Tumour mutations in long noncoding RNAs enhance cell fitness.

Long noncoding RNAs (lncRNAs) are linked to cancer via pathogenic changes in their expression levels. Yet, it remains unclear whether lncRNAs can also impact tumour cell fitness via function-altering somatic "driver" mutations. To search for such driver-lncRNAs, we here perform a genome-wide analysis of fitness-altering single nucleotide variants (SNVs) across a cohort of 2583 primary and 3527 metastatic tumours. The resulting 54 mutated and positively-selected lncRNAs are significantly enriched for previously-reported cancer genes and a range of clinical and genomic features. A number of these lncRNAs promote tumour cell proliferation when overexpressed in in vitro models. Our results also highlight a dense SNV hotspot in the widely-studied NEAT1 oncogene. To directly evaluate the functional significance of NEAT1 SNVs, we use in cellulo mutagenesis to introduce tumour-like mutations in the gene and observe a significant and reproducible increase in cell fitness, both in vitro and in a mouse model. Mechanistic studies reveal that SNVs remodel the NEAT1 ribonucleoprotein and boost subnuclear paraspeckles. In summary, this work demonstrates the utility of driver analysis for mapping cancer-promoting lncRNAs, and provides experimental evidence that somatic mutations can act through lncRNAs to enhance pathological cancer cell fitness.

A DNA-PK phosphorylation site on MET regulates its signaling interface with the DNA damage response.

The DNA damage response (DDR) is intertwined with signaling pathways downstream of oncogenic receptor tyrosine kinases (RTKs). To drive research into the application of targeted therapies as radiosensitizers, a better understanding of this molecular crosstalk is necessary. We present here the characterization of a previously unreported MET RTK phosphosite, Serine 1016 (S1016) that represents a potential DDR-MET interface. MET S1016 phosphorylation increases in response to irradiation and is mainly targeted by DNA-dependent protein kinase (DNA-PK). Phosphoproteomics unveils an impact of the S1016A substitution on the overall long-term cell cycle regulation following DNA damage. Accordingly, the abrogation of this phosphosite strongly perturbs the phosphorylation of proteins involved in the cell cycle and formation of the mitotic spindle, enabling cells to bypass a G2 arrest upon irradiation and leading to the entry into mitosis despite compromised genome integrity. This results in the formation of abnormal mitotic spindles and a lower proliferation rate. Altogether, the current data uncover a novel signaling mechanism through which the DDR uses a growth factor receptor system for regulating and maintaining genome stability.

An oncogene addiction phosphorylation signature and its derived scores inform tumor responsiveness to targeted therapies.

PURPOSE: Oncogene addiction provides important therapeutic opportunities for precision oncology treatment strategies. To date the cellular circuitries associated with driving oncoproteins, which eventually establish the phenotypic manifestation of oncogene addiction, remain largely unexplored. Data suggest the DNA damage response (DDR) as a central signaling network that intersects with pathways associated with deregulated addicting oncoproteins with kinase activity in cancer cells. EXPERIMENTAL: DESIGN: We employed a targeted mass spectrometry approach to systematically explore alterations in 116 phosphosites related to oncogene signaling and its intersection with the DDR following inhibition of the addicting oncogene alone or in combination with irradiation in MET-, EGFR-, ALK- or BRAF (V600)-positive cancer models. An NSCLC tissue pipeline combining patient-derived xenografts (PDXs) and ex vivo patient organotypic cultures has been established for treatment responsiveness assessment. RESULTS: We identified an 'oncogene addiction phosphorylation signature' (OAPS) consisting of 8 protein phosphorylations (ACLY S455, IF4B S422, IF4G1 S1231, LIMA1 S490, MYCN S62, NCBP1 S22, P3C2A S259 and TERF2 S365) that are significantly suppressed upon targeted oncogene inhibition solely in addicted cell line models and patient tissues. We show that the OAPS is present in patient tissues and the OAPS-derived score strongly correlates with the ex vivo responses to targeted treatments. CONCLUSIONS: We propose a score derived from OAPS as a quantitative measure to evaluate oncogene addiction of cancer cell samples. This work underlines the importance of protein phosphorylation assessment for patient stratification in precision oncology and corresponding identification of tumor subtypes sensitive to inhibition of a particular oncogene.

The simple regularities in the dynamics of online news impact.

