scholarly journals Expanding discovery from cancer genomes by integrating protein network analyses with in vivo tumorigenesis assays

2017 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Candace R. Chouinard ◽  
Yashaswi Shrestha ◽  
Jessica Xin Hu ◽  
...  
Keyword(s):  
Statistical Tests ◽  
Cancer Genes ◽  
Driver Genes ◽  
Network Information ◽  
Network Analyses ◽  
Genome Data ◽  
Cancer Genomes ◽  
Complement Gene ◽  
Tumor Types

AbstractApproaches that integrate molecular network information and tumor genome data could complement gene-based statistical tests to identify likely new cancer genes, but are challenging to validate at scale and their predictive value remains unclear. We developed a robust statistic (NetSig) that integrates protein interaction networks and data from 4,742 tumor exomes and used it to accurately classify known driver genes in 60% of tested tumor types and to predict 62 new candidates. We designed a quantitative experimental framework to compare the in vivo tumorigenic potential of NetSig candidates, known oncogenes and random genes in mice showing that NetSig candidates induce tumors at rates comparable to known oncogenes and 10-fold higher than random genes. By reanalyzing nine tumor-inducing NetSig candidates in 242 patients with oncogene-negative lung adenocarcinomas, we find that two (AKT2 and TFDP2) are significantly amplified. Overall, we illustrate a scalable integrated computational and experimental workflow to expand discovery from cancer genomes.

scholarly journals Pathway and network analysis of more than 2,500 whole cancer genomes

2018 ◽  
Author(s):  
Matthew A. Reyna ◽  
David Haan ◽  
Marta Paczkowska ◽  
Lieven P.C. Verbeke ◽  
Miguel Vazquez ◽  
...  
Keyword(s):  
Rna Splicing ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Protein Coding ◽  
Cancer Driver ◽  
Network Analyses ◽  
Protein Coding Genes ◽  
Cancer Genomes ◽  
Tumor Types ◽  
Promoter Mutations

AbstractThe catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notablyTERTpromoter mutations, have been reported. Motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes, we performed multi-faceted pathway and network analyses of non-coding mutations across 2,583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project. While few non-coding genomic elements were recurrently mutated in this cohort, we identified 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression inTP53, TLE4, andTCF4. We found that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing was primarily targeted by non-coding mutations in this cohort, with samples containing non-coding mutations exhibiting similar gene expression signatures as coding mutations in well-known RNA splicing factors. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

scholarly journals A comparative analysis of network mutation burdens across 21 tumor types augments discovery from cancer genomes

2015 ◽  
Author(s):  
Heiko Horn ◽  
Michael S. Lawrence ◽  
Jessica Xin Hu ◽  
Elizabeth Worstell ◽  
Nina Ilic ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Cancer Biology ◽  
Low Frequency ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Sequencing Data ◽  
Driver Genes ◽  
Significance Level ◽  
Cancer Genomes ◽  
Tumor Types

Heterogeneity across cancer makes it difficult to find driver genes with intermediate (2-20%) and low frequency (<2%) mutations, and we are potentially missing entire classes of networks (or pathways) of biological and therapeutic value. Here, we quantify the extent to which cancer genes across 21 tumor types have an increased burden of mutations in their immediate gene network derived from functional genomics data. We formalize a classifier that accurately calculates the significance level of a gene’s network mutation burden (NMB) and show it can accurately predict known cancer genes and recently proposed driver genes in the majority of tested tumours. Our approach predicts 62 putative cancer genes, including 35 with clear connection to cancer and 27 genes, which point to new cancer biology. NMB identifies proportionally more (4x) low-frequency mutated genes as putative cancer genes than gene-based tests, and provides molecular clues in patients without established driver mutations. Our quantitative and comparative analysis of pan-cancer networks across 21 tumour types gives new insights into the biological and genetic architecture of cancers and enables additional discovery from existing cancer genomes. The framework we present here should become increasingly useful with more sequencing data in the future.

scholarly journals In silico saturation mutagenesis of cancer genes

2020 ◽  
Author(s):  
Ferran Muiños ◽  
Francisco Martinez-Jimenez ◽  
Oriol Pich ◽  
Abel Gonzalez-Perez ◽  
Nuria Lopez-Bigas
Keyword(s):  
In Silico ◽  
Tumor Development ◽  
Saturation Mutagenesis ◽  
Driver Mutations ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Mutation Probability ◽  
Tumor Types ◽  
Tumor Somatic Mutations

