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Lymphoma

A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study

Abstract

We aimed to establish a discriminative gene-expression-based classifier to predict survival outcomes of T-cell lymphoblastic lymphoma (T-LBL) patients. After exploring global gene-expression profiles of progressive (n = 22) vs. progression-free (n = 28) T-LBL patients, 43 differentially expressed mRNAs were identified. Then an eleven-gene-based classifier was established using LASSO Cox regression based on NanoString quantification. In the training cohort (n = 169), high-risk patients stratified using the classifier had significantly lower progression-free survival (PFS: hazards ratio 4.123, 95% CI 2.565–6.628; p < 0.001), disease-free survival (DFS: HR 3.148, 95% CI 1.857–5.339; p < 0.001), and overall survival (OS: HR 3.790, 95% CI 2.237–6.423; p < 0.001) compared with low-risk patients. The prognostic accuracy of the classifier was validated in the internal testing (n = 84) and independent validation cohorts (n = 360). A prognostic nomogram consisting of five independent variables including the classifier, lactate dehydrogenase levels, ECOG-PS, central nervous system involvement, and NOTCH1/FBXW7 status showed significantly greater prognostic accuracy than each single variable alone. The addition of a five-miRNA-based signature further enhanced the accuracy of this nomogram. Furthermore, patients with a nomogram score ≥154.2 significantly benefited from the BFM protocol. In conclusion, our nomogram comprising the 11-gene-based classifier may make contributions to individual prognosis prediction and treatment decision-making.

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Fig. 1: The study flowchart.
Fig. 2: Kaplan–Meier curves of progression-free survival, disease-free survival, and overall survival according to eleven-gene-based classifier in three cohorts.
Fig. 3: Nomogram A to predict progression-free survival of T-LBL patients.
Fig. 4: Selection of patients who benefit from BFM chemotherapeutic regimen with Nomogram.
Fig. 5: Nomogram B and Nomogram C to predict survival outcomes in three cohorts.

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Data availability

The key raw data have been uploaded onto the Research Data Deposit public platform (RDD), with the approval RDD number of (RDDA2019000567). The microarray data have been deposited online under accession number (GSE138300).

References

  1. Cortelazzo S, Ponzoni M, Ferreri AJ, Hoelzer D. Lymphoblastic lymphoma. Crit Rev Oncol/Hematol. 2011;79:330–43.

    Article  Google Scholar 

  2. Cortelazzo S, Ferreri A, Hoelzer D, Ponzoni M. Lymphoblastic lymphoma. Crit Rev Oncol/Hematol. 2017;113:304–17.

    Article  Google Scholar 

  3. Lepretre S, Touzart A, Vermeulin T, Picquenot JM, Tanguy-Schmidt A, Salles G, et al. Pediatric-like acute lymphoblastic leukemia therapy in adults with lymphoblastic lymphoma: the GRAALL-LYSA LL03 Study. J Clin Oncol Off J Am Soc Clin Oncol. 2016;34:572–80.

    Article  CAS  Google Scholar 

  4. Portell CA, Sweetenham JW. Adult lymphoblastic lymphoma. Cancer J. 2012;18:432–8.

    Article  CAS  Google Scholar 

  5. Tian XP, Huang WJ, Huang HQ, Liu YH, Wang L, Zhang X, et al. Prognostic and predictive value of a microRNA signature in adults with T-cell lymphoblastic lymphoma. Leukemia. 2019; https://doi.org/10.1038/s41375-019-0466-0.

  6. Paraskevopoulou MD, Vlachos IS, Karagkouni D, Georgakilas G, Kanellos I, Vergoulis T, et al. DIANA-LncBase v2: indexing microRNA targets on non-coding transcripts. Nucleic Acids Res. 2016;44:D231–38.

    Article  CAS  Google Scholar 

  7. Sparano JA, Gray RJ, Makower DF, Pritchard KI, Albain KS, Hayes DF, et al. Prospective validation of a 21-gene expression assay in breast cancer. N Engl J Med. 2015;373:2005–14.

    Article  CAS  Google Scholar 

  8. Tang XR, Li YQ, Liang SB, Jiang W, Liu F, Ge WX, et al. Development and validation of a gene expression-based signature to predict distant metastasis in locoregionally advanced nasopharyngeal carcinoma: a retrospective, multicentre, cohort study. Lancet Oncol. 2018;19:382–93.

