Assessment of Heterogeneity of Cytochrome P450 Activity in Cancer-Cell Population by Cytometry of Reaction Rate Constant is Robust to Variation in Substrate Concentration
Bibliographic record
Abstract
ABSTRACT Enzymes of the cytochrome P450 (CYP) family catalyze the metabolism of chemotherapeutic agents and are among the key players in primary and acquired chemoresistance of cancer. The activity of CYP is heterogeneous in tumor tissues, and the quantitative characteristics of this heterogeneity can be used to predict chemoresistance. Cytometry of reaction rate constant (CRRC) is a kinetic approach to assess cell population heterogeneity by measuring rates of processes at the single-cell level via time-lapse imaging. CRRC was shown to be an accurate and robust method for assessing the heterogeneity of drug-extrusion activity catalyzed by ABC transporters, which are also key players in cancer chemoresistance. We hypothesized that CRRC is also a reliable method for assessing the heterogeneity of CYP activity. Here, we evaluated the robustness of assessing the heterogeneity of CYP activity by CRRC with respect to controlled variation in the concentration of a CYP substrate by comparing CRRC with non-kinetic approaches. We found that changing the substrate concentration by 20% resulted only in minimal changes in the position, width, and asymmetry of the peak in the CRRC histogram, while these parameters varied greatly in the non-kinetic histograms. Moreover, the Kolmogorov-Smirnov statistical test showed that the distribution of the cell population in CRRC histograms was not significantly different; the result was opposite for non-kinetic histograms. In conclusion, we were able to demonstrate the robustness of CRRC with respect to changes in substrate concentration when evaluating CYP activity at the single-cell level.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 0.000 |
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.
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 teacher head, 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".