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Disrupting cellular memory to overcome drug resistance

ABSTRACT: Disrupting cellular memory to overcome drug resistance

PROVIDER: PRJNA994430 | ENA |

REPOSITORIES: ENA

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			Action	DRS
	SRR25257300_1.fastq.gz	Fastqsanger.gz
	SRR25257300_2.fastq.gz	Fastqsanger.gz
	SRR25257301_1.fastq.gz	Fastqsanger.gz
	SRR25257301_2.fastq.gz	Fastqsanger.gz
	SRR25257302_1.fastq.gz	Fastqsanger.gz

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Disrupting cellular memory to overcome drug resistance

Project description:Plasticity enables cells to change their gene expression state in the absence of a genetic change. At the single-cell level, these gene expression states can persist for different lengths of time which is a quantitative measurement referred to as gene expression memory. Because plasticity is not encoded by genetic changes, these cell states can be reversible, and therefore, are amenable to modulation by disrupting gene expression memory. However, we currently do not have robust methods to find the regulators of memory or to track state switching in plastic cell populations. Here, we developed a lineage tracing-based technique to quantify gene expression memory and to identify single cells as they undergo cell state transitions. Applied to human melanoma cells, we quantified long-lived fluctuations in gene expression that underlie resistance to targeted therapy. Further, we identified the PI3K and TGF-β pathways as modulators of these state dynamics. Applying the gene expression signatures derived from this technique, we find that these expression states are generalizable to in vivo models and present in scRNA-seq from patient tumors. Leveraging the PI3K and TGF-β pathways as dials on memory between plastic states, we propose a "pretreatment" model in which we first use a PI3K inhibitor to modulate the expression states of the cell population and then apply targeted therapy. This plasticity informed dosing scheme ultimately yields fewer resistant colonies than targeted therapy alone. Taken together, we describe a technique to find modulators of gene expression memory and then apply this knowledge to alter plastic cell states and their connected cell fates.

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Project description:Clinical drug resistance is one major concern in the treatment of human cancers, most of them being found resistant to therapy at the time of drug presentation. Deciphering the molecular mechanisms that contribute to such innate drug resistance should improve both the prediction of treatment failure and the development of new strategies to overcome resistance. Our study reports for the first time a systems approach toward understanding the in vivo cellular states of clinical samples collected from colorectal cancer (CRC) patients prior to their exposure to combined chemotherapy of folinic acid (FA), 5-fluorouracil (5-FU) and irinotecan (CPT-11). Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established which intrinsically differentiate, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative-PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses. Molecular interaction networks were described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations. Keywords: Drug response

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