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.

Inflammatory bowel disease (IBD) is a chronic intestinal disorder, with two main types: Crohn’s disease (CD) and ulcerative colitis (UC), whose molecular pathology is not well understood. The majority of IBD-associated SNPs are located in non-coding regions and are hard to characterize since regulatory regions in IBD are not known. Here we profile transcription start sites (TSSs) and enhancers in the descending colon of 94 IBD patients and controls. IBD-upregulated promoters and enhancers are highly enriched for IBD-associated SNPs and are bound by the same transcription factors. IBD-specific TSSs are associated to genes with roles in both inflammatory cascades and gut epithelia while TSSs distinguishing UC and CD are associated to gut epithelia functions. We find that as few as 35 TSSs can distinguish active CD, UC, and controls with 85% accuracy in an independent cohort. Our data constitute a foundation for understanding the molecular pathology, gene regulation, and genetics of IBD.

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.

Apoptosis is an essential defensive mechanism against tumorigenesis. Proteins of the B-cell lymphoma-2 (Bcl-2) family regulate programmed cell death by the mitochondrial apoptosis pathway. In response to intracellular stress, the apoptotic balance is governed by interactions of three distinct subgroups of proteins; the activator/sensitizer BH3 (Bcl-2 homology 3)-only proteins, the pro-survival, and the pro-apoptotic executioner proteins. Changes in expression levels, stability, and functional impairment of pro-survival proteins can lead to an imbalance in tissue homeostasis. Their overexpression or hyperactivation can result in oncogenic effects. Pro-survival Bcl-2 family members carry out their function by binding the BH3 short linear motif of pro-apoptotic proteins in a modular way, creating a complex network of protein-protein interactions. Their dysfunction enables cancer cells to evade cell death. The critical role of Bcl-2 proteins in homeostasis and tumorigenesis, coupled with mounting insight in their structural properties, make them therapeutic targets of interest. A better understanding of gene expression, mutational profile, and molecular mechanisms of pro-survival Bcl-2 proteins in different cancer types, could help to clarify their role in cancer development and may guide advancement in drug discovery. Here, we shed light on the pro-survival Bcl-2 proteins in breast cancer using different bioinformatic approaches, linking -omics with structural data. We analyzed the changes in the expression of the Bcl-2 proteins and their BH3-containing interactors in breast cancer samples. We then studied, at the structural level, a selection of interactions, accounting for effects induced by mutations found in the breast cancer samples. We find two complexes between the up-regulated Bcl2A1 and two down-regulated BH3-only candidates (i.e., Hrk and Nr4a1) as targets associated with reduced apoptosis in breast cancer samples for future experimental validation. Furthermore, we predict L99R, M75R as damaging mutations altering protein stability, and Y120C as a possible allosteric mutation from an exposed surface to the BH3-binding site.

Intrinsically disordered proteins (IDPs) are involved in many pivotal cellular processes including phosphorylation and signalling. The structural and functional effects of phosphorylation of IDPs remain poorly understood and difficult to predict. Thus, a need exists to identify motifs that confer phosphorylation-dependent perturbation of the local preferences for forming e.g. helical structures as well as motifs that do not. The disordered distal tail of the Na/H exchanger 1 (NHE1) is six-times phosphorylated (S693, S723, S726, S771, T779, S785) by the mitogen activated protein kinase 2 (MAPK1, ERK2). Using NMR spectroscopy, we found that two out of those six phosphorylation sites had a stabilizing effect on transient helices. One of these was further investigated by circular dichroism and NMR spectroscopy as well as by molecular dynamic simulations, which confirmed the stabilizing effect and resulted in the identification of a short linear motif for helix stabilisation: [S/T]-P-{3}-[R/K] where [S/T] is the phosphorylation-site. By analysing IDP and phosphorylation site databases we found that the motif is significantly enriched around known phosphorylation sites, supporting a potential wider-spread role in phosphorylation-mediated regulation of intrinsically disordered proteins. The identification of such motifs is important for understanding the molecular mechanism of cellular signalling, and is crucial for the development of predictors for the structural effect of phosphorylation; a tool of relevance for understanding disease-promoting mutations that for example interfere with signalling for instance through constitutive active and often cancer-promoting signalling.

