Prediction of Outcomes with a Computational Biology Model in Newly Diagnosed Glioblastoma Patients Treated with Radiation Therapy and Temozolomide.

PURPOSE: Precision medicine has been most successful in targeting single mutations, but personalized medicine using broader genomic tumor profiles for individual patients is less well-developed. We evaluate a genomics-informed computational biology model (CBM) to predict outcomes from standard treatments and to suggest novel therapy recommendations in glioblastoma (GBM). METHODS AND MATERIALS: In this retrospective study, 98 patients with newly diagnosed GBM undergoing surgery followed by radiation therapy and temozolomide at a single institution with available genomic data were identified. Incorporating mutational and copy number aberration data, a CBM was used to simulate the response of GBM tumor cells and generate efficacy predictions for radiation therapy (RTeff) and temozolomide (TMZeff). RTeff and TMZeff were evaluated for association with overall survival (OS) and progression-free survival in a Cox regression model. To demonstrate a CBM-based individualized therapy strategy, treatment recommendations were generated for each patient by testing a panel of 45 CNS-penetrant FDA-approved agents. RESULTS: High RTeff scores were associated with longer survival on univariable analysis (UVA) (P<0.001), which persisted after controlling for age, extent of resection, performance status, MGMT and IDH status (P=0.017). High RTeff patients had a longer OS compared to low RTeff patients (median 27.7 vs. 14.6 months). High TMZeff was also associated with longer survival on UVA (P=0.007) but did not hold on multivariable analysis, suggesting an interplay with MGMT status. Among predictions of the three most efficacious combination therapies for each patient, only 2.4% (7 of 294) of two-drug recommendations produced by the CBM included TMZ. CONCLUSIONS: CBM-based predictions of RT and TMZ effectiveness were associated with survival in newly diagnosed GBM patients treated with those therapies, suggesting a possible predictive utility. Furthermore, the model was able to suggest novel individualized monotherapies and combinations. Prospective evaluation of such a personalized treatment strategy in clinical trials is needed.