Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

Lentiviral vector–based insertional mutagenesis identifies genes associated with liver cancer

Nature Methods volume 10, pages 155–161 (2013)Cite this article

Subjects

Abstract

Transposons and γ-retroviruses have been efficiently used as insertional mutagens in different tissues to identify molecular culprits of cancer. However, these systems are characterized by recurring integrations that accumulate in tumor cells and that hamper the identification of early cancer-driving events among bystander and progression-related events. We developed an insertional mutagenesis platform based on lentiviral vectors (LVVs) by which we could efficiently induce hepatocellular carcinoma (HCC) in three different mouse models. By virtue of the LVV's replication-deficient nature and broad genome-wide integration pattern, LVV-based insertional mutagenesis allowed identification of four previously unknown liver cancer–associated genes from a limited number of integrations. We validated the oncogenic potential of all the identified genes in vivo, with different levels of penetrance. The newly identified genes are likely to play a role in human cancer because they are upregulated, amplified and/or deleted in human HCCs and can predict clinical outcomes of patients.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Lentiviral vector–mediated induction of HCC.
Figure 2: Identification and validation of liver cancer genes.
Figure 3: LVV integrations at CISs upregulate the targeted genes.
Figure 4: Transcriptome deregulations in LVV-induced HCCs.
Figure 5: The newly identified liver cancer genes are implicated in human hepatocarcinogenesis.

Similar content being viewed by others

Accession codes

Primary accessions

Gene Expression Omnibus

Referenced accessions

Gene Expression Omnibus

References

  1. Stratton, M.R., Campbell, P.J. & Futreal, P.A. The cancer genome. Nature 458, 719–724 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Kool, J. & Berns, A. High-throughput insertional mutagenesis screens in mice to identify oncogenic networks. Nat. Rev. Cancer 9, 389–399 (2009).

    Article  CAS  PubMed  Google Scholar 

  3. Montini, E. et al. The genotoxic potential of retroviral vectors is strongly modulated by vector design and integration site selection in a mouse model of HSC gene therapy. J. Clin. Invest. 119, 964–975 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Naldini, L., Blomer, U., Gage, F.H., Trono, D. & Verma, I.M. Efficient transfer, integration, and sustained long-term expression of the transgene in adult rat brains injected with a lentiviral vector. Proc. Natl. Acad. Sci. USA 93, 11382–11388 (1996).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Croci, C. et al. Cerebellar neurons and glial cells are transducible by lentiviral vectors without decrease of cerebellar functions. Dev. Neurosci. 28, 216–221 (2006).

    Article  CAS  PubMed  Google Scholar 

  6. Dolcetta, D. et al. Design and optimization of lentiviral vectors for transfer of GALC expression in Twitcher brain. J. Gene Med. 8, 962–971 (2006).

    Article  CAS  PubMed  Google Scholar 

  7. Céfaï, D. et al. Multiply attenuated, self-inactivating lentiviral vectors efficiently transduce human coronary artery cells in vitro and rat arteries in vivo. J. Mol. Cell. Cardiol. 38, 333–344 (2005).

    Article  CAS  PubMed  Google Scholar 

  8. Bonci, D. et al. 'Advanced' generation lentiviruses as efficient vectors for cardiomyocyte gene transduction in vitro and in vivo. Gene Ther. 10, 630–636 (2003).

    Article  CAS  PubMed  Google Scholar 

  9. Buckley, S.M. et al. Lentiviral transduction of the murine lung provides efficient pseudotype and developmental stage-dependent cell-specific transgene expression. Gene Ther. 15, 1167–1175 (2008).

    Article  CAS  PubMed  Google Scholar 

  10. Di Nunzio, F. et al. Correction of laminin-5 deficiency in human epidermal stem cells by transcriptionally targeted lentiviral vectors. Mol. Ther. 16, 1977–1985 (2008).

    Article  CAS  PubMed  Google Scholar 

  11. Tedesco, F.S. et al. Transplantation of genetically corrected human iPSC-derived progenitors in mice with limb-girdle muscular dystrophy. Sci. Transl. Med. 4, 140ra189 (2012).

