Pathway-based signatures predict patient outcome, chemotherapy benefit and synthetic lethal dependencies in invasive lobular breast cancer.

BACKGROUND: Invasive Lobular Carcinoma (ILC) is a morphologically distinct breast cancer subtype that represents up to 15% of all breast cancers. Compared to Invasive Breast Carcinoma of No Special Type (IBC-NST), ILCs exhibit poorer long-term outcome and a unique pattern of metastasis. Despite these differences, the systematic discovery of robust prognostic biomarkers and therapeutically actionable molecular pathways in ILC remains limited. METHODS: Pathway-centric multivariable models using statistical machine learning were developed and tested in seven retrospective clinico-genomic cohorts (n = 996). Further external validation was performed using a new RNA-Seq clinical cohort of aggressive ILCs (n = 48). RESULTS AND CONCLUSIONS: mRNA dysregulation scores of 25 pathways were strongly prognostic in ILC (FDR-adjusted P < 0.05). Of these, three pathways including Cell-cell communication, Innate immune system and Smooth muscle contraction were also independent predictors of chemotherapy response. To aggregate these findings, a multivariable machine learning predictor called PSILC was developed and successfully validated for predicting overall and metastasis-free survival in ILC. Integration of PSILC with CRISPR-Cas9 screening data from breast cancer cell lines revealed 16 candidate therapeutic targets that were synthetic lethal with high-risk ILCs. This study provides interpretable prognostic and predictive biomarkers of ILC which could serve as the starting points for targeted drug discovery for this disease.

Extracting information from multiplex networks.

Multiplex networks are generalized network structures that are able to describe networks in which the same set of nodes are connected by links that have different connotations. Multiplex networks are ubiquitous since they describe social, financial, engineering, and biological networks as well. Extending our ability to analyze complex networks to multiplex network structures increases greatly the level of information that is possible to extract from big data. For these reasons, characterizing the centrality of nodes in multiplex networks and finding new ways to solve challenging inference problems defined on multiplex networks are fundamental questions of network science. In this paper, we discuss the relevance of the Multiplex PageRank algorithm for measuring the centrality of nodes in multilayer networks and we characterize the utility of the recently introduced indicator function Θ̃(S) for describing their mesoscale organization and community structure. As working examples for studying these measures, we consider three multiplex network datasets coming for social science.

Validation of prediction models for radiation-induced late rectal bleeding: Evidence from a large pooled population of prostate cancer patients.

PURPOSE: To validate published models for the risk estimate of grade ≥ 1 (G1+), grade ≥ 2 (G2+) and grade = 3 (G3) late rectal bleeding (LRB) after radical radiotherapy for prostate cancer in a large pooled population from three prospective trials. MATERIALS AND METHODS: The external validation population included patients from Europe, and Oceanian centres enrolled between 2003 and 2014. Patients received 3DCRT or IMRT at doses between 66-80 Gy. IMRT was administered with conventional or hypofractionated schemes (2.35-2.65 Gy/fr). LRB was prospectively scored using patient-reported questionnaires (LENT/SOMA scale) with a 3-year follow-up. All Normal Tissue Complication Probability (NTCP) models published until 2021 based on the Equivalent Uniform Dose (EUD) from the rectal Dose Volume Histogram (DVH) were considered for validation. Model performance in validation was evaluated through calibration and discrimination. RESULTS: Sixteen NTCP models were tested on data from 1633 patients. G1+ LRB was scored in 465 patients (28.5%), G2+ in 255 patients (15.6%) and G3 in 112 patients (6.8%). The best performances for G2+ and G3 LRB highlighted the importance of the medium-high doses to the rectum (volume parameters n = 0.24 and n = 0.18, respectively). Good performance was seen for models of severe LRB. Moreover, a multivariate model with two clinical factors found the best calibration slope. CONCLUSION: Five published NTCP models developed on non-contemporary cohorts were able to predict a relative increase in the toxicity response in a more recent validation population. Compared to QUANTEC findings, dosimetric results pointed toward mid-high doses of rectal DVH. The external validation cohort confirmed abdominal surgery and cardiovascular diseases as risk factors.

Dynamic Changes in the NK-, Neutrophil-, and B-cell Immunophenotypes Relevant in High Metastatic Risk Post Neoadjuvant Chemotherapy-Resistant Early Breast Cancers.

