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Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq

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.

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Figure 1: SB mutagenesis drives ML development in mice.
Figure 2: Trunk drivers are enriched in key signaling pathways and exhibit significant interactions.
Figure 3: The SBCapSeq method for sequencing transposon insertions sites from tumors.
Figure 4: Comparison of SBCapSeq to SB splink 454.
Figure 5: WGS confirms SB insertions drive ML.
Figure 6: Single-cell analysis reveals mutually exclusive tumor cell populations.
Figure 7: Clonally selected SB insertion events affect gene expression in SB-ML.

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Acknowledgements

The authors thank D. Adams, T. Whipp, R. Rance and the Wellcome Trust Sanger Institute sequencing and informatics teams for 454 sequencing; the Institute for Molecular and Cell Biology Histopathology Core; P. Cheok, N. Lim, D. Chen and C. Wee for assistance with tumor monitoring and animal husbandry at IMCB (Singapore), H. Lee and E. Freiter for assistance with animal husbandry at HMRI (Houston), R. Zahr (Integrated DNA Technologies, Inc.) for assistance with SBCapture probe design and D. Adams and C. Print for valuable discussions and critical reading of the manuscript. Histology work was performed by the Advanced Molecular Pathology Laboratory, IMCB, A*STAR. This work was supported by the Cancer Prevention Research Institute of Texas (N.G.C. and N.A.J.), the Biomedical Research Council, Agency for Science, Technology, and Research, Singapore (N.G.C. and N.A.J.), Cancer Research UK (A.G.R.), the Medical Research Council, UK (A.G.R.) and the Wellcome Trust (A.G.R.).

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Authors and Affiliations

Authors

Contributions

K.M.M., M.B.M., N.G.C. and N.A.J. designed the study, directed the research, interpreted the data and wrote the manuscript. K.M.M. and M.B.M. performed experimental work, designed the SBCapSeq oligos, coordinated sequencing efforts and analyzed the data. M.A.B., J.M.W., A.G.R. and N.N. contributed to the experimental design. J.Y.N., M.A.B. and A.G.R. provided essential statistical and bioinformatics resources. J.Y.N. wrote the Python code for SBCapSeq and splink HiSeq workflows and gCIS analysis; performed statistical and bioinformatic analysis for SBCapSeq, splink HiSeq, and RNA-seq data; and managed compute resources and data archiving of SBCapSeq and splink HiSeq data. M.A.B. wrote the R code and performed statistical and bioinformatic analysis for microarray, RNA-seq and WGS data analysis. A.G.R. wrote the R and Perl code for splink 454 workflow and managed resources and data archiving of splink 454 data. K.R. and S.M.R. performed and directed necropsy and histopathological analysis. J.M.W. performed and directed veterinary pathology analysis, including tumor grading and diagnosis. L.v.d.W. optimized library preparation for splink 454 sequencing. C.C.K.Y. performed bioinformatic analysis for splink 454 data. L.A.M. and L.S. isolated RNA and performed microarray hybridizations. K.M.M. developed and optimized staining protocols for FACS analysis. J.L.W., M.L.L. and N.N. isolated single cells by FACS and performed WGA of single-cell genomes. D.J.J. performed and optimized library preparation for SBCapSeq method and performed library preparation for splink HiSeq sequencing. L.G.-R. and F.A.-M. performed and optimized capture hybridizations for the SBCapSeq method and performed Ion Torrent sequencing for SBCapSeq, RNA-seq and WGS experiments. T.K. optimized library preparation splink HiSeq sequencing. All coauthors contributed to editing the manuscript before submission. N.G.C. and N.A.J. provided laboratory resources and personnel for animal husbandry, specimen archiving, sequencing and computer management.

Corresponding authors

Correspondence to Neal G Copeland or Michael B Mann.

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Integrated supplementary information

Supplementary Figure 1 Histological analysis of SB-induced hematopoietic disease.

Histological analysis of SB-induced leukemias revealed a disease that resembled myeloid leukemia in an undifferentiated state. (a) End-stage disease was characterized by splenomegaly (every tick-mark in grid represents 2 mm) and enlarged liver lobes (not shown). (b) Immunohistochemical analysis of TRP53 accumulation was performed in splenic sections. TRP53 staining was mainly observed in the red pulp, consistent with the site of myeloid disease (200X). (c) TRP53 staining was nuclear and present in cells cytologically defined as myeloid (400X). (d) R172H animals demonstrated significantly greater accumulation of Trp53 in the spleen than either KO or WT animals. This difference was most striking between the cohorts that developed ML (WT vs. KO vs. R172H, P<0.0001). Accumulation of TRP53 was also observed in R172H animals without SB (R172H-no SB) and the amount of Trp53 in the spleens of these animals was significantly greater than that quantitated in the spleens of animals with either wild type (WT-no SB) or those lacking Trp53 (KO-no SB, P<0.05) that were used for controls to assess background staining. (e) Foci of undifferentiated myeloid leukemic cells were also observed in the liver, in addition to the spleen (see text) (1,000X). (f) 40% of tumor cells also expressed PAX5, an early B-cell marker that has recently been shown to play a role in myeloid differentiation (400X).

