Research highlight by Jeongah Yoon and Thomas S. Deisboeck, Massachusetts General Hospital, Charlestown, MA

Targeting receptor tyrosine kinases (RTKs) is currently thought to be a promising anti-cancer strategy (Baselga, 2006). However, clinical trials with RTK inhibitors demonstrated that some solid tumors are sensitive to these drugs while others are not. For instance, only a subset of non small cell lung cancer (NSCLC) tumors with EGFR-activating mutations seems to respond to EGFR inhibitors (Lynch et al, 2004).

The recent study by Guo et al (2008) aims to shed more light on the causes for such selective drug sensitivity by investigating the downstream signaling pathways of several NSCLC cell lines and a gastric cancer cell line. Using a quantitative global proteomic analysis (PhosphoScan-SILAC) they analyzed the EGFR and c-Met networks, treated with the EGFR inhibitor gefitinib and the c-Met inhibitor Su11274, respectively.

The results show a dramatic decrease in EGFR phosphorylation from 5- to 200-fold after gefitinib treatment as well as a reduction of some adaptor proteins (e.g., Her3, Gab1, and Shc1), adhesion and cytoskeletal proteins. Furthermore, a c-Met-driven gastric cancer cell line demonstrated sensitivity to the c-Met inhibitor, Su11274. The authors observed that the inhibited EGFR and c-Met signaling networks share a number of molecular components which underscores that amplified c-Met can drive the activity of (mutated) EGFR and vice versa. In both cases, the targeted kinase is positioned on top of the hierarchical signaling network and thus controls downstream signaling.

In conclusion, this interesting study suggests that there is a common sub-cellular signaling module that processes drug sensitivity and that the effect of an anti-RTK therapeutic compound is maximized when the targeted kinase uniquely controls the downstream signaling networks.

Baselga J (2006) Targeting tyrosine kinases in cancer: the second wave. Science 312:1175-8

Guo A, Villén J, Kornhauser J, Lee KA, Stokes MP, Rikova K, Possemato A, Nardone J, Innocenti G, Wetzel R, Wang Y, MacNeill J, Mitchell J, Gygi SP, Rush J, Polakiewicz RD, Comb MJ (2008) Signaling networks assembled by oncogenic EGFR and c-Met. Proc Natl Acad Sci U S A. 105:692-7

Lynch TJ, Bell DW, Sordella R, Gurubhagavatula S, Okimoto RA, Brannigan BW, Harris PL, Haserlat SM, Supko JG, Haluska FG, Louis DN, Christiani DC, Settleman J, Haber DA (2004) Activating mutations in the epidermal growth factor receptor underlying responsiveness of non-small-cell lung cancer to gefitinib. N Engl J Med. 350:2129-39

In a very recent lecture (see full video from NIH VideoCasting) given for the NIH Systems Biology Special Interest Group, Trey Ideker presents a great overview of the various strategies his group has been developing in the recent years in order to integrate multiple types of large scale datasets. While one of the most pervasive 'meme' about high-throughput measurement is that they are "notoriously unreliable" (see Hakes et al, 2008, for a recent example), Trey beautifully illustrates how predictive computational models and novel biological insights can be generated by sophisticated data integration strategies. Three types of applications are presented in his talk:

1. mapping of transcriptional response pathways
2. functional mapping of protein complexes
3. disease diagnosis and stratification

In the last section, Trey presents the study recently published in Molecular Systems Biology (Chuang et al, 2007, video: 00hr:39min:15sec) where the information provided by microarray expression profiling is superposed to a protein-protein physical interaction network to identify 'subnetwork' biomarkers that classify metastatic vs non-metastatic breast tumors.