PURPOSE: To identify potential immune targets in post-neoadjuvant chemotherapy (NAC)-resistant triple-negative breast cancer (TNBC) and ER+HER2- breast cancer disease. EXPERIMENTAL DESIGN: Following pathology review, 153 patients were identified as having residual cancer burden (RCB) II/III disease (TNBC n = 80; ER+HER2-n = 73). Baseline pre-NAC samples were available for evaluation for 32 of 80 TNBC and 36 of 73 ER+HER2- cases. Bright-field hematoxylin and eosin assessment allowed for tumor-infiltrating lymphocyte (TIL) evaluation in all cases. Multiplexed immunofluorescence was used to identify the abundance and distribution of immune cell subsets. Levels of checkpoints including PD-1/PD-L1 expression were also quantified. Findings were then validated using expression profiling of cancer and immune-related genes. Cytometry by time-of-flight characterized the dynamic changes in circulating immune cells with NAC. RESULTS: RCB II/III TNBC and ER+HER2- breast cancer were immunologically "cold" at baseline and end of NAC. Although the distribution of immune cell subsets across subtypes was similar, the mRNA expression profiles were both subtype- and chemotherapy-specific. TNBC RCB II/III disease was enriched with genes related to neutrophil degranulation, and displayed strong interplay across immune and cancer pathways. We observed similarities in the dynamic changes in B-cell biology following NAC irrespective of subtype. However, NAC induced changes in the local and circulating tumor immune microenvironment (TIME) that varied by subtype and response. Specifically, in TNBC residual disease, we observed downregulation of stimulatory (CD40/OX40L) and inhibitory (PD-L1/PD-1) receptor expression and an increase in NK cell populations (especially non-cytolytic, exhausted CD56dimCD16-) within both the local TIME and peripheral white cell populations. CONCLUSIONS: This study identifies several potential immunologic pathways in residual disease, which may be targeted to benefit high-risk patients.

Visibility Graphs for Image Processing.

The family of image visibility graphs (IVG/IHVGs) have been recently introduced as simple algorithms by which scalar fields can be mapped into graphs. Here we explore the usefulness of such\an operator in the scenario of image processing and image classification. We demonstrate that the link architecture of the image visibility graphs encapsulates relevant information on the structure of the images and we explore their potential as image filters. We introduce several graph features, including the novel concept of Visibility Patches, and show through several examples that these features are highly informative, computationally efficient and universally applicable for general pattern recognition and image classification tasks.

Extraction and Integration of Genetic Networks from Short-Profile Omic Data Sets.

Mass spectrometry technologies are widely used in the fields of ionomics and metabolomics to simultaneously profile the intracellular concentrations of, e.g., amino acids or elements in genome-wide mutant libraries. These molecular or sub-molecular features are generally non-Gaussian and their covariance reveals patterns of correlations that reflect the system nature of the cell biochemistry and biology. Here, we introduce two similarity measures, the Mahalanobis cosine and the hybrid Mahalanobis cosine, that enforce information from the empirical covariance matrix of omics data from high-throughput screening and that can be used to quantify similarities between the profiled features of different mutants. We evaluate the performance of these similarity measures in the task of inferring and integrating genetic networks from short-profile ionomics/metabolomics data through an analysis of experimental data sets related to the ionome and the metabolome of the model organism S. cerevisiae. The study of the resulting ionome-metabolome Saccharomyces cerevisiae multilayer genetic network, which encodes multiple omic-specific levels of correlations between genes, shows that the proposed measures can provide an alternative description of relations between biological processes when compared to the commonly used Pearson's correlation coefficient and have the potential to guide the construction of novel hypotheses on the function of uncharacterised genes.

Multilink communities of multiplex networks.

Multiplex networks describe a large number of complex social, biological and transportation networks where a set of nodes is connected by links of different nature and connotation. Here we uncover the rich community structure of multiplex networks by associating a community to each multilink where the multilinks characterize the connections existing between any two nodes of the multiplex network. Our community detection method reveals the rich interplay between the mesoscale structure of the multiplex networks and their multiplexity. For instance some nodes can belong to many layers and few communities while others can belong to few layers but many communities. Moreover the multilink communities can be formed by a different number of relevant layers. These results point out that mesoscopically there can be large differences in the compressibility of multiplex networks.

A combinatorial framework to quantify peak/pit asymmetries in complex dynamics.

We explore a combinatorial framework which efficiently quantifies the asymmetries between minima and maxima in local fluctuations of time series. We first showcase its performance by applying it to a battery of synthetic cases. We find rigorous results on some canonical dynamical models (stochastic processes with and without correlations, chaotic processes) complemented by extensive numerical simulations for a range of processes which indicate that the methodology correctly distinguishes different complex dynamics and outperforms state of the art metrics in several cases. Subsequently, we apply this methodology to real-world problems emerging across several disciplines including cases in neurobiology, finance and climate science. We conclude that differences between the statistics of local maxima and local minima in time series are highly informative of the complex underlying dynamics and a graph-theoretic extraction procedure allows to use these features for statistical learning purposes.

Visibility graphs of random scalar fields and spatial data.

We extend the family of visibility algorithms to map scalar fields of arbitrary dimension into graphs, enabling the analysis of spatially extended data structures as networks. We introduce several possible extensions and provide analytical results on the topological properties of the graphs associated to different types of real-valued matrices, which can be understood as the high and low disorder limits of real-valued scalar fields. In particular, we find a closed expression for the degree distribution of these graphs associated to uncorrelated random fields of generic dimension. This result holds independently of the field's marginal distribution and it directly yields a statistical randomness test, applicable in any dimension. We showcase its usefulness by discriminating spatial snapshots of two-dimensional white noise from snapshots of a two-dimensional lattice of diffusively coupled chaotic maps, a system that generates high dimensional spatiotemporal chaos. The range of potential applications of this combinatorial framework includes image processing in engineering, the description of surface growth in material science, soft matter or medicine, and the characterization of potential energy surfaces in chemistry, disordered systems, and high energy physics. An illustration on the applicability of this method for the classification of the different stages involved in carcinogenesis is briefly discussed.

