Targeting stem cells with oncolytic viruses: a mathematical modelling approach
Bibliographic record
Abstract
Intratumoural epigenetic heterogeneity, which affects the outcome of many cancer treatments, results from stem cell-differentiated cell hierarchy. Cancer stem cells, also known as tumour-initiating cells, are a pluripotent subpopulation of tumour cells capable of creating a tumour clone through self-renewal and differentiation. Oncolytic viral therapy is a category of cancer therapeutics with high specificity in targeting cancer cells while leaving normal cells unharmed. More recently, oncolytic viruses have been developed that target tumour initiating cells with some promising results. The question is what values for virus infectivity and stem cell specificity result in the best clinical outcome. To address this question, we model interactions between uninfected and infected cancer cells, within a stem cell-differentiated cell hierarchy, during oncolytic viral therapy. We calculate the basic reproduction number and use it to constrain the infectivity rates of initiating and differentiated cancer cells. Long-term tumour shrinkage is observable when this constraint is met; otherwise, treatment fails. Our results suggest that stem cell specificity of an oncolytic virus depends both on the average infectivity and mitotic rates of infected cells. There is a positive correlation between the average infectivity rate and stem cell specificity for nonmitotic infected cells: when average infectivity is high, an oncolytic virus with higher stem cell specificity leads to smaller tumours. In contrast, when average infectivity is low, the minimum tumour size is obtained when an oncolytic virus with higher potency targeting differentiated cells is used. For the perfect stem cell targeting regimen, we derive the condition that leads to the minimum tumour size.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".