-Author name in bold denotes the presenting author
-Asterisk * with author name denotes a Non-ASH member
Clinically Relevant Abstract denotes an abstract that is clinically relevant.

PhD Trainee denotes that this is a recommended PHD Trainee Session.

Ticketed Session denotes that this is a ticketed session.

2591 A Clinical Measure of DNA Methylation Predicts Outcome in De Novo AML

Acute Myeloid Leukemia: Biology, Cytogenetics and Molecular Markers in Diagnosis and Prognosis
Program: Oral and Poster Abstracts
Session: 617. Acute Myeloid Leukemia: Biology, Cytogenetics and Molecular Markers in Diagnosis and Prognosis: Poster II
Sunday, December 6, 2015, 6:00 PM-8:00 PM
Hall A, Level 2 (Orange County Convention Center)

Marlise R. Luskin, MD1, Phyllis Gimotty, PhD2*, Catherine Smith3*, Alison Loren, MD, MS4, Maria Figueroa, MD5*, Jenna Harrison3*, Zhuoxin Sun, PhD6*, Martin S Tallman, MD7, Elisabeth Paietta, PhD8, Mark R Litzow, MD9, Ross L. Levine, MD10, Hugo F. Fernandez, M.D.11, Selina Luger, MD12, Martin Carroll, MD12, Stephen Master, MD, PhD13* and Gerald Wertheim, MD, PhD14*

1Brigham and Women's Hospital, Boston, MA
2Department of Biostatistics & Epidemiology, University of Pennsylvania, Philadelphia, PA
3Department of Pathology, Children's Hospital of Philadelphia, Philadelphia, PA
4Abramson Cancer Center of the University of Pennsylvania, Philadelphia, PA
5University of Michigan Medical School, Ann Arbor, MI
6Dana-Farber Cancer Institute, Boston, MA
7Leukemia Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY
8Cancer Center, The North Division of Montefiore Medical Center, Bronx, NY
9Division of Hematology, Mayo Clinic, Rochester, MN
10Memorial Sloan Kettering Cancer Center, New York, NY
11Blood and Marrow Transplantation, H. Lee Moffitt Cancer Center, Tampa, FL
12Division of Hematology & Oncology, University of Pennsylvania, Philadelphia, PA
13Department of Pathology & Laboratory Medicine, Weill Cornell Medical College, New York, NY
14Department of Pathology & Laboratory Medicine, University of Pennsylvania, Philadelphia, PA

Background: Despite advances, the majority of patients (pts) with acute myeloid leukemia (AML) will die of their disease.  Current genetics-based risk classification schemes inadequately predict outcome.  We sought to determine if a novel methylation-based biomarker could enhance risk stratification in AML.

Methods: Using a novel methylation assay (xMELP) validated for the clinical laboratory, we performed DNA methylation assessment at 17 previously identified prognostic loci to calculate a summary methylation statistic (M-score, Wertheim et al. Clin Chem 2015) for 166 pts with de novo AML treated at the University of Pennsylvania (UPENN).  Targeted next-generation sequencing (NGS) of 33 hematologic malignancy-associated genes was performed for a 136 pt subset.  The association of M-score with other prognostic variables and outcome [complete remission (CR) and overall survival (OS)] was evaluated.  Median follow-up was 68.1 mos (range 1.4-150.2) among 38 survivors and 10.5 mos (range 0.1-95.2) among those (n=128) deceased.  The optimal M-score for identifying groups with differing OS in the total UPENN cohort defined a binary classifier.  The classifier was validated in UPENN subgroups and in a separate ECOG ACRIN E1900 trial cohort.

Results: The mean and median M-score for the UPENN cohort was 92.3 (95% confidence interval [CI], 87.4-97.2) and 91.4 (range, 30.8-97.3), respectively; the M-score was not significantly associated with age, gender, specimen type, blast percent, NPM1, or FLT3-ITD.  Patients with favorable cytogenetics had a lower mean M-score than those with non-favorable cytogenetics; there was no difference between intermediate and unfavorable cytogenetic groups.  Univariate analyses demonstrated that a 10-unit increase in M-score was associated with a 10% increase in the hazard of death (HR 1.1; P<.0001) and a 20% increase in the odds of failing to achieve CR (OR 1.2, P<.0001).   In multivariable Cox analysis, higher M-score (HR 1.1, P=.011) and older age (P=.001) were significantly associated with increased hazard of death, while NPM1+/FLT3-ITD- status (P=.031) was associated with decreased hazard of death.  In a multivariable logistic analysis, higher M-score (HR, 1.1, P=.034) and older age (P=.007) were associated with increased odds of failing to achieve CR, while favorable cytogenetics (P=.030) was associated with achievement of CR.

Based on the maximization of the log-rank statistic, the optimal M-score cutpoint was 86, which defined a binary risk classifier (hazard of death for high vs low M-score: unadjusted HR 2.5, P<.0001; adjusted HR 1.9, P=.003).  Median OS was 26.6 vs. 10.6 mos for low and high M-score groups (Figure).  The CR rates for low and high M-score groups were 84% and 61%, respectively.  The performance of the M-score classifier was confirmed in pts ≤ 60 yrs with intermediate cytogenetics (log-rank P=.001, OS in high vs low M-score groups: 36.4 vs 14.0 mos) and among pts who achieved CR with initial therapy (log-rank P<.00001, OS 43.9 vs 17.2 mos).  The M-score classifier identified groups with different outcome regardless of allogeneic transplant.

The M-score classifier was validated in the E1900 cohort (n=383).  The mean and median M-scores were similar to the UPENN cohort and M-score was significantly associated with OS on multivariable analysis (P<0.0001).  The M-score classifier identified E1900 subgroups with different OS (log-rank P<.00001; OS 29.5 vs 12.6 mos), a finding confirmed in the intermediate cytogenetics group, in those aged <50 and ≥50 yrs, and among pts assigned to both standard and high dose daunorubicin.  Notably, high dose daunorubicin benefited patients with high M-scores (P=.001), but not those with low M-scores (P=.328).

We also evaluated the prognostic value of the M-score in the context of an extended molecular profile.  Random forest analysis of the UPENN cohort showed that M-score and age are the most robust predictors of OS, while a subset of recurrent mutations (FLT3-ITD, NPM1, IDH2, TET2, TP53, NRAS, CEBPA) also contribute to prognosis, but to a lesser degree.

Conclusion: The M-score and associated classifier represent promising tools for clinical decision-making in AML and deserve further investigation.  The prognostic value of the M-score may be superior to mutational analysis.  Optimal methods for integration of M-score, clinical and genetic information are being defined.

Figure 1

Disclosures: Loren: Merck: Research Funding . Levine: Foundation Medicine: Consultancy ; CTI BioPharma: Membership on an entity’s Board of Directors or advisory committees ; Loxo Oncology: Membership on an entity’s Board of Directors or advisory committees .

*signifies non-member of ASH