The role of single-cell sequencing in studying tumour evolution

Mossner, Maximilian; Baker, Ann-Marie C; Graham, Trevor A.

doi:10.12703/r/10-49

Cited by 1 publication

(1 citation statement)

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“…[1][2][3][4] As patients undergo therapies, such single cell omics approaches are uncovering the evolution of cancer cell clones with different fitness. 5,6 While single cell omics help define the complexity and heterogeneity of primary human cancer cells, they do not directly measure functional behaviors of individual cells. Moreover, cancer cell behaviors can be significantly modified by the presence of other cell types and soluble and insoluble factors in the tumor microenvironment (TME), which further complicates the ability to extract tumor cell behavioral information, such as drug response or resistance, from single cell omics approaches.…”

Section: Introductionmentioning

confidence: 99%

Timelapse viability assay to detect division and death of primary multiple myeloma cells in response to drug treatments with single cell resolution

Mark

Callander

Chng

et al. 2022

Integrative Biology

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Heterogeneity among cancer cells and in the tumor microenvironment (TME) is thought to be a significant contributor to the heterogeneity of clinical therapy response observed between patients and can evolve over time. A primary example of this is multiple myeloma (MM), a generally incurable cancer where such heterogeneity contributes to the persistent evolution of drug resistance. However, there is a paucity of functional assays for studying this heterogeneity in patient samples or for assessing the influence of the patient TME on therapy response. Indeed, the population-averaged data provided by traditional drug response assays and the large number of cells required for screening remain significant hurdles to advancement. To address these hurdles, we developed a suite of accessible technologies for quantifying functional drug response to a panel of therapies in ex vivo three-dimensional culture using small quantities of a patient’s own cancer and TME components. This suite includes tools for label-free single-cell identification and quantification of both cell division and death events with a standard brightfield microscope, an open-source software package for objective image analysis and feasible data management of multi-day timelapse experiments, and a new approach to fluorescent detection of cell death that is compatible with long-term imaging of primary cells. These new tools and capabilities are used to enable sensitive, objective, functional characterization of primary MM cell therapy response in the presence of TME components, laying the foundation for future studies and efforts to enable predictive assessment drug efficacy for individual patients.

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