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Analysis pipelines for cancer genome sequencing in mice

Abstract

Mouse models of human cancer have transformed our ability to link genetics, molecular mechanisms and phenotypes. Both reverse and forward genetics in mice are currently gaining momentum through advances in next-generation sequencing (NGS). Methodologies to analyze sequencing data were, however, developed for humans and hence do not account for species-specific differences in genome structures and experimental setups. Here, we describe standardized computational pipelines specifically tailored to the analysis of mouse genomic data. We present novel tools and workflows for the detection of different alteration types, including single-nucleotide variants (SNVs), small insertions and deletions (indels), copy-number variations (CNVs), loss of heterozygosity (LOH) and complex rearrangements, such as in chromothripsis. Workflows have been extensively validated and cross-compared using multiple methodologies. We also give step-by-step guidance on the execution of individual analysis types, provide advice on data interpretation and make the complete code available online. The protocol takes 2–7 d, depending on the desired analyses.

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Fig. 1: Overview of mouse cancer genome analysis workflow.
Fig. 2: Genetic alterations in human and murine tumors.
Fig. 3: Systematic comparison of SNV callers.
Fig. 4: Systematic comparison of callers for the detection of small indels.
Fig. 5: Performance of CopywriteR for detecting copy-number changes.
Fig. 6: Analysis of copy-number changes across one mouse cancer cohort.
Fig. 7: Identification of heterozygous variant positions in the mouse germline.
Fig. 8: Mouse-specific limitations of LOH detection.
Fig. 9: Visualization of LOH in human and mouse cancer genomes.
Fig. 10: Examples of chromothripsis in mouse cancer genomes.
Fig. 11: WGS-based inference of chromothripsis in mouse cancer genomes.
Fig. 12: Features of chromothripsis.
Fig. 13: The mutant Kras allele is present in both tumor and matched normal tissue.
Fig. 14: CNV and LOH profiles for sample S821.
Fig. 15: Patterns of genomic changes affecting oncogenes.
Fig. 16: Patterns of genomic changes affecting tumor suppressor genes.

Data availability

NGS data from mouse pancreatic cancer cell cultures are available from the European Nucleotide Archive using study accession no. PRJEB23787. The validation datasets generated during the current study are available from the corresponding author upon request.

Code availability

The source code for all pipelines is available for public use at https://github.com/roland-rad-lab/MoCaSeq under the MIT license. In addition, the main workflow described in this protocol is packaged as a Docker container, available at https://cloud.docker.com/repository/docker/rolandradlab/mocaseq.

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Acknowledgements

D.S. is supported by the European Research Council (Consolidator Grant 648521) and the Deutsche Forschungsgemeinschaft (SA1374/4-2; SFB 1321). I.V. is supported by the European Research Council (Starting Grant INTRAHETEROSEQ) and the Spanish Goverment (SAF2016-76758-R). R.R. is supported by the European Research Council (Consolidator Grants PACA-MET and MSCA-ITN-ETN PRECODE), the Deutsche Forschungsgemeinschaft (DFG RA1629/2-1; SFB1243; SFB1321; SFB1335), the German Cancer Consortium Joint Funding Program, and the Deutsche Krebshilfe (70112480).

Author information

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Authors

Contributions

S.L., T.E., M.Z., S.M., L.G.-S., I.V. and R.R. conceptualized, designed or developed analysis workflows, tools or procedures. S.L. integrated and validated bioinformatic workflows. S.M., R.M., M.J.F., R.B. and F.Y. performed wet-lab experiments. G.S., G.S.V. and D.S. provided biological resources and critical input during protocol development. S.L. and R.R. wrote the manuscript with input from T.E., S.M., R.M., M.J.F and I.V.

Corresponding author

Correspondence to Roland Rad.

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The authors declare no competing interests.

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Peer review information Nature Protocols thanks Malachi Griffith and other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Key references using this protocol

Mueller, S. et al. Nature 554, 62–68 (2018): https://doi.org/10.1038/nature25459

Rad, R. et al. Cancer Cell 24, 15–29 (2013): https://doi.org/10.1016/j.ccr.2013.05.014

Key data used in this protocol

Mueller, S. et al. Nature 554, 62–68 (2018): https://doi.org/10.1038/nature25459

Integrated supplementary information

Supplementary Figure 1 Performance of CNVKit calling copy number changes in mouse primary pancreatic cancer cell cultures.

Sensitivity and precision of CNVKit in primary pancreatic cancer cell cultures (n = 38). CNV segments were compared on the gene-level to corresponding reference aCGH data. Segments with a log2 ratio between -0.25 and +0.25 were regarded as copy number neutral. Samples are sorted by the fraction of the genome affected by CNV.

Supplementary Figure 2 Detection of an intragenic EGFR-deletion in human glioblastoma.

a and b, Copy number profiles generated by CopywriteR (a) and CNVKit (b) of a glioblastoma based on WES. Chr7 containing the EGFR locus is shown. DNA and RNA were extracted from FFPE slides and library preparation was performed using Agilent SureSelect Human V6 enrichment kit and Illumina TruSeq Stranded Total RNA kit respectively. Top, Copy number profile of Chr7. Bottom, zoom-in of Chr7 containing the EGFR locus. While CopywriteR detects the amplification of EGFR (~25 copies), CNVKit shows that only Exons 1 to 24 of the EGFR locus are amplified and that exons 25 to 28 remain in the copy number neutral state (arrow). Through RNA-Seq, the copy number neutral state of Exons 25 to 28 could be confirmed. Because CopywriteR only bins off-target” reads (small dots in the middle panel), this small copy number change is not detected. CNVKit correctly detects that Exons 25 to 28 are not included in the amplification by using both on-target and off-target reads.