    Article  CAS  Google Scholar 

  12. Follenzi, A., Sabatino, G., Lombardo, A., Boccaccio, C. & Naldini, L. Efficient gene delivery and targeted expression to hepatocytes in vivo by improved lentiviral vectors. Hum. Gene Ther. 13, 243–260 (2002).

    Article  CAS  PubMed  Google Scholar 

  13. Serrano, M. et al. Role of the INK4a locus in tumor suppression and cell mortality. Cell 85, 27–37 (1996).

    Article  CAS  PubMed  Google Scholar 

  14. Brown, B.D. et al. In vivo administration of lentiviral vectors triggers a type I interferon response that restricts hepatocyte gene transfer and promotes vector clearance. Blood 109, 2797–2805 (2007).

    Article  CAS  PubMed  Google Scholar 

  15. Tannapfel, A. et al. INK4a-ARF alterations and p53 mutations in hepatocellular carcinomas. Oncogene 20, 7104–7109 (2001).

    Article  CAS  PubMed  Google Scholar 

  16. Farazi, P.A. & DePinho, R.A. Hepatocellular carcinoma pathogenesis: from genes to environment. Nat. Rev. Cancer 6, 674–687 (2006).

    Article  CAS  PubMed  Google Scholar 

  17. Horie, Y. et al. Hepatocyte-specific Pten deficiency results in steatohepatitis and hepatocellular carcinomas. J. Clin. Invest. 113, 1774–1783 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Hu, T.H. et al. Expression and prognostic role of tumor suppressor gene PTEN/MMAC1/TEP1 in hepatocellular carcinoma. Cancer 97, 1929–1940 (2003).

    Article  CAS  PubMed  Google Scholar 

  19. Vigna, E. et al. Efficient Tet-dependent expression of human factor IX in vivo by a new self-regulating lentiviral vector. Mol. Ther. 11, 763–775 (2005).

    Article  CAS  PubMed  Google Scholar 

  20. Abel, U. et al. Real-time definition of non-randomness in the distribution of genomic events. PLoS ONE 2, e570 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wu, X., Luke, B.T. & Burgess, S.M. Redefining the common insertion site. Virology 344, 292–295 (2006).

    Article  CAS  PubMed  Google Scholar 

  22. Collier, L.S., Carlson, C.M., Ravimohan, S., Dupuy, A.J. & Largaespada, D.A. Cancer gene discovery in solid tumours using transposon-based somatic mutagenesis in the mouse. Nature 436, 272–276 (2005).

    Article  CAS  PubMed  Google Scholar 

  23. Gureasko, J. et al. Role of the histone domain in the autoinhibition and activation of the Ras activator Son of Sevenless. Proc. Natl. Acad. Sci. USA 107, 3430–3435 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Brown, B.D. et al. A microRNA-regulated lentiviral vector mediates stable correction of hemophilia B mice. Blood 110, 4144–4152 (2007).

    Article  CAS  PubMed  Google Scholar 

  25. Wurmbach, E. et al. Genome-wide molecular profiles of HCV-induced dysplasia and hepatocellular carcinoma. Hepatology 45, 938–947 (2007).

