Bioinformatique et biologie des systèmes du cancer

Publications de l’équipe

Année de publication : 2016

Chenjie Zeng, Xingyi Guo, Jirong Long, Karoline B Kuchenbaecker, Arnaud Droit, Kyriaki Michailidou, Maya Ghoussaini, Siddhartha Kar, Adam Freeman, John L Hopper, Roger L Milne, Manjeet K Bolla, Qin Wang, Joe Dennis, Simona Agata, Shahana Ahmed, Kristiina Aittomäki, Irene L Andrulis, Hoda Anton-Culver, Natalia N Antonenkova, Adalgeir Arason, Volker Arndt, Banu K Arun, Brita Arver, Francois Bacot, Daniel Barrowdale, Caroline Baynes, Alicia Beeghly-Fadiel, Javier Benitez, Marina Bermisheva, Carl Blomqvist, William J Blot, Natalia V Bogdanova, Stig E Bojesen, Bernardo Bonanni, Anne-Lise Borresen-Dale, Judith S Brand, Hiltrud Brauch, Paul Brennan, Hermann Brenner, Annegien Broeks, Thomas Brüning, Barbara Burwinkel, Saundra S Buys, Qiuyin Cai, Trinidad Caldes, Ian Campbell, Jane Carpenter, Jenny Chang-Claude, Ji-Yeob Choi, Kathleen B M Claes, Christine Clarke, Angela Cox, Simon S Cross, Kamila Czene, Mary B Daly, Miguel de la Hoya, Kim De Leeneer, Peter Devilee, Orland Diez, Susan M Domchek, Michele Doody, Cecilia M Dorfling, Thilo Dörk, Isabel Dos-Santos-Silva, Martine Dumont, Miriam Dwek, Bernd Dworniczak, Kathleen Egan, Ursula Eilber, Zakaria Einbeigi, Bent Ejlertsen, Steve Ellis, Debra Frost, Fiona Lalloo, , Peter A Fasching, Jonine Figueroa, Henrik Flyger, Michael Friedlander, Eitan Friedman, Gaetana Gambino, Yu-Tang Gao, Judy Garber, Montserrat García-Closas, Andrea Gehrig, Francesca Damiola, Fabienne Lesueur, Sylvie Mazoyer, Dominique Stoppa-Lyonnet, , Graham G Giles, Andrew K Godwin, David E Goldgar, Anna González-Neira, Mark H Greene, Pascal Guénel, Lothar Haeberle, Christopher A Haiman, Emily Hallberg, Ute Hamann, Thomas V O Hansen, Steven Hart, Jaana M Hartikainen, Mikael Hartman, Norhashimah Hassan, Sue Healey, Frans B L Hogervorst, Senno Verhoef, , Carolyn B Hendricks, Peter Hillemanns, Antoinette Hollestelle, Peter J Hulick, David J Hunter, Evgeny N Imyanitov, Claudine Isaacs, Hidemi Ito, Anna Jakubowska, Ramunas Janavicius, Katarzyna Jaworska-Bieniek, Uffe Birk Jensen, Esther M John, Charles Joly Beauparlant, Michael Jones, Maria Kabisch, Daehee Kang, Beth Y Karlan, Saila Kauppila, Michael J Kerin, Sofia Khan, Elza Khusnutdinova, Julia A Knight, Irene Konstantopoulou, Peter Kraft, Ava Kwong, Yael Laitman, Diether Lambrechts, Conxi Lazaro, Loic Le Marchand, Chuen Neng Lee, Min Hyuk Lee, Jenny Lester, Jingmei Li, Annelie Liljegren, Annika Lindblom, Artitaya Lophatananon, Jan Lubinski, Phuong L Mai, Arto Mannermaa, Siranoush Manoukian, Sara Margolin, Frederik Marme, Keitaro Matsuo, Lesley McGuffog, Alfons Meindl, Florence Menegaux, Marco Montagna, Kenneth Muir, Anna Marie Mulligan, Katherine L Nathanson, Susan L Neuhausen, Heli Nevanlinna, Polly A Newcomb, Silje Nord, Robert L Nussbaum, Kenneth Offit, Edith Olah, Olufunmilayo I Olopade, Curtis Olswold, Ana Osorio, Laura Papi, Tjoung-Won Park-Simon, Ylva Paulsson-Karlsson, Stephanie Peeters, Bernard Peissel, Paolo Peterlongo, Julian Peto, Georg Pfeiler, Catherine M Phelan, Nadege Presneau, Paolo Radice, Nazneen Rahman, Susan J Ramus, Muhammad Usman Rashid, Gad Rennert, Kerstin Rhiem, Anja Rudolph, Ritu Salani, Suleeporn Sangrajrang, Elinor J Sawyer, Marjanka K Schmidt, Rita K Schmutzler, Minouk J Schoemaker, Peter Schürmann, Caroline Seynaeve, Chen-Yang Shen, Martha J Shrubsole, Xiao-Ou Shu, Alice Sigurdson, Christian F Singer, Susan Slager, Penny Soucy, Melissa Southey, Doris Steinemann, Anthony Swerdlow, Csilla I Szabo, Sandrine Tchatchou, Manuel R Teixeira, Soo H Teo, Mary Beth Terry, Daniel C Tessier, Alex Teulé, Mads Thomassen, Laima Tihomirova, Marc Tischkowitz, Amanda E Toland, Nadine Tung, Clare Turnbull, Ans M W van den Ouweland, Elizabeth J van Rensburg, David Ven den Berg, Joseph Vijai, Shan Wang-Gohrke, Jeffrey N Weitzel, Alice S Whittemore, Robert Winqvist, Tien Y Wong, Anna H Wu, Drakoulis Yannoukakos, Jyh-Cherng Yu, Paul D P Pharoah, Per Hall, Georgia Chenevix-Trench, , , Alison M Dunning, Jacques Simard, Fergus J Couch, Antonis C Antoniou, Douglas F Easton, Wei Zheng (2016 Jul 28)

Identification of independent association signals and putative functional variants for breast cancer risk through fine-scale mapping of the 12p11 locus.

Breast cancer research : BCR : 64 : DOI : 10.1186/s13058-016-0718-0 En savoir plus
Résumé

Multiple recent genome-wide association studies (GWAS) have identified a single nucleotide polymorphism (SNP), rs10771399, at 12p11 that is associated with breast cancer risk.

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Rafael Ríos-Tamayo, Carmen Belén Lupiañez, Daniele Campa, Thomas Hielscher, Niels Weinhold, Joaquin Martínez-López, Andrés Jerez, Stefano Landi, Krzysztof Jamroziak, Charles Dumontet, Marzena Wątek, Fabienne Lesueur, Rui Manuel Reis, Herlander Marques, Artur Jurczyszyn, Ulla Vogel, Gabriele Buda, Ramón García-Sanz, Enrico Orciuolo, Mario Petrini, Annette J Vangsted, Federica Gemignani, Asta Försti, Hartmut Goldschmidt, Kari Hemminki, Federico Canzian, Manuel Jurado, Juan Sainz (2016 Jul 21)

A common variant within the HNF1B gene is associated with overall survival of multiple myeloma patients: results from the IMMEnSE consortium and meta-analysis.

Oncotarget : 59029-59048 : DOI : 10.18632/oncotarget.10665 En savoir plus
Résumé

Diabetogenic single nucleotide polymorphisms (SNPs) have recently been associated with multiple myeloma (MM) risk but their impact on overall survival (OS) of MM patients has not been analysed yet. In order to investigate the impact of 58 GWAS-identified variants for type 2 diabetes (T2D) on OS of patients with MM, we analysed genotyping data of 936 MM patients collected by the International Multiple Myeloma rESEarch (IMMENSE) consortium and an independent set of 700 MM patients recruited by the University Clinic of Heidelberg. A meta-analysis of the cox regression results of the two sets showed that rs7501939 located in the HNF1B gene negatively impacted OS (HRRec= 1.44, 95% CI = 1.18-1.76, P = 0.0001). The meta-analysis also showed a noteworthy gender-specific association of the SLC30A8rs13266634 SNP with OS. The presence of each additional copy of the minor allele at rs13266634 was associated with poor OS in men whereas no association was seen in women (HRMen-Add = 1.32, 95% CI 1.13-1.54, P = 0.0003). In conclusion, these data suggest that the HNF1Brs7501939 SNP confers poor OS in patients with MM and that a SNP in SLC30A8 affect OS in men.

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Adrien Rougny, Christine Froidevaux, Laurence Calzone, Loïc Paulevé (2016 Jun 17)

Qualitative dynamics semantics for SBGN process description.

BMC systems biology : 42 : DOI : 10.1186/s12918-016-0285-0 En savoir plus
Résumé

Qualitative dynamics semantics provide a coarse-grain modeling of networks dynamics by abstracting away kinetic parameters. They allow to capture general features of systems dynamics, such as attractors or reachability properties, for which scalable analyses exist. The Systems Biology Graphical Notation Process Description language (SBGN-PD) has become a standard to represent reaction networks. However, no qualitative dynamics semantics taking into account all the main features available in SBGN-PD had been proposed so far.

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Anne-Laure Renault, Fabienne Lesueur, Yan Coulombe, Stéphane Gobeil, Penny Soucy, Yosr Hamdi, Sylvie Desjardins, Florence Le Calvez-Kelm, Maxime Vallée, Catherine Voegele, , John L Hopper, Irene L Andrulis, Melissa C Southey, Esther M John, Jean-Yves Masson, Sean V Tavtigian, Jacques Simard (2016 Jun 9)

ABRAXAS (FAM175A) and Breast Cancer Susceptibility: No Evidence of Association in the Breast Cancer Family Registry.

PloS one : e0156820 : DOI : 10.1371/journal.pone.0156820 En savoir plus
Résumé

Approximately half of the familial aggregation of breast cancer remains unexplained. This proportion is less for early-onset disease where familial aggregation is greater, suggesting that other susceptibility genes remain to be discovered. The majority of known breast cancer susceptibility genes are involved in the DNA double-strand break repair pathway. ABRAXAS is involved in this pathway and mutations in this gene impair BRCA1 recruitment to DNA damage foci and increase cell sensitivity to ionizing radiation. Moreover, a recurrent germline mutation was reported in Finnish high-risk breast cancer families. To determine if ABRAXAS could be a breast cancer susceptibility gene in other populations, we conducted a population-based case-control mutation screening study of the coding exons and exon/intron boundaries of ABRAXAS in the Breast Cancer Family Registry. In addition to the common variant p.Asp373Asn, sixteen distinct rare variants were identified. Although no significant difference in allele frequencies between cases and controls was observed for the identified variants, two variants, p.Gly39Val and p.Thr141Ile, were shown to diminish phosphorylation of gamma-H2AX in MCF7 human breast adenocarcinoma cells, an important biomarker of DNA double-strand breaks. Overall, likely damaging or neutral variants were evenly represented among cases and controls suggesting that rare variants in ABRAXAS may explain only a small proportion of hereditary breast cancer.

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Marick Laé, Philippe La Rosa, Jonas Mandel, Fabien Reyal, Philippe Hupé, Philippe Terrier, Jérôme Couturier (2016 May 22)

Whole-genome profiling helps to classify phyllodes tumours of the breast.

Journal of clinical pathology : DOI : jclinpath-2016-203684 En savoir plus
Résumé

The aim of this study was to analyse a series of borderline and malignant phyllodes tumours (PTs) of the breast by whole-genome profiling to identify genomic markers that could help to recognise potentially malignant tumours within borderline tumours.

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Catherine Tcheandjieu, Fabienne Lesueur, Marie Sanchez, Dominique Baron-Dubourdieu, Anne-Valerie Guizard, Claire Mulot, Pierre Laurent-Puig, Claire Schvartz, Therese Truong, Pascal Guenel (2016 Mar 19)

Fine-mapping of two differentiated thyroid carcinoma susceptibility loci at 9q22.33 and 14q13.3 detects novel candidate functional SNPs in Europeans from metropolitan France and Melanesians from New Caledonia.

International journal of cancer : 617-27 : DOI : 10.1002/ijc.30088 En savoir plus
Résumé

Incidence of differentiated thyroid carcinoma varies considerably between countries and ethnic groups, with particularly high incidence rates in Melanesians of New Caledonia. Differentiated thyroid cancer (DTC) has a familial relative risk higher than other cancers, highlighting the contribution of inherited factors to the disease. Recently, genome-wide association studies (GWAS) identified several DTC susceptibility loci. The most robust associations were reported at loci 9q22 (rs965513 and rs1867277) and 14q13 (rs944289 and rs116909734). In this study, we performed a fine-mapping study of the two gene regions among Europeans and Melanesians from Metropolitan France and New Caledonia. We examined 81 single nucleotide polymorphisms (SNPs) at 9q22 and 561 SNPs at 14q13 in Europeans (625 cases/776 controls) and in Melanesians (244 cases/189 controls). The association with the four SNPs previously identified in GWAS was replicated in Europeans while only rs944289 was replicated in Melanesians. Among Europeans, we found that the two SNPs previously reported at 9q22 were not independently associated to DTC and that rs965513 was the predominant signal; at 14q13, we showed that the haplotype rs944289[C]-rs116909374[C]-rs999460[T] was significantly associated with DTC risk and that the association with rs116909374 differed by smoking status (p-interaction = 0.03). Among Melanesians, a new independent signal was observed at 14q13 for rs1755774 which is strongly correlated to rs2787423; this latter is potentially a functional variant. Significant interactions with parity (p < 0.05) and body mass index were observed for rs1755774 and rs2787423. This study contributed to a better characterization of the DTC loci 9q22 and 14q13 in Europeans and in Melanesians and has identified novel variants to be prioritized for further functional studies.

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Katia Ancelin, Laurène Syx, Maud Borensztein, Noémie Ranisavljevic, Ivaylo Vassilev, Luis Briseño-Roa, Tao Liu, Eric Metzger, Nicolas Servant, Emmanuel Barillot, Chong-Jian Chen, Roland Schüle, Edith Heard (2016 Feb 3)

Maternal LSD1/KDM1A is an essential regulator of chromatin and transcription landscapes during zygotic genome activation.

eLife : DOI : 10.7554/eLife.08851 En savoir plus
Résumé

Upon fertilization, the highly specialised sperm and oocyte genomes are remodelled to confer totipotency. The mechanisms of the dramatic reprogramming events that occur have remained unknown, and presumed roles of histone modifying enzymes are just starting to be elucidated. Here, we explore the function of the oocyte-inherited pool of a histone H3K4 and K9 demethylase, LSD1/KDM1A during early mouse development. KDM1A deficiency results in developmental arrest by the two-cell stage, accompanied by dramatic and stepwise alterations in H3K9 and H3K4 methylation patterns. At the transcriptional level, the switch of the maternal-to-zygotic transition fails to be induced properly and LINE-1 retrotransposons are not properly silenced. We propose that KDM1A plays critical roles in establishing the correct epigenetic landscape of the zygote upon fertilization, in preserving genome integrity and in initiating new patterns of genome expression that drive early mouse development.

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E L Young, B J Feng, A W Stark, F Damiola, G Durand, N Forey, T C Francy, A Gammon, W K Kohlmann, K A Kaphingst, S McKay-Chopin, T Nguyen-Dumont, J Oliver, A M Paquette, M Pertesi, N Robinot, J S Rosenthal, M Vallee, C Voegele, J L Hopper, M C Southey, I L Andrulis, E M John, M Hashibe, J Gertz, , F Le Calvez-Kelm, F Lesueur, D E Goldgar, S V Tavtigian (2016 Jan 21)

Multigene testing of moderate-risk genes: be mindful of the missense.

Journal of medical genetics : 366-76 : DOI : 10.1136/jmedgenet-2015-103398 En savoir plus
Résumé

Moderate-risk genes have not been extensively studied, and missense substitutions in them are generally returned to patients as variants of uncertain significance lacking clearly defined risk estimates. The fraction of early-onset breast cancer cases carrying moderate-risk genotypes and quantitative methods for flagging variants for further analysis have not been established.

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Olga M Sinilnikova, Marie-Gabrielle Dondon, Séverine Eon-Marchais, Francesca Damiola, Laure Barjhoux, Morgane Marcou, Carole Verny-Pierre, Valérie Sornin, Lucie Toulemonde, Juana Beauvallet, Dorothée Le Gal, Noura Mebirouk, Muriel Belotti, Olivier Caron, Marion Gauthier-Villars, Isabelle Coupier, Bruno Buecher, Alain Lortholary, Catherine Dugast, Paul Gesta, Jean-Pierre Fricker, Catherine Noguès, Laurence Faivre, Elisabeth Luporsi, Pascaline Berthet, Capucine Delnatte, Valérie Bonadona, Christine M Maugard, Pascal Pujol, Christine Lasset, Michel Longy, Yves-Jean Bignon, Claude Adenis, Laurence Venat-Bouvet, Liliane Demange, Hélène Dreyfus, Marc Frenay, Laurence Gladieff, Isabelle Mortemousque, Séverine Audebert-Bellanger, Florent Soubrier, Sophie Giraud, Sophie Lejeune-Dumoulin, Annie Chevrier, Jean-Marc Limacher, Jean Chiesa, Anne Fajac, Anne Floquet, François Eisinger, Julie Tinat, Chrystelle Colas, Sandra Fert-Ferrer, Clotilde Penet, Thierry Frebourg, Marie-Agnès Collonge-Rame, Emmanuelle Barouk-Simonet, Valérie Layet, Dominique Leroux, Odile Cohen-Haguenauer, Fabienne Prieur, Emmanuelle Mouret-Fourme, François Cornélis, Philippe Jonveaux, Odile Bera, Eve Cavaciuti, Anne Tardivon, Fabienne Lesueur, Sylvie Mazoyer, Dominique Stoppa-Lyonnet, Nadine Andrieu (2016 Jan 14)

GENESIS: a French national resource to study the missing heritability of breast cancer.

BMC cancer : 13 : DOI : 10.1186/s12885-015-2028-9 En savoir plus
Résumé

Less than 20% of familial breast cancer patients who undergo genetic testing for BRCA1 and BRCA2 carry a pathogenic mutation in one of these two genes. The GENESIS (GENE SISter) study was designed to identify new breast cancer susceptibility genes in women attending cancer genetics clinics and with no BRCA1/2 mutation.

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Daria Iakovishina, Isabelle Janoueix-Lerosey, Emmanuel Barillot, Mireille Regnier, Valentina Boeva (2016 Jan 8)

SV-Bay: structural variant detection in cancer genomes using a Bayesian approach with correction for GC-content and read mappability.

Bioinformatics (Oxford, England) : 984-92 : DOI : 10.1093/bioinformatics/btv751 En savoir plus
Résumé

Whole genome sequencing of paired-end reads can be applied to characterize the landscape of large somatic rearrangements of cancer genomes. Several methods for detecting structural variants with whole genome sequencing data have been developed. So far, none of these methods has combined information about abnormally mapped read pairs connecting rearranged regions and associated global copy number changes automatically inferred from the same sequencing data file. Our aim was to create a computational method that could use both types of information, i.e. normal and abnormal reads, and demonstrate that by doing so we can highly improve both sensitivity and specificity rates of structural variant prediction.

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Année de publication : 2015

David P A Cohen, Loredana Martignetti, Sylvie Robine, Emmanuel Barillot, Andrei Zinovyev, Laurence Calzone (2015 Nov 4)

Mathematical Modelling of Molecular Pathways Enabling Tumour Cell Invasion and Migration.

PLoS computational biology : e1004571 : DOI : 10.1371/journal.pcbi.1004571 En savoir plus
Résumé

Understanding the etiology of metastasis is very important in clinical perspective, since it is estimated that metastasis accounts for 90% of cancer patient mortality. Metastasis results from a sequence of multiple steps including invasion and migration. The early stages of metastasis are tightly controlled in normal cells and can be drastically affected by malignant mutations; therefore, they might constitute the principal determinants of the overall metastatic rate even if the later stages take long to occur. To elucidate the role of individual mutations or their combinations affecting the metastatic development, a logical model has been constructed that recapitulates published experimental results of known gene perturbations on local invasion and migration processes, and predict the effect of not yet experimentally assessed mutations. The model has been validated using experimental data on transcriptome dynamics following TGF-β-dependent induction of Epithelial to Mesenchymal Transition in lung cancer cell lines. A method to associate gene expression profiles with different stable state solutions of the logical model has been developed for that purpose. In addition, we have systematically predicted alleviating (masking) and synergistic pairwise genetic interactions between the genes composing the model with respect to the probability of acquiring the metastatic phenotype. We focused on several unexpected synergistic genetic interactions leading to theoretically very high metastasis probability. Among them, the synergistic combination of Notch overexpression and p53 deletion shows one of the strongest effects, which is in agreement with a recent published experiment in a mouse model of gut cancer. The mathematical model can recapitulate experimental mutations in both cell line and mouse models. Furthermore, the model predicts new gene perturbations that affect the early steps of metastasis underlying potential intervention points for innovative therapeutic strategies in oncology.

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Urszula Czerwinska, Laurence Calzone, Emmanuel Barillot, Andrei Zinovyev (2015 Aug 15)

DeDaL: Cytoscape 3 app for producing and morphing data-driven and structure-driven network layouts.

BMC systems biology : 46 : DOI : 10.1186/s12918-015-0189-4 En savoir plus
Résumé

Visualization and analysis of molecular profiling data together with biological networks are able to provide new mechanistic insights into biological functions. Currently, it is possible to visualize high-throughput data on top of pre-defined network layouts, but they are not always adapted to a given data analysis task. A network layout based simultaneously on the network structure and the associated multidimensional data might be advantageous for data visualization and analysis in some cases.

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I Kuperstein, E Bonnet, H-A Nguyen, D Cohen, E Viara, L Grieco, S Fourquet, L Calzone, C Russo, M Kondratova, M Dutreix, E Barillot, A Zinovyev (2015 Jul 21)

Atlas of Cancer Signalling Network: a systems biology resource for integrative analysis of cancer data with Google Maps.

Oncogenesis : e160 : DOI : 10.1038/oncsis.2015.19 En savoir plus
Résumé

Cancerogenesis is driven by mutations leading to aberrant functioning of a complex network of molecular interactions and simultaneously affecting multiple cellular functions. Therefore, the successful application of bioinformatics and systems biology methods for analysis of high-throughput data in cancer research heavily depends on availability of global and detailed reconstructions of signalling networks amenable for computational analysis. We present here the Atlas of Cancer Signalling Network (ACSN), an interactive and comprehensive map of molecular mechanisms implicated in cancer. The resource includes tools for map navigation, visualization and analysis of molecular data in the context of signalling network maps. Constructing and updating ACSN involves careful manual curation of molecular biology literature and participation of experts in the corresponding fields. The cancer-oriented content of ACSN is completely original and covers major mechanisms involved in cancer progression, including DNA repair, cell survival, apoptosis, cell cycle, EMT and cell motility. Cell signalling mechanisms are depicted in detail, together creating a seamless ‘geographic-like’ map of molecular interactions frequently deregulated in cancer. The map is browsable using NaviCell web interface using the Google Maps engine and semantic zooming principle. The associated web-blog provides a forum for commenting and curating the ACSN content. ACSN allows uploading heterogeneous omics data from users on top of the maps for visualization and performing functional analyses. We suggest several scenarios for ACSN application in cancer research, particularly for visualizing high-throughput data, starting from small interfering RNA-based screening results or mutation frequencies to innovative ways of exploring transcriptomes and phosphoproteomes. Integration and analysis of these data in the context of ACSN may help interpret their biological significance and formulate mechanistic hypotheses. ACSN may also support patient stratification, prediction of treatment response and resistance to cancer drugs, as well as design of novel treatment strategies.

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Mathurin Dorel, Emmanuel Barillot, Andrei Zinovyev, Inna Kuperstein (2015 Jun 19)

Network-based approaches for drug response prediction and targeted therapy development in cancer.

Biochemical and biophysical research communications : 386-91 : DOI : 10.1016/j.bbrc.2015.06.094 En savoir plus
Résumé

Signaling pathways implicated in cancer create a complex network with numerous regulatory loops and redundant pathways. This complexity explains frequent failure of one-drug-one-target paradigm of treatment, resulting in drug resistance in patients. To overcome the robustness of cell signaling network, cancer treatment should be extended to a combination therapy approach. Integrating and analyzing patient high-throughput data together with the information about biological signaling machinery may help deciphering molecular patterns specific to each patient and finding the best combinations of candidates for therapeutic targeting. We review state of the art in the field of targeted cancer medicine from the computational systems biology perspective. We summarize major signaling network resources and describe their characteristics with respect to applicability for drug response prediction and intervention targets suggestion. Thus discuss methods for prediction of drug sensitivity and intervention combinations using signaling networks together with high-throughput data. Gradual integration of these approaches into clinical routine will improve prediction of response to standard treatments and adjustment of intervention schemes.

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Laurence Calzone, Emmanuel Barillot, Andrei Zinovyev (2015 May 12)

Predicting genetic interactions from Boolean models of biological networks.

Integrative biology : quantitative biosciences from nano to macro : 921-9 : DOI : 10.1039/c5ib00029g En savoir plus
Résumé

Genetic interaction can be defined as a deviation of the phenotypic quantitative effect of a double gene mutation from the effect predicted from single mutations using a simple (e.g., multiplicative or linear additive) statistical model. Experimentally characterized genetic interaction networks in model organisms provide important insights into relationships between different biological functions. We describe a computational methodology allowing us to systematically and quantitatively characterize a Boolean mathematical model of a biological network in terms of genetic interactions between all loss of function and gain of function mutations with respect to all model phenotypes or outputs. We use the probabilistic framework defined in MaBoSS software, based on continuous time Markov chains and stochastic simulations. In addition, we suggest several computational tools for studying the distribution of double mutants in the space of model phenotype probabilities. We demonstrate this methodology on three published models for each of which we derive the genetic interaction networks and analyze their properties. We classify the obtained interactions according to their class of epistasis, dependence on the chosen initial conditions and the phenotype. The use of this methodology for validating mathematical models from experimental data and designing new experiments is discussed.

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