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Predicting Oncogene Addiction with Math
Some cancer cells are dependent or “addicted” to the continued activity of oncoproteins such as Myc. Drugs that target these oncoproteins induce the addicted cancer cells to die rapidly, a phenomenon called “oncogene addiction.” Marked clinical responses have been reported in some cancer patients, particularly those with lung cancer, after treatment with drugs targeting oncoproteins. However, only a distinct subset of human cancer patients have tumors that exhibit this behavior of oncogene addiction. The ability to predict when a tumor will exhibit oncogene addiction would be useful not only for developing new oncoprotein-targeted therapies but also for selecting which cancer patients are likely to respond best to such drugs. In a new study, Tran et al. use a systems approach to mathematically model the behavior of oncogene-addicted lung and lymphoma tumors. Starting from the simple premise that oncogene addiction could be deduced from the balance between converging signals from cell survival and cell death pathways in the tumors, Tran and colleagues were able to predict the clinical behavior of lung and lymphoma cells in mice treated with oncoprotein-targeted therapies. They also showed that their mathematical model for oncogene addiction could predict the distinct temporal relationships of signaling molecules from converging survival and death pathways. The group then used quantitative imaging of mouse tumors to train an algorithm called support vector machine to accurately differentiate mice that responded to oncoprotein-targeted therapy from those mice that did not respond. They then extended this quantitative imaging approach to humans with lung cancer who had been treated with oncoprotein-targeted therapy. Their model was able to differentiate patients who had lung tumors that were oncogene-addicted and hence were most likely to benefit from this treatment from those who did not. Although much more work needs to be done, the Tran et al. study demonstrates that mathematical modeling could be a useful tool for helping guide the management of cancer patients. Qualitative approaches alone are not sufficient given the complexity and heterogeneity of tumors, so the ability to quantify complex systems will be of benefit to the field of oncology. Finally, quantitative imaging combined with classifier algorithms may be useful clinically for distinguishing those cancer patients who are most likely to derive clinical benefit from oncoprotein-targeted therapeutics.
Footnotes
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↵* These authors contributed equally to this work.
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↵† Present address: Departments of Radiation Oncology and Molecular Radiation Sciences, and Oncology, Johns Hopkins University School of Medicine, Sidney Kimmel Comprehensive Cancer Center, Baltimore, MD 21231, USA.
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↵‡ D.P. and D.W.F. are co-senior authors.
- Copyright © 2011, American Association for the Advancement of Science