Background: Genomic initiatives such as The Cancer Genome Atlas (TCGA) contain data from-omics profiling of thousands of tumor samples, which may be used to decipher cancer signaling, and related alterations. Managing and analyzing data from large-scale projects, such as TCGA, is a demanding task. It is difficult to dissect the high complexity hidden in genomic data and to account for inter-tumor heterogeneity adequately. Methods: In this study, we used a robust statistical framework along with the integration of diverse bioinformatic tools to analyze next-generation sequencing data from more than 1000 patients from two different lung cancer subtypes, i.e., the lung adenocarcinoma (LUAD) and the squamous cell carcinoma (LUSC). Results: We used the gene expression data to identify co-expression modules and differentially expressed genes to discriminate between LUAD and LUSC. We identified a group of genes which could act as specific oncogenes or tumor suppressor genes in one of the two lung cancer types, along with two dual role genes. Our results have been validated against other transcriptomics data of lung cancer patients. Conclusions: Our integrative approach allowed us to identify two key features: a substantial up-regulation of genes involved in O-glycosylation of mucins in LUAD, and a compromised immune response in LUSC. The immune-profile associated with LUSC might be linked to the activation of three oncogenic pathways, which promote the evasion of the antitumor immune response. Collectively, our results provide new future directions for the design of target therapies in lung cancer.

Particular N‐glycan structures are known to be associated with breast malignancies by coordinating various regulatory events within the tumor and corresponding microenvironment, thus implying that N‐glycan patterns may be used for cancer stratification and as predictive or prognostic biomarkers. However, the association between N‐glycans secreted by breast tumor and corresponding clinical relevance remain to be elucidated. We profiled N‐glycans by HILIC UPLC across a discovery dataset composed of tumor interstitial fluids (TIF, n = 85), paired normal interstitial fluids (NIF, n = 54) and serum samples (n = 28) followed by independent evaluation, with the ultimate goal of identifying tumor‐related N‐glycan patterns in blood of patients with breast cancer. The segregation of N‐linked oligosaccharides revealed 33 compositions, which exhibited differential abundances between TIF and NIF. TIFs were depleted of bisecting N‐glycans, which are known to play essential roles in tumor suppression. An increased level of simple high mannose N‐glycans in TIF strongly correlated with the presence of tumor infiltrating lymphocytes within tumor. At the same time, a low level of highly complex N‐glycans in TIF inversely correlated with the presence of infiltrating lymphocytes within tumor. Survival analysis showed that patients exhibiting increased TIF abundance of GP24 had better outcomes, whereas low levels of GP10, GP23, GP38, and coreF were associated with poor prognosis. Levels of GP1, GP8, GP9, GP14, GP23, GP28, GP37, GP38, and coreF were significantly correlated between TIF and paired serum samples. Cross‐validation analysis using an independent serum dataset supported the observed correlation between TIF and serum, for five of nine N‐glycan groups: GP8, GP9, GP14, GP23, and coreF. Collectively, our results imply that profiling of N‐glycans from proximal breast tumor fluids is a promising strategy for determining tumor‐derived glyco‐signature(s) in the blood. N‐glycans structures validated in our study may serve as novel biomarkers to improve the diagnostic and prognostic stratification of patients with breast cancer.

Mutations resulting in amino acid substitution influence the stability of proteins along with their binding to other biomolecules. A molecular understanding of the effects induced by protein mutations are both of biotechnological and medical relevance. The availability of empirical free energy functions that quickly estimate the free energy change upon mutation (ΔΔG) can be exploited for systematic screenings of proteins and protein complexes. Indeed, in silico saturation mutagenesis can guide the design of new experiments or rationalize the consequences of already-known mutations at the atomic level. Often software such as FoldX, while fast and reliable, lack the necessary automation features to make them useful in high-throughput scenarios. Here we introduce MutateX, a software which aims to automate the prediction of ΔΔGs associated with the systematic mutation of each available residue within a protein or protein complex to all other possible residue types, by employing the FoldX energy function. MutateX also supports ΔΔG calculations over protein ensembles and the estimation of the changes in free energy upon post-translational modifications. At the heart of MutateX lies an automated pipeline engine that handles input preparation, performs parallel runs with FoldX and outputs publication-ready figures. We here illustrate the MutateX protocol applied to the study of the mutational landscape of cancer-related proteins, industrial enzymes and protein-protein interfaces. The results of the high-throughput scan provided by our tools could help in different applications, such as the analysis of disease-associated mutations, or in the design of protein variants for experimental studies or industrial applications. MutateX is a collection of Python tools that relies on Open Source libraries and requires the FoldX software to be installed beforehand. It is available free of charge and under the GNU General Public License from https://github.com/ELELAB/mutatex.