Skip to main content

Advertisement

SpringerLink
Log in

A new ETV6-NTRK3 cell line model reveals MALAT1 as a novel therapeutic target - a short report

  • Report
  • Published:
Cellular Oncology Aims and scope Submit manuscript

Abstract

Background

Previously, the chromosomal translocation t(12;15)(p13;q25) has been found to recurrently occur in both solid tumors and leukemias. This translocation leads to ETV6-NTRK3 (EN) gene fusions resulting in ectopic expression of the NTRK3 neurotropic tyrosine receptor kinase moiety as well as oligomerization through the donated ETV6-sterile alpha motif domain. As yet, no in vitro cell line model carrying this anomaly is available. Here we genetically characterized the acute promyelocytic leukemia (APL) cell line AP-1060 and, by doing so, revealed the presence of a t(12;15)(p13;q25). Subsequently, we evaluated its suitability as a model for this important clinical entity.

Methods

Spectral karyotyping, fluorescence in situ hybridization (FISH), and genomic and transcriptomic microarray-based profiling were used to screen for the presence of EN fusions. qRT-PCR was used for quantitative expression analyses. Responses to AZ-23 (NTRK) and wortmannin (PI3K) inhibitors, as well as to arsenic trioxide (ATO), were assessed using colorimetric assays. An AZ-23 microarray screen was used to define the EN targetome, which was parsed bioinformatically. MAPK1 and MALAT1 activation were assayed using Western blotting and RNA-FISH, respectively, whereas an AML patient cohort was used to assess the clinical occurrence of MALAT1 activation.

Results

An EN fusion was detected in AP1060 cells which, accordingly, turned out to be hypersensitive to AZ-23. We also found that AZ-23 can potentiate the effect of ATO and inhibit the phosphorylation of its canonical target MAPK1. The AZ-23 microarray screen highlighted a novel EN target, MALAT1, which also proved sensitive to wortmannin. Finally, we found that MALAT1 was massively up-regulated in a subset of AML patients.

Conclusions

From our data we conclude that AP-1060 may serve as a first publicly available preclinical model for EN. In addition, we conclude that these EN-positive cells are sensitive to the NTRK inhibitor AZ-23 and that this inhibitor may potentiate the therapeutic efficacy of ATO. Our data also highlight a novel AML EN target, MALAT1, which was so far only conspicuous in solid tumors.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3

References

  1. S.R. Knezevich, D.E. McFadden, W. Tao, J.F. Lim, P.H. Sorensen, A novel ETV6-NTRK3 gene fusion in congenital fibrosarcoma. Nat. Genet. 18, 184–187 (1998)

    Article  CAS  PubMed  Google Scholar 

  2. S.R. Knezevich, M.J. Garnett, T.J. Pysher, J.B. Beckwith, P.E. Grundy, P.H. Sorensen, ETV6-NTRK3 gene fusions and trisomy 11 establish a histogenetic link between mesoblastic nephroma and congenital fibrosarcoma. Cancer Res. 58, 5046–5048 (1998)

    CAS  PubMed  Google Scholar 

  3. C. Tognon, M. Garnett, E. Kenward, R. Kay, K. Morrison, P.H. Sorensen, The chimeric protein tyrosine kinase ETV6-NTRK3 requires both Ras-Erk1/2 and PI3-kinase-Akt signaling for fibroblast transformation. Cancer Res. 61, 8909–8916 (2001)

    CAS  PubMed  Google Scholar 

  4. C.L. Lannon, P.H. Sorensen, ETV6-NTRK3 a chimeric protein tyrosine kinase with transformation activity in multiple cell lineages. Semin. Cancer Biol. 15, 215–223 (2005)

    Article  CAS  PubMed  Google Scholar 

  5. A. Amatu, A. Sartore-Bianchi, S. Siena, NTRK gene fusions as novel targets of cancer therapy across multiple tumour types. ESMO Open 1, e000023 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  6. M. Eguchi, M. Eguchi-Ishimae, A. Tojo, K. Morishita, K. Suzuki, Y. Sato, S. Kudoh, K. Tanaka, M. Setoyama, F. Nagamura, S. Asano, N. Kamada, Fusion of ETV6 to neurotrophin-3 receptor TRKC in acute myeloid leukemia with t(12;15)(p13;q25). Blood 93, 1355–1363 (1999)

