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Identifying the optimal conditioning intensity for stem cell transplantation in patients with myelodysplastic syndrome: a machine learning analysis

Bone Marrow Transplantation volume 58, pages 186–194 (2023)Cite this article

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Abstract

A conditioning regimen is an essential prerequisite of allogeneic hematopoietic stem cell transplantation for patients with myelodysplastic syndrome (MDS). However, the optimal conditioning intensity for a patient may be difficult to establish. This study aimed to identify optimal conditioning intensity (reduced-intensity conditioning regimen [RIC] or myeloablative conditioning regimen [MAC]) for patients with MDS. Overall, 2567 patients with MDS who received their first HCT between 2009 and 2019 were retrospectively analyzed. They were divided into a training cohort and a validation cohort. Using a machine learning-based model, we developed a benefit score for RIC in the training cohort. The validation cohort was divided into a high-score and a low-score group, based on the median benefit score. The endpoint was progression-free survival (PFS). The benefit score for RIC was developed from nine baseline variables in the training cohort. In the validation cohort, the hazard ratios of the PFS in the RIC group compared to the MAC group were 0.65 (95% confidence interval [CI]: 0.48–0.90, P = 0.009) in the high-score group and 1.36 (95% CI: 1.06–1.75, P = 0.017) in the low-score group (P for interaction < 0.001). Machine-learning-based scoring can be useful for the identification of optimal conditioning regimens for patients with MDS.

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Fig. 1: Progression-free survival in each cohort.
Fig. 2: Progression-free survival in each cohort after dividing by the established model.
Fig. 3: Overall survival, relapse, and non-relapse mortality in the validation cohort divided by the established model.

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Data availability

The data used in this study are available from the corresponding author upon reasonable request.

References

  1. Cazzola M. Myelodysplastic syndromes. N Engl J Med. 2020;383:1358–74.

    Article  CAS  Google Scholar 

  2. Platzbecker U. Treatment of MDS. Blood. 2019;133:1096–107.

    Article  CAS  Google Scholar 

  3. de Witte T, Bowen D, Robin M, Malcovati L, Niederwieser D, Yakoub-Agha I, et al. Allogeneic hematopoietic stem cell transplantation for MDS and CMML: recommendations from an international expert panel. Blood. 2017;129:1753–62.

    Article  Google Scholar 

  4. Gagelmann N, Kröger N. Dose intensity for conditioning in allogeneic hematopoietic cell transplantation: can we recommend “when and for whom” in 2021? Haematologica. 2021;106:1794–804.

    Article  CAS  Google Scholar 

  5. McDonald GB, Sandmaier BM, Mielcarek M, Sorror M, Pergam SA, Cheng G-S, et al. Survival, nonrelapse mortality, and relapse-related mortality after allogeneic hematopoietic cell transplantation: comparing 2003–2007 versus 2013–2017 cohorts. Ann Intern Med. 2020;172:229.

    Article  Google Scholar 

  6. Sengsayadeth S, Savani BN, Blaise D, Malard F, Nagler A, Mohty M. Reduced intensity conditioning allogeneic hematopoietic cell transplantation for adult acute myeloid leukemia in complete remission – a review from the Acute Leukemia Working Party of the EBMT. Haematologica. 2015;100:859–69.

    Article  Google Scholar 

  7. Kröger N, Iacobelli S, Franke G-N, Platzbecker U, Uddin R, Hübel K, et al. Dose-reduced versus standard conditioning followed by allogeneic stem-cell transplantation for patients with myelodysplastic syndrome: a prospective Randomized Phase III Study of the EBMT (RICMAC Trial). J Clin Oncol. 2017;35:2157–64.

    Article  Google Scholar 

  8. Scott BL, Pasquini MC, Logan BR, Wu J, Devine SM, Porter DL, et al. Myeloablative versus reduced-intensity hematopoietic cell transplantation for acute myeloid leukemia and myelodysplastic syndromes. J Clin Oncol. 2017;35:1154–61.

    Article  Google Scholar 

  9. Rashidi A, Meybodi MA, Cao W, Chu H, Warlick ED, Devine S, et al. Myeloablative versus reduced-intensity hematopoietic cell transplantation in myelodysplastic syndromes: systematic review and meta-analysis. Biol Blood Marrow Transplant. 2020;26:e138–41.

