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| 001 | 285002 | ||
| 005 | 20240229155059.0 | ||
| 024 | 7 | _ | |a 10.1186/s13148-023-01580-z |2 doi |
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| 037 | _ | _ | |a DKFZ-2023-02175 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 610 |
| 100 | 1 | _ | |a Schmutz, Maximilian |0 P:(DE-He78)0a8e51582529e502d5e1aa688679ded5 |b 0 |e First author |
| 245 | _ | _ | |a Predictive value of DNA methylation patterns in AML patients treated with an azacytidine containing induction regimen. |
| 260 | _ | _ | |a [Erscheinungsort nicht ermittelbar] |c 2023 |b BioMed Central |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1698412438_6647 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
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| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 500 | _ | _ | |a #EA:B370#LA:B370# |
| 520 | _ | _ | |a 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. |
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| 650 | _ | 7 | |a AML |2 Other |
| 650 | _ | 7 | |a Azacytidine |2 Other |
| 650 | _ | 7 | |a DNA methylation patterns |2 Other |
| 650 | _ | 7 | |a DNA-methylation |2 Other |
| 650 | _ | 7 | |a Epigenetics |2 Other |
| 650 | _ | 7 | |a HMA-treatment |2 Other |
| 650 | _ | 7 | |a Predictive biomarker |2 Other |
| 650 | _ | 7 | |a Predictive signature |2 Other |
| 700 | 1 | _ | |a Zucknick, Manuela |0 P:(DE-HGF)0 |b 1 |
| 700 | 1 | _ | |a Schlenk, Richard F |b 2 |
| 700 | 1 | _ | |a Mertens, Daniel |0 P:(DE-He78)833c06a995d272b78f3a20df3eba6e9e |b 3 |u dkfz |
| 700 | 1 | _ | |a Benner, Axel |0 P:(DE-He78)e15dfa1260625c69d6690a197392a994 |b 4 |u dkfz |
| 700 | 1 | _ | |a Weichenhan, Dieter |0 P:(DE-He78)ff4024f7bc236e7897d9c18ee19c451f |b 5 |u dkfz |
| 700 | 1 | _ | |a Mücke, Oliver |0 P:(DE-He78)aaef108f2a1ba0a78fff60ac60d3368c |b 6 |u dkfz |
| 700 | 1 | _ | |a Döhner, Konstanze |b 7 |
| 700 | 1 | _ | |a Plass, Christoph |0 P:(DE-He78)4301875630bc997edf491c694ae1f8a9 |b 8 |u dkfz |
| 700 | 1 | _ | |a Bullinger, Lars |0 P:(DE-HGF)0 |b 9 |
| 700 | 1 | _ | |a Claus, Rainer |0 P:(DE-HGF)0 |b 10 |e Last author |
| 773 | _ | _ | |a 10.1186/s13148-023-01580-z |g Vol. 15, no. 1, p. 171 |0 PERI:(DE-600)2553921-8 |n 1 |p 171 |t Clinical epigenetics |v 15 |y 2023 |x 1868-7075 |
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