PERSONALIZED ENGINE FOR CANCER INTEGRATIVE STUDY AND EVALUATION

CORDIS fornisce collegamenti ai risultati finali pubblici e alle pubblicazioni dei progetti ORIZZONTE.

I link ai risultati e alle pubblicazioni dei progetti del 7° PQ, così come i link ad alcuni tipi di risultati specifici come dataset e software, sono recuperati dinamicamente da .OpenAIRE .

Risultati finali

Ultra-deep sequencing of prognostic biomarkers

This report will provide targeted profiles of selected biopsies and will be used improve clone inference in WP1, prognostic-biomarker inference in WP3, tumour classification WP4, sample and assay selection in WP6. Results will influence the construction of amplicon deliverables in D2.3.

Targeted ultra-deep sequencing of cancer-gene loci

This report will provide targeted profiles of selected biopsies and will be used to improve clone inference in WP1, prognostic-biomarker inference in WP3, and tumour classification in WP4. Results will influence the construction of amplicon deliverables in D2.2 and D2.3.

Generate amplicon sequencing profiles from sample punches prepared in D.6.2

This deliverable converts the validation cohort tissue samples into quantitative genomic amplicon profiles.

Robust cross-cohort clinical patient classifier

We will provide molecular signatures and/or biomarkers of clinical groups based on the integration of all analyzed data types.

Generic model

In this deliverable, we will construct a generic logical model that will include altered signalling pathways identified by WP3 and WP4, complemented by pathways that are known to be frequently altered in cancer.

Data Management Plan

The purpose of the DMP is to provide an analysis of the main elements of the data management policy that will be used by the applications with regard to all the datasets that will be generated by the project. The DMP is not a fixed document, but evolves during the lifespan of the project.

Project quality plan

The project quality plan (the project handbook) constitutes a set of project templates, explanations on the project management process, review process, quality checks, meeting organisation, which is communicated to all partners.

Generate cell line drug sensitivity/resistance validation assays

This deliverable will validate the drug predictions for prostatic cell lines inferred in WP5.

Proteomic data sets in cancer cell lines

In this deliverable the data required to train the logical models will be provided.

Clonal classification of tumours

Classification of tumours according to dominant clonal content.

Final clone inference

Refined clonality models and associated biomarkers.

Generate SWATH proteome profiles from sample punches prepared in D.6.2

This deliverable converts the validation cohort tissue samples into quantitative protein profiles.

1st Interim Progress Report

The interim project progress report will address the main achievements and concrete key outcomes of the first project year (project summary, work performed and main results, risk assessment, list of scientific publications and dissemination activities). All work packages will summarize their work, challenges and outcomes in order to contribute to this report.

Targeted profiling of prospective cohort

This report will provide profiles of selected biopsies and will be used to inform sample and assay selection in WP6.

Final regulatory network inference

Refined reversed engineered regulatory networks in PC tumours based on updated methodology for network integration and analyses of small datasets.

A complete catalogue of targeted profiles

This report will provide normalized molecular profiles, including DNA and protein-expression profiles, of all biopsies studied in WP2.

2nd Interim Progress Report

The interim project progress report will address the main achievements and concrete key outcomes of the second project year (project summary, work performed and main results, risk assessment, list of scientific publications and dissemination activities). All work packages will summarize their work, challenges and outcomes in order to contribute to this report.

Catalogue of molecular alterations and dysregulated pathways

We will provide lists of molecular alterations and targeted pathways. The list will be segregated according to pathological stage (Gleason score), clonal structure and patient.

Integrate methods, including ACSN and Watson

We will finalize dashboard implementation and interface with data access and depository, refactored methods, ACSN, and Watson. Integration of data and methods will be followed by extensive application testing.

First data-driven reconstruction of context-specific network

Proteome networks based on MS and phospho-MS data from prostatic cells lines and from samples of the proCOC and MetaProC biopsies.

Computational pipeline to extract prior network information at the proteomic level

Provides a computational tool to extract and to mathematically aggregate prior information for later use in data-driven network reconstruction.

Network reconstruction algorithms for MS data

Provides novel algorithms for protein network reconstruction tailored to the MS data format of partner ETH and evaluated on the already existing prostatic cell line data of ETH.

Re-implement methods

Analyses methods will be refactored and re-implemented within the framework.

