Konstantinos Zormpas-Petridis1, Matthew D. Blackledge1, Louis Chesler2, Yinyin Yuan3, Simon P. Robinson1, and Yann Jamin1
1Radiotherapy and Imaging, Institute of Cancer Research, London, Sutton, United Kingdom, 2Clinical studies, Institute of Cancer Research, London, Sutton, United Kingdom, 3Molecular pathology, Institute of Cancer Research, London, Sutton, United Kingdom
Synopsis
We use Gaussian mixture modeling, computational
pathology and MRI-histopathology registered datasets to evaluate i) the sensitivity of diffusion-weighted
imaging to the rich histopathology of childhood neuroblastoma and ii) the sensitivity of the derived
apparent diffusion coefficient, ADC, as a biomarker of response to a potent
MYCN-targeted small molecule inhibitor in the Th-MYCN mouse, a faithful model of high-risk MYCN-driven disease.
Introduction
The
clinical outcome of children with high-risk neuroblastoma, a cancer of the
sympathetic nervous system, remains poor.1 The oncogene MYCN plays a central role in the biology of
high-risk neuroblastoma. Small molecule inhibitors targeting the stability of the MYCN protein have shown strong anti-tumor activity, via the induction of
apoptosis in the Th-MYCN transgenic
mouse model of neuroblastoma. Selective inhibitors of mTOR activity, such as vistusertib,
which induce MYCN phosphorylation necessary to trigger its proteasomal
degradation are currently being evaluated in early-phase pediatric clinical
trials. Imaging biomarkers of response would be a valuable addition to these
trials especially since conventional pharmacodynamic biomarkers are difficult
to evaluate in children. Diffusion-weighted MRI (DWI) is increasingly being used for assisting neuroblastoma diagnosis.2 Purpose
The aims of this study were to evaluate whether the apparent diffusion
coefficient (ADC) could provide a biomarker of response to vistusertib in the
Th-MYCN mouse and to decipher the
pathological determinant(s) contributing to global and regional variations in intratumor
ADC using computational pathology and Gaussian mixture models (GMMs).Methods
MRI: Tumor-bearing Th-MYCN mice were imaged at 7T, prior to and
24 hours after treatment with 25mg/kg vistusertib (AstraZeneca, n=14) or
vehicle control (n=12). Diffusion-weighted
imaging was performed using an EPI readout (5 b-values=200-1000 s.mm-2,
TE=32ms, TR=1500ms, NS=4, FOV=3x3cm, 128x128 matrix, 1mm slice-thickness). ADC
was calculated voxel-wise using a robust Bayesian approach.3
Computational Pathology: Guided
by T2w-MRI, tumors (ntreated=7, nvehicle=11) were
carefully excised and orientated for histopathological processing.
Formalin-fixed and paraffin-embedded tumors were sectioned (3μm). Hematoxylin
and eosin (H&E)-stained whole-slide images were digitized (20x
magnification, 0.46μm pixel resolution, Hamamatsu NanoZoomer-XR) and analysed using CRImage
(Bioconductor).4 Cells were
automatically segmented and classified into 5 categories: undifferentiated
neuroblasts, differentiating neuroblasts, apoptotic cells, lymphocytes, stromal
cells. Density maps of segmented cells and classified undifferentiated,
apoptotic and differentiating neuroblasts matching the MRI resolution were
generated and automatically registered as previously described.5,6
Gaussian mixture modeling for tumor clustering: From visual inspection of the registered ADC & H&E images, we identified
5 pathological determinants of regional heterogeneity on ADC maps (Figure 1). We used GMMs to
cluster ADC values, using Bayesian and Akaike information criteria (BIC, AIC)
to determine the appropriate number of clusters (2-10 were tested). GMMs were applied in 6
tumours from the vehicle-control group, containing all the areas of interest. A
GMM was applied 1000 times on the ADC data with random initialisations and the
median value of the threshold for each cluster was selected. All tumors were
classified using the defined clusters.Results
Treatment with
vistusertib was associated with significantly lower fraction of
undifferentiated neuroblasts (66±4% vs 21±3%,
p<0.0001) and higher fraction in apoptotic cells (17±3%
vs 57±3%, p<0.0001) at 24h compared to vehicle control and a significant
reduction in tumor burden in the Th-MYCN
model (Table 1). There was no
significant change in tumor ADC over 24h treatment with vistusertib.