Online news can quickly reach and affect millions of people, yet we do not know yet whether there exist potential dynamical regularities that govern their impact on the public. We use data from two major news outlets, BBC and New York Times, where the number of user comments can be used as a proxy of news impact. We find that the impact dynamics of online news articles does not exhibit popularity patterns found in many other social and information systems. In particular, we find that a simple exponential distribution yields a better fit to the empirical news impact distributions than a power-law distribution. This observation is explained by the lack or limited influence of the otherwise omnipresent rich-get-richer mechanism in the analyzed data. The temporal dynamics of the news impact exhibits a universal exponential decay which allows us to collapse individual news trajectories into an elementary single curve. We also show how daily variations of user activity directly influence the dynamics of the article impact. Our findings challenge the universal applicability of popularity dynamics patterns found in other social contexts. Supplementary Information: The online version contains supplementary material available at 10.1007/s42001-021-00140-w.

DNA-PK in human malignant disorders: Mechanisms and implications for pharmacological interventions.

The DNA-PK holoenzyme is a fundamental element of the DNA damage response machinery (DDR), which is responsible for cellular genomic stability. Consequently, and predictably, over the last decades since its identification and characterization, numerous pre-clinical and clinical studies reported observations correlating aberrant DNA-PK status and activity with cancer onset, progression and responses to therapeutic modalities. Notably, various studies have established in recent years the role of DNA-PK outside the DDR network, corroborating its role as a pleiotropic complex involved in transcriptional programs that operate biologic processes as epithelial to mesenchymal transition (EMT), hypoxia, metabolism, nuclear receptors signaling and inflammatory responses. In particular tumor entities as prostate cancer, immense research efforts assisted mapping and describing the overall signaling networks regulated by DNA-PK that control metastasis and tumor progression. Correspondingly, DNA-PK emerges as an obvious therapeutic target in cancer and data pertaining to various pharmacological approaches have been published, largely in context of combination with DNA-damaging agents (DDAs) that act by inflicting DNA double strand breaks (DSBs). Currently, new generation inhibitors are tested in clinical trials. Several excellent reviews have been published in recent years covering the biology of DNA-PK and its role in cancer. In the current article we are aiming to systematically describe the main findings on DNA-PK signaling in major cancer types, focusing on both preclinical and clinical reports and present a detailed current status of the DNA-PK inhibitors repertoire.

Time-invariant degree growth in preferential attachment network models.

Preferential attachment drives the evolution of many complex networks. Its analytical studies mostly consider the simplest case of a network that grows uniformly in time despite the accelerating growth of many real networks. Motivated by the observation that the average degree growth of nodes is time invariant in empirical network data, we study the degree dynamics in the relevant class of network models where preferential attachment is combined with heterogeneous node fitness and aging. We propose an analytical framework based on the time invariance of the studied systems and show that it is self-consistent only for two special network growth forms: the uniform and the exponential network growth. Conversely, the breaking of such time invariance explains the winner-takes-all effect in some model settings, revealing the connection between the Bose-Einstein condensation in the Bianconi-Barabási model and similar gelation in superlinear preferential attachment. Aging is necessary to reproduce realistic node degree growth curves and can prevent the winner-takes-all effect under weak conditions. Our results are verified by extensive numerical simulations.

History-dependent percolation on multiplex networks.

The structure of interconnected systems and its impact on the system dynamics is a much-studied cross-disciplinary topic. Although various critical phenomena have been found in different models, study of the connections between different percolation transitions is still lacking. Here we propose a unified framework to study the origins of the discontinuous transitions of the percolation process on interacting networks. The model evolves in generations with the result of the present percolation depending on the previous state, and thus is history-dependent. Both theoretical analysis and Monte Carlo simulations reveal that the nature of the transition remains the same at finite generations but exhibits an abrupt change for the infinite generation. We use brain functional correlation and morphological similarity data to show that our model also provides a general method to explore the network structure and can contribute to many practical applications, such as detecting the abnormal structures of human brain networks.

Targeting the MET Receptor Tyrosine Kinase as a Strategy for Radiosensitization in Locoregionally Advanced Head and Neck Squamous Cell Carcinoma.

