ellipsoidFN: a tool for identifying a heterogeneous set of cancer biomarkers based on gene expressions

Computationally identifying effective biomarkers for cancers from gene expression profiles is an important and challenging task. The challenge lies in the complicated pathogenesis of cancers that often involve the dysfunction of many genes and regulatory interactions. Thus, sophisticated classification model is in pressing need. In this study, we proposed an efficient approach, called ellipsoidFN (ellipsoid Feature Net), to model the disease complexity by ellipsoids and seek a set of heterogeneous biomarkers. Our approach achieves a non-linear classification scheme for the mixed samples by the ellipsoid concept, and at the same time uses a linear programming framework to efficiently select biomarkers from high-dimensional space. ellipsoidFN reduces the redundancy and improves the complementariness between the identified biomarkers, thus significantly enhancing the distinctiveness between cancers and normal samples, and even between cancer types. Numerical evaluation on real prostate cancer, breast cancer and leukemia gene expression datasets suggested that ellipsoidFN outperforms the state-of-the-art biomarker identification methods, and it can serve as a useful tool for cancer biomarker identification in the future. The Matlab code of ellipsoidFN is freely available from http://doc.aporc.org/wiki/EllipsoidFN.     

Approximate Bayesian Computation

Approximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).

This is a “Topic Page” article for PLOS Computational Biology.

     

Evolving Digital Ecological Networks

“It is hard to realize that the living world as we know it is just one among many possibilities” [1]. Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs (i.e., digital organisms) that experience the same major ecological interactions as biological organisms (e.g., competition, predation, parasitism, and mutualism). Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations (hereafter tasks) that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology (e.g., the extent to which the architecture of multispecies networks shape coevolutionary outcomes, and the processes involved).

This is a “Topic Page” article for PLOS Computational Biology.

     

Viral Phylodynamics

Viral phylodynamics is defined as the study of how epidemiological, immunological, and evolutionary processes act and potentially interact to shape viral phylogenies. Since the coining of the term in 2004, research on viral phylodynamics has focused on transmission dynamics in an effort to shed light on how these dynamics impact viral genetic variation. Transmission dynamics can be considered at the level of cells within an infected host, individual hosts within a population, or entire populations of hosts. Many viruses, especially RNA viruses, rapidly accumulate genetic variation because of short generation times and high mutation rates. Patterns of viral genetic variation are therefore heavily influenced by how quickly transmission occurs and by which entities transmit to one another. Patterns of viral genetic variation will also be affected by selection acting on viral phenotypes. Although viruses can differ with respect to many phenotypes, phylodynamic studies have to date tended to focus on a limited number of viral phenotypes. These include virulence phenotypes, phenotypes associated with viral transmissibility, cell or tissue tropism phenotypes, and antigenic phenotypes that can facilitate escape from host immunity. Due to the impact that transmission dynamics and selection can have on viral genetic variation, viral phylogenies can therefore be used to investigate important epidemiological, immunological, and evolutionary processes, such as epidemic spread [2], spatio-temporal dynamics including metapopulation dynamics [3], zoonotic transmission, tissue tropism [4], and antigenic drift [5]. The quantitative investigation of these processes through the consideration of viral phylogenies is the central aim of viral phylodynamics.

This is a “Topic Page” article for PLOS Computational Biology.

     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

Homogeneous datasets of triple negative breast cancers enable the identification of novel prognostic and predictive signatures

Background

Current prognostic gene signatures for breast cancer mainly reflect proliferation status and have limited value in triple-negative (TNBC) cancers. The identification of prognostic signatures from TNBC cohorts was limited in the past due to small sample sizes.

Methodology/Principal Findings

We assembled all currently publically available TNBC gene expression datasets generated on Affymetrix gene chips. Inter-laboratory variation was minimized by filtering methods for both samples and genes. Supervised analysis was performed to identify prognostic signatures from 394 cases which were subsequently tested on an independent validation cohort (n = 261 cases).