SummaryExtensive bioinformatics analysis of datasets of tumor somatic mutations data have revealed the presence of some 500-600 cancer driver genes. The identification of all potential driver mutations affecting cancer genes is essential to implement precision cancer medicine and to understand the interplay of mutation probability and selection in tumor development. Here, we present an in silico saturation mutagenesis approach to identify all driver mutations in 568 cancer genes across 66 tumor types. For most cancer genes the mutation probability across tissues --underpinned by active mutational processes-- influences which driver variants have been observed, although this differs significantly between tumor suppressor and oncogenes. The role of selection is apparent in some of the latter, the observed and unobserved driver mutations of which are equally likely to occur. The number of potential driver mutations in a cancer gene roughly determines how many mutations are available for detection across newly sequenced tumors.

scholarly journals EIF4G1 and RAN as Possible Drivers for Malignant Pleural Mesothelioma

2020 ◽  
Vol 21 (14) ◽  
pp. 4856 ◽  
Author(s):  
Irene Dell’Anno ◽  
Marcella Barbarino ◽  
Elisa Barone ◽  
Antonio Giordano ◽  
Luca Luzzi ◽  
...  
Keyword(s):  
Cell Lines ◽  
Malignant Pleural Mesothelioma ◽  
Colony Formation ◽  
Mesothelial Cell ◽  
In Vivo Studies ◽  
Pleural Mesothelioma ◽  
Cancer Genes ◽  
Driver Genes

For malignant pleural mesothelioma (MPM) novel therapeutic strategies are urgently needed. In a previous study, we identified 51 putative cancer genes over-expressed in MPM tissues and cell lines. Here, we deepened the study on nine of them (ASS1, EIF4G1, GALNT7, GLUT1, IGF2BP3 (IMP3), ITGA4, RAN, SOD1, and THBS2) to ascertain whether they are truly mesothelial cancer driver genes (CDGs) or genes overexpressed in an adaptive response to the tumoral progression (“passenger genes”). Through a fast siRNA-based screening, we evaluated the consequences of gene depletion on migration, proliferation, colony formation capabilities, and caspase activities of four MPM (Mero-14, Mero-25, IST-Mes2, and NCI-H28) and one SV40-immortalized mesothelial cell line (MeT-5A) as a non-malignant model. The depletion of EIF4G1 and RAN significantly reduced cell proliferation and colony formation and increased caspase activity. In particular, the findings for RAN resemble those observed for other types of cancer. Thus, we evaluated the in vitro effects of importazole (IPZ), a small molecule inhibitor of the interaction between RAN and importin-β. We showed that IPZ could have effects similar to those observed following RAN gene silencing. We also found that primary cell lines from one out of three MPM patients were sensitive to IPZ. As EIF4G1 and RAN deserve further investigation with additional in vitro and in vivo studies, they emerged as promising CDGs, suggesting that their upregulation could play a role in mesothelial tumorigenesis and aggressiveness. Furthermore, present data propose the molecular pathways dependent on RAN as a putative pharmacological target for MPM patients in the view of a future personalized medicine.

Cellular Localization of Enzymatically Active Thrombin in Intact Human Tissues by Hirudin Binding

1995 ◽  
Vol 73 (05) ◽  
pp. 793-797 ◽  
Author(s):  
Leo R Zacharski ◽  
Vincent A Memoli ◽  
William D Morain ◽  
Jean-Marc Schlaeppi ◽  
Sandra M Rousseau
Keyword(s):  
Tumor Cell ◽  
Cell Carcinoma ◽  
Cellular Localization ◽  
Thrombin Generation ◽  
Normal Lung ◽  
Cancer Tissue ◽  
Immunohistochemical Techniques ◽  
Tumor Types ◽  
Negative Controls

SummaryCellular sites of coagulation activation within complex, intact tissues have been studied by immunohistochemical techniques. Hirudin, a specific and high affinity inihibitor of the active site of thrombin, together with antibody to hirudin were applied to sections of AMeX-fixed specimens of normal lung, kidney, placenta, freshly incised skin and unperturbed skin obtained at fresh autopsy; to rheumatoid synovial tissue; and to malignant tissue from a variety of tumor types. Staining for thrombin was observed selectively on pulmonary alveolar, rheumatoid synovial, and placental macrophages that express an intact extrinsic coagulation pathway. Staining was also observed restricted to the endothelium of capillaries in freshly incised skin but not in either unperturbed skin or in aged incisions. Staining of tumor cell bodies was observed in small cell carcinoma of the lung, renal cell carcinoma, and malignant melanoma tissues that we found previously to show tumor cell-associated procoagulant activity. This staining occurred commonly on cells within the tumor mass that were distant from stromal fibrinogen/fibrin. By contrast, tumor-associated macrophage but not tumor cell staining was seen in adenocarcinoma and squamous cell carcinoma of the lung, and little or no staining was seen in colon cancer tissue. Negative controls in which either the hirudin probe or its antibody were omitted failed to show staining. These results are in accord with previous findings and suggest that such techniques may be useful for studying the cellular sites of thrombin generation in intact tissues. We postulate that administration of potent and specific thrombin antagonists, such as hirudin, to patients with relevant tumor types might be followed by homing of hirudin to tumor cells in vivo so that effects of local thrombin generation on malignant progression can be determined.

