Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma

By Anoop P. Patel, I. Tirosh, John J. Trombetta, A. Shalek, Shawn M. Gillespie, H. Wakimoto, D. Cahill, B. Nahed, W. Curry, R. Martuza and 5 more. Science, 2014

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Cancer at single-cell resolution Single-cell sequencing can illuminate the genetic properties of brain cancers and reveal heterogeneity within a tumor. Patel et al. examined the genome sequence of single cells isolated from brain glioblastomas. The findings revealed shared chromosomal changes but also extensive transcription variation, including genes related to signaling, which represent potential therapeutic targets. The authors suggest that the variation in tumor cells reflects neural development and that such variation among cancer cells may prove to have clinical significance. Science, this issue p. 1396 Screening individual cancer cells within a brain tumor may help to guide treatment and predict prognosis. Human cancers are complex ecosystems composed of cells with distinct phenotypes, genotypes, and epigenetic states, but current models do not adequately reflect tumor composition in patients. We used single-cell RNA sequencing (RNA-seq) to profile 430 cells from five primary glioblastomas, which we found to be inherently variable in their expression of diverse transcriptional programs related to oncogenic signaling, proliferation, complement/immune response, and hypoxia. We also observed a continuum of stemness-related expression states that enabled us to identify putative regulators of stemness in vivo. Finally, we show that established glioblastoma subtype classifiers are variably expressed across individual cells within a tumor and demonstrate the potential prognostic implications of such intratumoral heterogeneity. Thus, we reveal previously unappreciated heterogeneity in diverse regulatory programs central to glioblastoma biology, prognosis, and therapy.

View Paper: https://www.semanticscholar.org/paper/f65ed87390df365fd928af269b77b5dc4e62f7a5

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Citations in the corpus

SCENIC: Single-cell regulatory network inference and clustering (2017, result): “Standard clustering of the oligodendroglioma expression matrix resulted in clusters of cancer cells by the tumour of origin, similar to previous observations 55,57 (Figure 4a), due to strong inter-tumor heterogeneity 54 .”
An integrative model of cellular states, plasticity and genetics for glioblastoma (2019, unknown): “…tumor, longitudinal analyses demonstrated that subtypes can change over time and through therapy, and single-cell RNA-sequencing (scRNA-seq) indicated that distinct cells in the same tumor recapitulate programs from distinct subtypes (Patel et al., 2014; Sottoriva et al., 2013; Wang et al., 2017).”
Sensitive detection of rare disease-associated cell subsets via representation learning (2016, methodology): “…cohort sizes in concerted initiatives as the The Cancer Genome Atlas (TCGA) and concomitant rise in their analysis with single-cell technologies 23 , we expect scalable representation learning approaches such as CellCnn to uniquely take advantage of the resulting data by enabling the discovery…”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “In particular, using only scRNA-seq data, it is difficult to deduce whether observed transcriptional differences represent functionally distinct populations of cells and further experimental validation is required (Islam et al., 2014; Patel et al., 2014).”
Single-cell analysis reveals fibroblast clusters linked to immunotherapy resistance in cancer. (2020, background): “In recent years, scRNA-seq appeared as a revolutionary technic for addressing intra-tumor cellular complexity (27,30,34-44).”
Glioblastoma: pathology, molecular mechanisms and markers (2015, background): “Moreover, a recent study analyzing expression signatures of single cells within GBM samples showed substantial intratumoral heterogeneity of expression subclasses within each tumor [124].”
A Patient-Derived Glioblastoma Organoid Model and Biobank Recapitulates Inter- and Intra-tumoral Heterogeneity. (2019, methodology): “For CNA analysis, inferCNV was used (Patel et al., 2014; Tirosh et al., 2016; Venteicher et al., 2017) on each patient sample individually with microglia/macrophages and T cells set as references (Figure S4A).”