Supplementary Figure 2 Specific subsets of SB-ML candidate cancer genes predict statistically significant clinical survival disparity among AML patient with mutation data from the TCGA.

We conducted overall survival analysis of 191 human AML patients with longitudinal data available from TCGA in cBioPortal1. Any human orthologs of mouse SB-ML candidate cancer genes (CCGs) for which there was a genomic aberration (mutation, copy number alteration) in these patients was considered. (a) Patients with one of more alterations in human orthologs of the 35 statistically defined SB-ML trunk driver genes had significantly decreased overall survival (P=0.00414) compared to patients with no alterations in these genes. (b) The survival disparity was significant (P=0.00021) for a subset of patients with alterations in orthologs of 6 SB-ML trunk drivers (AKT1, ERG, ETS1, GHR, NCOA2, NOTCH1) present in the clonal populations of the D5 leukemic spleen identified by single cell sequencing. (c) We next focused on human orthologs of the 32 SB-ML CCGs that participate in JAK-STAT signaling and found a significant decrease in overall survival for patients with one or more alterations (P=0.0288). (d) Patients with alterations in one or more human orthologs of the 44 SB-ML CCGs that participate in MAP Kinase signaling had an even greater disparity in overall survival (P=0.0018).

1Cerami, E. et al. The cBio cancer genomics portal: an open platform for exploring multidimensional cancer genomics data. Cancer Discov 2, 401-404 (2012).

Supplementary Figure 3 SBCapSeq workflow and bioinformatics pipeline.

(a) DNA libraries of 200bp fragments were generated using an AB Library builder. These fragments were then subjected to a series of purification and size-selection steps. PCR enrichment post-capture yielded pmol quantities of DNA that was subsequently sequenced on an Ion Torrent Proton PI chip. (b) Post-processing of the sequence output from the Ion Torrent software suite involved a series of filtering steps to enrich for sequences with a MAP quality score >30, that contained transposon inverted repeats along with unique mouse genomic sequence, that lacked vector sequence, and had a unique TA dinucleotide to which the SB insertion could be mapped. Sequences that passed these filters were compiled in a bed file that was then used for downstream statistical analysis.

Supplementary Figure 4 Breakdown of reads in the SBCapSeq analysis pipeline.

(a) SBCapSeq identifies sequences mapping to both ends of the transposon. Black bars represent reads mapping to an IRDR that spans the TA junction, while gray bars represent the remaining reads that map to the SB construct. There is a small bias in reads mapping to the IRDRL (56%) relative to the IRDR (44%). (b) Breakdown of reads sequenced by SBCapSeq for a typical tumor. Reads that map to a TA site (red) are used to identify mutated loci (refer to Supplemental Figure 3, panel B for reads that pass through Filter 4) and represent ~3% of all reads. The majority of reads that are filtered away by the SBCapSeq pipeline lack an exact 15 base pair match to the IRDR of the transposon (Filter 1). A small proportion (0.2%) of reads are unmappable; these contain the exact IRDR sequence but lack the adjacent Bam HI restriction site.

Supplementary Figure 5 Assessing the reproducibility of SBCapSeq experiments at various read depths.

We plotted individual insertion sites based on read depth from two D5-Spleen libraries and found high reproducibility between individual capture reactions at moderate-to-high read depths, supported by both Pearson’s (r= 0.88) and Spearman’s (ρ=0.82) correlations (a). A threshold to separate reproducible sites from non-reproducible sites in these two libraries was determined using precision-recall analysis (b). At a depth of 226 reads, 95 sites are identified with a precision of 0.955 and a recall of 0.505 (green dot), whereas the optimal tradeoff between precision and recall (F1 score=0.664) occurs at 188 reads (yellow dot, 103 sites, precision=0.834, recall=0.551). When we consider sites with a read depth of less than 226, we see poor correlation between the libraries (c), indicating that these are likely background passenger insertion events that are not reproducibly sequenced. Panels d–f show similar analyses using fragments instead of reads. As shown in panel e, the cutoff at which we achieve 0.958 precision is at four fragments (green dot, recall=0.615, F1 score=0.749), which is also the optimal F1 score. At this cutoff, 115 sites reproduce across the samples, which is 21% more than what was detected using reads. Relying on fragments therefore allows us to identify selected insertions with lower sequencing representation compared to reads.