    Article  CAS  PubMed  Google Scholar 

  26. Chen, X. et al. Gene expression patterns in human liver cancers. Mol. Biol. Cell 13, 1929–1939 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Woo, H.G. et al. Identification of a cholangiocarcinoma-like gene expression trait in hepatocellular carcinoma. Cancer Res. 70, 3034–3041 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Uren, A.G et al. Large-scale mutagenesis in p19ARF- and p53-deficient mice identifies cancer genes and their collaborative networks. Cell 133, 727–741 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Starr, T.K. et al. A transposon-based genetic screen in mice identifies genes altered in colorectal cancer. Science 323, 1747–1750 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Rad, R. et al. PiggyBac transposon mutagenesis: a tool for cancer gene discovery in mice. Science 330, 1104–1107 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Keng, V.W. et al. A conditional transposon-based insertional mutagenesis screen for genes associated with mouse hepatocellular carcinoma. Nat. Biotechnol. 27, 264–274 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Colombino, M. et al. BRAF and PIK3CA genes are somatically mutated in hepatocellular carcinoma among patients from South Italy. Cell Death Dis. 3, e259 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ciampi, R. et al. Oncogenic AKAP9-BRAF fusion is a novel mechanism of MAPK pathway activation in thyroid cancer. J. Clin. Invest. 115, 94–101 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Palanisamy, N. et al. Rearrangements of the RAF kinase pathway in prostate cancer, gastric cancer and melanoma. Nat. Med. 16, 793–798 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Guichard, C. et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat. Genet. 44, 694–698 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Schneider, A., Mehmood, T., Pannetier, S. & Hanauer, A. Altered ERK/MAPK signaling in the hippocampus of the mrsk2_KO mouse model of Coffin-Lowry syndrome. J. Neurochem. 119, 447–459 (2011).

    Article  CAS  PubMed  Google Scholar 

  37. Douville, E. & Downward, J. EGF induced SOS phosphorylation in PC12 cells involves P90 RSK-2. Oncogene 15, 373–383 (1997).

    Article  CAS  PubMed  Google Scholar 

  38. Llovet, J.M. et al. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med. 359, 378–390 (2008).

    Article  CAS  PubMed  Google Scholar 

  39. Donsante, A. et al. AAV vector integration sites in mouse hepatocellular carcinoma. Science 317, 477 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Dupuy, A.J. et al. A modified sleeping beauty transposon system that can be used to model a wide variety of human cancers in mice. Cancer Res. 69, 8150–8156 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Montini, E. et al. Hematopoietic stem cell gene transfer in a tumor-prone mouse model uncovers low genotoxicity of lentiviral vector integration. Nat. Biotechnol. 24, 687–696 (2006).

    Article  CAS  PubMed  Google Scholar 

  42. Themis, M. et al. Oncogenesis following delivery of a nonprimate lentiviral gene therapy vector to fetal and neonatal mice. Mol. Ther. 12, 763–771 (2005).

    Article  CAS  PubMed  Google Scholar 

  43. Follenzi, A., Ailles, L.E., Bakovic, S., Geuna, M. & Naldini, L. Gene transfer by lentiviral vectors is limited by nuclear translocation and rescued by HIV-1 pol sequences. Nat. Genet. 25, 217–222 (2000).

    Article  CAS  PubMed  Google Scholar 

  44. Kim, W.Y. & Sharpless, N.E. The regulation of INK4/ARF in cancer and aging. Cell 127, 265–275 (2006).

    Article  CAS  PubMed  Google Scholar 

  45. Müller, U. et al. Functional role of type I and type II interferons in antiviral defense. Science 264, 1918–1921 (1994).

    Article  PubMed  Google Scholar 

  46. Clawson, G.A. Mechanisms of carbon tetrachloride hepatotoxicity. Pathol. Immunopathol. Res. 8, 104–112 (1989).

    Article  CAS  PubMed  Google Scholar 

  47. Weber, L.W., Boll, M. & Stampfl, A. Hepatotoxicity and mechanism of action of haloalkanes: carbon tetrachloride as a toxicological model. Crit. Rev. Toxicol. 33, 105–136 (2003).

    Article  CAS  PubMed  Google Scholar 

  48. Bosman, F.T., Carneiro, F., Hruban, R.H. & Theise, N.D. WHO Classification of Tumours of the Digestive System 4th edn. (World Health Organization, 2010).

  49. Ishak, K.G., Goodman, Z.D. & Stocker,, J.T. Atlas of Tumor Pathology: Tumors of the Liver and Intrahepatic Bile Ducts (Armed Forces Institute of Pathology; Washington, DC; 2001).

  50. Hadjantonakis, A.K., Gertsenstein, M., Ikawa, M., Okabe, M. & Nagy, A. Generating green fluorescent mice by germline transmission of green fluorescent ES cells. Mech. Dev. 76, 79–90 (1998).

    Article  CAS  PubMed  Google Scholar 

  51. Schmidt, M. et al. A model for the detection of clonality in marked hematopoietic stem cells. Ann. NY Acad. Sci. 938, 146–156 (2001).