    CAS  PubMed  Google Scholar 

  7. J.M. Kralik, W. Kranewitter, H. Boesmueller, R. Marschon, G. Tschurtschenthaler, H. Rumpold, K. Wiesinger, M. Erdel, A.L. Petzer, G. Webersinke, Characterization of a newly identified ETV6-NTRK3 fusion transcript in acute myeloid leukemia. Diagn. Pathol. 6, 19 (2011)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. C.A. Kim, M.L. Phillips, W. Kim, M. Gingery, H.H. Tran, M.A. Robinson, S. Faham, J.U. Bowie, Polymerization of the SAM domain of TEL in leukemogenesis and transcriptional repression. EMBO J. 20, 4173–4182 (2001)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. D.H. Wai, S.R. Knezevich, T. Lucas, B. Jansen, R.J. Kay, P.H. Sorensen, The ETV6-NTRK3 gene fusion encodes a chimeric protein tyrosine kinase that transforms NIH3T3 cells. Oncogene 19, 906–915 (2000)

    Article  CAS  PubMed  Google Scholar 

  10. D.R. Kaplan, F.D. Miller, Neurotrophin signal transduction in the nervous system. Curr. Opin. Neurobiol. 10, 381–391 (2000)

    Article  CAS  PubMed  Google Scholar 

  11. M.S. Kim, G.M. Kim, Y.J. Choi, H.J. Kim, Y.J. Kim, W. Jin, TrkC promotes survival and growth of leukemia cells through Akt-mTOR-dependent up-regulation of PLK-1 and Twist-1. Mol. Cell 36, 177–184 (2013)

    Article  CAS  Google Scholar 

  12. M. Eguchi, M. Eguchi-Ishimae, A. Green, T. Enver, M. Greaves, Directing oncogenic fusion genes into stem cells via an SCL enhancer. Proc. Natl. Acad. Sci. U. S. A. 102, 1133–1138 (2005)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. T.L. Gu, L. Popova, C. Reeves, J. Nardone, J. Macneill, J. Rush, S.D. Nimer, R.D. Polakiewicz, Phosphoproteomic analysis identifies the M0-91 cell line as a cellular model for the study of TEL-TRKC fusion-associated leukemia. Leukemia 21, 563–566 (2007)

    Article  CAS  PubMed  Google Scholar 

  14. K. Thress, T. Macintyre, H. Wang, D. Whitston, Z.Y. Liu, E. Hoffman, T. Wang, J.L. Brown, K. Webster, C. Omer, P.E. Zage, L. Zeng, P.A. Zweidler-McKay, Identification and preclinical characterization of AZ-23, a novel, selective, and orally bioavailable inhibitor of the Trk kinase pathway. Mol. Cancer Ther. 8, 1818–1827 (2009)

    Article  CAS  PubMed  Google Scholar 

  15. M. Vitiello, A. Tuccoli, L. Poliseno, Long non-coding RNAs in cancer: implications for personalized therapy. Cell. Oncol. 38, 17–28 (2015)

    Article  CAS  Google Scholar 

  16. V. Taucher, H. Mangge, J. Haybaec, Non-coding RNAs in pancreatic cancer: challenges and opportunities for clinical application. Cell. Oncol. 39, 295–318 (2016)

    Article  CAS  Google Scholar 

  17. Y. Sun, S.H. Kim, D.C. Zhou, W. Ding, E. Paietta, F. Guidez, A. Zelent, K.H. Ramesh, L. Cannizzaro, R.P. Warrell, R.E. Gallagher, Acute promyelocytic leukemia cell line AP-1060 established as a cytokine-dependent culture from a patient clinically resistant to all-trans retinoic acid and arsenic trioxide. Leukemia 18, 1258–1269 (2004)

    Article  CAS  PubMed  Google Scholar 

  18. R.A. MacLeod, M. Kaufmann, H.G. Drexler, Cytogenetic harvesting of commonly used tumor cell lines. Nature Prot. 2, 372–382 (2007)

    Article  CAS  Google Scholar 

  19. T.S. Wan, Cancer cytogenetics: methodology revisited. Ann. Lab. Med. 34, 413–425 (2014)

    Article  PubMed  PubMed Central  Google Scholar 

  20. H. Dai, S. Ehrentraut, S. Nagel, S. Eberth, C. Pommerenke, W.G. Dirks, R. Geffers, S. Kalavalapalli, M. Kaufmann, C. Meyer, S. Faehnrich, S. Chen, H.G. Drexler, R.A. MacLeod, Genomic landscape of primary mediastinal B-cell lymphoma cell lines. PLoS One 10, e0139663 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

