Exploring the non-genetic (i.e. ePIgenetic) mechanisms that contribute to therapy Escape in melanoma

Informations projet

EscaPI

N° de convention de subvention: 896827

DOI

10.3030/896827

Projet clôturé le 25 Juin 2021

Date de signature de la CE 20 Mars 2020

Date de début 1 Mai 2020

Date de fin 30 Avril 2022

Financé au titre de

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Coût total

€ 178 320,00

Contribution de l’UE

€ 178 320,00

Coordonné par

VIB VZW
Belgium

Periodic Reporting for period 1 - EscaPI (Exploring the non-genetic (i.e. ePIgenetic) mechanisms that contribute to therapy Escape in melanoma)

Période du rapport: 2020-05-01 au 2022-04-30

Résumé du contexte et des objectifs généraux du projet

Resistance to anticancer drugs, which often develops from a heterogeneous pool of drug-tolerant cells known as minimal residual disease (MRD), is thought to mainly occur through acquisition of genetic alterations. Emerging evidence indicates that drug resistance may also be acquired in absence of a genetic cause. It remains unclear, however, whether genetic versus non-genetic mechanisms of resistance are selected in a stochastic manner, and what are the epigenomics mechanisms underlying the transition from drug-tolerance to resistance. To tackle those questions, I took advantage of scRNA-seq and scATAC-seq data generated on a melanoma PDX model displaying non-genetic resistance, at different timepoints before and during treatment with dabrafenib and trametinib. Analyzing those data, I aimed to provide a dynamic and integrated view of the evolution of transcriptional and epigenomic profiles -at single-cell resolution- before, during and after acquisition of drug resistance phenotypes in a in vivo clinically-relevant context. All together, this longitudinal study, at the level of single-cell, exploring both transcriptome and chromatin, aimed to provide precious inside about non-genetic mechanisms of resistance to therapy.

Travail effectué depuis le début du projet jusqu’à la fin de la période considérée dans le rapport et principaux résultats atteints jusqu’à présent

PDX melanoma of non-genetic resistance Mel006 was investigated at deep resolution on transcriptional and chromatin levels. 10X scRNA-seq and 10X scATAC-seq data were generated in this model at 5 different timepoints during treatment with DT. On transcriptional part, several subpopulations known to be present at MRD with different technologies were found back, validating the quality of our data : NCSC, invasive cells, SMC cells, hyper pigmented cells. Moreover, a highly distinct new subpopulation, not characterized yet in this model, emerged. This subpopulation has specific markers related to stress through AP-1 signaling and shows a strong gene regulatory network regulated by AP-1 factors using SCENIC tool. This subpopulation will be of high interest for further investigations as AP-1 already been shown to be involved in invasion and early resistance to treatment. Looking at dynamic of subpopulations with time under treatment, NCSC state was found to be a transient state mainly present at MRD, while cells enriched for pigmentation signatures were found to be enriched on early time after relapse, raising the question of their involvement in resistance. scATAC-seq data shown good quality. However, integrating those scATAC-seq data with scRNA-seq,specific enhancers of subpopulations of interest were failed to be identified for subsequent lineage tracing investigations. To circumvent this issue, the new 10X Multi-Ome technology that allows to get transcriptional and chromatin accessibility measures simultaneously in same cells was performed on same model. Those data were pre-analyzed and shows overall good quality and will need further investigation.

Progrès au-delà de l’état des connaissances et impact potentiel prévu (y compris l’impact socio-économique et les conséquences sociétales plus larges du projet jusqu’à présent)

This project presents for first time a longitudinal analysis of a non-genetic model of resistance to treatment in melanoma. More than describing heterogeneity in subpopulations, it allowed to describe the dynamic changes in composition and proportion with time, raising hypothesis regarding subpopulations involved in non-genetic resistance. It applied highly new technologies to invest chromatin changes during this process, highlighting difficulties to capture chromatin heterogeneity in subpopulations induced by treatment.

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