Literature DB >> 22658276

Multiplex high-throughput gene mutation analysis in acute myeloid leukemia.

Jennifer Dunlap1, Carol Beadling, Andrea Warrick, Tanaya Neff, William H Fleming, Marc Loriaux, Michael C Heinrich, Tibor Kovacsovics, Katalin Kelemen, Nicky Leeborg, Ken Gatter, Rita M Braziel, Richard Press, Christopher L Corless, Guang Fan.   

Abstract

Classification of acute myeloid leukemia increasingly depends on genetic analysis. However, the number of known mutations in acute myeloid leukemia is expanding rapidly. Therefore, we tested a high-throughput screening method for acute myeloid leukemia mutation analysis using a multiplex mass spectrometry-based approach. To our knowledge, this is the first reported application of this approach to genotype leukemias in a clinical setting. One hundred seven acute myeloid leukemia cases were screened for mutations using a panel that covers 344 point mutations across 31 genes known to be associated with leukemia. The analysis was performed by multiplex polymerase chain reaction for mutations in genes of interest followed by primer extension reactions. Products were analyzed on a Sequenom MassARRAY system (San Diego, CA). The multiplex panel yielded mutations in 58% of acute myeloid leukemia cases with normal cytogenetics and 21% of cases with abnormal cytogenetics. Cytogenetics and routine polymerase chain reaction-based screening of NPM1, CEBPA, FLT3-ITD, and KIT was also performed on a subset of cases. When combined with the results of these standard polymerase chain reaction-based tests, the mutation frequency reached 78% in cases with normal cytogenetics. Of these, 42% harbored multiple mutations primarily involving NPM1 with NRAS, KRAS, CEBPA, PTPN11, IDH1, or FLT3. In contrast, cases with abnormal cytogenetics rarely harbored more than 1 mutation (1.5%), suggesting different underlying biology. This study demonstrates the feasibility and utility of broad-based mutation profiling of acute myeloid leukemia in a clinical setting. This approach will be helpful in defining prognostic subgroups of acute myeloid leukemia and contribute to the selection of patients for enrollment into trials with novel inhibitors.
Copyright © 2012 Elsevier Inc. All rights reserved.

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Year:  2012        PMID: 22658276     DOI: 10.1016/j.humpath.2012.03.002

Source DB:  PubMed          Journal:  Hum Pathol        ISSN: 0046-8177            Impact factor:   3.466


  8 in total

1.  Acute monocytic leukaemia with t(11; 12) (p15; q13) chromosomal changes: A case report and literature review.

Authors:  Jiasheng Hu; Xiuli Hong; Zhe Li; Quanyi Lu
Journal:  Oncol Lett       Date:  2015-07-20       Impact factor: 2.967

2.  Clonal dynamics in a single AML case tracked for 9 years reveals the complexity of leukemia progression.

Authors:  T Kim; K Yoshida; Y K Kim; M S Tyndel; H J Park; S H Choi; J-S Ahn; S-H Jung; D-H Yang; J-J Lee; H J Kim; G Kong; S Ogawa; Z Zhang; H J Kim; D D Kim
Journal:  Leukemia       Date:  2015-10-01       Impact factor: 11.528

3.  Association between phosphatase related gene variants and coronary artery disease: case-control study and meta-analysis.

Authors:  Xia Han; Lijun Zhang; Zhiqiang Zhang; Zengtang Zhang; Jianchun Wang; Jun Yang; Jiamin Niu
Journal:  Int J Mol Sci       Date:  2014-08-13       Impact factor: 5.923

4.  Clonal Evolution and Changes in Two AML Patients Detected with A Novel Single-Cell DNA Sequencing Platform.

Authors:  Liwen Xu; Robert Durruthy-Durruthy; Dennis J Eastburn; Maurizio Pellegrino; Omid Shah; Everett Meyer; James Zehnder
Journal:  Sci Rep       Date:  2019-07-31       Impact factor: 4.379

5.  Gene mutation profile and risk stratification in AML1‑ETO‑positive acute myeloid leukemia based on next‑generation sequencing.

Authors:  Guopan Yu; Changxin Yin; Fuqun Wu; Ling Jiang; Zhongxin Zheng; Dan Xu; Jiaheng Zhou; Xuejie Jiang; Qifa Liu; Fanyi Meng
Journal:  Oncol Rep       Date:  2019-10-15       Impact factor: 3.906

6.  An integrated approach for identifying wrongly labelled samples when performing classification in microarray data.

Authors:  Yuk Yee Leung; Chun Qi Chang; Yeung Sam Hung
Journal:  PLoS One       Date:  2012-10-17       Impact factor: 3.240

7.  MDM2 Amplification and PI3KCA Mutation in a Case of Sclerosing Rhabdomyosarcoma.

Authors:  Ken Kikuchi; George R Wettach; Christopher W Ryan; Arthur Hung; Jody E Hooper; Carol Beadling; Andrea Warrick; Christopher L Corless; Susan B Olson; Charles Keller; Atiya Mansoor
Journal:  Sarcoma       Date:  2013-05-20

8.  Germline SAMD9 and SAMD9L mutations are associated with extensive genetic evolution and diverse hematologic outcomes.

Authors:  Jasmine C Wong; Victoria Bryant; Tamara Lamprecht; Jing Ma; Michael Walsh; Jason Schwartz; Maria Del Pilar Alzamora; Charles G Mullighan; Mignon L Loh; Raul Ribeiro; James R Downing; William L Carroll; Jeffrey Davis; Stuart Gold; Paul C Rogers; Sara Israels; Rochelle Yanofsky; Kevin Shannon; Jeffery M Klco
Journal:  JCI Insight       Date:  2018-07-26
  8 in total

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