  21. S. Nagel, C. Pommerenke, C. Meyer, M. Kaufmann, H.G. Drexler, R.A. MacLeod, Deregulation of polycomb repressor complex 1 modifier AUTS2 in T-cell leukemia. Oncotarget 7, 45398–45413 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  22. M.A. Frohman, M.K. Dush, G. Martin, Rapid production of full-length cDNAs from rare transcripts: Amplification using a single gene-specific oligonucleotide primer. Proc. Natl. Acad. Sci. U. S. A. 85, 8998–9002 (1988)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. J.J. van Dongen, E.A. Macintyre, J.A. Gabert, E. Delabesse, V. Rossi, G. Saglio, E. Gottardi, A. Rambaldi, G. Dotti, F. Griesinger, A. Parreira, P. Gameiro, M.G. Diáz, M. Malec, A.W. Langerak, J.F. San Miguel, A. Biondi, Standardized RT-PCR analysis of fusion gene transcripts from chromosome aberrations in acute leukemia for detection of minimal residual disease. Leukemia 13, 1901–1928 (1999)

    Article  PubMed  Google Scholar 

  24. R.A. MacLeod, Z.B. Hu, M. Kaufmann, H.G. Drexler, Cohabiting t(12;22) and inv(3) primary rearrangements in an acute myelomonocytic leukemia (FAB M4) cell line. Genes Chromosom. Cancer 16, 144–148 (1996)

    Article  CAS  PubMed  Google Scholar 

  25. J. Redondo-Munoz, E. Escobar-Diaz, M. Hernandez Del Cerro, A. Pandiella, M.J. Terol, J.A. Garcia-Marco, A. Garcia-Pardo, Induction of B-chronic lymphocytic leukemia cell apoptosis by arsenic trioxide involves suppression of the phosphoinositide 3-kinase/Akt survival pathway via c-jun-NH2 terminal kinase activation and PTEN upregulation. Clin. Cancer Res. 16, 4382–4391 (2010)

    Article  CAS  PubMed  Google Scholar 

  26. I. Amigo-Jiménez, E. Bailón, N. Aguilera-Montilla, J.A. García-Marco, A. García-Pardo, Gene expression profile induced by arsenic trioxide in chronic lymphocytic leukemia cells reveals a central role for heme oxygenase-1 in apoptosis and regulation of matrix metalloproteinase-9. Oncotarget 7, 83359–83377 (2016)

    Article  PubMed  PubMed Central  Google Scholar 

  27. C. Xu, M. Yang, J. Tian, X. Wang, Z. Li, MALAT-1 a long non-coding RNA and its important 3′ end functional motif in colorectal cancer metastasis. Int. J. Oncol. 39, 169–175 (2011)

    PubMed  Google Scholar 

  28. J.A. West, C.P. Davis, H. Sunwoo, M.D. Simon, R.I. Sadreyev, P.I. Wang, M.Y. Tolstorukov, R.E. Kingston, The long noncoding RNAs NEAT1 and MALAT1 bind active chromatin sites. Mol. Cell 55, 791–802 (2014)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. I. Shmulevich, I. Gluhovsky, R.F. Hashimoto, E.R. Dougherty, W. Zhang, Steady-state analysis of genetic regulatory networks modelled by probabilistic boolean networks. Comp. Funct. Genomics 4, 601–608 (2003)

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. L. Calzone, E. Barillot, A. Zinovyev, Predicting genetic interactions from Boolean models of biological networks. Integr. Biol. Camb. 7, 921–929 (2015)

    Article  CAS  PubMed  Google Scholar 

  31. J. Zhao, L. Li, L. Peng, MAPK1 up-regulates the expression of MALAT1 to promote the proliferation of cardiomyocytes through PI3K/AKT signaling pathway. Int. J. Clin. Exp. Pathol. 8, 15947–15953 (2015)

    PubMed  PubMed Central  Google Scholar 

  32. X.Y. Ma, J.H. Wang, J.L. Wang, C.X. Ma, X.C. Wang, F.S. Liu, Malat1 as an evolutionarily conserved lncRNA, plays a positive role in regulating proliferation and maintaining undifferentiated status of early-stage hematopoietic cells. BMC Genomics 16, 676 (2015)

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

The authors thank the cell line donors, and Poul Sorensen for critical reading of the manuscript and useful suggestions.

