Comparative molecular characterization of typical and exceptional responders in glioblastoma

Abstract

Glioblastoma (GBM) is the most common and the deadliest type of primary brain tumor, with a median survival time of only 15 months despite aggressive treatment. Although most patients have an extremely poor prognosis, a relatively small number of patients survive far beyond the median survival time. Investigation of these exceptional responders has sparked a great deal of interest and is becoming an important focus in the field of cancer research. To investigate the molecular differences between typical and exceptional responders in GBM, comparative analyses of somatic mutations, copy number, methylation, and gene expression datasets from The Cancer Genome Atlas were performed, and the results of these analyses were integrated via gene ontology and pathway analyses to assess the functional significance of the differential aberrations. Less severe copy number loss of CDKN2A, lower expression of CXCL8, and FLG mutations are all associated with an exceptional response. Typical responders are characterized by upregulation of NF-κB signaling and of pro-inflammatory cytokines, while exceptional responders are characterized by upregulation of Alzheimer’s and Parkinson’s disease pathways as well as of genes involved in synaptic transmission. The upregulated pathways and processes in typical responders are consistently associated with more aggressive tumor phenotypes, while those in the exceptional responders suggest a retained ability in tumor cells to undergo cell death in response to treatment. With the upcoming launch of the National Cancer Institute’s Exceptional Responders Initiative, similar studies with much larger sample sizes will likely become possible, hopefully providing even more insight into the molecular differences between typical and exceptional responders.

The Genetic Landscape of Human Glioblastoma and Matched Primary Cancer Stem Cells Reveals Intratumour Similarity and Intertumour Heterogeneity

“…GBM is characterized by rapid progression and invasion, cell infiltration, intense angiogenesis, resistance to radio- and chemotherapies, and high frequency of relapse. This aggressive behaviour is responsible for the poor prognosis, with a median survival of about 14 months and a 5-year survival rate of 5.1% [ 1 ]. GBM resistance to therapies and the high frequency of recurrence are mainly due to a subpopulation of tumourigenic stem-like cells, known as GBM stem cells (GSCs), able to hierarchically initiate, maintain, and spread the neoplasm.…”

Case Report of Complete Radiological Response of a Thalamic Glioblastoma After Treatment With Proton Therapy Followed by Temozolomide and Tumor-Treating Fields

“…Further molecular analyses demonstrated a deletion of cyclin-dependent kinase inhibitor 2A (CDKN2A). In this patient, a CDKN2A deletion was detected, which is associated with shortened survival and limited response to radiation therapy in patients with GBM (13,14). Resistance to TTFields therapy was assumed in a prior reported case with this constellation of characteristics (15), but TTFields resistance was not evident in this case study.…”

DeepSurvNet: deep survival convolutional network for brain cancer survival rate classification based on histopathological images

“…FLG is a high-affinity receptor of basic fibroblast growth factor (bFGF), and a recent report by Wipfler et al has shown that FLG has a significantly different distribution of patients affected by somatic nonsynonymous mutations. Of these, 25% of exceptional responders had one mutation each in FLG [13]. In contrast, overexpression of FLG is associated with low immune cell infiltration and short survival rates in melanoma and ovarian cancer [55], while the loss of function mutations in FLG is associated with lower cancer risk in several cancers [56].…”

“…Thus, we feel that in addition to the classification of brain tumours that have been done so far, it is also equally important to stratify brain cancer patients based on their survival characteristics and which will permit us to clinicians to tailor both the timing and the type of treatments to patients [12,13]. This will, for example, be helpful or avoid overtreating those patients with more stable disease.…”

“…This will, for example, be helpful or avoid overtreating those patients with more stable disease. Moreover, classification of brain tumours as a function of brain cancer survival will help us to reveal key characteristics that make these tumours very aggressive and for those patients that present long survival, what are the molecular signatures that contribute to it [13].…”