    Article  CAS  Google Scholar 

  10. Shimoni A, Robin M, Iacobelli S, Beelen D, Mufti GJ, Ciceri F, et al. Allogeneic hematopoietic cell transplantation in patients with myelodysplastic syndrome using treosulfan based compared to other reduced‐intensity or myeloablative conditioning regimens. A report of the chronic malignancies working party of the EBMT. Br J Haematol. 2021;195:417–28.

    Article  CAS  Google Scholar 

  11. Aoki K, Ishikawa T, Ishiyama K, Aoki J, Itonaga H, Fukuda T, et al. Allogeneic haematopoietic cell transplantation with reduced-intensity conditioning for elderly patients with advanced myelodysplastic syndromes: a nationwide study. Br J Haematol. 2015;168:463–6.

    Article  Google Scholar 

  12. Shimomura Y, Hara M, Konuma T, Itonaga H, Doki N, Ozawa Y. et al. Allogeneic hematopoietic stem cell transplantation for myelodysplastic syndrome in adolescent and young adult patients. Bone Marrow Transplant. 2021;56:2510–7. https://doi.org/10.1038/s41409-021-01324-8.

    Article  CAS  Google Scholar 

  13. Basu S, Sussman JB, Hayward RA. Detecting heterogeneous treatment effects to guide personalized blood pressure treatment: a modeling study of randomized clinical trials. Ann Intern Med. 2017;166:354–60.

    Article  Google Scholar 

  14. Chen S, Tian L, Cai T, Yu M. A general statistical framework for subgroup identification and comparative treatment scoring. Biometrics. 2017;73:1199–209.

    Article  Google Scholar 

  15. VanderWeele TJ, Knol MJ. Interpretation of subgroup analyses in randomized trials: heterogeneity versus secondary interventions. Ann Intern Med. 2011;154:680–3.

    Article  Google Scholar 

  16. Baum A, Scarpa J, Bruzelius E, Tamler R, Basu S, Faghmous J. Targeting weight loss interventions to reduce cardiovascular complications of type 2 diabetes: a machine learning-based post-hoc analysis of heterogeneous treatment effects in the Look AHEAD trial. Lancet Diabetes Endocrinol. 2017;5:808–15.

    Article  Google Scholar 

  17. Huling JD, Yu M. Subgroup identification using the personalized package. J Stat Softw. 2021;98:1–60. https://doi.org/10.18637/jss.v098.i05.

  18. Giralt S, Ballen K, Rizzo D, Bacigalupo A, Horowitz M, Pasquini M, et al. Reduced-Intensity Conditioning Regimen Workshop: defining the dose spectrum. Report of a workshop convened by the Center for International Blood and Marrow Transplant Research. Biol Blood Marrow Transplant. 2009;15:367–9.

    Article  Google Scholar 

  19. Bacigalupo A, Ballen K, Rizzo D, Giralt S, Lazarus H, Ho V, et al. Defining the intensity of conditioning regimens: working definitions. Biol Blood Marrow Transplant. 2009;15:1628–33.

    Article  Google Scholar 

  20. Sorror ML, Maris MB, Storb R, Baron F, Sandmaier BM, Maloney DG, et al. Hematopoietic cell transplantation (HCT)-specific comorbidity index: a new tool for risk assessment before allogeneic HCT. Blood. 2005;106:2912–9.

    Article  CAS  Google Scholar 

  21. Stekhoven DJ, Bühlmann P. MissForest–non-parametric missing value imputation for mixed-type data. Bioinforma Oxf Engl. 2012;28:112–8.

    Article  CAS  Google Scholar 

  22. Waljee AK, Mukherjee A, Singal AG, Zhang Y, Warren J, Balis U, et al. Comparison of imputation methods for missing laboratory data in medicine. BMJ Open. 2013;3:e002847.

    Article  Google Scholar 

  23. Shah AD, Bartlett JW, Carpenter J, Nicholas O, Hemingway H. Comparison of random forest and parametric imputation models for imputing missing data using MICE: a CALIBER study. Am J Epidemiol. 2014;179:764–74.

    Article  Google Scholar 

  24. Slade E, Naylor MG. A fair comparison of tree-based and parametric methods in multiple imputation by chained equations. Stat Med. 2020;39:1156–66.

    Article  Google Scholar 

  25. Atallah E, Logan B, Chen M, Cutler C, Deeg J, Jacoby M. et al. Comparison of patient age groups in transplantation for myelodysplastic syndrome: the medicare coverage with evidence development study. JAMA Oncol. 2020;6:486–93. https://doi.org/10.1001/jamaoncol.2019.5140.

    Article  Google Scholar 

  26. Gilleece MH, Labopin M, Yakoub-Agha I, Volin L, Socié G, Ljungman P, et al. Measurable residual disease, conditioning regimen intensity, and age predict outcome of allogeneic hematopoietic cell transplantation for acute myeloid leukemia in first remission: a registry analysis of 2292 patients by the Acute Leukemia Working Party E. Am J Hematol. 2018;93:1142–52.

    Article  Google Scholar 

  27. Gilleece MH, Labopin M, Savani BN, Yakoub-Agha I, Socié G, Gedde-Dahl T, et al. Allogeneic haemopoietic transplantation for acute myeloid leukaemia in second complete remission: a registry report by the Acute Leukaemia Working Party of the EBMT. Leukemia. 2020;34:87–99.

    Article  CAS  Google Scholar 

  28. Saraceni F, Labopin M, Forcade E, Kröger N, Socié G, Niittyvuopio R, et al. Allogeneic stem cell transplant in patients with acute myeloid leukemia and karnofsky performance status score less than or equal to 80%: a study from the acute leukemia working party of the European Society for Blood and Marrow Transplantation (EBMT). Cancer Med. 2021;10:23–33.

    Article  Google Scholar 

  29. Rambaldi A, Grassi A, Masciulli A, Boschini C, Micò MC, Busca A, et al. Busulfan plus cyclophosphamide versus busulfan plus fludarabine as a preparative regimen for allogeneic haemopoietic stem-cell transplantation in patients with acute myeloid leukaemia: an open-label, multicentre, randomised, phase 3 trial. Lancet Oncol. 2015;16:1525–36.

    Article  CAS  Google Scholar 

  30. Konuma T, Kondo T, Mizuno S, Doki N, Aoki J, Fukuda T, et al. Conditioning intensity for allogeneic hematopoietic cell transplantation in acute myeloid leukemia patients with poor-prognosis cytogenetics in first complete remission. Biol Blood Marrow Transplant. 2020;26:463–71.

    Article  CAS  Google Scholar 

  31. Fein JA, Shimoni A, Labopin M, Shem-Tov N, Yerushalmi R, Magen H, et al. The impact of individual comorbidities on non-relapse mortality following allogeneic hematopoietic stem cell transplantation. Leukemia. 2018;32:1787–94.

    Article  Google Scholar 

  32. Hourigan CS, Dillon LW, Gui G, Logan BR, Fei M, Ghannam J, et al. Impact of conditioning intensity of allogeneic transplantation for acute myeloid leukemia with genomic evidence of residual disease. J Clin Oncol. 2020;38:1273–83.

    Article  CAS  Google Scholar 

  33. Passweg JR, Labopin M, Cornelissen J, Volin L, Socié G, Huynh A, et al. Conditioning intensity in middle-aged patients with AML in first CR: no advantage for myeloablative regimens irrespective of the risk group–an observational analysis by the Acute Leukemia Working Party of the EBMT. Bone Marrow Transplant. 2015;50:1063–8.

    Article  CAS  Google Scholar 

  34. Lindsley RC, Saber W, Mar BG, Redd R, Wang T, Haagenson MD, et al. Prognostic mutations in myelodysplastic syndrome after stem-cell transplantation. N Engl J Med. 2017;376:536–47.

    Article  CAS  Google Scholar 

  35. Spyridonidis A, Labopin M, Savani BN, Niittyvuopio R, Blaise D, Craddock C, et al. Redefining and measuring transplant conditioning intensity in current era: a study in acute myeloid leukemia patients. Bone Marrow Transplant. 2020;55:1114–25.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

The authors thank all the physicians and data managers at the centers who contributed to the collection of data on transplantation for the Japanese Data Center for Hematopoietic Cell Transplantation and Transplant Registry Unified Management Program 2. We express our gratitude to the Japan Society of Clinical Research for their support.

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Authors and Affiliations

  1. Department of Hematology, Kobe City Hospital Organization Kobe City Medical Center General Hospital, Minamimati 2-1-1, Minatojima, Chuo-ku, Kobe, 650-0047, Japan

    Yoshimitsu Shimomura

  2. Department of Environmental Medicine and Population Science, Graduate School of Medicine, Osaka University, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Yoshimitsu Shimomura, Tetsuhisa Kitamura & Tomotaka Sobue

  3. Division of Biomedical Statistics, Department of Integrated Medicine Osaka University, Graduate School of Medicine, 2-2 Yamadaoka, Suita, Osaka, 565-0871, Japan

    Sho Komukai

  4. Division of Stem Cell and Molecular Medicine, The Institute of Medical Science, The University of Tokyo, 4-6-1, Shiroganedai, Minato-ku, Tokyo, 108-0071, Japan

    Shuhei Kurosawa

  5. Hematology Division, Tokyo Metropolitan Cancer and Infectious Diseases Center, Komagome Hospital, 3-18-22 Honkomagome, Bunkyo-ku, Tokyo, 113-8677, Japan

    Noriko Doki

  6. Department of Hematology, Hiroshima Red Cross Hospital & Atomic-bomb Survivors Hospital, 1-9-6 Sendamachi, Naka-ku, Hiroshima, 730-8619, Japan

    Yuta Katayama

  7. Department of Hematology, Japanese Red Cross Aichi Medical Center Nagoya Daiichi Hospital, 3-35, Michishita-tyo, Nakamura-ku, Nagoya, 453-8511, Japan

    Yukiyasu Ozawa

  8. Department of Hematology and Oncology, Okayama University Hospital, 2-5-1 Shikata-cho, Kita-ku, Okayama, 700-0914, Japan

    Ken-ichi Matsuoka

  9. Department of Hematopoietic Stem Cell Transplantation, National Cancer Center Hospital, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan

    Takashi Tanaka & Takahiro Fukuda

  10. Division of Hematology, Jichi Medical University Saitama Medical Center, 1-847 Amanuma-cho, Omiya-ku, Saitama, 330-8503, Japan

    Shinichi Kako

  11. Department of Hematology and Oncology, Anjo Kosei Hospital, 28 Higashihirokute, Anjo-cho, Anjo-shi, Aichi, 446-8602, Japan

    Masashi Sawa

  12. Division of Hematology, Jichi Medical University, 3311-1, Yaushiji, Shimotsuke, Tochigi, 329-0498, Japan

    Yoshinobu Kanda

  13. Department of Hematology, Graduate School of Medicine, Osaka City University, 1-4-3 Asahi-machi, Abeno-ku, Osaka, 545-8585, Japan

    Hirohisa Nakamae

  14. Department of Hematology, Shinshu University School of Medicine, 3-1-1 Asahi, Matsumoto, Nagano 390-8621, Japan

    Hideyuki Nakazawa

  15. Department of Hematology/Oncology and Transfusion and Hemapheresis Center, Kurashiki Central Hospital, 1-1-1 Miwa, Kurashiki-shi, Okayama, 710-8602, Japan

    Yasunori Ueda

  16. Department of Hematology and Oncology, Graduate School of Medicine, Kyoto University, Yoshida-Konoe-cho, Sakyo-ku, Kyoto, 606-8501, Japan

    Junya Kanda

  17. Japanese Data Center for Hematopoietic Cell Transplantation, 1-1 Yazakokariata, Nagakute, 480-1195, Japan

    Yoshiko Atsuta

  18. Department of Registry Science for Transplant and Cellular Therapy, Aichi Medical University School of Medicine, 1-1 Yazakokariata, Nagakute, 480-1195, Japan

    Yoshiko Atsuta

  19. Department of Hematology, Kanazawa University Hospital, 13-1 Takaramachi, Ishikawa, 920-8641, Japan

    Ken Ishiyama

Authors
  1. Yoshimitsu Shimomura
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  2. Sho Komukai
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  3. Tetsuhisa Kitamura
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  4. Tomotaka Sobue
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  5. Shuhei Kurosawa
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  6. Noriko Doki
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  7. Yuta Katayama
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  8. Yukiyasu Ozawa
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  10. Takashi Tanaka
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  11. Shinichi Kako
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  12. Masashi Sawa
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  14. Hirohisa Nakamae
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  20. Ken Ishiyama
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Contributions

YS designed the study, developed the models, performed the statistical analysis, and wrote the first draft of the manuscript. Sho Komukai and TK contributed to the development of the model and data analysis. Sho Komukai, TK, TS, Shuhei Kurosawa, and KI critically reviewed the data analysis and manuscript. All the other authors contributed to data collection. All the authors approved the final version of the manuscript.

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Correspondence to Yoshimitsu Shimomura.

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Shimomura, Y., Komukai, S., Kitamura, T. et al. Identifying the optimal conditioning intensity for stem cell transplantation in patients with myelodysplastic syndrome: a machine learning analysis. Bone Marrow Transplant 58, 186–194 (2023). https://doi.org/10.1038/s41409-022-01871-8

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