Author information

Author notes

  1. Stefan Nagel and Bjoern Schneider contributed equally to this work.

Authors and Affiliations

  1. Department of Human and Animal Cell Lines, DSMZ - German Collection of Microorganisms and Cell Cultures, Inhoffenstrasse 7b, 38124, Braunschweig, Germany

    Suning Chen, Stefan Nagel, Bjoern Schneider, Haiping Dai, Maren Kaufmann, Corinna Meyer, Claudia Pommerenke, Hilmar Quentmeier, Hans G. Drexler & Roderick A. F. MacLeod

  2. Jiangsu Institute of Hematology, Key Laboratory of Thrombosis and Hemostasis of Ministry of Health, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China

    Suning Chen & Haiping Dai

  3. Institute of Pathology and Molecular Pathology, University of Rostock, Rostock, Germany

    Bjoern Schneider

  4. Genome Analytics Research Group, Helmholtz Centre for Infection Research, Braunschweig, Germany

    Robert Geffers

  5. AstraZeneca Oncology Innovative Medicines, Waltham, MA, USA

    Kenneth S. Thress

  6. Department of Hematology, Yixing People’s Hospital of Jiangsu Province, Yixing, People’s Republic of China

    Jiao Li

Authors
  1. Suning Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Stefan Nagel
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bjoern Schneider
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Haiping Dai
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robert Geffers
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Maren Kaufmann
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Corinna Meyer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Claudia Pommerenke
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Kenneth S. Thress
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Jiao Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Hilmar Quentmeier
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Hans G. Drexler
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Roderick A. F. MacLeod
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Roderick A. F. MacLeod.

Ethics declarations

Conflict of interest

The authors have no conflicting interests to disclose.

Electronic supplementary material

Table S1

(XLSX 57 kb)

(XLSX 36 kb)

Table S2

(XLSX 10 kb)

Figure S1

FISH of ETV6 and NTKR3. FISH for ETV6 and PML (RP11-96B23) (A) and NTRK3 (B) yield breakpoint data shown in Fig 1b. Bacterial artificial chromosome (BAC) and fosmid clones were obtained from BACPAC Resources, (Children’s Hospital, Oakland, CA/USA) and labelled by nick translation with dUTP fluors (Dyomics, Jena/Germany). Slides were mounted in Vectashield (Vector Labs, Burlinghame, CA/USA) containing 0.01% DAPI (4′,6-diamidino-2-phenylindole) (Sigma) and images recorded using a Zeiss Axioimager microscope (Zeiss, Oberkochen/Germany) configured to a HiSKY imaging system (Applied Spectral Imaging, Neckarhausen/Germany) (GIF 384 kb)

High resolution image (TIFF 586 kb)

Figure S2

Copy number variations (CNV) and zygosity in AP-1060 cells. CNV were investigated with high density combined oligonucleotide SNP arrays. Images depict the following 12 mainly exonic gene regions: 3q26/MECOM (conspicuously high); 4q12/FIP1L1 (inconspicuous); 5p15/TERT; 5q21/translocation breakpoint (extragenic); 8q12/LYN (underexpression); 9p24/JAK2 (underexpression); 10q26/DMBT1 (inconspicuous); 11p13 biallelic WT1 (conspicuously silent); 11p15.4/TRIM5 (inconspicuous) and TRIM22 (overexpressed); 11p15.5/MRPL23 (underexpressed); 11q14/GAB2 (overexpressed); 12p12/BCAT1-intronic (conspicuously silent); 12q22/NUDT4 (underexpressed); 15q24/PML (breakpoint region); 15q25/NTRK3 (breakpoint region); 17q21/ATP5G1 translocation breakpoint (inconspicuous); 17q25/3′-ACOX1 (overexpressed); Xq24/IL13RA1 (splicing?). Cytoscan images show (top-to-bottom): genomic copy number (violet), losses of heterozygosity (plum); Database of Genomic Variation (DGV) showing recurrent losses (red) and gains (blue); BAC clones (yellow); Genbank genes with exons (pink); Online Mendelian Inheritance in Man (OMIM) genes (green and grey), and at the bottom genome coordinates (GRCh37/hg19). CytoScan High Density Arrays combining oligonucleotide and single nucleotide polymorphism (SNP) probes (Affymetrix, High Wycombe/UK) were employed to analyse genomic copy number alterations (CNA), loss of heterozygosity (LOH) and unbalanced chromosome translocation breakpoints as described recently [20]. Briefly, DNA was extracted using the Qiagen Gentra Puregene Kit (Hilden/Germany). Labeling, hybridization, washing and CytoScan HD arrays were performed per manufacturer’s recommendations and quality control criteria. Data were analysed using the Chromosome Analysis Suite software version 2.0.1.2 (Affymetrix) which links to the Database of Genomic Variants for identification of polymorphic CNV (http://dgv.tcag.ca/dgv/app/home). (GIF 126 kb)

