@article {49799,
	title = {System-wide Clinical Proteomics of Breast Cancer Reveals Global Remodeling of Tissue Homeostasis.},
	journal = {Cell Syst},
	volume = {2},
	year = {2016},
	month = {2016 Mar 23},
	pages = {172-84},
	abstract = {<p>The genomic and transcriptomic landscapes of breast cancer have been extensively studied, but the proteomes of breast tumors are far less characterized. Here, we use high-resolution, high-accuracy mass spectrometry to perform a deep analysis of luminal-type breast cancer progression using clinical breast samples from primary tumors, matched lymph node metastases, and healthy breast epithelia. We used a super-SILAC mix to quantify over 10,000 proteins with high accuracy, enabling us to identify key proteins and pathways associated with tumorigenesis and metastatic spread. We found high expression levels of proteins associated with protein synthesis and degradation in cancer tissues, accompanied by metabolic alterations that may facilitate energy production in cancer cells within their natural environment. In addition, we found proteomic differences between breast cancer stages and minor differences between primary tumors and their matched lymph node metastases. These results highlight the potential of proteomic technology in the elucidation of clinically relevant cancer signatures.</p>},
	issn = {2405-4712},
	doi = {10.1016/j.cels.2016.02.001},
	author = {Pozniak, Yair and Balint-Lahat, Nora and Rudolph, Jan Daniel and Lindskog, Cecilia and Katzir, Rotem and Avivi, Camilla and Pont{\'e}n, Fredrik and Ruppin, Eytan and Barshack, Iris and Geiger, Tamar}
}
@article {49598,
	title = {Synthetic dosage lethality in the human metabolic network is highly predictive of tumor growth and cancer patient survival.},
	journal = {Proc Natl Acad Sci U S A},
	year = {2015},
	month = {2015 Sep 14},
	abstract = {<p>Synthetic dosage lethality (SDL) denotes a genetic interaction between two genes whereby the underexpression of gene A combined with the overexpression of gene B is lethal. SDLs offer a promising way to kill cancer cells by inhibiting the activity of SDL partners of activated oncogenes in tumors, which are often difficult to target directly. As experimental genome-wide SDL screens are still scarce, here we introduce a network-level computational modeling framework that quantitatively predicts human SDLs in metabolism. For each enzyme pair (A, B) we systematically knock out the flux through A combined with a stepwise flux increase through B and search for pairs that reduce cellular growth more than when either enzyme is perturbed individually. The predictive signal of the emerging network of 12,000 SDLs is demonstrated in five different ways. (i) It can be successfully used to predict gene essentiality in shRNA cancer cell line screens. Moving to clinical tumors, we show that (ii) SDLs are significantly underrepresented in tumors. Furthermore, breast cancer tumors with SDLs active (iii) have smaller sizes and (iv) result in increased patient survival, indicating that activation of SDLs increases cancer vulnerability. Finally, (v) patient survival improves when multiple SDLs are present, pointing to a cumulative effect. This study lays the basis for quantitative identification of cancer SDLs in a model-based mechanistic manner. The approach presented can be used to identify SDLs in species and cell types in which "omics" data necessary for data-driven identification are missing.</p>},
	issn = {1091-6490},
	doi = {10.1073/pnas.1508573112},
	author = {Megchelenbrink, Wout and Katzir, Rotem and Lu, Xiaowen and Ruppin, Eytan and Notebaart, Richard A}
}