BIC and AIC criteria suggested using between 3 to 8 clusters, which we fixed to 5 based on our
initial hypothesis and which was corroborated by the sub-regional analysis (Figures 1 & 2.A). The 5 compartments
were categorised as: ADCvl
(0-188.10-6 mm2.s-1), ADCl (188-452.10-6 mm2.s-1), ADCm (452-850.10-6 mm2.s-1), ADCh (850-1400.10-6 mm2.s-1) and ADCvh (1400-2000.10-6 mm2.s-1). ADCm and ADCh contained higher density of undifferentiated
neuroblasts than the rest of the tumor. In the treated tumors, areas of high
density of apoptotic neuroblasts corresponded to both low ADC (ADCvl and ADCl) and higher ADC values and
compartments (Figure 2.B).
Quantitative analysis
(Figure 3) showed that the fraction
of apoptotic cells in treated tumors positively correlated with the fraction of
pixels belonging to the ADCvl and ADCl compartments (hemorrhage and
restricted diffusion) and inversely correlated with the fraction of pixels belonging to the ADCm+h
compartments (typically richer in undifferentiated cells). Additionally,
the ADCh compartment was sensitive
to regions rich in differentiating neuroblasts in three differentiating tumors
(Figure 4).Discussion
Our
study demonstrates the sensitivity of DWI to the rich histopathology of
neuroblastoma but reinforces the caution raised by other studies regarding the
sensitivity of median tumor ADC as a biomarker of tumor response to therapy.7
Factors affecting the ADC response are the small amount of extracellular space
(high-risk neuroblastoma is stroma-poor), diffuse apoptosis, inter-tumor
heterogeneity and the presence of coagulative (low ADC) and liquefactive (high
ADC) tissue damage. We used our insights of neuroblastoma histology to guide GMM
clustering to show that i) the
increase in apoptosis and extended tissue damage following treatment actually
correlated with regions where water diffusion is restricted (low ADC) and ii) the sensitivity of DWI to
differentiation used clinically to discriminate stroma-rich differentiating
benign form of neuroblastoma from stroma-poor aggressive neuroblastoma could
potentially be extended to detect differentiating regions within stroma-poor
aggressive neuroblastoma.2Conclusion
Global
tumor ADC is not a sensitive imaging biomarker of tumor response to widespread
apoptosis induced by vistusertib in the Th-MYCN
model of neuroblastoma. Based on our insights of neuroblastoma histopathology, we
guided the application of GMMs to derive ADC compartments and demonstrated the
sensitivity of low ADC values for treatment-induced apoptosis and the sensitivity
of higher ADC values to regions rich in differentiating neuroblasts.Acknowledgements
The
Institute of Cancer Research Cancer Research UK Cancer Imaging Centre in
association with the MRC and Department of Health grant C1060/A10334, Rosetrees
Trust, Children with Cancer UK, Cancer
Research UK grant C16412/A27725.References
1. Matthay
KK, Maris JM, Schleiermacher G et al. Neuroblastoma. Nat Rev Dis Primers.
2016;2:16078.
2. Wen
Y, Peng Y, Duan XM et al. Role of diffusion-weighted imaging in distinguishing
thoracoabdominal neuroblastic tumours of various histological types and
differentiation grades. J Med Imaging Radiat Oncol. 2017;61(6):718-724.
3.
Walker-Samuel S, Orton M, Boult JKR et al. Improving apparent diffusion
coefficient estimates and elucidating tumor heterogeneity using Bayesian
adaptive smoothing.
4. Pau G, Fuchs F, Sklyar O, et al. EBImage - an
R package for image processing with applications to cellular phenotypes.
Bioinformatics. 2010;26:979-981
5.
Zormpas-Petridis K, Blackledge MD, Clarke M et al. T1 mapping of neuroblastoma
pathology: insight from a computational pathology study in the Th-MYCN
transgenic mouse model. Proc Int Soc Magn Res Med. 2019;0584.
6. Zormpas-Petridis
K, Jerome NP, Blackledge MD et al. MRI Imaging of the Hemodynamic Vasculature
of Neuroblastoma Predicts Response to Anti-angiogenic Treatment. Cancer Res.
2019;79:2978-91.
7. Sinkus R, Van Beers BE, Vilgrain V et al. Apparent
diffusion coefficient from magnetic resonance imaging as a biomarker in
oncology drug development. Eur. J. Cancer. 2019;48:425-431.