Conclusions/Significance

Using two distinct false discovery rate thresholds, 25% and <3.5%, a larger (n = 264 probesets) and a smaller (n = 26 probesets) prognostic gene sets were identified and used as prognostic predictors. Most of these genes were positively associated with poor prognosis and correlated to metagenes for inflammation and angiogenesis. No correlation to other previously published prognostic signatures (recurrence score, genomic grade index, 70-gene signature, wound response signature, 7-gene immune response module, stroma derived prognostic predictor, and a medullary like signature) was observed. In multivariate analyses in the validation cohort the two signatures showed hazard ratios of 4.03 (95% confidence interval [CI] 1.71–9.48; P = 0.001) and 4.08 (95% CI 1.79–9.28; P = 0.001), respectively. The 10-year event-free survival was 70% for the good risk and 20% for the high risk group. The 26-gene signatures had modest predictive value (AUC = 0.588) to predict response to neoadjuvant chemotherapy, however, the combination of a B-cell metagene with the prognostic signatures increased its response predictive value. We identified a 264-gene prognostic signature for TNBC which is unrelated to previously known prognostic signatures.     

Scale Insects,edition 2, a tool for the identification of potential pest scales at U.S.A. ports-of-entry (Hemiptera,Sternorrhyncha, Coccoidea)

We provide a general overview of features and technical specifications of an online, interactive tool for the identification of scale insects of concern to the U.S.A. ports-of-entry. Full lists of terminal taxa included in the keys (of which there are four), a list of features used in them, and a discussion of the structure of the tool are provided. We also briefly discuss the advantages of interactive keys for the identification of potential scale insect pests. The interactive key is freely accessible on http://idtools.org/id/scales/index.php     

Network analysis of gene lists for finding reproducible prognostic breast cancer gene signatures

Many genome-scale studies in molecular biology deliver results in the form of a ranked list of gene names, accordingly to some scoring method. There is always the question how many top-ranked genes to consider for further analysis, for example, in order creating a diagnostic or predictive gene signature for a disease. This question is usually approached from a statistical point of view, without considering any biological properties of top-ranked genes or how they are related to each other functionally. Here we suggest a new method for selecting a number of genes in a ranked gene list such that this set forms the Optimally Functionally Enriched Network (OFTEN), formed by known physical interactions between genes or their products. The method allows associating a network with the gene list, providing easier interpretation of the results and classifying the genes or proteins accordingly to their position in the resulting network. We demonstrate the method on four breast cancer datasets and show that 1) the resulting gene signatures are more reproducible from one dataset to another compared to standard statistical procedures and 2) the overlap of these signatures has significant prognostic potential. The method is implemented in BiNoM Cytoscape plugin (http://binom.curie.fr).     

PHProteomicDB： A Module for Two-dimensional Gel Electrophoresis Database Creation on Personal Web Sites

PHProteomicDB is a PHP-written module to help researchers in proteomics to share two-dimenslonal gel electrophoresis data using personal web sites. No technical or PHP knowledge is necessary except a few basics about web site management. PHProteomicDB has a user-friendly administration interface to enter and update data. It creates web pages on the fly displaying gel characteristics, gel pictures, and numbered gel spots with their related identifications pointing to their reference pages in protein databanks. The module is freely available at http：//www.huvec.com/index.php3？rub=Download.     

BioGRID REST Service, BiogridPlugin2 and BioGRID WebGraph: new tools for access to interaction data at BioGRID

The Biological General Repository for Interaction Datasets (BioGRID) representational state transfer (REST) service allows full URL-based access to curated protein and genetic interaction data at the BioGRID database. Appending URL parameters allows filtering of data by various attributes including gene names and identifiers, PubMed ID and evidence type. We also describe two visualization tools that interface with the REST service, the BiogridPlugin2 for Cytoscape and the BioGRID WebGraph. Availability and implementation: BioGRID data and applications are completely free for commercial and non-commercial use. http://webservice.thebiogrid.org/resources/interactions (REST Service), http://wiki.thebiogrid.org/doku.php/biogridrest(REST Service parameter list and help), http://webservice.thebiogrid.org/resources/application.wadl(REST Service WADL), http://thebiogrid.org/download.php (BiogridPlugin2, v2.1 download), http://wiki.thebiogrid.org/doku.php/biogridplugin2 (BiogridPlugin2 help) and http://tyerslab.bio.ed.ac.uk/tools/BioGRID_webgraph.php(BioGRID WebGraph).     

