Glioblastoma (GBM) is the most common and lethal brain cancer. It has a dismal prognosis with a 5-year survival rate of 5%. The current standard of care consists of maximal surgical resection, followed by radiotherapy with concomitant and adjuvant chemotherapy with temozolomide (TMZ), the only drug approved. Unfortunately, drug resistance is the main reason behind treatment failure and responsible for the poor prognosis of patients. Cells initially respond to treatment but they eventually change their phenotype and adapt to it. That is, they modify their behavior or phenotype in response to the changes in the environment (in this case, drug exposure). This resistance is enhanced by hypoxia, a defining feature of GBM. Therefore, it is important to study how hypoxia governs cellular adaptation in GBM, since it can improve our understanding treatment response and, eventually, GBM prognosis. In view of the above, there is a need for strategies that help us understand how resistance is triggered and how it can be prevented with the final aim of designing new successful treatments. Towards this end, mathematical models enable the systematic study of these processes and, with sufficient validation with experimental data, may serve as decision support tools in clinical practice and help clinicians to determine the best treatment strategy. However, in order for them to capture the process of resistance acquisition, they must take into account plasticity, which has been included among the cancer hallmarks as a defining characteristic of the disease due to its fundamental role in processes such as drug resistance. In this context, one of the most relevant mechanisms is the go-or-grow hypothesis, which describes the phenotypic switch between proliferative and migratory states. This dichotomy not only underlies tumor invasion but also contributes to intratumoral heterogeneity, as cells within the tumor exhibit different behaviors driven by environmental pressures such as hypoxia. In this work, we introduce the dependence of proliferation on oxygen into an image-informed mathematical model of GBM calibrated with rat MRI data, implemented in FEniCsX. By integrating this phenotypic plasticity into our model, we investigate its ability to predict tumor evolution and capture key features of GBM progression.