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.