"Are Protein-Protein interactions likely to be valid therapeutic targets for cancer therapy?"
In her paper entitled “Protein-Protein interactions and cancer: small molecules going in for the kill” Michelle Arkin raises questions regarding the grooved and linear regions that permit protein-protein bonds, the binding peptides also found in those regions, if those interfaces are tractable for small molecule discovery and what compounds might serve as bonding agents. Further investigation of compounds would include if they are active, inhibiting, and do they help further characterize the target. She documents tubulin and several others that have potential. Her conclusion confirms that those interfaces do accept binding compounds and wisely observes that while there are hundreds of good binders that does not necessarily mean they will be good drugs, and more research is needed. CITATION Sun10 \l 1033
Christopher G. Cummings and Andrew D. Hamilton look to target protein-protein interactions with small molecules in their paper entitled “Disrupting protein-protein interactions with non-peptic, small molecule a-helix mimetics.” They observe that PPIs are involved in chemotaxis, proliferation, differentiation, endocytosis and apoptosis and because of this effect the cause and treatment of a variety of diseases. This makes the secondary structures at the PPIs’ interfaces targets for small molecule mimicry. They document research done on a number of a-helix mediated PPIs and reach the conclusion that the terphenls are a subset of scaffolds that can be finely tuned to many a-helix mediated PPIs and that continued development in this direction would yield PPI inhibitors. . CITATION Sun10 \l 1033
In their article “Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets”, Liang-Hui Chu and Bor-Sen Chen present a thorough investigation into the methods of identifying cancer-perturbed protein-protein interactions involved in apoptosis and identifies potential molecular targets from the selection of proteins known for roles in apoptosis as “core nodes” with a focus on identifying cancer-perturbed protein-protein interaction networks for use as targets in the development of anti-cancer drugs. After a rigorous process, they were unable to determine the qualification of all possible interactions of a target protein and were unable to determine the significance of the interaction so they used a statistical approach using model validation to evaluate significance levels and refine the network. Interactions were evaluated based upon bonding where an interaction detected only a HeLa cell was a “gain-of-function” and interactions detected in normal cell was a “los-of-function” because the on line databases had incomplete information protein complexes were only considered. Their contribution to the body of knowledge is the refinement of the process used to develop and determine a refined protein-protein interaction network for HeLa and normal cells. CITATION Sun10 \l 1033
Alexander B. Sigalov chose to work with “The SCHOOL of nature III From mechanistic understanding to novel therapies” to develop a greater understanding of protein-protein interactions and their use in biological processes. In particular, their function as targets for drug design and development and as targets for small molecule inhibitors, modulatory peptides and peptidomimetics. In particular, he is concerned with signaling chain homooligomerization or SCHOOL platform as universal therapeutic targets. The research is also concerned with the global therapeutic strategies targeting protein-protein interaction in receptor triggering and transmembrane signal transduction, and how these strategies can transfer between seemingly disparate disorders. Also addressed by this research are the human viruses and how these viruses use the SCHOOL-like strategies. This research addressed cancer, and a variety of other diseases. In regards to cancer, it found RTKs and some SRs, members of the tumor necrosis factor TNF) receptor superfamily can exist as pre-assembled dimers/oligomers on the surface of unstimulated cells. By the SCHOOL platform, binding to the multivalent ligand results in re-orientation of receptors and an interunit geometry permissive for CYTO homointeractions and further receptor activation. The conclusion followed the hypothesis that within the single and multichain receptor families the architecture of the receptors dictates similar mechanisms and suggests similar therapeutic targets. It also reveals the discovery of biophysical phenomenon that led to the general platform and signaled the SCHOOL platform. The SCHOOL platform can readily describe molecular signaling of any particular receptor in that the platform indicates molecular mechanisms for the majority of unexplained observations. It also reveals key points of control in receptor triggering as novel universal therapeutic targets for receptor -mediated disorders and improves our understanding of cancer and many other human viruses. CITATION Sun10 \l 1033
Adrain P.Quayle, Asim S. Diddiqui and Steven J.M. Jones present “Perturbation of Interaction Networks for Application to Cancer Therapy” which presents the computational approach for studying the effect of potential drug combinations on the protein networks associated with tumor cells. In particular, they take the unique approach of looking at multiple simultaneous protein-protein interaction networks and the combination of many interacting genes and proteins that characterize most diseased states. Because most diseased states are polygenic treatment has increased efficacy if it address a broad spectrum. Highly connected proteins have an essential role in this, and all cellular processes. The methodology was split into two stages; the integrate protein interaction and gene expression data, and the model network perturbations to search for target combinations. The method was developed, and served to reveal significant bias in curetted interaction data sources towards targets as compared with data sources form model organisms. This approach can be applied to other diseased cell types. They developed and studied a novel method for predicting target and drug combinations, which has added to the body of knowledge of how to facilitate a search for novel target combinations that significantly select for cancer-associated targets CITATION Sun10 \l 1033 .