    Article  CAS  PubMed  Google Scholar 

  52. Ott, M.G. et al. Correction of X-linked chronic granulomatous disease by gene therapy, augmented by insertional activation of MDS1-EVI1, PRDM16 or SETBP1. Nat. Med. 12, 401–409 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Schmidt, M. et al. Clonal evidence for the transduction of CD34+ cells with lymphomyeloid differentiation potential and self-renewal capacity in the SCID-X1 gene therapy trial. Blood 105, 2699–2706 (2005).

    Article  CAS  PubMed  Google Scholar 

  54. Schmidt, M. et al. Detection and direct genomic sequencing of multiple rare unknown flanking DNA in highly complex samples. Hum. Gene Ther. 12, 743–749 (2001).

    Article  CAS  PubMed  Google Scholar 

  55. Schmidt, M. et al. High-resolution insertion-site analysis by linear amplification-mediated PCR (LAM-PCR). Nat. Methods 4, 1051–1057 (2007).

    Article  CAS  PubMed  Google Scholar 

  56. Pfaffl, M.W. A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 29, e45 (2001).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Zhao, S. & Fernald, R.D. Comprehensive algorithm for quantitative real-time polymerase chain reaction. J. Comput. Biol. 12, 1047–1064 (2005).

    Article  CAS  PubMed  Google Scholar 

  58. Hellemans, J., Mortier, G., De Paepe, A., Speleman, F. & Vandesompele, J. qBase relative quantification framework and software for management and automated analysis of real-time quantitative PCR data. Genome Biol. 8, R19 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl. Acad. Sci. USA 102, 15545–15550 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We are grateful to M. Rocchi, A. Vino, S. Oldoni and M. Marini for technical help; G. Santambrogio for collaboration in human HCC collection at HSR; M. Volpin for help in animal handling; S. Annunziato and J. Sgualdino for help in molecular biology; M.A. Venneri and D. Biziato for suggestions regarding immunofluorescent staining; V. Neguembor, D. Cabianca and V. Casà for the suggestions regarding western blotting; and M. De Palma and D. Gabellini for critical reading of this manuscript. We would like to acknowledge the PhD program in Cellular and Molecular Biology at San Raffaele University, as M.R. conducted this study as partial fulfillment of his PhD in Molecular Medicine within that program. This work was supported by grants from the Association for International Cancer Research (AICR 09-0784 to E.M.), Telethon Foundation (TGT11D1 to E.M.), European Union (Clinigene NoE LSHB-CT-2006-018933 to E.M. and PERSIST to L.N.), Italian Ministries of Health (GR-2007-684057 to E.M. and ONC-34/07 to L.N.), and Basic Research Laboratory program through the National Research Foundation of Korea funded by the Ministry of Education, Science and Technology (2011-0001564 to Y.J.K.).

Author information

Author notes

  1. Daniela Cesana and Cynthia C Bartholomae: These authors contributed equally to the work.

Authors and Affiliations

  1. San Raffaele-Telethon Institute for Gene Therapy, San Raffaele Scientific Institute, Milan, Italy

    Marco Ranzani, Daniela Cesana, Fabrizio Benedicenti, Pierangela Gallina, Lucia Sergi Sergi, Stefania Merella, Luigi Naldini & Eugenio Montini

