WeSME: uncovering mutual exclusivity of cancer drivers and beyond.

Motivation: Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute P -values. Results: To reduce the computing cost and perform less restricted mutual exclusivity analysis, we developed an efficient method to estimate P -values while controlling the mutation rates of individual patients and genes similar to the permutation test. A comprehensive mutual exclusivity analysis allowed us to uncover mutually exclusive pairs, some of which may have relatively low mutation rates. These pairs often included likely cancer drivers that have been missed in previous analyses. More importantly, our results demonstrated that mutual exclusivity can also provide information that goes beyond the interactions between cancer drivers and can, for example, elucidate different mutagenic processes in different cancer groups. In particular, including frequently mutated, long genes such as TTN in our analysis allowed us to observe interesting patterns of APOBEC activity in breast cancer and identify a set of related driver genes that are highly predictive of patient survival. In addition, we utilized our mutual exclusivity analysis in support of a previously proposed model where APOBEC activity is the underlying process that causes TP53 mutations in a subset of breast cancer cases. Availability and Implementation: http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme. Contact: przytyck@ncbi.nlm.nih.gov. Supplementary information: Supplementary data are available at Bioinformatics online.

BeWith: A Between-Within method to discover relationships between cancer modules via integrated analysis of mutual exclusivity, co-occurrence and functional interactions.

The analysis of the mutational landscape of cancer, including mutual exclusivity and co-occurrence of mutations, has been instrumental in studying the disease. We hypothesized that exploring the interplay between co-occurrence, mutual exclusivity, and functional interactions between genes will further improve our understanding of the disease and help to uncover new relations between cancer driving genes and pathways. To this end, we designed a general framework, BeWith, for identifying modules with different combinations of mutation and interaction patterns. We focused on three different settings of the BeWith schema: (i) BeME-WithFun, in which the relations between modules are enriched with mutual exclusivity, while genes within each module are functionally related; (ii) BeME-WithCo, which combines mutual exclusivity between modules with co-occurrence within modules; and (iii) BeCo-WithMEFun, which ensures co-occurrence between modules, while the within module relations combine mutual exclusivity and functional interactions. We formulated the BeWith framework using Integer Linear Programming (ILP), enabling us to find optimally scoring sets of modules. Our results demonstrate the utility of BeWith in providing novel information about mutational patterns, driver genes, and pathways. In particular, BeME-WithFun helped identify functionally coherent modules that might be relevant for cancer progression. In addition to finding previously well-known drivers, the identified modules pointed to other novel findings such as the interaction between NCOR2 and NCOA3 in breast cancer. Additionally, an application of the BeME-WithCo setting revealed that gene groups differ with respect to their vulnerability to different mutagenic processes, and helped us to uncover pairs of genes with potentially synergistic effects, including a potential synergy between mutations in TP53 and the metastasis related DCC gene. Overall, BeWith not only helped us uncover relations between potential driver genes and pathways, but also provided additional insights on patterns of the mutational landscape, going beyond cancer driving mutations. Implementation is available at https://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/software/bewith.html.

Comparative validation of the D. melanogaster modENCODE transcriptome annotation.

Accurate gene model annotation of reference genomes is critical for making them useful. The modENCODE project has improved the D. melanogaster genome annotation by using deep and diverse high-throughput data. Since transcriptional activity that has been evolutionarily conserved is likely to have an advantageous function, we have performed large-scale interspecific comparisons to increase confidence in predicted annotations. To support comparative genomics, we filled in divergence gaps in the Drosophila phylogeny by generating draft genomes for eight new species. For comparative transcriptome analysis, we generated mRNA expression profiles on 81 samples from multiple tissues and developmental stages of 15 Drosophila species, and we performed cap analysis of gene expression in D. melanogaster and D. pseudoobscura. We also describe conservation of four distinct core promoter structures composed of combinations of elements at three positions. Overall, each type of genomic feature shows a characteristic divergence rate relative to neutral models, highlighting the value of multispecies alignment in annotating a target genome that should prove useful in the annotation of other high priority genomes, especially human and other mammalian genomes that are rich in noncoding sequences. We report that the vast majority of elements in the annotation are evolutionarily conserved, indicating that the annotation will be an important springboard for functional genetic testing by the Drosophila community.

