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AAV-mediated direct in vivo CRISPR screen identifies functional suppressors in glioblastoma

Nature Neuroscience volume 20, pages 1329–1341 (2017)Cite this article

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Abstract

A causative understanding of genetic factors that regulate glioblastoma pathogenesis is of central importance. Here we developed an adeno-associated virus–mediated, autochthonous genetic CRISPR screen in glioblastoma. Stereotaxic delivery of a virus library targeting genes commonly mutated in human cancers into the brains of conditional-Cas9 mice resulted in tumors that recapitulate human glioblastoma. Capture sequencing revealed diverse mutational profiles across tumors. The mutation frequencies in mice correlated with those in two independent patient cohorts. Co-mutation analysis identified co-occurring driver combinations such as B2m–Nf1, Mll3–Nf1 and Zc3h13–Rb1, which were subsequently validated using AAV minipools. Distinct from Nf1-mutant tumors, Rb1-mutant tumors are undifferentiated and aberrantly express homeobox gene clusters. The addition of Zc3h13 or Pten mutations altered the gene expression profiles of Rb1 mutants, rendering them more resistant to temozolomide. Our study provides a functional landscape of gliomagenesis suppressors in vivo.

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Figure 1: Autochthonous brain tumorigenesis induced by an AAV-mediated CRISPR library.
Figure 2: AAV-mTSG-induced brain tumors recapitulate pathological features of GBM.
Figure 3: Targeted-capture sequencing of sgRNA sites in AAV-mTSG-induced mouse GBM.
Figure 4: Integrative analysis of functional mutations in driving tumorigenesis.
Figure 5: Co-mutation analysis uncovers synergistic gene pairs in GBM.
Figure 6: Validation of driver combinations.
Figure 7: Transcriptional profiling of mouse GBM driver combinations.
Figure 8: Transcriptional profiling of mouse GBM driver combinations in the presence and absence of a chemotherapeutic agent.

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  • 21 August 2017

    In the PDF version of this article initially published online, one of the Online Methods headings read “vRNA-seq differential expression analysis”; this has been changed to “RNA-seq differential expression analysis.” The error has been corrected in the print and PDF versions of this article.

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Acknowledgements

We thank all members of the Chen, Sharp, Zhang and Platt laboratories, as well as our colleagues in the Yale Department of Genetics, Systems Biology Institute, Yale Cancer Center and Stem Cell Center, Koch Institute and Broad Institute at MIT for assistance and/or discussions. We thank the Center for Genome Analysis, Center for Molecular Discovery, High Performance Computing Center, West Campus Analytical Chemistry Core and West Campus Imaging Core and Keck Biotechnology Resource Laboratory at Yale, as well as Swanson Biotechnology Center at MIT, for technical support. S.C. is supported by Yale SBI/Genetics Startup Fund, Damon Runyon (DRG-2117-12; DFS-13-15), Melanoma Research Alliance (412806, 16-003524), St. Baldrick's Foundation (426685), American Cancer Society (IRG 58-012-54), Breast Cancer Alliance, Cancer Research Institute (CLIP), AACR (499395), DoD (W81XWH-17-1-0235) and NIH/NCI (1U54CA209992, 5P50CA196530-A10805, 4P50CA121974-A08306). R.J.P. is supported by NCCRMSE and ETH Zurich, the McGovern Institute and NSF (1122374). P.A.S. is supported by NIH (R01-CA133404, R01-GM034277, CCNE), Skoltech Center and the Casimir-Lambert Fund. F.Z. is supported by the NIH/NIMH (5DP1-MH100706 and 1R01-MH110049), NSF, NY Stem Cell Foundation, HHMI, Poitras, Simons, Paul G. Allen Family, Vallee Foundations, D.R. Cheng and B. Metcalfe. C.D.G. and P.R. are supported by an NIH Graduate Training Grant (T32GM007499). R.D.C., M.B.D. and M.W.Y. are supported by an NIH MSTP training grant (T32GM007205). F.S. is supported by NCCRMSE and ETH Zurich. G.W. is supported by RJ Anderson and CRI Irvington Postdoctoral Fellowships.

