Mutational Strand Asymmetries in Cancer Genomes Reveal Mechanisms of DNA Damage and Repair.

Mutational processes constantly shape the somatic genome, leading to immunity, aging, cancer, and other diseases. When cancer is the outcome, we are afforded a glimpse into these processes by the clonal expansion of the malignant cell. Here, we characterize a less explored layer of the mutational landscape of cancer: mutational asymmetries between the two DNA strands. Analyzing whole-genome sequences of 590 tumors from 14 different cancer types, we reveal widespread asymmetries across mutagenic processes, with transcriptional ("T-class") asymmetry dominating UV-, smoking-, and liver-cancer-associated mutations and replicative ("R-class") asymmetry dominating POLE-, APOBEC-, and MSI-associated mutations. We report a striking phenomenon of transcription-coupled damage (TCD) on the non-transcribed DNA strand and provide evidence that APOBEC mutagenesis occurs on the lagging-strand template during DNA replication. As more genomes are sequenced, studying and classifying their asymmetries will illuminate the underlying biological mechanisms of DNA damage and repair.

Widespread macromolecular interaction perturbations in human genetic disorders.

How disease-associated mutations impair protein activities in the context of biological networks remains mostly undetermined. Although a few renowned alleles are well characterized, functional information is missing for over 100,000 disease-associated variants. Here we functionally profile several thousand missense mutations across a spectrum of Mendelian disorders using various interaction assays. The majority of disease-associated alleles exhibit wild-type chaperone binding profiles, suggesting they preserve protein folding or stability. While common variants from healthy individuals rarely affect interactions, two-thirds of disease-associated alleles perturb protein-protein interactions, with half corresponding to "edgetic" alleles affecting only a subset of interactions while leaving most other interactions unperturbed. With transcription factors, many alleles that leave protein-protein interactions intact affect DNA binding. Different mutations in the same gene leading to different interaction profiles often result in distinct disease phenotypes. Thus disease-associated alleles that perturb distinct protein activities rather than grossly affecting folding and stability are relatively widespread.

A proteome-scale map of the human interactome network.

Just as reference genome sequences revolutionized human genetics, reference maps of interactome networks will be critical to fully understand genotype-phenotype relationships. Here, we describe a systematic map of ?14,000 high-quality human binary protein-protein interactions. At equal quality, this map is ?30% larger than what is available from small-scale studies published in the literature in the last few decades. While currently available information is highly biased and only covers a relatively small portion of the proteome, our systematic map appears strikingly more homogeneous, revealing a "broader" human interactome network than currently appreciated. The map also uncovers significant interconnectivity between known and candidate cancer gene products, providing unbiased evidence for an expanded functional cancer landscape, while demonstrating how high-quality interactome models will help "connect the dots" of the genomic revolution.

An atlas of combinatorial transcriptional regulation in mouse and man.

Combinatorial interactions among transcription factors are critical to directing tissue-specific gene expression. To build a global atlas of these combinations, we have screened for physical interactions among the majority of human and mouse DNA-binding transcription factors (TFs). The complete networks contain 762 human and 877 mouse interactions. Analysis of the networks reveals that highly connected TFs are broadly expressed across tissues, and that roughly half of the measured interactions are conserved between mouse and human. The data highlight the importance of TF combinations for determining cell fate, and they lead to the identification of a SMAD3/FLI1 complex expressed during development of immunity. The availability of large TF combinatorial networks in both human and mouse will provide many opportunities to study gene regulation, tissue differentiation, and mammalian evolution.

ConsensusPathDB 2022: molecular interactions update as a resource for network biology.

