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  • American Association for Cancer Research (AACR)  (3)
  • Fulton, Robert S.  (3)
  • Mardis, Elaine R.  (3)
Type of Medium
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  • American Association for Cancer Research (AACR)  (3)
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Subjects(RVK)
  • 1
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_1 ( 2015-11-15), p. PR11-PR11
    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 ( & 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. 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 Translation of the Cancer Genome; Feb 7-9, 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 1):Abstract nr PR11.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 15_Supplement ( 2015-08-01), p. 4109-4109
    Abstract: Introduction: Death from colorectal cancer (CRC) occurs via sequelae of metastases. Our lack of understanding of the mechanisms driving metastatic formation is a critical barrier to the identification and direct targeting of critical genes and pathways. This is further complicated by tumor heterogeneity and subclonal architecture. To reconstruct the patterns of tumor evolution and metastasis in CRC, we have conducted the first comprehensive clonality analysis of ten patients. Methods: Primary tumor, metastases in multiple liver segments, and matched normal tissues were procured from consented patients during operative resection. Deep exome (∼200x coverage) and whole genome sequencing (∼50x coverage) were used to identify somatic mutations and estimate variant allele frequency (VAF) for somatic single nucleotide variants (SNVs). Clonal architecture and evolution models were derived from the SNVs by VAF-based clustering, clonal ordering, and phylogeny analysis. Results: Non-silent somatic alterations were enriched in genes known to be involved in CRC and other major cancers, including APC, TP53, KRAS, PIK3CA and TCF7L2. Each patient had a founding clone originating from the primary tumor (carrying non-silent mutations in at least one cancer driver gene) that survived to metastasis, possibly following evolution and acquisition of additional somatic mutations. Branched evolution was common and spatially-distinct liver metastases within the same patient sometimes arose from different (sub)clones in the primary tumor. Unique subclones appeared to arise in all metastatic samples, and in some cases, were shared among various metastases of the same patient. This suggests that one metastasis seeded another or an ancestor common to those metastases was present in the primary tumor or elsewhere, but not observed due to spatial heterogeneity. In several cases, mutations in the dominant clone of the primary tumor were absent from metastases, suggesting these were subclonal events in more aggressive cancer cells that arose in the primary tumor after metastasis. These additional somatic events may involve (possibly novel) cancer driver genes. Conclusions: Understanding the genomic events driving tumor evolution and metastasis is critical for explaining why existing therapies fail and determining optimal treatment strategies. Our analyses have outlined several clonal evolution patterns in metastatic CRC. We are currently using ultra-deep targeted and multi-region sequencing to validate genomic alterations in our CRC cohort to refine clonal evolution models and evaluate which subclones may be biologically relevant to disease progression and treatment resistance. Additionally, by revealing critical altered genes and pathways associated with metastatic clones we can improve our understanding of the mechanisms driving metastasis in CRC that may lead to novel targeted cancer therapies. Citation Format: Ha X. Dang, Julie Grossman, Brian S. White, Matthew Strand, David E. Larson, Jason Walker, Elizabeth Pittman, Timothy Fleming, Peter S. Goedegebuure, Robert S. Fulton, Christopher A. Miller, Malachi Griffith, Kian H. Lim, Timothy J. Ley, Richard K. Wilson, Elaine R. Mardis, A.Craig Lockhart, Ryan C. Fields, Christopher A. Maher. Clonal evolution of metastatic colorectal cancer. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr 4109. doi:10.1158/1538-7445.AM2015-4109
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
  • 3
    In: Cancer Research, American Association for Cancer Research (AACR), Vol. 75, No. 22_Supplement_2 ( 2015-11-15), p. PR03-PR03
    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 ( & 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.
    Type of Medium: Online Resource
    ISSN: 0008-5472 , 1538-7445
    RVK:
    RVK:
    Language: English
    Publisher: American Association for Cancer Research (AACR)
    Publication Date: 2015
    detail.hit.zdb_id: 2036785-5
    detail.hit.zdb_id: 1432-1
    detail.hit.zdb_id: 410466-3
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
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