Major finding: Differential changes in death and survival signaling underlie oncogene addiction.
Impact: A simple, noninvasive method can identify patients who may benefit from targeted therapy.
Approach: Mathematical modeling is combined with imaging to predict oncogene addiction.
It remains unclear which cellular properties underlie a tumor's dependence on a single oncogenic signaling pathway. Tran and colleagues sought to elucidate the general underlying mechanisms of oncogene addiction with the ultimate goal of developing a strategy to quickly determine whether oncogene-targeted treatment will be effective. First, using a mouse model of Kras-addicted lung cancer, they combined immunohistochemistry of several prosurvival and prodeath effector molecules at multiple time points before and after Kras inactivation with the proliferative and apoptotic indices to generate an ordinary differential equation model of aggregate survival and death signals over time. This model fit serial imaging data and showed that the response of oncogene-addicted tumors could be explained almost entirely by a sharp attenuation of the survival signal and a more gradual decline in the death signal. The authors also used this model to successfully predict the effects of particular prosurvival (Stat, Akt) and prodeath (p53) pathways on oncogene-addicted tumor growth, and to predict the effect of Myc inactivation in Myc-addicted murine lymphoma, suggesting that this model is applicable to oncogene-addicted tumors regardless of oncogene or tumor type. The authors then used serial imaging data to determine whether tumor growth and regression kinetics were sufficient for a support vector machine (SVM) learning algorithm to distinguish an oncogene-addicted genotype. After only the first 2 weekly scans following oncogene inactivation in either Kras-addicted or Myc-induced (but not addicted) murine lung cancers, the SVM could successfully classify tumors. Using the mouse SVM classifier on CT scan imaging data from lung cancer patients treated with erlotinib, the authors were able to predict the EGFR genotype and progression-free survival of patients after 4 weekly CT scans. Although EGFR mutational status is a known predictor of response to erlotinib, it is not always possible to obtain a sample for biopsy or screen for EGFR mutations. Quantitative imaging algorithms may therefore aid in the personalized management of patients treated with targeted therapies.
Note: Research Watch is written by Cancer Discovery Science Writers. Readers are encouraged to consult the original articles for full details. For more Research Watch, visit Cancer Discovery online at www.AACR.org/CDnews.