  2. National Center for Tumor Diseases, Heidelberg, Germany

    Cynthia C Bartholomae, Christof von Kalle & Manfred Schmidt

  3. Department of Pathology, San Raffaele Scientific Institute, Milan, Italy

    Francesca Sanvito & Claudio Doglioni

  4. Mouse Histopathology, San Raffaele Scientific Institute, Milan, Italy

    Francesca Sanvito

  5. BioFlag Ltd., Cagliari, Italy

    Mauro Pala & Alessandro Bulfone

  6. Vita-Salute San Raffaele University, Milan, Italy

    Claudio Doglioni & Luigi Naldini

  7. Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Korea

    Yoon Jun Kim

  8. Liver Research Institute, Seoul National University College of Medicine, Seoul, Korea

    Yoon Jun Kim

  9. Functional Genomic Cancer Unit, San Raffaele Scientific Institute, Milan, Italy

    Giovanni Tonon

Authors
  1. Marco Ranzani
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Daniela Cesana
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Cynthia C Bartholomae
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Francesca Sanvito
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Mauro Pala
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Fabrizio Benedicenti
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Pierangela Gallina
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Lucia Sergi Sergi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Stefania Merella
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Alessandro Bulfone
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Claudio Doglioni
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Christof von Kalle
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Yoon Jun Kim
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Manfred Schmidt
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Giovanni Tonon
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Luigi Naldini
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Eugenio Montini
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.R. designed and performed experiments, and wrote the manuscript. D.C. performed all gene expression analyses and revised the manuscript. C.C.B. performed 454 pyrosequencing. F.S. and C.D. performed histopathological analyses. M.P., A.B. and G.T. analyzed microarray data. F.B., P.G., L.S.S. and S.M. performed experiments. C.v.K. and M.S. supervised 454 pyrosequencing and mapping of vector integrations. Y.J.K. provided clinical data of HCC patients. L.N. and E.M. supervised the project, and revised the manuscript. E.M. and L.N. share senior authorship.

Corresponding author

Correspondence to Eugenio Montini.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Figures 1–5 and Supplementary Notes 1–4 (PDF 7232 kb)

Supplementary Table 1

Different experimental groups of mice and tumors generated by LVV-mediated insertional mutagenesis. Each row represents an independent tumor. Columns are as follows. (a) Experimental mouse identifier. (b) Genotype of each different mouse. (c) Vector identifier (LV.ET.LTR) is indicated when mice were injected with the genotoxic LVV. (d) Titer expressed in transducing units (TU) per ml of the vector preparation. (e) Total transducing units administered to each mouse. (f) Administration of CCl4 is indicated. (g) Sex of each mouse: M, male; F, female. (h) Percentage of liver/body weight ratio. (i) Age of each mouse at the moment of euthanasia expressed in days. (j) Number of hepatocellular carcinomas identified in each mouse. (k) Number of hepatocellular adenomas identified in each mouse. (l) Presence or absence of steatosis as defined by histopathology. (m) Vector copy number measured by qPCR in nontumoral liver parenchyma. (n) Experimental tumor identifier from each independently collected liver mass. (o) Availability of RNA from the liver tumor, Y = yes; N = no. (p) Vector copy number detected by qPCR in each liver tumor. (q) Grade of each liver tumor as defined by histopathology. n.a., not analyzed; n.s., not sampled because the tumoral mass was too small. (XLSX 22 kb)

Supplementary Table 2a

Univocally mapped integration sites retrieved from LVV-induced tumors. Each row represents an independent integration. Columns are as follows. (a) Experimental mouse identifier. (b) Genotype of each different mouse. (c) Tumor ID of the liver mass from which the integration was retrieved. (d) Grade of each liver tumor as defined by histopathology. (e) Sequence counts from 454 pyrosequencing, representing how many times the same LAM-PCR product (from each integration site) was sequenced from each sample. (f) Chromosome on which the integration maps. (g) Integration site position on chromosome in nucleotides. (h) RefSeq gene ID of the gene closest to vector integration. (i) RefSeq gene symbol of the gene closest to vector integration; genes targeted by CIS integrations are outlined in bold font and green background. (j) Position of the intragenic integrations relative to the percentile of gene over the total gene length (from gene start to the integration position); none is indicated when integration is intergenic. (k) Distance in nucleotides from the integration position to the transcription start site (TSS) of the targeted gene. (l) Orientation of vector LTR with respect to the direction of transcription of the targeted gene, indicated as opposite or same. Integration sites are sorted for chromosome and integration locus. (XLSX 26 kb)

