Dataset Information

Spectrum of Mutations in Myeloid Neoplasms

ABSTRACT:

Patients with myeloid malignancies bearing high-risk cytogenetic abnormalities lack effective therapies and have a poor overall survival. -7/del(7q) is identified in half of high-risk myeloid neoplasms. We recently identified CUX1 to be a haploinsufficient myeloid tumor suppressor gene located within the commonly deleted segment of 7q22. Here we identify the spectrum of somatic mutations that co-occur with loss of CUX1 and chromosome 7 in patients with de novo acute myeloid leukemia (AML) or a therapy-related myeloid neoplasm. -7/del(7q) leukemias have a distinct mutational profile characterized by low frequencies of alterations in major leukemogenic pathways, including genes encoding transcription factors, cohesin, and DNA-methylation-related proteins. In contrast, RAS pathway activating mutations occurred in 40% of -7/del(7q) samples, a significantly higher frequency than other AMLs and higher than previously reported. As targeted therapeutics advance, our data provide guidance for which pathways are most relevant in the treatment of adverse-risk myeloid leukemia.

PROVIDER: phs000759 | dbGaP |

SECONDARY ACCESSION(S): PRJNA252637PRJNA252636

REPOSITORIES: dbGaP

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Dataset's files

Source:

			Action	DRS
	GapExchange_phs000759.v1.p1.xml	Xml
	dbGaPEx2.1.5.xsd	Other
	Study_Report.phs000759.MyeloidNeoplasms.v1.p1.MULTI.pdf	Pdf
	manifest_phs000759.MyeloidNeoplasms.v1.p1.c1.GRU-MDS.pdf	Pdf
	datadict_v2.xsl	Other

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Project description:Background: Gene expression profiling has shown its ability to identify with high accuracy low cytogenetic risk acute myeloid leukemia such as acute promyelocytic leukemia and leukemias with t(8;21) or inv(16). The aim of this gene expression profiling study was to evaluate to what extent suboptimal samples with low leukemic blast load (range, 2-59%) and/or poor quality control criteria could also be correctly identified. Methods: Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set). Results: With 10-marker classifiers, all training set samples as well as 97 of the 101 test samples with a low blast load, and all 10 samples with poor quality control criteria were correctly classified. Regarding test set samples, the overall error rate of the class prediction was below 4 percent, even though the leukemic blast load was as low as 2%. Sensitivity, specificity, negative and positive predictive values of the class assignments ranged from 91% to 100%. Of note, for acute promyelocytic leukemia and leukemias with t(8;21) or inv(16), the confidence level of the class assignment was influenced by the leukemic blast load. Conclusion: Gene expression profiling and a supervised method requiring 10-marker classifiers enable the identification of favorable cytogenetic risk acute myeloid leukemia even when samples contain low leukemic blast loads or display poor quality control criterion. Specific signatures were first defined so that all 71 acute promyelocytic leukemia, leukemia with t(8;21) or inv(16)-AML as well as cytogenetically normal acute myeloid leukemia samples with at least 60% blasts and good quality control criteria were correctly classified (training set). The classifiers were then evaluated for their ability to assign to the expected class 111 samples considered as suboptimal because of a low leukemic blast load (n=101) and/or poor quality control criteria (n=10) (test set).

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Spectrum of Mutations in Myeloid Neoplasms

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