Title |
Association Between Mutation Clearance After Induction Therapy and Outcomes in Acute Myeloid Leukemia
|
---|---|
Published in |
JAMA: Journal of the American Medical Association, August 2015
|
DOI | 10.1001/jama.2015.9643 |
Pubmed ID | |
Authors |
Jeffery M. Klco, Christopher A. Miller, Malachi Griffith, Allegra Petti, David H. Spencer, Shamika Ketkar-Kulkarni, Lukas D. Wartman, Matthew Christopher, Tamara L. Lamprecht, Nicole M. Helton, Eric J. Duncavage, Jacqueline E. Payton, Jack Baty, Sharon E. Heath, Obi L. Griffith, Dong Shen, Jasreet Hundal, Gue Su Chang, Robert Fulton, Michelle O'Laughlin, Catrina Fronick, Vincent Magrini, Ryan T. Demeter, David E. Larson, Shashikant Kulkarni, Bradley A. Ozenberger, John S. Welch, Matthew J. Walter, Timothy A. Graubert, Peter Westervelt, Jerald P. Radich, Daniel C. Link, Elaine R. Mardis, John F. DiPersio, Richard K. Wilson, Timothy J. Ley |
Abstract |
Tests that predict outcomes for patients with acute myeloid leukemia (AML) are imprecise, especially for those with intermediate risk AML. To determine whether genomic approaches can provide novel prognostic information for adult patients with de novo AML. Whole-genome or exome sequencing was performed on samples obtained at disease presentation from 71 patients with AML (mean age, 50.8 years) treated with standard induction chemotherapy at a single site starting in March 2002, with follow-up through January 2015. In addition, deep digital sequencing was performed on paired diagnosis and remission samples from 50 patients (including 32 with intermediate-risk AML), approximately 30 days after successful induction therapy. Twenty-five of the 50 were from the cohort of 71 patients, and 25 were new, additional cases. Whole-genome or exome sequencing and targeted deep sequencing. Risk of identification based on genetic data. Mutation patterns (including clearance of leukemia-associated variants after chemotherapy) and their association with event-free survival and overall survival. Analysis of comprehensive genomic data from the 71 patients did not improve outcome assessment over current standard-of-care metrics. In an analysis of 50 patients with both presentation and documented remission samples, 24 (48%) had persistent leukemia-associated mutations in at least 5% of bone marrow cells at remission. The 24 with persistent mutations had significantly reduced event-free survival vs the 26 who cleared all mutations (median [95% CI]: 6.0 months [95% CI, 3.7-9.6] for persistent mutations vs 17.9 months [95% CI, 11.3-40.4] for cleared mutations, log-rank P < .001; hazard ratio [HR], 3.67 [95% CI, 1.93-7.11], P < .001) and reduced overall survival (median [95% CI]: 10.5 months [95% CI, 7.5-22.2] for persistent mutations vs 42.2 months [95% CI, 20.6-not estimable] for cleared mutations, log-rank P = .003; HR, 2.86 [95% CI, 1.39-5.88], P = .004). Among the 32 patients with intermediate cytogenetic risk, the 14 patients with persistent mutations had reduced event-free survival compared with the 18 patients who cleared all mutations (median [95% CI]: 8.8 months [95% CI, 3.7-14.6] for persistent mutations vs 25.6 months [95% CI, 11.4-not estimable] for cleared mutations, log-rank P = .003; HR, 3.32 [95% CI, 1.44-7.67], P = .005) and reduced overall survival (median [95% CI]: 19.3 months [95% CI, 7.5-42.3] for persistent mutations vs 46.8 months [95% CI, 22.6-not estimable] for cleared mutations, log-rank P = .02; HR, 2.88 [95% CI, 1.11-7.45], P = .03). The detection of persistent leukemia-associated mutations in at least 5% of bone marrow cells in day 30 remission samples was associated with a significantly increased risk of relapse, and reduced overall survival. These data suggest that this genomic approach may improve risk stratification for patients with AML. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 46 | 45% |
India | 5 | 5% |
Canada | 3 | 3% |
Bosnia and Herzegovina | 2 | 2% |
Brazil | 2 | 2% |
United Kingdom | 1 | <1% |
Chile | 1 | <1% |
Sweden | 1 | <1% |
Switzerland | 1 | <1% |
Other | 5 | 5% |
Unknown | 35 | 34% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 61 | 60% |
Scientists | 29 | 28% |
Practitioners (doctors, other healthcare professionals) | 10 | 10% |
Science communicators (journalists, bloggers, editors) | 2 | 2% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Italy | 2 | <1% |
Spain | 1 | <1% |
United States | 1 | <1% |
Australia | 1 | <1% |
Unknown | 236 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Researcher | 44 | 18% |
Student > Ph. D. Student | 41 | 17% |
Other | 30 | 12% |
Student > Master | 23 | 10% |
Professor > Associate Professor | 17 | 7% |
Other | 46 | 19% |
Unknown | 40 | 17% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 83 | 34% |
Biochemistry, Genetics and Molecular Biology | 48 | 20% |
Agricultural and Biological Sciences | 34 | 14% |
Pharmacology, Toxicology and Pharmaceutical Science | 6 | 2% |
Immunology and Microbiology | 4 | 2% |
Other | 19 | 8% |
Unknown | 47 | 20% |