Data input and input interface

We will input data and implement a framework for depositing future data into SmartBiobank.

Design and integrate pathway visualization

We will provide visual profiles of data from patients, clones and cell lines using ACSN and networks created in WP3 and WP5.

Identification of systematic alterations of networks for different prognosis and for different clonal composition

Found alterations enable the comparison with the genomic analysis of WP1 and provide predictions to be validated in WP6.

Interactome of molecular interactions in prostate cancer

This deliverable provides comprehensive information of all known and inferred interactions in prostate cancer.

Internal and external IT communication infrastructure and project website

The external IT communication infrastructure constitutes a guideline for communication of the PrECISE project to external target groups including conferences, marketing measures and communication channels. Furthermore this deliverable constitutes the launch of the internal PrECISE communication infrastructure including the establishment of mailing lists or a subversion server, and the PrECISE website.

Pubblicazioni

Inferring clonal composition from multiple tumor biopsies

Autori: Matteo Manica, Philippe Chouvarine, Roland Mathis, Ulrich Wagner, Kathrin Oehl, Karim Saba, Laura De Vargas Roditi, Arati N Pati, Maria Rodriguez-Martinez, Peter J Wild, Pavel Sumazin
Pubblicato in: ISMB 2017, 2017
Editore: ISMB conference
DOI: 10.5281/zenodo.841110

DeepGRN: Deciphering gene deregulation in cancer development using deep learning

Autori: Mathis, Roland; Manica, Matteo; Rodriguez Martinez, Maria
Pubblicato in: ISMB 2017, 2017
Editore: ISMB conference
DOI: 10.5281/zenodo.841164

Inferring network statistics from high-dimensional undersampled time-course data

Autori: Linzner, Dominik Koeppl, Heinz
Pubblicato in: ISMB 2017, 2017
Editore: ISMB Conference
DOI: 10.5281/zenodo.841160

Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

Autori: Al- Sayed, Sara Department of Electrical Engineering Technische Universität Darmstadt, Germany ; Koeppl, Heinz
Pubblicato in: IEEE MLSP 2018, 2018
Editore: IEEE MLSP conference
DOI: 10.5281/zenodo.1488636

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

Autori: Oskooei, Ali; Born, Jannis; Manica, Matteo; Subramanian, Vigneshwari; Saez-Rodriguez, Julio; Rodriguez- Martinez, Maria
Pubblicato in: 32nd Conference on Neural Information Processing Systems (NIPS 2018), 2018
Editore: NeurIPs 2018
DOI: 10.5281/zenodo.1967105

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis

Autori: Yang, Sikun Koeppl, Heinz
Pubblicato in: IEEE International conference on data mining (ICDM 2018), 2018
Editore: IEEE ICDM
DOI: 10.5281/zenodo.1966177

Cluster Variatonal Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

Autori: Linzner, Dominik Koeppl, Heinz
Pubblicato in: 32nd Conference on Neural Information Processing Systems (NeurIPs 2018), 2018
Editore: NeurIPs 2018
DOI: 10.5281/zenodo.1966609

A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks

Autori: Yang, Sikun; Koeppl, Heinz
Pubblicato in: AAAI 2018 - Association for the Advancement of Artificial Intelligence 2018, Numero 3, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.1242987

Dependent Relational Gamma Process Models for Longitudinal Networks

Autori: Yang, Sikun; Koeppl, Heinz
Pubblicato in: Numero 10, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.1314290

Logic modeling in quantitative systems pharmacology (Poster)

Autori: Traynard, Pauline; Tobalina, Luis; Eduati, Federica; Calzone, Laurence; Saez-Rodriguez, Julio
Pubblicato in: Numero 1, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.841126

PaccMann: Prediction of anticancer compound sensitivity with multi-modal attention-based neural networks

Autori: Oskooei, Ali; Born, Jannis; Manica, Matteo; Subramanian, Vigneshwari; Saez-Rodriguez, Julio; Rodriguez- Martinez, Maria
Pubblicato in: Numero 7, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.1967104

Cluster Variatonal Approximations for Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data

Autori: LInzner, Dominik; Koeppl, Heinz
Pubblicato in: Numero 10, 2018
Editore: Arxiv
DOI: 10.5281/zenodo.1966608