Supplementary Figure 6 Comparison of splink 454 or splink HiSeq and SBCapSeq results.

SBCapSeq genome-wide insertion data from 10 ML tumors was compared to genome-wide insertion data generated using a splinkerette PCR approach (see Methods) and sequenced on either the Roche 454 (splink_454) or Illumina HiSeq (splink_HiSeq) platforms. Dendograms of shared insertions across two or more platforms were constructed using the Hamming distance metric showed that SBCapSeq and splink_HiSeq results were more comparable to each other than were the two different splinkerette methods. (a–j) The scatter plots show high read-depth sites are reproducibly detected across the two different methods, with a high correlation coefficient (Pearson correlation (r) >0.8 and Spearman correlation (ρ) >0.5). (k–n) The same analysis of genome-wide insertion data from the D5 leukemic spleen sample sequenced by SBCapSeq, whole genome sequencing and splink_HiSeq showed SBCapSeq and splink_HiSeq results were more closely related at a similar level to that observed in the 10 ML above, even though the D5 leukemic spleen was sequenced to a much greater depth with SBCapSeq (r=0.8). SBCapSeq and WGS showed a higher correlation (r=0.92) than splink_HiSeq and WGS (r=0.76).

Supplementary Figure 7 Whole-genome sequencing reveals few non-SB genomic aberrations.

Whole genome sequencing of a single end-stage leukemic spleen to >100X coverage using Illumina HiSeq, revealed a chromosomally stable genome, devoid of translocations and significant copy-number alterations. (a) A summary table of the statistically significant SNVs/SNPs and InDels detected by WGS shows that few events mapped to exonic regions of the genome. (b) A circular genome plot shows the CNA data, where red dots indicate focal gains and green dots indicate focal losses. A single gene (Olfr429) contained a genome-wide significant InDel, while only 4 genes (Krt5, Lactb, Tenm3 and Trdr6) contained genome-wide significant SNVs. Genomic DNA isolated from the tail of a sibling mouse served as the control.

Supplementary Figure 8 Single-cell sequencing revealed a high level of SB insertional heterogeneity in ML.

(a) FACS analysis of cells isolated from a spleen with myeloid leukemia showed that ~50% were CD71+. A smaller proportion of cells were Ter119+, with an equal proportion of CD71+/ Ter119+ double positive cells. We used these markers to isolate live single leukemia cells before whole genome amplification. Next, we assayed the amplified single cell DNA for a subset of highly represented SB insertions present in the bulk tumor. (b, top) Primers were designed to amplify the junctions between the SB transposon (blue arrow) and flanking mouse DNA (orange arrow) based on the mapped insertion site sequences. (b, bottom) We identified 26 insertion patterns for six CCGs from 84 single cells. Filled bars indicate a positive result; red denotes Ter119+ cells, while blue denotes CD71+ cells. Two housekeeping genes, Ppia and Actb were amplified as controls. We selected 26 representative cells for sequencing SB insertions using SBCapSeq. (c) We considered any insertion in a ML CCG observed in the single cells and illustrated these using hierarchical clustering. Evidence for insertions in the bulk tumor from WGS and SBCapSeq is depicted on the right side of the graph by black bars. Single cell insertions on the non-donor chromosome are depicted in dark blue, while insertions on the donor chromosome are depicted in light blue; green bars represent trunk drivers.

Supplementary Figure 9 Insertion maps for trunk drivers identified by bulk tumor and single-cell sequencing.

(a–e) We have drawn insertion maps for the trunk drivers Erg, Ghr, Ets1, Notch1 and Akt1 identified at the population level and confirmed by single cell sequencing from the D5 tumor. All known normal gene transcripts are depicted, with bars representing exons. Above each gene, bars represent the mapped insertions, where blue bars denote activating insertions in the population (168 ML samples sequenced by splink 454) and red bars denote inactivating insertions. Green bars denote activating insertions detected in the bulk D5 tumor, while black bars denote activating insertions in the single cell samples. Recurrent insertions have been collapsed down to one bar for simplicity.

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Mann, K., Newberg, J., Black, M. et al. Analyzing tumor heterogeneity and driver genes in single myeloid leukemia cells with SBCapSeq. Nat Biotechnol 34, 962–972 (2016). https://doi.org/10.1038/nbt.3637

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