Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Gillette, Michael A.; Satpathy, Shankha; Cao, Song; Dhanasekaran, Saravana M.; Vasaikar, Suhas; Krug, Karsten; Petralia, Francesca; Li, Yize; Liang, Wen-Wei; Reva, Boris; Krek, Azra; Ji, Jiayi; Song, Xiaoyu; Liu, Wenke; Hong, Runyu; Yao, Lijun; Blumenberg, Lili M.; Savage, Sara R.; Wendl, Michael C.; Wen, Bo; Li, Kai; Tang, Lihao; MacMullan, Melanie A.; Avanessian, Shayan C.; Kane, M. Harry; Newton, Chelsea J.; Cornwell, MacIntosh; Kothadia, Ramani B.; Ma, Wanli; Yoo, Seungyeul; Mannan, Rahul; Vats, Pankaj; Kumar‐Sinha, Chandan; Kawaler, Emily; Omelchenko, Tatiana; Colaprico, Antonio; Geffen, Yifat; Maruvka, Yosef E.; Leprevost, Felipe da Veiga; Wiznerowicz, Maciej; Gümüş, Zeynep H.; Veluswamy, Rajwanth; Hostetter, Galen; Heiman, David I.; Wyczalkowski, Matthew A.; Hiltke, Tara; Mesri, Mehdi; Kinsinger, Christopher R.; Boja, Emily S.; Omenn, Gilbert S.; Chinnaiyan, Arul M.; Rodriguez, Henry; Li, Qing Kay; Jewell, Scott D.; Thiagarajan, Mathangi; Getz, Gad; Zhang, Bing; Fenyö, David; Ruggles, Kelly V.; Cieślik, Marcin; Robles, Ana I.; Clauser, Karl R.; Govindan, Ramaswamy; Wang, Pei; Nesvizhskii, Alexey I.; Li, Ding; Mani, D. R.; Carr, Steven A.; Webster, Alex; Francis, Alicia; Charamut, Alyssa; Paulovich, Amanda G.; Perou, Amy M.; Godwin, Andrew K.; Karnuta, Andrii; Marrero-Oliveras, Annette; Hindenach, Barbara; Pruetz, Barbara L.; Kubisa, Bartosz; Druker, Brian J.; Birger, Chet; Jones, Corbin D.; Valley, Dana R.; Rohrer, Daniel C.; Zhou, Daniel Cui; Chan, Daniel W.; Chesla, David; Clark, David; Rykunov, Dmitry; Tan, Donghui; Пономарева, Е. В.; Duffy, Elizabeth; Burks, Eric; Schadt, Eric E.; Bergstrom, Erik J.; Fedorov, Eugene S.; Malc, Ewa; Wilson, George; Chen, Haiquan; Krzystek, Halina M.; Liu, Hongwei; Culpepper, Houston; Sun, Hua; Zhang, Hui; Day, Jacob; Suh, James; Whiteaker, Jeffrey R.; Eschbacher, Jennifer; McGee, John P.; Ketchum, Karen A.; Rodland, Karin D.; Robinson, Karna; Hoadley, Katherine A.; Suzuki, Kei; Um, Ki Sung; Elburn, Kim; Wang, Liang-Bo; Chen, Lijun; Hannick, Linda; Qi, Liqun; Sokoll, Lori J.; Wojtyś, Małgorzata; Domagalski, Marcin J.; Gritsenko, Marina; Beasley, Mary Beth; Monroe, Matthew E.; Ellis, Matthew J.; Dyer, Maureen A.; Burke, Meghan C.; Borucki, Melissa; Sun, Menghong; Roehrl, Michael H. A.; Birrer, Michael J.; Noble, Michael S.; Schnaubelt, Michael; Vernon, Michael; Chaikin, Michelle; Krotevich, Mikhail; Khan, Munziba; Selvan, Myvizhi Esai; Roche, Nancy; Edwards, Nathan; Vatanian, Negin; Potapova, Olga; Grady, Pamela; McGarvey, Peter B.; Mieczkowski, Piotr A.; Hariharan, Pushpa; Madan, Rashna; Thangudu, Ratna R.; Smith, Richard; Welsh, Robert J.; Zelt, Robert; Mehra, Rohit; Matteotti, Ronald; Mareedu, Sailaja; Payne, Samuel H.; Cottingham, Sandra; Markey, Sanford P.; Chugh, Seema; Smith, Shaleigh; Tsang, Shirley; Cai, Shuang; Boca, Simina M.; Carter, Sonya; Gabriel, Stacey; Young, Stephanie; Stein, Stephen E.; Shankar, Sunita; Krubit, Tanya; Liu, Tao; Skelly, Tara; Bauer, Thomas; Velvulou, Uma; Özbek, Umut; Petyuk, Vladislav; Sovenko, Volodymyr; Bocik, William; Maggio, William W.; Chen, Xi; Shi, Yan; Wu, Yige; Hu, Yingwei; Liao, Yuxing; Zhang, Zhen; Shi, Zhiao

doi:10.1016/j.cell.2020.06.013

Cited by 516 publications

(471 citation statements)

