fMetastatic potential in clonal melanoma cells is driven by a rare, early-invading subpopulation

Kaur, Amanpreet; Cuenca, Luciann; Kiani, Karun; Busch, Gianna T.; Fingerman, Dylan; Dunagin, Margaret C.; Li, Jingxin; Dardani, Ian; Sanford, Eric M.; Pemberton, Jordan; Goyal, Yogesh; Weeraratna, Ashani T.; Herlyn, Meenhard; Raj, Arjun

doi:10.1101/2022.04.17.488591

Cited by 3 publications

(1 citation statement)

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“…Memory of gene expression states can exist over a range of different timescales and is ultimately a quantitative measurement (Angel et al 2011; Shaffer et al 2020). For cancer, an intermediate timescale of memory has been associated with a number of important phenotypes including, stemness and differentiation (Chaligne et al 2021; Gupta et al 2011), metastasis (Kaur et al 2022), and therapy resistance (Shaffer et al 2020; Torre et al 2021; Sharma et al 2010; Oren et al 2021). In these examples, the cellular state underlying the phenotype is stable enough to persist through multiple cell divisions, but is ultimately not permanent, and thus amenable to switching states.…”

Section: Introductionmentioning

confidence: 99%

Disrupting cellular memory to overcome drug resistance

Harmange

Hueros

Schaff

et al. 2022

Preprint

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Cellular memory describes the length of time a particular transcriptional state exists at the single-cell level. Transcriptional states with memory can underlie important processes in biology, including therapy resistance in cancer. Here we present a new experimental and computational approach for identifying gene expression states with memory at single-cell resolution by combining single-cell RNA sequencing (scRNA-seq) with cellular barcoding. With this technique, we can systematically quantify the full expression profile of these memory states as well as their population dynamics including the relative growth rates of cells within different states and the rates of switching between states. We applied this approach to human melanoma cells and uncovered memory gene expression states that are predictive of which cells will be resistant to combination BRAF and MEK inhibition. While these cells have not been treated with targeted therapies, they already express markers of resistance, and thus are referred to here as “primed” for resistance. From the scRNA-seq data alone, the drug-susceptible and primed cells appear as two distinct and unrelated cell populations. From the lineage barcodes, we found that most cells remain within the same state, demonstrating memory of gene expression. However, we also directly observe state switching as we find that about 18% of lineages contain cells that have switched states. While the molecular drivers of state switching are not immediately apparent from scRNA-seq data alone, we specifically analyzed lineages that undergo state switching and identified TGF-β and PI3K as drivers of state switching at the single-cell level. Through functional validation and single-cell RNA-sequencing, we show mechanistic single-cell manipulation of state switching that ultimately yields changes in cellular phenotype. We leverage these mechanisms of state switching to delay resistance by demonstrating that reducing the number of primed cells with a PI3K inhibitor prior to BRAF and MEK inhibition ultimately leads to fewer resistant colonies. Our results show that modulation of cellular signaling can globally regulate gene expression programs to overcome therapy resistance.

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Section: Introductionmentioning

confidence: 99%

Disrupting cellular memory to overcome drug resistance

Harmange

Hueros

Schaff

et al. 2022

Preprint

Self Cite

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Disrupting cellular memory to overcome drug resistance

Harmange,

Hueros,

Schaff

et al. 2023

Nat Commun

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Gene expression states persist for varying lengths of time at the single-cell level, a phenomenon known as gene expression memory. When cells switch states, losing memory of their prior state, this transition can occur in the absence of genetic changes. However, we lack robust methods to find regulators of memory or track state switching. Here, we develop a lineage tracing-based technique to quantify memory and identify cells that switch states. Applied to melanoma cells without therapy, we quantify long-lived fluctuations in gene expression that are predictive of later resistance to targeted therapy. We also identify the PI3K and TGF-β pathways as state switching modulators. We propose a pretreatment model, first applying a PI3K inhibitor to modulate gene expression states, then applying targeted therapy, which leads to less resistance than targeted therapy alone. Together, we present a method for finding modulators of gene expression memory and their associated cell fates.

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