Personalized Engine for Cancer Integrative Study and Evaluation

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Welcome to PrECISE

The PrECISE project is a pilot project that combines hypothesis-driven strategies with data-driven analysis in a novel mathematical and computational methodology for the integration of genomic, epigenetic, transcriptomic, proteomic, and clinical data with the goal of risk-stratifying patients and suggesting personalized therapeutic interventions. We have the following specific objectives:

  • Development of a comprehensive computational methodology:
  • To integrate publicly available multi-omics datasets, well-characterized multiple-biopsies cohorts, and literature-driven knowledge powered by the Watson cognitive computer, developed at IBM.

  • Characterization of intra-tumour heterogeneity:
  • We will apply PrECISE to prostate cancer molecular cohorts where multiple biopsies have been generated from each patient.

  • Suggestion of chemotherapy drugs and targeted therapies for each patient:
  • We will investigate molecular mechanisms, identify suitable intervention points for therapy and suggest personalized therapies based on patient’s clonal signatures, and we will validate our predictions in a panel of prostatic cell lines.

  • Development of PrECISE into deployable, easy to use software tool:
  • We will integrate the developed computational modules with the Watson cognitive technology developed at IBM in a user-friendly interface and make PrECISE accessible to the clinical research community.


This section is dedicated to the output and results of research activities within the PrECISE project. For any requests or questions, please contact the coordinator.

Below we list some computational tools developed and/or applied in PrECISE.

SERVICE: OmniPath - literature curated human signaling pathways: a comprehensive collection of literature curated human signaling pathways accompanied by pypath, a powerful Python module for molecular networks and pathways analysis.

TOOLBOX: CellNetOptimizer (CellNOpt): a toolbox for creating logic-based models of signal transduction networks, and training them against high-throughput biochemical data, and is freely available both for R and MATLAB.

TOOL: PHONEMeS: a tool to build logic models from discovery mass-spectrometry based Phosphoproteomic data.

SERVICE: PIMKL - Pathway Induced Multiple Kernel Learning: a novel methodology to reliably classify samples that can also help gain insights into the molecular mechanisms that underlie the classification. PIMKL exploits prior knowledge in the form of a molecular interaction network and annotated gene sets, by optimizing a mixture of pathway-induced kernels using a Multiple Kernel Learning (MKL) algorithm, an approach that has demonstrated excellent performance in different machine learning applications. Credentials can be requested via the login page.

SERVICE: INtERAcT - Interaction Network Inference from Vector Representations of Words: a novel approach to extract protein-protein interactions from a corpus of biomedical articles related to a broad range of scientific domains in a completely unsupervised way. INtERAcT exploits vector representation of words, computed on a corpus of domain specific knowledge, and implements a new metric that estimates an interaction score between two molecules in the space where the corresponding words are embedded. Credentials can be requested via the login page.

SERVICE: Chimæra - clonality inference from mutations across biopsies: is an optimization algorithm that accounts for the effects of copy number variations (CNVs) in multiple same-tumor biopsies to estimate both mutation frequencies and copy number of mutated alleles. We show that mutation-frequency estimates by Chimæra are consistently more accurate in unstable genomes. When studying profiles of multiple biopsies of a high-risk prostate tumor, we show that Chimæra inferences allow for reconstructing its clonal evolution. Credentials can be requested via the login page.

SERVICE: COSIFER: is a web based platform providing a service for inference of molecular networks using a consensus between state-of-the-art methodologies given molecular measurements and a list of molecular entities of interest. Intracellular networks regulate every kind of cellular decision such as differentiation, proliferation and apoptosis and when these control mechanisms fail, cancer and other diseases may arise. Complexity of these networks originates from the large number and various interactions of molecules involved. High-throughput technologies such as microarrays and RNA sequencing provide snapshots of the transcriptome and enable insights into the internal regulatory apparatus of a cell. However, inferring the topology of these networks and identifying its key regulators is a challenging task and international consortia have intensively worked on the development of computational methods tackling this problem. Despite the effort of comparison and development of gene regulatory network inference methods, the research community still lacks easy to access inference tools available to everyone. Credentials can be requested via the login page.

SERVICE: LongHorn predicts modulation of canonical regulators (or effectors), including miRNA, RBP, and TF, by lncRNAs. Leveraging large-scale cancer genomics datasets from TCGA, LongHorn identifies four models for lncRNA regulation (1) Decoy: binds and inhibits the activity of effectors by affecting their availability to regulate their protein-coding targets; (2) Co-factor: binds proximal promoters of protein-coding genes and alter their regulation by TFs; (3) Guide: facilitates regulation of protein-coding genes by TFs; (4) Switch: alters the activity of TFs and RBPs across multiple targets. LongHorn has been implemented in MATLAB. Please download it from here.

The PrECISE consortium is constantly publishing scientific articles. For all these publications open access is ensured via the open access repository Zenodo. We also created a dedicated community focusing on PrECISE related content. Click on the logo on the left to get directly forwarded to the PrECISE community.

Moreover, we provide open access to all officially accepted public deliverables of the PrECISE project.


A Poisson Gamma Probabilistic Model for Latent Node-group Memberships in Dynamic Networks
Sikun Yang, Heinz Koeppl
Thirty-Second AAAI Conference on Artificial Intelligence (AAAI2018), February, 2018.

Logical versus kinetic modeling of biological networks: applications in cancer research
Laurence Calzone, Emmanuel Barillot and Andrei Zinovyev
COSB - Current Opinion in Chemical Engineering (Review), September, 2018.

