Skip to main content

Advertisement

Log in

Improved prediction of clinical outcome in chronic myeloid leukemia

  • Original Article
  • Published:
International Journal of Hematology Aims and scope Submit manuscript

Abstract

We sought to develop and compare prognostic models, based on clinical and/or morphometric diagnostic data, to enable better prediction of complete cytogenetic response (CCgR). This prospective longitudinal study included a consecutive series of patients with chronic myeloid leukemia (CML) who were started on imatinib therapy. Logistic regression analysis using backward selection was performed with CCgR at 6, 12, and 18 months as the outcome variables. We evaluated both calibration and discrimination of the model. Internal validation of the model was performed with bootstrapping techniques. A total of 40 patients on imatinib therapy were included in the final analysis. Of these, 25 (62.5 %), 29 (72.5 %), and 32 (80 %), respectively, achieved CCgR at 6, 12, and 18 months after initiation of imatinib. Models included EUTOS score on diagnosis and one of the following morphometric parameters: microvascular density, length of the minor axis, area or circularity of the blood vessel. Models including morphometric parameters and EUTOS score were superior for prediction of CCgR at 6, 12, and 18 months. In particular, the superior models showed better specificity than EUTOS score alone. Using morphometric parameters in conjunction with EUTOS score improves prediction of CCgR. If validated, these models could aid in individual patient risk stratification.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4

Similar content being viewed by others

References

  1. Shiffer C. BCR-ABL tyrosine kinase inhibitors for chronic myelogenous leukemia. N Engl J Med. 2007;357:258–65.

    Article  Google Scholar 

  2. Druker J, Guilhot F, O’Brien S, Gathamann I, Kantarjian HM, Gatterman N, et al. Fife-year follow-up of patients receiving imatinib for chronic myeloid leukemia. N Engl J Med. 2006;355:2408–17.

    Article  CAS  PubMed  Google Scholar 

  3. de Lavallade H, Apperley JF, Khorashad JS, Milojković D, Reid A, Bua M, et al. Imatinib for newly diagnosed patients with chronic myeloid leukemia: incidence of sustained responses in an intention to treat analysis. J Clin Oncol. 2008;26:3358–63.

    Article  PubMed  Google Scholar 

  4. Kantarjian H, Talpaz M, O’Brien S, Garcia-Manero G, Verstovsek S, Giles F, et al. High dose imatinib mesylate therapy in newly diagnosed Philadelphia chromosome-positive chronic phase chronic myeloid leukemia. Blood. 2004;103:2873–8.

    Article  CAS  PubMed  Google Scholar 

  5. Kantarjian H, Shah NP, Hochhaus A, Cortes J, Shah S, Ayala M, et al. Dasatinib versus imatinib in newly diagnosed chronic-phase chronic myeloid leukemia. N Engl J Med. 2010;362:2260–70.

    Article  CAS  PubMed  Google Scholar 

  6. Kantarjian HM, Hochhaus A, Saglio G, De Souza C, Flinn IW, Stenke L, et al. Nilotinib versus imatinib for the treatment of patients with newly diagnosed chronic phase, Philadelphia chromosome-positive, chronic myeloid leukaemia: 24-month minimum follow-up of the phase 3 randomised ENESTnd trial. Lancet Oncol. 2011;12:841–51.

    Article  CAS  PubMed  Google Scholar 

  7. Yamamoto E, Fujisawa S, Hagihara M, Tanaka M, Fujimaki K, Kishimoto K, et al. European treatment and outcome study score does not predict imatinib treatment response and outcome in chronic myeloid leukemia patients. Cancer Sci. 2014;105:105–9.

    Article  CAS  PubMed  Google Scholar 

  8. Sokal JE, Cox EB, Baccarani M, Tura S, Gomez GA, Robertson JE, et al. Prognostic discrimination in “good risk” chronic granulocytic leukemia. Blood. 1984;63(4):789–99.

    CAS  PubMed  Google Scholar 

  9. Hasford J, Pfirmann M, Hehlmann R, Alann NC, Baccarani M, Kluin-Nelemans JC, et al. A new prognostic score for survival of patients with chronic myeloid leukemia treated with interferon alfa. J Natl Cancer Inst. 1998;90:850–8.