Horng D. Ou, Andrew P. May and Clodagh C. OShea’s recent research entitled “The critical protein interactions and structures that elicit growth deregulation in cancer and viral replication” is intended to define the critical targets and network interactions that can elicit growth deregulation in cells. In particular, their study looks how key molecular targets and how they interact they look at small DNA viruses evolved to target inherent weaknesses in cellular protein interact. The study includes the similar mechanisms that drive unchecked cellular replication. It explores how viruses can be used as tools to probe system-wide protein-protein interactions and structures. The study concluded the interactions of small DNA virus proteins could reveal the critical protein structures and networks that regulate cellular growth and survival and how small viral proteins have found optimized solutions. This provides the background for further study of systematic structural and procedures. CITATION Sun10 \l 1033
Zhiwei Wang, Yiwei and Fazlul H. Sarkar find “Notch Signaling Proteins: Legitimate targets for Cancer Therapy” They explore the Notch signaling pathway proteins known to play roles in the balance between cell proliferation differentiation and apoptosis, and suggested that Notch may be responsible for the development and progression. Because of the signaling pathway, proteins may provide therapeutic targets. To that end, they describe the role of the Notch proteins, deregulation and progression, metastasis and the demise of patients. Then they summarize the role of sever inhibitors that could present strategies targeting Notch signaling. After evaluating the existing research from this prospective, they find that the known-regulation of Notch pathway by “natural agents” could help prevent tumor progression and the treatment of human cancers since these natural agents are general non-toxic and can be used for targeted elimination of drug-resistant CSCs, which hold the potential for targeted elimination of tumor cells. CITATION Sun10 \l 1033
“A Network-based biomarker approach for molecular investigation and diagnosis of lung cancer” by Yu-Chao Wang and Bor-Sen Chen looks at lung cancer for a network-based method for carcinogenesis characterization and diagnosis from the systems perspective. To do this they use the systems biology method and integrate microarray gene expression profiles and protein-protein interaction information to develop a network-based biomarker to diagnose lung cancer. This network-based biomarker is made of two protein association networks for cancer and non-cancer samples. They found 40 significant proteins identified with carcinogenesis and the network-based biomarker that proved effective in diagnosing smokers with signs of lung cancer in this they added significantly to the body of knowledge regarding the diagnosis of carcinogenesis and cancer. This network-based biomarker is not only a tool in diagnosis it may also prove to be a potential therapeutic target to combat cancer. CITATION Sun10 \l 1033
In “Ras superfamiily GEFs and GAPs: validated and tractable targets for cancer therapy?” Dominico Vigil, Jacqueline Cherfils, Kent L. Rossman and Channing J. Der loot at the causal role of aberrant activity of the Ras super family of small GTPases in human cancers, which act as GDP-GTP, regulated binary switches controlling many fundamental cellular processes. They assess the association of GEFs and GAPs with cancer and druggability for cancer therapeutics. They found there is increasing evidence that the aberrant activity of numerous members of the Ras super family of small GTPasses contribute to cancer. Unlike Ras proteins, GTPases are deregulated by indirect mechanisms, involving altered expression or activity of their regulatory proteins. That GEFs and GTPase activating proteins (GAPs) that control the GDP-GTP cycle have shown to add to cancer by either promoting of suppressing tumor progression and growth. GEFs and GAPs are deregulated in cancer by somatic mutation, changes in gene expression and through post-translational mechanisms that include aberrant signaling from changes in upstream oncogene or tumor suppressor function. The GEFs AND GAPs contribute to their regulation by signaling mechanisms and may identify therapeutic approaches although they are not classically druggable targets. CITATION Sun10 \l 1033
