Cell population heterogeneity and evolution towards drug resistance in cancer: Biological and mathematical assessment, theoretical treatment optimisation

Abstract

BACKGROUND. Drug-induced drug resistance in cancer has been attributed to diverse biological mechanisms at the individual cell or cell population scale, relying on stochastically or epigenetically varying expression of phenotypes at the single cell level, and on the adaptability of tumours at the cell population level. SCOPE OF REVIEW.We focus on intra-tumour heterogeneity, namely betweencell variability within cancer cell populations, to account for drug resistance. To shed light on such heterogeneity, we review evolutionary mechanisms that encompass the great evolution that has designed multicellular organisms, as well as smaller windows of evolution on the time scale of human disease. We also present mathematical models used to predict drug resistance in cancer and optimal control methods that can circumvent it in combined therapeutic strategies.MAJOR CONCLUSIONS. Plasticity in cancer cells, i.e., partial reversal to a stemlike status in individual cells and resulting adaptability of cancer cell populations, may be viewed as backward evolution making cancer cell populations resistant to drug insult. This reversible plasticity is captured by mathematical models that incorporate between-cell heterogeneity through continuous phenotypic variables. Such models have the benefit of being compatible with optimal control methods for the design of optimised therapeutic protocols involving combinations of cytotoxic and cytostatic treatments with epigenetic drugs and immunotherapies.GENERAL SIGNIFICANCE. Gathering knowledge from cancer and evolutionary biology with physiologically based mathematical models of cell population dynamics should provide oncologists with a rationale to design optimised therapeutic strategies to circumvent drug resistance, that still remains a major pitfall of cancer therapeutics.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance

“…Here we describe that KDM5B is a regulator of cellular transcriptomic heterogeneity in ER + luminal breast cancer and its higher expression in ER + breast tumors is associated with higher transcriptomic, but not genetic heterogeneity and shorter overall survival. Higher cell-to-cell variability increases the probability of therapeutic resistance (Chisholm et al, 2016). Most studies analyzing intratumor heterogeneity have focused on genetic alterations and in many cases therapeutic resistance is due to mutations in genes and pathways targeted by the treatment (McGranahan and Swanton, 2017).…”

Long-Term Effects of Metabolic Stress on Human Malignant Melanoma Cell Line

“…One reason behind this barrier in successful treatment could be the heterogeneity in tumour cells and the metabolic status that they bear. 15,16 In the presented research, malignant melanoma cells' reaction towards different levels of metabolic stress is evaluated and studied. Cell lines, such as this A375 melanoma cell line, usually require a media supplemented with 10% serum.…”

“…It is now a known fact that a tumour is not a homogenous tissue and in recent years, many researchers have focused on the heterogeneity of the tumour tissue. [14][15][16][17][18] The outer layers of a tumour can proliferate easily. These cells have enough space to proliferate, and sufficient access to blood as a source of oxygen and nutrition and a readily accessible stream for getting rid of all the metabolic wastes.…”

Why Is Evolution Important in Cancer and What Mathematics Should Be Used to Treat Cancer? Focus on Drug Resistance

“…In the latter case, these effects may be described either by their molecular effects on known drug targets (keeping in mind that precision targeting is often alluring, since drugs may have unpredictable effects on non recognised targets) or by their functional effects on the possible fates of cell populations, namely proliferation, extinction, differentiation or senescence. The respective advantages of these two points of view are also discussed, with examples, in [Chisholm et al 2016a] 6 . Whatever the chosen point of view, molecular or functional, the goal of these models is here clearly established as understanding and improving the efficacy of anticancer treatments (the physician's viewpoint).…”

“…They belong to two general classes: agent-based models, ruled by stochastic rules of growth (for division, death, motion, interactions with the environment) in which the individual agents are cancer cells, and continuous models that rely on ordinary or partial differential equations, sometimes delay differential equations, whose solutions are densities of cancer cell populations. The benefits and limitations of these two respective classes of models, with examples, are discussed, e.g.., in [Chisholm et al 2016a] 6 . As regards anticancer treatments, the continuous version allows to take advantage of mathematical optimisation and optimal control algorithms that have been designed in this framework, originally in engineering settings.…”