Radiotherapy (RT) along with surgery is the mainstay of treatment in head and neck squamous cell carcinoma (HNSCC). Radioresistance represents a major source of treatment failure, underlining the urgent necessity to explore and implement effective radiosensitization strategies. The MET receptor widely participates in the acquisition and maintenance of an aggressive phenotype in HNSCC and modulates the DNA damage response following ionizing radiation (IR). Here, we assessed MET expression and mutation status in primary and metastatic lesions within a cohort of patients with advanced HNSCC. Moreover, we investigated the radiosensitization potential of the MET inhibitor tepotinib in a panel of cell lines, in vitro and in vivo, as well as in ex vivo patient-derived organotypic tissue cultures (OTC). MET was highly expressed in 62.4% of primary tumors and in 53.6% of lymph node metastases (LNM), and in 6 of 9 evaluated cell lines. MET expression in primaries and LNMs was significantly associated with decreased disease control in univariate survival analyses. Tepotinib abrogated MET phosphorylation and to distinct extent MET downstream signaling. Pretreatment with tepotinib resulted in variable radiosensitization, enhanced DNA damage, cell death, and G2-M-phase arrest. Combination of tepotinib with IR led to significant radiosensitization in one of two tested in vivo models. OTCs revealed differential patterns of response toward tepotinib, irradiation, and combination of both modalities. The molecular basis of tepotinib-mediated radiosensitization was studied by a CyTOF-based single-cell mass cytometry approach, which uncovered that MET inhibition modulated PI3K activity in cells radiosensitized by tepotinib but not in the resistant ones.

ProtRank: bypassing the imputation of missing values in differential expression analysis of proteomic data.

BACKGROUND: Data from discovery proteomic and phosphoproteomic experiments typically include missing values that correspond to proteins that have not been identified in the analyzed sample. Replacing the missing values with random numbers, a process known as "imputation", avoids apparent infinite fold-change values. However, the procedure comes at a cost: Imputing a large number of missing values has the potential to significantly impact the results of the subsequent differential expression analysis. RESULTS: We propose a method that identifies differentially expressed proteins by ranking their observed changes with respect to the changes observed for other proteins. Missing values are taken into account by this method directly, without the need to impute them. We illustrate the performance of the new method on two distinct datasets and show that it is robust to missing values and, at the same time, provides results that are otherwise similar to those obtained with edgeR which is a state-of-art differential expression analysis method. CONCLUSIONS: The new method for the differential expression analysis of proteomic data is available as an easy to use Python package.

Comprehensive Genomic Profiling of Patient-matched Head and Neck Cancer Cells: A Preclinical Pipeline for Metastatic and Recurrent Disease.

Metastases and tumor recurrence have a major prognostic impact in head and neck squamous cell carcinoma (HNSCC); however, cellular models that comprehensively characterize metastatic and recurrent HNSCC are lacking. To this end, we obtained genomic, transcriptomic, and copy number profiles of the UM-SCC cell line panel, encompassing patient-matched metastatic and recurrent cells. UM-SCC cells recapitulate the most prevalent genomic alterations described in HNSCC, featuring common TP53, PI3K, NOTCH, and Hippo pathway mutations. This analysis identified a novel F977Y kinase domain PIK3CA mutation exclusively present in a recurrent cell line (UM-SCC14B), potentially conferring resistance to PI3K inhibitors. Small proline-rich protein 2A (SPRR2A), a protein involved in epithelial homeostasis and invasion, was one of the most consistently downregulated transcripts in metastatic and recurrent UM-SCC cells. Assessment of SPRR2A protein expression in a clinical cohort of patients with HNSCC confirmed common SPRR2A downregulation in primary tumors (61.9% of cases) and lymph node metastases (31.3%), but not in normal tissue. High expression of SPRR2A in lymph node metastases was, along with nonoropharyngeal location of the primary tumor, an independent prognostic factor for regional disease recurrence after surgery and radiotherapy (HR 2.81; 95% CI, 1.16-6.79; P = 0.02). These results suggest that SPRR2A plays a dual role in invasion and therapeutic resistance in HNSCC, respectively through its downregulation and overexpression. IMPLICATIONS: The current study reveals translationally relevant mechanisms underlying metastasis and recurrence in HNSCC and represents an adjuvant tool for preclinical research in this disease setting. Underlining its discovery potential this approach identified a PIK3CA-resistant mutation as well as SPRR2A as possible theragnostic markers.

Randomizing growing networks with a time-respecting null model.

