P11.25 Assessing electrical properties of cells as predictive marker for patient-specific TTFields response and optimal frequency
Bibliographic record
Abstract
Abstract BACKGROUND Tumor treating fields (TTFields) are currently approved for the treatment of glioblastoma multiforme (GBM, using 200 kHz), and being tested in other tumor types such as non-small cell lung cancer and brain metastases occurring in this indication (LUNAR and METIS trials, using 150 kHz). Response to TTFields in cancer cells was empirically shown to be frequency-dependent specific for cell type; however, there are no markers available predicting optimal frequency or response in different cancer types or individual patients to date. There is evidence indicating electrical properties determine the optimal anti-mitotic frequency. This study analyzed the correlation of electrical properties of cells with their optimal TTFields frequency and sensitivity using the 3DEP reader (LabTech) to determine the electrical properties with the help of Dielectrophoresis (DEP) force. With this technique, cell movements within electric fields of different frequencies can by analyzed based on the physical effect of DEP, exercising a force on polarizable particles inside a non-homogeneous electric field. MATERIAL AND METHODS We used the 3DEP reader to obtain baseline properties (permittivity and conductivity) of 17 different cell lines of several tumor types. The resulting curves were analyzed by a 2-way ANOVA. Additionally, we determined the optimal frequency for maximum cytotoxic effect for each cell line using the inovitroTM system and eventually compared with the detected electrical properties. RESULTS We found cell lines with an optimal TTFields frequency of 150 kHz (corresponding to cells with a membrane capacitance in the lower range of the observed 3DEP curves, n=9) to possess significantly different (p<0.001) electrical properties from cells with an optimal TTFields frequency of 200 kHz (n=8). According to the curve differences in the lower frequency range, the measure of membrane capacitance served as a good predictor for TTFields response. CONCLUSION This study showed a correlation of cell membrane capacitance and optimal TTFields frequency and response. Our results provide a substantial rationale for further studies investigating the predictive potential of electrical properties of tumor cells as a measure for the optimal frequency and sensitivity to TTFields in individual patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".