scholarly journals Predicting pathogenic non-coding SVs disrupting the 3D genome in 1646 whole cancer genomes using multiple instance learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Marleen M. Nieboer ◽  
Luan Nguyen ◽  
Jeroen de Ridder
Keyword(s):  
Multiple Instance Learning ◽  
Cancer Diagnostics ◽  
Common Mechanism ◽  
Open Chromatin ◽  
Driver Genes ◽  
3D Genome ◽  
Whole Genomes ◽  
Cancer Genomes ◽  
Cancer Types ◽  
The Impact

AbstractOver the past years, large consortia have been established to fuel the sequencing of whole genomes of many cancer patients. Despite the increased abundance in tools to study the impact of SNVs, non-coding SVs have been largely ignored in these data. Here, we introduce svMIL2, an improved version of our Multiple Instance Learning-based method to study the effect of somatic non-coding SVs disrupting boundaries of TADs and CTCF loops in 1646 cancer genomes. We demonstrate that svMIL2 predicts pathogenic non-coding SVs with an average AUC of 0.86 across 12 cancer types, and identifies non-coding SVs affecting well-known driver genes. The disruption of active (super) enhancers in open chromatin regions appears to be a common mechanism by which non-coding SVs exert their pathogenicity. Finally, our results reveal that the contribution of pathogenic non-coding SVs as opposed to driver SNVs may highly vary between cancers, with notably high numbers of genes being disrupted by pathogenic non-coding SVs in ovarian and pancreatic cancer. Taken together, our machine learning method offers a potent way to prioritize putatively pathogenic non-coding SVs and leverage non-coding SVs to identify driver genes. Moreover, our analysis of 1646 cancer genomes demonstrates the importance of including non-coding SVs in cancer diagnostics.

scholarly journals Ranking cancer drivers via betweenness-based outlier detection and random walks

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Cesim Erten ◽  
Aissa Houdjedj ◽  
Hilal Kazan
Keyword(s):  
Cancer Genomics ◽  
Interaction Network ◽  
Molecular Data ◽  
Alternative Methods ◽  
Patient Specific ◽  
Cancer Genes ◽  
Driver Genes ◽  
Cancer Driver ◽  
Protein Protein Interaction ◽  
Genomic Studies

Abstract Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

scholarly journals Ubiquitylome Analysis Reveals a Central Role for the Ubiquitin-Proteasome System in Plant Innate Immunity

2021 ◽  
Author(s):  
Xiyu Ma ◽  
Chao Zhang ◽  
Do Young Kim ◽  
Yanyan Huang ◽  
Elizabeth Chatt ◽  
...  
Keyword(s):  
Plant Immunity ◽  
High Sensitivity ◽  
Ubiquitin Proteasome System ◽  
Immune Recognition ◽  
Atpase Subunit ◽  
Ample Evidence ◽  
Network Analyses ◽  
Affinity Enrichment ◽  
Regulatory Loop

Abstract Protein ubiquitylation profoundly expands proteome functionality and diversifies cellular signaling processes, with recent studies providing ample evidence for its importance to plant immunity. To gain a proteome-wide appreciation of ubiquitylome dynamics during immune recognition, we employed a two-step affinity enrichment protocol based on a 6His-tagged ubiquitin (Ub) variant coupled with high sensitivity mass spectrometry to identify Arabidopsis proteins rapidly ubiquitylated upon plant perception of the microbe-associated molecular pattern (MAMP) peptide flg22. The catalog from 2-week-old seedlings treated for 30 minutes with flg22 contained 690 conjugates, 64 Ub footprints, and all seven types of Ub linkages, and included previously uncharacterized conjugates of immune components. In vivo ubiquitylation assays confirmed modification of several candidates upon immune elicitation, and revealed distinct modification patterns and dynamics for key immune components, including poly- and monoubiquitylation, as well as induced or reduced levels of ubiquitylation. Gene ontology and network analyses of the collection also uncovered rapid modification of the Ub-proteasome system itself, suggesting a critical auto-regulatory loop necessary for an effective MAMP-triggered immune response and subsequent disease resistance. Included targets were UBIQUITIN-CONJUGATING ENZYME 13 (UBC13) and proteasome component REGULATORY PARTICLE NON-ATPASE SUBUNIT 8b (RPN8b), whose subsequent biochemical and genetic analyses implied negative roles in immune elicitation. Collectively, our proteomic analyses further strengthened the connection between ubiquitylation and flg22-based immune signaling, identified components and pathways regulating plant immunity, and increased the database of ubiquitylated substrates in plants.