Understanding Genotype-Phenotype Effects in Cancer via Network Approaches.

Cancer is now increasingly studied from the perspective of dysregulated pathways, rather than as a disease resulting from mutations of individual genes. A pathway-centric view acknowledges the heterogeneity between genomic profiles from different cancer patients while assuming that the mutated genes are likely to belong to the same pathway and cause similar disease phenotypes. Indeed, network-centric approaches have proven to be helpful for finding genotypic causes of diseases, classifying disease subtypes, and identifying drug targets. In this review, we discuss how networks can be used to help understand patient-to-patient variations and how one can leverage this variability to elucidate interactions between cancer drivers.

MEMCover: integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types.

MOTIVATION: The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated. RESULTS: We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks.

Computational design of mRNA vaccines.

mRNA technology has emerged as a successful vaccine platform that offered a swift response to the COVID-19 pandemic. Accumulating evidence shows that vaccine efficacy, thermostability, and other important properties, are largely impacted by intrinsic properties of the mRNA molecule, such as RNA sequence and structure, both of which can be optimized. Designing mRNA sequence for vaccines presents a combinatorial problem due to an extremely large selection space. For instance, due to the degeneracy of the genetic code, there are over 10632 possible mRNA sequences that could encode the spike protein, the COVID-19 vaccines' target. Moreover, designing different elements of the mRNA sequence simultaneously against multiple objectives such as translational efficiency, reduced reactogenicity, and improved stability requires an efficient and sophisticated optimization strategy. Recently, there has been a growing interest in utilizing computational tools to redesign mRNA sequences to improve vaccine characteristics and expedite discovery timelines. In this review, we explore important biophysical features of mRNA to be considered for vaccine design and discuss how computational approaches can be applied to rapidly design mRNA sequences with desirable characteristics.

Chapter 5: Network biology approach to complex diseases.

Complex diseases are caused by a combination of genetic and environmental factors. Uncovering the molecular pathways through which genetic factors affect a phenotype is always difficult, but in the case of complex diseases this is further complicated since genetic factors in affected individuals might be different. In recent years, systems biology approaches and, more specifically, network based approaches emerged as powerful tools for studying complex diseases. These approaches are often built on the knowledge of physical or functional interactions between molecules which are usually represented as an interaction network. An interaction network not only reports the binary relationships between individual nodes but also encodes hidden higher level organization of cellular communication. Computational biologists were challenged with the task of uncovering this organization and utilizing it for the understanding of disease complexity, which prompted rich and diverse algorithmic approaches to be proposed. We start this chapter with a description of the general characteristics of complex diseases followed by a brief introduction to physical and functional networks. Next we will show how these networks are used to leverage genotype, gene expression, and other types of data to identify dysregulated pathways, infer the relationships between genotype and phenotype, and explain disease heterogeneity. We group the methods by common underlying principles and first provide a high level description of the principles followed by more specific examples. We hope that this chapter will give readers an appreciation for the wealth of algorithmic techniques that have been developed for the purpose of studying complex diseases as well as insight into their strengths and limitations.

Influence network model uncovers relations between biological processes and mutational signatures.