High resolution image (TIFF 55 kb)

(GIF 221 kb)

High resolution image (TIFF 148 kb)

(GIF 220 kb)

High resolution image (TIFF 124 kb)

Figure S3

Heatmap shows expression at genomically rearranged loci. Note conspicuous upregulation of three genes: MECOM which, though lacking detectible chromosome rearrangement at 3q26 bears a short intronic deletion (Supplementary Fig. S2); NTRK3 fused with ETV6 via t(12;15); and WT1 which showed conspicuous silencing attributable to biallelic deletions which were embedded within a ca. 1.8 Mbp LOH tract, one of several covering most of the 11p region, consistent with uniparental disomy (Supplementary Fig. S2). Cell lines were as follows AP-1060 and NB-4 (APL) and F-36P, GDM-1, GF-D8, HEL, KG-1, MB-02, MEG-01, MOLM-17, MUTZ-3, MUTZ-8, MV4–11, OCI-M1, OCI-M2, PLB-985 (HL-60 subclone), SAML-2, SET-2, THP-1, UKE-1, and UOC-M1.. Approximately 7.5 μg RNA prepared as for RQPCR was biotinylated using the 3′ IVT Express Kit (Affymetrix). cDNA was fragmented and placed in a cocktail along with hybridization controls (BioB, BioC, BioD, and Cre) per manufacturer’s recommendations. Samples were hybridized to Affymetrix GeneChip HG-U133 2.0 Plus for 16 h at 45 °C. Washing and staining were performed using a fluidics station 450 according to the recommended FS450 protocol. Image analysis was performed on GCS3000 Scanner and GCOS1.2 Software Suite (Affymetrix). Comparison datasets were kindly supplied by Prof. Andreas Rosenwald (University of Würzburg, Germany) or downloaded from the BROAD Institute (www.broadinstitute.org). For heatmaps we used CLUSTER 2.11 and TREEVIEW 1.60 (http://rana.lbl.gov/EisenSoftware.htm). Array data were parsed using the Broad/GSEA (http://www.broadinstitute.org/gsea/index.jsp), and STRING (http://string-db.org/) platforms. Signaling pathway models were cross-checked using PathwayNet software ( http://pathwaynet.princeton.edu/ ). (GIF 220 kb)

High resolution image (TIFF 135 kb)

Figure S4

FISH of MECOM . Image shows FISH analysis of MECOM locus using tilepath BAC clones. Note wild type configuration of both MECOM alleles at 3q26 remote from t(3;14) breakpoint at 3p14. Centromeric (RP11-94 J18) and telomeric (RP11-101 L8) were labeled and analysed as described above. (GIF 314 kb)

High resolution image (TIFF 303 kb)

Figure S5

Comparative transcriptomics of AP-1060 and NB-4. Shows microarray expression of genes conspicuously upregulated (upper figure) or downregulated (lower) in AP-1060 with respect to NB-4 cells, together with unsupervised data from other cell lines. As well as singular activation of NTRK3 and MECOM commented above, consistent upregulation of several genes with known or potential leukemic activity: BAALC, BRAF, CCR6, CLEC7A, F2RL1, FRMD6, IL1R1, IL32, LOC100507645/MALAT1, OSMR, RGL4, SELL, SLITRK4, SOCS3 and VSTM1 (red arrows). In addition to WT1, top downregulated genes included: ANXA5, BCAT1, E2F1, HNRPLL, PON2, RAB13, TPD52, TRIM4 and ZAK (green arrows). Transcriptional profiling and heatmapping were performed as described. (GIF 301 kb)

High resolution image (TIFF 290 kb)