Pan-cancer analysis of lncRNA regulation supports their targeting of cancer genes in each tumor context
Hua-Sheng Chiu, Sonal Somvanshi, Ektaben Patel, Ting-Wen Chen, Vivek P. Singh, Barry Zorman, Sagar L. Patil, Yinghong Pan, Sujash S. Chatterjee, The Cancer Genome Atlas Research Network (TCGA), Anil K. Sood, Preethi H. Gunaratne, Pavel Sumazin
Cell Reports, April, 2018.


Community assessment of cancer drug combination screens identifies strategies for synergy prediction
Michael P Menden, Dennis Wang, Yuanfang Guan, Michael Mason, Bence Szalai, Krishna C Bulusu, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, DREAM Consortium, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric KY Tang, Mathew J Garnett, Giovanni Di Veroli, Steve Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R. Dry, Julio Saez-Rodriguez
DREAM challenges, October, 2017.

Application of network diffusion approaches to drug screenings: A perspective on multi-layered networks derived from cell lines and drugs (Poster)
Vigneshwari Subramanian, Bence Szalai, Luis Tobalina, Julio Saez-Rodriguez
17th Workshop on Network Tools and Applications for Biology: Methods, tools and platforms for Personalized Medicine in the Big Data Era (NETTAB) , October, 2017.

Logic modeling in quantitative systems pharmacology (Journal Paper)
Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
CPT: Pharmacometrics and Systems Pharmacology, Volume 6, Issue 8, August 2017.

MaBoSS 2.0: an environment for stochastic Boolean modeling (Journal Paper)
Gautier Stoll, Barthelemy Caron, Eric Viara, Aurelian Dugourd, Andrei Zinovyev, Aurelien Naldi, Guido Kroemer, Emmanue Barillot, Laurence Calzone
Bioinformatics, Volume 33, Issue 14, July 2017.

Selection of stable biomarker signature for prediction of metabolic phenotypes (Poster)
Jelena Čuklina, Yibo Wu, Evan G. Williams, María Rodríguez Martínez, Ruedi Aebersold
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

DeepGRN: Deciphering gene deregulation in cancer development using deep learning (Poster)
Roland Mathis, Matteo Manica, Maria Rodriguez Martinez
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Inferring network statistics from high-dimensional undersampled time-course data (Poster)
Dominik Linzner, Heinz Koeppl
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Fast biological network reconstruction from high-dimensional time-course perturbation data using sparse multivariate Gaussian processes (Poster)
Sara Al-Sayed, Heinz Koeppl
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Logic modeling in quantitative systems pharmacology (Poster)
Pauline Traynard, Luis Tobalina, Federica Eduati, Laurence Calzone, Julio Saez-Rodriguez
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Incorporating patient-specific molecular data into a logic model of prostate cancer (Poster)
Pauline Traynard, Jonas Beal, Luis Tobalina, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.

Inferring clonal composition from multiple tumor biopsies (Technical Note)
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
25th Conference on Intelligent Systems for Molecular Biology / 16th European Conference on Computational Biology (ISMB/ECCB), July 2017.


Integration of Multi-omics Data for Prediction of Metabolic Traits (Poster)
Jelena Čuklina, Yibo Wu, Evan. G. Williams, Maria Rodríguez-Martínez; Ruedi Aebersold
LATSIS Symposium on Personalized Medicine (LATSIS), 2016.

Pypath and Omnipath: integrate, analyze and extract signaling networks from literature curated resources (Poster)
Dénes Türei, Luis Tobalina, David Henriques, Pauline Traynard, Laurence Calzone, Tamás Korcsmáros, Julio Saez-Rodriguez
17th International Conference on Systems Biology (ICSB), 2016.

Building a Boolean model of signaling pathways altered in prostate cancer (Poster)
Pauline Traynard, Luis Tobalina, David Henriques, Emmanuel Barillot, Julio Saez-Rodriguez, Laurence Calzone
17th International Conference on Systems Biology (ICSB), 2016.

Stratification of prostate cancer patients based on molecular interaction profiles (Poster)
Roland Mathis, Matteo Manica, María Rodríguez Martínez
All Day, 2016.

CoDON: a learning framework for linking genomics and transcriptomics data to protein expression (Poster)
Matteo Manica, Roland Mathis, María Rodríguez Martínez
All Day, 2016.

Proteome heterogeneity in benign and malignant prostate tissue (Poster)
Tiannan Guo, Li Li, Qing Zhong, Niels J. Rupp, Konstantina Charmpi, Christine E. Wong, Ulrich Wagner, Jan H. Rueschoff, Wolfram Jochum, Christian Fankhauser, Karim Saba, Cedric Poyet, Peter J. Wild, Ruedi Aebersold, Andreas Beyer
All Day, 2016.

An integrative Systems Biology approach to advance in the understanding and treatment of prostate cancer (Poster)
Luis Tobalina, David Henriques, Julio Saez-Rodriguez
Bioinformatics for Young inTernational researchers (byteMAL), 2016.

tec MErCuRIC is a multicentre phase Ib/II clinical trial which will assess a novel therapeutic strategy (combined treatment of a MEK inhibitor MEK-162 with a MET inhibitor PF-02341066) to combat metastasis, improve survival and change current clinical practice for CRC patients with KRAS mutant (MT) and KRAS wild type (WT) (with aberrant c-MET) tumours. The consortium will go beyond the current state-of-the- art by (i) employing a novel treatment strategy targeting the biology of the disease and by (ii) using next generation sequencing (NGS) and ‘xenopatients’ to identify CRC patient subgroups who will maximally benefit from this novel treatment strategy.

tec COLOSSUS is an EU-funded H2020 project that aims to provide new and more effective ways to classify patients with a difficult-to-treat subtype of metastatic colorectal cancer (microsatellite stable RAS mutant metastatic colorectal cancer or MSS RAS mt mCRC) and to develop new treatment options for them. Our ultimate goal is to deliver a personalised medicine approach for patients with MSS RAS mt mCRC that is currently not available.


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