Molecular interactions are key drivers of biological function. Providing interaction resources to the research community is important since they allow functional interpretation and network-based analysis of molecular data. ConsensusPathDB (http://consensuspathdb.org) is a meta-database combining interactions of diverse types from 31 public resources for humans, 16 for mice and 14 for yeasts. Using ConsensusPathDB, researchers commonly evaluate lists of genes, proteins and metabolites against sets of molecular interactions defined by pathways, Gene Ontology and network neighborhoods and retrieve complex molecular neighborhoods formed by heterogeneous interaction types. Furthermore, the integrated protein-protein interaction network is used as a basis for propagation methods. Here, we present the 2022 update of ConsensusPathDB, highlighting content growth, additional functionality and improved database stability. For example, the number of human molecular interactions increased to 859 848 connecting 200 499 unique physical entities such as genes/proteins, metabolites and drugs. Furthermore, we integrated regulatory datasets in the form of transcription factor-, microRNA- and enhancer-gene target interactions, thus providing novel functionality in the context of overrepresentation and enrichment analyses. We specifically emphasize the use of the integrated protein-protein interaction network as a scaffold for network inferences, present topological characteristics of the network and discuss strengths and shortcomings of such approaches.

NetSig: network-based discovery from cancer genomes.

Methods that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes; but such approaches are challenging to validate at scale, and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks with data from 4,742 tumor exomes. NetSig can accurately classify known driver genes in 60% of tested tumor types and predicts 62 new driver candidates. Using a quantitative experimental framework to determine in vivo tumorigenic potential in mice, we found that NetSig candidates induce tumors at rates that are comparable to those of known oncogenes and are ten-fold higher than those of random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Our study presents a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

Genomic analyses of PMBL reveal new drivers and mechanisms of sensitivity to PD-1 blockade.

Primary mediastinal large B-cell lymphomas (PMBLs) are aggressive tumors that typically present as large mediastinal masses in young women. PMBLs share clinical, transcriptional, and molecular features with classical Hodgkin lymphoma (cHL), including constitutive activation of nuclear factor κB (NF-κB), JAK/STAT signaling, and programmed cell death protein 1 (PD-1)-mediated immune evasion. The demonstrated efficacy of PD-1 blockade in relapsed/refractory PMBLs led to recent approval by the US Food and Drug Administration and underscored the importance of characterizing targetable genetic vulnerabilities in this disease. Here, we report a comprehensive analysis of recurrent genetic alterations -somatic mutations, somatic copy number alterations, and structural variants-in a cohort of 37 newly diagnosed PMBLs. We identified a median of 9 genetic drivers per PMBL, including known and newly identified components of the JAK/STAT and NF-κB signaling pathways and frequent B2M alterations that limit major histocompatibility complex class I expression, as in cHL. PMBL also exhibited frequent, newly identified driver mutations in ZNF217 and an additional epigenetic modifier, EZH2. The majority of these alterations were clonal, which supports their role as early drivers. In PMBL, we identified several previously uncharacterized molecular features that may increase sensitivity to PD-1 blockade, including high tumor mutational burden, microsatellite instability, and an apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutational signature. The shared genetic features between PMBL and cHL provide a framework for analyzing the mechanism of action of PD-1 blockade in these related lymphoid malignancies.

Comparison of algorithms for the detection of cancer drivers at subgene resolution.

Understanding genetic events that lead to cancer initiation and progression remains one of the biggest challenges in cancer biology. Traditionally, most algorithms for cancer-driver identification look for genes that have more mutations than expected from the average background mutation rate. However, there is now a wide variety of methods that look for nonrandom distribution of mutations within proteins as a signal for the driving role of mutations in cancer. Here we classify and review such subgene-resolution algorithms, compare their findings on four distinct cancer data sets from The Cancer Genome Atlas and discuss how predictions from these algorithms can be interpreted in the emerging paradigms that challenge the simple dichotomy between driver and passenger genes.

Dynamic protein-protein interaction wiring of the human spliceosome.

