE1) and ORF1p S18, S27, S33 and T203 phosphorylation in 5 cancer types: breast (n=94; (Krug et al, 2020) ), ovarian (n=97; (McDermott et al, 2020) ), colon (n=93) (Vasaikar et al, 2019) , clear cell kidney (n=106) (Clark et al, 2019) and endometrial (n=88) (Dou et al, 2020) . We also used the Mobile Element Locator Tool (MELT) to quantify somatic LINE-1 insertions in kidney and endometrial tumors, where matched tumor and whole blood WGS was available.…”

Section: Quantifications Of Line-1 Expression Orf1p Phosphorylationmentioning

confidence: 99%

“…Transcriptomic and genomic data for CPTAC 3 (endometrial and kidney cancers) can be found in the Genomic Data Commons ( https://gdc.cancer.gov/ ). Additionally data for each cancer type can be found in the corresponding publication: colon (Vasaikar et al, 2019), breast (Krug et al, 2020), ovarian (McDermott et al, 2020), endometrial (Dou et al, 2020), and kidney (Clark et al, 2019). L1EM code can be found at https://github.com/FenyoLab/L1EM .…”

Section: Data and Code Availabilitymentioning

confidence: 99%

LINE-1 expression in cancer correlates with DNA damage response, copy number variation, and cell cycle progression

McKerrow

Wang

Mita

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

Retrotransposons are genomic DNA sequences that are capable of copying themselves to new genomic locations via RNA intermediates; LINE-1 is the only retrotransposon that remains autonomous and active in the human genome. The mobility of LINE-1 is largely repressed in somatic tissues, but LINE-1 is active in many cancers. Recent studies using LINE-1 constructs indicate that host cells activate a DNA damage response (DDR) to repair retrotransposition intermediates and resolve conflicts between LINE-1 and DNA replication. Using multi-omic data from the CPTAC project, we found correlations between LINE-1 expression and ATM-MRN-SMC DDR signalling in endometrial cancer and between LINE-1 and the ATR-CHEK1 pathway in p53 wild type breast cancer. This provides evidence that conflicts between LINE-1 and DNA replication occur in at least some human cancers. Furthermore, LINE-1 expression in these cancers is correlated with the total amount of copy number variation genome wide, indicating that, when active in cancer, pointing to a direct impact of LINE-1 associated DNA damage on genome structure. We also find that, in endometrial and ovarian cancer, LINE-1 expression is correlated with the expression of genes that drive cycle progression including E2F3, PLK1 and Aurora kinase B. This study provides evidence, supporting recent work in model cell lines, of a LINE-1/DDR connection in human tumors and raises the possibility of additional interactions between LINE-1 and the cell cycle.

show abstract

Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma

Cited by 453 publications

References 190 publications

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

PRDM16 suppresses HIF-targeted gene expression in kidney cancer

ProTrack: An Interactive Multi-Omics Data Browser for Proteogenomic Studies

LINE-1 expression in cancer correlates with DNA damage response, copy number variation, and cell cycle progression

Contact Info

Product

Resources

About