Halliday A Idikio’s “Human Cancer Classification: A systems Biology based Model Intigrating Morphology, Cancer stem Cells, Proteomics, and Genomics” uses proteins as a part of the method to identify and classify cancers. One of the hallmarks of cancer cells is the protein-protein interaction and signaling networks. In an effort to take into account and individualize cancer treatment, it is useful to employ genomic cancer medicine and encourage the use of integrated classification. Proteomics are part of this process that utilizes protein-antigen arrays and includes cDNA profiling Copy number variation Methylation status and miRNA profiling. gene sequencing and cancer grading. The integrated model uses comprehensive bioinformatics and data mining tools. Their conclusion is that integrated models of cells that capture every essence of a cancer could enhance treatment for maximum effect. CITATION Sun10 \l 1033
The “Human Cancer Protein-Protein Interaction Network: A Structural Perspective” written by Gozde Kar, Attila Gursoy and Ozlem Keskin looks at the protein-protein interaction network to provide a global picture of cellular function. From the hub proteins that are highly connected to other proteins, which only have a few interactions there is the potential for disease causing dysfunction. They set forth a methodology to integrate protein interfaces into cancer treatment using cancer interaction networks(ciSPIN) . There is a detailed analysis of cancer related protein-protein interfaces and topological properties of the cancer network. Cancer related proteins have smaller, more planar, more charged and less hydrophobic binding sites than non-cancer interactions. This may indicate low affinity and high specificity in cancer-related interactions. Using protein-protein interaction and cancer associated protein interaction dataset to study the interaction. Those interactions that were redundant were eliminated. The resulting list was collected alone with a set of known cancer genes and mapped. Hubs and bottlenecks were defined and essential genes determined. Using a PRISM server the protein-protein algorithm produced scores to predict the most possible interactions. The results reviled that the affinity of interactions of the multi-interface hub tends to be higher that the single interface hub which may be useful to get new targets in cancer. CITATION Sun10 \l 1033
“Prediction protein-protein interactions in Arabidopsis thaliana through integration of orthology, gene ontology and co-expression” by Stefanie De Bodt, Sebastian Proost, Dlass Vandepoele, Pierre Roze and Yves Van de Peer, works with large scale identification of the interrelationships between cellular components. This includes the interactions between proteins and unraveling of large scale protein-protein interaction maps which can be laborious and expensive. To simplify this process they developed a computational method using existing knowledge of protein-protein interactions in diverse species through orthologous relations and functional association data to predict protein-protein interactions in Arabidopsis thaliana. Various data was used to identify the protein-protein interactions and the identified interactome that showed interactions between proteins exist between different lineages and are active in specific processes including cancer metastasis. This allowed them to conclude that the integration of orthology with functional association date as adequate to predict protein-protein interactions and many novel protein-protein interactions wit different roles were discovered including protein-protein interactions worthy of future analyses.
“Predicting cancer involvement of genes from heterogeneous data” by Ramon Aragues, Chris Sander and Baldo Oliva gives systematic approaches for identifying proteins involved in different types of cancer. Using a systematic method of PIANA software they produced lists of genes likely to be involved in cancer by analyzing protein-protein interactions; data and structural and functional properties of genes. Protein interaction networks are a useful tool to help understand cellular biology. The topology of the networks and the location of the protein in the network are useful to characterize proteins. One of the tendencies is for proteins related to a disease to have high connectivity between. this increase in inherited diseases, including cancer. Somatic cancer genes are more likely than other genes to encode proteins with many interaction partners or hubs. Some approaches have used these protein hubs to identify cancer genes, while others have based their predictions on sequence, structure and functional properties. This method incorporates identifying cancer genomes by identifying protein hubs and the protein-protein interface along with other data. This produced predictive values between 23% and 73%. An analysis of a list of 20 cancer gene predictions revealed that in recent literature these gene predictions were recently linked to cancer. CITATION Sun10 \l 1033
