Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

CHRONIC MYELOGENOUS LEUKEMIA

Predictive scoring systems for molecular responses in persons with chronic phase chronic myeloid leukemia receiving initial imatinib therapy

Abstract

It is vital for physicians and persons with chronic myeloid leukemia (CML) to accurately predict the likelihood of achieving a major molecular response (MMR) and a deep molecular response (DMR; at least MR4) at the start of imatinib-therapy, which could help in decision making of treatment goals and strategies. To answer this question, we interrogated data from 1369 consecutive subjects with chronic phase CML receiving initial imatinib-therapy to identify predictive co-variates. Subjects were randomly-assigned to training (n = 913) and validation (n = 456) datasets. Male sex, higher WBC concentration, lower haemoglobin concentration, higher percentage blood blasts and larger spleen size were significantly-associated with lower cumulative incidences of MMR and MR4 in training dataset. Using Fine-Gray model, we developed the predictive scoring systems for MMR and MR4 which classified subjects into the low-, intermediate- and high-risk cohorts with significantly-different cumulative incidences of MMR and MR4 with good predictive discrimination and accuracy in training and validation cohorts with high area under the receiver-operator characteristic curve (AUROC) values. These data may help physicians decide appropriateness of initial imatinib therapy.

This is a preview of subscription content

Access options

Buy article

Get time limited or full article access on ReadCube.

$32.00

All prices are NET prices.

Fig. 1
Fig. 2: Cumulative incidences of MMR and MR4 using the predictive scoring systems in the training dataset.
Fig. 3: Cumulative incidences of MMR and MR4 using the predictive scoring systems in the validation dataset.
Fig. 4: Concordance analyses of the predictive scoring systems for MMR and MR4.
Fig. 5: The impact of switching TKI-therapy on responses by the predictive scoring systems for MMR and MR4.

References

  1. Berman E. How I treat chronic phase chronic myelogenous leukemia. Blood. 2021;139:3138–47.

  2. Jabbour E. Chronic myeloid leukemia: First-line drug of choice. Am J Hematol. 2016;91:59–66.

    Article  Google Scholar 

  3. Oehler VG. First-generation vs second-generation tyrosine kinase inhibitors: which is best at diagnosis of chronic phase chronic myeloid leukemia? Hematol Am Soc Hematol Educ Program. 2020;2020:228–36.

    Article  Google Scholar 

  4. Branford S, Yeung DT, Ross DM, Prime JA, Field CR, Altamura HK, et al. Early molecular response and female sex strongly predict stable undetectable BCR-ABL1, the criteria for imatinib discontinuation in patients with CML. Blood. 2013;121:3818–24.

    CAS  Article  Google Scholar 

  5. Castagnetti F, Gugliotta G, Breccia M, Iurlo A, Levato L, Albano F, et al. The BCR-ABL1 transcript type influences response and outcome in Philadelphia chromosome-positive chronic myeloid leukemia patients treated frontline with imatinib. Am J Hematol. 2017;92:797–805.

    CAS  Article  Google Scholar 

  6. Ercaliskan A, Eskazan AE. The impact of BCR-ABL1 transcript type on tyrosine kinase inhibitor responses and outcomes in patients with chronic myeloid leukemia. Cancer. 2018;124:3806–18.

    CAS  Article  Google Scholar 

  7. Ko PS, Yu YB, Liu YC, Wu YT, Hung MH, Gau JP, et al. Moderate anemia at diagnosis is an independent prognostic marker of the EUTOS, Sokal, and Hasford scores for survival and treatment response in chronic-phase, chronic myeloid leukemia patients with frontline imatinib. Curr Med Res Opin. 2017;33:1737–44.

    CAS  Article  Google Scholar 

  8. Nteliopoulos G, Bazeos A, Claudiani S, Gerrard G, Curry E, Szydlo R, et al. Somatic variants in epigenetic modifiers can predict failure of response to imatinib but not to second-generation tyrosine kinase inhibitors. Haematologica. 2019;104:2400–9.

    CAS  Article  Google Scholar 

  9. Qin YZ, Jiang Q, Jiang H, Lai YY, Zhu HH, Liu YR, et al. Combination of white blood cell count at presentation with molecular response at 3 months better predicts deep molecular responses to imatinib in newly diagnosed chronic-phase chronic myeloid leukemia patients. Medicine. 2016;95:e2486.