More than 200 proteins copurify with spliceosomes, the compositionally dynamic RNPs catalyzing pre-mRNA splicing. To better understand protein - protein interactions governing splicing, we systematically investigated interactions between human spliceosomal proteins. A comprehensive Y2H interaction matrix screen generated a protein interaction map comprising 632 interactions between 196 proteins. Among these, 242 interactions were found between spliceosomal core proteins and largely validated by coimmunoprecipitation. To reveal dynamic changes in protein interactions, we integrated spliceosomal complex purification information with our interaction data and performed link clustering. These data, together with interaction competition experiments, suggest that during step 1 of splicing, hPRP8 interactions with SF3b proteins are replaced by hSLU7, positioning this second step factor close to the active site, and that the DEAH-box helicases hPRP2 and hPRP16 cooperate through ordered interactions with GPKOW. Our data provide extensive information about the spliceosomal protein interaction network and its dynamics.

Comprehensive assessment of cancer missense mutation clustering in protein structures.

Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg2+, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.

The ConsensusPathDB interaction database: 2013 update.

Knowledge of the various interactions between molecules in the cell is crucial for understanding cellular processes in health and disease. Currently available interaction databases, being largely complementary to each other, must be integrated to obtain a comprehensive global map of the different types of interactions. We have previously reported the development of an integrative interaction database called ConsensusPathDB (http://ConsensusPathDB.org) that aims to fulfill this task. In this update article, we report its significant progress in terms of interaction content and web interface tools. ConsensusPathDB has grown mainly due to the integration of 12 further databases; it now contains 215 541 unique interactions and 4601 pathways from overall 30 databases. Binary protein interactions are scored with our confidence assessment tool, IntScore. The ConsensusPathDB web interface allows users to take advantage of these integrated interaction and pathway data in different contexts. Recent developments include pathway analysis of metabolite lists, visualization of functional gene/metabolite sets as overlap graphs, gene set analysis based on protein complexes and induced network modules analysis that connects a list of genes through various interaction types. To facilitate the interactive, visual interpretation of interaction and pathway data, we have re-implemented the graph visualization feature of ConsensusPathDB using the Cytoscape.js library.

Dual coordination of post translational modifications in human protein networks.

Post-translational modifications (PTMs) regulate protein activity, stability and interaction profiles and are critical for cellular functioning. Further regulation is gained through PTM interplay whereby modifications modulate the occurrence of other PTMs or act in combination. Integration of global acetylation, ubiquitination and tyrosine or serine/threonine phosphorylation datasets with protein interaction data identified hundreds of protein complexes that selectively accumulate each PTM, indicating coordinated targeting of specific molecular functions. A second layer of PTM coordination exists in these complexes, mediated by PTM integration (PTMi) spots. PTMi spots represent very dense modification patterns in disordered protein regions and showed an equally high mutation rate as functional protein domains in cancer, inferring equivocal importance for cellular functioning. Systematic PTMi spot identification highlighted more than 300 candidate proteins for combinatorial PTM regulation. This study reveals two global PTM coordination mechanisms and emphasizes dataset integration as requisite in proteomic PTM studies to better predict modification impact on cellular signaling.

IntScore: a web tool for confidence scoring of biological interactions.

Knowledge of all molecular interactions that potentially take place in the cell is a key for a detailed understanding of cellular processes. Currently available interaction data, such as protein-protein interaction maps, are known to contain false positives that inevitably diminish the accuracy of network-based inferences. Interaction confidence scoring is thus a crucial intermediate step after obtaining interaction data and before using it in an interaction network-based inference approach. It enables to weight individual interactions according to the likelihood that they actually take place in the cell, and can be used to filter out false positives. We describe a web tool called IntScore which calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore is user-friendly and extensively documented. It is freely available at http://intscore.molgen.mpg.de.

Discovery of YAP1/TAZ pathway inhibitors through phenotypic screening with potent anti-tumor activity via blockade of Rho-GTPase signaling.

