Abstract 3725: Numerical simulation of tumor treating fields effects on cell structures: Mechanism and signaling pathway candidates
Bibliographic record
Abstract
Abstract Tumor Treating Fields (TTFields) have become a fourth modality for cancer treatment. Mild electric fields (~1-4 V/cm) produce few side effects and significantly extend overall survival of glioblastoma patients, and TTFields are in clinical trials for a variety of tumor cell types. Our goal is to uncover TTFields’ mechanism and cell signaling pathways by numerically modeling their effects on sub-cellular structures, such as microtubules (MTs) and their interactions with motor proteins. METHODS: We have built finite element models in COMSOL Multiphysics (tm) of the MT and its micro-environment to test hypotheses on TTFields’ mechanism of action by predicting effects on sub-cellular structures. RESULTS: One model prediction is that current density induced in the MT counter-ion layer by TTFields essentially shunts electric current within them. The strongest current flows through the counter-ion layer surrounding the MT’s C-termini and energy density in this layer likely exceeds the level to disrupt motor protein ‘walk’ along the MT. The energy density is predicted at 10-20 Joules when both the field and the MTs are aligned with the cell axis. A second mechanism examined by our model is disruption of the ‘foot’ of kinesin, released from its C-terminus contact by ATP (10-19 Joules). The final phase of the walk is driven by thermal buffeting of the forward foot randomly positioning it near enough to the C-terminus for electrostatic forces to bind it. A stall force ~10-19 - 10-16 N from TTFields would prevent diffusion and disrupt the kinesin walk. A recent clinical study segregating patient cohorts treated vs. not treated with dexamethasone found overall survival indefinitely for the non-dexamethosone cohort, leading us to hypothesize that TTFields activate the intrinsic Bcl2-mediated apoptotic signaling pathway. Future modeling will seek to tie disruption of motor protein transport along MTs to activating intrinsic apoptosis, e.g. via failure to silence the G2 cell cycle checkpoint. CONCLUSION: Our modeling predicts that TTFields in cytosol induce electric currents along MTs that are strong enough to disrupt key cellular functions such as the kinesin walk and C-termini transitions, both of which are crucial for motor protein transport. Hence, TTFields disrupt the most delicate mechanisms involved in the carefully-orchestrated succession of steps in mitosis. Citation Format: Kristen W. Carlson, Nirmal Paudel, Jack A. Tuszynski, Zeev Bomzon. Numerical simulation of tumor treating fields effects on cell structures: Mechanism and signaling pathway candidates [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 3725.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".