Optimising gene editing for cancer therapy

Gene editing holds promise for the treatment of cancers that are driven by well-characterised molecular alterations. A study now provides a proof of concept for the feasibility of in vivo gene editing to correct TERT mutations in glioblastoma, providing a platform for the direct manipulation of genetic alterations to reduce tumour growth.

The discovery and adaption of CRISPR–Cas9 gene editing has ushered in a renaissance period for the field of molecular biology and opened up unprecedented possibilities1. Now broadly applied as a basic research tool, this technology is constantly evolving to allow increased speed, precision, and efficiency of editing, accelerating the pace of research. Gene editing also offers unique insights into a variety of genetic diseases, including cancer, and in some instances may be of use therapeutically. In this issue of Nature Cell Biology, Li, Qian et al.2 capitalise on CRISPR and programmable base editing (PBE) methods to target mutational drivers in glioblastoma, the most common primary malignant brain tumour for which the prognosis remains poor despite the use of aggressive treatment strategies3. The findings reported here provide additional models for the study of this disease and have the potential to open up previously unappreciated therapeutic avenues for glioblastoma, as well as other cancer types.

Characterising the mutational landscape of tumour cells remains one of the biggest barriers in the development of more efficacious therapies to treat cancer. In their paper, Li, Qian et al.2 focus on mutations in the telomerase reverse transcriptase promoter (pTERT), which are present in over 80% of primary glioblastoma tumours4 and associated with an increased risk of developing the disease5. These pTERT mutations enhance the activity of telomerase, an enzyme responsible for extending the ends of chromosomes6. Enhanced telomerase activity limits cellular senescence, which in turn inhibits apoptosis and permits tumour growth7. The authors decided to select a pTERT mutation hotspot commonly found in primary glioblastoma, located at position 124 (cytosine to thymine, C>T), as the proof-of-concept mutation for this study. This mutation creates a consensus binding site for certain transcription factors, including ETS1 and GABPA, inducing TERT promoter activity and driving tumour cell survival8,9.

Li, Qian et al.2 used a PBE system to efficiently target the -124C>T pTERT mutation and employed elements of the CRISPR–Cas9 platform to induce single nucleotide changes in DNA. They first designed a vector that included a catalytically impaired Cas9 from Campylobacter Jejuni fused to an adenine base editor (CjABE) as well as a single guide RNA (sgRNA) for precise targeting. This system provides an advantage over traditional editing methods, as it allows single base editing without needing to induce DNA breaks10. The engineered vector was expressed in human glioblastoma cell models harbouring the -124 C>T TERT promoter mutation (U87 and U251 cells) or the wild-type TERT promoter (LN18 and SVG cells). Initial gene-sequencing results demonstrated that the sgRNA-guided CjABE converted the -124C>T mutated cells back to -124C but had no effect on the TERT promoter in control cells (with a non-targeting sgRNA) or those with the wild-type TERT promoter (Fig. 1). Li, Qian et al.2 subsequently performed analyses to determine the functional consequences of correcting the -124C>T mutation in glioblastoma cells. Using chromatin immunoprecipitation assays, the authors revealed that editing of the pTERT mutation in U87 and U251 cells prevented binding of the transcription factors ETS1 and GABPA to the promoter site. These findings further highlight the importance of GABPA binding for TERT activity in glioblastoma. Earlier work implicated a genetic disruption of this transcription factor in the reversal of the replicative capacity of glioblastoma tumour cells11. Further analysis of glioblastoma cells with the TERT promoter mutation confirmed enhanced activity of the TERT promoter compared to that of the wild type, which was abrogated upon expression of the sgRNA-guided CjABE. Furthermore, the authors revealed that TERT expression, both at mRNA and protein levels, was reduced in the -124C edited cells.

Fig. 1: TERT promoter activity in glioblastoma tumour cells.

Li, Qian et al.2 demonstrate the utility of programmable base editing technology to correct a TERT promoter mutation in glioblastoma (GBM) cells. a, Many glioblastoma tumours harbour hotspot C>T TERT promoter mutations at position 124. This mutation creates a binding site for several transcription factors (TF), leading to TERT activation and preservation of telomeres in these cells, ultimately driving tumour cell survival. b, Programmable base editing with a fused construct consisting of catalytically dead Cas9 and an adenosine deaminase, driven by a single guide RNA (sgRNA), results in the correction of the TERT mutation. Conversion of the TERT promoter back to –124C prohibits TF binding, thereby inhibiting the action of telomerase and inducing tumour cell senescence and apoptosis. Reprinted with permission, Cleveland Clinic Center for Medical Art & Photography © 2020. All Rights Reserved.

TERT promoter mutations have prognostic value in glioblastoma and have been associated with the proliferative and self-renewal capacity of these tumour cells12. The authors first measured telomere length using quantitative fluorescence in situ hybridisation to determine whether correction of pTERT -124C>T in glioblastoma alters the cell phenotype. They observed that cells expressing the sgRNA-guided CjABE demonstrated a more rapid atrophy of telomere length compared to those expressing the non-target CjABE. Li, Qian et al.2 also investigated whether a corresponding increase in senescence would accompany a reduction in telomere length via examination of senescence-associated biomarker staining. A strong signal in the edited U87 and U251 cells that was undetectable in control cells confirmed a direct relationship between TERT promoter activity and cellular senescence. In addition, the tumour cells expressing the -124T sgRNA-guided CjABE exhibited impaired proliferation compared to mutant cells.