    Article  Google Scholar 

  10. Togasaki E, Takeda J, Yoshida K, Shiozawa Y, Takeuchi M, Oshima M, et al. Frequent somatic mutations in epigenetic regulators in newly diagnosed chronic myeloid leukemia. Blood cancer J. 2017;7:e559.

    CAS  Article  Google Scholar 

  11. Zhang XS, Gale RP, Huang XJ, Jiang Q. Is the Sokal or EUTOS long-term survival (ELTS) score a better predictor of responses and outcomes in persons with chronic myeloid leukemia receiving tyrosine-kinase inhibitors? Leukemia. 2022;36:482–91.

    Article  Google Scholar 

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

    CAS  Article  Google Scholar 

  13. 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.

    CAS  Article  Google Scholar 

  14. Hasford J, Pfirrmann M, Hehlmann R, Allan NC, Baccarani M, Kluin-Nelemans JC, et al. A new prognostic score for survival of patients with chronic myeloid leukemia treated with interferon alfa. Writing Committee for the Collaborative CML Prognostic Factors Project Group. J Natl Cancer Inst. 1998;90:850–8.

    CAS  Article  Google Scholar 

  15. Baccarani M, Cortes J, Pane F, Niederwieser D, Saglio G, Apperley J, et al. Chronic myeloid leukemia: An update of concepts and management recommendations of European LeukemiaNet. J Clin Oncol: Off J Am Soc Clin Oncol. 2009;27:6041–51.

    CAS  Article  Google Scholar 

  16. 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.

    CAS  Article  Google Scholar 

  17. Baccarani M, Saglio G, Goldman J, Hochhaus A, Simonsson B, Appelbaum F, et al. Evolving concepts in the management of chronic myeloid leukemia: recommendations from an expert panel on behalf of the European LeukemiaNet. Blood. 2006;108:1809–20.

    CAS  Article  Google Scholar 

  18. Hochhaus A, Baccarani M, Silver RT, Schiffer C, Apperley JF, Cervantes F, et al. European LeukemiaNet 2020 recommendations for treating chronic myeloid leukemia. Leukemia. 2020;34:966–84.

    CAS  Article  Google Scholar 

  19. Pfirrmann M, Baccarani M, Saussele S, Guilhot J, Cervantes F, Ossenkoppele G, et al. Prognosis of long-term survival considering disease-specific death in patients with chronic myeloid leukemia. Leukemia. 2016;30:48–56.

    CAS  Article  Google Scholar 

  20. Qin YZ, Jiang Q, Jiang H, Li JL, Li LD, Zhu HH, et al. Which method better evaluates the molecular response in newly diagnosed chronic phase chronic myeloid leukemia patients with imatinib treatment, BCR-ABL(IS) or log reduction from the baseline level? Leuk Res. 2013;37:1035–40.

    CAS  Article  Google Scholar 

  21. Guilhot J, Baccarani M, Clark RE, Cervantes F, Guilhot F, Hochhaus A, et al. Definitions, methodological and statistical issues for phase 3 clinical trials in chronic myeloid leukemia: a proposal by the European LeukemiaNet. Blood. 2012;119:5963–71.

    CAS  Article  Google Scholar 

  22. Royston P, Sauerbrei W. Building multivariable regression models with continuous covariates in clinical epidemiology−with an emphasis on fractional polynomials. Methods Inf Med. 2005;44:561–71.

    CAS  Article  Google Scholar 

  23. Sauerbrei W, Meier-Hirmer C, Benner A, Royston PJCS, Analysis D. Multivariable regression model building by using fractional polynomials: Description of SAS, STATA and R programs. 2006;50:3464–85.

  24. Kuk D, Varadhan R. Model selection in competing risks regression. Stat Med. 2013;32:3077–88.

    Article  Google Scholar 

  25. Vrieze SI. Model selection and psychological theory: a discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC). Psychological Methods. 2012;17:228–43.

    Article  Google Scholar 

  26. Altman DG, Lausen B, Sauerbrei W, Schumacher M. Dangers of using “optimal” cutpoints in the evaluation of prognostic factors. J Natl Cancer Inst. 1994;86:829–35.