Collapsed Variational Inference for Nonparametric Bayesian Group Factor Analysis

Autori: Yang, Sikun; Koeppl, Heinz
Pubblicato in: Numero 3, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.1966176

Inferring network statistics from high-dimensional undersampled time-course data

Autori: Linzner, Dominik; Koepply, Heinz
Pubblicato in: ISMB 2017 / CMSB 2017, 2017
Editore: Zenodo
DOI: 10.5281/zenodo.841159

Batch effects in large-scale proteomic studies: diagnostics and correction

Autori: Cuklina, Jelena; Lee, Chloe; Williams, Evan G.; Sajic, Tatjana; Collins, Ben; Rodriguez-Martinez, Maria; Pedrioli, Patrick; Aebersold, Ruedi
Pubblicato in: Numero 10, 2018
Editore: Zenodo
DOI: 10.5281/zenodo.1446001

Incorporating patient-specific molecular data into a logic model of prostate cancer

Autori: Traynard, Pauline; Beal, Jonas; Tobalina, Luis; Barillot, Emmanuel; Saez-Rodriguez, Julio; Calzone, Laurence
Pubblicato in: ISMB 2017, Numero 9, 2017
Editore: ISMB conference
DOI: 10.5281/zenodo.841116

Network Reconstruction From Time-Course Perturbation Data Using Multivariate Gaussian Processes

Autori: Al- Sayed, Sara; Koeppl, Heinz
Pubblicato in: (MLSP 2018) 2018 IEEE International Workshop on Machine Learning for Signal Processing, Numero 2, 2018
Editore: IEEE
DOI: 10.5281/zenodo.1488635

DeepGRN: Deciphering gene deregulation in cancer development using deep learning

Autori: Mathis, Roland; Manica, Matteo; Rodriguez Martinez, Maria
Pubblicato in: Numero 2, 2017
Editore: Zenodo
DOI: 10.5281/zenodo.841163

Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes

Autori: Al-Sayed, Sara; Koeppl, Heinz
Pubblicato in: ISMB 2017, Numero 5, 2017
Editore: ISMB Conference
DOI: 10.5281/zenodo.841132

Selection of stable biomarker signature for prediction of metabolic phenotypes

Autori: Cuklina, Jelena; Wu, Yibo; Williams, Evan G.; Rodriguez Martinez, Maria; Aebersold, Ruedi
Pubblicato in: ISMB 2017, Numero 6, 2017
Editore: ISMB Conference
DOI: 10.5281/zenodo.841208

Application of network diffusion approaches to drug screenings: A perspective on multilayered networks derived from drugs and cell lines

Autori: Subramanian, Vigneshwari; Szalai, Bence; Tobalina, Luis; Saez-Rodriguez, Julio
Pubblicato in: NETTAB 2017, Numero 4, 2017
Editore: NETTAB conference
DOI: 10.5281/zenodo.1066906

Stratification of prostate cancer patients based on molecular interaction profiles

Autori: Mathis, Roland; Manica, Matteo; Martinez Rodriguez, Maria
Pubblicato in: Numero 9, 2016
Editore: ROCKY 2016
DOI: 10.5281/zenodo.840078

Pypath & Omnipath: integrate, analyze and extract signaling networks from literature curated resources

Autori: Türei, Denes; Tobalina, Luis; Henriques, David; Traynard, Pauline; Calzone, Laurence; Korcsmáros, Tamás; Saez-Rodriguez, Julio
Pubblicato in: Numero 4, 2016
Editore: ICSB 2016
DOI: 10.5281/zenodo.840094

Building a Boolean model of signaling pathways altered in prostate cancer

Autori: Traynard, Pauline; Tobalina, Luis; Henriques, David; Barillot, Emmanuel; Saez-Rodriguez, Julio; Calzone, Laurence
Pubblicato in: Numero 11, 2016
Editore: ICSB 2016
DOI: 10.5281/zenodo.840084

CoDON: a learning framework for linking genomics and transcriptomics data to protein expression

Autori: Manica, Matteo; Mathis, Roland; Martinez Rodriguez, Maria
Pubblicato in: Numero 6, 2016
Editore: All SystemsX.ch Day 2016
DOI: 10.5281/zenodo.839692

An integrative Systems Biology approach to advance in the understanding and treatment of prostate cancer

