title: A Modified Integrated Genetic Model for Risk Prediction in Younger Patients with Acute Myeloid Leukemia.
abstract: BACKGROUND
  Although cytogenetics-based prognostication systems are well described in acute myeloid leukemia (AML), overall survival (OS) remains highly variable within risk groups. An integrated genetic prognostic (IGP) model using cytogenetics plus mutations in nine genes was recently proposed for patients ≤60 years to improve classification. This model has not been validated in clinical practice.
  
  
  RESULTS
  We retrospectively studied 197 patients with newly diagnosed de novo AML. We compared OS curves among the mutational profiles defined by the IGP model. The IGP model assigned patients with intermediate cytogenetics as having favorable, intermediate or unfavorable mutational profiles. The IGP model reassigned 50 of 137 patients with intermediate cytogenetics to favorable or unfavorable mutational profiles. Median OS was 2.8 years among 14 patients with intermediate cytogenetics and favorable mutational profiles (mutant NPM1 and mutant IDH1 or IDH2) and 1.3 years among patients with intermediate mutational profiles. Among patients with intermediate cytogenetics labeled as having unfavorable mutational profiles, median OS was 0.8 years among 24 patients with FLT3-ITD positive AML and high-risk genetic changes (trisomy 8, TET2 and/or DNMT3A) and 1.7 years among 12 patients with FLT3-ITD negative AML and high-risk mutations (TET2, ASXL1 and/or PHF6). OS for patients with intermediate cytogenetics and favorable mutational profiles was similar to OS for patients with favorable cytogenetics (p = 0.697) and different from patients with intermediate cytogenetics and intermediate mutational profiles (p = 0.028). OS among patients with FLT3-ITD positive AML and high-risk genetic changes was similar to patients with unfavorable cytogenetics (p = 0.793) and different from patients with intermediate IGP profile (p = 0.022). Patients with FLT3-ITD negative AML and high-risk mutations, defined as 'unfavorable' in the IGP model, had OS similar to patients with intermediate IGP profile (p = 0.919).
  
  
  CONCLUSIONS
  The IGP model was not completely validated in our cohort. However, mutations in six out of the nine genes can be used to characterize survival (NPMI, IDH1, IDH2, FLT3-ITD, TET2, DNMT3A) and allow for more robust prognostication in the patients who are re-categorized by the IGP model. These mutations should be incorporated into clinical testing for younger patients outside of clinical trials, in order to guide therapy.
authors.affiliation: Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Caroline E
authors.initials: CE
authors.lastname: Sloan
authors.affiliation: Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Marlise R
authors.initials: MR
authors.lastname: Luskin
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Anne M
authors.initials: AM
authors.lastname: Boccuti
authors.affiliation: Division of Hematology and Oncology, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, United States of America.
authors.forename: Alison R
authors.initials: AR
authors.lastname: Sehgal
authors.affiliation: Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Jianhua
authors.initials: J
authors.lastname: Zhao
authors.affiliation: Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Robert D
authors.initials: RD
authors.lastname: Daber
authors.affiliation: Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Jennifer J D
authors.initials: JJ
authors.lastname: Morrissette
authors.affiliation: Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Selina M
authors.initials: SM
authors.lastname: Luger
authors.affiliation: Department of Pathology and Laboratory Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Adam
authors.initials: A
authors.lastname: Bagg
authors.affiliation: Department of Biostatistics and Epidemiology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.
authors.forename: Phyllis A
authors.initials: PA
authors.lastname: Gimotty
authors.affiliation: Division of Hematology and Oncology, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, Pennsylvania, United States of America.|Philadelphia Veterans Administration Medical Center, Philadelphia, Pennsylvania, United States of America.
authors.forename: Martin
authors.initials: M
authors.lastname: Carroll
mesh_terms: D000293:Adolescent; D000328:Adult; D005260:Female; D020022:Genetic Predisposition to Disease; D006801:Humans; D015470:Leukemia, Myeloid, Acute / Q000235:genetics*; D008297:Male; D008875:Middle Aged; D008957:Models, Genetic; D000090243:Nucleophosmin; D011379:Prognosis; D012189:Retrospective Studies; D012307:Risk Factors; D055815:Young Adult