BACKGROUND: There has been a growing appreciation recently that mutagenic processes can be studied through the lenses of mutational signatures, which represent characteristic mutation patterns attributed to individual mutagens. However, the causal links between mutagens and observed mutation patterns as well as other types of interactions between mutagenic processes and molecular pathways are not fully understood, limiting the utility of mutational signatures. METHODS: To gain insights into these relationships, we developed a network-based method, named GENESIGNET that constructs an influence network among genes and mutational signatures. The approach leverages sparse partial correlation among other statistical techniques to uncover dominant influence relations between the activities of network nodes. RESULTS: Applying GENESIGNET to cancer data sets, we uncovered important relations between mutational signatures and several cellular processes that can shed light on cancer-related processes. Our results are consistent with previous findings, such as the impact of homologous recombination deficiency on clustered APOBEC mutations in breast cancer. The network identified by GENESIGNET also suggest an interaction between APOBEC hypermutation and activation of regulatory T Cells (Tregs), as well as a relation between APOBEC mutations and changes in DNA conformation. GENESIGNET also exposed a possible link between the SBS8 signature of unknown etiology and the Nucleotide Excision Repair (NER) pathway. CONCLUSIONS: GENESIGNET provides a new and powerful method to reveal the relation between mutational signatures and gene expression. The GENESIGNET method was implemented in python, and installable package, source codes and the data sets used for and generated during this study are available at the Github site https://github.com/ncbi/GeneSigNet.

Identifying causal genes and dysregulated pathways in complex diseases.

In complex diseases, various combinations of genomic perturbations often lead to the same phenotype. On a molecular level, combinations of genomic perturbations are assumed to dys-regulate the same cellular pathways. Such a pathway-centric perspective is fundamental to understanding the mechanisms of complex diseases and the identification of potential drug targets. In order to provide an integrated perspective on complex disease mechanisms, we developed a novel computational method to simultaneously identify causal genes and dys-regulated pathways. First, we identified a representative set of genes that are differentially expressed in cancer compared to non-tumor control cases. Assuming that disease-associated gene expression changes are caused by genomic alterations, we determined potential paths from such genomic causes to target genes through a network of molecular interactions. Applying our method to sets of genomic alterations and gene expression profiles of 158 Glioblastoma multiforme (GBM) patients we uncovered candidate causal genes and causal paths that are potentially responsible for the altered expression of disease genes. We discovered a set of putative causal genes that potentially play a role in the disease. Combining an expression Quantitative Trait Loci (eQTL) analysis with pathway information, our approach allowed us not only to identify potential causal genes but also to find intermediate nodes and pathways mediating the information flow between causal and target genes. Our results indicate that different genomic perturbations indeed dys-regulate the same functional pathways, supporting a pathway-centric perspective of cancer. While copy number alterations and gene expression data of glioblastoma patients provided opportunities to test our approach, our method can be applied to any disease system where genetic variations play a fundamental causal role.

Mutational Signatures as Sensors of Environmental Exposures: Analysis of Smoking-Induced Lung Tissue Remodeling.

Smoking is a widely recognized risk factor in the emergence of cancers and other lung diseases. Studies of non-cancer lung diseases typically investigate the role that smoking has in chronic changes in lungs that might predispose patients to the diseases, whereas most cancer studies focus on the mutagenic properties of smoking. Large-scale cancer analysis efforts have collected expression data from both tumor and control lung tissues, and studies have used control samples to estimate the impact of smoking on gene expression. However, such analyses may be confounded by tumor-related micro-environments as well as patient-specific exposure to smoking. Thus, in this paper, we explore the utilization of mutational signatures to study environment-induced changes of gene expression in control lung tissues from lung adenocarcinoma samples. We show that a joint computational analysis of mutational signatures derived from sequenced tumor samples, and the gene expression obtained from control samples, can shed light on the combined impact that smoking and tumor-related micro-environments have on gene expression and cell-type composition in non-neoplastic (control) lung tissue. The results obtained through such analysis are both supported by experimental studies, including studies utilizing single-cell technology, and also suggest additional novel insights. We argue that the study provides a proof of principle of the utility of mutational signatures to be used as sensors of environmental exposures not only in the context of the mutational landscape of cancer, but also as a reference for changes in non-cancer lung tissues. It also provides an example of how a database collected with the purpose of understanding cancer can provide valuable information for studies not directly related to the disease.

Modeling information flow in biological networks.

