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Disrupting cellular memory to overcome drug resistance

Biology

Disrupting cellular memory to overcome drug resistance

G. Harmange, R. A. R. Hueros, et al.

Discover the fascinating world of gene expression memory! This research, conducted by Guillaume Harmange and colleagues, unveils a novel technique called scMemorySeq that quantifies memory states in melanoma cells, revealing fluctuations that may predict therapy resistance. Notably, the study identifies key pathways influencing state switching and offers insights on overcoming treatment challenges.

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Playback language: English
Introduction
Gene expression memory, the duration of a specific expression state in a cell or lineage, varies across timescales. In cancer, intermediate-timescale memory is linked to phenotypes like stemness, differentiation, metastasis, and drug resistance. Melanoma cells, for instance, fluctuate between drug-susceptible and primed-for-resistance states, even without therapy. The primed state, upon treatment, leads to higher resistance. Understanding the molecular cues driving state switching is crucial for developing therapeutic strategies that can prevent resistance by shifting primed cells to drug-susceptible states. Existing techniques, such as bulk RNA-seq and scRNA-seq, have limitations in capturing both memory and state switching dynamics across relevant timescales (days to weeks). This research aimed to develop a robust method to address these limitations and identify molecular regulators of state switching in melanoma cells.
Literature Review
Previous research has highlighted the role of gene expression memory in various cancer phenotypes, including drug resistance. Studies have shown that cancer cells can exist in multiple states, some of which are more susceptible to treatment than others. The concept of cellular memory, where a cell's transcriptional state persists through multiple divisions, is central to understanding the emergence of drug resistance. However, limited methods exist to study these long-term dynamics at the single-cell level. While single-cell RNA sequencing (scRNA-seq) can capture cellular heterogeneity, it struggles to resolve the duration of different states within individual cells. Existing computational and experimental methods often focus on short timescales (hours), not the longer timescales relevant to understanding memory and state switching relevant to drug resistance. The development of high-throughput cellular barcoding technologies provides a new avenue to track cellular lineages over extended periods, enabling a more comprehensive understanding of gene expression memory and state transitions.
Methodology
The researchers developed scMemorySeq, a method combining cellular barcoding and scRNA-seq to measure cellular memory and state switching. Drug-naive human melanoma cells (WM989) were transduced with a high-complexity viral barcode library, allowing lineage tracking across multiple cell divisions. Cells were sorted based on known markers (EGFR and NGFR) into primed and mixed (primarily drug-susceptible) populations. After approximately four doublings (12-14 days), cells were harvested for scRNA-seq and barcode sequencing. This approach provided paired lineage and transcriptome data, enabling the identification of heritable gene expression states and state-switching events. The methodology further involved the analysis of additional melanoma cell lines (WM983B) and patient samples using scRNA-seq and RNA FISH to validate the findings. The data were analyzed using a stochastic two-state model to estimate proliferation and state-switching rates. Pathway analysis was performed to identify molecular mechanisms driving state transitions. Finally, sequential dosing experiments were performed where melanoma cells were pretreated with either TGF-β1, TGF-β receptor inhibitor (TGFBRi), or PI3K inhibitor (PI3Ki) for 5 days before the addition of BRAF/MEK inhibitors for 4 weeks to assess the impact on drug resistance.
Key Findings
scMemorySeq identified two major transcriptionally distinct populations in melanoma cells: drug-susceptible and primed for resistance. A novel marker, NTSE, was identified to accurately capture the entire primed cell population. Lineage tracing revealed that TGF-β and PI3K pathways regulate state switching. TGF-β1 treatment increased the percentage of primed cells, while PI3K inhibition decreased it, demonstrating the sufficiency of TGF-β for inducing the primed state but not its necessity for maintaining it. A stochastic model showed that drug-susceptible cells switch to the primed state less frequently than primed cells switch to the drug-susceptible state. Analysis of lineages identified a transient intermediate state characterized by downregulation of UV response and upregulation of EMT and hypoxia pathways. Sequential dosing experiments demonstrated that PI3K inhibition as a pretreatment significantly reduced drug resistance compared to BRAF/MEK inhibitors alone. Pretreatment with TGF-β1, however, increased resistance.
Discussion
This study demonstrates the power of scMemorySeq in characterizing gene expression memory and state switching in cancer cells. The identification of TGF-β and PI3K pathways as key regulators of this process opens new avenues for therapeutic intervention. The finding that brief PI3K inhibition before targeted therapy can significantly reduce drug resistance suggests a novel strategy for improving treatment efficacy while potentially mitigating toxicity associated with prolonged combination therapy. The observed transient intermediate state highlights the dynamic nature of cell state transitions and provides potential targets for future drug development. The successful validation of findings in multiple melanoma cell lines and patient samples strengthens the clinical relevance of these observations.
Conclusion
scMemorySeq provides a powerful tool for studying gene expression memory and state switching. The study identifies TGF-β and PI3K as crucial regulators of melanoma cell state transitions, highlighting the potential of PI3K inhibition as a pretreatment strategy to reduce drug resistance. Future research should focus on optimizing pretreatment strategies and exploring the potential of combining state-switching modulators with drug holidays to further enhance the efficacy of targeted therapies.
Limitations
The study focused primarily on melanoma cell lines and patient samples. Further research is needed to determine the generalizability of these findings to other cancer types. The stochastic model used for analyzing state switching made several assumptions that may require further validation. The intermediate state identified may represent a spectrum of related states rather than a single distinct state. The effect of PI3K inhibitor pretreatment may not be generalizable to other types of cancer or to all melanoma subtypes.
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