Response Classification Based on a Minimal Model of Glioblastoma Growth Is Prognostic for Clinical Outcomes and Distinguishes Progression from Pseudoprogression

Abstract

Glioblastoma multiforme (GBM) is the most aggressive type of primary brain tumor. GBM growth dynamics vary widely across patients, making it difficult to accurately gauge their response to treatment. We developed a model-based metric of therapy response called Days Gained that accounts for this heterogeneity. Here we demonstrate in 63 newly diagnosed GBM patients that Days Gained scores from a simple GBM growth model computed at the time of the first post-radiation therapy MRI scan are prognostic for time to tumor recurrence and overall patient survival. After radiation treatment, Days Gained also distinguished patients with pseudoprogression from those with true progression. Since Days Gained scores can be easily computed with routinely available clinical imaging devices, it offers immediate potential to be used in ongoing prospective studies.

Gene therapy enhances chemotherapy tolerance and efficacy in glioblastoma patients

“…We then predicted the growth of each patient's tumor, if left untreated, in terms of millimeters (mm) of radial tumor growth per day ( Figure 4). The resulting patient-specific "untreated virtual control" (UVC) provided the baseline against which treatment effect was measured ( Figure 4, plot of untreated radial tumor growth versus time) (12,13). Treatment effect was described in two ways: first, as the x axis deflection from the UVC, days gained (DG, defined as number of days the treatment delayed tumor progression), and second, as the y axis deflection from the UVC, radial treatment response (RTR, defined as the radius of tumor growth prevented).…”

“…To characterize the treatment benefits achieved with O 6 BG added to the TMZ regimen, we applied a precision medicine, biomathematical model to compare the treatment strategy employed in study patients with standard care regimens in patients treated at our institution (12,13,(16)(17)(18)(19)(20). We show that model-predicted, untreated tumor growth was deflected by a greater degree in study patients compared with matched control patients, at lower cumulative TMZ doses when administered with O 6 BG.…”

“…Biomathematical modeling studies were performed in the laboratory of Kristin Swanson (Northwestern University, Evanston, Illinois, USA). The model used is a biologically based, patient-specific mathematical model for quantifying the growth of gliomas through the estimation of patientcalibrated net rates of proliferation (ρ) and invasion (D) of tumor cells, referred to as the proliferation-invasion (PI) model (12,13,(16)(17)(18)(19)(20)(22)(23)(24)(25), which has previously been shown to generate accurate predictions of GBM growth in untreated (19,21,24) and treated contexts (20). The model was applied to predict the growth of the tumor, if left untreated, holding rate parameters constant.…”

“…For these analyses, values of ρ and D were calculated for purposes of matching tumor growth characteristics between case and control patients, and velocity was used to simulate untreated tumor growth (13). As previously reported (16), the parameters were tuned by fitting two serial, pretreatment MRI scans (one obtained at the time of symptom presentation and the other at the time of surgery) obtained on 1.5T and 3.0T Philips machines with routine clinical MR pulse sequences; slice thickness in the axial plane varied from 1 mm to 6 mm.…”

Investigating the Reproducibility of In Vitro Cell Biology Assays Using Mathematical Models

“…The Fisher-Kolmogorov model has been used to describe collective cell spreading in both in vitro [76,77,100,104] and in vivo [47,70,93] contexts. Calibrating the solution of the Fisher-Kolmogorov model to experimental data can be used to quantify the roles of cell migration and cell proliferation [76,77,100].…”