Designing and interpreting 'multi-omic' experiments that may change our understanding of biology.

Most biological mechanisms involve more than one type of biomolecule, and hence operate not solely at the level of either genome, transcriptome, proteome, metabolome or ionome. Datasets resulting from single-omic analysis are rapidly increasing in throughput and quality, rendering multi-omic studies feasible. These should offer a comprehensive, structured and interactive overview of a biological mechanism. However, combining single-omic datasets in a meaningful manner has so far proved challenging, and the discovery of new biological information lags behind expectation. One reason is that experiments conducted in different laboratories can typically not to be combined without restriction. Second, the interpretation of multi-omic datasets represents a significant challenge by nature, as the biological datasets are heterogeneous not only for technical, but also for biological, chemical, and physical reasons. Here, multi-layer network theory and methods of artificial intelligence might contribute to solve these problems. For the efficient application of machine learning however, biological datasets need to become more systematic, more precise - and much larger. We conclude our review with basic guidelines for the successful set-up of a multi-omic experiment.

Sequential visibility-graph motifs.

Visibility algorithms transform time series into graphs and encode dynamical information in their topology, paving the way for graph-theoretical time series analysis as well as building a bridge between nonlinear dynamics and network science. In this work we introduce and study the concept of sequential visibility-graph motifs, smaller substructures of n consecutive nodes that appear with characteristic frequencies. We develop a theory to compute in an exact way the motif profiles associated with general classes of deterministic and stochastic dynamics. We find that this simple property is indeed a highly informative and computationally efficient feature capable of distinguishing among different dynamics and robust against noise contamination. We finally confirm that it can be used in practice to perform unsupervised learning, by extracting motif profiles from experimental heart-rate series and being able, accordingly, to disentangle meditative from other relaxation states. Applications of this general theory include the automatic classification and description of physical, biological, and financial time series.

Sequential motif profile of natural visibility graphs.

The concept of sequential visibility graph motifs-subgraphs appearing with characteristic frequencies in the visibility graphs associated to time series-has been advanced recently along with a theoretical framework to compute analytically the motif profiles associated to horizontal visibility graphs (HVGs). Here we develop a theory to compute the profile of sequential visibility graph motifs in the context of natural visibility graphs (VGs). This theory gives exact results for deterministic aperiodic processes with a smooth invariant density or stochastic processes that fulfill the Markov property and have a continuous marginal distribution. The framework also allows for a linear time numerical estimation in the case of empirical time series. A comparison between the HVG and the VG case (including evaluation of their robustness for short series polluted with measurement noise) is also presented.

Emergence of Multiplex Communities in Collaboration Networks.

Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

Mesoscopic structures reveal the network between the layers of multiplex data sets.

Multiplex networks describe a large variety of complex systems, whose elements (nodes) can be connected by different types of interactions forming different layers (networks) of the multiplex. Multiplex networks include social networks, transportation networks, or biological networks in the cell or in the brain. Extracting relevant information from these networks is of crucial importance for solving challenging inference problems and for characterizing the multiplex networks microscopic and mesoscopic structure. Here we propose an information theory method to extract the network between the layers of multiplex data sets, forming a "network of networks." We build an indicator function, based on the entropy of network ensembles, to characterize the mesoscopic similarities between the layers of a multiplex network, and we use clustering techniques to characterize the communities present in this network of networks. We apply the proposed method to study the Multiplex Collaboration Network formed by scientists collaborating on different subjects and publishing in the American Physical Society journals. The analysis of this data set reveals the interplay between the collaboration networks and the organization of knowledge in physics.

Triadic closure as a basic generating mechanism of communities in complex networks.

Most of the complex social, technological, and biological networks have a significant community structure. Therefore the community structure of complex networks has to be considered as a universal property, together with the much explored small-world and scale-free properties of these networks. Despite the large interest in characterizing the community structures of real networks, not enough attention has been devoted to the detection of universal mechanisms able to spontaneously generate networks with communities. Triadic closure is a natural mechanism to make new connections, especially in social networks. Here we show that models of network growth based on simple triadic closure naturally lead to the emergence of community structure, together with fat-tailed distributions of node degree and high clustering coefficients. Communities emerge from the initial stochastic heterogeneity in the concentration of links, followed by a cycle of growth and fragmentation. Communities are the more pronounced, the sparser the graph, and disappear for high values of link density and randomness in the attachment procedure. By introducing a fitness-based link attractivity for the nodes, we find a phase transition where communities disappear for high heterogeneity of the fitness distribution, but a different mesoscopic organization of the nodes emerges, with groups of nodes being shared between just a few superhubs, which attract most of the links of the system.