Figure S6

Comparative transcriptomics of AP-1060 and NB-4 highlight MALAT1. To define transcriptional responses of AP-1060 cells to AZ-23, expression array data were substractively compared with NB-4 cells lacking EN. Top inhibitory and stimulatory targets were MALAT1 (upper figure) and MIR4680 (lower), respectively Lanes were (top-to-bottom): 1/2 untreated AP-1060 /NB-4; 3/4 AP-1060/NB-4 untreated/vehicle; 5/6 AP-1060/NB-4 + AZ-23. Note singular inhibition of MALAT1 restricted to AP-1060 highlighted as potential EN target (arrows). (GIF 399 kb)

High resolution image (TIFF 387 kb)

Figure S7

Gene networks of NTRK3 signaling. A) STRING database of known and predicted protein-protein interactions was used to assemble a gene network of the top 300 genes inhibited by NTRK3 in AP-1060 cells. Note central, tightly interconnected network including NTRK3, MAPK1 and ESR1 (red). B) High confidence PathWay Net inference of “post-translational regulation” shows network linking seeding input genes ESR1, IL32, NTRK3 and TP53 (red lines). Note unprompted inclusion of MAPK1 and PML, contrasted with boycotting of IL32 from close partner network. Minimum confidence = 0.94. (MALAT1 is ipso facto excluded from protein based analyses.) (GIF 262 kb)

High resolution image (TIFF 266 kb)

(GIF 300 kb)

High resolution image (TIFF 470 kb)

Figure S8

Immunofluorescence of MAPK1 and PML expression. Immunofluoresence shows inhibition by AZ-23 of activation (phosphorylation) of MAPK1 while sparing expression. Neither endpoint is affected in HG-23, paralleling western blot data (Fig. 3a). PML (bottom row), in contrast, was unaffected by inhibiting NTRK3 showing both nuclear and cytoplasmic staining in HG-3, while expression was mainly cytoplasmic in AP-1060 reflecting depletion of nuclear bodies therein. Microscope slides bearing cyto-centrifuged cells from log-phase cultures were fixed in methanol for 15 min at room temperature (RT), air dried and incubated in blocking buffer (5% BSA/PBS) for 30 min under soft plastic coverslips (Grace Bio-Labs, Bend, OR/USA) in a humidified atmosphere at RT. Slides were then incubated with primary antibodies (as detailed above for Westerns), together with anti-PML (Santa Cruz). MAPK1/pMAPK1 and PML antibodies were detected using anti-rabbit (SC-2359) and anti-mouse (SC-1030) FITC-labelled secondary antibodies (Santa Cruz). All procedures followed manufacturers` protocols. After ethanol dehydration and air-drying slides were mounted and as described for DNA FISH. (GIF 217 kb)

High resolution image (TIFF 171 kb)

Figure S9

NTRK3 expression in high vs low MALAT1 expressing AML patients. Shows RqPCR expression data for exons-14/15/16 (retained in NTRK3 fusion mRNA). Note NTRK3 upregulation in (patient)-S1 [t(15;17)] absent from S2 [t(8;21)] and all MALAT1 LOW patients tested. (GIF 512 kb)

High resolution image (TIFF 782 kb)

Figure S10

Co-expression of NTRK3 and MALAT1 in Cancer Patients. Public gene regulatory network data show positive association between NTRK3 and MALAT1 expression. Public National Cancer Institute data (https://gdc-portal.nci.nih.gov/projects/TCGA-HNSC) were mined to reveal Boolean relationships between expression of gene pairs. Variable thresholds are shown as blue lines which partition variables into low or high levels. Green and purple lines are, respectively, −0.5 and +0.5 standard deviations from X axis thresholds. Samples between the green/purple vertical lines (X-axis) and yellow/blue horizontal lines (Y-axis) were ignored. Each point represents a tumor sample (n = 528). Boolean implications are generated between variables when one quadrant is sparsely inhabited, thus detecting L-shaped relationships which may be overlooked by linear methods. Here, peak MALAT1 and NTRK3 expression appear cohabitory implying thresholding. (GIF 153 kb)

High resolution image (TIFF 73 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Chen, S., Nagel, S., Schneider, B. et al. A new ETV6-NTRK3 cell line model reveals MALAT1 as a novel therapeutic target - a short report. Cell Oncol. 41, 93–101 (2018). https://doi.org/10.1007/s13402-017-0356-2

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13402-017-0356-2

Keywords

Search

Navigation