Efficient treatment of heterogeneous malignant cell populations

TL;DR

This paper introduces a stochastic framework to predict the treatment outcomes of heterogeneous malignant cell populations, addressing limitations of traditional homogeneous assumptions and improving the accuracy of remission timing predictions.

Contribution

It develops an analytical model that accounts for cellular heterogeneity, enabling better prediction of treatment efficacy and remission times compared to average-based methods.

Findings

Average cellular parameters often fail to predict outcomes in heterogeneous populations.

Incorporating variability improves prediction of remission likelihood and timescales.

The model provides a full trajectory prediction of cell population dynamics.

Abstract

When confronted with an undesired cell population, such as bacterial infections or tumors, we seek the most effective treatment, designed to eliminate the population as rapidly as possible. A common practice is to monitor the cells short-term response to the treatment, and from that, extrapolate the eventual treatment outcome, i.e. will it eradicate the cells, and if yes at what timescales. Underlying this approach is the assumption that the cells exhibit a homogeneous response to the treatment, and hence the early response patterns can be naturally extended to later times. Recent experiments on cancer cell populations, however, indicate a significant level of cellular heterogeneity, undermining this classic assessment protocol of treatment efficacy. We, therefore, develop here a stochastic framework, to analytically predict the temporal dynamics of a heterogeneous cell population. Quite often, we find, the average cellular parameters, governing the short-term response, fail to predict the actual treatment outcome. In contrast, our analysis, which also incorporates the populations variability, helps identify the relevant statistical parameters, which in turn, enable us to predict the full trajectory of the cell population, and specifically - the likelihood and typical timescales for remission.