Risk classification at diagnosis predicts post-HCT outcomes in intermediate-, adverse-risk, and KMT2A-rearranged AML.
Kamal MenghrajaniAlexandra Gomez-ArteagaRafael Madero-MarroquinMei-Jie ZhangKhalid Bo-SubaitJonathan SanchezHai-Lin WangMahmoud AljurfAmer AssalVera Ulrike BacherSherif M BadawyNelli BejanyanVijaya Raj BhattChristopher N BredesonMichael ByrnePaul CastilloJan CernySaurabh ChhabraStefan Octavian CiureaZachariah DeFilippNosha FarhadfarShahinaz M GadallaRobert Peter GaleSiddhartha GangulyLohith GowdaMichael Richard GrunwaldShahrukh K. HashmiGerhard Carl HildebrandtChristopher G KanakryAnkit KansagraFarhad KhimaniMaxwell M KremHillard M LazarusHongtao LiuRodrigo Martino BofarullFotios V MichelisSunita NathanTaiga NishihoriRichard F OlssonRan ReshefDavid A RizzieriJacob M RoweBipin P SavaniSachiko SeoAkshay SharmaMelhem SolhCelalettin UstunLeo F VerdonckChristopher S HouriganBrenda M SandmaierMark R LitzowPartow KebriaeiDaniel J WeisdorfYanming ZhangMartin S TallmanWael SaberPublished in: Blood advances (2021)
Little is known about whether risk classification at diagnosis predicts post-hematopoietic cell transplantation (HCT) outcomes for acute myeloid leukemia (AML) patients. We evaluated 8709 AML patients from the CIBMTR database and, after selection and manual curation of cytogenetics data, 3779 patients in CR1 were included in the final analysis: 2384 with intermediate-risk, 969 with adverse-risk, and 426 with KMT2A-rearranged disease. An adjusted multivariable analysis compared to intermediate-risk patients detected an increased risk of relapse for KMT2A-rearranged and adverse-risk patients (HR 1.27, p = 0.01 and HR 1.71, p < 0.001, respectively). Leukemia-free survival (LFS) was similar for KMT2A and adverse-risk patients (HR 1.26, p = 0.002 and HR 1.47, p < 0.001), as was overall survival (OS) (HR 1.32, p < 0.001 and HR 1.45, p < 0.001). No differences in outcome could be detected when patients were stratified by KMT2A fusion partner. This is the largest study conducted to date on post-HCT outcomes in AML using manually curated cytogenetics for risk stratification. Our work demonstrates that risk classification at diagnosis remains predictive of post-HCT outcomes in AML. It also highlights the critical need to develop novel treatment strategies for patients with KMT2A rearrangements and adverse-risk disease.
Keyphrases
- acute myeloid leukemia
- end stage renal disease
- ejection fraction
- chronic kidney disease
- type diabetes
- emergency department
- free survival
- metabolic syndrome
- signaling pathway
- patient reported outcomes
- deep learning
- bone marrow
- adipose tissue
- acute lymphoblastic leukemia
- human immunodeficiency virus
- weight loss
- patient reported
- big data
- artificial intelligence