加拿大研究人员周日表示，观察各种蛋白质在肿瘤中互相作用的情况有助于预测乳腺癌患者幸存的概率，从而可以使医生更好地调整治疗方式。
如果一开始就知道患者病情不好的话，医生就能立即对其采取积极的治疗方式。但通常情况下，很难预测乳腺癌患者的病情发展情况。
研究人员对美国和欧洲约350名乳腺癌患者乳房肿瘤组织中的蛋白质网络进行了分析。结果显示，痊愈幸存者癌细胞中的蛋白质网络组织形式和死亡者的大不一样。
研究人员在《自然-生物技术》（Nature Biotechnology）上撰文指出，通过观测这些蛋白质的相互作用，对乳腺癌患者未来病情走势预测的准确率可以达到82%。
“这有助于对不同的患者采取适合她们的治疗方法。”
研究人员对约8,000种蛋白质的30,000种作用方式进行了研究，发现其中约250种蛋白质对预测病情发展至关重要。（生物谷Bioon.com）
生物谷推荐原始出处：
Nature Biotechnology Published online: 1 February 2009 doi:10.1038/nbt.1522
Dynamic modularity in protein interaction networks predicts breast cancer outcome
Ian W Taylor1,2, Rune Linding1,3, David Warde-Farley4,5, Yongmei Liu1, Catia Pesquita4, Daniel Faria4, Shelley Bull1,5, Tony Pawson1,2, Quaid Morris4 & Jeffrey L Wrana1,2
Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may be useful as an indicator of breast cancer prognosis.
1 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, Ontario M5G 1X5, Canada.2 Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Room 4396, Toronto, Ontario M5S 1A8, Canada.3 Cellular & Molecular Logic Team, Institute of Cancer Research (ICR), Section of Cell, Molecular & Systems Section, 237 Fulham Road, London, SW3 6JB, UK.4 The Terrence Donnelly Centre for Cellular and Biomolecular Research, 160 College St., Toronto, Ontario M5S 3E1, Canada.5 Department of Computer Science, University of Toronto, 10 King's College Road, Room 3303, Toronto, Ontario M5S 3G4, Canada.6 Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016, Lisbon, Portugal.7 Dalla Lana, School of Public Health, University of Toronto, 155 College St., Toronto, ON M5T 3M7, Canada.
如果一开始就知道患者病情不好的话，医生就能立即对其采取积极的治疗方式。但通常情况下，很难预测乳腺癌患者的病情发展情况。
研究人员对美国和欧洲约350名乳腺癌患者乳房肿瘤组织中的蛋白质网络进行了分析。结果显示，痊愈幸存者癌细胞中的蛋白质网络组织形式和死亡者的大不一样。
研究人员在《自然-生物技术》（Nature Biotechnology）上撰文指出，通过观测这些蛋白质的相互作用，对乳腺癌患者未来病情走势预测的准确率可以达到82%。
“这有助于对不同的患者采取适合她们的治疗方法。”
研究人员对约8,000种蛋白质的30,000种作用方式进行了研究，发现其中约250种蛋白质对预测病情发展至关重要。（生物谷Bioon.com）
生物谷推荐原始出处：
Nature Biotechnology Published online: 1 February 2009 doi:10.1038/nbt.1522
Dynamic modularity in protein interaction networks predicts breast cancer outcome
Ian W Taylor1,2, Rune Linding1,3, David Warde-Farley4,5, Yongmei Liu1, Catia Pesquita4, Daniel Faria4, Shelley Bull1,5, Tony Pawson1,2, Quaid Morris4 & Jeffrey L Wrana1,2
Changes in the biochemical wiring of oncogenic cells drives phenotypic transformations that directly affect disease outcome. Here we examine the dynamic structure of the human protein interaction network (interactome) to determine whether changes in the organization of the interactome can be used to predict patient outcome. An analysis of hub proteins identified intermodular hub proteins that are co-expressed with their interacting partners in a tissue-restricted manner and intramodular hub proteins that are co-expressed with their interacting partners in all or most tissues. Substantial differences in biochemical structure were observed between the two types of hubs. Signaling domains were found more often in intermodular hub proteins, which were also more frequently associated with oncogenesis. Analysis of two breast cancer patient cohorts revealed that altered modularity of the human interactome may be useful as an indicator of breast cancer prognosis.
1 Samuel Lunenfeld Research Institute, Mount Sinai Hospital, 600 University Ave., Toronto, Ontario M5G 1X5, Canada.2 Department of Molecular Genetics, University of Toronto, 1 King's College Circle, Room 4396, Toronto, Ontario M5S 1A8, Canada.3 Cellular & Molecular Logic Team, Institute of Cancer Research (ICR), Section of Cell, Molecular & Systems Section, 237 Fulham Road, London, SW3 6JB, UK.4 The Terrence Donnelly Centre for Cellular and Biomolecular Research, 160 College St., Toronto, Ontario M5S 3E1, Canada.5 Department of Computer Science, University of Toronto, 10 King's College Road, Room 3303, Toronto, Ontario M5S 3G4, Canada.6 Faculty of Sciences, University of Lisbon, Campo Grande, 1749-016, Lisbon, Portugal.7 Dalla Lana, School of Public Health, University of Toronto, 155 College St., Toronto, ON M5T 3M7, Canada.
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