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A recurrent inactivating mutation in RHOA GTPase in angioimmunoblastic T cell lymphoma

Nature Genetics volume 46, pages 371–375 (2014)Cite this article

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Abstract

The molecular mechanisms underlying angioimmunoblastic T cell lymphoma (AITL), a common type of mature T cell lymphoma of poor prognosis, are largely unknown. Here we report a frequent somatic mutation in RHOA (encoding p.Gly17Val) using exome and transcriptome sequencing of samples from individuals with AITL. Further examination of the RHOA mutation encoding p.Gly17Val in 239 lymphoma samples showed that the mutation was specific to T cell lymphoma and was absent from B cell lymphoma. We demonstrate that the RHOA mutation encoding p.Gly17Val, which was found in 53.3% (24 of 45) of the AITL cases examined, is oncogenic in nature using multiple molecular assays. Molecular modeling and docking simulations provided a structural basis for the loss of GTPase activity in the RHOA Gly17Val mutant. Our experimental data and modeling results suggest that the RHOA mutation encoding p.Gly17Val is a driver mutation in AITL. On the basis of these data and through integrated pathway analysis, we build a comprehensive signaling network for AITL oncogenesis.

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Figure 1: Mutation profiles for AITL cases.
Figure 2: Comparison of the mutation profiles in AITL, Burkitt lymphoma and DLBCL.
Figure 3: Mutation structure and functional domains of the RHOA and CD28 proteins.
Figure 4: Effect of the p.Gly17Val alteration on RHOA activity.
Figure 5: Structural model of RHOA-GTP binding via docking simulation.
Figure 6: Pathway model of AITL.

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Acknowledgements

We appreciate helpful discussion and comments from P.J. Park (Harvard Medical School). This work was supported by grants from the Samsung Biomedical Research Institute (SP2-B2-04 to Y.H.K., W.S.K. and H.Y.Y. and GE1-B2-071 to H.Y.Y.), the Samsung Cancer Research Institute (cancer genomics project SCRI-12-02), the National Research Foundation of Korea (NRF-2012M3A9D1054744 and NRF-2012M3A9B9036673 to S.L. and NRF-2011-0019745), the GIST (Gwangju Institute of Science and Technology) Systems Biology Infrastructure Establishment Grant through ERCSB (S.L. and J.K.) and the Ewha Global Top5 Grant of Ewha Womans University.

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  1. Hae Yong Yoo and Min Kyung Sung: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Health Sciences and Technology, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University, Seoul, Korea

    Hae Yong Yoo & Seung Ho Lee

  2. Samsung Biomedical Research Institute, Research Institute for Future Medicine, Samsung Medical Center, Seoul, Korea

    Hae Yong Yoo, Hyunjung Ju, Seok Jin Kim, Won Seog Kim & Young Hyeh Ko

  3. Korean Bioinformation Center (KOBIC), Korea Research Institute of Bioscience and Biotechnology, Daejeon, Korea

    Min Kyung Sung, Seongjin Park, Sang Cheol Kim & Byungwook Lee

  4. Ewha Research Center for Systems Biology (ERCSB), Ewha Womans University, Seoul, Korea

    Sangok Kim, Haeseung Lee, Wankyu Kim, Jaesang Kim & Sanghyuk Lee

  5. Department of Life Science, Ewha Womans University, Seoul, Korea

    Sangok Kim, Haeseung Lee, Wankyu Kim, Jaesang Kim & Sanghyuk Lee

  6. DNA Link, Inc., Seoul, Korea

    Kyoohyoung Rho & Jong-Eun Lee

  7. School of Systems Biomedical Science, Soongsil University, Seoul, Korea

    Kwang-Hwi Cho

  8. Division of Hematology and Oncology, Department of Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Seok Jin Kim & Won Seog Kim

  9. Department of Pathology, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea

    Young Hyeh Ko

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Contributions

Y.H.K., S.L., W.S.K., S.J.K. and H.Y.Y. conceptualized the research program and designed the experiments. S.J.K., W.S.K. and Y.H.K. were involved in sample collection and clinical interpretation. Y.H.K. reviewed pathology. H.Y.Y., S.H.L. and H.J. conducted laboratory experiments. M.K.S., S.K., S.P., S.C.K., B.L. and K.R. analyzed the high-throughput sequencing and microarray data. H.L., K.-H.C. and W.K. performed the structural modeling of proteins. J.-E.L. supervised data generation. H.Y.Y., J.K., S.L. and Y.H.K. participated in preparing the manuscript.

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Correspondence to Sanghyuk Lee or Young Hyeh Ko.

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Integrated supplementary information

Supplementary Figure 1 Representative histologic evaluation of the tumor tissue.

(a) Classical morphology with effacement of normal architecture and marked vascular proliferation associated with aggregates of atypical lymphoid cells (H&E staining). (b) The majority of atypical cells express CD3. (c) CD21 immunostaining showing hyperplastic follicular dendritic cell meshwork. (d) A portion of tumor cells express CD10. (e) Some tumor cells express CXCL13. (f) PD-1 is expressed in some neoplastic cells.

