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PrECISE





  • PrECISE

    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



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Recent studies have demonstrated the presence of clonal diversity in prostate cancer and highlighted the difficulties in designing diagnostic and therapeutic strategies based on morphological evaluation and single-sample biopsies. We propose an in-depth characterization of the tumour clonal architecture through deep-sequencing the state-of-the-art- quantitative proteomics. We will develop mathematical models to gain a mechanistic understanding of clone-specific lesions and predict personalized drug therapies based on individual molecular profiles.


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