Theoretical study of electron transfer process between fullerenes and neurotransmitters; acetylcholine,dopamine, serotonin and epinephrine in nanostructures [neurotransmitters].C<Subscript><Emphasis Type="Italic">n</Emphasis></Subscript> complexes

Neurotransmitters are the compounds which allow the transmission of signals from one neuron to the next across synapses. They are the brain chemicals that communicate information throughout brain and body. Fullerenes are a family of carbonallotropes, molecules composed entirely of carbon, that take the forms of spheres, ellipsoids, and cylinders. Various empty carbon fullerenes (C_n) with different carbon atoms have been obtained and investigated. Topological indices have been successfully used to construct effective and useful mathematical methods to establish clear relationships between structural data and the physical properties of these materials. In this study, the number of carbon atoms in the fullerenes was used as an index to establish a relationship between the structures of neurotransmitters (NTs) acetylcholine (AC) 1, dopamine (DP) 2, serotonin (SE) 3, and epinephrine (EP) 4 as the well-known redox systems and fullerenes C_n (n = 60, 70, 76, 82, and 86) which create [NT].C_n; A-1 to A-5 up to D-1 to D-5. The relationship between the number of carbon atoms and the free energy of electron transfer (ΔG_et(n); n = 1–4) is assessed using the Rehm-Weller equation for A-1 to A-5 up to D-1 to D-5 supramolecular [NT].C_n complexes. The calculations are presented for the four reduction potentials (^Red.E₁ to ^Red.E₄) of fullerenes C_n. The results were used to calculate the four free energy values of electron transfer (ΔG_et(1) to ΔG_et(4)) of the supramolecular complexes A-1 to A-8 up to D-1 to D-8 for fullerenes C₆₀ to C₁₂₀. The first to fourth free activation energy values of electron transfer and the maximum wavelength of the electron transfers, ΔG^#_et(n) and λ_et (n = 1–4), respectively, were also calculated in this study for A-1 to A-8 up to D-1 to D-8 in accordance with the Marcus theory.     

Comparative Analysis of Pdf-Mediated Circadian Behaviors Between Drosophila melanogaster and D. virilis

PAR proteins (partitioning defective) are major regulators of cell polarity and asymmetric cell division. One of the par genes, par-1, encodes a Ser/Thr kinase that is conserved from yeast to mammals. In Caenorhabditis elegans, par-1 governs asymmetric cell division by ensuring the polar distribution of cell fate determinants. However the precise mechanisms by which PAR-1 regulates asymmetric cell division in C. elegans remain to be elucidated. We performed a genomewide RNAi screen and identified six genes that specifically suppress the embryonic lethal phenotype associated with mutations in par-1. One of these suppressors is mpk-1, the C. elegans homolog of the conserved mitogen activated protein (MAP) kinase ERK. Loss of function of mpk-1 restored embryonic viability, asynchronous cell divisions, the asymmetric distribution of cell fate specification markers, and the distribution of PAR-1 protein in par-1 mutant embryos, indicating that this genetic interaction is functionally relevant for embryonic development. Furthermore, disrupting the function of other components of the MAPK signaling pathway resulted in suppression of par-1 embryonic lethality. Our data therefore indicates that MAP kinase signaling antagonizes PAR-1 signaling during early C. elegans embryonic polarization.ASYMMETRIC cell division, a process in which a mother cell divides in two different daughter cells, is a fundamental mechanism to achieve cell diversity during development. We use the early embryo of Caenorhabditis elegans as a model system to study asymmetric cell division. The C. elegans one-cell embryo divides asymmetrically along its anteroposterior axis, generating two cells of different sizes and fates: the larger anterior daughter cell will generate somatic tissues while the smaller posterior daughter cell will generate the germline (Sulston et al. 1983).A group of proteins called PAR proteins (partitioning defective) is required for asymmetric cell division in C. elegans (Kemphues et al. 1988). Depletion of any of the seven par genes (par-1 to -6 and pkc-3) leads to defects in asymmetric cell division and embryonic lethality (Kemphues et al. 1988; Kirby et al. 1990; Tabuse et al. 1998; Hung and Kemphues 1999; Hao et al. 2006). PAR-3 and PAR-6 are conserved proteins that contain PDZ-domains and form a complex with PKC-3 (Etemad-Moghadam et al. 1995; Izumi et al. 1998; Tabuse et al. 1998; Hung and Kemphues 1999). This complex becomes restricted to the anterior cortex of the embryo in response to spatially defined actomyosin contractions occurring in the embryo upon fertilization (Goldstein and Hird 1996; Munro et al. 2004). The posterior cortex of the embryo that becomes devoid of the anterior PAR proteins is occupied by the RING protein PAR-2 and the Ser/Thr kinase PAR-1 (Guo and Kemphues 1995; Boyd et al. 1996; Cuenca et al. 2003). Once polarized, the anterior and posterior PAR proteins mutually exclude each other from their respective cortices (Etemad-Moghadam et al. 1995; Boyd et al. 1996; Cuenca et al. 2003; Hao et al. 2006). Loss of function of the gene par-1, as opposed to loss of most other par genes, results in embryos that exhibit only subtle effects on the polarized cortical domains occupied by the other PAR proteins (Cuenca et al. 2003). However defects in this gene are associated with a more symmetric division in size, an aberrant distribution of cell fate specification markers, altered cell fates of the daughter cells of the embryo, and ultimately embryonic lethality (Kemphues et al. 1988; Guo and Kemphues 1995).PAR-1 controls asymmetric cell division and cell fate specification by regulating the localization of the two highly similar CCCH-type zinc-finger proteins MEX-5 and MEX-6 (referred to as MEX-5/6). MEX-5 and MEX-6 are 70% identical in their amino acid sequence and fulfill partially redundant functions in the embryo (Schubert et al. 2000). In wild-type animals, endogenous MEX-5 and GFP fusions of MEX-6 localize primarily to the anterior of the embryo while both proteins are evenly distributed in par-1 mutant embryos (Schubert et al. 2000; Cuenca et al. 2003). This suggests that in wild-type animals, PAR-1 acts in part by restricting MEX-5 and MEX-6 to the anterior of the embryo. The precise mechanism of this regulation is not known, but an elegant study performed for MEX-5 indicates that differential protein mobility in the anterior and posterior cytoplasm of the one-cell embryo contributes to this asymmetry (Tenlen et al. 2008). While increased mobility in the posterior of the one-cell embryo correlates with a par-1- and par-4-dependent phosphorylation on MEX-5, the kinase directly phosphorylating MEX-5 remains to be identified (Tenlen et al. 2008).Some of the phenotypes associated with loss of par-1 function are dependent on the function of mex-5 and mex-6. First, loss of function of par-1 leads to a decreased stability and aberrant localization of the posterior cell fate specification marker PIE-1, a protein that is usually inherited by the posterior daughter cell in wild-type animals and ensures the correct specification of the germline (Mello et al. 1996; Seydoux et al. 1996). This decreased stability is dependent on mex-5/6 function as PIE-1 levels are restored, albeit with symmetrical distribution, in mex-6(RNAi); mex-5(RNAi); par-1(b274) embryos (Schubert et al. 2000; Cuenca et al. 2003; Derenzo et al. 2003). Second, embryos lacking par-1 function exhibit decreased amounts of P granules in the one-cell embryo, while these markers are present in mex-6(pk440); mex-5(zu199); par-1(RNAi) embryos of comparable age (Cheeks et al. 2004). Third, in par-1(RNAi) one-cell embryos the posterior cortical domain occupied by the polarity protein PAR-2 is extended anteriorly, when compared to wild-type embryos (Cuenca et al. 2003). This anterior extension is rescued in embryos deficient for both par-1 and mex-5/6 (Cuenca et al. 2003). Taken together, these results indicate that par-1 acts in the embryo—at least in part—by regulating the localization and/or activity of the proteins MEX-5 and MEX-6. However, it remains unclear whether other proteins can modulate PAR-1 function to affect MEX-5/6 activity.To gain insight into the mechanisms of par-1 function in the embryo, we sought to identify genes that act together with par-1 during embryonic development. We performed an RNAi-based screen for genetic interactors of the temperature-sensitive allele par-1(zu310), using the embryonic lethal phenotype of this mutant as a readout. This method has proven successful in previous screens to identify genes involved in early embryonic processes (Labbé et al. 2006; O''Rourke et al. 2007). We were able to identify six genes that, upon disruption of their function, suppress the embryonic lethal phenotype of par-1 mutant embryos. One of these genes is mpk-1, the C. elegans homolog of the highly conserved MAP kinase ERK. Closer analysis subsequently showed that reduction of function of mpk-1 not only increases viability of par-1 mutant embryos, but also reverts several polarity phenotypes associated with loss of function of par-1. Our data indicate that mpk-1 antagonizes par-1 activity to regulate polarization and asymmetric cell divisions in the early embryo.     

Progress and prospects for the discovery of biomarkers for gastric cancer: a focus on proteomics

Introduction: Patient outcomes from gastric cancer vary due to the complexity of stomach carcinogenesis. Recent research using proteomic technologies has targeted components of all of these systems in order to develop biomarkers to aid the early diagnosis of gastric cancer and to assist in prognostic stratification.

Areas covered: This review is comprised of evidence obtained from literature searches from PubMed. It covers the evidence of diagnostic, prognostic, and predictive biomarkers for gastric cancer using proteomic technologies, and provides up-to-date references.