    Article  CAS  PubMed  Google Scholar 

  10. Hasford J, Baccarani M, Hoffmann V, Guilhot J, Saussele S, Rosti G, et al. Predicting complete cytogenetic response and subsequent progression-free survival in 2060 patients with CML on imatinib treatment: the EUTOS score. Blood. 2011;118:686–92.

    Article  CAS  PubMed  Google Scholar 

  11. Marin D, Ibrahim AR, Goldman JM. European treatment and out come study (EUTOS) score for chronic myeloid leukemia still require more confirmation. J Clin Oncol. 2011;29:3944–5.

    Article  PubMed  Google Scholar 

  12. Jabbour E, Cortes J, Nazha A, O’Brien S, Quintas-Cardama A, Pierce S, et al. EUTOS score is not predictive for survival and outcome in patients with early chronic phase chronic myeloid leukemia treated with tyrosine kinase inhibitors: a single institution experience. Blood. 2012;119:4524–6.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  13. Korkolopoulou P, Viniou N, Kavantzas N, Patsouris E, Thymara I, Pavlopoulos PM, et al. Clinicopathologic correlations of bone marrow angiogenesis in chronic myeloid leukemia: a morphometric study. Leukemia. 2003;17:89–97.

    Article  CAS  PubMed  Google Scholar 

  14. Baccarani M, Cortes J, Pane FD, Niederwieser D, Saglio D, Apperley J, et al. Chronic myeloid leukemia. An update of concepts and management recommendations of the European LeukemiaNet. J Clin Oncol. 2009;27:6041–51.

    Article  CAS  PubMed  Google Scholar 

  15. Baccarani M, Deininger MW, Rosti G, Hochhaus A, Soverini S, Apperley JF, et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood. 2013;122:872–84.

    Article  CAS  PubMed  Google Scholar 

  16. Hsu SM, Raine L, Fanger L. User of avidin-biotin-peroxidase complex (ABC) in immunoperoxidase techniques: a comparison between ABC and unlabelled antibody (PAP) procedures. J Hisohem Cytochem. 1981;29:77–80.

    Google Scholar 

  17. Padró T, Ruiz S, Bieker R, Burger H, Steins M, Kienast J, et al. Increased angiogenesis in the bone marrow of patients with acute myeloid leukemia. Blood. 2000;95(8):2637–44.

    PubMed  Google Scholar 

  18. Vermeulan PB, Gasparini G, Fox SB, Toj M, Martin L, McCulloch P, et al. Quantification of angiogenesis in solid human tumorous; an international consensus on the methodology and criteria of evaluation. Eur J Cancer. 1996;32:2474–84.

    Article  Google Scholar 

  19. Box GE, Tidwell PW. Transformation of the independent variables. Technometrics. 1962;4:531–50.

    Article  Google Scholar 

  20. Pregibon D. Logistic regression diagnostics. Ann Stat. 1981;9:705–24.

    Article  Google Scholar 

  21. Hosmer DW, Lemeshow S. Applied logistic regression. New York: Wiley; 2000.

    Book  Google Scholar 

  22. Kleinbaum DG, Klein M. Logistic regression: a self-learning text 3rd edition, Springer Science Business Media LLC, 2010.

  23. Hanley J, McNeil B. A method of comparing the areas under receiver operating characteristic curves derived from the same cases. Radiology. 1983;148:839–43.

    Article  CAS  PubMed  Google Scholar 

  24. Efron B, Tibshirani R. An introduction to the bootstrap. London: Chapman and Hall; 1993.

    Book  Google Scholar 

  25. Lundberg LG, Lerner R, Sundelin P, Rogers R, Folkman J, Palmblad J. Bone marrow in polycythemia vera, chronic myelocytic leukemia and myelofibrosis has an increased vascularity. Am J Pathol. 2000;157:15–9.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  26. Aguayo A, Kantarjian HM, Manshouri T, Gidel C, Estey E, Thomas D, et al. Angigenesis in acute and chronic leukemias and myelodysplastic syndromes. Blood. 2000;96:2240–5.

    CAS  PubMed  Google Scholar 

  27. Trask P, Mitra D, Iyer S, Candrilli S, Kaye J. Patterns and prognostic indicators of response to CML treatment in a multi-country medical record review study. Int J Hematol. 2012;95:535–44.

    Article  PubMed  Google Scholar 

  28. Cortes JE, Talpaz M, Giles F, O’Brien S, Rios MB, Shan J, et al. Prognostic significance of cytogenetic clonal evolution in patients with chronic myelogenous leukemia on imatinib mesylate therapy. Blood. 2003;101:3794–800.

    Article  CAS  PubMed  Google Scholar 

  29. Verma D, Kantarjian H, Shan J, O’Brien S, Estrov Z, Garcia-Manero G, et al. Survival outcomes for clonal evolution in chronic myeloid leukemia patients on second generation tyrosine kinase inhibitor therapy. Cancer. 2010;116:2673–81.

    CAS  PubMed Central  PubMed  Google Scholar 

  30. Stagno F, Vigneri P, Del Fabro V, Stella S, Cupri A, Massimino M, et al. Influence of complex variant chromosomal translocations in chronic myeloid leukemia patients treated with tyrosine kinase inhibitors. Acta Oncol. 2010;49:506–8.

    Article  CAS  PubMed  Google Scholar 

  31. Lucas CM, Harris RJ, Giannoudis A, Davies A, Knight K, Watmough SJ, et al. Chronic myeloid leukemia patients with the e13a2 BCR-ABL fusion transcript have inferior responses to imatinib compared to patients with the e14a2 transcript. Haematologica. 2009;94:1362–7.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  32. White DL, Dang P, Engler J, Frede A, Zrim S, Osborn M, et al. Functional activity of the OCT-1 protein is predictive of long-term outcome in patients with chronic-phase chronic myeloid leukemia treated with imatinib. J Clin Oncol. 2010;28:2761–7.

    Article  CAS  PubMed  Google Scholar 

  33. Laupacis A, Sekar N, Stiell IG. Clinical prediction rules: a review and suggested modifications of methodological standards. JAMA. 1997;277:488–94.

    Article  CAS  PubMed  Google Scholar 

  34. Brotman DJ, Walker E, Lauer MS, O’Brien RG. In search of fewer independent risk factors. Arch Intern Med. 2005;165:138–45.

    Article  PubMed  Google Scholar 

  35. Moons KG, Royston P, Vergouwe Y, Grobbee DE, Altman DG. Prognosis and prognostic research: what, why and how? BMJ. 2009;338:b375.

    Article  PubMed  Google Scholar 

  36. Gil TM. The central role of prognosis in clinical decision making. JAMA. 2012;11(307):199–200.

    Article  Google Scholar 

  37. Steyerberg E. Clinical prediction models: a practical approach to development, validation, and updating. New York: Springer Science Business Media; 2009.

    Book  Google Scholar 

  38. Gwilliam B, Keeley V, Todd C, Roberts C, Gittins M, Kelly L, et al. Prognosticating in patients with advanced cancer-observational study comparing the accuracy of clinicians ‘and patients’ estimates of survival. Ann Oncol. 2012;24:482–8.

    Article  PubMed  Google Scholar 

  39. Lukić S, Ćojbašić Ž, Jović N, Popović M, Bjelaković B, Dimitrijević L, Bjelaković Lj. Artificial neural networks based prediction of cerebral palsy in infants with central coordination disturbance. Early Human Dev. 2012;88:547–53.

    Article  Google Scholar 

Download references

Conflict of interest

None of the authors has any conflict of interest to disclose.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Irena Ćojbašić.

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ćojbašić, I., Mačukanović-Golubović, L., Mihailović, D. et al. Improved prediction of clinical outcome in chronic myeloid leukemia. Int J Hematol 101, 173–183 (2015). https://doi.org/10.1007/s12185-014-1726-4

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12185-014-1726-4

Keywords

Navigation