Autori: Tobalina, Luis; Henriques, David; Saez-Rodriguez, Julio
Pubblicato in: 2016
Editore: byteMAL
DOI: 10.5281/zenodo.835692

Proteome heterogeneity in benign and malignant prostate tissue

Autori: Guo, Tiannan; Li, Li; Zhong, Qing; Rupp, Niels J.; Charmpi, Konstantina; Wong, Christine E.; Wagner, Ulrich; Rueschoff, Jan H.; Jochum, Wolfram; Fankhauser, Christian; Saba, Karim; Poyet, Cedric; Wild, Peter; Aebersold, Ruedi; Beyer, Andreas
Pubblicato in: Numero 1, 2016
Editore: All SystemsX.ch Day 2016
DOI: 10.5281/zenodo.841216

Integration of Multi-omics Data for Prediction of Metabolic Traits

Autori: Čuklina, Jelena; Wu, Yibo; Williams, Evan. G.; Rodríguez-Martínez, María; Aebersold, Ruedi
Pubblicato in: Numero 8, 2016
Editore: LATSIS Symposium on Personalized Medicine
DOI: 10.5281/zenodo.846702

Logic modeling in quantitative systems pharmacology

Autori: Traynard, Pauline; Tobalina, Luis; Eduati, Federica; Calzone, Laurence; Saez-Rodriguez, Julio
Pubblicato in: ISMB 2017, 2017
Editore: ISMB conference
DOI: 10.5281/zenodo.841127

Batch effects in large-scale proteomic studies: diagnostics and correction

Autori: Cuklina, Jelena; Lee, Chloe; Williams, Evan G.; Sajic, Tatjana; Collins, Ben; Rodriguez-Martinez, Maria; Pedrioli, Patrick; Aebersold, Ruedi
Pubblicato in: HUPO 2018, 2018
Editore: HUPO conference
DOI: 10.5281/zenodo.1446001

A logic modelling workflow for systems pharmacology

Autori: Tobalina, Luis
Pubblicato in: Logic and System Biology Workshop, 2018
Editore: Logic and System Biology Workshop
DOI: 10.5281/zenodo.1474213

Community assessment of cancer drug combination screens identifies strategies for synergy prediction

Autori: Menden, Michael P; Wang, Dennis; Guan, Yuanfang; Mason, Michael; Szalai, Bence; Bulusu, Krishna C; Yu, Thomas; Kang, Jaewoo; Jeon, Minji; Wolfinger, Russ; Nguyen, Tin; Zaskavskiy, Mikhail; DREAM consortium; Jang, In Sock; Ghazoui, Zara; Ahsen, Mehmet Eren; Vogel, Robert; Neto, Elias Chaibub; Norman, Thea; Tang, Eric KY; Garnett, Matthew J; Di Veroli, Giovanni; Fawell, Steve; Stolovitzky, Gustavo;
Pubblicato in: DREAM Challenges 2017, 2017
Editore: DREAM Challenges
DOI: 10.1101/200451

Patient-specific prostate logical models allow clinical stratification of patients and personalized drug treatment

Autori: Arnau Montagud, Jonas Béal, Pauline Traynard, Luis Tobalina, Julio Sáez-Rodríguez, Emmanuel Barillot and Laurence Calzone
Pubblicato in: 2018
Editore: ECCB 2018
DOI: 10.5281/zenodo.2416618

Instantiation of Patient-Specific Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Autori: Jonas Béal, Arnau Montagud, Pauline Traynard, Emmanuel Barillot and Laurence Calzone
Pubblicato in: 2017
Editore: ECCB 2018
DOI: 10.5281/zenodo.2417118

How to find the right drug for each patient? Advances and challenges in pharmacogenomics

Autori: Angeliki Kalamara, Luis Tobalina, Julio Saez-Rodriguez
Pubblicato in: Current Opinion in Systems Biology, Numero 10, 2018, Pagina/e 53-62, ISSN 2452-3100
Editore: ELSEVIER
DOI: 10.1016/j.coisb.2018.07.001

LIN28 Selectively Modulates a Subclass of Let-7 MicroRNAs

Autori: Dmytro Ustianenko, Hua-Sheng Chiu, Thomas Treiber, Sebastien M. Weyn-Vanhentenryck, Nora Treiber, Gunter Meister, Pavel Sumazin, Chaolin Zhang
Pubblicato in: Molecular Cell, Numero 71/2, 2018, Pagina/e 271-283.e5, ISSN 1097-2765
Editore: Cell Press
DOI: 10.1016/j.molcel.2018.06.029