References 242 publications

(201 reference statements)

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“…Clinical data contains patient demographic information, descriptive data for the tumor, and patient follow up. See the original publication for a detailed description of data acquisition and analysis methods [1][2][3][4][5] . The data API is designed to provide frictionless access to quantitative data tables; its goal is to provide the data in the most convenient format with the least effort.…”

Section: Resultsmentioning

confidence: 99%

“…They amass both a number of samples and diversity of measurements that requires a large collaborative effort. In addition to the primary analysis done by the consortium and published as flagship manuscripts [1][2][3][4][5][6] , these datasets are designed to be a resource to the scientific community to explore new questions or apply novel methodologies [7][8][9][10][11] .…”

Section: Introductionmentioning

confidence: 99%

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Simplified and unified access to cancer proteogenomic data

Lindgren

Adams

Kimball

et al. 2020

Preprint

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Comprehensive cancer datasets recently generated by the Clinical Proteomic Tumor Analysis Consortium (CPTAC) offer great potential for advancing our understanding of how to combat cancer. These datasets include DNA, RNA, protein, and clinical characterization for tumor and normal samples from large cohorts in many different cancer types. The raw data are publicly available at various Cancer Research Data Commons. However, widespread re-use of these datasets is also facilitated by easy access to the processed quantitative data tables. We have created a Python package, cptac, which is a data API that distributes the finalized processed CPTAC datasets in a consistent, up-to-date format. This consistency makes it easy to integrate the data with common graphing, statistical, and machine learning packages for advanced analysis. Additionally, consistent formatting across all cancer types promotes the investigation of pan-cancer trends. The data API structure of directly streaming data within a programming environment enhances reproducibility. Finally, with the accompanying tutorials, this package provides a novel resource for cancer research education.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Simplified and unified access to cancer proteogenomic data

Lindgren

Adams

Kimball

et al. 2020

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…These activities empowered the recent creation of the International Proteogenome Consortium (ICPC; icpc.cancer.gov) 108 . Collectively, CPTAC and ICPC collaborators have comprehensively characterized 13 cancer types at the proteogenomics level, with all datasets publicly accessible [109][110][111][112][113][114][115][116][117] .…”

Section: Translating Proteomics To Precision Medicinementioning

confidence: 99%

A high-stringency blueprint of the human proteome

et al. 2020

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The Human Proteome Organization (HUPO) launched the Human Proteome Project (HPP) in 2010, creating an international framework for global collaboration, data sharing, quality assurance and enhancing accurate annotation of the genome-encoded proteome. During the subsequent decade, the HPP established collaborations, developed guidelines and metrics, and undertook reanalysis of previously deposited community data, continuously increasing the coverage of the human proteome. On the occasion of the HPP’s tenth anniversary, we here report a 90.4% complete high-stringency human proteome blueprint. This knowledge is essential for discerning molecular processes in health and disease, as we demonstrate by highlighting potential roles the human proteome plays in our understanding, diagnosis and treatment of cancers, cardiovascular and infectious diseases.

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“…However, the benefits of the integration of proteomics with genomics and transcriptomics have already proven useful to generate important and unique biological insights that were not possible with single omics data analysis (Zhang et al, 2014;Mertins et al, 2016). For instance, integration of multi-omics levels was used to uncover the novel altered molecular features and possible therapeutic/prognostic targets in lung adenocarcinoma (Gillette et al, 2020), ovarian cancer (Zhang et al, 2016), breast cancer (Huang et al, 2017) and colorectal cancer (Zhang et al, 2014). In addition to proteomics, phosphoproteomics studies were also conducted simultaneously on the same samples except for rectum adenocarcinoma whereas glycoproteomics was only performed for ovarian serous cystadenocarcinoma (OV) ( Table 3).…”

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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Proteogenomic Characterization Reveals Therapeutic Vulnerabilities in Lung Adenocarcinoma

Cited by 516 publications

References 242 publications

Simplified and unified access to cancer proteogenomic data

Simplified and unified access to cancer proteogenomic data

A high-stringency blueprint of the human proteome

Integration of Online Omics-Data Resources for Cancer Research

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