Single-cell trajectories of melanoma cell resistance to targeted treatment

Abstract

Objective: Cellular heterogeneity is regarded as a major factor affecting treatment response and resistance in malignant melanoma. Recent developments in single-cell sequencing technology have provided deeper insights into these mechanisms. Methods: Here, we analyzed a BRAF V600E -mutant melanoma cell line by single-cell RNA-seq under various conditions: cells sensitive to BRAF inhibition with BRAF inhibitor vemurafenib and cells resistant to BRAF inhibition with vemurafenib alone or vemurafenib in combination with the MEK1/2 inhibitors cobimetinib or trametinib. Dimensionality reduction by t-distributed stochastic neighbor embedding and self-organizing maps identified distinct trajectories of resistance development clearly separating the 4 treatment conditions in cell and gene state space. Results: Trajectories associated with resistance to single-agent treatment involved cell cycle, extracellular matrix, and de-differentiation programs. In contrast, shifts detected in double-resistant cells primarily affected translation and mitogen-activated protein kinase pathway reactivation, with a small subpopulation showing markers of pluripotency. These findings were validated in pseudotime analyses and RNA velocity measurements. Conclusions: The single-cell transcriptomic analyses reported here employed a spectrum of bioinformatics methods to identify mechanisms of melanoma resistance to single-and double-agent treatments. This study deepens our understanding of treatmentinduced cellular reprogramming and plasticity in melanoma cells and identifies targets of potential relevance to the management of treatment resistance.

Classifying Germinal Center Derived Lymphomas—Navigate a Complex Transcriptional Landscape

“…As for lymphomas, also the transcriptional states along the GC reaction form a continuum on cellular resolution rather than a set of well-separated single cell states [ 46 ]. Notably, SOM-landscapes in general are able to recapitulate time-dependent trajectories along such a continuum in gene state space [ 66 ] as was shown for a wide spectrum of time-scales ranging from cell and tissue development [ 67 ] to evolutionary processes [ 68 , 69 ] using transcriptomic and genetic features.…”

Single-cell RNA sequencing in melanoma: what have we learned so far?

“…For example, scRNA-seq of BRAF-mutant melanoma cells treated with the BRAF inhibitor vemurafenib, alone or in combination with MEK inhibitor cobimetinib or trametinib, showed seven separate cell clusters representative of distinct transcriptional states. Each treatment group showed enrichment of particular transcriptional states, 52 suggesting that the frequencies of these states change with treatment, and this dynamic change contributes to the overall heterogeneity. Similarly, in NRAS-mutant melanoma cells treated with combination MEK and CDK4/6 inhibitors, scRNA-seq revealed distinct cell clusters reflecting heterogeneity in transcriptional response to treatment, and differences in response was postulated to cause outgrowth of intrinsically resistant melanoma subpopulations.…”

“…This study proposed therapeutic targets to circumvent treatment resistance. GSE164897 52 Four NRAS-mutant melanoma cell lines treated with MEK and CDK4/6 inhibitors Melanoma cells Differentiated distinct early vs late transcriptional profiles in response to drug treatment. This study identified the ATP-gated ion channel P2RX7 as a potential target in delaying acquired resistance to CDK4/6 inhibitors.…”

“…For instance, scRNA-seq studies of melanoma cultures exposed to different treatments showed induction of distinct transcriptional programs that could lead to drug resistance; BRAF-mutant melanoma cells resistant to BRAF inhibitor alone showed a high cell cycling and proliferative phenotype while those resistant to combination BRAF/MEK inhibitors showed signatures of high translational activity, PI3K pathway activation and alternate MAPK pathway reactivation. 52 Treatment of NRAS-mutant melanoma cells with MEK and CDK4/6 inhibitors induced two divergent drug responses: fast-adapting cells characterised by increased ion transmembrane transport and an immune-like state, and slow-adapting cells with features of senescence. 53 Hence, it is provocative to suggest that understanding the mechanisms of treatment response may provide novel approaches to circumvent melanoma cell adaptation and prevent the emergence of drug resistance.…”

Heterogeneity in Melanoma

“…Several studies have employed single-cell RNA sequencing (scRNA-seq) to identify subpopulations present within melanoma tumours based on gene expression signatures ( Figure 1 C). Despite the use of different approaches to identify cell states, there are similarities in the findings [ 54 , 106 , 107 , 108 , 109 , 110 , 111 , 112 ]. Thus far, up to seven distinct transcriptional melanoma cell states have been identified, although a consensus regarding the naming of these populations is yet to be reached ( Table 2 ).…”