Overall, Li, Qian et al.2 elegantly demonstrate the ability to target TERT in a unique manner, laying the foundation for additional gene-targeting approaches. Even though the authors focused on correcting mutations for therapeutic purposes in this case, the technology also provides an opportunity for the development of mouse models that recapitulate a variety of cancers and integrate patient-specific mutations. In combination with an accelerated knowledge base of the cancer mutational landscape at high resolution from the Pan-Cancer Analysis of Whole Genomes13, this approach opens up the possibility of deeper patient-specific insight into cancer, including rare cancer types for which there are limited models, and identification of alternative therapeutic targets.

Li, Qian et al.2 conclude with an in vivo application of PBE to correct the TERT promoter mutation directly in glioblastoma tumours. By implanting luciferase-expressing U87 and patient-derived xenograft (PDX) cells into the brains of mice and subsequently treating the animals with the engineered vectors, the authors were able to visualise impaired tumour growth in mice receiving the -124T sgRNA-guided CjABE compared to the non-target group. The reduction in tumour growth correlated with improvements in survival of both groups receiving the targeted vector compared to controls. In addition, immunohistochemical staining of the tumours revealed a reduction in markers of TERT activity and proliferation, as well as increases in apoptosis in the tumours treated with the vector. Intriguingly, in vivo editing frequencies were similar to those seen in vitro, further highlighting the therapeutic potential of this gene-editing system.

Although gene-manipulation approaches are now commonly used in gain-of-function and loss-of-function studies for a variety of diseases, gene-editing approaches are also being integrated into many in vitro studies. However, several limitations still exist, among them inefficacious in vivo delivery. The results obtained by Li, Qian et al.2 confirm the general feasibility of in vivo manipulation in models of complex diseases such as glioblastoma. The authors provide clear evidence of attenuation of tumour growth in glioblastoma models with fairly high efficacy; however, the level of distribution of the sgRNA-expressing viruses remains unclear. For future clinical translation, the assessment of viral delivery distribution and stability will be necessary, as well as a careful interrogation of potential side effects of such approaches. Possible tumour-immune interactions will have to be accounted for and the use of immune-competent models in these studies will be crucial. In particular, studies that focus on glioblastoma will require appropriate assessment of brain-specific delivery and stability of these vectors. In the future, the evaluation of these approaches in combination with the current standard of care should take priority.

Taken together, the work by Li, Qian et al.2 presents an innovative approach to the long-standing problem of how to alter mutational burden in cancer to reduce tumour growth and improve patient outcomes. Although more extensive research is needed to demonstrate the safety and efficacy of PBE, the findings reported in the current study have potentially broad applications across various disciplines in cancer research.


  1. 1.

    Doudna, J. A. & Charpentier, E. Science 346, 1258096 (2014).

    Article  Google Scholar 

  2. 2.

    Li, X. et al. Nat. Cell Biol. (2020).

  3. 3.

    Ostrom, Q. T. et al. Neuro-oncol. 21, v1–v100 (2019).

    Article  Google Scholar 

  4. 4.

    Killela, P. J. et al. Proc. Natl Acad. Sci. USA 110, 6021–6026 (2013).

    CAS  Article  Google Scholar 

  5. 5.

    Walsh, K. M. et al. Nat. Genet. 46, 731–735 (2014).

    CAS  Article  Google Scholar 

  6. 6.

    Blackburn, E. H. Nature 350, 569–573 (1991).

    CAS  Article  Google Scholar 

  7. 7.

    Campisi, J., Kim, S. H., Lim, C. S. & Rubio, M. Exp. Gerontol. 36, 1619–1637 (2001).

    CAS  Article  Google Scholar 

  8. 8.

    Horn, S. et al. Science 339, 959–961 (2013).

    CAS  Article  Google Scholar 

  9. 9.

    Bell, R. J. A. et al. Science 348, 1036–1039 (2015).

    CAS  Article  Google Scholar 

  10. 10.

    Tan, J., Zhang, F., Karcher, D. & Bock, R. Nat. Commun. 10, 1 (2019).

    Article  Google Scholar 

  11. 11.

    Mancini, A. et al. Cancer Cell 34, 513–528.e8 (2018).

    CAS  Article  Google Scholar 

  12. 12.

    Jeong, D. E. et al. Oncol. Lett. 14, 8213–8219 (2017).

    PubMed  PubMed Central  Google Scholar 

  13. 13.

    Alexandrov, L. B. et al. Nature 578, 94–101 (2020).

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Justin D. Lathia.

Ethics declarations

Competing interests

The authors declare no competing interests.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Troike, K., Lathia, J.D. Optimising gene editing for cancer therapy. Nat Cell Biol 22, 259–261 (2020).

Download citation

Further reading


Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing