Page 15 - 2019_12-Haematologica-web
P. 15

Editorials
Figure 2. Treatment algorithm of chronic myeloid leukemia (CML) patients in future medicine incorporating next-generation sequencing (NGS)-based risk assess- ment and up-front tyrosine kinase inhibitor (TKI) drug selection.
tion of the finding before appropriate recommendations References
can be made, with the inclusion of NGS screening at ini- tial diagnosis of CML.13 Moreover, clinical risk scores at diagnosis may inform the selection of patients for NGS- based screening. Figure 2 shows an example of future therapies incorporating NGS-based testing at diagnosis in CML management. Once the diagnosis of CML is made, the next step for risk assessment will include NGS- based risk assessment in addition to clinical disease stag- ing (chronic phase vs. accelerated phase vs. blastic phase) or Sokal risk score calculation. In the case of advanced disease phase, intermediate to high Sokal risk score or those with a somatic mutation in epigenetic modifiers pathway such as ASXL1, DMNT3A, TET2 will be strong candidates for upfront therapy using the 2nd generation TKI.
In the context of somatic mutation profile in CML, some questions remain: 1) what is the role of age-related clonal hematopoiesis in the development of cardiovascu- lar toxicity following TKI therapy; 2) what is the role of somatic mutations in TKI switch for TKI resistant cases without carrying ABL1 kinase domain mutation; 3) what is the clinical relevance of somatic mutations with respect to treatment-free remission? Future studies are warranted to answer these questions so that somatic mutation pro- files can be incorporated into future CML practice not only for upfront TKI drug selection but also during follow up with TKI therapy.
There is a limitation in the study by Nteliopoulos et al.14 the study cohort did not consist of a consecutive set of patients. Thus, further study is strongly warranted to reach a clearer conclusion with a larger prospectively col- lected cohort. Upon successful validation of these data, this approach using NGS-based precision medicine will eventually be incorporated into a clinical algorithm of CML management such as future ELN recommenda- tions. Precision medicine will soon be part of our practice even in CML.
1. Bower H, Björkholm M, Dickman PW, Höglund M, Lambert PC, Andersson TML. Life expectancy of patients with chronic myeloid leukemia approaches the life expectancy of the general population. J Clin Oncol. 2016;34(24):2851-2857.
2. Hughes T, Deininger M, Hochhaus A, et al. Monitoring CML patients responding to treatment with tyrosine kinase inhibitors: Review and recommendations for harmonizing current methodology for detecting BCR-ABL transcripts and kinase domain mutations and for expressing results. Blood. 2006;108(1):28-37.
3. Baccarani M, Deininger MW, Rosti G, et al. European LeukemiaNet recommendations for the management of chronic myeloid leukemia: 2013. Blood. 2013;122(6):872-884.
4. PavlůJ,SzydloRM,GoldmanJM,ApperleyJF.Threedecadesoftrans- plantation for chronic myeloid leukemia: What have we learned? Blood. 2011;117(3):755-763.
5. Papaemmanuil E, Gerstung M, Bullinger L, et al. Genomic Classification and Prognosis in Acute Myeloid Leukemia. N Engl J Med. 2016;374(23):2209-2221.
6. Guglielmelli P, Lasho TL, Rotunno G, et al. MIPSS70: Mutation- enhanced international prognostic score system for transplantation-age patients with primary myelofibrosis. J Clin Oncol. 2018;36(4):310-318.
7. DöhnerH,EsteyE,GrimwadeD,etal.Diagnosisandmanagementof AML in adults: 2017 ELN recommendations from an international expert panel. Blood. 2017;129(4):424-447.
8. Jongen-Lavrencic M, Grob T, Hanekamp D, et al. Molecular Minimal Residual Disease in Acute Myeloid Leukemia. N Engl J Med. 2018;378(13):1189-1199.
9. Kim T, Moon JH, Ahn J-S, et al. Next-generation sequencing based post-transplant monitoring of acute myeloid leukemia identifies patients at high risk of relapse. Blood. 2018;132(15):1604-1613.
10. Kim T, Tyndel MS, Kim HJ, et al. Spectrum of somatic mutation dynamics in chronic myeloid leukemia following tyrosine kinase inhibitor therapy. Blood. 2017;129(1):38-47.
11. Schmidt M, Rinke J, Schäfer V, et al. Molecular-defined clonal evolution in patients with chronic myeloid leukemia independent of the BCR- ABL status. Leukemia. 2014;28(12):2292-2299.
12. Branford S, Wang P, Yeung DT, et al. Integrative genomic analysis reveals cancer-associated mutations at diagnosis of CML in patients with high risk disease. Blood. 2018;132(9):948-961.
13. Branford S, Kim DDH, Apperley JF, et al. Laying the foundation for genomically-based risk assessment in chronic myeloid leukemia. Leukemia. 2019;33(8):1835-1850.
14. Nteliopoulos G, Bazeos A, Claudiani S, et al. Somatic variants in epige- netic modifiers can predict failure of response to imatinib but not to second generation tyrosine kinase inhibitors. Haematologica. 2019;104(12):2400-2409.
haematologica | 2019; 104(12)
2329


































































































   13   14   15   16   17