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Record W2887441655 · doi:10.1158/1538-7445.am2018-3209

Abstract 3209: Numerical modeling of intracellular mechanisms in tumor-treating fields

2018· article· en· W2887441655 on OpenAlexaff
Kristen W. Carlson, Jack A. Tuszyński, Socrates Dokos

Bibliographic record

VenueCancer Research · 2018
Typearticle
Languageen
FieldEngineering
TopicMolecular Communication and Nanonetworks
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsBiophysicsMicrotubuleTubulinIntracellularMitosisCytoskeletonCytosolMaterials scienceChemistryCellBiologyCell biologyBiochemistryEnzyme

Abstract

fetched live from OpenAlex

Abstract Tumor Treating Fields (TTFields) are 100-500 kHz electric fields with intensities of about 1-4 V/cm. They are known to exert an antimitotic effect on cancer cells, most likely by exerting forces on highly polar tubulin dimers, thereby disrupting spindle formation. Calculations show that TTFields-tubulin interaction energy is negligible compared to the thermal energy in the cell (1). Therefore, this interaction is unlikely to disrupt cellular function. Conductivity of polymerized tubulin, microtubules (MTs), was measured to be 20 S/m, which is 400 times higher than that of the ambient cytosol (0.05 S/m) (2). Thus when TTFields penetrate the cytosol, they may induce electric currents along MTs that are strong enough to disrupt key cellular functions. In particular, if the power (energy per unit time) deposited by these currents is on par with that the power consumed by the molecular motor kinesin, then TTFields may disrupt the forces needed for cell division, thereby disrupting mitosis. To test this hypothesis, we performed numerical simulations evaluating the magnitude of the electric current along MTs exposed to TTFields at 200 kHz. Based on studies of MTs and their microenvironment, we model the MT as a layered cylindrical structure (1): Innermost is the lumen (15 nm in thickness), surrounded by 13 strands of alpha-beta tubulin dimers linked in a helix (4.5 nm). C-termini extend out from the helix with a thickness of 3.5 nm. MTs carry net negative charge; thus they are surrounded by a counter-ion layer (2 nm), and an outer nonconductive Bjerrum layer (3 nm). We built a finite element model in COMSOL Multiphysics (tm) incorporating these layers and examined the current density induced in each layer by TTFields for MTs varying in length from 1-10 µm within an ambient 200 kHz AC electric field of 1-4 V/cm. Our model shows that MTs act as electrical "shunts" that conduct electric current within them. The highest current flows through the counter-ion layer surrounding the C-termini. The current density in this layer exceeds the level likely to disrupt the motor protein kinesin "walk" along the C-termini. The current density is highest when both the field and the MTs are aligned with the cell axis, in accord with in vitro experiments (3). Our model is consistent with the hypothesis that when cells are exposed to TTFields, MTs act as cables carrying high-density electric currents strong enough to disrupt the function of molecular motors, ultimately disrupting mitosis. References: 1. Tuszynski JA et al. An overview of sub-cellular mechanisms involved in the action of TTFields. Int J Environ Res Public Health 2016. 2. Santelices IB et al. Response to alternating electric fields of tubulin dimers and microtubule ensembles in electrolytic solutions. Sci Rep 2017. 3. Kirson ED et al. Alternating electric fields arrest cell proliferation in animal tumor models and human brain tumors. Proc Natl Acad Sci U S A 2007. Citation Format: Kristen W. Carlson, Jack A. Tuszynski, Socrates Dokos. Numerical modeling of intracellular mechanisms in tumor-treating fields [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3209.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.353
Threshold uncertainty score0.367

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.057
GPT teacher head0.349
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2018
Admission routes1
Has abstractyes

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