Supplementary Table 2b

Univocally mapped integration sites retrieved from LVV-transduced nontumoral livers. Newborn mice for each genotype (total n = 5) were administered with 108 TU of LV.ET.LTR and euthanized 2 weeks after the treatment. Each row represents an independent integration. Columns are as follows. (a) Experimental mouse ID for each LVV-treated mouse. (b) Genotype of each different mouse. (c) Sequence count from 454 pyrosequencing, representing how many times the same LAM-PCR product (from each integration site) was sequenced. (d) Chromosome on which the integration maps. (e) LTR position on chromosome in nucleotides. (f) RefSeq gene identifier of the gene closest to vector integration. (g) RefSeq gene symbol of the gene closest to vector integration. (h) Position of the intragenic integrations relative to the percentile of gene over the total gene length (from gene start to the integration position). (i) Distance in nucleotides from the integration position to the TSS of the targeted gene. (j) Orientation of vector LTR with respect to the direction of transcription of the targeted gene, indicated as opposite or same. Integration sites are sorted for chromosome and integration locus. (XLSX 24 kb)

Supplementary Table 3

Mice administered SINLVs for the validation of cancer genes. Each row represents a mouse. Columns are as follows. (a) Mouse genotype; wild-type mice received also the treatment regimen with CCl4. (b) Vector identifier indicates which vector was administered to the different experimental groups: SINLV.ET.Braf_trunc that expresses truncated Braf open reading frame (ORF); SINLV.ET.Fign_full that expresses full-length Fign ORF; SINLV.ET.Fign_trunc that expresses truncated Fign ORF; SINLV.ET.Rtl1_full that expresses full-length Rtl1 ORF; SINLV.ET.Sos1_full that expresses full-length Sos1 ORF; SINLV.ET.Sos1_trunc that expresses truncated Sos1 ORF; SINL.PGK.GFP (Montini et al., 2006) that expresses GFP marker gene as a neutral vector. (c) Titer expressed in TU per ml of the vector preparation. (d) Total TU administered to each mouse. (e) Experimental mouse identifier. (f) Sex of each mouse: M, male; F, female. (g) Age of each mouse at the moment of euthanasia expressed in days. (h) Number of hepatocellular carcinomas identified in the liver parenchyma of each mouse. (i) Grade of the different tumors found in the liver of each mouse, as defined by histopathology. > indicates that many different masses (>10) were found in the liver parenchyma. LM, lung metastasis; n.a., not analyzed. (XLSX 12 kb)

Supplementary Table 4a

GSEA overrepresented molecular pathways and functions in HCC groups with respect to normal livers: Cellular Process. Overrepresented gene sets of 'Cellular Process' by GSEA using setting: Real. Dataset: expression levels of Braf, Fign and Rtl1 groups were compared with respect to the expression of pooled normal livers; Trend: upregulation (Up) or downregulation (Down) of genes belonging to a given pathway is indicated; Gene Set: Description of significantly overrepresented gene sets belonging to specific Canonical Pathway; Size: size of the given Canonical Pathways gene set. ES, enrichment score; NES, normalized enrichment score; positive values indicate downregulation, and negative values indicate upregulation. NOM p-val, nominal P value; FDR q_val: false discovery rate (only gene classes with FDR <0.05 are shown); FWER p_val: family-wise error rate–corrected P value. Gene sets with a FWER P value <0.05 passed the multiple testing error correction, and their description is in bold and the significant FWER P value is highlighted by a pink background. (XLSX 16 kb)

Supplementary Table 4b

GSEA overrepresented molecular pathways and functions in HCC groups with respect to normal livers: transcription factor targets. Overrepresented gene sets of transcription factor targets. Legends as in a. In Rtl1 tumors the only significantly overrepresented gene set of transcription factor targets was for SF1 (also known as Nr5a1). (XLSX 13 kb)

Supplementary Table 5

Sequences of primer used to perform PCR-based mouse genotyping and to perform RT-PCR for the detection of chimeric transcripts between LVV and CIS genes. (XLSX 10 kb)

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ranzani, M., Cesana, D., Bartholomae, C. et al. Lentiviral vector–based insertional mutagenesis identifies genes associated with liver cancer. Nat Methods 10, 155–161 (2013). https://doi.org/10.1038/nmeth.2331

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nmeth.2331

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer