First steps to creating a platform for high throughput simulation of TTFields

Hershkovich, Hadas Sara; Bomzon, Zéev; Wenger, Cornelia; Urman, Noa; Chaudhry, Aafia; Garcia-Carracedo, Dario; Kirson, Eilon D.; Weinberg, Uri; Wassermann, Yoram; Palti, Yoram

doi:10.1109/embc.2016.7591203

Cited by 4 publications

(5 citation statements)

References 8 publications

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“…This is based on the prevailing understanding that TTFields interferes with mitosis of rapidly dividing tumor cells, which results in cancer cell death. In addition, computational modeling studies of TTFields in cell culture are currently driven by cell count as the primary outcome of the model 11 , 56 , 57 . As additional mechanisms of action of TTFields (e.g., increase in cellular permeability described in the current study) emerge, additional read-outs based on these mechanisms will follow suit.…”

Section: Discussionmentioning

confidence: 99%

Tumor treating fields increases membrane permeability in glioblastoma cells

Chang

Patel

Pohling

et al. 2018

Cell Death Discovery

127

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Glioblastoma is the most common yet most lethal of primary brain cancers with a one-year post-diagnosis survival rate of 65% and a five-year survival rate of barely 5%. Recently the U.S. Food and Drug Administration approved a novel fourth approach (in addition to surgery, radiation therapy, and chemotherapy) to treating glioblastoma; namely, tumor treating fields (TTFields). TTFields involves the delivery of alternating electric fields to the tumor but its mechanisms of action are not fully understood. Current theories involve TTFields disrupting mitosis due to interference with proper mitotic spindle assembly. We show that TTFields also alters cellular membrane structure thus rendering it more permeant to chemotherapeutics. Increased membrane permeability through the imposition of TTFields was shown by several approaches. For example, increased permeability was indicated through increased bioluminescence with TTFields exposure or with the increased binding and ingress of membrane-associating reagents such as Dextran-FITC or ethidium D or with the demonstration by scanning electron microscopy of augmented number and sizes of holes on the cellular membrane. Further investigations showed that increases in bioluminescence and membrane hole production with TTFields exposure disappeared by 24 h after cessation of alternating electric fields thus demonstrating that this phenomenom is reversible. Preliminary investigations showed that TTFields did not induce membrane holes in normal human fibroblasts thus suggesting that the phenomenom was specific to cancer cells. With TTFields, we present evidence showing augmented membrane accessibility by compounds such as 5-aminolevulinic acid, a reagent used intraoperatively to delineate tumor from normal tissue in glioblastoma patients. In addition, this mechanism helps to explain previous reports of additive and synergistic effects between TTFields and other chemotherapies. These findings have implications for the design of combination therapies in glioblastoma and other cancers and may significantly alter standard of care strategies for these diseases.

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Section: Discussionmentioning

confidence: 99%

Tumor treating fields increases membrane permeability in glioblastoma cells

Chang

Patel

Pohling

et al. 2018

Cell Death Discovery

127

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“…One small study started to investigate the effect of different head size and thicknesses of outer tissue layers. It would be possible to deform the virtual tumor model into different shapes and scale tissue to produce a more individual model [107]. However, these techniques might underestimate altered anatomy due to disease (edema, midline shifts, etc.)…”

Section: B Realistic Human Head Modeling 1) Materials and Methodsmentioning

confidence: 99%

A Review on Tumor-Treating Fields (TTFields): Clinical Implications Inferred From Computational Modeling

Wenger

Miranda

Salvador

et al. 2018

IEEE Rev. Biomed. Eng.

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Tumor-treating fields (TTFields) are a cancer treatment modality that uses alternating electric fields of intermediate frequency (∼100-500 kHz) and low intensity (1-3 V/cm) to disrupt cell division. TTFields are delivered by transducer arrays placed on the skin close to the tumor and act regionally and noninvasively to inhibit tumor growth. TTFields therapy is U.S. Food and Drug Administration approved for the treatment of glioblastoma multiforme, the most common and aggressive primary human brain cancer. Clinical trials testing the safety and efficacy of TTFields for other solid tumor types are underway. The objective of this paper is to review computational approaches used to characterize TTFields. The review covers studies of the macroscopic spatial distribution of TTFields generated in the human head, and of the microscopic field distribution in tumor cells. In addition, preclinical and clinical findings related to TTFields and principles of its operation are summarized. Particular emphasis is put on outlining the potential clinical value inferred from computational modeling.