Personalization of Logical Models With Multi-Omics Data Allows Clinical Stratification of Patients

Autori: Jonas Béal, Arnau Montagud, Pauline Traynard, Emmanuel Barillot, Laurence Calzone
Pubblicato in: Frontiers in Physiology, Numero 9, 2019, ISSN 1664-042X
Editore: Frontiers Research Foundation
DOI: 10.3389/fphys.2018.01965

PhysiBoSS: a multi-scale agent-based modelling framework integrating physical dimension and cell signalling

Autori: Gaelle Letort, Arnau Montagud, Gautier Stoll, Randy Heiland, Emmanuel Barillot, Paul Macklin, Andrei Zinovyev, Laurence Calzone
Pubblicato in: Bioinformatics, 2018, ISSN 1367-4803
Editore: Oxford University Press
DOI: 10.1093/bioinformatics/bty766

Logical versus kinetic modeling of biological networks: applications in cancer research

Autori: Calzone, Laurence; Barillot, Emmanuel; Zinovyev, Andrei
Pubblicato in: Current Opinion in Chemical Engineering 21 22-31, Numero 1, 2018, ISSN 2211-3398
Editore: Elsevier BV
DOI: 10.5281/zenodo.1243004

Logic Modeling in Quantitative Systems Pharmacology

Autori: Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
Pubblicato in: CPT: Pharmacometrics & Systems Pharmacology, Numero 6/8, 2017, Pagina/e 499-511, ISSN 2163-8306
Editore: Nature Publishing Group
DOI: 10.1002/psp4.12225

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Autori: 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
Pubblicato in: Cell Reports, Numero 23/1, 2018, Pagina/e 297-312.e12, ISSN 2211-1247
Editore: Cell Press
DOI: 10.1016/j.celrep.2018.03.064

MaBoSS 2.0: an environment for stochastic Boolean modeling

Autori: Stoll, Gautier; Caron, Barthelemy; Viara, Eric; Dugourd, Aurelien; Zinovyev, Andrei; Naldi, Aurelien; Kroemer, Guido
Pubblicato in: Bioinformatics, Numero 5, 2017, Pagina/e 2226–2228, ISSN 1367-4803
Editore: Oxford University Press
DOI: 10.5281/zenodo.841168

Logic Modeling in Quantitative Systems Pharmacology (Journal Article)

Autori: Traynard, Pauline; Tobalina, Luis; Eduati, Federica; Calzone, Laurence; Saez-Rodriguez, Julio
Pubblicato in: CPT: Pharmacometrics and Systems Pharmacology, Numero 5, 2017, ISSN 2163-8306
Editore: Nature Publishing Group
DOI: 10.5281/zenodo.841206

Systems pharmacology using mass spectrometry identifies critical response nodes in prostate cancer

Autori: H. Alexander Ebhardt, Alex Root, Yansheng Liu, Nicholas Paul Gauthier, Chris Sander, Ruedi Aebersold
Pubblicato in: npj Systems Biology and Applications, Numero 4/1, 2018, ISSN 2056-7189
Editore: Zenodo
DOI: 10.1038/s41540-018-0064-1

Multi-region proteome analysis quantifies spatial heterogeneity of prostate tissue biomarkers

Autori: 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
Pubblicato in: Life Science Alliance, Numero 1/2, 2018, Pagina/e e201800042, ISSN 2575-1077
Editore: Life Science Alliance
DOI: 10.26508/lsa.201800042

PIMKL: Pathway-Induced Multiple Kernel Learning

Autori: Matteo Manica, Joris Cadow, Roland Mathis, María Rodríguez Martínez
Pubblicato in: npj Systems Biology and Applications, Numero 5/1, 2019, ISSN 2056-7189
Editore: Cornell University
DOI: 10.1038/s41540-019-0086-3

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INTERACTION NETWORK INFERENCE FROM VECTOR REPRESENTATION OF WORDS

Numero candidatura/pubblicazione: US 94520169
Data: 2017-12-01
Candidato/i: IBM RESEARCH GMBH

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