This study describes the identification and target deconvolution of small molecule inhibitors of oncogenic Yes-associated protein (YAP1)/TAZ activity with potent anti-tumor activity in vivo. A high-throughput screen (HTS) of 3.8 million compounds was conducted using a cellular YAP1/TAZ reporter assay. Target deconvolution studies identified the geranylgeranyltransferase-I (GGTase-I) complex as the direct target of YAP1/TAZ pathway inhibitors. The small molecule inhibitors block the activation of Rho-GTPases, leading to subsequent inactivation of YAP1/TAZ and inhibition of cancer cell proliferation in vitro. Multi-parameter optimization resulted in BAY-593, an in vivo probe with favorable PK properties, which demonstrated anti-tumor activity and blockade of YAP1/TAZ signaling in vivo.

ConsensusPathDB: toward a more complete picture of cell biology.

ConsensusPathDB is a meta-database that integrates different types of functional interactions from heterogeneous interaction data resources. Physical protein interactions, metabolic and signaling reactions and gene regulatory interactions are integrated in a seamless functional association network that simultaneously describes multiple functional aspects of genes, proteins, complexes, metabolites, etc. With 155,432 human, 194,480 yeast and 13,648 mouse complex functional interactions (originating from 18 databases on human and eight databases on yeast and mouse interactions each), ConsensusPathDB currently constitutes the most comprehensive publicly available interaction repository for these species. The Web interface at http://cpdb.molgen.mpg.de offers different ways of utilizing these integrated interaction data, in particular with tools for visualization, analysis and interpretation of high-throughput expression data in the light of functional interactions and biological pathways.

Cluster-based assessment of protein-protein interaction confidence.

BACKGROUND: Protein-protein interaction networks are key to a systems-level understanding of cellular biology. However, interaction data can contain a considerable fraction of false positives. Several methods have been proposed to assess the confidence of individual interactions. Most of them require the integration of additional data like protein expression and interaction homology information. While being certainly useful, such additional data are not always available and may introduce additional bias and ambiguity. RESULTS: We propose a novel, network topology based interaction confidence assessment method called CAPPIC (cluster-based assessment of protein-protein interaction confidence). It exploits the network's inherent modular architecture for assessing the confidence of individual interactions. Our method determines algorithmic parameters intrinsically and does not require any parameter input or reference sets for confidence scoring. CONCLUSIONS: On the basis of five yeast and two human physical interactome maps inferred using different techniques, we show that CAPPIC reliably assesses interaction confidence and its performance compares well to other approaches that are also based on network topology. The confidence score correlates with the agreement in localization and biological process annotations of interacting proteins. Moreover, it corroborates experimental evidence of physical interactions. Our method is not limited to physical interactome maps as we exemplify with a large yeast genetic interaction network. An implementation of CAPPIC is available at http://intscore.molgen.mpg.de.

DIPSBC--data integration platform for systems biology collaborations.

BACKGROUND: Modern biomedical research is often organized in collaborations involving labs worldwide. In particular in systems biology, complex molecular systems are analyzed that require the generation and interpretation of heterogeneous data for their explanation, for example ranging from gene expression studies and mass spectrometry measurements to experimental techniques for detecting molecular interactions and functional assays. XML has become the most prominent format for representing and exchanging these data. However, besides the development of standards there is still a fundamental lack of data integration systems that are able to utilize these exchange formats, organize the data in an integrative way and link it with applications for data interpretation and analysis. RESULTS: We have developed DIPSBC, an interactive data integration platform supporting collaborative research projects, based on Foswiki, Solr/Lucene, and specific helper applications. We describe the main features of the implementation and highlight the performance of the system with several use cases. All components of the system are platform independent and open-source developments and thus can be easily adopted by researchers. An exemplary installation of the platform which also provides several helper applications and detailed instructions for system usage and setup is available at http://dipsbc.molgen.mpg.de. CONCLUSIONS: DIPSBC is a data integration platform for medium-scale collaboration projects that has been tested already within several research collaborations. Because of its modular design and the incorporation of XML data formats it is highly flexible and easy to use.

