Cancer driver gene alterations influence cancer development, occurring in oncogenes, tumor suppressors, and dual role genes. Discovering dual role cancer genes is difficult because of their elusive context-dependent behavior. We define oncogenic mediators as genes controlling biological processes. With them, we classify cancer driver genes, unveiling their roles in cancer mechanisms. To this end, we present Moonlight, a tool that incorporates multiple -omics data to identify critical cancer driver genes. With Moonlight, we analyze 8000+ tumor samples from 18 cancer types, discovering 3310 oncogenic mediators, 151 having dual roles. By incorporating additional data (amplification, mutation, DNA methylation, chromatin accessibility), we reveal 1000+ cancer driver genes, corroborating known molecular mechanisms. Additionally, we confirm critical cancer driver genes by analysing cell-line datasets. We discover inactivation of tumor suppressors in intron regions and that tissue type and subtype indicate dual role status. These findings help explain tumor heterogeneity and could guide therapeutic decisions.

SCAN domains in zinc-finger transcription factors are crucial mediators of protein-protein interactions. Up to 240 SCAN-domain encoding genes have been identified throughout the human genome. These include cancer-related genes, such as the myeloid zinc finger 1 (MZF1), an oncogenic transcription factor involved in the progression of many solid cancers. The mechanisms by which SCAN homo- and heterodimers assemble and how they alter the transcriptional activity of zinc-finger transcription factors in cancer and other diseases remain to be investigated. Here, we provide the first description of the conformational ensemble of the MZF1 SCAN domain cross-validated against NMR experimental data, which are probes of structure and dynamics on different timescales. We investigated the protein-protein interaction network of MZF1 and how it is perturbed in different cancer types by the analyses of high-throughput proteomics and RNASeq data. Collectively, we integrated many computational approaches, ranging from simple empirical energy functions to all-atom microsecond molecular dynamics simulations and network analyses to unravel the effects of cancer-related substitutions in relation to MZF1 structure and interactions.

Shotgun lipidomics is a powerful tool that enables simultaneous and fast quantification of diverse lipid classes through mass spectrometry based analyses of directly infused crude lipid extracts. We present here a shotgun lipidomics platform established to quantify 38 lipid classes belonging to four lipid categories present in mammalian samples and show the fine-tuning and comprehensive evaluation of its experimental parameters and performance. We first determined for all the targeted lipid classes the collision energy levels optimal for the recording of their lipid class- and species-specific fragment ions and fine-tuned the energy levels applied in the platform. We then performed a series of titrations to define the boundaries of linear signal response for the targeted lipid classes, and demonstrated that the dynamic quantification range spanned more than 3 orders of magnitude and reached sub picomole levels for 35 lipid classes. The platform identified 273, 261, and 287 lipid species in brain, plasma, and cultured fibroblast samples, respectively, at the respective optimal working sample amounts. The platform properly quantified the majority of these identified lipid species, while lipid species measured to be below the limit of quantification were efficiently removed from the data sets by the use of statistical analyses of data reproducibility or a cutoff threshold. Finally, we demonstrated that a series of parameters of cell culture conditions influence lipidomics outcomes, including confluency, medium supplements, and use of transfection reagents. The present study provides a guideline for setting up and using a simple and efficient platform for quantitatively exploring the mammalian lipidome.

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.