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“…This approach obviates the need for electrode placement, and it is easier to implement computationally. The third proposed solution, which is principally related to the one described previously, involves scaling a pre-segmented human head, an atlas, to match the patient anatomy under analysis (Hershkovich et al 2016). Strengths of this approach are the automated analysis and minimal software dependencies, such that the barrier to entry is low.…”

Section: Discussionmentioning

confidence: 99%

“…But the idealization of the tumor as an ellipsoid, which is an approximation, would have failed for two of the four patient anatomies used here as test cases. Both patients had diffuse and non-spherical tumors that would not have been accurately represented in the prototype solution proposed in Hershkovich et al (2016). Furthermore, stretching a pre-segmented atlas brain based on manual measurement from the user is imprecise, particularly given the existing registration algorithms that automate the task (Klein et al 2009).…”

Section: Discussionmentioning

confidence: 99%

“…The difficulty in automated segmentation, cleanup, and electrode placement for FEA has prompted the development of two heuristic solutions. One involves scaling a pre-segmented patient brain to match the approximate dimensions of the patient MRI under analysis, with an ellipsoid representing the tumor (Hershkovich et al 2016), while another simplifies the patient anatomy into deformable convex hulls (Wenger et al 2016). But we have found in our previous works (Lok et al 2015(Lok et al , 2017 that small variations in patient anatomy, tumor shape, and cerebrospinal fluid volume can have profound implications for the predicted electric fields.…”

Section: Introductionmentioning

confidence: 94%

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End-to-end workflow for finite element analysis of tumor treating fields in glioblastomas

Timmons¹,

Lok²,

San³

et al. 2017

Phys. Med. Biol.

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Tumor Treating Fields (TTFields) therapy is an approved modality of treatment for glioblastoma. Patient anatomy-based finite element analysis (FEA) has the potential to reveal not only how these fields affect tumor control but also how to improve efficacy. While the automated tools for segmentation speed up the generation of FEA models, multi-step manual corrections are required, including removal of disconnected voxels, incorporation of unsegmented structures and the addition of 36 electrodes plus gel layers matching the TTFields transducers. Existing approaches are also not scalable for the high throughput analysis of large patient volumes. A semi-automated workflow was developed to prepare FEA models for TTFields mapping in the human brain. Magnetic resonance imaging (MRI) pre-processing, segmentation, electrode and gel placement, and post-processing were all automated. The material properties of each tissue were applied to their corresponding mask in silico using COMSOL Multiphysics (COMSOL, Burlington, MA, USA). The fidelity of the segmentations with and without post-processing was compared against the full semi-automated segmentation workflow approach using Dice coefficient analysis. The average relative differences for the electric fields generated by COMSOL were calculated in addition to observed differences in electric field-volume histograms. Furthermore, the mesh file formats in MPHTXT and NASTRAN were also compared using the differences in the electric field-volume histogram. The Dice coefficient was less for auto-segmentation without versus auto-segmentation with post-processing, indicating convergence on a manually corrected model. An existent but marginal relative difference of electric field maps from models with manual correction versus those without was identified, and a clear advantage of using the NASTRAN mesh file format was found. The software and workflow outlined in this article may be used to accelerate the investigation of TTFields in glioblastoma patients by facilitating the creation of FEA models derived from patient MRI datasets.

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First steps to creating a platform for high throughput simulation of TTFields

Cited by 4 publications

References 8 publications

Tumor treating fields increases membrane permeability in glioblastoma cells

Tumor treating fields increases membrane permeability in glioblastoma cells

A Review on Tumor-Treating Fields (TTFields): Clinical Implications Inferred From Computational Modeling

End-to-end workflow for finite element analysis of tumor treating fields in glioblastomas

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