Integrated pathway-level analysis of transcriptomics and metabolomics data with IMPaLA.

SUMMARY: Pathway-level analysis is a powerful approach enabling interpretation of post-genomic data at a higher level than that of individual biomolecules. Yet, it is currently hard to integrate more than one type of omics data in such an approach. Here, we present a web tool 'IMPaLA' for the joint pathway analysis of transcriptomics or proteomics and metabolomics data. It performs over-representation or enrichment analysis with user-specified lists of metabolites and genes using over 3000 pre-annotated pathways from 11 databases. As a result, pathways can be identified that may be disregulated on the transcriptional level, the metabolic level or both. Evidence of pathway disregulation is combined, allowing for the identification of additional pathways with changed activity that would not be highlighted when analysis is applied to any of the functional levels alone. The tool has been implemented both as an interactive website and as a web service to allow a programming interface. AVAILABILITY: The web interface of IMPaLA is available at http://impala.molgen.mpg.de. A web services programming interface is provided at http://impala.molgen.mpg.de/wsdoc. CONTACT: kamburov@molgen.mpg.de; r.cavill@imperial.ac.uk; h.keun@imperial.ac.uk SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells.

Using transcriptomic and metabolomic measurements from the NCI60 cell line panel, together with a novel approach to integration of molecular profile data, we show that the biochemical pathways associated with tumour cell chemosensitivity to platinum-based drugs are highly coincident, i.e. they describe a consensus phenotype. Direct integration of metabolome and transcriptome data at the point of pathway analysis improved the detection of consensus pathways by 76%, and revealed associations between platinum sensitivity and several metabolic pathways that were not visible from transcriptome analysis alone. These pathways included the TCA cycle and pyruvate metabolism, lipoprotein uptake and nucleotide synthesis by both salvage and de novo pathways. Extending the approach across a wide panel of chemotherapeutics, we confirmed the specificity of the metabolic pathway associations to platinum sensitivity. We conclude that metabolic phenotyping could play a role in predicting response to platinum chemotherapy and that consensus-phenotype integration of molecular profiling data is a powerful and versatile tool for both biomarker discovery and for exploring the complex relationships between biological pathways and drug response.

Prognosis and oncogenomic profiling of patients with tropomyosin receptor kinase fusion cancer in the 100,000 genomes project.

INTRODUCTION: Neurotrophic tyrosine receptor kinase (NTRK) gene fusions are oncogenic drivers in various tumor types. Limited data exist on the overall survival (OS) of patients with tumors with NTRK gene fusions and on the co-occurrence of NTRK fusions with other oncogenic drivers. MATERIALS AND METHODS: This retrospective study included patients enrolled in the Genomics England 100,000 Genomes Project who had linked clinical data from UK databases. Patients who had undergone tumor whole genome sequencing between March 2016 and July 2019 were included. Patients with and without NTRK fusions were matched. OS was analyzed along with oncogenic alterations in ALK, BRAF, EGFR, ERBB2, KRAS, and ROS1, and tumor mutation burden (TMB) and microsatellite instability (MSI). RESULTS: Of 15,223 patients analyzed, 38 (0.25%) had NTRK gene fusions in 11 tumor types, the most common were breast cancer, colorectal cancer (CRC), and sarcoma. Median OS was not reached in both the NTRK gene fusion-positive and -negative groups (hazard ratio 1.47, 95% CI 0.39-5.57, P = 0.572). A KRAS mutation was identified in two (5%) patients with NTRK gene fusions, and both had hepatobiliary cancer. High TMB and MSI were both more common in patients with NTRK gene fusions, due to the CRC subset. While there was a higher risk of death in patients with NTRK gene fusions compared to those without, the difference was not statistically significant. CONCLUSION: This study supports the hypothesis that NTRK gene fusions are primary oncogenic drivers and the co-occurrence of NTRK gene fusions with other oncogenic alterations is rare.