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@ARTICLE{Schmutz:285002,
      author       = {M. Schmutz$^*$ and M. Zucknick$^*$ and R. F. Schlenk and D.
                      Mertens$^*$ and A. Benner$^*$ and D. Weichenhan$^*$ and O.
                      Mücke$^*$ and K. Döhner and C. Plass$^*$ and L.
                      Bullinger$^*$ and R. Claus$^*$},
      title        = {{P}redictive value of {DNA} methylation patterns in {AML}
                      patients treated with an azacytidine containing induction
                      regimen.},
      journal      = {Clinical epigenetics},
      volume       = {15},
      number       = {1},
      issn         = {1868-7075},
      address      = {[Erscheinungsort nicht ermittelbar]},
      publisher    = {BioMed Central},
      reportid     = {DKFZ-2023-02175},
      pages        = {171},
      year         = {2023},
      note         = {#EA:B370#LA:B370#},
      abstract     = {Acute myeloid leukemia (AML) is a heterogeneous disease
                      with a poor prognosis. Dysregulation of the epigenetic
                      machinery is a significant contributor to disease
                      development. Some AML patients benefit from treatment with
                      hypomethylating agents (HMAs), but no predictive biomarkers
                      for therapy response exist. Here, we investigated whether
                      unbiased genome-wide assessment of pre-treatment
                      DNA-methylation profiles in AML bone marrow blasts can help
                      to identify patients who will achieve a remission after an
                      azacytidine-containing induction regimen.A total of n = 155
                      patients with newly diagnosed AML treated in the AMLSG 12-09
                      trial were randomly assigned to a screening and a refinement
                      and validation cohort. The cohorts were divided according to
                      azacytidine-containing induction regimens and response
                      status. Methylation status was assessed for 664,227
                      500-bp-regions using methyl-CpG immunoprecipitation-seq,
                      resulting in 1755 differentially methylated regions (DMRs).
                      Top regions were distilled and included genes such as WNT10A
                      and GATA3. $80\%$ of regions identified as a hit were
                      represented on HumanMethlyation 450k Bead Chips.
                      Quantitative methylation analysis confirmed $90\%$ of these
                      regions (36 of 40 DMRs). A classifier was trained using
                      penalized logistic regression and fivefold cross validation
                      containing 17 CpGs. Validation based on mass spectra
                      generated by MALDI-TOF failed (AUC 0.59). However,
                      discriminative ability was maintained by adding neighboring
                      CpGs. A recomposed classifier with 12 CpGs resulted in an
                      AUC of 0.77. When evaluated in the non-azacytidine
                      containing group, the AUC was 0.76.Our analysis evaluated
                      the value of a whole genome methyl-CpG screening assay for
                      the identification of informative methylation changes. We
                      also compared the informative content and discriminatory
                      power of regions and single CpGs for predicting response to
                      therapy. The relevance of the identified DMRs is supported
                      by their association with key regulatory processes of
                      oncogenic transformation and support the idea of relevant
                      DMRs being enriched at distinct loci rather than evenly
                      distribution across the genome. Predictive response to
                      therapy could be established but lacked specificity for
                      treatment with azacytidine. Our results suggest that a
                      predictive epigenotype carries its methylation information
                      at a complex, genome-wide level, that is confined to
                      regions, rather than to single CpGs. With increasing
                      application of combinatorial regimens, response prediction
                      may become even more complicated.},
      keywords     = {AML (Other) / Azacytidine (Other) / DNA methylation
                      patterns (Other) / DNA-methylation (Other) / Epigenetics
                      (Other) / HMA-treatment (Other) / Predictive biomarker
                      (Other) / Predictive signature (Other)},
      cin          = {B370 / W010 / B061 / C060 / BE01},
      ddc          = {610},
      cid          = {I:(DE-He78)B370-20160331 / I:(DE-He78)W010-20160331 /
                      I:(DE-He78)B061-20160331 / I:(DE-He78)C060-20160331 /
                      I:(DE-He78)BE01-20160331},
      pnm          = {312 - Funktionelle und strukturelle Genomforschung
                      (POF4-312)},
      pid          = {G:(DE-HGF)POF4-312},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:37885041},
      doi          = {10.1186/s13148-023-01580-z},
      url          = {https://inrepo02.dkfz.de/record/285002},
}