    CAS  Article  Google Scholar 

  27. Hinkley, DV. Bootstrap methods and their application. Bootstrap methods and their application, 1997.

  28. Kulesa A, Krzywinski M, Blainey P, Altman N. Sampling distributions and the bootstrap. Nat Methods. 2015;12:477–8.

    CAS  Article  Google Scholar 

  29. Wang S, Wang J, Chung FL. Kernel density estimation, kernel methods, and fast learning in large data sets. IEEE Trans Cybern. 2014;44:1–20.

    Article  Google Scholar 

  30. Kamarudin AN, Cox T, Kolamunnage-Dona R. Time-dependent ROC curve analysis in medical research: Current methods and applications. BMC Med Res Methodol. 2017;17:53.

    Article  Google Scholar 

  31. Van Calster B, Nieboer D, Vergouwe Y, De Cock B, Pencina MJ, Steyerberg EW. A calibration hierarchy for risk models was defined: From utopia to empirical data. J Clin Epidemiol. 2016;74:167–76.

    Article  Google Scholar 

  32. Van Calster B, Wynants L, Verbeek JFM, Verbakel JY, Christodoulou E, Vickers AJ, et al. Reporting and interpreting decision curve analysis: A guide for investigators. Eur Urol. 2018;74:796–804.

    Article  Google Scholar 

  33. Breccia M, Alimena G. The significance of early, major and stable molecular responses in chronic myeloid leukemia in the imatinib era. Crit Rev Oncol/Hematol. 2011;79:135–43.

    Article  Google Scholar 

  34. Cortes J, Rea D, Lipton JH. Treatment-free remission with first- and second-generation tyrosine kinase inhibitors. Am J Hematol. 2019;94:346–57.

    PubMed  Google Scholar 

  35. Sharf G, Marin C, Bradley JA, Pemberton-Whiteley Z, Bombaci F, Christensen RIO, et al. Treatment-free remission in chronic myeloid leukemia: the patient perspective and areas of unmet needs. Leukemia. 2020;34:2102–12.

    Article  Google Scholar 

  36. D’Adda M, Farina M, Schieppati F, Borlenghi E, Bottelli C, Cerqui E, et al. The e13a2 BCR-ABL transcript negatively affects sustained deep molecular response and the achievement of treatment-free remission in patients with chronic myeloid leukemia who receive tyrosine kinase inhibitors. Cancer. 2019;125:1674–82.

    Article  Google Scholar 

  37. 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.

    CAS  Article  Google Scholar 

  38. Qin YZ, Jiang Q, Jiang H, Lai YY, Shi HX, Chen WM, et al. Prevalence and outcomes of uncommon BCR-ABL1 fusion transcripts in patients with chronic myeloid leukaemia: data from a single centre. Br J Haematol. 2018;182:693–700.

    CAS  Article  Google Scholar 

Download references

Acknowledgements

We thank medical staff and patient participants. QJ acknowledges support from the National Natural Science Foundation of China (No. 81970140). RPG acknowledges support from the National Institute of Health Research (NIHR) Biomedical Research Centre funding scheme. Funded, in part, by the National Nature Science Foundation of China (No. 81970140) and CAMS Innovation Fund for Medical Sciences (CIFMS) (No. 2019-I2M-5-034).

Author information

Authors and Affiliations

Authors

Contributions

QJ designed the study. QJ, XSZ, ZYL, and MYZ collected and analyzed the data. QJ, XJH, XSZ, RPG, ZYL, and MYZ prepared the typescript. All authors approved the final typescript, take responsibility for the content, and agree to submit for publication.

Corresponding authors

Correspondence to Xiao-jun Huang or Qian Jiang.

Ethics declarations

Competing interests

RPG is a consultant to NexImmune Inc. and Ananexa Pharma Ascentage Pharm Group, Antengene Biotech LLC, Medical Director, FFF Enterprises Inc.; partner, AZAC Inc.; Board of Directors, Russian Foundation for Cancer Research Support; and Scientific Advisory Board: StemRad Ltd.

Ethics approval

The study was approved by the Ethics Committee of People’s Hospital Beijing compliant with principles of the Helsinki Declaration.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary information

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Zhang, Xs., Gale, R.P., Li, Zy. et al. Predictive scoring systems for molecular responses in persons with chronic phase chronic myeloid leukemia receiving initial imatinib therapy. Leukemia (2022). https://doi.org/10.1038/s41375-022-01616-y

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1038/s41375-022-01616-y

Search

Quick links