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.

Welcome to PrECISE

The PrECISE project is planned to run for 36 months. It is organized into nine work packages with significant dependencies and expected synergies between them which are described shortly in the following.

WP1: Regulatory network and clonality inference in prostate cancer tumours

WP1 provides the basis for clone identification and associated biomarkers. Therefore, regulatory networks and clonality analyses of proCOC tumour biopsies as well as public prostate genomic profiles are used.

WP2: Identification of sub-clonal genomic alterations

This WP identifies sub-clonal genomic alterations and describes intra-tumour heterogeneity in prostate cancer through protein profiling and ultra-deep sequencing in the proCOC samples and additional selected CRPC biopsies.

WP3: Reconstruction of protein interaction networks from high-dimensional proteomic maps and IBM – Watson technology

WP3 provides data-driven context-specific interaction networks out of the SWATH mass spectrometry for the proCOC samples, prostatic cell lines and public proteomic data. Another focus will be on network reconstruction algorithms that incorporate automatic text-mining capabilities through the Watson cognitive computer.

WP4: Linking genetic variation to protein expression

WP4 aims at incorporating genomic, transcriptomic and proteomic maps into a comprehensive molecular map, which is then utilized to detect clusters of proteins enriched in genomic alterations. It shall also identify key pathways and molecular mechanisms that underlie cancer progression specific clones and it shall help to develop a multidata patient classifier to group patients in clinically meaningful groups.

WP5: Logic models of prostate cancer patients: predicting personalized drug therapies

The objective of WP5 is to develop a mathematical model that includes the key molecular players identified in upstream WPs and that can provide a qualitative understanding of cancer underlying molecular mechanisms.

WP6: Experimental validation of prognostic biomarkers and targeted drug predictions

WP6 is a validation WP that creates quantitative proteomic and genomic datasets from selected samples. These new data will be used to validate inferred biomarkers and dysregulated pathways.

WP7: Graphical user interface

This WP provides an interface for analyses and prognostic inference from molecular profiles. A dashboard is established that contains all project data in order to select, control, execute or display the analyses.

WP8: Dissemination, Communication, Exploitation and Training

WP8 focuses on communication and dissemination of scientific research results achieved within the individual WPs to outside parties as well as to participating entities. Furthermore, this WP will support the partners to exploit the achieved results and impact the European as well as the international market. Moreover, WP8 results will lead to contributions in terms of trainings.

WP9: Project, Risk and Innovation Management

Finally, WP9 ensures a successful project lifetime with respect to risk and innovation management. There are dependencies to all other work packages as this WP coordinates the tasks so that they are in line with the project work plan in order to reach the objectives of PrECISE.

The PrECISE Consortium will:

  • produce innovative methods based on state of the art statistical and machine learning approaches to distinguish the many indolent prostate cancer tumours from the minority of lethal ones;
  • develop new methodologies to understand the personal disease history and most likely evolution of each patient based on the most accurate molecular data and clinical information of each patient;
  • investigate the molecular mechanisms that underlie progression to metastasis and predict the optimal intervention for each patient in order to prevent cancer progression; and
  • create a user-friendly web interface that will be made available to clinicians and hospitals with the goal of accelerating the translation of important scientific discoveries into better patient treatment.

  • Mission of PrECISE

    PrECISE aims to improve patient risk-stratification and treatment in prostate cancer by developing new computational approaches to exploit next generation molecular data. The expected outcome is the development of a predictive computational technology that can exploit molecular and clinical data to improve the understanding of disease mechanism and to inform clinicians about optimized treatment strategies.


    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. PrECISE aims 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 will focus on two urgent clinical needs in prostate cancer:

    • distinguishing the many indolent tumours from the minority of lethal ones, and
    • providing rationally chosen treatment options for patients with advanced disease.

    July 2016

    11th - 12th of July 2016, Technical Meeting, Villach, Austria


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