Abstract # 2716 Predictive Modeling Of Tumor Regression Kinetics Using A Murine Model Of Oncogene-addicted Lung Cancers

Presenter: Tran, Phuoc

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These transgenic mice develop primary lung adenocarcinomas with variable latency (26-51 weeks) and clinical presentation that is dependent on their genotype. Upon oncogene-inactivation in these murine models, we have found that Ras, but not Myc induced lung adenocarcinomas regress in a matter of weeks completely. We measured quantitatively the clinical behavior of murine lung tumors in situ after oncogene-inactivation by use of serial micro-computed tomography (microCT) imaging. By modeling the regression curves of each transgenic system, we were able to express the difference mathematically between the tumor regression of Ras- and Myc-induced tumors. The modeling also allowed us to distinguish that double mutant, Ras/Myc-induced tumors, were likely composed of two dominant populations with behavior similar to single Ras- and Myc-induced tumors. We then used our data array as a training set for a predictive support vector machine algorithm which is highly accurate at predicting the regression of murine tumors based on only three serial weekly microCT scans at the initiation of oncogene-inactivation.