Dataset Information

4D label-free quantitative proteomics of 32 leukemia patient samples

ABSTRACT: Early T-cell precursor acute lymphoblastic leukemia (ETP-ALL) is a high-risk T-ALL subtype with distinct immunophenotypes and genetic alterations, complicating diagnosis and treatment. Although cytogenetic and gene expression studies have advanced the diagnostic and therapeutic strategies, the proteomics signatures of ETP-ALL remain underexplored. Herein, we performed 4D label-free proteomic analyses on mononuclear cells isolated from ETP-ALL, T-ALL, acute myeloid leukemia (AML), and healthy donor samples to identify molecular signatures specific to ETP-ALL.

INSTRUMENT(S):

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Mononuclear Cell Of Bone Marrow, Bone Marrow

DISEASE(S): Acute Leukemia

SUBMITTER: Feng Xian

LAB HEAD: Yinghui Zhu

PROVIDER: PXD069989 | Pride | 2025-12-30

REPOSITORIES: Pride

ACCESS DATA

Dataset's files

Source:

			Action	DRS
	Search.zip	Other
	XA02052B2LQ_S1_1_Slot2-1_1_10372.d.zip	Other
	XA02052B2LQ_S1_3_Slot2-2_1_10373.d.zip	Other
	XA02052B2LQ_S1_4_Slot2-3_1_10374.d.zip	Other
	XA02052B2LQ_S1_5_Slot2-4_1_10376.d.zip	Other

Items per page:

1 - 5 of 34

Similar Datasets

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).

Dataset Information

4D label-free quantitative proteomics of 32 leukemia patient samples

Dataset's files

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets