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PrECISE

PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION

Total Cost €

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EC-Contrib. €

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Partnership

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 PrECISE project word cloud

Explore the words cloud of the PrECISE project. It provides you a very rough idea of what is the project "PrECISE" about.

clinicians    mechanisms    implicated    cohorts    strategies    biomarker    predictive    deposit    throughput    mathematical    lines    failed    treatment    industrial    tools    translational    therapeutic    share    inform    driving    ibm    integration    patients    proof    translate    men    screenings    competing    promise    industries    framework    therapy    tumour    multiple    coexist    intuitive    biomedical    software    cells    watson    therapies    recommend    visualize    cognitive    single    types    last    drug    prone    infer    proteomic    primary    amongst    aggressive    generation    analyze    alterations    elucidate    subclones    renders    accordingly    cancer    individual    genomic    recommendations    metastatic    lies    clinical    accessible    graphical    disease    difficult    panel    heterogeneity    optimized    molecular    technologies    despite    statements    interface    actionable    prospective    computational    tumours    combines    transcriptomic    death    cell    prognosis    models    clinic    prostate    serve    data    intervention    computer   

Project "PrECISE" data sheet

The following table provides information about the project.

Coordinator
TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH 

Organization address
address: BURGPLATZ 3A
city: VILLACH
postcode: 9500
website: https://urldefense.com/v3/__https://technikon.com__;!!DOxrgLBm!XQj8lX3wvrhxnrAflv0OBz99-qlU79olpwsKnLk7T4NxnEuLngVJcmEmxcnyX1OKvqUsEW_8DocHehV2MbBX$

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Austria [AT]
 Project website https://precise-project.eu/
 Total cost 5˙695˙712 €
 EC max contribution 3˙090˙312 € (54%)
 Programme 1. H2020-EU.3.1.1. (Understanding health, wellbeing and disease)
 Code Call H2020-PHC-2015-two-stage
 Funding Scheme RIA
 Starting year 2016
 Duration (year-month-day) from 2016-01-01   to  2018-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    TECHNIKON FORSCHUNGS- UND PLANUNGSGESELLSCHAFT MBH AT (VILLACH) coordinator 358˙125.00
2    UNIVERSITAETSKLINIKUM AACHEN DE (AACHEN) participant 620˙937.00
3    BAYLOR COLLEGE OF MEDICINE US (HOUSTON TX) participant 561˙250.00
4    TECHNISCHE UNIVERSITAT DARMSTADT DE (DARMSTADT) participant 539˙375.00
5    INSTITUT CURIE FR (PARIS) participant 535˙625.00
6    ASTRIDBIO TECHNOLOGIES KFT HU (SZEGED) participant 475˙000.00
7    EIDGENOESSISCHE TECHNISCHE HOCHSCHULE ZUERICH CH (ZUERICH) participant 0.00
8    IBM RESEARCH GMBH CH (RUESCHLIKON) participant 0.00
9    UNIVERSITAT ZURICH CH (ZURICH) participant 0.00

Map

 Project objective

Despite of their great promise, high-throughput technologies in cancer research have often failed to translate to major therapeutic advances in the clinic. One challenge has been tumour heterogeneity, where multiple competing subclones coexist within a single tumour. Genomic heterogeneity renders it difficult to identify all driving molecular alterations, and thus results in therapies that only target subsets of aggressive tumour cells. Another challenge lies in the integration of multiple types of molecular data into mathematical disease models that can make actionable clinical statements.

We aim to develop predictive computational technology that can exploit molecular and clinical data to improve our understanding of disease mechanisms and to inform clinicians about optimized strategies for therapeutic intervention. We propose to focus on prostate cancer, a leading cause of cancer death amongst men in Europe, but also prone to over-treatment. Our approach combines the exploitation of genomic, transcriptomic, proteomic, and clinical data in primary and metastatic tumours, prospective cohorts of well characterized prostate cancer patients, drug screenings in cell lines, and the use of the Watson technology, a last generation cognitive computer developed at IBM.

The translational objective of this study is to develop technology for identifying disease mechanisms and produce treatment recommendations for individual patients based on a therapeutic biomarker panel. The proposed software framework will be accessible through a graphical interface that will facilitate its dissemination and use by researchers, clinicians, and biomedical industries. The framework will provide intuitive tools to deposit, share, analyze, and visualize molecular and clinical data; as well as to infer prognosis, elucidate implicated mechanisms and recommend therapy accordingly. This software framework will serve as a proof of concept for future development by industrial partners in Europe.

 Deliverables

List of deliverables.
Generate SWATH proteome profiles from sample punches prepared in D.6.2 Documents, reports 2020-03-25 14:53:42
1st Interim Progress Report Documents, reports 2020-03-25 14:53:42
Network reconstruction algorithms for MS data Other 2020-03-25 14:53:43
Re-implement methods Other 2020-03-25 14:53:42
2nd Interim Progress Report Documents, reports 2020-03-25 14:53:42
Generate amplicon sequencing profiles from sample punches prepared in D.6.2 Documents, reports 2020-03-25 14:53:42
Proteomic data sets in cancer cell lines Documents, reports 2020-03-25 14:53:42
Data input and input interface Other 2020-03-25 14:53:42
Robust cross-cohort clinical patient classifier Documents, reports 2020-03-25 14:53:43
A complete catalogue of targeted profiles Documents, reports 2020-03-25 14:53:42
Clonal classification of tumours Documents, reports 2020-03-25 14:53:42
Targeted profiling of prospective cohort Documents, reports 2020-03-25 14:53:42
Integrate methods, including ACSN and Watson Other 2020-03-25 14:53:42
Generate cell line drug sensitivity/resistance validation assays Documents, reports 2020-03-25 14:53:42
Catalogue of molecular alterations and dysregulated pathways Documents, reports 2020-03-25 14:53:42
Identification of systematic alterations of networks for different prognosis and for different clonal composition Other 2020-03-25 14:53:42
Final clone inference Documents, reports 2020-03-25 14:53:42
Internal and external IT communication infrastructure and project website Websites, patent fillings, videos etc. 2020-03-25 14:53:42
Project quality plan Documents, reports 2020-03-25 14:53:42
Interactome of molecular interactions in prostate cancer Other 2020-03-25 14:53:42
Generic model Documents, reports 2020-03-25 14:53:42
First data-driven reconstruction of context-specific network Other 2020-03-25 14:53:42
Computational pipeline to extract prior network information at the proteomic level Other 2020-03-25 14:53:42
Targeted ultra-deep sequencing of cancer-gene loci Documents, reports 2020-03-25 14:53:42
Data Management Plan Documents, reports 2020-03-25 14:53:42
Final regulatory network inference Documents, reports 2020-03-25 14:53:42
Ultra-deep sequencing of prognostic biomarkers Documents, reports 2020-03-25 14:53:42
Design and integrate pathway visualization Other 2020-03-25 14:53:41

Take a look to the deliverables list in detail:  detailed list of PrECISE deliverables.

 Publications

year authors and title journal last update
List of publications.
2018 H. Alexander Ebhardt, Alex Root, Yansheng Liu, Nicholas Paul Gauthier, Chris Sander, Ruedi Aebersold
Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer
published pages: , ISSN: 2056-7189, DOI: 10.1038/s41540-018-0064-1
npj Systems Biology and Applications 4/1 2020-03-25
2019 Jonas Béal, Arnau Montagud, Pauline Traynard, Emmanuel Barillot, Laurence Calzone
Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients
published pages: , ISSN: 1664-042X, DOI: 10.3389/fphys.2018.01965
Frontiers in Physiology 9 2020-03-25
2018 Yang, Sikun; Koeppl, Heinz
Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis
published pages: , ISSN: , DOI: 10.5281/zenodo.1966176
3 2020-03-25
2018 Angeliki Kalamara, Luis Tobalina, Julio Saez-Rodriguez
How to find the right drug for each patient? Advances and challenges in pharmacogenomics
published pages: 53-62, ISSN: 2452-3100, DOI: 10.1016/j.coisb.2018.07.001
Current Opinion in Systems Biology 10 2020-03-25
2018 Arnau Montagud, Jonas Béal, Pauline Traynard, Luis Tobalina, Julio Sáez-Rodríguez, Emmanuel Barillot and Laurence Calzone
Patient-specific prostate logical models allow clinical stratification of patients and personalized drug treatment
published pages: , ISSN: , DOI: 10.5281/zenodo.2416618
2020-03-25
2018 Yang, Sikun; Koeppl, Heinz
Dependent Relational Gamma Process Models for Longitudinal Networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1314290
10 2020-03-25
2018 Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei
Logical versus kinetic modeling of biological networks: applications in cancer research
published pages: , ISSN: 2211-3398, DOI: 10.5281/zenodo.1243004
Current Opinion in Chemical Engineering 21 22-31 1 2020-03-25
2018 Dmytro Ustianenko, Hua-Sheng Chiu, Thomas Treiber, Sebastien M. Weyn-Vanhentenryck, Nora Treiber, Gunter Meister, Pavel Sumazin, Chaolin Zhang
LIN28 Selectively Modulates a Subclass of Let-7 MicroRNAs
published pages: 271-283.e5, ISSN: 1097-2765, DOI: 10.1016/j.molcel.2018.06.029
Molecular Cell 71/2 2020-03-25
2017 Linzner, Dominik; Koepply, Heinz
Inferring network statistics from high-dimensional undersampled time-course data
published pages: , ISSN: , DOI: 10.5281/zenodo.841159
ISMB 2017 / CMSB 2017 2020-03-25
2017 Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
Logic Modeling in Quantitative Systems Pharmacology
published pages: 499-511, ISSN: 2163-8306, DOI: 10.1002/psp4.12225
CPT: Pharmacometrics & Systems Pharmacology 6/8 2020-03-25
2018 Oskooei, Ali; Born, Jannis; Manica, Matteo; Subramanian, Vigneshwari; Saez-Rodriguez, Julio; Rodriguez- Martinez, Maria
PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1967104
7 2020-03-25
2017 Mathis, Roland; Manica, Matteo; Rodriguez Martinez, Maria
DeepGRN: Deciphering gene deregulation in cancer development using deep learning
published pages: , ISSN: , DOI: 10.5281/zenodo.841163
2 2020-03-25
2017 Jonas Béal, Arnau Montagud, Pauline Traynard, Emmanuel Barillot and Laurence Calzone
Instantiation of Patient-Specific Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients
published pages: , ISSN: , DOI: 10.5281/zenodo.2417118
2020-03-25
2018 Yang, Sikun; Koeppl, Heinz
A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
published pages: , ISSN: , DOI: 10.5281/zenodo.1242987
AAAI 2018 - Association for the Advancement of Artificial Intelligence 2018 3 2020-03-25
2018 Hua-Sheng Chiu, Sonal Somvanshi, Ektaben Patel, Ting-Wen Chen, Vivek P. Singh, Barry Zorman, Sagar L. Patil, Yinghong Pan, Sujash S. Chatterjee, Anil K. Sood, Preethi H. Gunaratne, Pavel Sumazin, Samantha J. Caesar-Johnson, John A. Demchok, Ina Felau, Melpomeni Kasapi, Martin L. Ferguson, Carolyn M. Hutter, Heidi J. Sofia, Roy Tarnuzzer, Zhining Wang, Liming Yang, Jean C. Zenklusen, Jiashan (Julia) Zhang, Sudha Chudamani, Jia Liu, Laxmi Lolla, Rashi Naresh, Todd Pihl, Qiang Sun, Yunhu Wan, Ye Wu, Juok Cho, Timothy DeFreitas, Scott Frazer, Nils Gehlenborg, Gad Getz, David I. Heiman, Jaegil Kim, Michael S. Lawrence, Pei Lin, Sam Meier, Michael S. Noble, Gordon Saksena, Doug Voet, Hailei Zhang, Brady Bernard, Nyasha Chambwe, Varsha Dhankani, Theo Knijnenburg, Roger Kramer, Kalle Leinonen, Yuexin Liu, Michael Miller, Sheila Reynolds, Ilya Shmulevich, Vesteinn Thorsson, Wei Zhang, Rehan Akbani, Bradley M. Broom, Apurva M. Hegde, Zhenlin Ju, Rupa S. Kanchi, Anil Korkut, Jun Li, Han Liang, Shiyun Ling, Wenbin Liu, Yiling Lu, Gordon B. Mills, Kwok-Shing Ng, Arvind Rao, Michael Ryan, Jing Wang, John N. Weinstein, Jiexin Zhang, Adam Abeshouse, Joshua Armenia, Debyani Chakravarty, Walid K. Chatila, Ino de Bruijn, Jianjiong Gao, Benjamin E. Gross, Zachary J. Heins, Ritika Kundra, Konnor La, Marc Ladanyi, Augustin Luna, Moriah G. Nissan, Angelica Ochoa, Sarah M. Phillips, Ed Reznik, Francisco Sanchez-Vega, Chris Sander, Nikolaus Schultz, Robert Sheridan, S. Onur Sumer, Yichao Sun, Barry S. Taylor, Jioajiao Wang, Hongxin Zhang, Pavana Anur, Myron Peto, Paul Spellman, Christopher Benz, Joshua M. Stuart, Christopher K. Wong, Christina Yau, D. Neil Hayes, Joel S. Parker, Matthew D. Wilkerson, Adrian Ally, Miruna Balasundaram, Reanne Bowlby, Denise Brooks, Rebecca Carlsen, Eric Chuah, Noreen Dhalla, Robert Holt, Steven J.M. Jones, Katayoon Kasaian, Darlene Lee, Yussanne Ma, Marco A. Marra, Michael Mayo, Richard A. Moore, Andrew J. Mungall, Karen Mungall, A. Gordon Robertson, Sara Sadeghi, Jacqueline E. Schein, Payal Sipahimalani, Angela Tam, Nina Thiessen, Kane Tse, Tina Wong, Ashton C. Berger, Rameen Beroukhim, Andrew D. Cherniack, Carrie Cibulskis, Stacey B. Gabriel, Galen F. Gao, Gavin Ha, Matthew Meyerson, Steven E. Schumacher, Juliann Shih, Melanie H. Kucherlapati, Raju S. Kucherlapati, Stephen Baylin, Leslie Cope, Ludmila Danilova, Moiz S. Bootwalla, Phillip H. Lai, Dennis T. Maglinte, David J. Van Den Berg, Daniel J. Weisenberger, J. Todd Auman, Saianand Balu, Tom Bodenheimer, Cheng Fan, Katherine A. Hoadley, Alan P. Hoyle, Stuart R. Jefferys, Corbin D. Jones, Shaowu Meng, Piotr A. Mieczkowski, Lisle E. Mose, Amy H. Perou, Charles M. Perou, Jeffrey Roach, Yan Shi, Janae V. Simons, Tara Skelly, Matthew G. Soloway, Donghui Tan, Umadevi Veluvolu, Huihui Fan, Toshinori Hinoue, Peter W. Laird, Hui Shen, Wanding Zhou, Michelle Bellair, Kyle Chang, Kyle Covington, Chad J. Creighton, Huyen Dinh, HarshaVardhan Doddapaneni, Lawrence A. Donehower, Jennifer Drummond, Richard A. Gibbs, Robert Glenn, Walker Hale, Yi Han, Jianhong Hu, Viktoriya Korchina, Sandra Lee, Lora Lewis, Wei Li, Xiuping Liu, Margaret Morgan, Donna Morton, Donna Muzny, Jireh Santibanez, Margi Sheth, Eve Shinbrot, Linghua Wang, Min Wang, David A. Wheeler, Liu Xi, Fengmei Zhao, Julian Hess, Elizabeth L. Appelbaum, Matthew Bailey, Matthew G. Cordes, Li Ding, Catrina C. Fronick, Lucinda A. Fulton, Robert S. Fulton, Cyriac Kandoth, Elaine R. Mardis, Michael D. McLellan, Christopher A. Miller, Heather K. Schmidt, Richard K. Wilson, Daniel Crain, Erin Curley, Johanna Gardner, Kevin Lau, David Mallery, Scott Morris, Joseph Paulauskis, Robert Penny, Candace Shelton, Troy Shelton, Mark Sherman, Eric Thompson, Peggy Yena, Jay Bowen, Julie M. Gastier-Foster, Mark Gerken, Kristen M. Leraas, Tara M. Lichtenberg, Nilsa C. Ramirez, Lisa Wise, Erik Zmuda, Niall Corcoran, Tony Costello, Christopher Hovens, Andre L. Carvalho, Ana C. de Carvalho, José H. Fregnani, Adhemar Longatto-Filho, Rui M. Rei
Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context
published pages: 297-312.e12, ISSN: 2211-1247, DOI: 10.1016/j.celrep.2018.03.064
Cell Reports 23/1 2020-03-25
2018 Traynard, Pauline; Tobalina, Luis; Eduati, Federica; Calzone, Laurence; Saez-Rodriguez, Julio
Logic modeling in quantitative systems pharmacology (Poster)
published pages: , ISSN: , DOI: 10.5281/zenodo.841126
1 2020-03-25
2018 LInzner, Dominik; Koeppl, Heinz
Cluster Variatonal Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data
published pages: , ISSN: , DOI: 10.5281/zenodo.1966608
10 2020-03-25
2018 Gaelle Letort, Arnau Montagud, Gautier Stoll, Randy Heiland, Emmanuel Barillot, Paul Macklin, Andrei Zinovyev, Laurence Calzone
PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling
published pages: , ISSN: 1367-4803, DOI: 10.1093/bioinformatics/bty766
Bioinformatics 2020-03-25
2018 Cuklina, Jelena; Lee, Chloe; Williams, Evan G.; Sajic, Tatjana; Collins, Ben; Rodriguez-Martinez, Maria; Pedrioli, Patrick; Aebersold, Ruedi
Batch effects in large-scale proteomic studies: diagnostics and correction
published pages: , ISSN: , DOI: 10.5281/zenodo.1446001
10 2020-03-25
2018 Tiannan Guo, Li Li, Qing Zhong, Niels J Rupp, Konstantina Charmpi, Christine E Wong, Ulrich Wagner, Jan H Rueschoff, Wolfram Jochum, Christian Daniel Fankhauser, Karim Saba, Cedric Poyet, Peter J Wild, Ruedi Aebersold, Andreas Beyer
Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers
published pages: e201800042, ISSN: 2575-1077, DOI: 10.26508/lsa.201800042
Life Science Alliance 1/2 2020-03-25
2018 Al- Sayed, Sara; Koeppl, Heinz
Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes
published pages: , ISSN: , DOI: 10.5281/zenodo.1488635
(MLSP 2018) 2018 IEEE International Workshop on Machine Learning for Signal Processing 2 2020-03-25
2019 Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez
PIMKL: Pathway-Induced Multiple Kernel Learning
published pages: , ISSN: 2056-7189, DOI: 10.1038/s41540-019-0086-3
npj Systems Biology and Applications 5/1 2020-03-25
2017 Traynard, Pauline; Beal, Jonas; Tobalina, Luis; Barillot, Emmanuel; Saez-Rodriguez, Julio; Calzone, Laurence
Incorporating patient-specific molecular data into a logic model of prostate cancer
published pages: , ISSN: , DOI: 10.5281/zenodo.841116
ISMB 2017 9 2020-03-25
2017 Al-Sayed, Sara; Koeppl, Heinz
Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes
published pages: , ISSN: , DOI: 10.5281/zenodo.841132
ISMB 2017 5 2020-03-25
2017 Subramanian, Vigneshwari; Szalai, Bence; Tobalina, Luis; Saez-Rodriguez, Julio
Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from drugs and cell lines
published pages: , ISSN: , DOI: 10.5281/zenodo.1066906
NETTAB 2017 4 2020-03-25
2017 Cuklina, Jelena; Wu, Yibo; Williams, Evan G.; Rodriguez Martinez, Maria; Aebersold, Ruedi
Selection of stable biomarker signature for prediction of metabolic phenotypes
published pages: , ISSN: , DOI: 10.5281/zenodo.841208
ISMB 2017 6 2020-03-25

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