“A comparative study of cancer proteins in the human protein-protein interaction network” by Jinchun Sun and Zhongming Zhao goes beyond the traditional approach of studying individual genes of loci and instead employs a systematic investigation of cancer proteins in the human protein-protein interaction network to provide biological information to uncover the molecular mechanisms of cancer. To employ this methodology they looked at the local and global network characteristics of cancer proteins in the human interactome. This revealed that the cancer proteins’ network topology was different from proteins encoded by essential genes or control genes. Cancer proteins tended to have higher degree and betweenness, shorter shortest path distance, and weaker clustering coefficient in the human interactome. By further separating the cancer proteins into recessive and dominant proteins and comparing their topological it became evident that recessive cancer proteins had higher betweenness than dominant cancer proteins and their degree distribution and characteristic shortest path distance were significantly different as well. The distribution of cancer proteins was not random and they connected strongly with each other. Based on the four topological measurements the results suggested that protein-protein interaction features of cancer genes are important to understand the etiology of cancers. CITATION Ara08 \l 1033
References
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General Search
1: Program-Abstracts Cancer Microenviron. 2009 September; 2(Suppl 1): 19–204. Published online 2009 September 26. doi: 10.1007/s12307-009-0029-4
PMCID: PMC2756345
2: Construction of a cancer-perturbed protein-protein interaction network for discovery of apoptosis drug targets
Liang-Hui Chu, Bor-Sen Chen
BMC Syst Biol. 2008; 2: 56. Published online 2008 June 30. doi: 10.1186/1752-0509-2-56
PMCID: PMC2488323
3: The SCHOOL of nature: III. From mechanistic understanding to novel therapies
Alexander B Sigalov
Self Nonself. 2010 Jul-Sep; 1(3): 192–224. Published online 2010 June 11. doi: 10.4161/self.1.3.12794
PMCID: PMC3047783
4: The 20th Aspen Cancer Conference: Mechanisms of Toxicity, Carcinogenesis, Cancer Prevention, and Cancer Therapy 2005
Miriam Sander, Benjamin F. Trump, Curtis C. Harris, Raymond W. Tennant
Mol Carcinog. Author manuscript; available in PMC 2009 September 1. Published in final edited form as: Mol Carcinog. 2008 September; 47 (9) : 707–732. doi: 10.1002/mc.20214
PMCID: PMC2610844
5: The critical protein interactions and structures that elicit growth deregulation in cancer and viral replication
Horng D. Ou, Andrew P. May, Clodagh C. O’Shea
Wiley Interdiscip Rev Syst Biol Med. Author manuscript; available in PMC 2012 January 1. Published in final edited form as: Wiley Interdiscip Rev Syst Biol Med. 2011 Jan–Feb; 3 (1) : 48–73. doi: 10.1002/wsbm.88
PMCID: PMC3053133
6: DEAR1 Is a Dominant Regulator of Acinar Morphogenesis and an Independent Predictor of Local Recurrence-Free Survival in Early-Onset Breast Cancer
Steven T. Lott, Nanyue Chen, Dawn S. Chandler, Qifeng Yang, Luo Wang, Marivonne Rodriguez, Hongyan Xie, Seetharaman Balasenthil, Thomas A. Buchholz, Aysegul A. Sahin, Katrina Chaung, Baili Zhang, Shodimu-Emmanu Olufemi, Jinyun Chen, Henry Adams, Vimla Band, Adel K. El-Naggar, Marsha L. Frazier, Khandan Keyomarsi, Kelly K. Hunt, Subrata Sen, Bruce Haffty, Stephen M. Hewitt, Ralf Krahe, Ann McNeill Killary
PLoS Med. 2009 May; 6(5): e1000068. Published online 2009 May 5. doi: 10.1371/journal.pmed.1000068
PMCID: PMC2673042
7: Perturbation of Interaction Networks for Application to Cancer Therapy
Adrian P. Quayle, Asim S. Siddiqui, Steven J. M. Jones
Cancer Inform. 2007; 5: 45–65. Published online 2007 April 1.
PMCID: PMC2666951
8: Principles of Cancer Therapy: Oncogene and Non-oncogene Addiction
Ji Luo, Nicole L. Solimini, Stephen J. Elledge
Cell. Author manuscript; available in PMC 2010 June 30. Published in final edited form as: Cell. 2009 March 6; 136 (5) : 823–837. doi: 10.1016/j.cell.2009.02.024
PMCID: PMC2894612
9: Imaging and Photodynamic Therapy: Mechanisms, Monitoring and Optimization
Jonathan P. Celli, Bryan Q. Spring, Imran Rizvi, Conor L. Evans, Kimberly S. Samkoe, Sarika Verma, Brian W. Pogue, Tayyaba Hasan
Chem Rev. Author manuscript; available in PMC 2011 May 12. Published in final edited form as: Chem Rev. 2010 May 12; 110 (5) : 2795–2838. doi: 10.1021/cr900300p
PMCID: PMC2896821
10: The ERBB3 receptor in cancer and cancer gene therapy
G Sithanandam, LM Anderson
Cancer Gene Ther. Author manuscript; available in PMC 2009 October 14. Published in final edited form as: Cancer Gene Ther. 2008 July; 15 (7) : 413–448. Published online 2008 April 11. doi: 10.1038/cgt.2008.15
PMCID: PMC2761714
11: Cancer-Associated Fibroblasts and Their Putative Role in Potentiating the Initiation and Development of Epithelial Ovarian Cancer
Isaiah G Schauer, Anil K Sood, Samuel Mok, Jinsong Liu
Neoplasia. 2011 May; 13(5): 393–405.