Author information

Author notes

  1. Ryan D Chow, Christopher D Guzman, Guangchuan Wang and Florian Schmidt: These authors contributed equally to this work.

  2. Mark W Youngblood and Lupeng Ye: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Genetics, Yale University School of Medicine, New Haven, Connecticut, USA

    Ryan D Chow, Christopher D Guzman, Guangchuan Wang, Mark W Youngblood, Lupeng Ye, Youssef Errami, Matthew B Dong, Michael A Martinez, Sensen Zhang, Paul Renauer, Kaya Bilguvar, Murat Gunel & Sidi Chen

  2. Systems Biology Institute, Yale University School of Medicine, West Haven, Connecticut, USA

    Ryan D Chow, Christopher D Guzman, Guangchuan Wang, Lupeng Ye, Youssef Errami, Matthew B Dong, Michael A Martinez, Sensen Zhang, Paul Renauer & Sidi Chen

  3. Medical Scientist Training Program, Yale University School of Medicine, New Haven, Connecticut, USA

    Ryan D Chow, Mark W Youngblood, Matthew B Dong, Murat Gunel & Sidi Chen

  4. Biological and Biomedical Sciences Program, Yale University School of Medicine, New Haven, Connecticut, USA

    Christopher D Guzman, Paul Renauer & Sidi Chen

  5. Immunobiology Program, Yale University School of Medicine, New Haven, Connecticut, USA

    Christopher D Guzman & Sidi Chen

  6. Department of Immunobiology, Yale University School of Medicine, New Haven, Connecticut, USA

    Christopher D Guzman

  7. Department of Biosystems Science and Engineering, ETH Zurich, Basel, Switzerland

    Florian Schmidt & Randall J Platt

  8. Department of Chemistry, University of Basel, Basel, Switzerland

    Florian Schmidt & Randall J Platt

  9. Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA

    Mark W Youngblood & Murat Gunel

  10. Yale Center for Genome Analysis, Yale University School of Medicine, New Haven, Connecticut, USA

    Kaya Bilguvar & Murat Gunel

  11. Koch Institute for Integrative Cancer Research, MIT, Cambridge, Massachusetts, USA

    Phillip A Sharp

  12. Department of Biology, MIT, Cambridge, Massachusetts, USA

    Phillip A Sharp

  13. Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA

    Feng Zhang

  14. Department of Biological Engineering, MIT, Cambridge, Massachusetts, USA

    Feng Zhang

  15. Comprehensive Cancer Center, Yale University School of Medicine, New Haven, Connecticut, USA

    Sidi Chen

  16. Stem Cell Center, Yale University School of Medicine, New Haven, Connecticut, USA

    Sidi Chen

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Contributions

S.C. and R.J.P. conceived the study, designed the study and performed the initial set of experiments. R.D.C. developed the algorithms and performed integrative analyses of all the data. C.D.G. performed validation, performed histology and established primary cell lines. G.W. performed exome-capture, mutant cell line generation, drug treatment and RNA-seq. F.S. performed AAV production. S.C. performed MRI. M.W.Y. contributed to data analysis. L.Y., Y.E., M.B.D., M.A.M., S.Z. and P.R. contributed to experiments including mouse breeding, genotyping, cloning, cell culture, virus prep, injection, necropsy and sample prep. K.B. assisted in captured and exome sequencing. M.G. provided clinical insights. P.A.S., F.Z., R.J.P. and S.C. jointly supervised the work. R.D.C. and S.C. wrote the manuscript with inputs from all authors.

Corresponding authors

Correspondence to Randall J Platt or Sidi Chen.

Ethics declarations

Competing interests

F.Z. is a cofounder of Editas Medicine and a scientific advisor for Editas Medicine and Horizon Discovery. A patent application has been filed on the methods pertaining to this work.

Integrated supplementary information

Supplementary Figure 1 Additional data for massively parallel GBM suppressor analysis by AAV-CRISPR library-mediated pooled mutagenesis

(a) Schematic of the AAV vector used in the study. The vector contains a cassette expressing Cre recombinase under a GFAP promoter, a p53 sgRNA under U6 promoter, and an empty cassette for expression of custom cloned sgRNA(s).

