Glioblastoma (GBM) is the most common malignant brain tumor of adults, highly aggressive, with dismall prognosis despite standard-of-care (SOC) treatment. It has low mutational burden, and thus is not amenable to targeted or immunotherapy. Although much research has been done in conventional or novel treatment modalities, the survival of patients with newly diagnosed GBM is not higher than 19 months. The main threat of GBM is recurrence, which depends upon survival capacity of remnant tumor cells after treatment, cell migration and adaptation to new environments, and reconstitution of the primary tumor after these previous steps. The study of GBM cells hability to adapt has been a possible approach to find novel robust treatments. In silico research has been increasingly used to model specific features of tumor biology, treatment, or outcomes. Given the complexity and broad scope of these studies, a great diversity of cancer-associated phenomena simulating computer models has been designed. Glioblastoma computer models have been used to study from blood-brain barrier dynamics to treatment response. These models have already brought useful insights into basic and clinical cancer research. One of their main challenges, however, is to translate meaningful parameters into clinical practice. Celiku et al have developed a GBM computational model based on patient data and exploratory adaption. They demonstrated that cell phenotype dynamics can be modelled in this manner and that it predicts an evolutionary landscape of phenotype pathways that could have implications to tumor treatment. They have used this modelling technique to explore phenotype stability response under a variety of perturbations in tumor microenvironment and have shown that a cycling between basic behavior-molecular matched cell tumor phenotypes is crucial for GBM progression. They have labeled the modelled cell tumor phenotypes GO (motile infiltrating cells), GROW (tumor-reconstituting cells), DORMANT (metabollically inactive cells), and APOPTOSIS (cells undergoing programmed-cell death). We sought to use these concepts to derive biomarkers for the evaluation of magnetic resonance imaging (MRI) of a test patient. Our objetive was to make a proof of concept translation of the evolutionary adaptive concepts of phenotype stability and exploratory adaptation to clinically useful endpoints for routine imaging analysis.

1. www.postersession.com
www.postersession.com
Glioblastoma (GBM) is the most common malignant brain tumor of adults, highly aggressive, with
dismall prognosis despite standard-of-care (SOC) treatment. It has low mutational burden, and thus is
not amenable to targeted or immunotherapy. Although much research has been done in conventional
or novel treatment modalities, the survival of patients with newly diagnosed GBM is not higher than
19 months (1). The main threat of GBM is recurrence, which depends upon survival capacity of
remnant tumor cells after treatment, cell migration and adaptation to new environments, and
reconstitution of the primary tumor after these previous steps. The study of GBM cells hability to
adapt has been a possible approach to find novel robust treatments (2).
In silico research has been increasingly used to model specific features of tumor biology, treatment, or
outcomes. Given the complexity and broad scope of these studies, a great diversity of cancer-
associated phenomena simulating computer models has been designed. Glioblastoma computer
models have been used to study from blood-brain barrier dynamics to treatment response. These
models have already brought useful insights into basic and clinical cancer research. One of their main
challenges, however, is to translate meaningful parameters into clinical practice (3).
Celiku et al have developed a GBM computational model based on patient data and exploratory
adaption. They demonstrated that cell phenotype dynamics can be modelled in this manner and that it
predicts an evolutionary landscape of phenotype pathways that could have implications to tumor
treatment (2). They have used this modelling technique to explore phenotype stability response under
a variety of perturbations in tumor microenvironment and have shown that a cycling between basic
behavior-molecular matched cell tumor phenotypes is crucial for GBM progression. They have
labeled the modelled cell tumor phenotypes GO (motile infiltrating cells), GROW (tumor-
reconstituting cells), DORMANT (metabollically inactive cells), and APOPTOSIS (cells undergoing
programmed-cell death) (4).
We sought to use these concepts to derive biomarkers for the evaluation of magnetic resonance
imaging (MRI) of a test patient. Our objetive was to make a proof of concept translation of the
evolutionary adaptive concepts of phenotype stability and exploratory adaptation to clinically useful
endpoints for routine imaging analysis.
Methods
Conclusions
BIOMARKERS FROM A GLIOBLASTOMA PROGRESSION
MODEL IN IMAGE ANALYSIS: PROOF OF CONCEPT.
Francisco Hélder Cavalcante Félix1; Juvenia Bezerra Fontenele2
1 Pediatric Cancer Center, Hospital Infantil Albert Sabin
2 Department of Pharmacy, Federal University of Ceará, UFC
Bibliography
1. Medikonda R, Dunn G, Rahman M, Fecci P, Lim M. A review of glioblastoma immunotherapy. J
Neurooncol. 2020 Apr 6. doi: 10.1007/s11060-020-03448-1. Epub ahead of print. PMID: 32253714.
2. Celiku, O., Gilbert, M.R. & Lavi, O. Computational modeling demonstrates that glioblastoma cells can
survive spatial environmental challenges through exploratory adaptation. Nat Commun 10, 5704 (2019).
https://doi.org/10.1038/s41467-019-13726-w
3. Computational modeling in glioblastoma: from the prediction of blood–brain barrier permeability to the
simulation of tumor behavior. Ana Miranda, Tânia Cova, João Sousa, Carla Vitorino, and Alberto Pais.
