Single-cell dissection of transcriptional heterogeneity in human colon tumors

By Piero Dalerba, T. Kalisky, D. Sahoo, Pradeep S. Rajendran, M. Rothenberg, A. Leyrat, Sopheak K. Sim, Jennifer Okamoto, Darius M. Johnston, Dalong Qian and 12 more. Nature Biotechnology, 2011

728 citations recorded by the source corpus.

Cancer is often viewed as a caricature of normal developmental processes, but the extent to which its cellular heterogeneity truly recapitulates multilineage differentiation processes of normal tissues remains unknown. Here we implement single-cell PCR gene-expression analysis to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single (n = 1) cell, we demonstrate that the transcriptional diversity of cancer tissues is largely explained by in vivo multilineage differentiation and not only by clonal genetic heterogeneity. Finally, we show that the different gene-expression programs linked to multilineage differentiation are strongly associated with patient survival. We develop two-gene classifier systems (KRT20 versus CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard ratios superior to those of pathological grade and comparable to those of microarray-derived multigene expression signatures.

Subfields: Cancer Research, Oncology, Pathology and Forensic Medicine

View Paper: https://www.semanticscholar.org/paper/b34321182e9ed0787585a857d48ee8a8ceaa3321

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cancer Research

In Oncology

In Pathology and Forensic Medicine

Citations in the corpus

Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images (2016, background): “other factors involved in tumor development [1], [2].”
TSCAN: Pseudo-time reconstruction and evaluation in single-cell RNA-seq analysis (2016, methodology): “Among these methods, MARS-seq (17) and SINCE-PCR (19) do not have associated software for others to use.”
The Cancer Stem Cell Niche: How Essential is the Niche in Regulating Stemness of Tumor Cells? (2015, neutral): “…only cells instigating or maintaining metastasis, the CSC-generated hierarchy of stem-like and differentiated tumor cells is able to initiate metastatic growth and is also seen in late-stage cancers and atmetastatic sites (Dalerba et al., 2011; Merlos-Suárez et al., 2011; Vermeulen et al., 2008).”
Mapping the Mouse Cell Atlas by Microwell-Seq. (2018, neutral): “Emerging methods, such as high-throughput single-cell qPCR (Dalerba et al., 2011; Guo et al., 2010; Han et al., 2017), single-cell mass cytometry (Bendall et al., 2011), and singlecell mRNA-seq (Hashimshony et al., 2012; Ramsköld et al., 2012; Shalek et al., 2013; Tang et al., 2009; Treutlein et…”
Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase. (2012, methodology): “…single-cell gene expression analysis has been applied previously to studies in the mouse intestine (Itzkovitz et al., 2012), human colon tumors (Dalerba et al., 2011), the mouse zygote and blastocyst (Guo et al., 2010; Tang et al., 2010), and human iPSCs (Narsinh et al., 2011), such an…”