Post-translational modifications on histone proteins (hPTMs) have long been recognized to be crucial epigenetic regulators that mediate Eukaryote protein expression and thus health and disease. However, their large-scale study has lagged behind compared to targeted epigenomic profiling techniques based on sequencing, such as BS-Seq and ChIPseq. This hampers a mechanistic understanding of this evolutionary conserved regulation mechanism, in turn affecting fields such as the study of disease mechanisms, toxicoepigenetics, drug development and pharmacoepigenetics. Here, I propose to fill this void with the realization of an untargeted mass spectrometry-based assay on histones that matches conventional proteomics workflows both in terms of identification false discovery rate and quantitative accuracy. In particular, I aim to crystalize the data-independent acquisition (DIA) workflow which was optimized in our lab over the past years (hSWATH) and to apply it for the first time to predict outcome of epidrug treatment in T-cell Acute Lymphoblastic Leukemia (T-ALL). To do this, I will (i) optimize the data quality and data analysis of the workflow, (ii) apply this on 20 different T-cell lines to create a histone library, (iii) treat these cell lines with an epidrug library and monitor resistance. By (iv) matching the initial histone fingerprints of resistant versus sensitive cell lines, a predictive pattern will arise that can (v) be used on patient samples to adjust treatment.