Paul Daniel

Radiomics analysis has had remarkable progress along with advances in medical imaging, most notability in central nervous system malignancies. Radiomics refers to the extraction of a large number of quantitative features that describe the intensity, texture and geometrical characteristics attributed to the tumor radiographic data. These features have been used to build predictive models for diagnosis, prognosis, and therapeutic response. Such models are being combined with clinical, biological, genetics and proteomic features to enhance reproducibility. Broadly, the four steps necessary for radiomic analysis are: (1) image acquisition, (2) segmentation or labeling, (3) feature extraction, and (4) statistical analysis. Major methodological challenges remain prior to clinical implementation. Essential steps include: adoption of an optimized standard imaging process, establishing a common criterion for performing segmentation, fully automated extraction of radiomic features without redundancy, and robust statistical modeling validated in the prospective setting. This review walks through these steps in detail, as it pertains to high grade gliomas. The impact on precision medicine will be discussed, as well as the challenges facing clinical implementation of radiomic in the current management of glioblastoma.

Glioblastoma (GBM) is a heterogeneous tumor of the brain with a poor prognosis due to recurrence and drug resistance following therapy. Genome-wide profiling has revealed the existence of distinct GBM molecular subtypes that respond differently to aggressive therapies. Despite this, molecular subtype does not predict recurrence or drug resistance and overall survival is similar across subtypes. One of the key features contributing to tumor recurrence and resistance to therapy is proposed to be an underlying subpopulation of resistant glioma stem cells (GSC). CD133 expression has been used as a marker of GSCs, however recent evidence suggests the relationship between CD133 expression, GSCs and molecular subtype is more complex than initially proposed. The expression of CD133, Olig2 and CD44 was investigated using patient derived glioma stem-like cells (PDGCs) in vitro and in vivo. Different PDGCs exhibited a characteristic equilibrium of distinct CD133+ and CD44+ subpopulations and the influence of environmental factors on the intra-tumor equilibrium of CD133+ and CD44+ cells in PDGCs was also investigated, with hypoxia inducing a CD44+ to CD133+ shift and chemo-radiotherapy inducing a CD133+ to CD44+ shift. These data suggest that surveillance and modulation of intra-tumor heterogeneity using molecular markers at initial surgery and surgery for recurrent GBM may be important for more effective management of GBM.

Background: Glioblastoma (GBM) is almost invariably fatal due to failure of standard therapy. The relapse of GBM following surgery, radiation, and systemic temozolomide (TMZ) is attributed to the ability of glioma stem cells (GSCs) to survive, evolve, and repopulate the tumor mass, events on which therapy exerts a poorly understood influence. Methods: Here we explore the molecular and cellular evolution of TMZ resistance as it emerges in vivo (xenograft models) in a series of human GSCs with either proneural (PN) or mesenchymal (MES) molecular characteristics. Results: We observed that the initial response of GSC-initiated intracranial xenografts to TMZ is eventually replaced by refractory growth pattern. Individual tumors derived from the same isogenic GSC line expressed divergent and complex profiles of TMZ resistance markers, with a minor representation of O6-methylguanine DNA methyltransferase (MGMT) upregulation. In several independent TMZ-resistant tumors originating from MES GSCs we observed a consistent diminution of mesenchymal features, which persisted in cell culture and correlated with increased expression of Nestin, decline in transglutaminase 2 and sensitivity to radiation. The corresponding mRNA expression profiles reflective of TMZ resistance and stem cell phenotype were recapitulated in the transcriptome of exosome-like extracellular vesicles (EVs) released by GSCs into the culture medium. Conclusions: Intrinsic changes in the tumor-initiating cell compartment may include loss of subtype characteristics and reciprocal alterations in sensitivity to chemo- and radiation therapy. These observations suggest that exploiting therapy-induced changes in the GSC phenotype and alternating cycles of therapy may be explored to improve GBM outcomes.

Glioma are the most common type of malignant brain tumor, with glioblastoma (GBM) representing the most common and most lethal type of glioma. Surgical resection followed by radiotherapy and chemotherapy using the alkylating agent Temozolomide (TMZ) remain the mainstay of treatment for glioma. While this multimodal regimen is sufficient to temporarily eliminate the bulk of the tumor mass, recurrence is inevitable and often poses major challenges for clinical management due to treatment resistance and failure to respond to targeted therapies. Improved tumor profiling capacity has enabled characterization of the genomic landscape of gliomas with the overarching goal to identify clinically relevant subtypes and inform treatment decisions. Increased tumor mutational load has been shown to correlate with higher levels of neoantigens and is indicative of the potential to induce a durable response to immunotherapy. Following treatment with TMZ, a subset of glioma has been identified to recur with increased tumor mutational load. These hypermutant recurrent glioma represent a subtype of recurrence with unique molecular vulnerabilities. In this review, we will elaborate on the current knowledge regarding the evolution of hypermutation in gliomas and the potential therapeutic opportunities that arise with TMZ-induced hypermutation in gliomas.