Proteogenomic Characterization of Endometrial Carcinoma

Dou, Yongchao; Kawaler, Emily; Zhou, Daniel Cui; Gritsenko, Marina; Huang, Chen; Blumenberg, Lili M.; Karpova, Alla; Petyuk, Vladislav; Savage, Sara R.; Satpathy, Shankha; Liu, Wenke; Wu, Yige; Tsai, Chia‐Feng; Wen, Bo; Li, Zhi; Cao, Song; Moon, Jamie; Shi, Zhiao; Cornwell, MacIntosh; Wyczalkowski, Matthew A.; Chu, Rosalie K.; Vasaikar, Suhas; Zhou, Hua; Gao, Qingsong; Moore, Ronald J.; Li, Kai; Sethuraman, Sunantha; Monroe, Matthew E.; Zhao, Rui; Heiman, David I.; Krug, Karsten; Clauser, Karl R.; Kothadia, Ramani B.; Maruvka, Yosef E.; Pico, Alexander; Oliphant, Amanda E.; Hoskins, Emily L.; Pugh, Samuel L.; Beecroft, Sean J.I.; Adams, David W.; Jarman, Jonathan C.; Kong, Andy T.; Chang, Hui-Yin; Reva, Boris; Liao, Yuxing; Rykunov, Dmitry; Colaprico, Antonio; Chen, Xi Steven; Czekański, Andrzej; Jędryka, Marcin; Matkowski, Rafał; Wiznerowicz, Maciej; Hiltke, Tara; Boja, Emily S.; Kinsinger, Christopher R.; Mesri, Mehdi; Robles, Ana I.; Rodriguez, Henry; Mutch, David G.; Fuh, Katherine C.; Ellis, Matthew J.; DeLair, Deborah F.; Thiagarajan, Mathangi; Mani, D. R.; Getz, Gad; Noble, Michael S.; Nesvizhskii, Alexey I.; Wang, Pei; Anderson, Matthew L.; Levine, Douglas A.; Smith, Richard; Payne, Samuel H.; Ruggles, Kelly V.; Rodland, Karin D.; Li, Ding; Zhang, Bing; Liu, Tao; Fenyö, David; Agarwal, Anupriya; Anurag, Meenakshi; Avtonomov, Dmitry M.; Birger, Chet; Birrer, Michael J.; Boca, Simina M.; Bocik, William; Borate, Uma; Borucki, Melissa; Burke, Meghan C.; Cai, Shuang; Calinawan, Anna; Carr, Steven A.; Carter, Sonya; Castro, Patricia; Cerda, Sandra; Chaikin, Michelle; Chan, Daniel W.; Chan, Doug W.; Charamut, Alyssa; Chen, Feng; Chen, Jin; Chen, Lijun; Chen, Lin S.; Chesla, David; Chheda, Milan G.; Chinnaiyan, Arul M.; Chowdhury, Shrabanti; Cieślik, Marcin; Clark, David; Cottingham, Sandra; Culpepper, Houston; Day, Jacob; Young, Stephanie; Demir, Emek; Dhanasekaran, Saravana M.; Dhir, Rajiv; Domagalski, Marcin J.; Dottino, Peter R.; Druker, Brian J.; Duffy, Elizabeth; Dyer, Maureen A.; Edwards, Nathan; Edwards, Robert A.; Elburn, Kim; Field, Jayson B.; Francis, Alicia; Gabriel, Stacey; Geffen, Yifat; Geiszler, Daniel; Gillette, Michael A.; Godwin, Andrew K.; Grady, Pamela; Hannick, Linda; Hariharan, Pushpa; Hilsenbeck, S. G.; Hindenach, Barbara; Hoadley, Katherine A.; Hong, Runyu; Hostetter, Galen; Hsieh, James J.; Hu, Yingwei; Ittmann, Michael; Jaehnig, Eric J.; Jewell, Scott D.; Ji, Jiayi; Jones, Corbin D.; Karabon, Renee; Ketchum, Karen A.; Khan, Munziba; Kim, Beom-Jun; Krek, Azra; Krubit, Tanya; Kumar‐Sinha, Chandan; Leprevost, Felipe da Veiga; Lewis, Michael R.; Li, Qing Kay; Li, Yize; Liu, Hongwei; Lubiński, Jan; Ma, Wanli; Madan, Rashna; Malc, Ewa; Malovannaya, Anna; Mareedu, Sailaja; Markey, Sanford P.; Marrero-Oliveras, Annette; Martignetti, John A.; McDermott, Jason; McGarvey, Peter B.; McGee, John P.; Mieczkowski, Piotr A.; Modugno, Francesmary; Montgomery, Rebecca; Newton, Chelsea J.; Omenn, Gilbert S.; Paulovich, Amanda G.; Perou, Amy M.; Petralia, Francesca; Piehowski, Paul; Polonskaya, Larisa; Qi, Liqun; Richey, Shannon; Robinson, Karna; Roche, Nancy; Rohrer, Daniel C.; Schadt, Eric E.; Schnaubelt, Michael; Shi, Yan; Skelly, Tara; Sokoll, Lori J.; Song, Xiaoyu; Stein, Stephen E.; Suh, James; Tan, Donghui; Tansil, Darlene; Teo, Guo Ci; Thangudu, Ratna R.; Tognon, Cristina E.; Traer, Elie; Tyner, Jeffrey W.; Um, Ki Sung; Valley, Dana R.; Vatanian, Negin; Vats, Pankaj; Velvulou, Uma; Vernon, Michael; Wang, Liang-Bo; Wang, Ying; Webster, Alex; Westbrook, Thomas F.; Wheeler, David A.; Whiteaker, Jeffrey R.; Wilson, George; Zakhartsev, Yuriy; Zelt, Robert; Zhang, Hui; Zhang, Yuping; Zhang, Zhen; Zhao, Grace

doi:10.1016/j.cell.2020.01.026

Cited by 347 publications

(327 citation statements)

