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

Informazioni relative al progetto

EscaPI

ID dell’accordo di sovvenzione: 896827

DOI

10.3030/896827

Progetto concluso il 25 Giugno 2021

Data della firma CE 20 Marzo 2020

Data di avvio 1 Maggio 2020

Data di completamento 30 Aprile 2022

Finanziato da

• EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions

Costo totale

€ 178 320,00

Contributo UE

€ 178 320,00

Coordinato da

VIB VZW
Belgium

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

Periodo di rendicontazione: 2020-05-01 al 2022-04-30

Sintesi del contesto e degli obiettivi generali del progetto

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.

Lavoro eseguito dall’inizio del progetto fino alla fine del periodo coperto dalla relazione e principali risultati finora ottenuti

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.

Progressi oltre lo stato dell’arte e potenziale impatto previsto (incluso l’impatto socioeconomico e le implicazioni sociali più ampie del progetto fino ad ora)

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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