Future Medicinal Chemistry 2018 10:1, 121-131
4. Rajapakse VN, Herrada S, Lavi O. Phenotype stability under dynamic brain-tumor environment stimuli
maps glioblastoma progression in patients. Sci Adv. 2020 May 27;6(22):eaaz4125. doi:
10.1126/sciadv.aaz4125. PMID: 32832595; PMCID: PMC7439317.
5. Felix, Francisco. (2016). Longitudinal observational study of pediatric patients with primary brain tumors:
establishment of a hospital-based registry. (Version 1.0.0) [Data set]. Zenodo.
http://doi.org/10.5281/zenodo.3576056
Introduction Results
Eighteen individual nodular-like enhancing sites were chosen in the serial images. Total area (TB) was 2183
mm² at timepoint A, 4297 mm² at timepoint B, and 6132 mm² at timepoint C. Mean and SDM for nodule area
were 93,5±123,5 mm² (A), 186±184,1 mm² (B), and 220,5±238,1 mm² (C). The difference between A and B
was statistically significant (p = 0,03), but not the difference between B and C (p = 0,16). The S nodules
remained stable after chemotherapy (TBS, A = 1350, B = 2496, C = 2572), the R nodules maintained continuous
growth after treatment (TBR, A = 304, B = 1332, C = 2353, and the D nodules diminished before treatment and
grew after chemotherapy (TBD, A = 529, B = 496, C = 1207). There were three new nodules at B and one new
nodule at C. (Figure 2)
De-identified patient data, including images, was extracted from a hospital-based registry of pediatric patients
with central nervous system tumors (5), built upon authorization of our institution IRB and after informed consent
from patients’ families. The data used was from a patient diagnosed with a left cingulate gyrus H3K27me3-
negative GBM that underwent SOC therapy and achieved complete tumor remission (CR). Two years after the
diagnosis a midline recurrence was noted that progressed swiftly involving the surface of the previous surgical
cavity. The patient underwent a new radiation therapy treatment, followed after 6 months by new progression. It
was then proposed palliative chemotherapy using an adaptive therapy framework, that resulted in continued
tumoral progression to no avail.
We used routine MRI to study the dynamics of apparent clonal cell tumor populations, individualized as nodular
growths in the images. Serial T1-weighted, contrast-enhanced images with 1 mm thickness were obtained at
predetermined 2-month intervals after disease recurrence (A, B, and C). Chemotherapy was administered after
timepoint B. Semi-automated elipsoids were marked at enhancing nodular-like sites in each image, and their areas
were machine determined and manually compared at each time point. An example of slice and nodule choosing is
depicted in figure 1. We used Carestream Vue Motion PACS Software (Carestream Health France, 2015).
Nodules identified in a timepoint were compared with the same nodules (identified by location) at each one of the
other timepoints. Nodule area as calculated by the software was plotted to inspect the overall behavior of each
nodule. Based on this, we could separate nodules in three groups: sensitive (S) (their growth was inhibited after
chemotherapy), resistant (R) (their growth continued relentlessly despite treatment), and disinhibited (D) (they
were stable or decreased before chemotherapy and increased after it). Figure 2 show examples. The sum of
nodule areas in each timepoint was called tumor burden (TB). Mean and standard deviation of mean (STM) were
calculated for nodules in each timepoint, and a repeated measures parametric comparison (t test) was performed.
Statistics were done in Google Sheets, 2020.
These results suggest that GBM has intrinsic phenotypic heterogeneity that is analogous to those that emerged
from the Lavi group model. The R nodules can be viewed as GROW cells, the S nodules as DORMANT cells, the
D cells could be analogous to APOPTOSIS cells that were repressed by darwinian pressure within the tumor
microenvironment, and the newly formed nodules as evidence of GO phenotype cells. Our observations sugest
that chemotherapy augmented tumor phenotype heterogeneity and induced derepression of cell clones that
otherwise would have not survived the competition within the tumor ambient, conceptualized as a dynamic,
evolving, multidimensional space of interacions that determine the tumor behavior. These phenomena have to be
accounted for if one is to develop succesfull treatment strategies for GBM.
Figure 1: these slices were chosen from
a total of 176 sagittal T1w MRI. Each
image was manually checked for the
presence of nodule-like enhancing
regions classified as tumor growth sites.
Ellipsoids were drawn with the help of
Carestream Vue Motion PACS software.
The software automatically calculated
nodule areas.
Figure 2: (a) Example of nodule behavior, showing S, R, and D types ,
comparing timepoints A, B, and C. A nodule was considered stable if its
area variation was < 25%; progressive if its area was ≥ 25% than previous
measure; or regressive if its area was <25% than previous measure. Stable
or regresive nodules after chemotherapy were classified as S. Progressive
nodules after chemotherapy were classified as R. Stable or regressive
nodules before chemotherapy that progressed after it were classified as D.
(b) Boxplot of nodule areas at A, B, and C. (c) Growth plots of all the
nodules measured at A, B, and C. (d) Growth plots of S nodules and TBS
(blue). (e) Growth plots of R nodules and TBR (red). (f) Growth plots of D
nodules and TBD (yellow). (g) Growth plots of TBS (blue), TBR (red), and
TBD (yellow).
C
B
A
(a)
R
R
S
S
D
D
(b) (c)
(d) (e)
(f) (g)