(b) Plasmid library representation of the AAV-CRISPR mTSG library (n = 2 plasmid library replicates, averaged).

(c) A representative AAV-mTSG injected mouse showing macrocephaly.

(d) Dissected whole brains from PBS, AAV-vector and AAV-mTSG injected mice (left) and sections (right) visualized under a fluorescent stereoscope.

(e) Full-spectrum MRI series of representative mouse brains in PBS, vector and mTSG group. Mice under anesthesia were imaged with a small animal MRI imaging system. 20 MRI sections are shown for each condition. Brain tumors were found in AAV-mTSG injected mice but not in matched PBS or AAV-vector injected mice.

Supplementary Figure 2 Full-scan histology images for special staining of mouse brain sections in vector and mTSG groups

Panels from top to bottom: Luxol fast blue Cresyl violet (LFB/CV) staining, Wight Giemsa staining, Masson staining and Alcian blue Periodic acidSchiff (AB/PAS) staining of representative mouse brain sections in vector and mTSG groups. Scale bar = 1 mm.

Supplementary Figure 3 Representative histopathology images of human GBM

(a) Representative images of H&E stained brain sections from human GBM patient samples from Yale Glioma tissue bank. Images from the three rows represent GBM with significant mutations in NF1, PTEN and RB1, respectively. Pathological features such as giant aneuploid cells with pleomorphic nuclei, angiogenesis, necrosis and hemorrhage were evident in these tumors. Scale bar = 0.5 mm.

(b) Representative images of anti-GFAP stained brain sections from human GBM patient samples from Yale Glioma tissue bank. Images from the two rows represent GBM with significant mutations in PTEN and RB1, respectively. PTEN tumors were mostly GFAP-positive. RB1 tumors have mixtures of GFAP-positive and GFAP-negative cells. NF1 tumors were not shown due to availability of GFAP staining sections. Scale bar = 0.5 mm.

Supplementary Figure 4 Early time point analysis of sgRNA cutting efficiency by molecular inversion probe sequencing

(a) Heatmap of sum variant frequencies for each sgRNA across the 3 in vivo infection replicates. Each row denotes one gene, while each column corresponds to a specific sgRNA and replicate. Variant frequencies are square-rooted to improve visibility.

(b) Dissected whole brain from an AAV-mTSG injected mouse for early time point analysis, visualized under a fluorescent stereoscope. GFP (green) is shown as an overlay on the brightfield image.

(c) Venn diagram detailing the overlap between cutting sgRNAs identified in early-stage mutagenesis and late-stage GBMs. Differences in the identified cutting sgRNAs were likely due to differential selection pressures, insufficient time for CRISPR mutagenesis to occur in early time point brains, and/or allele frequencies below detection limit of capture sequencing.

Supplementary Figure 5 Mutational signatures of all GBM mice induced with AAV-CRISPR mTSG library

Waterfall plots of significantly mutated sgRNA sites across all mTSG brain samples, sorted by sum variant frequency. Two samples (mTSG brain 1, mTSG brain 7) are not shown, as these samples were not found to have any significantly mutated sgRNA sites per the stringent variant calling strategy. The extensive mutational landscape in these samples shows strong positive selection for loss-of-function mutations in mTSGs during gliomagenesis.

Supplementary Figure 6 Additional analysis of mutational signatures

(a) Scatterplots of the number of samples with an SMS call per sgRNA (left) or SMG call per gene (right), using two different thresholds for calling SMSs. In conjunction with the FDR approach, the use of either a flat 5% or 10% variant frequency cutoff did not affect the results at either the sgRNA or gene level. Spearman correlation coefficients and associated p-values are shown on the plots.

(b) Gaussian kernel density estimate of variant frequencies within each mTSG brain sample. The number of peaks in the kernel density estimate is an approximation for the clonality of each sample. From this analysis, most (20/22) samples appeared to be composed of multiple clones, with only two (mTSG brain 15, mTSG brain 20) monoclonal samples. Of note, 3/25 sequenced mTSG brain samples did not have sufficient high-frequency variants for clustering analysis.

