The proteomic landscape of triple-negative breast cancer.
The proteomic landscape of triple-negative breast cancer.
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DOI：
10.1016/j.celrep.2015.03.050
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发表时间：
2015-04-28
期刊：
影响因子：
8.8
通讯作者：
Villén J
中科院分区：
文献类型：
--
作者：
Lawrence RT;Perez EM;Hernández D;Miller CP;Haas KM;Irie HY;Lee SI;Blau CA;Villén J
三阴性乳腺癌是一种异质性疾病，其特点是临床预后差且缺乏靶向治疗方案。为了发现三阴性乳腺癌的分子特征，我们对20种人源乳腺细胞系和4个原发性乳腺肿瘤进行了定量蛋白质组学分析，分析深度涵盖超过12000种不同的蛋白质。我们利用这些数据在蛋白质水平上识别乳腺癌亚型，并通过质谱法展示了生物标志物、信号蛋白和生物通路的精确定量。我们将蛋白质组学数据与外显子组序列资源相结合，以识别影响蛋白质表达的基因组畸变。我们进行了高通量药物筛选，以识别药物敏感性的蛋白质标志物并了解耐药机制。基因组和蛋白质组提供了互补信息，当两者结合时，为治疗发现提供了强大的动力。这一资源可供癌症研究界使用，以促进进一步的分析和研究。
Triple-negative breast cancer is a heterogeneous disease characterized by poor clinical outcomes and a shortage of targeted treatment options. To discover molecular features of triple-negative breast cancer, we performed quantitative proteomics analysis of twenty human-derived breast cell lines and four primary breast tumors to a depth of more than 12,000 distinct proteins. We used this data to identify breast cancer subtypes at the protein level and demonstrate the precise quantification of biomarkers, signaling proteins, and biological pathways by mass spectrometry. We integrated proteomics data with exome sequence resources to identify genomic aberrations that affect protein expression. We performed a high-throughput drug screen to identify protein markers of drug sensitivity and understand the mechanisms of drug resistance. The genome and proteome provide complementary information that, when combined, provide a powerful engine for therapeutic discovery. This resource is available to the cancer research community to catalyze further analysis and investigation.
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影响因子：
64.8
作者：
Kim, Min-Sik;Pinto, Sneha M.;Getnet, Derese;Nirujogi, Raja Sekhar;Manda, Srikanth S.;Chaerkady, Raghothama;Madugundu, Anil K.;Kelkar, Dhanashree S.;Isserlin, Ruth;Jain, Shobhit;Thomas, Joji K.;Muthusamy, Babylakshmi;Leal-Rojas, Pamela;Kumar, Praveen;Sahasrabuddhe, Nandini A.;Balakrishnan, Lavanya;Advani, Jayshree;George, Bijesh;Renuse, Santosh;Selvan, Lakshmi Dhevi N.;Patil, Arun H.;Nanjappa, Vishalakshi;Radhakrishnan, Aneesha;Prasad, Samarjeet;Subbannayya, Tejaswini;Raju, Rajesh;Kumar, Manish;Sreenivasamurthy, Sreelakshmi K.;Marimuthu, Arivusudar;Sathe, Gajanan J.;Chavan, Sandip;Datta, Keshava K.;Subbannayya, Yashwanth;Sahu, Apeksha;Yelamanchi, Soujanya D.;Jayaram, Savita;Rajagopalan, Pavithra;Sharma, Jyoti;Murthy, Krishna R.;Syed, Nazia;Goel, Renu;Khan, Aafaque A.;Ahmad, Sartaj;Dey, Gourav;Mudgal, Keshav;Chatterjee, Aditi;Huang, Tai-Chung;Zhong, Jun;Wu, Xinyan;Shaw, Patrick G.;Freed, Donald;Zahari, Muhammad S.;Mukherjee, Kanchan K.;Shankar, Subramanian;Mahadevan, Anita;Lam, Henry;Mitchell, Christopher J.;Shankar, Susarla Krishna;Satishchandra, Parthasarathy;Schroeder, John T.;Sirdeshmukh, Ravi;Maitra, Anirban;Leach, Steven D.;Drake, Charles G.;Halushka, Marc K.;Prasad, T. S. Keshava;Hruban, Ralph H.;Kerr, Candace L.;Bader, Gary D.;Iacobuzio-Donahue, Christine A.;Gowda, Harsha;Pandey, Akhilesh
通讯作者：
Pandey, Akhilesh
DOI：
10.1038/nrc1299
发表时间：
2004-03
期刊：
Nature reviews. Cancer
影响因子：
--
作者：
通讯作者：
--
影响因子：
3.7
作者：
Chatterjee P;Choudhary GS;Sharma A;Singh K;Heston WD;Ciezki J;Klein EA;Almasan A
通讯作者：
Almasan A
影响因子：
64.8
作者：
Curtis, Christina;Shah, Sohrab P.;Chin, Suet-Feung;Turashvili, Gulisa;Rueda, Oscar M.;Dunning, Mark J.;Speed, Doug;Lynch, Andy G.;Samarajiwa, Shamith;Yuan, Yinyin;Graef, Stefan;Ha, Gavin;Haffari, Gholamreza;Bashashati, Ali;Russell, Roslin;McKinney, Steven;Langerod, Anita;Green, Andrew;Provenzano, Elena;Wishart, Gordon;Pinder, Sarah;Watson, Peter;Markowetz, Florian;Murphy, Leigh;Ellis, Ian;Purushotham, Arnie;Borresen-Dale, Anne-Lise;Brenton, James D.;Tavare, Simon;Caldas, Carlos;Aparicio, Samuel
通讯作者：
Aparicio, Samuel
DOI：
10.1083/jcb.200408161
发表时间：
2004-11-08
期刊：
The Journal of cell biology
影响因子：
--
作者：
Manning BD
通讯作者：
Manning BD