Large-scale molecular interaction networks are being increasingly used to provide a system level view of cellular processes. Modeling communications between nodes in such huge networks as information flows is useful for dissecting dynamical dependences between individual network components. In the information flow model, individual nodes are assumed to communicate with each other by propagating the signals through intermediate nodes in the network. In this paper, we first provide an overview of the state of the art of research in the network analysis based on information flow models. In the second part, we describe our computational method underlying our recent work on discovering dysregulated pathways in glioma. Motivated by applications to inferring information flow from genotype to phenotype in a very large human interaction network, we generalized previous approaches to compute information flows for a large number of instances and also provided a formal proof for the method.

Network integration meets network dynamics.

Molecular interaction networks provide a window on the workings of the cell. However, combining various types of networks into one coherent large-scale dynamic model remains a formidable challenge. A recent paper in BMC Systems Biology describes a promising step in this direction.

RepairSig: Deconvolution of DNA damage and repair contributions to the mutational landscape of cancer.

Cancer genomes accumulate a large number of somatic mutations resulting from a combination of stochastic errors in DNA processing, cancer-related aberrations of the DNA repair machinery, or carcinogenic exposures; each mutagenic process leaves a characteristic mutational signature. A key challenge is understanding the interactions between signatures, particularly as DNA repair deficiencies often modify the effects of other mutagens. Here, we introduce RepairSig, a computational method that explicitly models additive primary mutagenic processes; non-additive secondary processes, which interact with the primary processes; and a mutation opportunity, that is, the distribution of sites across the genome that are vulnerable to damage or preferentially repaired. We demonstrate that RepairSig accurately recapitulates experimentally identified signatures, identifies autonomous signatures of deficient DNA repair processes, and explains mismatch repair deficiency in breast cancer by de novo inference of both primary and secondary signatures from patient data. RepairSig is freely available for download at https://github.com/ncbi/RepairSig.

Mutational Signatures: From Methods to Mechanisms.

Mutations are the driving force of evolution, yet they underlie many diseases, in particular, cancer. They are thought to arise from a combination of stochastic errors in DNA processing, naturally occurring DNA damage (e.g., the spontaneous deamination of methylated CpG sites), replication errors, and dysregulation of DNA repair mechanisms. High-throughput sequencing has made it possible to generate large datasets to study mutational processes in health and disease. Since the emergence of the first mutational process studies in 2012, this field is gaining increasing attention and has already accumulated a host of computational approaches and biomedical applications.

Identifying Drug Sensitivity Subnetworks with NETPHIX.

Phenotypic heterogeneity in cancer is often caused by different patterns of genetic alterations. Understanding such phenotype-genotype relationships is fundamental for the advance of personalized medicine. We develop a computational method, named NETPHIX (NETwork-to-PHenotype association with eXclusivity) to identify subnetworks of genes whose genetic alterations are associated with drug response or other continuous cancer phenotypes. Leveraging interaction information among genes and properties of cancer mutations such as mutual exclusivity, we formulate the problem as an integer linear program and solve it optimally to obtain a subnetwork of associated genes. Applied to a large-scale drug screening dataset, NETPHIX uncovered gene modules significantly associated with drug responses. Utilizing interaction information, NETPHIX modules are functionally coherent and can thus provide important insights into drug action. In addition, we show that modules identified by NETPHIX together with their association patterns can be leveraged to suggest drug combinations.

Network-based approaches elucidate differences within APOBEC and clock-like signatures in breast cancer.

