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  current news   Press   selected story    
     
  5 August 2016  
 
Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq
 
 




Authors
Karen M Mann1,2,7, Justin Y Newberg1, Michael A Black3, Devin J Jones1, Felipe Amaya-Manzanares1,
Liliana Guzman-Rojas1, Takahiro Kodama1, Jerrold M Ward2,6, Alistair G Rust4,6, Louise van der Weyden4, Christopher Chin Kuan Yew2,6, Jill L Waters5,6, Marco L Leung5, Keith Rogers2, Susan M Rogers2, Leslie A McNoe3, Luxmanan Selvanesan3,6, Nicholas Navin5, Nancy A Jenkins1,2, Neal G Copeland1,2 & Michael B Mann1,2,7

1   Cancer Research Program, Houston Methodist Research Institute, Houston, Texas, USA.
2   Institute of Molecular and Cell Biology, Singapore, Republic of Singapore.
3   Department of Biochemistry, University of Otago, Dunedin, New Zealand.
4   Experimental Cancer Genetics, Wellcome Trust Sanger Institute, Hinxton, Cambridge, UK.
5   Department of Genetics, University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
6   Present addresses: Global VetPathology, Montgomery Village, Maryland, USA (J.M.W.), Tumour     Profiling Unit, the Institute of Cancer Research, Chester Beatty Laboratories, London, UK (A.G.R.),     National Heart Research Institute Singapore, Republic of Singapore (C.C.K.Y.), Illumina, Inc., San     Diego, California, USA (J.L.W.) and Pacific Edge Limited, Dunedin, Otago, New Zealand (L.S.).
7   These authors contributed equally to this work.

Correspondence should be addressed to N.G.C. (ncopeland@houstonmethodist.org) or M.B.M. (mbmann@houstonmethodist.org).

Published online ahead of print in Nature Biotechnology on 1st August 2016.

Abstract

A central challenge in oncology is how to kill tumors containing heterogeneous cell populations defined by different combinations of mutated genes. Identifying these mutated genes and understanding how they cooperate requires single-cell analysis, but current single-cell analytic methods, such as PCR-based strategies or whole-exome sequencing, are biased, lack sequencing depth or are cost prohibitive. Transposon-based mutagenesis allows the identification of early cancer drivers, but current sequencing methods have limitations that prevent single-cell analysis. We report a liquid-phase, capture-based sequencing and bioinformatics pipeline, Sleeping Beauty (SB) capture hybridization sequencing (SBCapSeq), that facilitates sequencing of transposon insertion sites from single tumor cells in a SB mouse model of myeloid leukemia (ML). SBCapSeq analysis of just 26 cells from one tumor revealed the tumor’s major clonal subpopulations, enabled detection of clonal insertion events not detected by other sequencing methods and led to the identification of dominant subclones, each containing a unique pair of interacting gene drivers along with three to six cooperating cancer genes with SB-driven expression changes.



Figure. 1.
SB mutagenesis drives ML development in mice. (a) Three cohorts of mice with wild-type Trp53 (Trp53+/+), Trp53+/- or Trp53R172H/+ were generated and aged for tumors. All mice developed hematopoietic disease but Trp53+/- and Trp53R172H/+ mice had significantly decreased survival compared to the WT cohort (P < 0.0001, log-rank). (b,c) Histological analysis of tumor cell differentiation in the red pulp of the spleen (b) and in the subcapsular area (c), shown at 1,000× magnification. Histological analysis was performed on all mice in each cohort. Tumor cell differentiation representative of all cohorts is shown in (b) for the red pulp in a Trp53+/+ animal and (c) for the subcapsular area in a Trp53+/- animal. (d,e) MPO staining in poorly differentiated regions (d) and PAX5 staining of tumor cells in the subcapsular area (e), shown at 400× magnification. Scale bars, 100 μm (b–e). MPO and PAX5 staining was performed on all animals in each cohort and half showed positive MPO staining (d) in poorly differentiated regions and half showed positive PAX5 staining (e) in the subcapsular area. Representative images from Trp53+/+ mice are shown. (f) Statistical analysis of SB insertions present in ≥3 tumors at a read depth of ≥5 (based on splink 454 sequencing) identified 35 CCGs that are highly represented in SB-ML. Genes are plotted on the basis of mean read depth (y axis) relative to the median number of reads (x axis); SB insertions are predicted to activate 80% of these genes (blue) and inactivate 20% (red). CIS, common insertion site.