Supplementary Figure 2 Frozen section of the tumor sample.

Frozen section of the tumor sample showing hypercellular lymphoid tissue without necrosis (H&E staining).

Supplementary Figure 3 Sanger sequencing traces of RHOA G17V mutation in T cell lymphoma patients.

Patient ID with RHOA G17V mutation is indicated by red text and arrows. Representative traces of wild-type and G17V mutant RHOA are shown in the top left panel. The horizontal red arrow shows the direction of genomic DNA sequencing. The mutant sequence shows a clear heterozygous mutation. Traces from tumor samples obtained from 45 AITL, 20 NK/T cell and 13 PTCL-NOS patients are shown in the rectangles. Patients 5, 6, 9, 10, 29, 30 and 43 in the AITL group showed the mutant as the dominant peak. The other 17 AITL patients showed the heterozygous mutation clearly. All three NK/T cell patients with somatic mutation showed the heterozygous genotype. Only one PTCL-NOS patient showed a heterozygous mutation. Sequencing trace chromatograms are representative of at least three independent experiments.

Supplementary Figure 4 Kaplan-Meier survival plot according to RHOA mutation.

Supplementary Figure 5 In vitro proliferation assays.

In vitro proliferation assays in (a) the SUP-T1 cell line and (b) the MOLT-4 cell line. Cells expressing inactive RHOA (G17V and T19N) displayed enhanced cell proliferation. P < 0.01 compared with the cells expressing WT. Each condition with three replicates was repeated three times and expressed as the ± s.d.

Supplementary Figure 6 Inhibition of the RHOA-ROCK pathway downregulates AKT phosphorylation and promotes cell proliferation.

Inhibition of the RHOA-ROCK pathway downregulates AKT phosphorylation and promotes cell proliferation. (a) After transfection of Jurkat cells with control siRNA or RHOA siRNA, AKT phosphorylation levels and cell proliferation were assessed. Cell lysates were prepared 48 h after transfection and processed for immunoblotting with the indicated antibodies. The extent of RHOA depletion was determined by immunoblotting with anti-RHOA antibodies. As a loading control, a-tubulin was used. Cells transfected with siRNA were incubated for 48 h, and relative cell proliferation was determined using a cell counting kit (CCK-8). Five replicates of each condition were repeated three times; the data are expressed as mean ± s.d. (b) After treatment with the ROCK inhibitor, Y-27632 (30 μM), AKT phosphorylation levels and cell proliferation were measured. Cell lysates were prepared 48 h after treatment and processed for immunoblotting with the indicated antibodies. Cells treated with Y-27632 were incubated for 48 h, and relative cell proliferation was determined using a cell counting kit (CCK-8). Five replicates of each condition were repeated three times, and the data are expressed as mean ± s.d.

Supplementary Figure 7 Structural models of wild-type and mutant RHOA proteins.

Structural models of wild-type and mutant RHOA mutant proteins. Homology modeling was based on the PDB structure of 3LXR. GTP substrate and its ribose moiety are indicated in yellow and pink, respectively. The glycine and valine residues are shown in green and red, respectively.

Supplementary Figure 8 Heat map from the hierarchical clustering of DEGs.

Heat map from hierarchical clustering of differentially expressed genes. AITL patients from our study (PAT1–PAT9) and GSE6338 (AITL1–AITL6) were included. Normal control samples are indicated as CD4+. Gene clusters from hierarchical classification were subjected to the DAVID web server for Gene Ontology analysis, and the most enriched term for each cluster was determined using the q value from the FDR test.

Supplementary Figure 9 Statistically significant KEGG pathways from GAGE gene set enrichment analysis.

Pathways are grouped into functional categories. Up- and downregulated pathways are indicated by pink and green backgrounds, respectively. Numbers of SNVs, indels, CNVs and DEGs indicate the number of genes affected in each pathway.

Supplementary Figure 10 Comparison of gene expression according to RHOA mutation status.

(a) Patients with wild-type RHOA (PAT1 and PAT3). (b) Patients with G17V RHOA (PAT2 and PAT4–PAT9).

Supplementary information

Supplementary Text and Figures

Supplementary Tables 1–3, 5–7 and 9, and Supplementary Figures 1–10 (PDF 2830 kb)

Supplementary Table 4

Full list of somatic SNVs. (XLS 294 kb)

Supplementary Table 8

Full list of differentially expressed genes. (XLS 979 kb)

Supplementary Table 10

List of genes affected by copy number variations. (XLS 3064 kb)

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Yoo, H., Sung, M., Lee, S. et al. A recurrent inactivating mutation in RHOA GTPase in angioimmunoblastic T cell lymphoma. Nat Genet 46, 371–375 (2014). https://doi.org/10.1038/ng.2916

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