Abstract PR03: Genomic approaches for risk assessment in acute myeloid leukemia
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, Jacqueline E. Payton, Jack Baty, Sharon E. Heath, Obi L. Griffith, Dong Shen, Jasreet Hundal, Gue Su Chang, Robert S. Fulton, Michelle O'laughlin, Catrina Fronick, Vincent Magrini, Ryan Demeter, David E. Larson, Shashikant Kulkarni, Bradley A. Ozenberger, John S. Welch, Matthew J. Walker, Timothy A. Graubert, Peter Westervelt, Jerald P. Radich, Daniel C. Link, Elaine R. Mardis, John F. DiPersio, Richard K. Wilson
Journal: Cancer Research (2015)

Abstract Acute myeloid leukemia is heterogeneous with respect to clinical outcome and molecular pathogenesis. Approximately 20% of AML cases are refractory to induction chemotherapy, and about 50% of patients ultimately relapse within a time interval that ranges from months to years. At the molecular level, diverse chromosomal abnormalities and genetic mutations have been observed across patients1. Although several clinical factors (age, white blood cell count), cytogenetic aberrations (t[15;17] translocation, loss of chromosome 5) 2-4, and genetic mutations (DNMT3A, FLT3) have been associated with differences in survival 5,6, these factors are of limited prognostic utility. Moreover, few studies have integrated sequence data with clinical and cytogentic factors to build predictive models of patient outcome. Here, we sought to identify genomic predictors of refractory disease or early relapse. We used whole genome and exome sequencing to analyze the genomes of 71 adult de novo AML patients treated with anthracycline and cytarabine-based induction chemotherapy. Of these, 34 had refractory disease or relapsed within 6 months, 12 relapsed in 6-12 months, and 25 had a long first remission (&amp;gt;12 months). We also developed an enhanced exome sequencing (EES) approach to identify and follow leukemia-associated variants over time. In 12 additional patients that achieved morphologic remission after induction chemotherapy, we used EES to identify and track variants at time of diagnosis, time of morphologic remission (roughly 30 days later), and a final time point corresponding to eventual relapse (n=8) or extended remission (n=4). No novel coding or non-coding variants present at the time of diagnosis were found to be predictive of refractory disease or early relapse. Using EES, however, we were able to detect leukemia-associated variants in the initial remission bone marrow in all eight patients who eventually relapsed. One persistent leukemia-associated variant was also detected in one patient still in remission, but all other variants in that patient were eliminated. We also detected 64 somatic variants that became enriched following chemotherapy, but were not detected in the original leukemic cells. These may represent relapse-specific variants or oligoclonal hematopoiesis after bone marrow recovery. Overall, our data suggest that the persistence of leukemia-associated variants after bone marrow recovery from cytotoxic therapy is strongly correlated with relapse, and may be used to complement more traditional, morphologic measures of leukemic cell clearance. 1. Cancer Genome Atlas Research N. Genomic and epigenomic landscapes of adult de novo acute myeloid leukemia. The New England Journal of Medicine 2013;368:2059-74. 2. Byrd JC, Mrozek K, Dodge RK, et al. Pretreatment cytogenetic abnormalities are predictive of induction success, cumulative incidence of relapse, and overall survival in adult patients with de novo acute myeloid leukemia: results from Cancer and Leukemia Group B (CALGB 8461). Blood 2002;100:4325-36. 3. Grimwade D, Hills RK, Moorman AV, et al. Refinement of cytogenetic classification in acute myeloid leukemia: determination of prognostic significance of rare recurring chromosomal abnormalities among 5876 younger adult patients treated in the United Kingdom Medical Research Council trials. Blood 2010;116:354-65. 4. Schlenk RF, Dohner K, Krauter J, et al. Mutations and treatment outcome in cytogenetically normal acute myeloid leukemia. The New England Journal of Medicine 2008;358:1909-18. 5. Kihara R, Nagata Y, Kiyoi H, et al. Comprehensive analysis of genetic alterations and their prognostic impacts in adult acute myeloid leukemia patients. Leukemia 2014;28:1586-95. 6. Ley TJ, Ding L, Walter MJ, et al. DNMT3A mutations in acute myeloid leukemia. The New England Journal of Medicine 2010;363:2424-33. This abstract is also presented as a poster at the Translation of the Cancer Genome conference. Citation Format: Jeffery M. Klco, Christopher A. Miller, Malachi Griffith, Allegra Petti, David H. Spencer, Shamika Ketkar-Kulkarni, Lukas D. Wartman, Matthew Christopher, Tamara L. Lamprecht, Jacqueline E. Payton, Jack Baty, Sharon E. Heath, Obi L. Griffith, Dong Shen, Jasreet Hundal, Gue Su Chang, Robert S. Fulton, Michelle O'laughlin, Catrina Fronick, Vincent Magrini, Ryan Demeter, David E. Larson, Shashikant Kulkarni, Bradley A. Ozenberger, John S. Welch, Matthew J. Walker, Timothy A. Graubert, Peter Westervelt, Jerald P. Radich, Daniel C. Link, Elaine R. Mardis, John F. DiPersio, Richard K. Wilson. Genomic approaches for risk assessment in acute myeloid leukemia. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR03.