scholarly journals driveR: a novel method for prioritizing cancer driver genes using somatic genomics data

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Ege Ülgen ◽  
O. Uğur Sezerman
Keyword(s):  
Biological Knowledge ◽  
Driver Gene ◽  
Driver Genes ◽  
Cancer Driver ◽  
Prior Biological Knowledge ◽  
Wilcoxon Rank Sum Test ◽  
Cancer Genomes ◽  
Novel Method ◽  
Cancer Driver Genes ◽  
Batch Analysis

Abstract Background Cancer develops due to “driver” alterations. Numerous approaches exist for predicting cancer drivers from cohort-scale genomics data. However, methods for personalized analysis of driver genes are underdeveloped. In this study, we developed a novel personalized/batch analysis approach for driver gene prioritization utilizing somatic genomics data, called driveR. Results Combining genomics information and prior biological knowledge, driveR accurately prioritizes cancer driver genes via a multi-task learning model. Testing on 28 different datasets, this study demonstrates that driveR performs adequately, achieving a median AUC of 0.684 (range 0.651–0.861) on the 28 batch analysis test datasets, and a median AUC of 0.773 (range 0–1) on the 5157 personalized analysis test samples. Moreover, it outperforms existing approaches, achieving a significantly higher median AUC than all of MutSigCV (Wilcoxon rank-sum test p < 0.001), DriverNet (p < 0.001), OncodriveFML (p < 0.001) and MutPanning (p < 0.001) on batch analysis test datasets, and a significantly higher median AUC than DawnRank (p < 0.001) and PRODIGY (p < 0.001) on personalized analysis datasets. Conclusions This study demonstrates that the proposed method is an accurate and easy-to-utilize approach for prioritizing driver genes in cancer genomes in personalized or batch analyses. driveR is available on CRAN: https://cran.r-project.org/package=driveR.

scholarly journals NDUFA4L2 promotes glioblastoma progression, is associated with poor survival, and can be effectively targeted by apatinib

2021 ◽  
Vol 12 (4) ◽  
Author(s):  
Zheng Chen ◽  
Xiangyu Wei ◽  
Xueyi Wang ◽  
Xuan Zheng ◽  
Bowen Chang ◽  
...  
Keyword(s):  
Tumor Cell ◽  
Hypoxia Inducible Factor ◽  
Mitochondrial Respiratory Chain ◽  
Metabolic Reprogramming ◽  
Tumor Cell Apoptosis ◽  
Survival Gene ◽  
A Subunit ◽  
Tumor Types

AbstractNADH dehydrogenase [ubiquinone] 1 alpha subcomplex, 4-like 2 (NDUFA4L2) is a subunit of Complex I of the mitochondrial respiratory chain, which is important in metabolic reprogramming and oxidative stress in multiple cancers. However, the biological role and molecular regulation of NDUFA4L2 in glioblastoma (GBM) are poorly understood. Here, we found that NDUFA4L2 was significantly upregulated in GBM; the elevated levels were correlated with reduced patient survival. Gene knockdown of NDUFA4L2 inhibited tumor cell proliferation and enhanced apoptosis, while tumor cells initiated protective mitophagy in vitro and in vivo. We used lentivirus to reduce expression levels of NDUFA4L2 protein in GBM cells exposed to mitophagy blockers, which led to a significant enhancement of tumor cell apoptosis in vitro and inhibited the development of xenografted tumors in vivo. In contrast to other tumor types, NDUFA4L2 expression in GBM may not be directly regulated by hypoxia-inducible factor (HIF)-1α, because HIF-1α inhibitors failed to inhibit NDUFA4L2 in GBM. Apatinib was able to effectively target NDUFA4L2 in GBM, presenting an alternative to the use of lentiviruses, which currently cannot be used in humans. Taken together, our data suggest the use of NDUFA4L2 as a potential therapeutic target in GBM and demonstrate a practical treatment approach.

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