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Phuoc T Tran,
Pavan K Bendapudi,
H Jill Lin,
Peter Choi,
Shan Koh,
Joy Chen, George Horng,
Nicholas P Hughes,
Lawrence H Schwartz,
Vincent A Miller,
Toshiyuki Kawashima,
Toshio Kitamura,
David Paik,
Dean W Felsher
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ABSTRACT: Cancers can exhibit marked tumor regression after oncogene inhibition through a phenomenon called "oncogene addiction." The ability to predict when a tumor will exhibit oncogene addiction would be useful in the development of targeted therapeutics. Oncogene addiction is likely the consequence of many cellular programs. However, we reasoned that many of these inputs may converge on aggregate survival and death signals. To test this, we examined conditional transgenic models of K-ras(G12D)--or MYC-induced lung tumors and lymphoma combined with quantitative imaging and an in situ analysis of biomarkers of proliferation and apoptotic signaling. We then used computational modeling based on ordinary differential equations (ODEs) to show that oncogene addiction could be modeled as differential changes in survival and death intracellular signals. Our mathematical model could be generalized to different imaging methods (computed tomography and bioluminescence imaging), different oncogenes (K-ras(G12D) and MYC), and several tumor types (lung and lymphoma). Our ODE model could predict the differential dynamics of several putative prosurvival and prodeath signaling factors [phosphorylated extracellular signal-regulated kinase 1 and 2, Akt1, Stat3/5 (signal transducer and activator of transcription 3/5), and p38] that contribute to the aggregate survival and death signals after oncogene inactivation. Furthermore, we could predict the influence of specific genetic lesions (p53⁻/⁻, Stat3-d358L, and myr-Akt1) on tumor regression after oncogene inactivation. Then, using machine learning based on support vector machine, we applied quantitative imaging methods to human patients to predict both their EGFR genotype and their progression-free survival after treatment with the targeted therapeutic erlotinib. Hence, the consequences of oncogene inactivation can be accurately modeled on the basis of a relatively small number of parameters that may predict when targeted therapeutics will elicit oncogene addiction after oncogene inactivation and hence tumor regression.
Science translational medicine 10/2011; 3(103):103ra99. · 7.80 Impact Factor
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ABSTRACT: We describe a new generation of tracers for molecular imaging of the cell surface receptors for epidermal growth factor (EGF). These receptors play a key role in the progression of many tumors and are major drug development targets. Our tracers are based on a recombinant human EGF expressed with a cysteine-containing tag that enables facile site-specific radiolabeling with (99m)Tc for single photon emission computed tomography or site-specific conjugation of (64)Cu PEGylated chelators for positron emission tomography. These tracers retain EGF activities in vitro and display selective and highly specific focal uptake in tumors in vivo. We expect that nuclear imaging of EGF receptors with these tracers will be useful for clinical diagnosis, therapeutic monitoring, and development of new drugs and treatment regimens.
Bioconjugate Chemistry 04/2009; 20(4):742-9. · 4.93 Impact Factor
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Phuoc T Tran,
Alice C Fan,
Pavan K Bendapudi,
Shan Koh,
Kim Komatsubara,
Joy Chen, George Horng,
David I Bellovin,
Sylvie Giuriato,
Craig S Wang,
Jeffrey A Whitsett,
Dean W Felsher
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ABSTRACT: Conditional transgenic models have established that tumors require sustained oncogene activation for tumor maintenance, exhibiting the phenomenon known as "oncogene-addiction." However, most cancers are caused by multiple genetic events making it difficult to determine which oncogenes or combination of oncogenes will be the most effective targets for their treatment.
To examine how the MYC and K-ras(G12D) oncogenes cooperate for the initiation and maintenance of tumorigenesis, we generated double conditional transgenic tumor models of lung adenocarcinoma and lymphoma. The ability of MYC and K-ras(G12D) to cooperate for tumorigenesis and the ability of the inactivation of these oncogenes to result in tumor regression depended upon the specific tissue context. MYC-, K-ras(G12D)- or MYC/K-ras(G12D)-induced lymphomas exhibited sustained regression upon the inactivation of either or both oncogenes. However, in marked contrast, MYC-induced lung tumors failed to regress completely upon oncogene inactivation; whereas K-ras(G12D)-induced lung tumors regressed completely. Importantly, the combined inactivation of both MYC and K-ras(G12D) resulted more frequently in complete lung tumor regression. To account for the different roles of MYC and K-ras(G12D) in maintenance of lung tumors, we found that the down-stream mediators of K-ras(G12D) signaling, Stat3 and Stat5, are dephosphorylated following conditional K-ras(G12D) but not MYC inactivation. In contrast, Stat3 becomes dephosphorylated in lymphoma cells upon inactivation of MYC and/or K-ras(G12D). Interestingly, MYC-induced lung tumors that failed to regress upon MYC inactivation were found to have persistent Stat3 and Stat5 phosphorylation.
Taken together, our findings point to the importance of the K-Ras and associated down-stream Stat effector pathways in the initiation and maintenance of lymphomas and lung tumors. We suggest that combined targeting of oncogenic pathways is more likely to be effective in the treatment of lung cancers and lymphomas.
PLoS ONE 02/2008; 3(5):e2125. · 4.09 Impact Factor
