Dissecting the multicellular ecosystem of metastatic melanoma by single-cell RNA-seq

By I. Tirosh, B. Izar, S. Prakadan, M. Wadsworth, D. Treacy, John J. Trombetta, Asaf Rotem, Christopher Rodman, C. Lian, G. Murphy and 27 more. Science, 2016

4,371 citations recorded by the source corpus.

Single-cell expression profiles of melanoma Tumors harbor multiple cell types that are thought to play a role in the development of resistance to drug treatments. Tirosh et al. used single-cell sequencing to investigate the distribution of these differing genetic profiles within melanomas. Many cells harbored heterogeneous genetic programs that reflected two different states of genetic expression, one of which was linked to resistance development. Following drug treatment, the resistance-linked expression state was found at a much higher level. Furthermore, the environment of the melanoma cells affected their gene expression programs. Science, this issue p. 189 Melanoma cells show transcriptional heterogeneity. To explore the distinct genotypic and phenotypic states of melanoma tumors, we applied single-cell RNA sequencing (RNA-seq) to 4645 single cells isolated from 19 patients, profiling malignant, immune, stromal, and endothelial cells. Malignant cells within the same tumor displayed transcriptional heterogeneity associated with the cell cycle, spatial context, and a drug-resistance program. In particular, all tumors harbored malignant cells from two distinct transcriptional cell states, such that tumors characterized by high levels of the MITF transcription factor also contained cells with low MITF and elevated levels of the AXL kinase. Single-cell analyses suggested distinct tumor microenvironmental patterns, including cell-to-cell interactions. Analysis of tumor-infiltrating T cells revealed exhaustion programs, their connection to T cell activation and clonal expansion, and their variability across patients. Overall, we begin to unravel the cellular ecosystem of tumors and how single-cell genomics offers insights with implications for both targeted and immune therapies.

Subfields: Molecular Biology, Oncology

View Paper: https://www.semanticscholar.org/paper/cca97ab86fc094eff85542ebce0d2e26eb72a001

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Citations in the corpus

Multi-stage differentiation defines melanoma subtypes with differential vulnerability to drug-induced iron-dependent oxidative stress (2018, background): “…of AXL are also predictive of intrinsic resistance to mitogen-activated protein kinase (MAPK) pathway inhibition (MAPKi) (Konieczkowski et al., 2014) and have been observed in patient tumors during disease progression on MAPKi therapy (Hugo et al., 2015; M€uller et al., 2014; Tirosh et al., 2016).”
Decoupling genetics, lineages, and microenvironment in IDH-mutant gliomas by single-cell RNA-seq (2017, background): “Interestingly, three of those genes were components of the complement system, a specialized arm of the innate immune system, as we recently observed in a similar analysis of fibroblast-immune cell interactions in melanoma (5).”
Dysfunctional CD8+ T cells form a proliferative, dynamically regulated compartment within human melanoma (2018, background): “…heterogeneous makeup of immune cell infiltrates, and this heterogeneity is likely to form a determining factor in therapy outcome (Azizi et al., 2018; Guo et al., 2018; Lavin et al., 2017; Sade-Feldman et al., 2018; Savas et al., 2018; Tirosh et al., 2016; Zhang et al., 2018; Zheng et al., 2017).”
TISCH: a comprehensive web resource enabling interactive single-cell transcriptome visualization of tumor microenvironment (2020, background): “TPM values were divided by 10 to lower the impact of varying dropout rates between genes (21,39).”
The Principles of Engineering Immune Cells to Treat Cancer (2017, background): “We have entered an era of remarkable precision bioinformatics, in which we are beginning to access highly detailed information about tumors—which antigens are present, which types of suppressor molecules are present, and how these are distributed in the different cells that make up the tumor ecosyst”
Tumor cell biodiversity drives microenvironmental reprogramming in liver cancer (2019, methodology): “…tumor ecosystems of leukemia, lung cancer, glioma, colon cancer, head and neck cancer, breast cancer as well as tumor immune cell landscape (Azizi et al., 2018; Giustacchini et al., 2017; Li et al., 2017; Puram et al., 2017; Tirosh et al., 2016b; Venteicher et al., 2017; Zheng et al., 2017).”