    Article  CAS  Google Scholar 

  9. Zhao SG, Chang SL, Spratt DE, Erho N, Yu M, Ashab HA, et al. Development and validation of a 24-gene predictor of response to postoperative radiotherapy in prostate cancer: a matched, retrospective analysis. Lancet Oncol. 2016;17:1612–20.

    Article  Google Scholar 

  10. Cheson BD, Pfistner B, Juweid ME, Gascoyne RD, Specht L, Horning SJ, et al. Revised response criteria for malignant lymphoma. J Clin Oncol Off J Am Soc Clin Oncol. 2007;25:579–86.

    Article  Google Scholar 

  11. Farre D, Roset R, Huerta M, Adsuara JE, Rosello L, Alba MM, et al. Identification of patterns in biological sequences at the ALGGEN server: PROMO and MALGEN. Nucleic acids Res. 2003;31:3651–3.

    Article  CAS  Google Scholar 

  12. Messeguer X, Escudero R, Farre D, Nunez O, Martinez J, Alba MM. PROMO: detection of known transcription regulatory elements using species-tailored searches. Bioinformatics. 2002;18:333–4.

    Article  CAS  Google Scholar 

  13. Veldman-Jones MH, Brant R, Rooney C, Geh C, Emery H, Harbron CG, et al. Evaluating robustness and sensitivity of the NanoString Technologies nCounter platform to enable multiplexed gene expression analysis of clinical samples. Cancer Res. 2015;75:2587–93.

    Article  CAS  Google Scholar 

  14. Scott DW, Chan FC, Hong F, Rogic S, Tan KL, Meissner B, et al. Gene expression-based model using formalin-fixed paraffin-embedded biopsies predicts overall survival in advanced-stage classical Hodgkin lymphoma. J Clin Oncol Off J Am Soc Clin Oncol. 2013;31:692–700.

    Article  Google Scholar 

  15. Balachandran VP, Gonen M, Smith JJ, DeMatteo RP. Nomograms in oncology: more than meets the eye. Lancet Oncol. 2015;16:e173–80.

    Article  Google Scholar 

  16. Dienstmann R, Salazar R, Tabernero J. Personalizing colon cancer adjuvant therapy: selecting optimal treatments for individual patients. J Clin Oncol Off J Am Soc Clin Oncol. 2015;33:1787–96.

    Article  CAS  Google Scholar 

  17. Deng L, Liu G, Zheng C, Zhang L, Kang Y, Yang F. Circ-LAMP1 promotes T-cell lymphoblastic lymphoma progression via acting as a ceRNA for miR-615-5p to regulate DDR2 expression. Gene. 2019;701:146–51.

    Article  CAS  Google Scholar 

  18. Deng R, Fan FY, Yi H, Liu F, He GC, Sun HP, et al. MEG3 affects the progression and chemoresistance of T-cell lymphoblastic lymphoma by suppressing epithelial-mesenchymal transition via the PI3K/mTOR pathway. J Cell Biochem. 2018;120:8144–53.

  19. Sestak I, Cuzick J, Dowsett M, Lopez-Knowles E, Filipits M, Dubsky P, et al. Prediction of late distant recurrence after 5 years of endocrine treatment: a combined analysis of patients from the Austrian breast and colorectal cancer study group 8 and arimidex, tamoxifen alone or in combination randomized trials using the PAM50 risk of recurrence score. J Clin Oncol Off J Am Soc Clin Oncol. 2015;33:916–22.

    Article  CAS  Google Scholar 

  20. Liu S, Walker SR, Nelson EA, Cerulli R, Xiang M, Toniolo PA, et al. Targeting STAT5 in hematologic malignancies through inhibition of the bromodomain and extra-terminal (BET) bromodomain protein BRD2. Mol Cancer Ther. 2014;13:1194–205.

    Article  CAS  Google Scholar 

  21. Cheung KL, Zhang F, Jaganathan A, Sharma R, Zhang Q, Konuma T, et al. Distinct roles of Brd2 and Brd4 in potentiating the transcriptional program for Th17 cell differentiation. Mol Cell. 2017;65:1068.e5–80.e5.

    Article  Google Scholar 

  22. Tiong KL, Chang KC, Yeh KT, Liu TY, Wu JH, Hsieh PH, et al. CSNK1E/CTNNB1 are synthetic lethal to TP53 in colorectal cancer and are markers for prognosis. Neoplasia. 2014;16:441–50.