Supplementary Figure 3 Comparison of CopywriteR, aCGH and M-FISH for sample R1035.

a-c, Copy number profile, generated from WES using CopywriteR (a), aCGH (b) and ten M-FISH karyotypes (c) for sample R1035, a murine primary pancreatic cancer cell culture.

Supplementary Figure 4 Comparison of CopywriteR, aCGH and M-FISH for sample 5123.

a–c, Copy number profile, generated from WES using CopywriteR (a), aCGH (b) and ten M-FISH karyotypes (c) for sample 5123, a murine primary pancreatic cancer cell culture.

Supplementary Figure 5 Comparison of CopywriteR, aCGH and M-FISH for sample S302.

a-c, Copy number profile, generated from WES using CopywriteR (a), aCGH (b) and ten M-FISH karyotypes (c) for sample S302, a murine primary pancreatic cancer cell culture.

Supplementary Figure 6 WGS-based inference of chromothripsis in mouse pancreatic cancer 8661.

a-f, The analysis workflow described in this protocol was used to perform testing of chromothripsis hallmarks from WGS data for sample 8661, a mouse pancreatic cancer primary cell culture. a, Clustering of breakpoints: The distribution of observed distances between breakpoints (n = 41) differs significantly from an exponential distribution (“expected”). P < 10−3; χ2 goodness-of-fit. b, Interspersed loss and retention of heterozygosity: Comparison of CNV and LOH plots for Chr4. Copy number changes cluster in the second half of the chromosome. Only three distinct copy number states (2, 1 and 0 copies) can be identified. The number of heterozygous germline variants is insufficient for LOH analysis. c, Regularity of oscillating copy number states: A Monte Carlo approach was used to simulate the sequential acquisition of observed rearrangements on Chr4 (n = 1000 simulations per number of structural variations). Black dots represent the mean copy number states. The associated 95% confidence interval are shown as black lines. Chr4 showed less copy number states than expected by sequential acquisition of observed rearrangements. d, Randomness of DNA fragment joins: All four types of structural variations are uniformly distributed in the chromothriptic chromosome. P = 0.82; χ2 goodness-of-fit. e, Randomness of DNA fragment order: Start and end positions of observed rearrangements (n = 42) were randomly reordered using a Monte Carlo approach (n = 1000 simulations) to generate a random background distribution. The segment order of sample 8661 is located within the null model of random distribution. Two-sided P = 0.56. f, Ability to walk the derivative chromosome: Rearrangement graph of Chr4 (n = 42 rearrangements). Each fragment is represented by two blocks, indicating the read-orientations (5’ or 3’, indicated in red or grey) for the start and end of each segment, when mapped to the reference genome. P < 10-5; Wald-Wolfowitz test. SV, structural variation.

Supplementary Figure 7 WGS-based inference of chromothripsis in mouse pancreatic cancer 5671.

a-f, The analysis workflow described in this protocol was used to perform testing of chromothripsis hallmarks from WGS data for sample 5671, a mouse pancreatic cancer primary cell culture. a, Clustering of breakpoints: The distribution of observed distances between breakpoints (n = 55) differs significantly from an exponential distribution (“expected”). P = 0.003; χ2 goodness-of-fit. b, Interspersed loss and retention of heterozygosity: Comparison of CNV and LOH plots for Chr15. Copy number changes cluster in the second half of the chromosome. Only three distinct copy number states (2 and 1 copies, ~20 copies for double minute chromosome) can be identified. Regions of loss and retention of heterozygosity alternate, with a very high overlap between regions of LOH and copy number loss. c, Regularity of oscillating copy number states: A Monte Carlo approach was used to simulate the sequential acquisition of observed rearrangements on Chr15 (n = 1000 simulations per number of structural variations). Black dots represent the mean copy number states. The associated 95% confidence interval are shown as black lines. Chr15 showed less copy number states than expected by sequential acquisition of observed rearrangements. d, Randomness of DNA fragment joins: All four types of structural variations are uniformly distributed in the chromothriptic chromosome. P = 0.23; χ2 goodness-of-fit. e, Randomness of DNA fragment order: Start and end positions of observed rearrangements (n = 56) were randomly reordered using a Monte Carlo approach (n = 1000 simulations) to generate a random background distribution. The segment order of sample 5671 is located within the null model of random distribution. Two-sided P = 0.2. f, Ability to walk the derivative chromosome: Rearrangement graph of Chr15 (n = 56 rearrangements). Each fragment is represented by two blocks, indicating the read-orientations (5’ or 3’, indicated in red or grey) for the start and end of each segment, when mapped to the reference genome. P = 0.004; Wald-Wolfowitz test. SV, structural variation.

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Supplementary Figs. 1–7, Supplementary Methods, Supplementary Tables 1–3

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Lange, S., Engleitner, T., Mueller, S. et al. Analysis pipelines for cancer genome sequencing in mice. Nat Protoc 15, 266–315 (2020). https://doi.org/10.1038/s41596-019-0234-7

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