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All Supplementary Data from Recognition of Recurrent Protein Expression Patterns in Pediatric Acute Myeloid Leukemia Identified New Therapeutic Targets

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posted on 2023-04-03, 16:46 authored by Fieke W. Hoff, Chenyue W. Hu, Yihua Qiu, Andrew Ligeralde, Suk-Young Yoo, Hasan Mahmud, Eveline S.J.M. de Bont, Amina A. Qutub, Terzah M. Horton, Steven M. Kornblau

Supplementary Table S1. Treatment protocols pediatric AML patients. Supplemental Table S2: Patient characteristics for the 73 pediatric acute lymphoblastic leukemia patients Supplementary Table S3. This table contains the "Rosetta Stone" for the antibody and protein nomenclature (HUGO, MiMI, GeneCards) together with the RPPA staining details. Supplementary Table S4. Protein membership for the 31 defined protein functional groups. Supplementary Table S5. AML protein constellation membership for the 136 protein clusters. Supplementary Table S6. This table shows the univariate and multivariate Cox Proportional Hazard Model Supplementary Table S7. The combined ALL and AML protein constellation membership for the 142 protein clusters. Supplementary Table S8. Protein cluster membership for the leukemic cell lines. Supplementary Figure S1: The co-occurrence probability matrices for the ''Hypoxia'' PFG and the PFG ''CREB''. Supplementary Fig. S2. Principal component analysis for the 95 pediatric acute myeloid leukemia samples stratified by source; peripheral blood (PB) samples or bone marrow samples (BM). Supplementary Figure S3: Unsupervised hierarchical clustering for all 95 AML patient samples. Supplementary Figure S4. The minimal set of 30 antibodies that enable classification into the defined protein expression signatures. Supplementary Figure S5. (A) Unsupervised hierarchical clustering (Ward linkage) for all mycoplasma negative cell lines samples (N=95) and the 95 acute myeloid leukemia patient samples.

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Takeda/Millennium

Foundation for Pediatric Oncology Groningen, the Netherlands

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ARTICLE ABSTRACT

Heterogeneity in the genetic landscape of pediatric acute myeloid leukemia (AML) makes personalized medicine challenging. As genetic events are mediated by the expression and function of proteins, recognition of recurrent protein patterns could enable classification of pediatric AML patients and could reveal crucial protein dependencies. This could help to rationally select combinations of therapeutic targets. To determine whether protein expression levels could be clustered into functionally relevant groups, custom reverse-phase protein arrays were performed on pediatric AML (n = 95) and CD34+ normal bone marrow (n = 10) clinical specimens using 194 validated antibodies. To analyze proteins in the context of other proteins, all proteins were assembled into 31 protein functional groups (PFG). For each PFG, an optimal number of protein clusters was defined that represented distinct transition states. Block clustering analysis revealed strong correlations between various protein clusters and identified the existence of 12 protein constellations stratifying patients into 8 protein signatures. Signatures were correlated with therapeutic outcome, as well as certain laboratory and demographic characteristics. Comparison of acute lymphoblastic leukemia specimens from the same array and AML pediatric patient specimens demonstrated disease-specific signatures, but also identified the existence of shared constellations, suggesting joint protein deregulation between the diseases.Implication: Recognition of altered proteins in particular signatures suggests rational combinations of targets that could facilitate stratified targeted therapy. Mol Cancer Res; 16(8); 1275–86. ©2018 AACR.See related article by Hoff et al., p. 1263

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