Supplementary Figure 7 Additional analysis of comutated pairs and exome sequencing

(a) Scatterplot of the co-occurrence rate of a given mutation pair, plotted against -log10 p-values. All pairs involving Trp53 were excluded from this analysis.

(b) Scatterplot of pairwise Spearman correlations plotted against -log10 p-values. All pairs involving Trp53 were excluded from this analysis.

(c) Scatterplot of the co-occurrence rate of a given mutation pair in the TCGA human GBM dataset, plotted against -log10 p-values.

(d) Venn diagram of co-occurring pairs identified in mouse GBM (Benjamini-Hochberg adjusted p < 0.05, either co-occurrence or Spearman correlation analysis) and/or in human GBM (p < 0.05). 7 gene pairs were found to be significant in both mouse and human GBM. The overlap between the two datasets was significant (hypergeometric test, p = 0.001).

(e) Whole-exome analysis of possible off-target mutations generated by AAV-CRISPR mTSG (n = 7). Chromosomal map of potential off-targets in AAV-CRISPR mTSG brain samples. Indels in mTSG genes are marked in red, while possible off-target mutations and AAV insertions are marked in blue.

Supplementary Figure 8 GFAP immunohistochemical characterization of brain sections from mice treated with AAV sgRNA minipools

GFAP immunohistochemistry of brain sections from mice treated with various AVV minipools. Brain tumors in Nf1, Nf1;Pten, and Nf1;B2m mice were strongly positive for GFAP, while tumors in Nf1;Mll3 mice were positive at an intermediate level. Brain tumors in Rb1, Rb1;Pten, and Rb1;Zc3h13 mice contained a mixture of GFAP positive and negative cells, similar to the GFAP staining pattern with human patient GBM samples. Brain tumors in Mll2 mice were variably GFAP positive. Scale bar = 0.5 mm.

Supplementary Figure 9 Additional supplemental data related to the study

(a) Kaplan-Meier overall survival curves for mice injected with control (n = 9), B2m (n = 4), Nf1 (n = 8), and Nf1;B2m (n = 4) AAV minipools. All control and B2m mice were tumor-free and survived the entire duration of the experiment; control and B2m curves are offset for visibility. Mice treated with Nf1;B2m AAVs had significantly worse survival compared to mice treated with Nf1 or B2m AAVs alone (Log-rank (LR) test, p = 0.0067).

(b) T7E1 nuclease assay to confirm mutagenesis by CRISPR/Cas9 at the indicated target genes. Indel frequencies are indicated.

(c-d) LentiCRISPR mTSG direct in vivo GBM screen

(c) IVIS imaging of mice injected with lenti-vector or lenti-mTSG library, showing luminescence in the brains of a fraction of lenti-mTSG injected mice, but not in vector injected mice. Mice were imaged at 6.5 months post injection (mpi), where 4/18 mice imaged were luciferase positive (10 were shown). These 4 mice were sacrificed as they developed poor body conditions and brain tumors, before the end of 10 mpi. Mice were imaged again at 11 mpi, where 6/14 mice imaged luciferase positive, which were subsequently sacrificed as they developed poor body conditions and brain tumors.

(d) Kaplan-Meier curves for overall survival (OS) of mice injected with PBS (n = 2), lenti-vector (n = 5) or lenti-mTSG library (n = 18). OS for PBS and vector groups were both 100%, where the curves are dashed and slightly offset for visibility. LR test, p < 0.0239, mTSG vs. vector or PBS; LR test, p = 1, vector vs. PBS.

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Supplementary Figures 1–9 (PDF 3540 kb)

Life Sciences Reporting Summary (PDF 129 kb)

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Supplementary Tables 1–33 (XLSX 10772 kb)

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Chow, R., Guzman, C., Wang, G. et al. AAV-mediated direct in vivo CRISPR screen identifies functional suppressors in glioblastoma. Nat Neurosci 20, 1329–1341 (2017). https://doi.org/10.1038/nn.4620

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