BACKGROUND: Studies of cancer mutations have typically focused on identifying cancer driving mutations that confer growth advantage to cancer cells. However, cancer genomes accumulate a large number of passenger somatic mutations resulting from various endogenous and exogenous causes, including normal DNA damage and repair processes or cancer-related aberrations of DNA maintenance machinery as well as mutations triggered by carcinogenic exposures. Different mutagenic processes often produce characteristic mutational patterns called mutational signatures. Identifying mutagenic processes underlying mutational signatures shaping a cancer genome is an important step towards understanding tumorigenesis. METHODS: To investigate the genetic aberrations associated with mutational signatures, we took a network-based approach considering mutational signatures as cancer phenotypes. Specifically, our analysis aims to answer the following two complementary questions: (i) what are functional pathways whose gene expression activities correlate with the strengths of mutational signatures, and (ii) are there pathways whose genetic alterations might have led to specific mutational signatures? To identify mutated pathways, we adopted a recently developed optimization method based on integer linear programming. RESULTS: Analyzing a breast cancer dataset, we identified pathways associated with mutational signatures on both expression and mutation levels. Our analysis captured important differences in the etiology of the APOBEC-related signatures and the two clock-like signatures. In particular, it revealed that clustered and dispersed APOBEC mutations may be caused by different mutagenic processes. In addition, our analysis elucidated differences between two age-related signatures-one of the signatures is correlated with the expression of cell cycle genes while the other has no such correlation but shows patterns consistent with the exposure to environmental/external processes. CONCLUSIONS: This work investigated, for the first time, a network-level association of mutational signatures and dysregulated pathways. The identified pathways and subnetworks provide novel insights into mutagenic processes that the cancer genomes might have undergone and important clues for developing personalized drug therapies.

Hidden Markov models lead to higher resolution maps of mutation signature activity in cancer.

Knowing the activity of the mutational processes shaping a cancer genome may provide insight into tumorigenesis and personalized therapy. It is thus important to characterize the signatures of active mutational processes in patients from their patterns of single base substitutions. However, mutational processes do not act uniformly on the genome, leading to statistical dependencies among neighboring mutations. To account for such dependencies, we develop the first sequence-dependent model, SigMa, for mutation signatures. We apply SigMa to characterize genomic and other factors that influence the activity of mutation signatures in breast cancer. We show that SigMa outperforms previous approaches, revealing novel insights on signature etiology. The source code for SigMa is publicly available at https://github.com/lrgr/sigma.

Sex- and tissue-specific functions of Drosophila doublesex transcription factor target genes.

Primary sex-determination "switches" evolve rapidly, but Doublesex (DSX)-related transcription factors (DMRTs) act downstream of these switches to control sexual development in most animal species. Drosophila dsx encodes female- and male-specific isoforms (DSX(F) and DSX(M)), but little is known about how dsx controls sexual development, whether DSX(F) and DSX(M) bind different targets, or how DSX proteins direct different outcomes in diverse tissues. We undertook genome-wide analyses to identify DSX targets using in vivo occupancy, binding site prediction, and evolutionary conservation. We find that DSX(F) and DSX(M) bind thousands of the same targets in multiple tissues in both sexes, yet these targets have sex- and tissue-specific functions. Interestingly, DSX targets show considerable overlap with targets identified for mouse DMRT1. DSX targets include transcription factors and signaling pathway components providing for direct and indirect regulation of sex-biased expression.

Module cover - a new approach to genotype-phenotype studies.

Uncovering and interpreting phenotype/genotype relationships are among the most challenging open questions in disease studies. Set cover approaches are explicitly designed to provide a representative set for diverse disease cases and thus are valuable in studies of heterogeneous datasets. At the same time pathway-centric methods have emerged as key approaches that significantly empower studies of genotype-phenotype relationships. Combining the utility of set cover techniques with the power of network-centric approaches, we designed a novel approach that extends the concept of set cover to network modules cover. We developed two alternative methods to solve the module cover problem: (i) an integrated method that simultaneously determines network modules and optimizes the coverage of disease cases. (ii) a two-step method where we first determined a candidate set of network modules and subsequently selected modules that provided the best coverage of the disease cases. The integrated method showed superior performance in the context of our application. We demonstrated the utility of the module cover approach for the identification of groups of related genes whose activity is perturbed in a coherent way by specific genomic alterations, allowing the interpretation of the heterogeneity of cancer cases.