PMCID: PMC3084616
12: Notch Signaling Proteins: Legitimate Targets for Cancer Therapy
Zhiwei Wang, Yiwei Li, Fazlul H. Sarkar
Curr Protein Pept Sci. Author manuscript; available in PMC 2011 May 16. Published in final edited form as: Curr Protein Pept Sci. 2010 September 1; 11 (6) : 398–408.
PMCID: PMC3094802
13: Discovering cancer genes by integrating network and functional properties
Li Li, Kangyu Zhang, James Lee, Shaun Cordes, David P Davis, Zhijun Tang
BMC Med Genomics. 2009; 2: 61. Published online 2009 September 19. doi: 10.1186/1755-8794-2-61
PMCID: PMC2758898
14: Protein-network modeling of prostate cancer gene signatures reveals essential pathways in disease recurrence
James L Chen, Jianrong Li, Walter M Stadler, Yves A Lussier
J Am Med Inform Assoc. 2011 July; 18(4): 392–402. doi: 10.1136/amiajnl-2011-000178
PMCID: PMC3128407
15: Human Cancer Classification: A Systems Biology- Based Model Integrating Morphology, Cancer Stem Cells, Proteomics, and Genomics
Halliday A Idikio
J Cancer. 2011; 2: 107–115. Published online 2011 February 22.
PMCID: PMC3072616
16: Roles of the Raf/MEK/ERK and PI3K/PTEN/Akt/mTOR pathways in controlling growth and sensitivity to therapy-implications for cancer and aging
Linda S. Steelman, William H. Chappell, Stephen L. Abrams, C. Ruth Kempf, Jacquelyn Long, Piotr Laidler, Sanja Mijatovic, Danijela Maksimovic-Ivanic, Franca Stivala, Maria C. Mazzarino, Marco Donia, Paolo Fagone, Graziella Malaponte, Ferdinando Nicoletti, Massimo Libra, Michele Milella, Agostino Tafuri, Antonio Bonati, Jörg Bäsecke, Lucio Cocco, Camilla Evangelisti, Alberto M. Martelli, Giuseppe Montalto, Melchiorre Cervello, James A. McCubrey
Aging (Albany NY) 2011 March; 3(3): 192–222. Published online 2011 March 10.
PMCID: PMC3091517
17: A network-based biomarker approach for molecular investigation and diagnosis of lung cancer
Yu-Chao Wang, Bor-Sen Chen
BMC Med Genomics. 2011; 4: 2. Published online 2011 January 6. doi: 10.1186/1755-8794-4-2
PMCID: PMC3027087
18: Antibodies targeting cancer stem cells: A new paradigm in immunotherapy?
Mahendra P Deonarain, Christina A Kousparou, Agamemnon A Epenetos
MAbs. 2009 Jan-Feb; 1(1): 12–25.
PMCID: PMC2715180
19: The Twenty-Second Aspen Cancer Conference: Mechanisms of Toxicity, Carcinogenesis, Cancer Prevention, and Cancer Therapy, 2007
Miriam Sander, Thomas J. Slaga, Benjamin F. Trump, Curtis C. Harris
Mol Carcinog. Author manuscript; available in PMC 2009 July 1. Published in final edited form as: Mol Carcinog. 2008 July; 47 (7) : 554–571. doi: 10.1002/mc.20408
PMCID: PMC2642617
20: Ras superfamily GEFs and GAPs: validated and tractable targets for cancer therapy?
Dominico Vigil, Jacqueline Cherfils, Kent L. Rossman, Channing J. Der
Nat Rev Cancer. Author manuscript; available in PMC 2011 June 27. Published in final edited form as: Nat Rev Cancer. 2010 December; 10 (12) : 842–857. Published online 2010 November 24. doi: 10.1038/nrc2960
PMCID: PMC3124093
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