De Maesschalck, A. | bioRxiv Science

bioRxiv2026

Focus on the edges: a biomolecular network of histone PTMs, metabolites and proteins unveils functional entanglement in AML

The cell phenotype is not a direct manifestation of the genotype but rather a product of cellular history and the environmental context. However, individual biomolecules cannot change independently and show coordinated behavior. To study this in acute myeloid leukemia (AML), we built a unique multi-omics biomolecular network made from proteins, metabolites and histone posttranslational modifications (hPTMs) sequentially extracted from each cell pellet. Edges between the nodes are measured directly using 400 LC-MSMS runs that cover 18 AML cell lines. We provide a novel conceptual framework to illustrate the different classes of functional entanglement between and within omics layers and present the data in three interactive data browsers to allow full community access. To help navigate the network, we approach it from the perspective of two biomolecular targets, i.e. CD34 and the epigenetic mark Histone H3 lysine 27 trimethylation (H3K27me3). Now, this easily accessible biomolecular network serves as a starting point for building and testing hypotheses and streamlining drug development, in the process positioning biomolecular associations center stage in understanding phenotypic complexity.

cancer biology↗

bioRxiv2025

Profiling histone post-translational modifications to identify signatures of epigenetic drug response in T-cell acute lymphoblastic leukemia

Epigenetic modifications are dynamic and reversible, making them attractive targets for therapeutic intervention in cancer. Although several epigenetic drugs (epidrugs) have been clinically approved, their application in T-cell acute lymphoblastic leukemia (T-ALL) remains limited, and predictive biomarkers of response are lacking. Here, we present a mass spectrometry (MS)-based pharmacoepigenetic approach to profile histone post-translational modifications (hPTMs) to identify signatures associated with epidrug sensitivity in T-ALL. Baseline hPTM landscapes were previously established by our group for 21 T-ALL cell lines using liquid chromatography-tandem mass spectrometry (LC-MS/MS). Here, we treated these cell lines with a panel of nine epidrugs including anthracyclines, histone deacetylase inhibitors, and DNA methyltransferase inhibitors. Correlation of cell viability data with hPTM levels revealed distinct hPTM signatures linked to sensitivity for each drug class. These signatures were subsequently evaluated in T-ALL patient-derived xenograft (PDX) models. However, we our analysis revealed substantial discepancies in hPTM sensitivity signatures compared to those observed in vitro. Co-variation network analysis highlighted divergence in hPTM-hPTM correlation between the two models, underscoring limitations of cell lines for modeling dynamic epigenetic regulation in vivo. Our findings establish a framework for MS-based hPTM profiling in T-ALL and emphasize the importance of model selection in developing predictive epigenetic biomarkers. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=134 SRC="FIGDIR/small/673463v1_ufig1.gif" ALT="Figure 1"> View larger version (32K): org.highwire.dtl.DTLVardef@1b15092org.highwire.dtl.DTLVardef@20bed5org.highwire.dtl.DTLVardef@1d0a687org.highwire.dtl.DTLVardef@1652999_HPS_FORMAT_FIGEXP M_FIG C_FIG Global hPTM profiling of 21 T-ALL cell lines was performed using LC-MS/MS, as previously published by Provez et al. In parallel, the 21 cell lines were treated with a dilution series of nine epidrugs, categorized into three distinct classes, to determine their IC50 values. Finally, Spearman correlation analysis was performed to assess the relationship between hPTM levels and drug sensitivity. Figure created with Biorender.com.

cancer biology↗