Complex networks are often used to represent systems that are not static but grow with time: People make new friendships, new papers are published and refer to the existing ones, and so forth. To assess the statistical significance of measurements made on such networks, we propose a randomization methodology-a time-respecting null model-that preserves both the network's degree sequence and the time evolution of individual nodes' degree values. By preserving the temporal linking patterns of the analyzed system, the proposed model is able to factor out the effect of the system's temporal patterns on its structure. We apply the model to the citation network of Physical Review scholarly papers and the citation network of US movies. The model reveals that the two data sets are strikingly different with respect to their degree-degree correlations, and we discuss the important implications of this finding on the information provided by paradigmatic node centrality metrics such as indegree and Google's PageRank. The randomization methodology proposed here can be used to assess the significance of any structural property in growing networks, which could bring new insights into the problems where null models play a critical role, such as the detection of communities and network motifs.

Link Prediction in Bipartite Nested Networks.

Real networks typically studied in various research fields-ecology and economic complexity, for example-often exhibit a nested topology, which means that the neighborhoods of high-degree nodes tend to include the neighborhoods of low-degree nodes. Focusing on nested networks, we study the problem of link prediction in complex networks, which aims at identifying likely candidates for missing links. We find that a new method that takes network nestedness into account outperforms well-established link-prediction methods not only when the input networks are sufficiently nested, but also for networks where the nested structure is imperfect. Our study paves the way to search for optimal methods for link prediction in nested networks, which might be beneficial for World Trade and ecological network analysis.

Information filtering based on corrected redundancy-eliminating mass diffusion.

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

Identification and impact of discoverers in online social systems.

Understanding the behavior of users in online systems is of essential importance for sociology, system design, e-commerce, and beyond. Most existing models assume that individuals in diverse systems, ranging from social networks to e-commerce platforms, tend to what is already popular. We propose a statistical time-aware framework to identify the users who differ from the usual behavior by being repeatedly and persistently among the first to collect the items that later become hugely popular. Since these users effectively discover future hits, we refer them as discoverers. We use the proposed framework to demonstrate that discoverers are present in a wide range of real systems. Once identified, discoverers can be used to predict the future success of new items. We finally introduce a simple network model which reproduces the discovery patterns observed in the real data. Our results open the door to quantitative study of detailed temporal patterns in social systems.

Model-based evaluation of scientific impact indicators.

Using bibliometric data artificially generated through a model of citation dynamics calibrated on empirical data, we compare several indicators for the scientific impact of individual researchers. The use of such a controlled setup has the advantage of avoiding the biases present in real databases, and it allows us to assess which aspects of the model dynamics and which traits of individual researchers a particular indicator actually reflects. We find that the simple average citation count of the authored papers performs well in capturing the intrinsic scientific ability of researchers, regardless of the length of their career. On the other hand, when productivity complements ability in the evaluation process, the notorious h and g indices reveal their potential, yet their normalized variants do not always yield a fair comparison between researchers at different career stages. Notably, the use of logarithmic units for citation counts allows us to build simple indicators with performance equal to that of h and g. Our analysis may provide useful hints for a proper use of bibliometric indicators. Additionally, our framework can be extended by including other aspects of the scientific production process and citation dynamics, with the potential to become a standard tool for the assessment of impact metrics.

Ranking nodes in growing networks: When PageRank fails.

PageRank is arguably the most popular ranking algorithm which is being applied in real systems ranging from information to biological and infrastructure networks. Despite its outstanding popularity and broad use in different areas of science, the relation between the algorithm's efficacy and properties of the network on which it acts has not yet been fully understood. We study here PageRank's performance on a network model supported by real data, and show that realistic temporal effects make PageRank fail in individuating the most valuable nodes for a broad range of model parameters. Results on real data are in qualitative agreement with our model-based findings. This failure of PageRank reveals that the static approach to information filtering is inappropriate for a broad class of growing systems, and suggest that time-dependent algorithms that are based on the temporal linking patterns of these systems are needed to better rank the nodes.

Network-driven reputation in online scientific communities.

The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.

Statistical validation of high-dimensional models of growing networks.

The abundance of models of complex networks and the current insufficient validation standards make it difficult to judge which models are strongly supported by data and which are not. We focus here on likelihood maximization methods for models of growing networks with many parameters and compare their performance on artificial and real datasets. While high dimensionality of the parameter space harms the performance of direct likelihood maximization on artificial data, this can be improved by introducing a suitable penalization term. Likelihood maximization on real data shows that the presented approach is able to discriminate among available network models. To make large-scale datasets accessible to this kind of analysis, we propose a subset sampling technique and show that it yields substantial model evidence in a fraction of time necessary for the analysis of the complete data.