References 173 publications

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“…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%

“…This analysis revealed that most of the high LINE-1 expressing tumors fall into the "CNV high" classification (Wilcox p=6.4x10 -6 , figure 4A). As the name suggests, CNV high tumors have a high degree of copy number variation (CNV) genome-wide and have frequent p53 mutations (Cancer Genome Atlas Research Network et al, 2013;Dou et al, 2020) . This is consistent with previous observations that LINE-1 expression is higher in p53 mutant tumors (Rodić et al, 2014;Wylie et al, 2016) , an observation that suggests an association between LINE-1 and persistent DNA damage.…”

Section: Atm-mrn-smc Signalling Cnv and Drivers Of Cell Cycle Progrementioning

confidence: 99%

“…For breast and endometrial cancer, global CNV scores were provided by the respective CPTAC working groups (Krug et al, 2020;(Dou et al, 2020). For colon cancer, the CNV score was recalculated according to the description provided (Vasaikar et al, 2019).…”

Section: Estimation Of Global Cnvmentioning

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%

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

McKerrow

Wang

Mita

et al. 2020

Preprint

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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.

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Section: Quantifications Of Line-1 Expression Orf1p Phosphorylationmentioning

confidence: 99%

Section: Atm-mrn-smc Signalling Cnv and Drivers Of Cell Cycle Progrementioning

confidence: 99%

Section: Estimation Of Global Cnvmentioning

confidence: 99%

Section: Data and Code Availabilitymentioning

confidence: 99%

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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

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show abstract

“…Integration of deep-scale proteomic analysis of human tumors with genomic data has been shown to improve speci city for identifying pathway alterations caused by tumor associated mutations. Furthermore, phosphoproteome measurements provide information on pathway activation not discernible from genetic measurements and thus offers unique insights into potential therapeutic targets (9)(10)(11)(12)(13)(14).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Sanford

Wang

Hansen

et al. 2020

Preprint

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Background: Global and phosphoproteome profiling has demonstrated great utility for the analysis of clinical specimens. One major barrier to the broad clinical application of proteomic profiling is the large amount of biological material required, particularly for phosphoproteomics—currently on the order of 25 mg wet tissue weight, depending on tissue type. For hematopoietic cancers such as acute myeloid leukemia (AML), the sample requirement is in excess of 10 million (1E7) peripheral blood mononuclear cells (PBMCs). Throughout the course of a prospective study, this requirement will certainly exceed what is obtainable from many of the individual patients/timepoints. For this reason, we were interested in examining the impact of differential peptide loading across multiplex channels on proteomic data quality. Methods: To achieve this, we tested a range of channel loading amounts (20, 40, 100, 200, and 400 μg of tryptic peptides, or approximately the material obtainable from 5E5, 1E6, 2.5E6, 5E6, and 1E7 AML patient cells) to assess proteome coverage, quantification reproducibility and accuracy in experiments utilizing isobaric tandem mass tag (TMT) labeling. Results: As expected, we found that fewer missing values are observed in TMT channels with higher peptide loading amounts compared to those with lower loading. Moreover, channels with lower loading amounts have greater quantitative variability than channels with higher loading amounts. Statistical analysis of the differences in means among the five loading groups showed that the 20 μg loading group was significantly different from the 400 μg loading group. However, no significant differences were detected among the 40, 100, 200 and 400 μg loading groups. Conclusions: These assessment data demonstrate the practical limits of loading differential quantities of peptides across channels in TMT multiplexes, and provide a basis for designing the optimal clinical proteomics study when specimen quantities are limited.

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Transgelin Promotes Glioblastoma Stem Cell Hypoxic Responses and Maintenance Through p53 Acetylation

Li,

Song,

Zhang

et al. 2023

Advanced Science

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Glioblastoma (GBM) is a lethal cancer characterized by hypervascularity and necrosis associated with hypoxia. Here, it is found that hypoxia preferentially induces the actin‐binding protein, Transgelin (TAGLN), in GBM stem cells (GSCs). Mechanistically, TAGLN regulates HIF1α transcription and stabilizes HDAC2 to deacetylate p53 and maintain GSC self‐renewal. To translate these findings into preclinical therapeutic paradigm, it is found that sodium valproate (VPA) is a specific inhibitor of TAGLN/HDAC2 function, with augmented efficacy when combined with natural borneol (NB) in vivo. Thus, TAGLN promotes cancer stem cell survival in hypoxia and informs a novel therapeutic paradigm.

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Proteogenomic Characterization of Endometrial Carcinoma

Cited by 347 publications

References 173 publications

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

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

Evaluation of Differential Peptide Loading on Tandem Mass Tag-Based Proteomic and Phosphoproteomic Data Quality

Transgelin Promotes Glioblastoma Stem Cell Hypoxic Responses and Maintenance Through p53 Acetylation

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