Optimal Scheduling of Therapy to Delay Cancer Drug Resistance

Grima

2022

Preprint

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In the noisy cellular environment, RNAs and proteins are subject to considerable stochastic fluctuations in copy numbers over time. As a consequence, single cells within the same isoclonal population can differ in their expression profile and reside in different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time makes it a particularly hard phenomenon to characterize. Here we propose a novel fluctuation-test approach to infer the kinetics of transitions between cell states. More specifically, single cells are randomly drawn from the population and grown into cell colonies. After growth for a fixed number of generations, the number of cells residing in different states is assayed for each colony. In a simple system with reversible switching between two cell states, our analysis shows that the extent of colony-to-colony fluctuations in the fraction of cells in a given state is monotonically related to the switching kinetics. Several closed-form formulas for inferring the switching rates from experimentally quantified fluctuations are presented. We further extend this approach to multiple cell states where harnessing fluctuation signatures can reveal both the topology and the rates of cell-state switching. In summary, our analysis provides a powerful approach for dissecting cell-state transitions based on a single time point measurement. This is especially important for scenarios where a measurement involves killing the cell (for example, performing single-cell RNA-seq or assaying whether a microbial/cancer cell is in a drug-sensitive or drug-tolerant state), and hence the state of the same cell cannot be measured at different time points.

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Section: Introductionmentioning

confidence: 94%

A fluctuation-based approach to infer kinetics and topology of cell-state switching

Grima

2022

Preprint

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“…Combining the colony-to-colony fluctuations in the number of surviving melanoma cells with the formula in Equation (5) revealed a drug-tolerant state with a transient heritability of roughly five to eight generations before melanoma cells switch back to being drug-sensitive (Saint-Antoine and Singh, 2022 ). From a therapeutic point of view, knowing these rates of switching can aid the design of drug therapy schedules to delay the emergence of cancer drug resistance (Paryad-Zanjani et al, 2021 ). These mathematical results also facilitated the development of a novel approach, Memory Sequencing (MemorySeq), that identifies all slowly fluctuating expression programs in rare cells (Shaffer et al, 2020 ).…”

Section: Inferring Transient Heritable Across Biology Systemsmentioning

confidence: 99%

Probing transient memory of cellular states using single-cell lineages

2023

Front. Microbiol.

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The inherent stochasticity in the gene product levels can drive single cells within an isoclonal population to different phenotypic states. The dynamic nature of this intercellular variation, where individual cells can transition between different states over time, makes it a particularly hard phenomenon to characterize. We reviewed recent progress in leveraging the classical Luria–Delbrück experiment to infer the transient heritability of the cellular states. Similar to the original experiment, individual cells were first grown into cell colonies, and then, the fraction of cells residing in different states was assayed for each colony. We discuss modeling approaches for capturing dynamic state transitions in a growing cell population and highlight formulas that identify the kinetics of state switching from the extent of colony-to-colony fluctuations. The utility of this method in identifying multi-generational memory of the both expression and phenotypic states is illustrated across diverse biological systems from cancer drug resistance, reactivation of human viruses, and cellular immune responses. In summary, this fluctuation-based methodology provides a powerful approach for elucidating cell-state transitions from a single time point measurement, which is particularly relevant in situations where measurements lead to cell death (as in single-cell RNA-seq or drug treatment) or cause an irreversible change in cell physiology.

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Estimating Rates of Switching Between Cell States Using a Single Time Point Measurement

2022

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