    Article  CAS  Google Scholar 

  23. Varghese RT, Young S, Pham L, Liang Y, Pridham KJ, Guo S, et al. Casein kinase 1 epsilon regulates glioblastoma cell survival. Sci Rep. 2018;8:13621.

    Article  Google Scholar 

  24. Minden A. PAK4-6 in cancer and neuronal development. Cell Logist. 2012;2:95–104.

    Article  Google Scholar 

  25. Lu H, Liu S, Zhang G, Bin W, Zhu Y, Frederick DT, et al. PAK signalling drives acquired drug resistance to MAPK inhibitors in BRAF-mutant melanomas. Nature. 2017;550:133–6.

    Article  CAS  Google Scholar 

  26. Pellizzoni L, Yong J, Dreyfuss G. Essential role for the SMN complex in the specificity of snRNP assembly. Science. 2002;298:1775–9.

    Article  CAS  Google Scholar 

  27. Matera AG, Wang Z. A day in the life of the spliceosome. Nat Rev Mol Cell Biol. 2014;15:108–21.

    Article  CAS  Google Scholar 

  28. Xiao W, Adhikari S, Dahal U, Chen YS, Hao YJ, Sun BF, et al. Nuclear m(6)A reader YTHDC1 regulates mRNA splicing. Mol Cell. 2016;61:507–19.

    Article  CAS  Google Scholar 

  29. Li F, Yi Y, Miao Y, Long W, Long T, Chen S, et al. N(6)-Methyladenosine Modulates Nonsense-Mediated mRNA Decay in Human Glioblastoma. Cancer Res. 2019;79:5785–98.

    Article  CAS  Google Scholar 

  30. Cao L, Wang Z, Zhu C, Zhao Y, Yuan W, Li J, et al. ZNF383, a novel KRAB-containing zinc finger protein, suppresses MAPK signaling pathway. Biochem Biophys Res Commun. 2005;333:1050–9.

    Article  CAS  Google Scholar 

  31. Coude MM, Braun T, Berrou J, Dupont M, Bertrand S, Masse A, et al. BET inhibitor OTX015 targets BRD2 and BRD4 and decreases c-MYC in acute leukemia cells. Oncotarget. 2015;6:17698–712.

    Article  Google Scholar 

  32. Siekmann IK, Dierck K, Prall S, Klokow M, Strauss J, Buhs S, et al. Combined inhibition of receptor tyrosine and p21-activated kinases as a therapeutic strategy in childhood ALL. Blood Adv. 2018;2:2554–67.

    Article  CAS  Google Scholar 

  33. Zhu MY, Wang H, Huang CY, Xia ZJ, Chen XQ, Geng QR, et al. A childhood chemotherapy protocol improves overall survival among adults with T-lymphoblastic lymphoma. Oncotarget. 2016;7:38884–91.

    Article  Google Scholar 

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Acknowledgements

This work was supported by grants from National Key R&D Program of China (2017YFC1309001, 2016YFC1302305), National Natural Science Foundation of China (81603137, 81672686, 81973384), and Special Support Program of Sun Yat-sen University Cancer Center (PT19020401).

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Contributions

X-PT, DX, W-JH, S-YM contributed equally. Q-QC and X-PT designed the study. W-JH, S-YM, LW, Y-HL, XZ, H-QH, T-YL, H-LR, ML, FL, FZ, L-YZ, LL, X-LL, JL, BL, Z-HL, Q-LT, QL, C-KS, Q-LZ, R-FC, QS, KR, XG, X-NL, KY, Y-RS, X-DC, WD, WS, CS, HL, Z-GZ, JR, Q-NG, YZ, X-LM, YZ, C-LH, Y-RJ, YZ, H-YG, W-JH, Z-JX., X-YP, HL, G-WL, LL, H-ZB, L-YS, and T-BK obtained and assembled data. X-PT, W-JH, DX, and Q-QC analyzed and interpreted the data. X-PT and Q-QC wrote the manuscript. X-PT and W-JH did the statistical analysis. All authors reviewed the manuscript and approved the final version.

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Correspondence to Qing-Qing Cai.

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Tian, XP., Xie, D., Huang, WJ. et al. A gene-expression-based signature predicts survival in adults with T-cell lymphoblastic lymphoma: a multicenter study. Leukemia 34, 2392–2404 (2020). https://doi.org/10.1038/s41375-020-0757-5

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