Summary Current therapies for medulloblastoma (MB), a highly malignant childhood brain tumor, impose debilitating effects on the developing child, warranting deployment of molecularly targeted treatments with reduced toxicities. Prior studies failed to disclose the full spectrum of driver genes and molecular processes operative in MB subgroups. Herein, we detail the somatic landscape across 491 sequenced MBs and molecular heterogeneity amongst 1,256 epigenetically analyzed cases, identifying subgroup-specific driver alterations including previously unappreciated actionable targets. Driver mutations explained the majority of Group 3 and Group 4 patients, remarkably enhancing previous knowledge. Novel molecular subtypes were differentially enriched for specific driver events, including hotspot in-frame insertions targeting KBTBD4 and ‘enhancer hijacking’ driving PRDM6 activation. Thus, application of integrative genomics to an unprecedented cohort of clinical samples derived from a single childhood cancer entity disclosed a series of new cancer genes and biologically relevant subtype diversity that represent attractive therapeutic targets for treating MB patients.

The data-independent acquisition (DIA) approach has recently been introduced as a novel mass spectrometric method that promises to combine the high content aspect of shotgun proteomics with the reproducibility and precision of selected reaction monitoring. Here, we evaluate, whether SWATH-MS type DIA effectively translates into a better protein profiling as compared with the established shotgun proteomics.We implemented a novel DIA method on the widely used Orbitrap platform and used retention-time-normalized (iRT) spectral libraries for targeted data extraction using Spectronaut. We call this combination hyper reaction monitoring (HRM). Using a controlled sample set, we show that HRM outperformed shotgun proteomics both in the number of consistently identified peptides across multiple measurements and quantification of differentially abundant proteins. The reproducibility of HRM in peptide detection was above 98%, resulting in quasi complete data sets compared with 49% of shotgun proteomics.Utilizing HRM, we profiled acetaminophen (APAP)1-treated three-dimensional human liver microtissues. An early onset of relevant proteome changes was revealed at subtoxic doses of APAP. Further, we detected and quantified for the first time human NAPQI-protein adducts that might be relevant for the toxicity of APAP. The adducts were identified on four mitochondrial oxidative stress related proteins (GATM, PARK7, PRDX6, and VDAC2) and two other proteins (ANXA2 and FTCD).Our findings imply that DIA should be the preferred method for quantitative protein profiling.

There is a pressing need to identify therapeutic targets in tumors with low mutation rates such as the malignant pediatric brain tumor medulloblastoma. To address this challenge, we quantitatively profiled global proteomes and phospho-proteomes of 45 medulloblastoma samples. Integrated analyses revealed that tumors with similar RNA expression vary extensively at the post-transcriptional and post-translational levels. We identified distinct pathways associated with two subsets of SHH tumors, and found post-translational modifications of MYC that are associated with poor outcomes in group 3 tumors. We found kinases associated with subtypes and showed that inhibiting PRKDC sensitizes MYC-driven cells to radiation. Our study shows that proteomics enables a more comprehensive, functional readout, providing a foundation for future therapeutic strategies.

The timing and localization of events during mitosis is controlled by the regulated phosphorylation of proteins by the mitotic kinases, which include Aurora A, Aurora B, Nek2, Plk1, and the cyclin-dependent kinase complex Cdk1/cyclin B. Although mitotic kinases can have overlapping subcellular localizations, each kinase appears to phosphorylate its substrates on distinct sites. To gain insight into the relative importance of local sequence context in kinase selectivity, identify previously unknown substrates of these five mitotic kinases, and explore potential mechanisms for substrate discrimination, we determined the optimal substrate motifs of these major mitotic kinases by Positional Scanning Oriented Peptide Library Screening (PS-OPLS). We verified individual motifs with in vitro peptide kinetic studies and used structural modeling to rationalize the kinase-specific selection of key motif-determining residues at the molecular level. Cross comparisons among the phosphorylation site selectivity motifs of these kinases revealed an evolutionarily conserved mutual exclusion mechanism in which the positively and negatively selected portions of the phosphorylation motifs of mitotic kinases, together with their subcellular localizations, result in proper substrate targeting in a coordinated manner during mitosis.

Answer ALS is a biological and clinical resource of patient-derived, induced pluripotent stem (iPS) cell lines, multi-omic data derived from iPS neurons and longitudinal clinical and smartphone data from over 1,000 patients with ALS. This resource provides population-level biological and clinical data that may be employed to identify clinical–molecular–biochemical subtypes of amyotrophic lateral sclerosis (ALS). A unique smartphone-based system was employed to collect deep clinical data, including fine motor activity, speech, breathing and linguistics/cognition. The iPS spinal neurons were blood derived from each patient and these cells underwent multi-omic analytics including whole-genome sequencing, RNA transcriptomics, ATAC-sequencing and proteomics. The intent of these data is for the generation of integrated clinical and biological signatures using bioinformatics, statistics and computational biology to establish patterns that may lead to a better understanding of the underlying mechanisms of disease, including subgroup identification. A web portal for open-source sharing of all data was developed for widespread community-based data analytics.