Expert commentary: The proteomic technologies have not only enabled the screening of a large number of samples, but also enabled the identification of diagnostic, prognostic and predictive biomarkers for gastric cancer. While major challenges still remain, to date, proteomic studies in gastric cancer have provided a wealth of information in revealing proteome alterations associated with the disease.     

A Novel Cholinergic Action of Alcohol and the Development of Tolerance to That Effect in Caenorhabditis elegans

The Prp43 DExD/H-box protein is required for progression of the biochemically distinct pre-messenger RNA and ribosomal RNA (rRNA) maturation pathways. In Saccharomyces cerevisiae, the Spp382/Ntr1, Sqs1/Pfa1, and Pxr1/Gno1 proteins are implicated as cofactors necessary for Prp43 helicase activation during spliceosome dissociation (Spp382) and rRNA processing (Sqs1 and Pxr1). While otherwise dissimilar in primary sequence, these Prp43-binding proteins each contain a short glycine-rich G-patch motif required for function and thought to act in protein or nucleic acid recognition. Here yeast two-hybrid, domain-swap, and site-directed mutagenesis approaches are used to investigate G-patch domain activity and portability. Our results reveal that the Spp382, Sqs1, and Pxr1 G-patches differ in Prp43 two-hybrid response and in the ability to reconstitute the Spp382 and Pxr1 RNA processing factors. G-patch protein reconstitution did not correlate with the apparent strength of the Prp43 two-hybrid response, suggesting that this domain has function beyond that of a Prp43 tether. Indeed, while critical for Pxr1 activity, the Pxr1 G-patch appears to contribute little to the yeast two-hybrid interaction. Conversely, deletion of the primary Prp43 binding site within Pxr1 (amino acids 102–149) does not impede rRNA processing but affects small nucleolar RNA (snoRNA) biogenesis, resulting in the accumulation of slightly extended forms of select snoRNAs, a phenotype unexpectedly shared by the prp43 loss-of-function mutant. These and related observations reveal differences in how the Spp382, Sqs1, and Pxr1 proteins interact with Prp43 and provide evidence linking G-patch identity with pathway-specific DExD/H-box helicase activity.     

Chromatin conformation signatures of cellular differentiation

One of the major genomics challenges is to better understand how correct gene expression is orchestrated. Recent studies have shown how spatial chromatin organization is critical in the regulation of gene expression. Here, we developed a suite of computer programs to identify chromatin conformation signatures with 5C technology http://Dostielab.biochem.mcgill.ca. We identified dynamic HoxA cluster chromatin conformation signatures associated with cellular differentiation. Genome-wide chromatin conformation signature identification might uniquely identify disease-associated states and represent an entirely novel class of human disease biomarkers.     

Identification of two poorly prognosed ovarian carcinoma subtypes associated with CHEK2 germ-line mutation and non-CHEK2 somatic mutation gene signatures

High-grade serous ovarian cancer (HG-SOC), a major histologic type of epithelial ovarian cancer (EOC), is a poorly-characterized, heterogeneous and lethal disease where somatic mutations of TP53 are common and inherited loss-of-function mutations in BRCA1/2 predispose to cancer in 9.5–13% of EOC patients. However, the overall burden of disease due to either inherited or sporadic mutations is not known.

We performed bioinformatics analyses of mutational and clinical data of 334 HG-SOC tumor samples from The Cancer Genome Atlas to identify novel tumor-driving mutations, survival-significant patient subgroups and tumor subtypes potentially driven by either hereditary or sporadic factors.

We identified a sub-cluster of high-frequency mutations in 22 patients and 58 genes associated with DNA damage repair, apoptosis and cell cycle. Mutations of CHEK2, observed with the highest intensity, were associated with poor therapy response and overall survival (OS) of these patients (P = 8.00e-05), possibly due to detrimental effect of mutations at the nuclear localization signal. A 21-gene mutational prognostic signature significantly stratifies patients into relatively low or high-risk subgroups with 5-y OS of 37% or 6%, respectively (P = 7.31e-08). Further analysis of these genes and high-risk subgroup revealed 2 distinct classes of tumors characterized by either germline mutations of genes such as CHEK2, RPS6KA2 and MLL4, or somatic mutations of other genes in the signature.

Our results could provide improvement in prediction and clinical management of HG-SOC, facilitate our understanding of this complex disease, guide the design of targeted therapeutics and improve screening efforts to identify women at high-risk of hereditary ovarian cancers distinct from those associated with BRCA1/2 mutations.